feat: Trading Bot V1.8 - Aggressive Mode + Infrastructure
## Major Features - Session Filter: NY-only trading (13:00-21:00 UTC) - SQLite Database: Structured trade logging - Telegram Bot: Real-time notifications (@Xausd_digger_bot) - Streamlit Dashboard: Visual monitoring & analytics - JSON Import: Historical data migration ## Infrastructure - trading_database.py: SQLite trade storage - telegram_notifier.py: Telegram integration - infrastructure_patch.py: Combined DB + Telegram - trading_dashboard.py: Real-time web dashboard - import_json_to_db.py: JSON to SQLite migration ## Session Filter (V1.8 Aggressive Mode) - session_filter_patch.py: Whitelist-based filter - Blocks: Asian, London, Overlap sessions - Active: NY session only (best performance: 47.6% WR) - Base confidence: 60% ## Documentation - V1.8_AGGRESSIVE_MODE_AKTIVIERT.md - FIX_DUPLICATE_SCHEDULER.md - DASHBOARD_WINDOWS_SERVER.md - SQLITE_TELEGRAM_SETUP.md - PROJECT_CLEANUP.md ## Cleanup - Archived old V1.1-V1.7 versions - Removed obsolete analysis scripts (replaced by dashboard) - Added .gitignore for secrets and temp files ## Breaking Changes - Requires telegram_config.json (use template) - Requires Python packages: streamlit, plotly 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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# Secrets & Credentials
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telegram_config.json
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*.env
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*.key
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*.secret
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# Database
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*.db
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*.sqlite
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*.sqlite3
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# Python
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__pycache__/
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*.py[cod]
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*$py.class
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*.so
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.Python
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*.egg-info/
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dist/
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build/
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# OS Files
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.DS_Store
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.DS_Store?
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._*
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.Spotlight-V100
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.Trashes
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ehthumbs.db
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Thumbs.db
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# IDE
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.vscode/
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.idea/
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*.swp
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*.swo
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*~
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# Logs
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*.log
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dashboard.log
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dashboard_*.log
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nohup.out
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# Jupyter
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.ipynb_checkpoints/
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*.ipynb:Zone.Identifier
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# Archive/Temp folders
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archive/
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archiv/
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misc/
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.goose/
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temp/
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tmp/
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# Performance data (optional - keep template)
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# Uncomment if you don't want to track trade data
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# trade_performance_*.json
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# Streamlit cache
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.streamlit/
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@@ -1,771 +0,0 @@
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{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
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||||
"execution_count": 2,
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||||
"id": "4cb5824d",
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"metadata": {},
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"outputs": [],
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"source": [
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"# 📦 Imports\n",
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"import MetaTrader5 as mt\n",
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"import pandas as pd\n",
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"import sqlite3 as db\n",
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"from datetime import datetime\n",
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"import time\n",
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"from scipy.signal import savgol_filter\n",
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"import pandas_ta as ta\n",
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||||
"import keyring as kr"
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||||
]
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||||
},
|
||||
{
|
||||
"cell_type": "code",
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||||
"execution_count": 3,
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||||
"id": "1279652c",
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"metadata": {},
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||||
"outputs": [],
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"source": [
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"# 📊 Verbindung zu SQLite\n",
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"def init_db(db_name=\"trading_log.db\"):\n",
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" conn = db.connect(db_name)\n",
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" c = conn.cursor()\n",
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" c.execute(\"\"\"\n",
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" CREATE TABLE IF NOT EXISTS trade_log (\n",
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" timestamp TEXT,\n",
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" symbol TEXT,\n",
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" order_type TEXT,\n",
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" price REAL,\n",
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" sl REAL,\n",
|
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" tp REAL,\n",
|
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" volume REAL,\n",
|
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" signal INTEGER,\n",
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||||
" comment TEXT\n",
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" )\n",
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" \"\"\")\n",
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" conn.commit()\n",
|
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" return conn"
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]
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},
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{
|
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"cell_type": "code",
|
||||
"execution_count": 7,
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||||
"id": "fc1eb959",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"True"
|
||||
]
|
||||
},
|
||||
"execution_count": 7,
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"metadata": {},
|
||||
"output_type": "execute_result"
|
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}
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],
|
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"source": [
|
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"# 🔐 MT5 Login einmalig initialisieren\n",
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"mt.initialize()\n",
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"login = 10800246\n",
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"server = \"VantageInternational-Demo\"\n",
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"password = kr.get_password(server, str(login))\n",
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"mt.login(login, password, server)"
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]
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},
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{
|
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"cell_type": "code",
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"execution_count": 8,
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"id": "840d41c6",
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"metadata": {},
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"outputs": [],
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"source": [
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"# 📥 MT5 Daten abrufen\n",
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"def get_mt5_data(symbol=\"XAUUSD\", timeframe=mt.TIMEFRAME_M5, n_bars=500):\n",
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" rates = mt.copy_rates_from_pos(symbol, timeframe, 0, n_bars)\n",
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" df = pd.DataFrame(rates)\n",
|
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" df[\"time\"] = pd.to_datetime(df[\"time\"], unit=\"s\")\n",
|
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" return df"
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]
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},
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{
|
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"cell_type": "code",
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"execution_count": 9,
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"id": "48445ef4",
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"metadata": {},
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"outputs": [],
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"source": [
|
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"df = get_mt5_data()"
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]
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},
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{
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"cell_type": "code",
|
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"execution_count": 11,
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"id": "964f927f",
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"metadata": {},
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"outputs": [],
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"source": [
|
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"# 🤖 Signale erzeugen\n",
|
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"def generate_signal(df):\n",
|
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" df[\"ema10\"] = df[\"close\"].ewm(span=10).mean()\n",
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" df[\"ema30\"] = df[\"close\"].ewm(span=30).mean()\n",
|
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" df[\"rsi\"] = ta.rsi(df[\"close\"], length=14)\n",
|
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" df[\"trend\"] = savgol_filter(df[\"close\"], 15, 3)\n",
|
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" df[\"signal\"] = 0\n",
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"\n",
|
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" for i in range(1, len(df)):\n",
|
||||
" if (\n",
|
||||
" df[\"ema10\"].iloc[i] > df[\"ema30\"].iloc[i]\n",
|
||||
" and df[\"ema10\"].iloc[i - 1] <= df[\"ema30\"].iloc[i - 1]\n",
|
||||
" and df[\"rsi\"].iloc[i] < 70\n",
|
||||
" and df[\"trend\"].iloc[i] > df[\"trend\"].iloc[i - 1]\n",
|
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" ):\n",
|
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" df.at[i, \"signal\"] = 1\n",
|
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" elif (\n",
|
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" df[\"ema10\"].iloc[i] < df[\"ema30\"].iloc[i]\n",
|
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" and df[\"ema10\"].iloc[i - 1] >= df[\"ema30\"].iloc[i - 1]\n",
|
||||
" and df[\"rsi\"].iloc[i] > 30\n",
|
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" and df[\"trend\"].iloc[i] < df[\"trend\"].iloc[i - 1]\n",
|
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" ):\n",
|
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" df.at[i, \"signal\"] = -1\n",
|
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" return df"
|
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]
|
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},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"id": "7494a5da",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
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"\n",
|
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" .dataframe thead th {\n",
|
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" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>time</th>\n",
|
||||
" <th>open</th>\n",
|
||||
" <th>high</th>\n",
|
||||
" <th>low</th>\n",
|
||||
" <th>close</th>\n",
|
||||
" <th>tick_volume</th>\n",
|
||||
" <th>spread</th>\n",
|
||||
" <th>real_volume</th>\n",
|
||||
" <th>ema10</th>\n",
|
||||
" <th>ema30</th>\n",
|
||||
" <th>rsi</th>\n",
|
||||
" <th>trend</th>\n",
|
||||
" <th>signal</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>2025-07-17 04:30:00</td>\n",
|
||||
" <td>3340.74</td>\n",
|
||||
" <td>3341.46</td>\n",
|
||||
" <td>3337.21</td>\n",
|
||||
" <td>3338.04</td>\n",
|
||||
" <td>1293</td>\n",
|
||||
" <td>18</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>3338.040000</td>\n",
|
||||
" <td>3338.040000</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>3338.208386</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>2025-07-17 04:35:00</td>\n",
|
||||
" <td>3338.03</td>\n",
|
||||
" <td>3341.03</td>\n",
|
||||
" <td>3337.72</td>\n",
|
||||
" <td>3341.03</td>\n",
|
||||
" <td>964</td>\n",
|
||||
" <td>18</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>3339.684500</td>\n",
|
||||
" <td>3339.584833</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>3340.377874</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>2025-07-17 04:40:00</td>\n",
|
||||
" <td>3341.03</td>\n",
|
||||
" <td>3341.48</td>\n",
|
||||
" <td>3339.96</td>\n",
|
||||
" <td>3341.26</td>\n",
|
||||
" <td>944</td>\n",
|
||||
" <td>18</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>3340.317841</td>\n",
|
||||
" <td>3340.180848</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>3341.873882</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>2025-07-17 04:45:00</td>\n",
|
||||
" <td>3341.27</td>\n",
|
||||
" <td>3343.45</td>\n",
|
||||
" <td>3341.27</td>\n",
|
||||
" <td>3342.47</td>\n",
|
||||
" <td>1099</td>\n",
|
||||
" <td>18</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>3341.026881</td>\n",
|
||||
" <td>3340.811593</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>3342.785860</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>2025-07-17 04:50:00</td>\n",
|
||||
" <td>3342.47</td>\n",
|
||||
" <td>3343.61</td>\n",
|
||||
" <td>3342.20</td>\n",
|
||||
" <td>3342.84</td>\n",
|
||||
" <td>1070</td>\n",
|
||||
" <td>18</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>3341.547378</td>\n",
|
||||
" <td>3341.273104</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>3343.203260</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>...</th>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>495</th>\n",
|
||||
" <td>2025-07-18 22:45:00</td>\n",
|
||||
" <td>3349.29</td>\n",
|
||||
" <td>3349.71</td>\n",
|
||||
" <td>3348.47</td>\n",
|
||||
" <td>3348.78</td>\n",
|
||||
" <td>928</td>\n",
|
||||
" <td>19</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>3350.024962</td>\n",
|
||||
" <td>3351.232735</td>\n",
|
||||
" <td>34.935115</td>\n",
|
||||
" <td>3348.405247</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>496</th>\n",
|
||||
" <td>2025-07-18 22:50:00</td>\n",
|
||||
" <td>3348.79</td>\n",
|
||||
" <td>3348.80</td>\n",
|
||||
" <td>3347.62</td>\n",
|
||||
" <td>3348.15</td>\n",
|
||||
" <td>859</td>\n",
|
||||
" <td>19</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>3349.684060</td>\n",
|
||||
" <td>3351.033849</td>\n",
|
||||
" <td>32.290909</td>\n",
|
||||
" <td>3348.186635</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>497</th>\n",
|
||||
" <td>2025-07-18 22:55:00</td>\n",
|
||||
" <td>3348.16</td>\n",
|
||||
" <td>3348.53</td>\n",
|
||||
" <td>3347.65</td>\n",
|
||||
" <td>3348.25</td>\n",
|
||||
" <td>1028</td>\n",
|
||||
" <td>19</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>3349.423322</td>\n",
|
||||
" <td>3350.854245</td>\n",
|
||||
" <td>33.155760</td>\n",
|
||||
" <td>3348.153324</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>498</th>\n",
|
||||
" <td>2025-07-18 23:00:00</td>\n",
|
||||
" <td>3348.24</td>\n",
|
||||
" <td>3348.88</td>\n",
|
||||
" <td>3347.72</td>\n",
|
||||
" <td>3348.06</td>\n",
|
||||
" <td>599</td>\n",
|
||||
" <td>19</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>3349.175445</td>\n",
|
||||
" <td>3350.673972</td>\n",
|
||||
" <td>32.311285</td>\n",
|
||||
" <td>3348.346744</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>499</th>\n",
|
||||
" <td>2025-07-18 23:05:00</td>\n",
|
||||
" <td>3348.06</td>\n",
|
||||
" <td>3349.25</td>\n",
|
||||
" <td>3347.92</td>\n",
|
||||
" <td>3348.88</td>\n",
|
||||
" <td>450</td>\n",
|
||||
" <td>19</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>3349.121728</td>\n",
|
||||
" <td>3350.558231</td>\n",
|
||||
" <td>39.476021</td>\n",
|
||||
" <td>3348.808327</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"<p>500 rows × 13 columns</p>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" time open high low close tick_volume \\\n",
|
||||
"0 2025-07-17 04:30:00 3340.74 3341.46 3337.21 3338.04 1293 \n",
|
||||
"1 2025-07-17 04:35:00 3338.03 3341.03 3337.72 3341.03 964 \n",
|
||||
"2 2025-07-17 04:40:00 3341.03 3341.48 3339.96 3341.26 944 \n",
|
||||
"3 2025-07-17 04:45:00 3341.27 3343.45 3341.27 3342.47 1099 \n",
|
||||
"4 2025-07-17 04:50:00 3342.47 3343.61 3342.20 3342.84 1070 \n",
|
||||
".. ... ... ... ... ... ... \n",
|
||||
"495 2025-07-18 22:45:00 3349.29 3349.71 3348.47 3348.78 928 \n",
|
||||
"496 2025-07-18 22:50:00 3348.79 3348.80 3347.62 3348.15 859 \n",
|
||||
"497 2025-07-18 22:55:00 3348.16 3348.53 3347.65 3348.25 1028 \n",
|
||||
"498 2025-07-18 23:00:00 3348.24 3348.88 3347.72 3348.06 599 \n",
|
||||
"499 2025-07-18 23:05:00 3348.06 3349.25 3347.92 3348.88 450 \n",
|
||||
"\n",
|
||||
" spread real_volume ema10 ema30 rsi trend \\\n",
|
||||
"0 18 0 3338.040000 3338.040000 NaN 3338.208386 \n",
|
||||
"1 18 0 3339.684500 3339.584833 NaN 3340.377874 \n",
|
||||
"2 18 0 3340.317841 3340.180848 NaN 3341.873882 \n",
|
||||
"3 18 0 3341.026881 3340.811593 NaN 3342.785860 \n",
|
||||
"4 18 0 3341.547378 3341.273104 NaN 3343.203260 \n",
|
||||
".. ... ... ... ... ... ... \n",
|
||||
"495 19 0 3350.024962 3351.232735 34.935115 3348.405247 \n",
|
||||
"496 19 0 3349.684060 3351.033849 32.290909 3348.186635 \n",
|
||||
"497 19 0 3349.423322 3350.854245 33.155760 3348.153324 \n",
|
||||
"498 19 0 3349.175445 3350.673972 32.311285 3348.346744 \n",
|
||||
"499 19 0 3349.121728 3350.558231 39.476021 3348.808327 \n",
|
||||
"\n",
|
||||
" signal \n",
|
||||
"0 0 \n",
|
||||
"1 0 \n",
|
||||
"2 0 \n",
|
||||
"3 0 \n",
|
||||
"4 0 \n",
|
||||
".. ... \n",
|
||||
"495 0 \n",
|
||||
"496 0 \n",
|
||||
"497 0 \n",
|
||||
"498 0 \n",
|
||||
"499 0 \n",
|
||||
"\n",
|
||||
"[500 rows x 13 columns]"
|
||||
]
|
||||
},
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"generate_signal(df)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"id": "0a8abc07",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"0.01"
|
||||
]
|
||||
},
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"mt.symbol_info('XAUUSD').point"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "64757821",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 📤 Order senden\n",
|
||||
"def send_market_order(symbol, volume, order_type, signal, conn, sl_pips=20, tp_pips=40, magic=1001):\n",
|
||||
" tick = mt.symbol_info_tick(symbol)\n",
|
||||
" price = tick.ask if order_type == mt.ORDER_TYPE_BUY else tick.bid\n",
|
||||
" point = mt.symbol_info(symbol).point\n",
|
||||
"\n",
|
||||
" sl = price - sl_pips * point if order_type == mt.ORDER_TYPE_BUY else price + sl_pips * point\n",
|
||||
" tp = price + tp_pips * point if order_type == mt.ORDER_TYPE_BUY else price - tp_pips * point\n",
|
||||
"\n",
|
||||
" request = {\n",
|
||||
" \"action\": mt.TRADE_ACTION_DEAL,\n",
|
||||
" \"symbol\": symbol,\n",
|
||||
" \"volume\": volume,\n",
|
||||
" \"type\": order_type,\n",
|
||||
" \"price\": price,\n",
|
||||
" \"sl\": round(sl, 5),\n",
|
||||
" \"tp\": round(tp, 5),\n",
|
||||
" \"deviation\": 20,\n",
|
||||
" \"magic\": magic,\n",
|
||||
" \"comment\": \"AutoSignalBot\",\n",
|
||||
" \"type_time\": mt.ORDER_TIME_GTC,\n",
|
||||
" \"type_filling\": mt.ORDER_FILLING_IOC,\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" result = mt.order_send(request)\n",
|
||||
" print(f\"[TRADE] {symbol} - {'BUY' if order_type==0 else 'SELL'} @ {price} | SL: {sl} | TP: {tp}\")\n",
|
||||
"\n",
|
||||
" # Logging in DB\n",
|
||||
" c = conn.cursor()\n",
|
||||
" c.execute(\"\"\"\n",
|
||||
" INSERT INTO trade_log (timestamp, symbol, order_type, price, sl, tp, volume, signal, comment)\n",
|
||||
" VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)\n",
|
||||
" \"\"\", (datetime.now(), symbol, \"BUY\" if order_type==0 else \"SELL\", price, sl, tp, volume, signal, \"AutoSignalBot\"))\n",
|
||||
" conn.commit()\n",
|
||||
" \n",
|
||||
" return result"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"id": "3bc5f11e",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 🔁 Hauptfunktion\n",
|
||||
"def run_live_trading(symbol=\"XAUUSD\", interval=300, volume=0.1, db_name=\"trading_log.db\"):\n",
|
||||
" conn = init_db(db_name)\n",
|
||||
" last_signal = 0\n",
|
||||
"\n",
|
||||
" print(f\"✅ Starte Auto-Trading für {symbol} – Intervall {interval}s\")\n",
|
||||
" while True:\n",
|
||||
" df = get_mt5_data(symbol)\n",
|
||||
" df = generate_signal(df)\n",
|
||||
" signal = df[\"signal\"].iloc[-1]\n",
|
||||
"\n",
|
||||
" if signal != 0 and signal != last_signal:\n",
|
||||
" order_type = mt.ORDER_TYPE_BUY if signal == 1 else mt.ORDER_TYPE_SELL\n",
|
||||
" send_market_order(symbol, volume, order_type, signal, conn)\n",
|
||||
" last_signal = signal\n",
|
||||
" else:\n",
|
||||
" print(f\"[{datetime.now()}] Kein neues Signal ({signal})\")\n",
|
||||
"\n",
|
||||
" time.sleep(interval)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"id": "71acdb0d",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"✅ Starte Auto-Trading für XAUUSD – Intervall 300s\n",
|
||||
"[2025-07-18 12:11:30.004688] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 12:16:30.083605] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 12:21:30.121793] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 12:26:30.174396] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 12:31:30.216996] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 12:36:30.258849] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 12:41:30.313072] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 12:46:30.356620] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 12:51:30.396600] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 12:56:30.442114] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 13:01:30.481155] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 13:06:30.530619] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 13:11:30.566248] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 13:16:30.606701] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 13:21:30.645462] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 13:26:30.686722] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 13:31:31.130100] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 13:36:31.193825] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 13:41:31.381330] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 13:46:31.434187] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 13:51:31.471695] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 13:56:31.507322] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 14:01:31.550436] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 14:06:31.587884] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 14:11:31.665002] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 14:16:31.704012] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 14:21:31.745292] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 14:26:31.783905] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 14:31:31.822325] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 14:36:31.873271] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 14:41:31.914303] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 14:46:31.951585] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 14:51:32.005271] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 14:56:32.046384] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 15:01:32.083259] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 15:06:32.132835] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 15:11:32.176714] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 15:16:32.221857] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 15:21:32.269876] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 15:26:32.313363] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 15:31:32.356954] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 15:36:32.403653] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 15:41:32.449777] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 15:46:32.489053] Kein neues Signal (0)\n",
|
||||
"[TRADE] XAUUSD - SELL @ 3354.56 | SL: 3354.7599999999998 | TP: 3354.16\n",
|
||||
"[2025-07-18 15:56:32.609729] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 16:01:32.650996] Kein neues Signal (0)\n",
|
||||
"[TRADE] XAUUSD - BUY @ 3358.95 | SL: 3358.75 | TP: 3359.35\n",
|
||||
"[2025-07-18 16:11:32.744972] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 16:16:32.791366] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 16:21:32.829930] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 16:26:32.871934] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 16:31:32.912571] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 16:36:32.954745] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 16:41:32.994281] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 16:46:33.048113] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 16:51:33.086281] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 16:56:33.127117] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 17:01:33.177813] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 17:06:33.219713] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 17:11:33.260138] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 17:16:33.302870] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 17:21:33.346259] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 17:26:33.390947] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 17:31:33.434736] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 17:36:33.476901] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 17:41:33.520435] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 17:46:33.575426] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 17:51:33.622573] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 17:56:33.662664] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 18:01:33.703019] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 18:06:33.748220] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 18:11:33.788812] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 18:16:33.827200] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 18:21:33.880825] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 18:26:33.931042] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 18:31:33.982017] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 18:36:34.021865] Kein neues Signal (1)\n",
|
||||
"[2025-07-18 18:41:34.059505] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 18:46:34.100395] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 18:51:34.148794] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 18:56:34.186791] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 19:01:34.227630] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 19:06:34.272331] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 19:11:34.309122] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 19:16:34.350483] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 19:21:34.391638] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 19:26:34.430235] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 19:31:34.476827] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 19:36:34.516804] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 19:41:34.551478] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 19:46:34.600704] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 19:51:34.657098] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 19:56:34.694871] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 20:01:34.750662] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 20:06:34.786445] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 20:11:34.941510] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 20:16:34.997686] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 20:21:35.041835] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 20:26:35.082677] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 20:31:35.135303] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 20:36:35.174403] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 20:41:35.211876] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 20:46:35.254113] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 20:51:35.297650] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 20:56:35.338598] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 21:01:35.378704] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 21:06:35.421315] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 21:11:35.446675] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 21:16:35.501085] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 21:21:35.547489] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 21:26:35.600292] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 21:31:35.642080] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 21:36:35.681283] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 21:41:35.719258] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 21:46:35.756743] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 21:51:35.802329] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 21:56:35.849136] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 22:01:35.907573] Kein neues Signal (0)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"ename": "KeyboardInterrupt",
|
||||
"evalue": "",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
|
||||
"Cell \u001b[1;32mIn[22], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;66;03m# ▶️ Live-Trading starten (z. B. alle 5 Minuten)\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m run_live_trading(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mXAUUSD\u001b[39m\u001b[38;5;124m\"\u001b[39m, interval\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m300\u001b[39m, volume\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.1\u001b[39m)\n",
|
||||
"Cell \u001b[1;32mIn[13], line 19\u001b[0m, in \u001b[0;36mrun_live_trading\u001b[1;34m(symbol, interval, volume, db_name)\u001b[0m\n\u001b[0;32m 16\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 17\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m[\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdatetime\u001b[38;5;241m.\u001b[39mnow()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m] Kein neues Signal (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00msignal\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m---> 19\u001b[0m time\u001b[38;5;241m.\u001b[39msleep(interval)\n",
|
||||
"\u001b[1;31mKeyboardInterrupt\u001b[0m: "
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# ▶️ Live-Trading starten (z. B. alle 5 Minuten)\n",
|
||||
"run_live_trading(\"XAUUSD\", interval=300, volume=0.1)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"id": "a7675469",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
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|
||||
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|
||||
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|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
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|
||||
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|
||||
"\n",
|
||||
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|
||||
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|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>timestamp</th>\n",
|
||||
" <th>symbol</th>\n",
|
||||
" <th>order_type</th>\n",
|
||||
" <th>price</th>\n",
|
||||
" <th>sl</th>\n",
|
||||
" <th>tp</th>\n",
|
||||
" <th>volume</th>\n",
|
||||
" <th>signal</th>\n",
|
||||
" <th>comment</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>2025-07-18 16:06:32.701648</td>\n",
|
||||
" <td>XAUUSD</td>\n",
|
||||
" <td>BUY</td>\n",
|
||||
" <td>3358.95</td>\n",
|
||||
" <td>3358.75</td>\n",
|
||||
" <td>3359.35</td>\n",
|
||||
" <td>0.1</td>\n",
|
||||
" <td>b'\\x01\\x00\\x00\\x00\\x00\\x00\\x00\\x00'</td>\n",
|
||||
" <td>AutoSignalBot</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>2025-07-18 15:51:32.545794</td>\n",
|
||||
" <td>XAUUSD</td>\n",
|
||||
" <td>SELL</td>\n",
|
||||
" <td>3354.56</td>\n",
|
||||
" <td>3354.76</td>\n",
|
||||
" <td>3354.16</td>\n",
|
||||
" <td>0.1</td>\n",
|
||||
" <td>b'\\xff\\xff\\xff\\xff\\xff\\xff\\xff\\xff'</td>\n",
|
||||
" <td>AutoSignalBot</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" timestamp symbol order_type price sl tp \\\n",
|
||||
"0 2025-07-18 16:06:32.701648 XAUUSD BUY 3358.95 3358.75 3359.35 \n",
|
||||
"1 2025-07-18 15:51:32.545794 XAUUSD SELL 3354.56 3354.76 3354.16 \n",
|
||||
"\n",
|
||||
" volume signal comment \n",
|
||||
"0 0.1 b'\\x01\\x00\\x00\\x00\\x00\\x00\\x00\\x00' AutoSignalBot \n",
|
||||
"1 0.1 b'\\xff\\xff\\xff\\xff\\xff\\xff\\xff\\xff' AutoSignalBot "
|
||||
]
|
||||
},
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# 🧾 Optional: SQLite-Datenbank anzeigen\n",
|
||||
"conn = db.connect(\"trading_log.db\")\n",
|
||||
"pd.read_sql(\"SELECT * FROM trade_log ORDER BY timestamp DESC LIMIT 10\", conn)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "b4512a2c",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "base",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@@ -1,2 +0,0 @@
|
||||
[ZoneTransfer]
|
||||
ZoneId=3
|
||||
@@ -1,841 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "4cb5824d",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 📦 Imports\n",
|
||||
"import MetaTrader5 as mt\n",
|
||||
"import pandas as pd\n",
|
||||
"import sqlite3 as db\n",
|
||||
"from datetime import datetime\n",
|
||||
"import time\n",
|
||||
"from scipy.signal import savgol_filter\n",
|
||||
"import pandas_ta as ta\n",
|
||||
"import keyring as kr"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "1279652c",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 📊 Verbindung zu SQLite\n",
|
||||
"def init_db(db_name=\"trading_log.db\"):\n",
|
||||
" conn = db.connect(db_name)\n",
|
||||
" c = conn.cursor()\n",
|
||||
" c.execute(\"\"\"\n",
|
||||
" CREATE TABLE IF NOT EXISTS trade_log (\n",
|
||||
" timestamp TEXT,\n",
|
||||
" symbol TEXT,\n",
|
||||
" order_type TEXT,\n",
|
||||
" price REAL,\n",
|
||||
" sl REAL,\n",
|
||||
" tp REAL,\n",
|
||||
" volume REAL,\n",
|
||||
" signal INTEGER,\n",
|
||||
" comment TEXT\n",
|
||||
" )\n",
|
||||
" \"\"\")\n",
|
||||
" conn.commit()\n",
|
||||
" return conn"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"id": "fc1eb959",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"True"
|
||||
]
|
||||
},
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# 🔐 MT5 Login einmalig initialisieren\n",
|
||||
"mt.initialize()\n",
|
||||
"login = 10800246\n",
|
||||
"server = \"VantageInternational-Demo\"\n",
|
||||
"password = kr.get_password(server, str(login))\n",
|
||||
"mt.login(login, password, server)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"id": "840d41c6",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 📥 MT5 Daten abrufen\n",
|
||||
"def get_mt5_data(symbol=\"XAUUSD\", timeframe=mt.TIMEFRAME_M5, n_bars=500):\n",
|
||||
" rates = mt.copy_rates_from_pos(symbol, timeframe, 0, n_bars)\n",
|
||||
" df = pd.DataFrame(rates)\n",
|
||||
" df[\"time\"] = pd.to_datetime(df[\"time\"], unit=\"s\")\n",
|
||||
" return df"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"id": "48445ef4",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df = get_mt5_data()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"id": "964f927f",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 🤖 Signale erzeugen\n",
|
||||
"def generate_signal(df):\n",
|
||||
" df[\"ema10\"] = df[\"close\"].ewm(span=10).mean()\n",
|
||||
" df[\"ema30\"] = df[\"close\"].ewm(span=30).mean()\n",
|
||||
" df[\"rsi\"] = ta.rsi(df[\"close\"], length=14)\n",
|
||||
" df[\"trend\"] = savgol_filter(df[\"close\"], 15, 3)\n",
|
||||
" df[\"signal\"] = 0\n",
|
||||
"\n",
|
||||
" for i in range(1, len(df)):\n",
|
||||
" if (\n",
|
||||
" df[\"ema10\"].iloc[i] > df[\"ema30\"].iloc[i]\n",
|
||||
" and df[\"ema10\"].iloc[i - 1] <= df[\"ema30\"].iloc[i - 1]\n",
|
||||
" and df[\"rsi\"].iloc[i] < 70\n",
|
||||
" and df[\"trend\"].iloc[i] > df[\"trend\"].iloc[i - 1]\n",
|
||||
" ):\n",
|
||||
" df.at[i, \"signal\"] = 1\n",
|
||||
" elif (\n",
|
||||
" df[\"ema10\"].iloc[i] < df[\"ema30\"].iloc[i]\n",
|
||||
" and df[\"ema10\"].iloc[i - 1] >= df[\"ema30\"].iloc[i - 1]\n",
|
||||
" and df[\"rsi\"].iloc[i] > 30\n",
|
||||
" and df[\"trend\"].iloc[i] < df[\"trend\"].iloc[i - 1]\n",
|
||||
" ):\n",
|
||||
" df.at[i, \"signal\"] = -1\n",
|
||||
" return df"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"id": "7494a5da",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
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|
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|
||||
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|
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" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
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|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>time</th>\n",
|
||||
" <th>open</th>\n",
|
||||
" <th>high</th>\n",
|
||||
" <th>low</th>\n",
|
||||
" <th>close</th>\n",
|
||||
" <th>tick_volume</th>\n",
|
||||
" <th>spread</th>\n",
|
||||
" <th>real_volume</th>\n",
|
||||
" <th>ema10</th>\n",
|
||||
" <th>ema30</th>\n",
|
||||
" <th>rsi</th>\n",
|
||||
" <th>trend</th>\n",
|
||||
" <th>signal</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>2025-07-17 04:30:00</td>\n",
|
||||
" <td>3340.74</td>\n",
|
||||
" <td>3341.46</td>\n",
|
||||
" <td>3337.21</td>\n",
|
||||
" <td>3338.04</td>\n",
|
||||
" <td>1293</td>\n",
|
||||
" <td>18</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>3338.040000</td>\n",
|
||||
" <td>3338.040000</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>3338.208386</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>2025-07-17 04:35:00</td>\n",
|
||||
" <td>3338.03</td>\n",
|
||||
" <td>3341.03</td>\n",
|
||||
" <td>3337.72</td>\n",
|
||||
" <td>3341.03</td>\n",
|
||||
" <td>964</td>\n",
|
||||
" <td>18</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>3339.684500</td>\n",
|
||||
" <td>3339.584833</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>3340.377874</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>2025-07-17 04:40:00</td>\n",
|
||||
" <td>3341.03</td>\n",
|
||||
" <td>3341.48</td>\n",
|
||||
" <td>3339.96</td>\n",
|
||||
" <td>3341.26</td>\n",
|
||||
" <td>944</td>\n",
|
||||
" <td>18</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>3340.317841</td>\n",
|
||||
" <td>3340.180848</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>3341.873882</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>2025-07-17 04:45:00</td>\n",
|
||||
" <td>3341.27</td>\n",
|
||||
" <td>3343.45</td>\n",
|
||||
" <td>3341.27</td>\n",
|
||||
" <td>3342.47</td>\n",
|
||||
" <td>1099</td>\n",
|
||||
" <td>18</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>3341.026881</td>\n",
|
||||
" <td>3340.811593</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>3342.785860</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>2025-07-17 04:50:00</td>\n",
|
||||
" <td>3342.47</td>\n",
|
||||
" <td>3343.61</td>\n",
|
||||
" <td>3342.20</td>\n",
|
||||
" <td>3342.84</td>\n",
|
||||
" <td>1070</td>\n",
|
||||
" <td>18</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>3341.547378</td>\n",
|
||||
" <td>3341.273104</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>3343.203260</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>...</th>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>495</th>\n",
|
||||
" <td>2025-07-18 22:45:00</td>\n",
|
||||
" <td>3349.29</td>\n",
|
||||
" <td>3349.71</td>\n",
|
||||
" <td>3348.47</td>\n",
|
||||
" <td>3348.78</td>\n",
|
||||
" <td>928</td>\n",
|
||||
" <td>19</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>3350.024962</td>\n",
|
||||
" <td>3351.232735</td>\n",
|
||||
" <td>34.935115</td>\n",
|
||||
" <td>3348.405247</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>496</th>\n",
|
||||
" <td>2025-07-18 22:50:00</td>\n",
|
||||
" <td>3348.79</td>\n",
|
||||
" <td>3348.80</td>\n",
|
||||
" <td>3347.62</td>\n",
|
||||
" <td>3348.15</td>\n",
|
||||
" <td>859</td>\n",
|
||||
" <td>19</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>3349.684060</td>\n",
|
||||
" <td>3351.033849</td>\n",
|
||||
" <td>32.290909</td>\n",
|
||||
" <td>3348.186635</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>497</th>\n",
|
||||
" <td>2025-07-18 22:55:00</td>\n",
|
||||
" <td>3348.16</td>\n",
|
||||
" <td>3348.53</td>\n",
|
||||
" <td>3347.65</td>\n",
|
||||
" <td>3348.25</td>\n",
|
||||
" <td>1028</td>\n",
|
||||
" <td>19</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>3349.423322</td>\n",
|
||||
" <td>3350.854245</td>\n",
|
||||
" <td>33.155760</td>\n",
|
||||
" <td>3348.153324</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>498</th>\n",
|
||||
" <td>2025-07-18 23:00:00</td>\n",
|
||||
" <td>3348.24</td>\n",
|
||||
" <td>3348.88</td>\n",
|
||||
" <td>3347.72</td>\n",
|
||||
" <td>3348.06</td>\n",
|
||||
" <td>599</td>\n",
|
||||
" <td>19</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>3349.175445</td>\n",
|
||||
" <td>3350.673972</td>\n",
|
||||
" <td>32.311285</td>\n",
|
||||
" <td>3348.346744</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>499</th>\n",
|
||||
" <td>2025-07-18 23:05:00</td>\n",
|
||||
" <td>3348.06</td>\n",
|
||||
" <td>3349.25</td>\n",
|
||||
" <td>3347.92</td>\n",
|
||||
" <td>3348.88</td>\n",
|
||||
" <td>450</td>\n",
|
||||
" <td>19</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>3349.121728</td>\n",
|
||||
" <td>3350.558231</td>\n",
|
||||
" <td>39.476021</td>\n",
|
||||
" <td>3348.808327</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"<p>500 rows × 13 columns</p>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" time open high low close tick_volume \\\n",
|
||||
"0 2025-07-17 04:30:00 3340.74 3341.46 3337.21 3338.04 1293 \n",
|
||||
"1 2025-07-17 04:35:00 3338.03 3341.03 3337.72 3341.03 964 \n",
|
||||
"2 2025-07-17 04:40:00 3341.03 3341.48 3339.96 3341.26 944 \n",
|
||||
"3 2025-07-17 04:45:00 3341.27 3343.45 3341.27 3342.47 1099 \n",
|
||||
"4 2025-07-17 04:50:00 3342.47 3343.61 3342.20 3342.84 1070 \n",
|
||||
".. ... ... ... ... ... ... \n",
|
||||
"495 2025-07-18 22:45:00 3349.29 3349.71 3348.47 3348.78 928 \n",
|
||||
"496 2025-07-18 22:50:00 3348.79 3348.80 3347.62 3348.15 859 \n",
|
||||
"497 2025-07-18 22:55:00 3348.16 3348.53 3347.65 3348.25 1028 \n",
|
||||
"498 2025-07-18 23:00:00 3348.24 3348.88 3347.72 3348.06 599 \n",
|
||||
"499 2025-07-18 23:05:00 3348.06 3349.25 3347.92 3348.88 450 \n",
|
||||
"\n",
|
||||
" spread real_volume ema10 ema30 rsi trend \\\n",
|
||||
"0 18 0 3338.040000 3338.040000 NaN 3338.208386 \n",
|
||||
"1 18 0 3339.684500 3339.584833 NaN 3340.377874 \n",
|
||||
"2 18 0 3340.317841 3340.180848 NaN 3341.873882 \n",
|
||||
"3 18 0 3341.026881 3340.811593 NaN 3342.785860 \n",
|
||||
"4 18 0 3341.547378 3341.273104 NaN 3343.203260 \n",
|
||||
".. ... ... ... ... ... ... \n",
|
||||
"495 19 0 3350.024962 3351.232735 34.935115 3348.405247 \n",
|
||||
"496 19 0 3349.684060 3351.033849 32.290909 3348.186635 \n",
|
||||
"497 19 0 3349.423322 3350.854245 33.155760 3348.153324 \n",
|
||||
"498 19 0 3349.175445 3350.673972 32.311285 3348.346744 \n",
|
||||
"499 19 0 3349.121728 3350.558231 39.476021 3348.808327 \n",
|
||||
"\n",
|
||||
" signal \n",
|
||||
"0 0 \n",
|
||||
"1 0 \n",
|
||||
"2 0 \n",
|
||||
"3 0 \n",
|
||||
"4 0 \n",
|
||||
".. ... \n",
|
||||
"495 0 \n",
|
||||
"496 0 \n",
|
||||
"497 0 \n",
|
||||
"498 0 \n",
|
||||
"499 0 \n",
|
||||
"\n",
|
||||
"[500 rows x 13 columns]"
|
||||
]
|
||||
},
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"generate_signal(df)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"id": "0a8abc07",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"0.01"
|
||||
]
|
||||
},
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"mt.symbol_info('XAUUSD').point"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "64757821",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 📤 Order senden\n",
|
||||
"def send_market_order(symbol, volume, order_type, signal, conn, sl_pips=20, tp_pips=40, magic=1001):\n",
|
||||
" tick = mt.symbol_info_tick(symbol)\n",
|
||||
" price = tick.ask if order_type == mt.ORDER_TYPE_BUY else tick.bid\n",
|
||||
" point = mt.symbol_info(symbol).point\n",
|
||||
"\n",
|
||||
" sl = price - sl_pips * point if order_type == mt.ORDER_TYPE_BUY else price + sl_pips * point\n",
|
||||
" tp = price + tp_pips * point if order_type == mt.ORDER_TYPE_BUY else price - tp_pips * point\n",
|
||||
"\n",
|
||||
" request = {\n",
|
||||
" \"action\": mt.TRADE_ACTION_DEAL,\n",
|
||||
" \"symbol\": symbol,\n",
|
||||
" \"volume\": volume,\n",
|
||||
" \"type\": order_type,\n",
|
||||
" \"price\": price,\n",
|
||||
" \"sl\": round(sl, 5),\n",
|
||||
" \"tp\": round(tp, 5),\n",
|
||||
" \"deviation\": 20,\n",
|
||||
" \"magic\": magic,\n",
|
||||
" \"comment\": \"AutoSignalBot\",\n",
|
||||
" \"type_time\": mt.ORDER_TIME_GTC,\n",
|
||||
" \"type_filling\": mt.ORDER_FILLING_IOC,\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" result = mt.order_send(request)\n",
|
||||
" print(f\"[TRADE] {symbol} - {'BUY' if order_type==0 else 'SELL'} @ {price} | SL: {sl} | TP: {tp}\")\n",
|
||||
"\n",
|
||||
" # Logging in DB\n",
|
||||
" c = conn.cursor()\n",
|
||||
" c.execute(\"\"\"\n",
|
||||
" INSERT INTO trade_log (timestamp, symbol, order_type, price, sl, tp, volume, signal, comment)\n",
|
||||
" VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)\n",
|
||||
" \"\"\", (datetime.now(), symbol, \"BUY\" if order_type==0 else \"SELL\", price, sl, tp, volume, signal, \"AutoSignalBot\"))\n",
|
||||
" conn.commit()\n",
|
||||
" \n",
|
||||
" return result"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"id": "3bc5f11e",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 🔁 Hauptfunktion\n",
|
||||
"def run_live_trading(symbol=\"XAUUSD\", interval=300, volume=0.1, db_name=\"trading_log.db\"):\n",
|
||||
" conn = init_db(db_name)\n",
|
||||
" last_signal = 0\n",
|
||||
"\n",
|
||||
" print(f\"✅ Starte Auto-Trading für {symbol} – Intervall {interval}s\")\n",
|
||||
" while True:\n",
|
||||
" df = get_mt5_data(symbol)\n",
|
||||
" df = generate_signal(df)\n",
|
||||
" signal = df[\"signal\"].iloc[-1]\n",
|
||||
"\n",
|
||||
" if signal != 0 and signal != last_signal:\n",
|
||||
" order_type = mt.ORDER_TYPE_BUY if signal == 1 else mt.ORDER_TYPE_SELL\n",
|
||||
" send_market_order(symbol, volume, order_type, signal, conn)\n",
|
||||
" last_signal = signal\n",
|
||||
" else:\n",
|
||||
" print(f\"[{datetime.now()}] Kein neues Signal ({signal})\")\n",
|
||||
"\n",
|
||||
" time.sleep(interval)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"id": "71acdb0d",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"✅ Starte Auto-Trading für XAUUSD – Intervall 300s\n",
|
||||
"[2025-07-18 12:11:30.004688] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 12:16:30.083605] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 12:21:30.121793] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 12:26:30.174396] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 12:31:30.216996] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 12:36:30.258849] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 12:41:30.313072] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 12:46:30.356620] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 12:51:30.396600] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 12:56:30.442114] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 13:01:30.481155] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 13:06:30.530619] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 13:11:30.566248] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 13:16:30.606701] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 13:21:30.645462] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 13:26:30.686722] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 13:31:31.130100] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 13:36:31.193825] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 13:41:31.381330] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 13:46:31.434187] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 13:51:31.471695] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 13:56:31.507322] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 14:01:31.550436] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 14:06:31.587884] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 14:11:31.665002] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 14:16:31.704012] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 14:21:31.745292] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 14:26:31.783905] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 14:31:31.822325] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 14:36:31.873271] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 14:41:31.914303] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 14:46:31.951585] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 14:51:32.005271] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 14:56:32.046384] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 15:01:32.083259] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 15:06:32.132835] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 15:11:32.176714] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 15:16:32.221857] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 15:21:32.269876] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 15:26:32.313363] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 15:31:32.356954] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 15:36:32.403653] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 15:41:32.449777] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 15:46:32.489053] Kein neues Signal (0)\n",
|
||||
"[TRADE] XAUUSD - SELL @ 3354.56 | SL: 3354.7599999999998 | TP: 3354.16\n",
|
||||
"[2025-07-18 15:56:32.609729] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 16:01:32.650996] Kein neues Signal (0)\n",
|
||||
"[TRADE] XAUUSD - BUY @ 3358.95 | SL: 3358.75 | TP: 3359.35\n",
|
||||
"[2025-07-18 16:11:32.744972] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 16:16:32.791366] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 16:21:32.829930] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 16:26:32.871934] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 16:31:32.912571] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 16:36:32.954745] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 16:41:32.994281] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 16:46:33.048113] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 16:51:33.086281] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 16:56:33.127117] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 17:01:33.177813] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 17:06:33.219713] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 17:11:33.260138] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 17:16:33.302870] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 17:21:33.346259] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 17:26:33.390947] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 17:31:33.434736] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 17:36:33.476901] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 17:41:33.520435] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 17:46:33.575426] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 17:51:33.622573] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 17:56:33.662664] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 18:01:33.703019] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 18:06:33.748220] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 18:11:33.788812] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 18:16:33.827200] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 18:21:33.880825] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 18:26:33.931042] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 18:31:33.982017] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 18:36:34.021865] Kein neues Signal (1)\n",
|
||||
"[2025-07-18 18:41:34.059505] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 18:46:34.100395] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 18:51:34.148794] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 18:56:34.186791] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 19:01:34.227630] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 19:06:34.272331] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 19:11:34.309122] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 19:16:34.350483] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 19:21:34.391638] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 19:26:34.430235] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 19:31:34.476827] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 19:36:34.516804] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 19:41:34.551478] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 19:46:34.600704] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 19:51:34.657098] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 19:56:34.694871] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 20:01:34.750662] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 20:06:34.786445] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 20:11:34.941510] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 20:16:34.997686] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 20:21:35.041835] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 20:26:35.082677] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 20:31:35.135303] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 20:36:35.174403] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 20:41:35.211876] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 20:46:35.254113] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 20:51:35.297650] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 20:56:35.338598] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 21:01:35.378704] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 21:06:35.421315] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 21:11:35.446675] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 21:16:35.501085] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 21:21:35.547489] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 21:26:35.600292] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 21:31:35.642080] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 21:36:35.681283] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 21:41:35.719258] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 21:46:35.756743] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 21:51:35.802329] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 21:56:35.849136] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 22:01:35.907573] Kein neues Signal (0)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"ename": "KeyboardInterrupt",
|
||||
"evalue": "",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
|
||||
"Cell \u001b[1;32mIn[22], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;66;03m# ▶️ Live-Trading starten (z. B. alle 5 Minuten)\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m run_live_trading(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mXAUUSD\u001b[39m\u001b[38;5;124m\"\u001b[39m, interval\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m300\u001b[39m, volume\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.1\u001b[39m)\n",
|
||||
"Cell \u001b[1;32mIn[13], line 19\u001b[0m, in \u001b[0;36mrun_live_trading\u001b[1;34m(symbol, interval, volume, db_name)\u001b[0m\n\u001b[0;32m 16\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 17\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m[\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdatetime\u001b[38;5;241m.\u001b[39mnow()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m] Kein neues Signal (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00msignal\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m---> 19\u001b[0m time\u001b[38;5;241m.\u001b[39msleep(interval)\n",
|
||||
"\u001b[1;31mKeyboardInterrupt\u001b[0m: "
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# ▶️ Live-Trading starten (z. B. alle 5 Minuten)\n",
|
||||
"run_live_trading(\"XAUUSD\", interval=300, volume=0.1)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"id": "a7675469",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>timestamp</th>\n",
|
||||
" <th>symbol</th>\n",
|
||||
" <th>order_type</th>\n",
|
||||
" <th>price</th>\n",
|
||||
" <th>sl</th>\n",
|
||||
" <th>tp</th>\n",
|
||||
" <th>volume</th>\n",
|
||||
" <th>signal</th>\n",
|
||||
" <th>comment</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>2025-07-18 16:06:32.701648</td>\n",
|
||||
" <td>XAUUSD</td>\n",
|
||||
" <td>BUY</td>\n",
|
||||
" <td>3358.95</td>\n",
|
||||
" <td>3358.75</td>\n",
|
||||
" <td>3359.35</td>\n",
|
||||
" <td>0.1</td>\n",
|
||||
" <td>b'\\x01\\x00\\x00\\x00\\x00\\x00\\x00\\x00'</td>\n",
|
||||
" <td>AutoSignalBot</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>2025-07-18 15:51:32.545794</td>\n",
|
||||
" <td>XAUUSD</td>\n",
|
||||
" <td>SELL</td>\n",
|
||||
" <td>3354.56</td>\n",
|
||||
" <td>3354.76</td>\n",
|
||||
" <td>3354.16</td>\n",
|
||||
" <td>0.1</td>\n",
|
||||
" <td>b'\\xff\\xff\\xff\\xff\\xff\\xff\\xff\\xff'</td>\n",
|
||||
" <td>AutoSignalBot</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" timestamp symbol order_type price sl tp \\\n",
|
||||
"0 2025-07-18 16:06:32.701648 XAUUSD BUY 3358.95 3358.75 3359.35 \n",
|
||||
"1 2025-07-18 15:51:32.545794 XAUUSD SELL 3354.56 3354.76 3354.16 \n",
|
||||
"\n",
|
||||
" volume signal comment \n",
|
||||
"0 0.1 b'\\x01\\x00\\x00\\x00\\x00\\x00\\x00\\x00' AutoSignalBot \n",
|
||||
"1 0.1 b'\\xff\\xff\\xff\\xff\\xff\\xff\\xff\\xff' AutoSignalBot "
|
||||
]
|
||||
},
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# 🧾 Optional: SQLite-Datenbank anzeigen\n",
|
||||
"conn = db.connect(\"trading_log.db\")\n",
|
||||
"pd.read_sql(\"SELECT * FROM trade_log ORDER BY timestamp DESC LIMIT 10\", conn)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "b4512a2c",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "ec808aea",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"\n",
|
||||
"import numpy as np\n",
|
||||
"\n",
|
||||
"def calculate_fib_levels(df, lookback=20):\n",
|
||||
" swing_high = df['high'].rolling(lookback).max().iloc[-1]\n",
|
||||
" swing_low = df['low'].rolling(lookback).min().iloc[-1]\n",
|
||||
" diff = swing_high - swing_low\n",
|
||||
" levels = {\n",
|
||||
" '0.0': swing_low,\n",
|
||||
" '0.236': swing_high - 0.236 * diff,\n",
|
||||
" '0.382': swing_high - 0.382 * diff,\n",
|
||||
" '0.5': swing_high - 0.5 * diff,\n",
|
||||
" '0.618': swing_high - 0.618 * diff,\n",
|
||||
" '0.786': swing_high - 0.786 * diff,\n",
|
||||
" '1.0': swing_high\n",
|
||||
" }\n",
|
||||
" return levels\n",
|
||||
"\n",
|
||||
"def calculate_atr(df, period=14):\n",
|
||||
" df['H-L'] = df['high'] - df['low']\n",
|
||||
" df['H-C'] = abs(df['high'] - df['close'].shift())\n",
|
||||
" df['L-C'] = abs(df['low'] - df['close'].shift())\n",
|
||||
" df['TR'] = df[['H-L', 'H-C', 'L-C']].max(axis=1)\n",
|
||||
" df['ATR'] = df['TR'].rolling(period).mean()\n",
|
||||
" return df\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "09b3f9df",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"\n",
|
||||
"def send_fib_trade(df, symbol, order_type, volume):\n",
|
||||
" fib = calculate_fib_levels(df)\n",
|
||||
" df = calculate_atr(df)\n",
|
||||
" atr = df['ATR'].iloc[-1]\n",
|
||||
" point = mt.symbol_info(symbol).point\n",
|
||||
" tick = mt.symbol_info_tick(symbol)\n",
|
||||
" price = tick.ask if order_type == mt.ORDER_TYPE_BUY else tick.bid\n",
|
||||
"\n",
|
||||
" sl = price - atr if order_type == mt.ORDER_TYPE_BUY else price + atr\n",
|
||||
" tp = price + 2 * atr if order_type == mt.ORDER_TYPE_BUY else price - 2 * atr\n",
|
||||
"\n",
|
||||
" request = {\n",
|
||||
" \"action\": mt.TRADE_ACTION_DEAL,\n",
|
||||
" \"symbol\": symbol,\n",
|
||||
" \"volume\": volume,\n",
|
||||
" \"type\": order_type,\n",
|
||||
" \"price\": price,\n",
|
||||
" \"sl\": round(sl, 5),\n",
|
||||
" \"tp\": round(tp, 5),\n",
|
||||
" \"deviation\": 20,\n",
|
||||
" \"magic\": 1010,\n",
|
||||
" \"comment\": \"FibATR_Trade\",\n",
|
||||
" \"type_time\": mt.ORDER_TIME_GTC,\n",
|
||||
" \"type_filling\": mt.ORDER_FILLING_IOC,\n",
|
||||
" }\n",
|
||||
" return mt.order_send(request)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "base",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@@ -1,885 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "4cb5824d",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 📦 Imports\n",
|
||||
"import MetaTrader5 as mt\n",
|
||||
"import pandas as pd\n",
|
||||
"import sqlite3 as db\n",
|
||||
"from datetime import datetime\n",
|
||||
"import time\n",
|
||||
"from scipy.signal import savgol_filter\n",
|
||||
"import pandas_ta as ta\n",
|
||||
"import keyring as kr"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "1279652c",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 📊 Verbindung zu SQLite\n",
|
||||
"def init_db(db_name=\"trading_log.db\"):\n",
|
||||
" conn = db.connect(db_name)\n",
|
||||
" c = conn.cursor()\n",
|
||||
" c.execute(\"\"\"\n",
|
||||
" CREATE TABLE IF NOT EXISTS trade_log (\n",
|
||||
" timestamp TEXT,\n",
|
||||
" symbol TEXT,\n",
|
||||
" order_type TEXT,\n",
|
||||
" price REAL,\n",
|
||||
" sl REAL,\n",
|
||||
" tp REAL,\n",
|
||||
" volume REAL,\n",
|
||||
" signal INTEGER,\n",
|
||||
" comment TEXT\n",
|
||||
" )\n",
|
||||
" \"\"\")\n",
|
||||
" conn.commit()\n",
|
||||
" return conn"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"id": "fc1eb959",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"True"
|
||||
]
|
||||
},
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# 🔐 MT5 Login einmalig initialisieren\n",
|
||||
"mt.initialize()\n",
|
||||
"login = 10800246\n",
|
||||
"server = \"VantageInternational-Demo\"\n",
|
||||
"password = kr.get_password(server, str(login))\n",
|
||||
"mt.login(login, password, server)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"id": "840d41c6",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 📥 MT5 Daten abrufen\n",
|
||||
"def get_mt5_data(symbol=\"XAUUSD\", timeframe=mt.TIMEFRAME_M5, n_bars=500):\n",
|
||||
" rates = mt.copy_rates_from_pos(symbol, timeframe, 0, n_bars)\n",
|
||||
" df = pd.DataFrame(rates)\n",
|
||||
" df[\"time\"] = pd.to_datetime(df[\"time\"], unit=\"s\")\n",
|
||||
" return df"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"id": "48445ef4",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df = get_mt5_data()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"id": "964f927f",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 🤖 Signale erzeugen\n",
|
||||
"def generate_signal(df):\n",
|
||||
" df[\"ema10\"] = df[\"close\"].ewm(span=10).mean()\n",
|
||||
" df[\"ema30\"] = df[\"close\"].ewm(span=30).mean()\n",
|
||||
" df[\"rsi\"] = ta.rsi(df[\"close\"], length=14)\n",
|
||||
" df[\"trend\"] = savgol_filter(df[\"close\"], 15, 3)\n",
|
||||
" df[\"signal\"] = 0\n",
|
||||
"\n",
|
||||
" for i in range(1, len(df)):\n",
|
||||
" if (\n",
|
||||
" df[\"ema10\"].iloc[i] > df[\"ema30\"].iloc[i]\n",
|
||||
" and df[\"ema10\"].iloc[i - 1] <= df[\"ema30\"].iloc[i - 1]\n",
|
||||
" and df[\"rsi\"].iloc[i] < 70\n",
|
||||
" and df[\"trend\"].iloc[i] > df[\"trend\"].iloc[i - 1]\n",
|
||||
" ):\n",
|
||||
" df.at[i, \"signal\"] = 1\n",
|
||||
" elif (\n",
|
||||
" df[\"ema10\"].iloc[i] < df[\"ema30\"].iloc[i]\n",
|
||||
" and df[\"ema10\"].iloc[i - 1] >= df[\"ema30\"].iloc[i - 1]\n",
|
||||
" and df[\"rsi\"].iloc[i] > 30\n",
|
||||
" and df[\"trend\"].iloc[i] < df[\"trend\"].iloc[i - 1]\n",
|
||||
" ):\n",
|
||||
" df.at[i, \"signal\"] = -1\n",
|
||||
" return df"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"id": "7494a5da",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
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|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>time</th>\n",
|
||||
" <th>open</th>\n",
|
||||
" <th>high</th>\n",
|
||||
" <th>low</th>\n",
|
||||
" <th>close</th>\n",
|
||||
" <th>tick_volume</th>\n",
|
||||
" <th>spread</th>\n",
|
||||
" <th>real_volume</th>\n",
|
||||
" <th>ema10</th>\n",
|
||||
" <th>ema30</th>\n",
|
||||
" <th>rsi</th>\n",
|
||||
" <th>trend</th>\n",
|
||||
" <th>signal</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>2025-07-17 04:30:00</td>\n",
|
||||
" <td>3340.74</td>\n",
|
||||
" <td>3341.46</td>\n",
|
||||
" <td>3337.21</td>\n",
|
||||
" <td>3338.04</td>\n",
|
||||
" <td>1293</td>\n",
|
||||
" <td>18</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>3338.040000</td>\n",
|
||||
" <td>3338.040000</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>3338.208386</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>2025-07-17 04:35:00</td>\n",
|
||||
" <td>3338.03</td>\n",
|
||||
" <td>3341.03</td>\n",
|
||||
" <td>3337.72</td>\n",
|
||||
" <td>3341.03</td>\n",
|
||||
" <td>964</td>\n",
|
||||
" <td>18</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>3339.684500</td>\n",
|
||||
" <td>3339.584833</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>3340.377874</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>2025-07-17 04:40:00</td>\n",
|
||||
" <td>3341.03</td>\n",
|
||||
" <td>3341.48</td>\n",
|
||||
" <td>3339.96</td>\n",
|
||||
" <td>3341.26</td>\n",
|
||||
" <td>944</td>\n",
|
||||
" <td>18</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>3340.317841</td>\n",
|
||||
" <td>3340.180848</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>3341.873882</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>2025-07-17 04:45:00</td>\n",
|
||||
" <td>3341.27</td>\n",
|
||||
" <td>3343.45</td>\n",
|
||||
" <td>3341.27</td>\n",
|
||||
" <td>3342.47</td>\n",
|
||||
" <td>1099</td>\n",
|
||||
" <td>18</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>3341.026881</td>\n",
|
||||
" <td>3340.811593</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>3342.785860</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>2025-07-17 04:50:00</td>\n",
|
||||
" <td>3342.47</td>\n",
|
||||
" <td>3343.61</td>\n",
|
||||
" <td>3342.20</td>\n",
|
||||
" <td>3342.84</td>\n",
|
||||
" <td>1070</td>\n",
|
||||
" <td>18</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>3341.547378</td>\n",
|
||||
" <td>3341.273104</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>3343.203260</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>...</th>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>495</th>\n",
|
||||
" <td>2025-07-18 22:45:00</td>\n",
|
||||
" <td>3349.29</td>\n",
|
||||
" <td>3349.71</td>\n",
|
||||
" <td>3348.47</td>\n",
|
||||
" <td>3348.78</td>\n",
|
||||
" <td>928</td>\n",
|
||||
" <td>19</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>3350.024962</td>\n",
|
||||
" <td>3351.232735</td>\n",
|
||||
" <td>34.935115</td>\n",
|
||||
" <td>3348.405247</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>496</th>\n",
|
||||
" <td>2025-07-18 22:50:00</td>\n",
|
||||
" <td>3348.79</td>\n",
|
||||
" <td>3348.80</td>\n",
|
||||
" <td>3347.62</td>\n",
|
||||
" <td>3348.15</td>\n",
|
||||
" <td>859</td>\n",
|
||||
" <td>19</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>3349.684060</td>\n",
|
||||
" <td>3351.033849</td>\n",
|
||||
" <td>32.290909</td>\n",
|
||||
" <td>3348.186635</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>497</th>\n",
|
||||
" <td>2025-07-18 22:55:00</td>\n",
|
||||
" <td>3348.16</td>\n",
|
||||
" <td>3348.53</td>\n",
|
||||
" <td>3347.65</td>\n",
|
||||
" <td>3348.25</td>\n",
|
||||
" <td>1028</td>\n",
|
||||
" <td>19</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>3349.423322</td>\n",
|
||||
" <td>3350.854245</td>\n",
|
||||
" <td>33.155760</td>\n",
|
||||
" <td>3348.153324</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>498</th>\n",
|
||||
" <td>2025-07-18 23:00:00</td>\n",
|
||||
" <td>3348.24</td>\n",
|
||||
" <td>3348.88</td>\n",
|
||||
" <td>3347.72</td>\n",
|
||||
" <td>3348.06</td>\n",
|
||||
" <td>599</td>\n",
|
||||
" <td>19</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>3349.175445</td>\n",
|
||||
" <td>3350.673972</td>\n",
|
||||
" <td>32.311285</td>\n",
|
||||
" <td>3348.346744</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>499</th>\n",
|
||||
" <td>2025-07-18 23:05:00</td>\n",
|
||||
" <td>3348.06</td>\n",
|
||||
" <td>3349.25</td>\n",
|
||||
" <td>3347.92</td>\n",
|
||||
" <td>3348.88</td>\n",
|
||||
" <td>450</td>\n",
|
||||
" <td>19</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>3349.121728</td>\n",
|
||||
" <td>3350.558231</td>\n",
|
||||
" <td>39.476021</td>\n",
|
||||
" <td>3348.808327</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"<p>500 rows × 13 columns</p>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" time open high low close tick_volume \\\n",
|
||||
"0 2025-07-17 04:30:00 3340.74 3341.46 3337.21 3338.04 1293 \n",
|
||||
"1 2025-07-17 04:35:00 3338.03 3341.03 3337.72 3341.03 964 \n",
|
||||
"2 2025-07-17 04:40:00 3341.03 3341.48 3339.96 3341.26 944 \n",
|
||||
"3 2025-07-17 04:45:00 3341.27 3343.45 3341.27 3342.47 1099 \n",
|
||||
"4 2025-07-17 04:50:00 3342.47 3343.61 3342.20 3342.84 1070 \n",
|
||||
".. ... ... ... ... ... ... \n",
|
||||
"495 2025-07-18 22:45:00 3349.29 3349.71 3348.47 3348.78 928 \n",
|
||||
"496 2025-07-18 22:50:00 3348.79 3348.80 3347.62 3348.15 859 \n",
|
||||
"497 2025-07-18 22:55:00 3348.16 3348.53 3347.65 3348.25 1028 \n",
|
||||
"498 2025-07-18 23:00:00 3348.24 3348.88 3347.72 3348.06 599 \n",
|
||||
"499 2025-07-18 23:05:00 3348.06 3349.25 3347.92 3348.88 450 \n",
|
||||
"\n",
|
||||
" spread real_volume ema10 ema30 rsi trend \\\n",
|
||||
"0 18 0 3338.040000 3338.040000 NaN 3338.208386 \n",
|
||||
"1 18 0 3339.684500 3339.584833 NaN 3340.377874 \n",
|
||||
"2 18 0 3340.317841 3340.180848 NaN 3341.873882 \n",
|
||||
"3 18 0 3341.026881 3340.811593 NaN 3342.785860 \n",
|
||||
"4 18 0 3341.547378 3341.273104 NaN 3343.203260 \n",
|
||||
".. ... ... ... ... ... ... \n",
|
||||
"495 19 0 3350.024962 3351.232735 34.935115 3348.405247 \n",
|
||||
"496 19 0 3349.684060 3351.033849 32.290909 3348.186635 \n",
|
||||
"497 19 0 3349.423322 3350.854245 33.155760 3348.153324 \n",
|
||||
"498 19 0 3349.175445 3350.673972 32.311285 3348.346744 \n",
|
||||
"499 19 0 3349.121728 3350.558231 39.476021 3348.808327 \n",
|
||||
"\n",
|
||||
" signal \n",
|
||||
"0 0 \n",
|
||||
"1 0 \n",
|
||||
"2 0 \n",
|
||||
"3 0 \n",
|
||||
"4 0 \n",
|
||||
".. ... \n",
|
||||
"495 0 \n",
|
||||
"496 0 \n",
|
||||
"497 0 \n",
|
||||
"498 0 \n",
|
||||
"499 0 \n",
|
||||
"\n",
|
||||
"[500 rows x 13 columns]"
|
||||
]
|
||||
},
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"generate_signal(df)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"id": "0a8abc07",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"0.01"
|
||||
]
|
||||
},
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"mt.symbol_info('XAUUSD').point"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "64757821",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 📤 Order senden\n",
|
||||
"def send_market_order(symbol, volume, order_type, signal, conn, sl_pips=20, tp_pips=40, magic=1001):\n",
|
||||
" tick = mt.symbol_info_tick(symbol)\n",
|
||||
" price = tick.ask if order_type == mt.ORDER_TYPE_BUY else tick.bid\n",
|
||||
" point = mt.symbol_info(symbol).point\n",
|
||||
"\n",
|
||||
" sl = price - sl_pips * point if order_type == mt.ORDER_TYPE_BUY else price + sl_pips * point\n",
|
||||
" tp = price + tp_pips * point if order_type == mt.ORDER_TYPE_BUY else price - tp_pips * point\n",
|
||||
"\n",
|
||||
" request = {\n",
|
||||
" \"action\": mt.TRADE_ACTION_DEAL,\n",
|
||||
" \"symbol\": symbol,\n",
|
||||
" \"volume\": volume,\n",
|
||||
" \"type\": order_type,\n",
|
||||
" \"price\": price,\n",
|
||||
" \"sl\": round(sl, 5),\n",
|
||||
" \"tp\": round(tp, 5),\n",
|
||||
" \"deviation\": 20,\n",
|
||||
" \"magic\": magic,\n",
|
||||
" \"comment\": \"AutoSignalBot\",\n",
|
||||
" \"type_time\": mt.ORDER_TIME_GTC,\n",
|
||||
" \"type_filling\": mt.ORDER_FILLING_IOC,\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" result = mt.order_send(request)\n",
|
||||
" print(f\"[TRADE] {symbol} - {'BUY' if order_type==0 else 'SELL'} @ {price} | SL: {sl} | TP: {tp}\")\n",
|
||||
"\n",
|
||||
" # Logging in DB\n",
|
||||
" c = conn.cursor()\n",
|
||||
" c.execute(\"\"\"\n",
|
||||
" INSERT INTO trade_log (timestamp, symbol, order_type, price, sl, tp, volume, signal, comment)\n",
|
||||
" VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)\n",
|
||||
" \"\"\", (datetime.now(), symbol, \"BUY\" if order_type==0 else \"SELL\", price, sl, tp, volume, signal, \"AutoSignalBot\"))\n",
|
||||
" conn.commit()\n",
|
||||
" \n",
|
||||
" return result"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"id": "3bc5f11e",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 🔁 Hauptfunktion\n",
|
||||
"def run_live_trading(symbol=\"XAUUSD\", interval=300, volume=0.1, db_name=\"trading_log.db\"):\n",
|
||||
" conn = init_db(db_name)\n",
|
||||
" last_signal = 0\n",
|
||||
"\n",
|
||||
" print(f\"✅ Starte Auto-Trading für {symbol} – Intervall {interval}s\")\n",
|
||||
" while True:\n",
|
||||
" df = get_mt5_data(symbol)\n",
|
||||
" df = generate_signal(df)\n",
|
||||
" signal = df[\"signal\"].iloc[-1]\n",
|
||||
"\n",
|
||||
" if signal != 0 and signal != last_signal:\n",
|
||||
" order_type = mt.ORDER_TYPE_BUY if signal == 1 else mt.ORDER_TYPE_SELL\n",
|
||||
" send_market_order(symbol, volume, order_type, signal, conn)\n",
|
||||
" last_signal = signal\n",
|
||||
" else:\n",
|
||||
" print(f\"[{datetime.now()}] Kein neues Signal ({signal})\")\n",
|
||||
"\n",
|
||||
" time.sleep(interval)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"id": "71acdb0d",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"✅ Starte Auto-Trading für XAUUSD – Intervall 300s\n",
|
||||
"[2025-07-18 12:11:30.004688] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 12:16:30.083605] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 12:21:30.121793] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 12:26:30.174396] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 12:31:30.216996] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 12:36:30.258849] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 12:41:30.313072] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 12:46:30.356620] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 12:51:30.396600] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 12:56:30.442114] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 13:01:30.481155] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 13:06:30.530619] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 13:11:30.566248] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 13:16:30.606701] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 13:21:30.645462] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 13:26:30.686722] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 13:31:31.130100] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 13:36:31.193825] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 13:41:31.381330] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 13:46:31.434187] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 13:51:31.471695] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 13:56:31.507322] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 14:01:31.550436] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 14:06:31.587884] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 14:11:31.665002] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 14:16:31.704012] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 14:21:31.745292] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 14:26:31.783905] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 14:31:31.822325] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 14:36:31.873271] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 14:41:31.914303] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 14:46:31.951585] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 14:51:32.005271] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 14:56:32.046384] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 15:01:32.083259] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 15:06:32.132835] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 15:11:32.176714] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 15:16:32.221857] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 15:21:32.269876] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 15:26:32.313363] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 15:31:32.356954] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 15:36:32.403653] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 15:41:32.449777] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 15:46:32.489053] Kein neues Signal (0)\n",
|
||||
"[TRADE] XAUUSD - SELL @ 3354.56 | SL: 3354.7599999999998 | TP: 3354.16\n",
|
||||
"[2025-07-18 15:56:32.609729] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 16:01:32.650996] Kein neues Signal (0)\n",
|
||||
"[TRADE] XAUUSD - BUY @ 3358.95 | SL: 3358.75 | TP: 3359.35\n",
|
||||
"[2025-07-18 16:11:32.744972] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 16:16:32.791366] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 16:21:32.829930] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 16:26:32.871934] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 16:31:32.912571] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 16:36:32.954745] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 16:41:32.994281] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 16:46:33.048113] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 16:51:33.086281] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 16:56:33.127117] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 17:01:33.177813] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 17:06:33.219713] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 17:11:33.260138] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 17:16:33.302870] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 17:21:33.346259] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 17:26:33.390947] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 17:31:33.434736] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 17:36:33.476901] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 17:41:33.520435] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 17:46:33.575426] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 17:51:33.622573] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 17:56:33.662664] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 18:01:33.703019] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 18:06:33.748220] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 18:11:33.788812] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 18:16:33.827200] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 18:21:33.880825] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 18:26:33.931042] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 18:31:33.982017] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 18:36:34.021865] Kein neues Signal (1)\n",
|
||||
"[2025-07-18 18:41:34.059505] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 18:46:34.100395] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 18:51:34.148794] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 18:56:34.186791] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 19:01:34.227630] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 19:06:34.272331] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 19:11:34.309122] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 19:16:34.350483] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 19:21:34.391638] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 19:26:34.430235] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 19:31:34.476827] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 19:36:34.516804] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 19:41:34.551478] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 19:46:34.600704] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 19:51:34.657098] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 19:56:34.694871] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 20:01:34.750662] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 20:06:34.786445] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 20:11:34.941510] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 20:16:34.997686] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 20:21:35.041835] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 20:26:35.082677] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 20:31:35.135303] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 20:36:35.174403] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 20:41:35.211876] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 20:46:35.254113] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 20:51:35.297650] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 20:56:35.338598] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 21:01:35.378704] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 21:06:35.421315] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 21:11:35.446675] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 21:16:35.501085] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 21:21:35.547489] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 21:26:35.600292] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 21:31:35.642080] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 21:36:35.681283] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 21:41:35.719258] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 21:46:35.756743] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 21:51:35.802329] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 21:56:35.849136] Kein neues Signal (0)\n",
|
||||
"[2025-07-18 22:01:35.907573] Kein neues Signal (0)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"ename": "KeyboardInterrupt",
|
||||
"evalue": "",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
|
||||
"Cell \u001b[1;32mIn[22], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;66;03m# ▶️ Live-Trading starten (z. B. alle 5 Minuten)\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m run_live_trading(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mXAUUSD\u001b[39m\u001b[38;5;124m\"\u001b[39m, interval\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m300\u001b[39m, volume\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.1\u001b[39m)\n",
|
||||
"Cell \u001b[1;32mIn[13], line 19\u001b[0m, in \u001b[0;36mrun_live_trading\u001b[1;34m(symbol, interval, volume, db_name)\u001b[0m\n\u001b[0;32m 16\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 17\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m[\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdatetime\u001b[38;5;241m.\u001b[39mnow()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m] Kein neues Signal (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00msignal\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m---> 19\u001b[0m time\u001b[38;5;241m.\u001b[39msleep(interval)\n",
|
||||
"\u001b[1;31mKeyboardInterrupt\u001b[0m: "
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# ▶️ Live-Trading starten (z. B. alle 5 Minuten)\n",
|
||||
"run_live_trading(\"XAUUSD\", interval=300, volume=0.1)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"id": "a7675469",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>timestamp</th>\n",
|
||||
" <th>symbol</th>\n",
|
||||
" <th>order_type</th>\n",
|
||||
" <th>price</th>\n",
|
||||
" <th>sl</th>\n",
|
||||
" <th>tp</th>\n",
|
||||
" <th>volume</th>\n",
|
||||
" <th>signal</th>\n",
|
||||
" <th>comment</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>2025-07-18 16:06:32.701648</td>\n",
|
||||
" <td>XAUUSD</td>\n",
|
||||
" <td>BUY</td>\n",
|
||||
" <td>3358.95</td>\n",
|
||||
" <td>3358.75</td>\n",
|
||||
" <td>3359.35</td>\n",
|
||||
" <td>0.1</td>\n",
|
||||
" <td>b'\\x01\\x00\\x00\\x00\\x00\\x00\\x00\\x00'</td>\n",
|
||||
" <td>AutoSignalBot</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>2025-07-18 15:51:32.545794</td>\n",
|
||||
" <td>XAUUSD</td>\n",
|
||||
" <td>SELL</td>\n",
|
||||
" <td>3354.56</td>\n",
|
||||
" <td>3354.76</td>\n",
|
||||
" <td>3354.16</td>\n",
|
||||
" <td>0.1</td>\n",
|
||||
" <td>b'\\xff\\xff\\xff\\xff\\xff\\xff\\xff\\xff'</td>\n",
|
||||
" <td>AutoSignalBot</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" timestamp symbol order_type price sl tp \\\n",
|
||||
"0 2025-07-18 16:06:32.701648 XAUUSD BUY 3358.95 3358.75 3359.35 \n",
|
||||
"1 2025-07-18 15:51:32.545794 XAUUSD SELL 3354.56 3354.76 3354.16 \n",
|
||||
"\n",
|
||||
" volume signal comment \n",
|
||||
"0 0.1 b'\\x01\\x00\\x00\\x00\\x00\\x00\\x00\\x00' AutoSignalBot \n",
|
||||
"1 0.1 b'\\xff\\xff\\xff\\xff\\xff\\xff\\xff\\xff' AutoSignalBot "
|
||||
]
|
||||
},
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# 🧾 Optional: SQLite-Datenbank anzeigen\n",
|
||||
"conn = db.connect(\"trading_log.db\")\n",
|
||||
"pd.read_sql(\"SELECT * FROM trade_log ORDER BY timestamp DESC LIMIT 10\", conn)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "b4512a2c",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "ec808aea",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"\n",
|
||||
"import numpy as np\n",
|
||||
"\n",
|
||||
"def calculate_fib_levels(df, lookback=20):\n",
|
||||
" swing_high = df['high'].rolling(lookback).max().iloc[-1]\n",
|
||||
" swing_low = df['low'].rolling(lookback).min().iloc[-1]\n",
|
||||
" diff = swing_high - swing_low\n",
|
||||
" levels = {\n",
|
||||
" '0.0': swing_low,\n",
|
||||
" '0.236': swing_high - 0.236 * diff,\n",
|
||||
" '0.382': swing_high - 0.382 * diff,\n",
|
||||
" '0.5': swing_high - 0.5 * diff,\n",
|
||||
" '0.618': swing_high - 0.618 * diff,\n",
|
||||
" '0.786': swing_high - 0.786 * diff,\n",
|
||||
" '1.0': swing_high\n",
|
||||
" }\n",
|
||||
" return levels\n",
|
||||
"\n",
|
||||
"def calculate_atr(df, period=14):\n",
|
||||
" df['H-L'] = df['high'] - df['low']\n",
|
||||
" df['H-C'] = abs(df['high'] - df['close'].shift())\n",
|
||||
" df['L-C'] = abs(df['low'] - df['close'].shift())\n",
|
||||
" df['TR'] = df[['H-L', 'H-C', 'L-C']].max(axis=1)\n",
|
||||
" df['ATR'] = df['TR'].rolling(period).mean()\n",
|
||||
" return df\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "09b3f9df",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"\n",
|
||||
"def send_fib_trade(df, symbol, order_type, volume):\n",
|
||||
" fib = calculate_fib_levels(df)\n",
|
||||
" df = calculate_atr(df)\n",
|
||||
" atr = df['ATR'].iloc[-1]\n",
|
||||
" point = mt.symbol_info(symbol).point\n",
|
||||
" tick = mt.symbol_info_tick(symbol)\n",
|
||||
" price = tick.ask if order_type == mt.ORDER_TYPE_BUY else tick.bid\n",
|
||||
"\n",
|
||||
" sl = price - atr if order_type == mt.ORDER_TYPE_BUY else price + atr\n",
|
||||
" tp = price + 2 * atr if order_type == mt.ORDER_TYPE_BUY else price - 2 * atr\n",
|
||||
"\n",
|
||||
" request = {\n",
|
||||
" \"action\": mt.TRADE_ACTION_DEAL,\n",
|
||||
" \"symbol\": symbol,\n",
|
||||
" \"volume\": volume,\n",
|
||||
" \"type\": order_type,\n",
|
||||
" \"price\": price,\n",
|
||||
" \"sl\": round(sl, 5),\n",
|
||||
" \"tp\": round(tp, 5),\n",
|
||||
" \"deviation\": 20,\n",
|
||||
" \"magic\": 1010,\n",
|
||||
" \"comment\": \"FibATR_Trade\",\n",
|
||||
" \"type_time\": mt.ORDER_TIME_GTC,\n",
|
||||
" \"type_filling\": mt.ORDER_FILLING_IOC,\n",
|
||||
" }\n",
|
||||
" return mt.order_send(request)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "8878e31f",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"\n",
|
||||
"def plot_fib_levels(df, levels):\n",
|
||||
" plt.figure(figsize=(12, 6))\n",
|
||||
" plt.plot(df['close'], label='Close Price', alpha=0.5)\n",
|
||||
" last_index = df.index[-1]\n",
|
||||
" start_index = df.index[-50] if len(df) >= 50 else df.index[0]\n",
|
||||
"\n",
|
||||
" for level, price in levels.items():\n",
|
||||
" plt.hlines(price, start_index, last_index, label=f'Fibo {level}: {round(price, 2)}', linestyles='--')\n",
|
||||
"\n",
|
||||
" plt.title(\"Fibonacci Retracement Levels\")\n",
|
||||
" plt.xlabel(\"Zeit\")\n",
|
||||
" plt.ylabel(\"Preis\")\n",
|
||||
" plt.legend()\n",
|
||||
" plt.grid(True)\n",
|
||||
" plt.tight_layout()\n",
|
||||
" plt.show()\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "ffa9c58a",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"\n",
|
||||
"def check_fib_entry(df, fib_levels, tolerance=0.01):\n",
|
||||
" current_price = df['close'].iloc[-1]\n",
|
||||
" level_price = fib_levels['0.382']\n",
|
||||
" diff = abs(current_price - level_price)\n",
|
||||
" fib_range = fib_levels['1.0'] - fib_levels['0.0']\n",
|
||||
" return diff < tolerance * fib_range\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "base",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@@ -1,66 +0,0 @@
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
from backtesting import Backtest, Strategy
|
||||
from backtesting.lib import crossover
|
||||
from ta.momentum import rsi
|
||||
from scipy.signal import savgol_filter
|
||||
|
||||
# Daten vorbereiten (df muss OHLCV-Daten enthalten: 'open', 'high', 'low', 'close', 'volume')
|
||||
df = pd.read_csv('xauusd_5min.csv', parse_dates=['time'])
|
||||
df.set_index('time', inplace=True)
|
||||
|
||||
# Indikatoren berechnen
|
||||
def calculate_indicators(df):
|
||||
df['ema21'] = df['close'].ewm(span=21).mean()
|
||||
df['ema50'] = df['close'].ewm(span=50).mean()
|
||||
df['rsi9'] = rsi(df['close'], length=9)
|
||||
df['rsi14'] = rsi(df['close'], length=14)
|
||||
df['trend'] = savgol_filter(df['close'], window_length=25, polyorder=3)
|
||||
return df
|
||||
|
||||
df = calculate_indicators(df)
|
||||
|
||||
# Strategie definieren
|
||||
class Gold5MinStrategy(Strategy):
|
||||
def init(self):
|
||||
# Indikatoren für den Plot
|
||||
self.add_indicator('EMA21', self.data.ema21)
|
||||
self.add_indicator('EMA50', self.data.ema50)
|
||||
|
||||
def next(self):
|
||||
current_index = len(self.data.close) - 1
|
||||
|
||||
# Long-Signal (Kauf)
|
||||
if (
|
||||
crossover(self.data.ema21, self.data.ema50)
|
||||
and self.data.rsi14[-1] < 65
|
||||
and self.data.rsi9[-1] > 50
|
||||
and self.data.trend[-1] > self.data.trend[-2]
|
||||
and not self.position.is_long
|
||||
):
|
||||
self.buy(sl=self.data.low[-1] * 0.995, tp=self.data.close[-1] * 1.01) # 0.5% SL, 1% TP
|
||||
|
||||
# Short-Signal (Verkauf)
|
||||
elif (
|
||||
crossover(self.data.ema50, self.data.ema21)
|
||||
and self.data.rsi14[-1] > 35
|
||||
and self.data.rsi9[-1] < 50
|
||||
and self.data.trend[-1] < self.data.trend[-2]
|
||||
and not self.position.is_short
|
||||
):
|
||||
self.sell(sl=self.data.high[-1] * 1.005, tp=self.data.close[-1] * 0.99) # 0.5% SL, 1% TP
|
||||
|
||||
# Backtest ausführen
|
||||
bt = Backtest(df, Gold5MinStrategy, commission=0.0002, margin=0.05) # 0.02% Kommission, 5% Margin
|
||||
stats = bt.run()
|
||||
print(stats)
|
||||
|
||||
# Optimierung (optional)
|
||||
# stats_opt = bt.optimize(
|
||||
# rsi_long_upper=[60, 65, 70],
|
||||
# rsi_short_lower=[30, 35, 40],
|
||||
# maximize='Return [%]'
|
||||
# )
|
||||
|
||||
# Ergebnisse plotten
|
||||
bt.plot()
|
||||
@@ -0,0 +1,508 @@
|
||||
# 📊 Streamlit Dashboard - Windows Server Deployment
|
||||
|
||||
## Übersicht
|
||||
|
||||
Diese Anleitung zeigt dir wie du das Trading Dashboard auf einem **Windows Server** (oder Windows VPS) zum Laufen bringst.
|
||||
|
||||
## 🚀 Installation auf Windows Server
|
||||
|
||||
### SCHRITT 1: Python & Dependencies prüfen
|
||||
|
||||
**1. Öffne PowerShell als Administrator**
|
||||
|
||||
```powershell
|
||||
# Prüfe Python Version
|
||||
python --version
|
||||
# Sollte Python 3.8+ zeigen
|
||||
|
||||
# Falls Python nicht gefunden wird:
|
||||
# Installiere von: https://www.python.org/downloads/
|
||||
```
|
||||
|
||||
**2. Navigiere zum Trading Bot Ordner**
|
||||
|
||||
```powershell
|
||||
# Beispiel (passe Pfad an):
|
||||
cd C:\TradingBot\placeorder
|
||||
```
|
||||
|
||||
**3. Installiere Streamlit & Dependencies**
|
||||
|
||||
```powershell
|
||||
pip install streamlit plotly pandas
|
||||
```
|
||||
|
||||
Falls `pip` nicht funktioniert:
|
||||
```powershell
|
||||
python -m pip install streamlit plotly pandas
|
||||
```
|
||||
|
||||
### SCHRITT 2: Dashboard-Datei kopieren
|
||||
|
||||
**Option A: Via RDP (Remote Desktop)**
|
||||
|
||||
1. Verbinde per RDP zu deinem Windows Server
|
||||
2. Öffne Browser auf dem Server
|
||||
3. Lade `trading_dashboard.py` herunter (z.B. via GitHub, Email, Cloud)
|
||||
4. Kopiere nach: `C:\TradingBot\placeorder\`
|
||||
|
||||
**Option B: Via SCP (von Mac aus)**
|
||||
|
||||
```bash
|
||||
# Auf deinem Mac (Terminal)
|
||||
scp "/Users/sebastianfrohlich/Library/Mobile Documents/com~apple~CloudDocs/Jupyter Notebooks/FinancialTrading/PlaceOrder/placeorder/trading_dashboard.py" user@windows-server-ip:C:/TradingBot/placeorder/
|
||||
```
|
||||
|
||||
**Hinweis:** Benötigt OpenSSH Server auf Windows. Siehe unten für Aktivierung.
|
||||
|
||||
**Option C: Manuell erstellen**
|
||||
|
||||
1. Auf Windows Server: Öffne Notepad++, VSCode oder Editor
|
||||
2. Erstelle neue Datei: `trading_dashboard.py`
|
||||
3. Copy-Paste den Code aus der Datei
|
||||
4. Speichere in: `C:\TradingBot\placeorder\`
|
||||
|
||||
### SCHRITT 3: Dashboard starten
|
||||
|
||||
**Option A: PowerShell (zum Testen)**
|
||||
|
||||
```powershell
|
||||
cd C:\TradingBot\placeorder
|
||||
streamlit run trading_dashboard.py --server.port 8501 --server.address 0.0.0.0
|
||||
```
|
||||
|
||||
Das Dashboard startet und sollte automatisch den Browser öffnen!
|
||||
|
||||
**Option B: CMD (Command Prompt)**
|
||||
|
||||
```cmd
|
||||
cd C:\TradingBot\placeorder
|
||||
streamlit run trading_dashboard.py --server.port 8501 --server.address 0.0.0.0
|
||||
```
|
||||
|
||||
### SCHRITT 4: Firewall-Regel erstellen
|
||||
|
||||
**Windows Firewall für Port 8501 öffnen:**
|
||||
|
||||
**Via GUI:**
|
||||
1. Windows-Taste → "Windows Defender Firewall"
|
||||
2. "Erweiterte Einstellungen"
|
||||
3. "Eingehende Regeln" → "Neue Regel..."
|
||||
4. "Port" auswählen → Weiter
|
||||
5. "TCP" → "Bestimmte lokale Ports": **8501** → Weiter
|
||||
6. "Verbindung zulassen" → Weiter
|
||||
7. Alle Profile aktivieren → Weiter
|
||||
8. Name: "Streamlit Dashboard" → Fertig
|
||||
|
||||
**Via PowerShell (als Administrator):**
|
||||
|
||||
```powershell
|
||||
New-NetFirewallRule -DisplayName "Streamlit Dashboard" -Direction Inbound -LocalPort 8501 -Protocol TCP -Action Allow
|
||||
```
|
||||
|
||||
### SCHRITT 5: Dashboard im Browser öffnen
|
||||
|
||||
**Lokal auf dem Server:**
|
||||
```
|
||||
http://localhost:8501
|
||||
```
|
||||
|
||||
**Von anderem Computer:**
|
||||
```
|
||||
http://WINDOWS-SERVER-IP:8501
|
||||
```
|
||||
|
||||
Beispiel:
|
||||
```
|
||||
http://192.168.1.100:8501
|
||||
```
|
||||
|
||||
## 🔄 Dashboard permanent laufen lassen
|
||||
|
||||
### Option 1: Als Windows Service (EMPFOHLEN)
|
||||
|
||||
**Mit NSSM (Non-Sucking Service Manager):**
|
||||
|
||||
**1. NSSM herunterladen:**
|
||||
```
|
||||
https://nssm.cc/download
|
||||
```
|
||||
|
||||
**2. NSSM installieren:**
|
||||
|
||||
```powershell
|
||||
# NSSM entpacken nach C:\nssm
|
||||
# PowerShell als Administrator öffnen
|
||||
cd C:\nssm\win64
|
||||
|
||||
# Service erstellen
|
||||
.\nssm.exe install TradingDashboard
|
||||
```
|
||||
|
||||
**3. Im NSSM GUI konfigurieren:**
|
||||
|
||||
- **Application Tab:**
|
||||
- Path: `C:\Python310\Scripts\streamlit.exe` (passe Python-Pfad an)
|
||||
- Startup directory: `C:\TradingBot\placeorder`
|
||||
- Arguments: `run trading_dashboard.py --server.port 8501 --server.address 0.0.0.0`
|
||||
|
||||
- **Details Tab:**
|
||||
- Display name: `Trading Dashboard`
|
||||
- Description: `Streamlit Dashboard for Trading Bot V1.8`
|
||||
|
||||
- **Log on Tab:**
|
||||
- Wähle Account (z.B. dein User-Account)
|
||||
|
||||
- **I/O Tab:**
|
||||
- Output (stdout): `C:\TradingBot\placeorder\dashboard_stdout.log`
|
||||
- Error (stderr): `C:\TradingBot\placeorder\dashboard_stderr.log`
|
||||
|
||||
**4. Service starten:**
|
||||
|
||||
```powershell
|
||||
# Service starten
|
||||
.\nssm.exe start TradingDashboard
|
||||
|
||||
# Service Status prüfen
|
||||
.\nssm.exe status TradingDashboard
|
||||
|
||||
# Service stoppen
|
||||
.\nssm.exe stop TradingDashboard
|
||||
|
||||
# Service entfernen (falls nötig)
|
||||
.\nssm.exe remove TradingDashboard
|
||||
```
|
||||
|
||||
**Oder via Windows Services:**
|
||||
1. Windows-Taste → "services.msc"
|
||||
2. Finde "Trading Dashboard"
|
||||
3. Rechtsklick → "Starten"
|
||||
|
||||
### Option 2: Batch-Datei + Task Scheduler
|
||||
|
||||
**1. Erstelle Batch-Datei:**
|
||||
|
||||
Erstelle `start_dashboard.bat`:
|
||||
|
||||
```batch
|
||||
@echo off
|
||||
echo Starting Trading Dashboard...
|
||||
cd /d C:\TradingBot\placeorder
|
||||
streamlit run trading_dashboard.py --server.port 8501 --server.address 0.0.0.0
|
||||
pause
|
||||
```
|
||||
|
||||
**2. Task Scheduler konfigurieren:**
|
||||
|
||||
1. Windows-Taste → "Task Scheduler"
|
||||
2. "Create Basic Task..."
|
||||
3. Name: "Trading Dashboard"
|
||||
4. Trigger: "When the computer starts"
|
||||
5. Action: "Start a program"
|
||||
6. Program: `C:\TradingBot\placeorder\start_dashboard.bat`
|
||||
7. Finish
|
||||
|
||||
**Task manuell starten:**
|
||||
- Task Scheduler → Rechtsklick auf Task → "Run"
|
||||
|
||||
### Option 3: PowerShell Background Job (temporär)
|
||||
|
||||
```powershell
|
||||
# Starte als Background Job
|
||||
Start-Process powershell -ArgumentList "-NoExit", "-Command", "cd C:\TradingBot\placeorder; streamlit run trading_dashboard.py --server.port 8501 --server.address 0.0.0.0"
|
||||
|
||||
# Prüfe laufende Prozesse
|
||||
Get-Process | Where-Object {$_.ProcessName -like "*streamlit*"}
|
||||
|
||||
# Stoppe Dashboard
|
||||
Stop-Process -Name streamlit -Force
|
||||
```
|
||||
|
||||
## 🔒 Sicherheit & Zugriff
|
||||
|
||||
### Option 1: SSH Tunnel (von Mac aus)
|
||||
|
||||
**Falls OpenSSH Server auf Windows aktiviert:**
|
||||
|
||||
```bash
|
||||
# Auf Mac
|
||||
ssh -L 8501:localhost:8501 user@windows-server-ip
|
||||
|
||||
# Dann Browser öffnen
|
||||
http://localhost:8501
|
||||
```
|
||||
|
||||
**OpenSSH Server auf Windows aktivieren:**
|
||||
|
||||
```powershell
|
||||
# PowerShell als Administrator
|
||||
Add-WindowsCapability -Online -Name OpenSSH.Server~~~~0.0.1.0
|
||||
|
||||
# SSH Service starten
|
||||
Start-Service sshd
|
||||
Set-Service -Name sshd -StartupType 'Automatic'
|
||||
|
||||
# Firewall-Regel (falls nötig)
|
||||
New-NetFirewallRule -Name sshd -DisplayName 'OpenSSH Server (sshd)' -Enabled True -Direction Inbound -Protocol TCP -Action Allow -LocalPort 22
|
||||
```
|
||||
|
||||
### Option 2: IIS Reverse Proxy (für HTTPS)
|
||||
|
||||
Falls du IIS verwendest, kannst du einen Reverse Proxy einrichten:
|
||||
|
||||
1. Installiere "Application Request Routing" (ARR)
|
||||
2. Installiere "URL Rewrite"
|
||||
3. Konfiguriere Reverse Proxy von Port 80/443 → 8501
|
||||
|
||||
### Option 3: VPN-Only Access
|
||||
|
||||
Öffne Port 8501 nur im VPN-Netzwerk, nicht öffentlich.
|
||||
|
||||
## 📱 Zugriff von außen (Internet)
|
||||
|
||||
### Router Port Forwarding (falls Home Server)
|
||||
|
||||
1. Router-Verwaltung öffnen
|
||||
2. Port Forwarding Regel erstellen:
|
||||
- External Port: 8501
|
||||
- Internal IP: Windows Server IP (z.B. 192.168.1.100)
|
||||
- Internal Port: 8501
|
||||
- Protocol: TCP
|
||||
|
||||
3. Zugriff via:
|
||||
```
|
||||
http://DEINE-ÖFFENTLICHE-IP:8501
|
||||
```
|
||||
|
||||
### Cloud VPS (z.B. Azure, AWS)
|
||||
|
||||
**Firewall/Security Group konfigurieren:**
|
||||
- Erlaube eingehenden Traffic auf Port 8501 (TCP)
|
||||
- Von deiner IP oder 0.0.0.0/0 (öffentlich, vorsichtig!)
|
||||
|
||||
## 🛠️ Troubleshooting
|
||||
|
||||
### Dashboard startet nicht
|
||||
|
||||
**1. Prüfe Python/Streamlit Installation:**
|
||||
|
||||
```powershell
|
||||
python --version
|
||||
streamlit --version
|
||||
```
|
||||
|
||||
**2. Prüfe ob Port belegt:**
|
||||
|
||||
```powershell
|
||||
netstat -ano | findstr :8501
|
||||
```
|
||||
|
||||
Falls Port belegt, finde Prozess:
|
||||
```powershell
|
||||
# Nutze die PID aus netstat output
|
||||
tasklist | findstr <PID>
|
||||
|
||||
# Prozess killen
|
||||
taskkill /PID <PID> /F
|
||||
```
|
||||
|
||||
**3. Prüfe Logs:**
|
||||
|
||||
Falls als Service läuft, schaue in:
|
||||
```
|
||||
C:\TradingBot\placeorder\dashboard_stdout.log
|
||||
C:\TradingBot\placeorder\dashboard_stderr.log
|
||||
```
|
||||
|
||||
### Dashboard nicht erreichbar von außen
|
||||
|
||||
**1. Firewall prüfen:**
|
||||
|
||||
```powershell
|
||||
Get-NetFirewallRule | Where-Object {$_.LocalPort -eq 8501}
|
||||
```
|
||||
|
||||
**2. Prüfe ob Streamlit auf richtiger IP hört:**
|
||||
|
||||
```powershell
|
||||
netstat -ano | findstr :8501
|
||||
```
|
||||
|
||||
Sollte zeigen: `0.0.0.0:8501` (nicht `127.0.0.1:8501`)
|
||||
|
||||
Falls falsch, starte mit `--server.address 0.0.0.0`:
|
||||
```powershell
|
||||
streamlit run trading_dashboard.py --server.port 8501 --server.address 0.0.0.0
|
||||
```
|
||||
|
||||
### Database Error
|
||||
|
||||
```powershell
|
||||
# Prüfe ob trading_bot.db existiert
|
||||
dir trading_bot.db
|
||||
|
||||
# Falls nicht:
|
||||
# 1. Starte zuerst den Trading Bot (Jupyter Notebook)
|
||||
# 2. Der erstellt automatisch die Datenbank
|
||||
```
|
||||
|
||||
## 📊 PowerShell Helper Scripts
|
||||
|
||||
### start_dashboard.ps1
|
||||
|
||||
```powershell
|
||||
# Trading Dashboard Starter Script
|
||||
Write-Host "Starting Trading Dashboard..." -ForegroundColor Green
|
||||
|
||||
$path = "C:\TradingBot\placeorder"
|
||||
Set-Location $path
|
||||
|
||||
# Check if database exists
|
||||
if (!(Test-Path "trading_bot.db")) {
|
||||
Write-Host "WARNING: trading_bot.db not found!" -ForegroundColor Yellow
|
||||
Write-Host "Make sure the Trading Bot is running first." -ForegroundColor Yellow
|
||||
}
|
||||
|
||||
# Start Streamlit
|
||||
Write-Host "Dashboard starting on http://localhost:8501" -ForegroundColor Cyan
|
||||
streamlit run trading_dashboard.py --server.port 8501 --server.address 0.0.0.0
|
||||
```
|
||||
|
||||
### stop_dashboard.ps1
|
||||
|
||||
```powershell
|
||||
# Stop Trading Dashboard
|
||||
Write-Host "Stopping Trading Dashboard..." -ForegroundColor Yellow
|
||||
|
||||
Get-Process | Where-Object {$_.ProcessName -like "*streamlit*"} | Stop-Process -Force
|
||||
|
||||
Write-Host "Dashboard stopped!" -ForegroundColor Green
|
||||
```
|
||||
|
||||
### check_dashboard.ps1
|
||||
|
||||
```powershell
|
||||
# Check Dashboard Status
|
||||
Write-Host "Checking Trading Dashboard Status..." -ForegroundColor Cyan
|
||||
|
||||
$process = Get-Process | Where-Object {$_.ProcessName -like "*streamlit*"}
|
||||
|
||||
if ($process) {
|
||||
Write-Host "Dashboard is RUNNING" -ForegroundColor Green
|
||||
Write-Host "Process ID: $($process.Id)" -ForegroundColor White
|
||||
Write-Host "URL: http://localhost:8501" -ForegroundColor Cyan
|
||||
} else {
|
||||
Write-Host "Dashboard is NOT running" -ForegroundColor Red
|
||||
}
|
||||
|
||||
# Check if port is listening
|
||||
$port = Get-NetTCPConnection -LocalPort 8501 -ErrorAction SilentlyContinue
|
||||
|
||||
if ($port) {
|
||||
Write-Host "Port 8501 is LISTENING" -ForegroundColor Green
|
||||
} else {
|
||||
Write-Host "Port 8501 is NOT listening" -ForegroundColor Yellow
|
||||
}
|
||||
```
|
||||
|
||||
## ✅ Installation Checklist
|
||||
|
||||
- [ ] Python 3.8+ installiert
|
||||
- [ ] Streamlit installiert (`pip install streamlit plotly pandas`)
|
||||
- [ ] trading_dashboard.py im richtigen Ordner
|
||||
- [ ] Windows Firewall Port 8501 geöffnet
|
||||
- [ ] Dashboard gestartet (manuell oder als Service)
|
||||
- [ ] Im Browser erreichbar (`http://localhost:8501`)
|
||||
- [ ] Von außen erreichbar (`http://SERVER-IP:8501`)
|
||||
- [ ] Als Windows Service konfiguriert (optional, empfohlen)
|
||||
- [ ] Auto-Start beim Boot konfiguriert (optional)
|
||||
|
||||
## 🎯 Empfohlenes Setup für Windows Server
|
||||
|
||||
**Beste Praxis:**
|
||||
|
||||
1. **Dashboard als Windows Service** (mit NSSM)
|
||||
- Auto-Start beim Boot
|
||||
- Läuft auch ohne User-Login
|
||||
|
||||
2. **Firewall konfiguriert**
|
||||
- Port 8501 nur von vertrauenswürdigen IPs
|
||||
|
||||
3. **Logs aktiviert**
|
||||
- stdout/stderr Logs für Debugging
|
||||
|
||||
4. **Auto-Refresh im Dashboard** (30s)
|
||||
|
||||
## 🎉 Quick Start Commands
|
||||
|
||||
```powershell
|
||||
# Dashboard starten (manuell)
|
||||
cd C:\TradingBot\placeorder
|
||||
streamlit run trading_dashboard.py --server.port 8501 --server.address 0.0.0.0
|
||||
|
||||
# Dashboard Status prüfen
|
||||
Get-Process | Where-Object {$_.ProcessName -like "*streamlit*"}
|
||||
|
||||
# Dashboard stoppen
|
||||
Stop-Process -Name streamlit -Force
|
||||
|
||||
# Service starten (falls als Service installiert)
|
||||
Start-Service TradingDashboard
|
||||
|
||||
# Service stoppen
|
||||
Stop-Service TradingDashboard
|
||||
```
|
||||
|
||||
## 📝 Zugriff URLs
|
||||
|
||||
**Lokal auf Server:**
|
||||
```
|
||||
http://localhost:8501
|
||||
```
|
||||
|
||||
**Vom Netzwerk:**
|
||||
```
|
||||
http://WINDOWS-SERVER-IP:8501
|
||||
```
|
||||
|
||||
**Von außen (Internet):**
|
||||
```
|
||||
http://ÖFFENTLICHE-IP:8501
|
||||
```
|
||||
|
||||
**Via SSH Tunnel:**
|
||||
```bash
|
||||
# Auf Mac
|
||||
ssh -L 8501:localhost:8501 user@windows-server-ip
|
||||
|
||||
# Browser öffnen
|
||||
http://localhost:8501
|
||||
```
|
||||
|
||||
## 🔗 Nützliche Links
|
||||
|
||||
- **NSSM Download:** https://nssm.cc/download
|
||||
- **Python Windows:** https://www.python.org/downloads/windows/
|
||||
- **Streamlit Docs:** https://docs.streamlit.io/
|
||||
|
||||
## 💡 Pro-Tipps
|
||||
|
||||
1. **Dedicated User Account:** Erstelle einen separaten Windows User nur für den Trading Bot
|
||||
2. **Scheduled Restart:** Task Scheduler für täglichen Neustart (z.B. 3:00 nachts)
|
||||
3. **Backup:** Sichere `trading_bot.db` regelmäßig
|
||||
4. **Monitoring:** Nutze Windows Performance Monitor für Ressourcen-Überwachung
|
||||
5. **Updates:** Halte Streamlit aktuell: `pip install --upgrade streamlit`
|
||||
|
||||
## 🚨 Wichtige Sicherheitshinweise
|
||||
|
||||
⚠️ **NIEMALS Port 8501 öffentlich ohne Authentifizierung öffnen!**
|
||||
|
||||
Empfohlene Absicherung:
|
||||
- VPN-Only Access
|
||||
- SSH Tunnel
|
||||
- IIS Reverse Proxy mit Basic Auth
|
||||
- IP Whitelist in Firewall
|
||||
|
||||
---
|
||||
|
||||
Du bist jetzt bereit das Dashboard auf Windows Server zu deployen! 🎉
|
||||
@@ -0,0 +1,306 @@
|
||||
# 🔧 FIX: Duplicate Scheduler Problem
|
||||
|
||||
## ❌ Problem Identifiziert
|
||||
|
||||
**Symptom:** Bot tradet während London Session trotz SESSION_WHITELIST_CONFIG mit nur NY aktiviert
|
||||
|
||||
**Root Cause:** **DUPLICATE Scheduler Jobs!**
|
||||
|
||||
Beweis aus deinen Logs:
|
||||
```
|
||||
2025-11-26 11:37:00 - INFO - Running job "create_session_filtered_check..." (scheduled at 11:37:00)
|
||||
2025-11-26 11:37:00 - INFO - ⏸️ Trading SKIP: Session blocked: London
|
||||
2025-11-26 11:37:00 - INFO - Running job "create_session_filtered_check..." (scheduled at 11:37:00)
|
||||
2025-11-26 11:37:00 - INFO - ⏸️ Trading SKIP: Session blocked: London
|
||||
```
|
||||
|
||||
**Was passiert:**
|
||||
1. ✅ **NEUER** Scheduler mit Session Filter → blockiert London korrekt
|
||||
2. ❌ **ALTER** Scheduler OHNE Session Filter → tradet weiter!
|
||||
|
||||
## 🔍 Wie ist das passiert?
|
||||
|
||||
**Szenario:**
|
||||
1. Du hast Cell 33 (Scheduler Start) mehrmals ausgeführt
|
||||
2. Jedes Mal wurde ein NEUER Scheduler erstellt
|
||||
3. ALTE Scheduler laufen weiter im Hintergrund
|
||||
4. Result: 2+ Jobs zur gleichen Zeit!
|
||||
|
||||
## ✅ Lösung: Scheduler Cleanup + Neustart
|
||||
|
||||
### SCHRITT 1: Alle laufenden Scheduler stoppen
|
||||
|
||||
**In einer NEUEN Notebook Cell ausführen:**
|
||||
|
||||
```python
|
||||
# ==========================================
|
||||
# SCHRITT 1: ALLE SCHEDULER STOPPEN
|
||||
# ==========================================
|
||||
|
||||
print("🔄 Stopping all schedulers...")
|
||||
|
||||
# Stop current scheduler
|
||||
try:
|
||||
scheduler.shutdown(wait=False)
|
||||
print("✅ Main scheduler stopped")
|
||||
except:
|
||||
print("⚠️ No main scheduler found")
|
||||
|
||||
# Check for orphaned schedulers
|
||||
import gc
|
||||
from apscheduler.schedulers.background import BackgroundScheduler
|
||||
|
||||
orphaned_count = 0
|
||||
for obj in gc.get_objects():
|
||||
try:
|
||||
if isinstance(obj, BackgroundScheduler):
|
||||
if obj.running:
|
||||
obj.shutdown(wait=False)
|
||||
orphaned_count += 1
|
||||
print(f"✅ Stopped orphaned scheduler {orphaned_count}")
|
||||
except:
|
||||
pass
|
||||
|
||||
print(f"\n✅ Cleanup complete! Stopped {orphaned_count + 1} scheduler(s)")
|
||||
print("⏸️ Bot is now PAUSED - no trading happening")
|
||||
```
|
||||
|
||||
**Erwartete Ausgabe:**
|
||||
```
|
||||
🔄 Stopping all schedulers...
|
||||
✅ Main scheduler stopped
|
||||
✅ Stopped orphaned scheduler 1
|
||||
✅ Stopped orphaned scheduler 2
|
||||
✅ Cleanup complete! Stopped 3 scheduler(s)
|
||||
```
|
||||
|
||||
### SCHRITT 2: Session Filter Configuration bestätigen
|
||||
|
||||
**Prüfe nochmal die Config:**
|
||||
|
||||
```python
|
||||
# ==========================================
|
||||
# SCHRITT 2: VERIFY SESSION FILTER CONFIG
|
||||
# ==========================================
|
||||
|
||||
from session_filter_patch import SESSION_WHITELIST_CONFIG, is_session_allowed
|
||||
|
||||
print("📊 CURRENT SESSION FILTER CONFIGURATION:")
|
||||
print("=" * 60)
|
||||
|
||||
for session in ['asian', 'london', 'overlap', 'ny']:
|
||||
allowed, reason = is_session_allowed(session)
|
||||
emoji = "✅ AKTIV" if allowed else "❌ BLOCKED"
|
||||
print(f"{emoji:12s} {session.upper():8s}: {reason}")
|
||||
|
||||
print("\n📋 Full Config:")
|
||||
print(f"Enabled Sessions: {SESSION_WHITELIST_CONFIG['enabled_sessions']}")
|
||||
print(f"Base Confidence: {SESSION_WHITELIST_CONFIG['base_confidence']}%")
|
||||
print(f"Aggressive Mode: {SESSION_WHITELIST_CONFIG.get('aggressive_mode', False)}")
|
||||
```
|
||||
|
||||
**Erwartete Ausgabe:**
|
||||
```
|
||||
❌ BLOCKED ASIAN : Session blocked: Asian has -$199 loss...
|
||||
❌ BLOCKED LONDON : Session blocked: London is break-even...
|
||||
❌ BLOCKED OVERLAP : Session blocked: Not in whitelist
|
||||
✅ AKTIV NY : NY allowed: +$372 profit, 50.0% win-rate
|
||||
```
|
||||
|
||||
### SCHRITT 3: Neuen Scheduler mit Session Filter starten
|
||||
|
||||
**Erstelle einen FRISCHEN Scheduler:**
|
||||
|
||||
```python
|
||||
# ==========================================
|
||||
# SCHRITT 3: CREATE FRESH SCHEDULER
|
||||
# ==========================================
|
||||
|
||||
from apscheduler.schedulers.background import BackgroundScheduler
|
||||
from session_filter_patch import create_session_filtered_check, SESSION_WHITELIST_CONFIG
|
||||
from infrastructure_patch import create_scheduled_reports
|
||||
|
||||
print("🚀 Creating FRESH scheduler with session filter...")
|
||||
|
||||
# Create NEW scheduler instance
|
||||
scheduler = BackgroundScheduler()
|
||||
|
||||
# Create session-filtered trading check
|
||||
adaptive_trading_check = create_session_filtered_check(
|
||||
rhythm_manager=rhythm_manager,
|
||||
execute_func=execute_trade_v2_adaptive,
|
||||
symbol=symbol,
|
||||
strategy_name=strategy_name,
|
||||
max_positions=max_positions,
|
||||
logger=logger,
|
||||
datetime=datetime,
|
||||
config=SESSION_WHITELIST_CONFIG # ✅ EXPLICITLY pass config
|
||||
)
|
||||
|
||||
# Add FILTERED trading job
|
||||
scheduler.add_job(
|
||||
func=adaptive_trading_check,
|
||||
trigger='cron',
|
||||
minute='*',
|
||||
id='adaptive_trading_check_v18', # NEW ID to avoid conflicts
|
||||
replace_existing=True
|
||||
)
|
||||
|
||||
# Add status report
|
||||
scheduler.add_job(
|
||||
func=print_status_report,
|
||||
trigger='cron',
|
||||
minute='0,30',
|
||||
id='status_report_v18',
|
||||
replace_existing=True
|
||||
)
|
||||
|
||||
# Add infrastructure reports
|
||||
create_scheduled_reports(infra, scheduler)
|
||||
|
||||
# Start scheduler
|
||||
scheduler.start()
|
||||
|
||||
print("\n✅ FRESH SCHEDULER STARTED!")
|
||||
print(f" Active Jobs: {len(scheduler.get_jobs())}")
|
||||
print("\n📋 Scheduled Jobs:")
|
||||
for job in scheduler.get_jobs():
|
||||
print(f" • {job.id} - {job.trigger}")
|
||||
|
||||
print("\n⚠️ SESSION FILTER ACTIVE:")
|
||||
for session, enabled in SESSION_WHITELIST_CONFIG['enabled_sessions'].items():
|
||||
status = "✅ TRADING" if enabled else "❌ BLOCKED"
|
||||
print(f" {session.upper():8s}: {status}")
|
||||
|
||||
print("\n🎯 Bot will ONLY trade during NY session (13:00-21:00 UTC)")
|
||||
print("🔒 All other sessions are BLOCKED")
|
||||
```
|
||||
|
||||
**Erwartete Ausgabe:**
|
||||
```
|
||||
✅ FRESH SCHEDULER STARTED!
|
||||
Active Jobs: 4
|
||||
|
||||
📋 Scheduled Jobs:
|
||||
• adaptive_trading_check_v18 - cron[minute='*']
|
||||
• status_report_v18 - cron[minute='0,30']
|
||||
• daily_report - cron[hour='22', minute='0']
|
||||
• weekly_report - cron[day_of_week='sun', hour='23', minute='0']
|
||||
|
||||
⚠️ SESSION FILTER ACTIVE:
|
||||
ASIAN : ❌ BLOCKED
|
||||
LONDON : ❌ BLOCKED
|
||||
OVERLAP : ❌ BLOCKED
|
||||
NY : ✅ TRADING
|
||||
```
|
||||
|
||||
### SCHRITT 4: Verification Test
|
||||
|
||||
**Warte 1-2 Minuten und prüfe die Logs:**
|
||||
|
||||
```python
|
||||
# ==========================================
|
||||
# SCHRITT 4: VERIFY LOGS
|
||||
# ==========================================
|
||||
|
||||
import time
|
||||
print("⏰ Waiting 90 seconds to collect logs...")
|
||||
time.sleep(90)
|
||||
|
||||
print("\n📊 Last few log entries should show:")
|
||||
print(" ⏸️ Trading SKIP: Session blocked: London")
|
||||
print("\n✅ If you see this → Session filter is working!")
|
||||
```
|
||||
|
||||
## 📊 Expected Behavior nach Fix
|
||||
|
||||
### Während London Session (08:00-13:00 UTC / 09:00-14:00 CET):
|
||||
```
|
||||
2025-11-26 11:37:00 - INFO - ⏸️ Trading SKIP: Session blocked: London
|
||||
2025-11-26 11:38:00 - INFO - ⏸️ Trading SKIP: Session blocked: London
|
||||
2025-11-26 11:39:00 - INFO - ⏸️ Trading SKIP: Session blocked: London
|
||||
```
|
||||
|
||||
**WICHTIG:** Nur EINE Log-Zeile pro Minute! (Nicht 2 wie vorher)
|
||||
|
||||
### Während NY Session (13:00-21:00 UTC / 14:00-22:00 CET):
|
||||
```
|
||||
2025-11-26 14:00:00 - INFO - ✅ Session: NY - NY allowed: +$372 profit
|
||||
2025-11-26 14:00:00 - INFO - 📊 Confidence Threshold: 60%
|
||||
[Normal trading analysis...]
|
||||
```
|
||||
|
||||
### Andere Sessions:
|
||||
- **Asian (00:00-08:00 UTC):** ⏸️ Trading SKIP
|
||||
- **Overlap (13:00-16:00 UTC):** ⏸️ Trading SKIP (auch wenn in NY Zeit!)
|
||||
|
||||
## 🔍 How to Monitor
|
||||
|
||||
### Quick Check jede Stunde:
|
||||
|
||||
```python
|
||||
# Check current session & status
|
||||
from datetime import datetime
|
||||
import pytz
|
||||
|
||||
now_utc = datetime.now(pytz.UTC)
|
||||
current_session = rhythm_manager.get_current_session()
|
||||
allowed, reason = is_session_allowed(current_session)
|
||||
|
||||
print(f"Time: {now_utc.strftime('%H:%M UTC')}")
|
||||
print(f"Session: {current_session.upper()}")
|
||||
print(f"Trading: {'✅ ACTIVE' if allowed else '❌ BLOCKED'}")
|
||||
print(f"Reason: {reason}")
|
||||
```
|
||||
|
||||
### Full Status:
|
||||
|
||||
```python
|
||||
check_adaptive_bot_status()
|
||||
```
|
||||
|
||||
## ⚠️ WICHTIG: Prevent Future Duplicates
|
||||
|
||||
**NIEMALS nochmal Cell 33 ausführen!**
|
||||
|
||||
Wenn du den Scheduler neu starten musst:
|
||||
|
||||
1. **ERST:** `scheduler.shutdown()` ausführen
|
||||
2. **DANN:** Neue Scheduler Cell ausführen
|
||||
|
||||
**ODER:** Kernel → Restart & Run All (safest!)
|
||||
|
||||
## 🎯 Summary
|
||||
|
||||
**Problem:**
|
||||
- Duplicate schedulers (mit/ohne Session Filter)
|
||||
|
||||
**Lösung:**
|
||||
1. ✅ Alle Scheduler stoppen
|
||||
2. ✅ Config verifizieren
|
||||
3. ✅ Frischen Scheduler mit Session Filter starten
|
||||
4. ✅ Logs monitoren
|
||||
|
||||
**Result:**
|
||||
- Nur 1 Scheduler läuft
|
||||
- Session Filter aktiv
|
||||
- Nur NY Session tradet
|
||||
- London/Asian/Overlap blockiert
|
||||
|
||||
**Expected Log:**
|
||||
```
|
||||
⏸️ Trading SKIP: Session blocked: London ← RICHTIG!
|
||||
(nur EINE Zeile pro Minute während London)
|
||||
```
|
||||
|
||||
## 📱 Next Steps
|
||||
|
||||
Nach dem Fix:
|
||||
|
||||
1. **Monitor 24h:** Prüfe dass KEIN Trade außerhalb NY passiert
|
||||
2. **Check Telegram:** Du solltest jetzt NUR NY-Trades sehen
|
||||
3. **Database Check:** Alle neuen Trades sollten `session='ny'` haben
|
||||
|
||||
**Falls weiterhin London Trades:**
|
||||
→ Kernel Restart erforderlich
|
||||
→ Alle Cells neu ausführen (Kernel → Restart & Run All)
|
||||
@@ -0,0 +1,279 @@
|
||||
# 🧹 Project Cleanup Guide
|
||||
|
||||
## Aktuelle Projekt-Struktur
|
||||
|
||||
### ✅ KEEP - Production Files (Aktiv genutzt)
|
||||
|
||||
**Core Trading Bot:**
|
||||
- `TradingBot_V1.6_Adaptive_Complete_CORRECTED.ipynb` ⭐ Main notebook
|
||||
- `session_filter_patch.py` ⭐ Session filter (V1.8)
|
||||
- `infrastructure_patch.py` ⭐ Database + Telegram integration
|
||||
- `trading_database.py` ⭐ SQLite database
|
||||
- `telegram_notifier.py` ⭐ Telegram bot
|
||||
- `telegram_config.json` ⭐ Your credentials (KEEP PRIVATE!)
|
||||
- `trading_bot.db` ⭐ Your trade data
|
||||
|
||||
**Dashboard & Tools:**
|
||||
- `trading_dashboard.py` ⭐ Streamlit dashboard
|
||||
- `import_json_to_db.py` ⭐ JSON import tool
|
||||
|
||||
**Data:**
|
||||
- `trade_performance_v16_XAUUSD_202510.json` 📊 Historical data (October)
|
||||
- `trade_performance_v16_XAUUSD_202511.json` 📊 Historical data (November)
|
||||
|
||||
**Documentation (Active):**
|
||||
- `V1.8_AGGRESSIVE_MODE_AKTIVIERT.md` 📖 Current version docs
|
||||
- `FIX_DUPLICATE_SCHEDULER.md` 📖 Critical fix guide
|
||||
- `DASHBOARD_WINDOWS_SERVER.md` 📖 Dashboard deployment
|
||||
- `SQLITE_TELEGRAM_SETUP.md` 📖 Infrastructure setup
|
||||
|
||||
---
|
||||
|
||||
### 📦 ARCHIVE - Old/Reference Files
|
||||
|
||||
**Old Documentation (Superseded by V1.8):**
|
||||
- `V1.6_CORRECTED_Dokumentation.md` → Archive
|
||||
- `V1.7_SESSION_FILTER_AKTIVIERT.md` → Archive (now V1.8)
|
||||
- `SESSION_FILTER_INSTALLATION.md` → Archive (covered in V1.8 docs)
|
||||
- `CONFIDENCE_THRESHOLD_GUIDE.md` → Archive (info in session_filter_patch.py)
|
||||
- `TradingBot_V1.6_Verbesserungsvorschlaege.md` → Archive
|
||||
- `Implementierungsplan_V1.6_to_V2.0.md` → Archive
|
||||
|
||||
**Development/Deployment Docs (Still useful but can be archived):**
|
||||
- `VPS_DEPLOYMENT_V1.8.md` → Keep or Archive (covered in DASHBOARD_WINDOWS_SERVER.md)
|
||||
- `DASHBOARD_VPS_DEPLOYMENT.md` → Archive (Windows version is newer)
|
||||
- `QUICK_START_INFRASTRUCTURE.md` → Archive (covered in SQLITE_TELEGRAM_SETUP.md)
|
||||
- `NOTEBOOK_FIX_TRADE_LOGGING.md` → Archive (fix already applied)
|
||||
|
||||
**Analysis Scripts (Optional - can archive if not used):**
|
||||
- `analyze_mt5_profitability.py` → Archive (Dashboard does this now)
|
||||
- `analyze_performance.py` → Archive (Dashboard does this now)
|
||||
- `analyze_performance_simple.py` → Archive (Dashboard does this now)
|
||||
- `check_database_logs.py` → Archive (Dashboard replaced this)
|
||||
|
||||
**Template:**
|
||||
- `telegram_config_template.json` → Keep (useful for setup on new systems)
|
||||
|
||||
---
|
||||
|
||||
## 🗂️ Recommended Folder Structure
|
||||
|
||||
```
|
||||
placeorder/
|
||||
├── 📁 production/ # Active files
|
||||
│ ├── TradingBot_V1.6_Adaptive_Complete_CORRECTED.ipynb
|
||||
│ ├── session_filter_patch.py
|
||||
│ ├── infrastructure_patch.py
|
||||
│ ├── trading_database.py
|
||||
│ ├── telegram_notifier.py
|
||||
│ ├── trading_dashboard.py
|
||||
│ ├── import_json_to_db.py
|
||||
│ ├── telegram_config.json
|
||||
│ └── trading_bot.db
|
||||
│
|
||||
├── 📁 docs/ # Current documentation
|
||||
│ ├── V1.8_AGGRESSIVE_MODE_AKTIVIERT.md
|
||||
│ ├── FIX_DUPLICATE_SCHEDULER.md
|
||||
│ ├── DASHBOARD_WINDOWS_SERVER.md
|
||||
│ ├── SQLITE_TELEGRAM_SETUP.md
|
||||
│ └── telegram_config_template.json
|
||||
│
|
||||
├── 📁 data/ # Trade data
|
||||
│ ├── trade_performance_v16_XAUUSD_202510.json
|
||||
│ └── trade_performance_v16_XAUUSD_202511.json
|
||||
│
|
||||
└── 📁 archive/ # Old files
|
||||
├── docs/
|
||||
│ ├── V1.6_CORRECTED_Dokumentation.md
|
||||
│ ├── V1.7_SESSION_FILTER_AKTIVIERT.md
|
||||
│ ├── SESSION_FILTER_INSTALLATION.md
|
||||
│ ├── CONFIDENCE_THRESHOLD_GUIDE.md
|
||||
│ ├── TradingBot_V1.6_Verbesserungsvorschlaege.md
|
||||
│ ├── Implementierungsplan_V1.6_to_V2.0.md
|
||||
│ ├── VPS_DEPLOYMENT_V1.8.md
|
||||
│ ├── DASHBOARD_VPS_DEPLOYMENT.md
|
||||
│ ├── QUICK_START_INFRASTRUCTURE.md
|
||||
│ └── NOTEBOOK_FIX_TRADE_LOGGING.md
|
||||
│
|
||||
└── scripts/
|
||||
├── analyze_mt5_profitability.py
|
||||
├── analyze_performance.py
|
||||
├── analyze_performance_simple.py
|
||||
└── check_database_logs.py
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 🚀 Quick Cleanup Actions
|
||||
|
||||
### Option 1: Manual Cleanup (Safest)
|
||||
|
||||
```bash
|
||||
# Create folders
|
||||
mkdir -p production docs data archive/docs archive/scripts
|
||||
|
||||
# Move production files
|
||||
mv TradingBot_V1.6_Adaptive_Complete_CORRECTED.ipynb production/
|
||||
mv session_filter_patch.py infrastructure_patch.py production/
|
||||
mv trading_database.py telegram_notifier.py production/
|
||||
mv trading_dashboard.py import_json_to_db.py production/
|
||||
mv telegram_config.json trading_bot.db production/
|
||||
|
||||
# Move current docs
|
||||
mv V1.8_AGGRESSIVE_MODE_AKTIVIERT.md docs/
|
||||
mv FIX_DUPLICATE_SCHEDULER.md docs/
|
||||
mv DASHBOARD_WINDOWS_SERVER.md docs/
|
||||
mv SQLITE_TELEGRAM_SETUP.md docs/
|
||||
mv telegram_config_template.json docs/
|
||||
|
||||
# Move data
|
||||
mv trade_performance_v16_XAUUSD_*.json data/
|
||||
|
||||
# Archive old docs
|
||||
mv V1.6_CORRECTED_Dokumentation.md archive/docs/
|
||||
mv V1.7_SESSION_FILTER_AKTIVIERT.md archive/docs/
|
||||
mv SESSION_FILTER_INSTALLATION.md archive/docs/
|
||||
mv CONFIDENCE_THRESHOLD_GUIDE.md archive/docs/
|
||||
mv TradingBot_V1.6_Verbesserungsvorschlaege.md archive/docs/
|
||||
mv Implementierungsplan_V1.6_to_V2.0.md archive/docs/
|
||||
mv VPS_DEPLOYMENT_V1.8.md archive/docs/
|
||||
mv DASHBOARD_VPS_DEPLOYMENT.md archive/docs/
|
||||
mv QUICK_START_INFRASTRUCTURE.md archive/docs/
|
||||
mv NOTEBOOK_FIX_TRADE_LOGGING.md archive/docs/
|
||||
|
||||
# Archive old scripts
|
||||
mv analyze_*.py archive/scripts/
|
||||
mv check_database_logs.py archive/scripts/
|
||||
```
|
||||
|
||||
### Option 2: Simple Cleanup (Just Archive)
|
||||
|
||||
```bash
|
||||
# Create archive folder
|
||||
mkdir -p archive
|
||||
|
||||
# Move old files
|
||||
mv V1.6_CORRECTED_Dokumentation.md archive/
|
||||
mv V1.7_SESSION_FILTER_AKTIVIERT.md archive/
|
||||
mv SESSION_FILTER_INSTALLATION.md archive/
|
||||
mv CONFIDENCE_THRESHOLD_GUIDE.md archive/
|
||||
mv TradingBot_V1.6_Verbesserungsvorschlaege.md archive/
|
||||
mv Implementierungsplan_V1.6_to_V2.0.md archive/
|
||||
mv VPS_DEPLOYMENT_V1.8.md archive/
|
||||
mv DASHBOARD_VPS_DEPLOYMENT.md archive/
|
||||
mv QUICK_START_INFRASTRUCTURE.md archive/
|
||||
mv NOTEBOOK_FIX_TRADE_LOGGING.md archive/
|
||||
mv analyze_*.py archive/
|
||||
mv check_database_logs.py archive/
|
||||
```
|
||||
|
||||
### Option 3: Aggressive Cleanup (Delete Archive)
|
||||
|
||||
**⚠️ ONLY if you're sure you don't need old docs!**
|
||||
|
||||
```bash
|
||||
# Delete old docs
|
||||
rm V1.6_CORRECTED_Dokumentation.md
|
||||
rm V1.7_SESSION_FILTER_AKTIVIERT.md
|
||||
rm SESSION_FILTER_INSTALLATION.md
|
||||
rm CONFIDENCE_THRESHOLD_GUIDE.md
|
||||
rm TradingBot_V1.6_Verbesserungsvorschlaege.md
|
||||
rm Implementierungsplan_V1.6_to_V2.0.md
|
||||
rm VPS_DEPLOYMENT_V1.8.md
|
||||
rm DASHBOARD_VPS_DEPLOYMENT.md
|
||||
rm QUICK_START_INFRASTRUCTURE.md
|
||||
rm NOTEBOOK_FIX_TRADE_LOGGING.md
|
||||
|
||||
# Delete old scripts (replaced by dashboard)
|
||||
rm analyze_mt5_profitability.py
|
||||
rm analyze_performance.py
|
||||
rm analyze_performance_simple.py
|
||||
rm check_database_logs.py
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 📋 Minimal Production Setup (What you NEED)
|
||||
|
||||
For clean Windows Server deployment, you only need these files:
|
||||
|
||||
**Essential Files (13 files):**
|
||||
```
|
||||
1. TradingBot_V1.6_Adaptive_Complete_CORRECTED.ipynb
|
||||
2. session_filter_patch.py
|
||||
3. infrastructure_patch.py
|
||||
4. trading_database.py
|
||||
5. telegram_notifier.py
|
||||
6. telegram_config.json
|
||||
7. trading_dashboard.py
|
||||
8. import_json_to_db.py (optional, for historical data)
|
||||
|
||||
Documentation:
|
||||
9. V1.8_AGGRESSIVE_MODE_AKTIVIERT.md
|
||||
10. FIX_DUPLICATE_SCHEDULER.md
|
||||
11. DASHBOARD_WINDOWS_SERVER.md
|
||||
12. SQLITE_TELEGRAM_SETUP.md
|
||||
13. telegram_config_template.json
|
||||
```
|
||||
|
||||
**Generated at Runtime:**
|
||||
- `trading_bot.db` (created by bot)
|
||||
- Performance JSON files (created by bot)
|
||||
|
||||
---
|
||||
|
||||
## 🎯 Recommendation
|
||||
|
||||
**My Suggestion: Option 2 (Simple Archive)**
|
||||
|
||||
1. Create `archive/` folder
|
||||
2. Move old docs/scripts there
|
||||
3. Keep everything accessible but organized
|
||||
4. Easy to reference if needed
|
||||
|
||||
**Benefits:**
|
||||
- ✅ Clean main directory
|
||||
- ✅ Easy to find current files
|
||||
- ✅ Old docs still accessible
|
||||
- ✅ Safe (nothing deleted)
|
||||
|
||||
**Execute with:**
|
||||
```bash
|
||||
cd "/Users/sebastianfrohlich/Library/Mobile Documents/com~apple~CloudDocs/Jupyter Notebooks/FinancialTrading/PlaceOrder/placeorder"
|
||||
|
||||
# Run Option 2 commands above
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 📦 What to Deploy to Windows Server
|
||||
|
||||
**Copy only these to Windows Server:**
|
||||
|
||||
```
|
||||
Essential Production Files:
|
||||
1. TradingBot_V1.6_Adaptive_Complete_CORRECTED.ipynb
|
||||
2. session_filter_patch.py
|
||||
3. infrastructure_patch.py
|
||||
4. trading_database.py
|
||||
5. telegram_notifier.py
|
||||
6. telegram_config.json
|
||||
7. trading_dashboard.py
|
||||
8. import_json_to_db.py (if you want to import JSON data)
|
||||
|
||||
Optional:
|
||||
9. trade_performance_v16_XAUUSD_*.json (historical data)
|
||||
```
|
||||
|
||||
**Don't copy:**
|
||||
- Documentation (read on Mac)
|
||||
- Analysis scripts (replaced by dashboard)
|
||||
- Archive folder (not needed)
|
||||
|
||||
---
|
||||
|
||||
Would you like me to execute the cleanup for you? Choose:
|
||||
- **A)** Option 1 - Full folder structure
|
||||
- **B)** Option 2 - Simple archive ⭐ Recommended
|
||||
- **C)** Option 3 - Delete old files
|
||||
- **D)** Do it manually
|
||||
@@ -1,106 +0,0 @@
|
||||
"""
|
||||
QUICK FIX: Position Control Patch
|
||||
Füge diese Funktionen zu deinem bestehenden TradingBot_V1.4_Complete.ipynb hinzu
|
||||
"""
|
||||
|
||||
def check_existing_positions_simple(symbol="XAUUSD", strategy_name="TradingBot_V1.4_Complete"):
|
||||
"""
|
||||
Einfache Position-Überprüfung für bestehende Notebooks
|
||||
"""
|
||||
try:
|
||||
positions = mt.positions_get(symbol=symbol)
|
||||
if positions is None:
|
||||
return False, 0
|
||||
|
||||
# Zähle Positionen mit unserem Strategy-Namen
|
||||
strategy_positions = 0
|
||||
for pos in positions:
|
||||
if strategy_name in pos.comment:
|
||||
strategy_positions += 1
|
||||
|
||||
return strategy_positions > 0, strategy_positions
|
||||
except:
|
||||
return False, 0
|
||||
|
||||
def execute_trade_v2_with_simple_position_control(
|
||||
symbol="XAUUSD",
|
||||
atr_mult=1.5,
|
||||
base_confidence=70,
|
||||
max_risk_per_trade=0.01,
|
||||
risk_filter=True,
|
||||
min_atr=0.0010,
|
||||
use_pullback_entry=True,
|
||||
debug=True
|
||||
):
|
||||
"""
|
||||
QUICK FIX: Füge diese Funktion zu deinem bestehenden Notebook hinzu
|
||||
Ersetzt deine bestehende execute_trade_v2() Funktion
|
||||
"""
|
||||
|
||||
# WICHTIG: Position-Check ZUERST!
|
||||
has_position, position_count = check_existing_positions_simple(symbol, strategy_name)
|
||||
|
||||
if has_position:
|
||||
if debug:
|
||||
print(f"🛑 TRADE BLOCKIERT: {position_count} Position(en) bereits aktiv für {symbol}")
|
||||
|
||||
# Zeige bestehende Positionen
|
||||
positions = mt.positions_get(symbol=symbol)
|
||||
if positions:
|
||||
for pos in positions:
|
||||
if strategy_name in pos.comment:
|
||||
profit_emoji = "🟢" if pos.profit >= 0 else "🔴"
|
||||
print(f" Aktive Position: {'BUY' if pos.type == 0 else 'SELL'} {pos.volume} @ {pos.price_open} | Profit: {profit_emoji} {pos.profit:.2f}")
|
||||
return None
|
||||
|
||||
print(f"✅ Position-Check OK: Keine aktiven Positionen für {symbol}")
|
||||
|
||||
# AB HIER: Deine bestehende execute_trade_v2 Logik
|
||||
# (Kopiere deine bestehende Funktion hier hin, aber mit Position-Check am Anfang)
|
||||
|
||||
# Vereinfachte Version für Demonstration:
|
||||
signal_info = extended_top_down_v2(symbol)
|
||||
if signal_info is None:
|
||||
print("❌ Signal-Analyse fehlgeschlagen")
|
||||
return None
|
||||
|
||||
entry_signal = signal_info["entry_signal"]
|
||||
confidence = signal_info["confidence"]
|
||||
adaptive_threshold = signal_info["adaptive_threshold"]
|
||||
|
||||
if entry_signal == 0:
|
||||
print(f"⏸️ No entry signal (Confidence: {confidence}%, Threshold: {adaptive_threshold}%)")
|
||||
return None
|
||||
|
||||
if confidence < adaptive_threshold:
|
||||
print(f"⏸️ Confidence too low: {confidence}% < {adaptive_threshold}%")
|
||||
return None
|
||||
|
||||
# FINAL POSITION CHECK vor Order (Sicherheit)
|
||||
final_check, _ = check_existing_positions_simple(symbol, strategy_name)
|
||||
if final_check:
|
||||
print(f"🛑 LAST-MINUTE BLOCK: Position wurde zwischen Checks eröffnet!")
|
||||
return None
|
||||
|
||||
# Hier würde deine normale Trading-Logik kommen
|
||||
print(f"🚀 WOULD EXECUTE TRADE: {'LONG' if entry_signal == 1 else 'SHORT'}")
|
||||
print(f"Confidence: {confidence}% | Quality: {signal_info['signal_quality']}")
|
||||
|
||||
# Für echte Ausführung: Uncomment die nächste Zeile und füge deine market_order Logik hinzu
|
||||
# return market_order(symbol, volume, order_type, stoploss, take_profit)
|
||||
|
||||
return "DEMO_ORDER_RESULT" # Placeholder für Demo
|
||||
|
||||
print("✅ Simple Position Control Patch ready")
|
||||
|
||||
# ANLEITUNG FÜR DEIN BESTEHENDES NOTEBOOK:
|
||||
print("\\n📋 ANLEITUNG für dein bestehendes Notebook:")
|
||||
print("1. Kopiere check_existing_positions_simple() in eine neue Zelle")
|
||||
print("2. Ersetze deine execute_trade_v2() durch execute_trade_v2_with_simple_position_control()")
|
||||
print("3. Oder füge am Anfang deiner bestehenden execute_trade_v2() den Position-Check hinzu:")
|
||||
print("\\n # Position-Check am Anfang der Funktion:")
|
||||
print(" has_position, position_count = check_existing_positions_simple(symbol, strategy_name)")
|
||||
print(" if has_position:")
|
||||
print(" print(f'🛑 TRADE BLOCKIERT: {position_count} Position(en) aktiv')")
|
||||
print(" return None")
|
||||
print("\\n4. Fertig! Dein Bot handelt jetzt maximal 1x gleichzeitig.")
|
||||
-244
@@ -1,244 +0,0 @@
|
||||
# TradingBot V1.4 - Optimized Version
|
||||
|
||||
## 🚀 Überblick
|
||||
|
||||
TradingBot V1.4 ist eine erheblich verbesserte Version deines ursprünglichen Trading Bots mit intelligenten Anpassungen an verschiedene Marktbedingungen.
|
||||
|
||||
## 📁 Dateien
|
||||
|
||||
- `TradingBot_V1.4_Optimized.py` - Hauptfunktionen der optimierten Logik
|
||||
- `TradingBot_V1.4_Integration.ipynb` - Jupyter Notebook mit vollständiger Integration
|
||||
- `config_v1.4.json` - Konfigurationsdatei für alle Parameter
|
||||
- `TradingBot_V1.3_backup_*` - Backup deiner ursprünglichen Datei
|
||||
|
||||
## 🎯 Hauptverbesserungen
|
||||
|
||||
### 1. **Adaptive Confidence Threshold**
|
||||
```python
|
||||
# Statt fixer 80% wird automatisch angepasst:
|
||||
Trending Markets: 60-75%
|
||||
Ranging Markets: 85%
|
||||
Volatile Markets: 90%
|
||||
```
|
||||
|
||||
### 2. **Market Regime Detection**
|
||||
- **Trending**: ADX > 25, klare Richtung
|
||||
- **Ranging**: ADX < 20, seitwärts bewegung
|
||||
- **Volatile**: Hohe Volatilitäts-Cluster
|
||||
|
||||
### 3. **Entry Timing Optimization**
|
||||
- Wartet auf Pullbacks zu EMA21
|
||||
- Bessere Risk/Reward durch optimiertes Timing
|
||||
- Regime-abhängige Entry-Kriterien
|
||||
|
||||
### 4. **Risk-Adjusted Signal Strength**
|
||||
```python
|
||||
signal_strength = confidence × trend_strength × rrr
|
||||
# Mindestens 100 für Entry, 150+ für "excellent" Signals
|
||||
```
|
||||
|
||||
### 5. **Performance Monitoring**
|
||||
- Automatisches Logging aller Signale
|
||||
- JSON-Export für Analyse
|
||||
- Regime-basierte Performance-Statistiken
|
||||
|
||||
## 📊 Vergleich V1.3 vs V1.4
|
||||
|
||||
| Feature | V1.3 | V1.4 |
|
||||
|---------|------|------|
|
||||
| Confidence Threshold | Fixed 80% | Adaptive 60-90% |
|
||||
| Market Regime | Ignoriert | Aktive Erkennung |
|
||||
| Entry Timing | Sofort | Pullback-optimiert |
|
||||
| Performance Tracking | Manuell | Automatisch |
|
||||
| Parameter | Statisch | Dynamisch |
|
||||
|
||||
## 🛠️ Installation & Setup
|
||||
|
||||
### 1. Dateien kopieren
|
||||
```bash
|
||||
# Alle V1.4 Dateien sind bereits in deinem Verzeichnis:
|
||||
/Users/sebastianfrohlich/Library/Mobile Documents/com~apple~CloudDocs/Jupyter Notebooks/FinancialTrading/PlaceOrder/placeorder/
|
||||
```
|
||||
|
||||
### 2. Jupyter Notebook öffnen
|
||||
```bash
|
||||
# Öffne: TradingBot_V1.4_Integration.ipynb
|
||||
# Führe alle Zellen aus
|
||||
```
|
||||
|
||||
### 3. Konfiguration anpassen (optional)
|
||||
```json
|
||||
// Editiere config_v1.4.json für deine Bedürfnisse
|
||||
{
|
||||
"trading_config": {
|
||||
"symbol": "XAUUSD",
|
||||
"base_confidence": 70,
|
||||
"max_risk_per_trade": 0.01
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## 🔄 Migration von V1.3 zu V1.4
|
||||
|
||||
### Option 1: Komplette Migration (empfohlen)
|
||||
1. Verwende `TradingBot_V1.4_Integration.ipynb` als neues Hauptnotebook
|
||||
2. Deine bestehenden Funktionen werden automatisch integriert
|
||||
3. Alle Verbesserungen sind sofort aktiv
|
||||
|
||||
### Option 2: Schrittweise Integration
|
||||
1. Kopiere einzelne Funktionen aus `TradingBot_V1.4_Optimized.py`
|
||||
2. Ersetze in deinem V1.3 Notebook:
|
||||
- `extended_top_down()` → `extended_top_down_v2()`
|
||||
- `execute_trade()` → `execute_trade_v2()`
|
||||
|
||||
## 📈 Verwendung
|
||||
|
||||
### Schnellstart
|
||||
```python
|
||||
from TradingBot_V1.4_Optimized import extended_top_down_v2, execute_trade_v2
|
||||
|
||||
# Analysiere aktuelles Signal
|
||||
signal_info = extended_top_down_v2("XAUUSD")
|
||||
print(f"Signal: {signal_info['entry_signal']}")
|
||||
print(f"Quality: {signal_info['signal_quality']}")
|
||||
print(f"Regime: {signal_info['market_regime']['regime']}")
|
||||
|
||||
# Führe optimierten Trade aus
|
||||
result = execute_trade_v2(
|
||||
symbol="XAUUSD",
|
||||
base_confidence=70,
|
||||
use_pullback_entry=True
|
||||
)
|
||||
```
|
||||
|
||||
### Automatisierung
|
||||
```python
|
||||
# Setup für automatisierten Handel
|
||||
from apscheduler.schedulers.background import BackgroundScheduler
|
||||
|
||||
scheduler = BackgroundScheduler()
|
||||
scheduler.add_job(
|
||||
execute_trade_v2,
|
||||
'cron',
|
||||
minute='*/5', # Alle 5 Minuten
|
||||
kwargs={'symbol': 'XAUUSD', 'debug': True}
|
||||
)
|
||||
scheduler.start()
|
||||
```
|
||||
|
||||
## 📊 Performance Monitoring
|
||||
|
||||
### Logs prüfen
|
||||
```python
|
||||
from TradingBot_V1.4_Optimized import analyze_performance
|
||||
|
||||
# Analysiere letzte 7 Tage
|
||||
analyze_performance("XAUUSD", days_back=7)
|
||||
```
|
||||
|
||||
### Output:
|
||||
```
|
||||
📊 PERFORMANCE ANALYSIS - Last 7 days
|
||||
Total Trades: 12
|
||||
|
||||
By Market Regime:
|
||||
TRENDING: 8 (66.7%)
|
||||
RANGING: 3 (25.0%)
|
||||
VOLATILE: 1 (8.3%)
|
||||
|
||||
By Signal Quality:
|
||||
EXCELLENT: 4 (33.3%)
|
||||
GOOD: 6 (50.0%)
|
||||
FAIR: 2 (16.7%)
|
||||
```
|
||||
|
||||
## ⚙️ Konfiguration
|
||||
|
||||
### Wichtige Parameter
|
||||
```python
|
||||
# In config_v1.4.json oder direkt im Code:
|
||||
|
||||
ADAPTIVE_THRESHOLDS = {
|
||||
'trending': -10, # 10% niedrigere Confidence in Trends
|
||||
'ranging': +15, # 15% höhere Confidence in Ranges
|
||||
'volatile': +20 # 20% höhere Confidence bei Volatilität
|
||||
}
|
||||
|
||||
RISK_SETTINGS = {
|
||||
'max_risk_per_trade': 0.01, # 1% pro Trade
|
||||
'max_daily_loss': 0.05, # 5% maximaler Tagesverlust
|
||||
'use_pullback_entry': True # Warte auf besseres Timing
|
||||
}
|
||||
```
|
||||
|
||||
## 🧪 Testing & Validation
|
||||
|
||||
### 1. Demo-Modus Test
|
||||
```python
|
||||
# Führe 1-2 Wochen im Demo-Modus aus
|
||||
# Überwache Performance-Logs
|
||||
# Validiere Verbesserungen gegenüber V1.3
|
||||
```
|
||||
|
||||
### 2. Parameter-Optimierung
|
||||
```python
|
||||
# Basierend auf Demo-Ergebnissen:
|
||||
# - Anpassung der Confidence-Schwellen
|
||||
# - Feintuning der Regime-Erkennung
|
||||
# - Optimierung der Entry-Timing-Parameter
|
||||
```
|
||||
|
||||
## ⚠️ Wichtige Hinweise
|
||||
|
||||
### Risiken
|
||||
- **Neue Logik**: Teste ausführlich im Demo-Modus
|
||||
- **Parameter**: Beginne mit konservativen Einstellungen
|
||||
- **Monitoring**: Überwache Performance täglich in der ersten Woche
|
||||
|
||||
### Backup
|
||||
```bash
|
||||
# Dein Original V1.3 ist gesichert als:
|
||||
TradingBot_V1.3_backup_YYYYMMDD_HHMMSS.ipynb
|
||||
```
|
||||
|
||||
### Support
|
||||
Bei Fragen oder Problemen:
|
||||
1. Prüfe die Debug-Ausgaben
|
||||
2. Analysiere Performance-Logs
|
||||
3. Validiere MT5-Verbindung
|
||||
|
||||
## 📝 Changelog V1.3 → V1.4
|
||||
|
||||
### ✅ Neue Features
|
||||
- Market Regime Detection
|
||||
- Adaptive Confidence Thresholds
|
||||
- Pullback Entry Timing
|
||||
- Risk-Adjusted Signal Strength
|
||||
- Automatic Performance Logging
|
||||
- Enhanced Debug Output
|
||||
|
||||
### 🔧 Verbesserungen
|
||||
- Weighted Timeframe Analysis
|
||||
- Dynamic Parameter Adjustment
|
||||
- Better Risk Management
|
||||
- Signal Quality Grading
|
||||
- Configuration Management
|
||||
|
||||
### 🐛 Fixes
|
||||
- Over-filtering durch zu hohe Confidence-Schwellen
|
||||
- Fehlende Markt-Adaptation
|
||||
- Späte Entry-Signale durch zu viele Filter
|
||||
|
||||
## 🎯 Nächste Schritte
|
||||
|
||||
1. **Sofort**: Teste V1.4 im Demo-Modus
|
||||
2. **1 Woche**: Analysiere erste Performance-Daten
|
||||
3. **2 Wochen**: Optimiere Parameter basierend auf Ergebnissen
|
||||
4. **1 Monat**: Bei positiven Ergebnissen → Live-Trading
|
||||
|
||||
---
|
||||
|
||||
**Version**: 1.4.0
|
||||
**Datum**: 2025-01-17
|
||||
**Status**: Ready for Demo Testing
|
||||
**Kompatibilität**: MT5, Python 3.7+
|
||||
@@ -0,0 +1,692 @@
|
||||
# 🗄️📱 SQLite + Telegram Integration Guide
|
||||
|
||||
**Version:** V1.8+
|
||||
**Datum:** 26. November 2025
|
||||
**Features:** Database Logging + Mobile Notifications
|
||||
|
||||
---
|
||||
|
||||
## 🎯 Was wird implementiert?
|
||||
|
||||
### 1. **SQLite Database** 🗄️
|
||||
- Strukturiertes Trade Logging (statt JSON)
|
||||
- Schnelle Performance-Queries
|
||||
- Session/Confidence-basierte Analysen
|
||||
- Historische Datenbank
|
||||
|
||||
### 2. **Telegram Notifications** 📱
|
||||
- Live Trade Entry/Exit Benachrichtigungen
|
||||
- Tägliche Performance Reports (22:00 UTC)
|
||||
- Wöchentliche Summaries (Sonntag 23:00 UTC)
|
||||
- Error Alerts
|
||||
- Bot Status Updates
|
||||
|
||||
---
|
||||
|
||||
## 📦 Neue Dateien
|
||||
|
||||
| Datei | Beschreibung |
|
||||
|-------|--------------|
|
||||
| `trading_database.py` | SQLite Database Core |
|
||||
| `telegram_notifier.py` | Telegram Integration |
|
||||
| `infrastructure_patch.py` | Integration mit TradingBot |
|
||||
| `telegram_config.json` | Telegram Konfiguration (erstellen!) |
|
||||
| `trading_bot.db` | SQLite Database (automatisch erstellt) |
|
||||
|
||||
---
|
||||
|
||||
## 🚀 Setup Guide - Schritt für Schritt
|
||||
|
||||
### **PHASE 1: Telegram Bot erstellen (5 Minuten)**
|
||||
|
||||
#### Schritt 1.1: Bot erstellen bei @BotFather
|
||||
|
||||
1. Öffne Telegram
|
||||
2. Suche nach `@BotFather`
|
||||
3. Sende `/newbot`
|
||||
4. Folge den Anweisungen:
|
||||
- Bot Name: z.B. "My Trading Bot"
|
||||
- Bot Username: z.B. "mytrading_bot" (muss auf "_bot" enden)
|
||||
5. **Kopiere den Bot Token** (z.B. `1234567890:ABCdefGHIjklMNOpqrsTUVwxyz`)
|
||||
|
||||
#### Schritt 1.2: Chat ID herausfinden
|
||||
|
||||
1. Suche nach `@userinfobot` in Telegram
|
||||
2. Sende `/start`
|
||||
3. **Kopiere deine User ID** (z.B. `987654321`)
|
||||
|
||||
#### Schritt 1.3: Telegram Config erstellen
|
||||
|
||||
Erstelle eine neue Datei `telegram_config.json`:
|
||||
|
||||
```json
|
||||
{
|
||||
"bot_token": "1234567890:ABCdefGHIjklMNOpqrsTUVwxyz",
|
||||
"chat_id": "987654321",
|
||||
"notifications": {
|
||||
"trade_entry": true,
|
||||
"trade_exit": true,
|
||||
"daily_report": true,
|
||||
"weekly_report": true,
|
||||
"error_alerts": true
|
||||
},
|
||||
"daily_report_time": "22:00",
|
||||
"weekly_report_day": "Sunday"
|
||||
}
|
||||
```
|
||||
|
||||
**Wichtig:** Ersetze `bot_token` und `chat_id` mit deinen echten Werten!
|
||||
|
||||
#### Schritt 1.4: Bot testen
|
||||
|
||||
```bash
|
||||
python telegram_notifier.py
|
||||
```
|
||||
|
||||
**Erwartete Ausgabe:**
|
||||
```
|
||||
✅ Telegram Bot connected: @mytrading_bot
|
||||
✅ Created telegram_config_template.json
|
||||
```
|
||||
|
||||
Wenn erfolgreich, sende eine Test-Nachricht:
|
||||
|
||||
```python
|
||||
from telegram_notifier import TelegramNotifier
|
||||
|
||||
notifier = TelegramNotifier(
|
||||
bot_token='YOUR_BOT_TOKEN',
|
||||
chat_id='YOUR_CHAT_ID'
|
||||
)
|
||||
|
||||
notifier.send_message("🎉 Telegram Bot Test erfolgreich!")
|
||||
```
|
||||
|
||||
Du solltest die Nachricht auf deinem Handy bekommen! 📱
|
||||
|
||||
---
|
||||
|
||||
### **PHASE 2: SQLite Database einrichten (2 Minuten)**
|
||||
|
||||
#### Schritt 2.1: Database erstellen
|
||||
|
||||
```bash
|
||||
python trading_database.py
|
||||
```
|
||||
|
||||
**Erwartete Ausgabe:**
|
||||
```
|
||||
🗄️ TRADING DATABASE - System Check
|
||||
======================================================================
|
||||
|
||||
📊 Tables created: 3
|
||||
✅ trades
|
||||
✅ performance_summary
|
||||
✅ bot_status
|
||||
|
||||
📈 Overall Statistics:
|
||||
Total Trades: 0
|
||||
Win Rate: 0%
|
||||
Net Profit: $0
|
||||
|
||||
======================================================================
|
||||
✅ Database ready!
|
||||
```
|
||||
|
||||
#### Schritt 2.2: Existierende JSON Daten migrieren (optional)
|
||||
|
||||
Wenn du bereits JSON Performance-Dateien hast:
|
||||
|
||||
```python
|
||||
from trading_database import TradingDatabase
|
||||
|
||||
db = TradingDatabase("trading_bot.db")
|
||||
|
||||
# Migriere alle JSON Files
|
||||
import glob
|
||||
json_files = glob.glob("trade_performance_*.json")
|
||||
|
||||
for json_file in json_files:
|
||||
print(f"Migrating {json_file}...")
|
||||
db.migrate_from_json(json_file)
|
||||
|
||||
db.close()
|
||||
```
|
||||
|
||||
#### Schritt 2.3: Database prüfen
|
||||
|
||||
```python
|
||||
from trading_database import TradingDatabase
|
||||
|
||||
db = TradingDatabase("trading_bot.db")
|
||||
|
||||
# Zeige alle Trades
|
||||
trades = db.get_recent_trades(limit=10)
|
||||
print(f"Total trades in DB: {len(trades)}")
|
||||
|
||||
# Session Performance
|
||||
session_perf = db.get_session_performance(days=30)
|
||||
for session, stats in session_perf.items():
|
||||
print(f"{session}: {stats['total_profit']} profit, {stats['win_rate']}% WR")
|
||||
|
||||
db.close()
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### **PHASE 3: Integration mit TradingBot (10 Minuten)**
|
||||
|
||||
#### Schritt 3.1: Imports hinzufügen
|
||||
|
||||
**Am Anfang deines Notebooks** (nach den MT5 Imports):
|
||||
|
||||
```python
|
||||
# ==========================================
|
||||
# INFRASTRUCTURE IMPORTS (NEU!)
|
||||
# ==========================================
|
||||
|
||||
from infrastructure_patch import (
|
||||
TradingInfrastructure,
|
||||
create_scheduled_reports
|
||||
)
|
||||
from trading_database import TradingDatabase
|
||||
from telegram_notifier import TelegramNotifier
|
||||
```
|
||||
|
||||
#### Schritt 3.2: Infrastructure initialisieren
|
||||
|
||||
**Nach der MT5 Verbindung und VOR dem Scheduler**:
|
||||
|
||||
```python
|
||||
# ==========================================
|
||||
# INITIALIZE INFRASTRUCTURE
|
||||
# ==========================================
|
||||
|
||||
print("🔧 Initializing Infrastructure...")
|
||||
|
||||
infra = TradingInfrastructure(
|
||||
db_path="trading_bot.db",
|
||||
enable_telegram=True, # Telegram aktivieren
|
||||
enable_database=True # SQLite aktivieren
|
||||
)
|
||||
|
||||
# Send Bot Started Notification
|
||||
from session_filter_patch import SESSION_WHITELIST_CONFIG
|
||||
|
||||
bot_config = {
|
||||
'version': 'V1.8',
|
||||
'enabled_sessions': SESSION_WHITELIST_CONFIG['enabled_sessions'],
|
||||
'base_confidence': SESSION_WHITELIST_CONFIG['base_confidence'],
|
||||
'max_risk_per_trade': SESSION_WHITELIST_CONFIG['max_risk_per_trade']
|
||||
}
|
||||
|
||||
infra.send_bot_started(bot_config)
|
||||
|
||||
print("✅ Infrastructure ready!")
|
||||
```
|
||||
|
||||
**Du solltest jetzt auf Telegram eine "Bot Started" Nachricht bekommen!** 📱🚀
|
||||
|
||||
#### Schritt 3.3: Trade Logging hinzufügen
|
||||
|
||||
**Modifiziere deine `execute_trade_v2_adaptive` Funktion:**
|
||||
|
||||
**VORHER** (ca. Zeile wo Position geöffnet wird):
|
||||
```python
|
||||
result = mt.order_send(request)
|
||||
|
||||
if result.retcode == mt.TRADE_RETCODE_DONE:
|
||||
logger.info(f"✅ {trade_type} position opened")
|
||||
# ... existing code ...
|
||||
```
|
||||
|
||||
**NACHHER** (mit Infrastructure Logging):
|
||||
```python
|
||||
result = mt.order_send(request)
|
||||
|
||||
if result.retcode == mt.TRADE_RETCODE_DONE:
|
||||
logger.info(f"✅ {trade_type} position opened")
|
||||
|
||||
# ==========================================
|
||||
# LOG TRADE ENTRY (NEU!)
|
||||
# ==========================================
|
||||
try:
|
||||
# Hole Position Info
|
||||
positions = mt.positions_get(symbol=symbol)
|
||||
if positions:
|
||||
position = positions[0]
|
||||
|
||||
# Erstelle Trade Data
|
||||
trade_data = {
|
||||
'ticket': position.ticket,
|
||||
'position_id': position.identifier,
|
||||
'symbol': symbol,
|
||||
'strategy_name': strategy_name,
|
||||
'type': 'BUY' if trade_type == 'BUY' else 'SELL',
|
||||
'volume': volume,
|
||||
'entry_price': position.price_open,
|
||||
'sl_price': position.sl,
|
||||
'tp_price': position.tp,
|
||||
'entry_time': datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
|
||||
'session': rhythm_manager.get_current_session(),
|
||||
'regime': regime,
|
||||
'quality': quality,
|
||||
'confidence': confidence,
|
||||
'timeframe_alignment': timeframe_alignment,
|
||||
'risk_amount': risk_amount,
|
||||
'risk_pct': max_risk_per_trade
|
||||
}
|
||||
|
||||
# Log to Database + Telegram
|
||||
infra.log_trade_entry(trade_data)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"⚠️ Infrastructure logging failed: {e}")
|
||||
# ==========================================
|
||||
|
||||
# ... existing code continues ...
|
||||
```
|
||||
|
||||
#### Schritt 3.4: Exit Logging hinzufügen
|
||||
|
||||
**Wenn Trade geschlossen wird** (z.B. durch SL/TP):
|
||||
|
||||
Dies hängt davon ab, wie dein Bot Exits erkennt. Falls du einen Position Monitor hast:
|
||||
|
||||
```python
|
||||
# Wenn Position geschlossen wurde
|
||||
if position_closed:
|
||||
exit_data = {
|
||||
'exit_price': close_price,
|
||||
'exit_time': datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
|
||||
'duration_hours': (close_time - open_time).total_seconds() / 3600,
|
||||
'profit': profit,
|
||||
'commission': commission,
|
||||
'swap': swap,
|
||||
'net_profit': net_profit,
|
||||
'profit_pct': (net_profit / risk_amount) * 100 if risk_amount else 0,
|
||||
'rr_ratio': abs(net_profit / risk_amount) if risk_amount else 0,
|
||||
'exit_reason': 'tp' if hit_tp else 'sl' if hit_sl else 'manual'
|
||||
}
|
||||
|
||||
# Log Exit
|
||||
infra.log_trade_exit(ticket, exit_data)
|
||||
```
|
||||
|
||||
#### Schritt 3.5: Scheduled Reports hinzufügen
|
||||
|
||||
**Nach dem Scheduler Setup**:
|
||||
|
||||
```python
|
||||
# ==========================================
|
||||
# SCHEDULER SETUP (existing)
|
||||
# ==========================================
|
||||
|
||||
scheduler = BackgroundScheduler()
|
||||
|
||||
# ... existing jobs ...
|
||||
|
||||
# ==========================================
|
||||
# ADD SCHEDULED REPORTS (NEU!)
|
||||
# ==========================================
|
||||
|
||||
create_scheduled_reports(infra, scheduler)
|
||||
|
||||
# Start Scheduler
|
||||
scheduler.start()
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### **PHASE 4: Testing (5 Minuten)**
|
||||
|
||||
#### Test 1: Infrastructure Status
|
||||
|
||||
```python
|
||||
# Prüfe ob alles läuft
|
||||
print("\n🔍 Infrastructure Status:")
|
||||
print(f" Database: {'✅' if infra.enable_database else '❌'}")
|
||||
print(f" Telegram: {'✅' if infra.enable_telegram else '❌'}")
|
||||
|
||||
# Prüfe DB Stats
|
||||
if infra.db:
|
||||
stats = infra.db.get_overall_statistics(days=7)
|
||||
print(f"\n📊 Last 7 Days:")
|
||||
print(f" Trades: {stats.get('total_trades', 0)}")
|
||||
print(f" Win Rate: {stats.get('win_rate', 0)}%")
|
||||
print(f" Net Profit: ${stats.get('net_profit', 0):.2f}")
|
||||
```
|
||||
|
||||
#### Test 2: Manual Telegram Test
|
||||
|
||||
```python
|
||||
# Sende Test Notification
|
||||
if infra.telegram:
|
||||
infra.telegram.send_message("🧪 Test: Infrastructure is working!")
|
||||
```
|
||||
|
||||
#### Test 3: Database Query
|
||||
|
||||
```python
|
||||
# Query recent trades
|
||||
if infra.db:
|
||||
trades = infra.db.get_recent_trades(limit=5)
|
||||
print(f"\n📝 Recent Trades: {len(trades)}")
|
||||
for trade in trades:
|
||||
print(f" {trade['symbol']} {trade['type']} @ {trade['entry_price']}")
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 📱 Was du auf Telegram sehen wirst
|
||||
|
||||
### Bot Started (sofort)
|
||||
```
|
||||
🚀 TradingBot Started
|
||||
|
||||
Version: V1.8
|
||||
Time: 2025-11-26 14:30:00 UTC
|
||||
|
||||
⚙️ Configuration:
|
||||
Active Sessions: NY
|
||||
Confidence Threshold: 60%
|
||||
Max Risk/Trade: 1.0%
|
||||
|
||||
✅ Bot is now monitoring the market
|
||||
```
|
||||
|
||||
### Trade Entry (bei jedem Trade)
|
||||
```
|
||||
🟢 Trade Opened
|
||||
|
||||
Symbol: XAUUSD
|
||||
Type: BUY
|
||||
Entry: 2650.50
|
||||
|
||||
SL: 2645.50 | TP: 2660.50
|
||||
|
||||
Risk: $50.00 (1.0%)
|
||||
Volume: 0.1 lots
|
||||
|
||||
🇺🇸 Session: NY
|
||||
📊 Confidence: 72.5%
|
||||
🎯 Quality: EXCELLENT
|
||||
|
||||
⏰ 2025-11-26 17:15:00 UTC
|
||||
```
|
||||
|
||||
### Trade Exit (bei jedem Close)
|
||||
```
|
||||
✅ Trade Closed 🟢
|
||||
|
||||
Symbol: XAUUSD
|
||||
Type: BUY
|
||||
|
||||
Entry: 2650.50
|
||||
Exit: 2660.50
|
||||
|
||||
Profit: +$100.00
|
||||
Duration: 2.5h
|
||||
|
||||
Exit Reason: TP
|
||||
R:R Ratio: 2.00
|
||||
|
||||
⏰ 2025-11-26 19:45:00 UTC
|
||||
```
|
||||
|
||||
### Daily Report (täglich 22:00 UTC)
|
||||
```
|
||||
📊 Daily Trading Report
|
||||
📅 2025-11-26
|
||||
|
||||
━━━━━━━━━━━━━━━━━━━━
|
||||
|
||||
Trades: 3
|
||||
Wins: 2 | Losses: 1
|
||||
🎯 Win Rate: 66.7%
|
||||
|
||||
💰 Net Profit: $150.00
|
||||
Gross Profit: $200.00
|
||||
Gross Loss: $-50.00
|
||||
|
||||
Avg Trade: $50.00
|
||||
|
||||
━━━━━━━━━━━━━━━━━━━━
|
||||
✅ Bot Status: Running
|
||||
```
|
||||
|
||||
### Weekly Report (Sonntag 23:00 UTC)
|
||||
```
|
||||
📈 Weekly Trading Report
|
||||
📅 2025-11-20 to 2025-11-26
|
||||
|
||||
━━━━━━━━━━━━━━━━━━━━
|
||||
|
||||
📊 Overall Performance
|
||||
Total Trades: 21
|
||||
Wins: 10 | Losses: 11
|
||||
Win Rate: 47.6%
|
||||
|
||||
💰 Net Profit: $660.00
|
||||
🟢 Profit Factor: 1.85
|
||||
Avg Trade: $31.43
|
||||
|
||||
━━━━━━━━━━━━━━━━━━━━
|
||||
|
||||
📍 Session Performance
|
||||
|
||||
🇺🇸 NY: +$660.00 (47.6% WR) ✅
|
||||
|
||||
━━━━━━━━━━━━━━━━━━━━
|
||||
✅ Bot Status: Running
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 🔍 Nützliche Database Queries
|
||||
|
||||
### Query 1: Session Performance (letzte 30 Tage)
|
||||
```python
|
||||
from trading_database import TradingDatabase
|
||||
|
||||
db = TradingDatabase("trading_bot.db")
|
||||
session_perf = db.get_session_performance(days=30)
|
||||
|
||||
for session, stats in session_perf.items():
|
||||
print(f"{session.upper()}:")
|
||||
print(f" Trades: {stats['count']}")
|
||||
print(f" Win Rate: {stats['win_rate']}%")
|
||||
print(f" Profit: ${stats['total_profit']:.2f}")
|
||||
print()
|
||||
```
|
||||
|
||||
### Query 2: Confidence Analysis
|
||||
```python
|
||||
conf_analysis = db.get_confidence_analysis(days=30)
|
||||
|
||||
for conf_range, stats in conf_analysis.items():
|
||||
print(f"Confidence {conf_range}:")
|
||||
print(f" Trades: {stats['count']}")
|
||||
print(f" Win Rate: {stats['win_rate']}%")
|
||||
print(f" Profit: ${stats['total_profit']:.2f}")
|
||||
print()
|
||||
```
|
||||
|
||||
### Query 3: Best/Worst Trades
|
||||
```python
|
||||
# Best Trades
|
||||
db.cursor.execute("""
|
||||
SELECT symbol, type, entry_price, exit_price, net_profit, session
|
||||
FROM trades
|
||||
WHERE status = 'closed'
|
||||
ORDER BY net_profit DESC
|
||||
LIMIT 5
|
||||
""")
|
||||
|
||||
print("🏆 Top 5 Trades:")
|
||||
for row in db.cursor.fetchall():
|
||||
print(f" {row['symbol']} {row['type']}: ${row['net_profit']:.2f} ({row['session']})")
|
||||
|
||||
# Worst Trades
|
||||
db.cursor.execute("""
|
||||
SELECT symbol, type, entry_price, exit_price, net_profit, session
|
||||
FROM trades
|
||||
WHERE status = 'closed'
|
||||
ORDER BY net_profit ASC
|
||||
LIMIT 5
|
||||
""")
|
||||
|
||||
print("\n💔 Bottom 5 Trades:")
|
||||
for row in db.cursor.fetchall():
|
||||
print(f" {row['symbol']} {row['type']}: ${row['net_profit']:.2f} ({row['session']})")
|
||||
|
||||
db.close()
|
||||
```
|
||||
|
||||
### Query 4: Daily Breakdown
|
||||
```python
|
||||
db.cursor.execute("""
|
||||
SELECT
|
||||
DATE(entry_time) as date,
|
||||
COUNT(*) as trades,
|
||||
SUM(CASE WHEN net_profit > 0 THEN 1 ELSE 0 END) as wins,
|
||||
ROUND(SUM(net_profit), 2) as daily_profit
|
||||
FROM trades
|
||||
WHERE status = 'closed'
|
||||
AND entry_time >= datetime('now', '-30 days')
|
||||
GROUP BY DATE(entry_time)
|
||||
ORDER BY date DESC
|
||||
""")
|
||||
|
||||
print("📅 Daily Performance (Last 30 Days):")
|
||||
for row in db.cursor.fetchall():
|
||||
print(f" {row['date']}: {row['trades']} trades, {row['wins']} wins, ${row['daily_profit']}")
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 🛠️ Troubleshooting
|
||||
|
||||
### Problem 1: Telegram nicht verbunden
|
||||
**Symptom:** `❌ Telegram connection failed`
|
||||
|
||||
**Lösung:**
|
||||
1. Prüfe `telegram_config.json` existiert
|
||||
2. Prüfe Bot Token ist korrekt
|
||||
3. Prüfe Chat ID ist korrekt
|
||||
4. Teste mit: `python telegram_notifier.py`
|
||||
|
||||
### Problem 2: Database locked
|
||||
**Symptom:** `database is locked`
|
||||
|
||||
**Lösung:**
|
||||
```python
|
||||
# Stelle sicher nur eine Connection offen ist
|
||||
db.close()
|
||||
|
||||
# ODER: Benutze Context Manager
|
||||
with TradingDatabase("trading_bot.db") as db:
|
||||
# queries...
|
||||
pass # automatisch geschlossen
|
||||
```
|
||||
|
||||
### Problem 3: Keine Notifications
|
||||
**Symptom:** Bot läuft, aber keine Telegram Nachrichten
|
||||
|
||||
**Lösung:**
|
||||
1. Prüfe `infra.enable_telegram` ist `True`
|
||||
2. Sende Test-Nachricht manuell
|
||||
3. Prüfe Bot ist nicht von @BotFather blockiert
|
||||
4. Starte eine Konversation mit deinem Bot (sende `/start`)
|
||||
|
||||
### Problem 4: Alte Trades nicht in DB
|
||||
**Symptom:** Database zeigt 0 Trades
|
||||
|
||||
**Lösung:**
|
||||
```python
|
||||
# Migriere JSON Daten
|
||||
from infrastructure_patch import TradingInfrastructure
|
||||
|
||||
infra = TradingInfrastructure()
|
||||
infra.migrate_json_files(".") # Migriert alle JSON Files
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 📊 Performance Vorteile
|
||||
|
||||
### Vorher (nur JSON):
|
||||
- ❌ Schwer zu analysieren
|
||||
- ❌ Manuelle Performance-Berechnung
|
||||
- ❌ Keine Live Updates
|
||||
- ❌ Keine Session-Analyse
|
||||
|
||||
### Nachher (SQLite + Telegram):
|
||||
- ✅ Instant Queries
|
||||
- ✅ Automatische Reports
|
||||
- ✅ Mobile Benachrichtigungen
|
||||
- ✅ Session/Confidence Analytics
|
||||
- ✅ Historische Datenbank
|
||||
|
||||
---
|
||||
|
||||
## 🎯 Nächste Schritte
|
||||
|
||||
### Nach erfolgreicher Integration:
|
||||
|
||||
1. **1 Woche monitoren:**
|
||||
- Prüfe tägliche Telegram Reports
|
||||
- Verifiziere Database logging
|
||||
- Teste Queries
|
||||
|
||||
2. **Performance analysieren:**
|
||||
```python
|
||||
# Nach 1 Woche
|
||||
stats = infra.get_performance_summary(days=7)
|
||||
print(stats)
|
||||
```
|
||||
|
||||
3. **Optional - Custom Queries:**
|
||||
- Erstelle eigene Performance-Metriken
|
||||
- Exportiere Daten für Excel/Charts
|
||||
- Backtesting mit historischen Daten
|
||||
|
||||
4. **Phase 2 Features** (später):
|
||||
- Position Scaling basierend auf Confidence
|
||||
- ML Signal Filter
|
||||
- Multi-Symbol Support
|
||||
|
||||
---
|
||||
|
||||
## ✅ Checklist - Ist alles fertig?
|
||||
|
||||
- [ ] Telegram Bot erstellt (@BotFather)
|
||||
- [ ] Chat ID erhalten (@userinfobot)
|
||||
- [ ] `telegram_config.json` erstellt mit echten Werten
|
||||
- [ ] `python telegram_notifier.py` läuft ohne Fehler
|
||||
- [ ] `python trading_database.py` erstellt DB
|
||||
- [ ] Alte JSON Daten migriert (optional)
|
||||
- [ ] Infrastructure im Notebook initialisiert
|
||||
- [ ] Trade Entry Logging hinzugefügt
|
||||
- [ ] Trade Exit Logging hinzugefügt (wenn möglich)
|
||||
- [ ] Scheduled Reports aktiviert
|
||||
- [ ] Test-Trade durchgeführt → Telegram Notification erhalten
|
||||
- [ ] Database Query funktioniert
|
||||
- [ ] Bot auf VPS deployed
|
||||
|
||||
---
|
||||
|
||||
## 🎉 Zusammenfassung
|
||||
|
||||
Mit **SQLite + Telegram** hast du jetzt:
|
||||
|
||||
🗄️ **Strukturierte Datenbank** für alle Trades
|
||||
📱 **Live Mobile Notifications** für jeden Trade
|
||||
📊 **Automatische Performance Reports** täglich & wöchentlich
|
||||
🔍 **Schnelle Analytics** mit SQL Queries
|
||||
📈 **Historische Daten** für Backtesting
|
||||
⚠️ **Error Alerts** wenn etwas schief geht
|
||||
|
||||
**V1.8 ist jetzt eine professionelle Trading-Platform!** 🚀
|
||||
|
||||
Bei Fragen oder Problemen: Check Troubleshooting oder frag nach! 💪
|
||||
@@ -1,354 +0,0 @@
|
||||
"""
|
||||
TradingBot Diagnosis Tool
|
||||
Hilft bei der Identifikation, warum der Bot nicht handelt
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import MetaTrader5 as mt
|
||||
import pandas_ta as ta
|
||||
from tabulate import tabulate
|
||||
|
||||
def diagnose_trading_issue(symbol="XAUUSD"):
|
||||
"""
|
||||
Umfassende Diagnose warum der Bot nicht handelt
|
||||
"""
|
||||
print("🔍 TRADING BOT DIAGNOSIS")
|
||||
print("=" * 50)
|
||||
|
||||
issues_found = []
|
||||
|
||||
# 1. MT5 Connection Check
|
||||
print("\n1️⃣ MT5 Connection Check:")
|
||||
terminal_info = mt.terminal_info()
|
||||
if terminal_info:
|
||||
print(" ✅ MT5 connected")
|
||||
print(f" Company: {terminal_info.company}")
|
||||
print(f" Connected: {terminal_info.connected}")
|
||||
print(f" Trade allowed: {terminal_info.trade_allowed}")
|
||||
if not terminal_info.trade_allowed:
|
||||
issues_found.append("Trading not allowed in terminal")
|
||||
else:
|
||||
print(" ❌ MT5 not connected")
|
||||
issues_found.append("MT5 connection failed")
|
||||
return issues_found
|
||||
|
||||
# 2. Account Info Check
|
||||
print("\n2️⃣ Account Info Check:")
|
||||
account_info = mt.account_info()
|
||||
if account_info:
|
||||
print(f" ✅ Account: {account_info.login}")
|
||||
print(f" Balance: {account_info.balance}")
|
||||
print(f" Equity: {account_info.equity}")
|
||||
print(f" Trade allowed: {account_info.trade_allowed}")
|
||||
print(f" Trade mode: {account_info.trade_mode}")
|
||||
if not account_info.trade_allowed:
|
||||
issues_found.append("Trading not allowed on account")
|
||||
else:
|
||||
print(" ❌ Cannot get account info")
|
||||
issues_found.append("Account info unavailable")
|
||||
|
||||
# 3. Symbol Info Check
|
||||
print(f"\n3️⃣ Symbol Info Check ({symbol}):")
|
||||
symbol_info = mt.symbol_info(symbol)
|
||||
if symbol_info:
|
||||
print(f" ✅ Symbol exists: {symbol_info.name}")
|
||||
print(f" Trade mode: {symbol_info.trade_mode}")
|
||||
print(f" Min volume: {symbol_info.volume_min}")
|
||||
print(f" Max volume: {symbol_info.volume_max}")
|
||||
print(f" Volume step: {symbol_info.volume_step}")
|
||||
if symbol_info.trade_mode == 0:
|
||||
issues_found.append(f"Trading disabled for {symbol}")
|
||||
else:
|
||||
print(f" ❌ Symbol {symbol} not found")
|
||||
issues_found.append(f"Symbol {symbol} not available")
|
||||
|
||||
# 4. Market Hours Check
|
||||
print("\n4️⃣ Market Hours Check:")
|
||||
tick = mt.symbol_info_tick(symbol)
|
||||
if tick:
|
||||
print(f" ✅ Current price: Bid={tick.bid}, Ask={tick.ask}")
|
||||
print(f" Last tick time: {pd.to_datetime(tick.time, unit='s')}")
|
||||
spread = tick.ask - tick.bid
|
||||
print(f" Spread: {spread:.5f}")
|
||||
if spread > 0.01: # Sehr hoher Spread
|
||||
issues_found.append(f"High spread: {spread:.5f}")
|
||||
else:
|
||||
print(" ❌ No current price data")
|
||||
issues_found.append("No price data available")
|
||||
|
||||
# 5. Data Availability Check
|
||||
print("\n5️⃣ Data Availability Check:")
|
||||
timeframes_to_check = ["M5", "M15", "M30", "H1", "H4", "D1"]
|
||||
tf_map = {"M5": mt.TIMEFRAME_M5, "M15": mt.TIMEFRAME_M15, "M30": mt.TIMEFRAME_M30,
|
||||
"H1": mt.TIMEFRAME_H1, "H4": mt.TIMEFRAME_H4, "D1": mt.TIMEFRAME_D1}
|
||||
|
||||
data_status = []
|
||||
for tf in timeframes_to_check:
|
||||
rates = mt.copy_rates_from_pos(symbol, tf_map[tf], 0, 50)
|
||||
if rates is not None and len(rates) > 0:
|
||||
data_status.append([tf, "✅", len(rates), pd.to_datetime(rates[-1]['time'], unit='s')])
|
||||
else:
|
||||
data_status.append([tf, "❌", 0, "No data"])
|
||||
issues_found.append(f"No data for {tf}")
|
||||
|
||||
print(tabulate(data_status, headers=["Timeframe", "Status", "Bars", "Last Update"], tablefmt="psql"))
|
||||
|
||||
return issues_found
|
||||
|
||||
def compare_v13_vs_v14_logic(symbol="XAUUSD"):
|
||||
"""
|
||||
Vergleicht warum V1.3 gehandelt hat aber V1.4 nicht
|
||||
"""
|
||||
print("\n🔄 COMPARING V1.3 vs V1.4 LOGIC")
|
||||
print("=" * 50)
|
||||
|
||||
try:
|
||||
# Simuliere V1.3 Logik (vereinfacht)
|
||||
from TradingBot_V1.4_Fixed import extended_top_down_v2, get_rates
|
||||
|
||||
# Hole aktuelle Daten
|
||||
signal_info = extended_top_down_v2(symbol, lookback=100)
|
||||
|
||||
if signal_info is None:
|
||||
print("❌ Cannot get signal info")
|
||||
return
|
||||
|
||||
confidence = signal_info['confidence']
|
||||
trend = signal_info['top_down_trend']
|
||||
signal_quality = signal_info['signal_quality']
|
||||
regime = signal_info['market_regime']['regime']
|
||||
adaptive_threshold = signal_info['adaptive_threshold']
|
||||
|
||||
# V1.3 Logic (fixed 80% threshold)
|
||||
v13_threshold = 80
|
||||
v13_would_trade = (trend != "sideways" and confidence >= v13_threshold)
|
||||
|
||||
# V1.4 Logic (adaptive threshold + quality filter)
|
||||
v14_would_trade = (signal_info['entry_signal'] != 0)
|
||||
|
||||
print(f"\n📊 SIGNAL COMPARISON:")
|
||||
comparison_data = [
|
||||
["Metric", "V1.3 (Old)", "V1.4 (New)", "Impact"],
|
||||
["Confidence", f"{confidence:.1f}%", f"{confidence:.1f}%", "Same"],
|
||||
["Threshold", f"{v13_threshold}%", f"{adaptive_threshold}%", f"{adaptive_threshold-v13_threshold:+d}%"],
|
||||
["Trend", trend, trend, "Same"],
|
||||
["Would Trade", "✅" if v13_would_trade else "❌", "✅" if v14_would_trade else "❌", ""],
|
||||
["Market Regime", "Not considered", regime, "New Filter"],
|
||||
["Signal Quality", "Not checked", signal_quality, "New Filter"],
|
||||
]
|
||||
|
||||
print(tabulate(comparison_data, headers="firstrow", tablefmt="psql"))
|
||||
|
||||
# Analyse warum nicht gehandelt wird
|
||||
print(f"\n🔍 WHY NOT TRADING:")
|
||||
|
||||
if not v13_would_trade and not v14_would_trade:
|
||||
print(" • Both versions agree: Confidence too low")
|
||||
print(f" • Need: {v13_threshold}% (V1.3) or {adaptive_threshold}% (V1.4)")
|
||||
print(f" • Have: {confidence:.1f}%")
|
||||
|
||||
elif v13_would_trade and not v14_would_trade:
|
||||
print(" 🛡️ V1.4 is MORE SELECTIVE (this is good!)")
|
||||
print(f" • V1.3 would trade with {confidence:.1f}% confidence")
|
||||
print(f" • V1.4 requires {adaptive_threshold}% in {regime} market")
|
||||
print(f" • Signal quality: {signal_quality}")
|
||||
|
||||
if regime == 'ranging':
|
||||
print(" • RANGING market detected - higher threshold prevents false breakouts")
|
||||
elif regime == 'volatile':
|
||||
print(" • VOLATILE market detected - avoiding choppy conditions")
|
||||
|
||||
elif not v13_would_trade and v14_would_trade:
|
||||
print(" 🚀 V1.4 FOUND OPPORTUNITY that V1.3 missed!")
|
||||
print(f" • Adaptive threshold {adaptive_threshold}% < fixed 80%")
|
||||
print(f" • Trading in {regime} market with {signal_quality} quality")
|
||||
|
||||
else:
|
||||
print(" 🤝 Both versions would trade - check other filters")
|
||||
|
||||
return {
|
||||
'v13_would_trade': v13_would_trade,
|
||||
'v14_would_trade': v14_would_trade,
|
||||
'confidence': confidence,
|
||||
'adaptive_threshold': adaptive_threshold,
|
||||
'regime': regime,
|
||||
'signal_quality': signal_quality
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Error in comparison: {e}")
|
||||
return None
|
||||
|
||||
def check_position_limits(symbol="XAUUSD"):
|
||||
"""
|
||||
Prüft ob Position-Limits das Trading verhindern
|
||||
"""
|
||||
print("\n📊 POSITION LIMITS CHECK")
|
||||
print("=" * 30)
|
||||
|
||||
# Aktuelle Positionen
|
||||
positions = mt.positions_get(symbol=symbol)
|
||||
total_positions = mt.positions_total()
|
||||
|
||||
print(f"Current positions for {symbol}: {len(positions) if positions else 0}")
|
||||
print(f"Total positions: {total_positions}")
|
||||
|
||||
if positions:
|
||||
print("\nExisting positions:")
|
||||
for pos in positions:
|
||||
print(f" • {pos.type_str} {pos.volume} lots @ {pos.price_open}")
|
||||
print(f" Comment: {pos.comment}")
|
||||
print(f" Profit: {pos.profit}")
|
||||
|
||||
# Position Limits prüfen
|
||||
# (Hier würdest du deine spezifischen Limits einbauen)
|
||||
max_positions = 3 # Beispiel
|
||||
if total_positions >= max_positions:
|
||||
return f"Position limit reached: {total_positions}/{max_positions}"
|
||||
|
||||
return None
|
||||
|
||||
def suggest_quick_fixes():
|
||||
"""
|
||||
Schlägt schnelle Lösungen vor
|
||||
"""
|
||||
print("\n🔧 QUICK FIXES TO TRY")
|
||||
print("=" * 30)
|
||||
|
||||
fixes = [
|
||||
"1️⃣ Lower adaptive threshold temporarily:",
|
||||
" adaptive_threshold = max(60, adaptive_threshold - 10)",
|
||||
"",
|
||||
"2️⃣ Bypass pullback entry timing:",
|
||||
" use_pullback_entry = False",
|
||||
"",
|
||||
"3️⃣ Reduce minimum signal quality:",
|
||||
" Allow 'fair' quality signals temporarily",
|
||||
"",
|
||||
"4️⃣ Check if V1.3 logic still works:",
|
||||
" Run your original extended_top_down() function",
|
||||
"",
|
||||
"5️⃣ Force a test trade:",
|
||||
" market_order(symbol, 0.01, 'buy') # Small test",
|
||||
]
|
||||
|
||||
for fix in fixes:
|
||||
print(fix)
|
||||
|
||||
def emergency_v13_mode(symbol="XAUUSD"):
|
||||
"""
|
||||
Notfall-Modus: Nutze V1.3 Logik mit V1.4 Verbesserungen
|
||||
"""
|
||||
print("\n🚨 EMERGENCY V1.3 MODE")
|
||||
print("=" * 30)
|
||||
|
||||
emergency_code = '''
|
||||
def emergency_trading_signal(symbol="XAUUSD"):
|
||||
"""
|
||||
Vereinfachte V1.3-ähnliche Logik als Fallback
|
||||
"""
|
||||
from TradingBot_V1.4_Fixed import get_rates
|
||||
import pandas_ta as ta
|
||||
from scipy.signal import savgol_filter
|
||||
from sklearn.linear_model import LinearRegression
|
||||
import numpy as np
|
||||
|
||||
# Hole H4 und M5 Daten
|
||||
h4_df = get_rates("h4", 150, symbol)
|
||||
m5_df = get_rates("m5", 100, symbol)
|
||||
|
||||
if h4_df is None or m5_df is None:
|
||||
return 0, "No data"
|
||||
|
||||
# Einfache Trend-Analyse H4
|
||||
h4_df['close_smooth'] = savgol_filter(h4_df['close'], 15, 3)
|
||||
X = np.arange(len(h4_df)).reshape(-1, 1)
|
||||
y = h4_df['close_smooth'].values
|
||||
model = LinearRegression().fit(X, y)
|
||||
slope = model.coef_[0]
|
||||
|
||||
atr = h4_df['atr'].iloc[-1]
|
||||
slope_threshold = atr * 0.0001
|
||||
|
||||
if slope > slope_threshold:
|
||||
h4_trend = "uptrend"
|
||||
elif slope < -slope_threshold:
|
||||
h4_trend = "downtrend"
|
||||
else:
|
||||
h4_trend = "sideways"
|
||||
|
||||
# Einfache Confidence (Prozent der letzten 6 Timeframes die aligned sind)
|
||||
# Vereinfacht: nur prüfen ob H4 und M5 aligned sind
|
||||
m5_df['close_smooth'] = savgol_filter(m5_df['close'], 15, 3)
|
||||
X_m5 = np.arange(len(m5_df)).reshape(-1, 1)
|
||||
y_m5 = m5_df['close_smooth'].values
|
||||
model_m5 = LinearRegression().fit(X_m5, y_m5)
|
||||
slope_m5 = model_m5.coef_[0]
|
||||
|
||||
atr_m5 = m5_df['atr'].iloc[-1]
|
||||
slope_threshold_m5 = atr_m5 * 0.0001
|
||||
|
||||
if slope_m5 > slope_threshold_m5:
|
||||
m5_trend = "uptrend"
|
||||
elif slope_m5 < -slope_threshold_m5:
|
||||
m5_trend = "downtrend"
|
||||
else:
|
||||
m5_trend = "sideways"
|
||||
|
||||
# Confidence basierend auf Alignment
|
||||
if h4_trend == m5_trend and h4_trend != "sideways":
|
||||
confidence = 85 # Hoch wenn aligned
|
||||
signal = 1 if h4_trend == "uptrend" else -1
|
||||
else:
|
||||
confidence = 45 # Niedrig wenn nicht aligned
|
||||
signal = 0
|
||||
|
||||
# V1.3-ähnliche Schwelle (niedriger als adaptive)
|
||||
threshold = 70 # Niedrigere Schwelle als V1.4
|
||||
|
||||
if confidence >= threshold and signal != 0:
|
||||
return signal, f"Emergency signal: {h4_trend}, confidence: {confidence}%"
|
||||
else:
|
||||
return 0, f"No signal: confidence {confidence}% < {threshold}%"
|
||||
|
||||
# Test:
|
||||
signal, reason = emergency_trading_signal("XAUUSD")
|
||||
print(f"Emergency Signal: {signal}")
|
||||
print(f"Reason: {reason}")
|
||||
'''
|
||||
|
||||
print("Copy this code to test emergency V1.3-like logic:")
|
||||
print(emergency_code)
|
||||
|
||||
if __name__ == "__main__":
|
||||
# Hauptdiagnose
|
||||
symbol = "XAUUSD"
|
||||
|
||||
print("🚨 TRADING BOT NOT WORKING - DIAGNOSIS")
|
||||
print("=" * 60)
|
||||
|
||||
# 1. Grundlegende Checks
|
||||
issues = diagnose_trading_issue(symbol)
|
||||
|
||||
# 2. Logic Comparison
|
||||
comparison = compare_v13_vs_v14_logic(symbol)
|
||||
|
||||
# 3. Position Limits
|
||||
position_issue = check_position_limits(symbol)
|
||||
if position_issue:
|
||||
issues.append(position_issue)
|
||||
|
||||
# 4. Zusammenfassung
|
||||
print(f"\n📋 ISSUES SUMMARY:")
|
||||
if issues:
|
||||
for i, issue in enumerate(issues, 1):
|
||||
print(f" {i}. {issue}")
|
||||
else:
|
||||
print(" ✅ No technical issues found")
|
||||
|
||||
# 5. Lösungsvorschläge
|
||||
suggest_quick_fixes()
|
||||
|
||||
# 6. Emergency Mode
|
||||
emergency_v13_mode(symbol)
|
||||
@@ -1,367 +0,0 @@
|
||||
"""
|
||||
Position Control für TradingBot V1.4
|
||||
Verhindert mehrfache gleichzeitige Trades
|
||||
"""
|
||||
|
||||
import MetaTrader5 as mt
|
||||
import pandas as pd
|
||||
from datetime import datetime
|
||||
|
||||
def check_existing_positions(symbol="XAUUSD", strategy_name="TradingBot_V1.4_Complete"):
|
||||
"""
|
||||
Überprüft ob bereits Positionen für das Symbol und die Strategie existieren
|
||||
|
||||
Returns:
|
||||
- has_position: bool - True wenn bereits Position existiert
|
||||
- position_info: dict - Info über bestehende Position(en)
|
||||
"""
|
||||
try:
|
||||
# Alle Positionen für das Symbol abrufen
|
||||
positions = mt.positions_get(symbol=symbol)
|
||||
|
||||
if positions is None:
|
||||
return False, {"count": 0, "details": []}
|
||||
|
||||
# Filter nach Strategie-Namen im Kommentar
|
||||
strategy_positions = []
|
||||
for pos in positions:
|
||||
if strategy_name in pos.comment:
|
||||
strategy_positions.append({
|
||||
"ticket": pos.ticket,
|
||||
"type": "BUY" if pos.type == 0 else "SELL",
|
||||
"volume": pos.volume,
|
||||
"price_open": pos.price_open,
|
||||
"profit": pos.profit,
|
||||
"comment": pos.comment,
|
||||
"time_open": pd.to_datetime(pos.time, unit='s')
|
||||
})
|
||||
|
||||
has_position = len(strategy_positions) > 0
|
||||
|
||||
position_info = {
|
||||
"count": len(strategy_positions),
|
||||
"details": strategy_positions
|
||||
}
|
||||
|
||||
return has_position, position_info
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error checking positions: {e}")
|
||||
return False, {"count": 0, "details": []}
|
||||
|
||||
def get_position_summary(symbol="XAUUSD", strategy_name="TradingBot_V1.4_Complete"):
|
||||
"""
|
||||
Gibt eine übersichtliche Zusammenfassung der aktuellen Positionen
|
||||
"""
|
||||
has_position, position_info = check_existing_positions(symbol, strategy_name)
|
||||
|
||||
print(f"\n📊 POSITION SUMMARY für {symbol}")
|
||||
print("=" * 50)
|
||||
|
||||
if not has_position:
|
||||
print("✅ Keine aktiven Positionen - bereit für neuen Trade")
|
||||
return False
|
||||
|
||||
print(f"⚠️ {position_info['count']} aktive Position(en) gefunden:")
|
||||
|
||||
for i, pos in enumerate(position_info['details'], 1):
|
||||
profit_emoji = "🟢" if pos['profit'] >= 0 else "🔴"
|
||||
print(f"\n Position {i}:")
|
||||
print(f" Ticket: {pos['ticket']}")
|
||||
print(f" Typ: {pos['type']}")
|
||||
print(f" Volumen: {pos['volume']}")
|
||||
print(f" Eröffnungspreis: {pos['price_open']}")
|
||||
print(f" Profit: {profit_emoji} {pos['profit']:.2f}")
|
||||
print(f" Eröffnungszeit: {pos['time_open']}")
|
||||
print(f" Kommentar: {pos['comment']}")
|
||||
|
||||
print(f"\n🛑 TRADING BLOCKIERT - Maximal 1 Position erlaubt")
|
||||
return True
|
||||
|
||||
def close_existing_positions(symbol="XAUUSD", strategy_name="TradingBot_V1.4_Complete", force_close=False):
|
||||
"""
|
||||
Schließt bestehende Positionen (optional)
|
||||
|
||||
Args:
|
||||
force_close: bool - Wenn True, schließt alle Positionen sofort
|
||||
"""
|
||||
has_position, position_info = check_existing_positions(symbol, strategy_name)
|
||||
|
||||
if not has_position:
|
||||
print("✅ Keine Positionen zum Schließen")
|
||||
return True
|
||||
|
||||
if not force_close:
|
||||
print(f"⚠️ {position_info['count']} Position(en) gefunden. Verwende force_close=True zum Schließen.")
|
||||
return False
|
||||
|
||||
print(f"🔄 Schließe {position_info['count']} Position(en)...")
|
||||
|
||||
success_count = 0
|
||||
for pos in position_info['details']:
|
||||
try:
|
||||
# Position schließen
|
||||
close_request = {
|
||||
"action": mt.TRADE_ACTION_DEAL,
|
||||
"symbol": symbol,
|
||||
"volume": pos['volume'],
|
||||
"type": mt.ORDER_TYPE_SELL if pos['type'] == "BUY" else mt.ORDER_TYPE_BUY,
|
||||
"position": pos['ticket'],
|
||||
"price": mt.symbol_info_tick(symbol).bid if pos['type'] == "BUY" else mt.symbol_info_tick(symbol).ask,
|
||||
"deviation": 20,
|
||||
"magic": 234000,
|
||||
"comment": f"Close {strategy_name}",
|
||||
"type_time": mt.ORDER_TIME_GTC,
|
||||
"type_filling": mt.ORDER_FILLING_IOC,
|
||||
}
|
||||
|
||||
result = mt.order_send(close_request)
|
||||
|
||||
if result.retcode == mt.TRADE_RETCODE_DONE:
|
||||
print(f"✅ Position {pos['ticket']} erfolgreich geschlossen")
|
||||
success_count += 1
|
||||
else:
|
||||
print(f"❌ Fehler beim Schließen von Position {pos['ticket']}: {result.comment}")
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Exception beim Schließen von Position {pos['ticket']}: {e}")
|
||||
|
||||
print(f"📊 {success_count}/{len(position_info['details'])} Positionen erfolgreich geschlossen")
|
||||
return success_count == len(position_info['details'])
|
||||
|
||||
def enhanced_execute_trade_v2_with_position_control(
|
||||
symbol="XAUUSD",
|
||||
atr_mult=1.5,
|
||||
base_confidence=70,
|
||||
max_risk_per_trade=0.01,
|
||||
risk_filter=True,
|
||||
min_atr=0.0010,
|
||||
use_pullback_entry=True,
|
||||
max_positions=1, # NEU: Maximale Anzahl Positionen
|
||||
strategy_name="TradingBot_V1.4_Complete",
|
||||
debug=True
|
||||
):
|
||||
"""
|
||||
Enhanced Execute Trade mit Position-Kontrolle
|
||||
"""
|
||||
|
||||
# WICHTIG: Zuerst Position-Check
|
||||
print(f"\n🔍 POSITION CHECK für {symbol}")
|
||||
has_position, position_info = check_existing_positions(symbol, strategy_name)
|
||||
|
||||
if has_position and position_info['count'] >= max_positions:
|
||||
if debug:
|
||||
print(f"🛑 TRADE BLOCKIERT: {position_info['count']}/{max_positions} Positionen bereits aktiv")
|
||||
for pos in position_info['details']:
|
||||
profit_emoji = "🟢" if pos['profit'] >= 0 else "🔴"
|
||||
print(f" Position: {pos['type']} {pos['volume']} @ {pos['price_open']} | Profit: {profit_emoji} {pos['profit']:.2f}")
|
||||
return None
|
||||
|
||||
print(f"✅ Position-Check OK: {position_info['count']}/{max_positions} Positionen")
|
||||
|
||||
# Hier würde deine bestehende execute_trade_v2 Logik kommen
|
||||
# Ich importiere sie von deinem bestehenden Code
|
||||
|
||||
try:
|
||||
# Import der bestehenden Funktionen (angepasst an deine Struktur)
|
||||
from TradingBot_V1_4_Complete import extended_top_down_v2, check_risk_limits, market_order, check_pullback_entry
|
||||
|
||||
# Signal-Analyse
|
||||
signal_info = extended_top_down_v2(symbol)
|
||||
if signal_info is None:
|
||||
print("❌ Signal-Analyse fehlgeschlagen")
|
||||
return None
|
||||
|
||||
entry_signal = signal_info["entry_signal"]
|
||||
confidence = signal_info["confidence"]
|
||||
adaptive_threshold = signal_info["adaptive_threshold"]
|
||||
signal_quality = signal_info["signal_quality"]
|
||||
market_regime = signal_info["market_regime"]
|
||||
|
||||
# Get Price/ATR from M5
|
||||
m5_info = signal_info["trend_info"]["M5"]
|
||||
price = m5_info["price"]
|
||||
atr = m5_info["atr"]
|
||||
|
||||
# Enhanced Pre-checks
|
||||
reason = ""
|
||||
|
||||
if confidence < adaptive_threshold:
|
||||
reason = f"Confidence {confidence}% < adaptive threshold {adaptive_threshold}%"
|
||||
elif entry_signal == 0:
|
||||
reason = f"No entry signal (Trend: {signal_info['top_down_trend']}, Regime: {market_regime['regime']})"
|
||||
elif price is None or atr is None:
|
||||
reason = "Price/ATR not available"
|
||||
elif risk_filter and atr < min_atr:
|
||||
reason = f"ATR {atr:.5f} < min_atr {min_atr}"
|
||||
elif signal_quality == "none":
|
||||
reason = "Signal quality insufficient"
|
||||
else:
|
||||
# Entry Timing Check
|
||||
if use_pullback_entry:
|
||||
pullback_ok, pullback_reason = check_pullback_entry(symbol, signal_info)
|
||||
if not pullback_ok:
|
||||
reason = f"Entry timing: {pullback_reason}"
|
||||
|
||||
if not reason:
|
||||
# Risk Checks
|
||||
risk_ok = check_risk_limits(symbol, max_risk_per_trade=max_risk_per_trade)
|
||||
if not risk_ok:
|
||||
reason = "Risk limits exceeded"
|
||||
|
||||
# Execute Trade if all checks pass
|
||||
if not reason:
|
||||
# Enhanced SL/TP calculation based on regime
|
||||
regime_mult = 1.0
|
||||
if market_regime['regime'] == 'volatile':
|
||||
regime_mult = 1.3 # Wider stops in volatile markets
|
||||
elif market_regime['regime'] == 'ranging':
|
||||
regime_mult = 0.8 # Tighter stops in ranging markets
|
||||
|
||||
adjusted_atr_mult = atr_mult * regime_mult
|
||||
|
||||
if entry_signal == 1: # Long
|
||||
stop_loss = price - adjusted_atr_mult * atr
|
||||
take_profit = price + adjusted_atr_mult * atr * 2.5
|
||||
else: # Short
|
||||
stop_loss = price + adjusted_atr_mult * atr
|
||||
take_profit = price - adjusted_atr_mult * atr * 2.5
|
||||
|
||||
# Dynamic Position Sizing
|
||||
stop_distance = adjusted_atr_mult * atr
|
||||
account_info = mt.account_info()
|
||||
if account_info:
|
||||
balance = account_info.balance
|
||||
risk_amount = balance * max_risk_per_trade
|
||||
if symbol == "XAUUSD":
|
||||
volume = min(0.1, max(0.01, risk_amount / (stop_distance * 100)))
|
||||
else:
|
||||
volume = 0.01
|
||||
else:
|
||||
volume = 0.01
|
||||
|
||||
# FINAL POSITION CHECK vor Order
|
||||
final_check, _ = check_existing_positions(symbol, strategy_name)
|
||||
if final_check:
|
||||
print(f"🛑 LAST-MINUTE BLOCK: Position wurde zwischen Checks eröffnet!")
|
||||
return None
|
||||
|
||||
# Log Enhanced Trade Info
|
||||
print(f"\n🚀 ENHANCED TRADE EXECUTION (mit Position Control)")
|
||||
print(f"Symbol: {symbol}")
|
||||
print(f"Direction: {'LONG' if entry_signal == 1 else 'SHORT'}")
|
||||
print(f"Price: {price:.5f}")
|
||||
print(f"Volume: {volume:.2f}")
|
||||
print(f"Stop Loss: {stop_loss:.5f}")
|
||||
print(f"Take Profit: {take_profit:.5f}")
|
||||
print(f"Confidence: {confidence}% (Threshold: {adaptive_threshold}%)")
|
||||
print(f"Signal Quality: {signal_quality.upper()}")
|
||||
print(f"Market Regime: {market_regime['regime'].upper()}")
|
||||
print(f"Position Limit: {position_info['count']}/{max_positions}")
|
||||
|
||||
# Execute the actual trade
|
||||
try:
|
||||
order_result = market_order(
|
||||
symbol=symbol,
|
||||
volume=volume,
|
||||
order_type="buy" if entry_signal == 1 else "sell",
|
||||
stoploss=stop_loss,
|
||||
take_profit=take_profit,
|
||||
comment=strategy_name # Wichtig für Position-Tracking
|
||||
)
|
||||
|
||||
if order_result and order_result.retcode == mt.TRADE_RETCODE_DONE:
|
||||
print(f"✅ Trade erfolgreich eröffnet! Ticket: {order_result.order}")
|
||||
|
||||
# Verify position was created
|
||||
new_check, new_info = check_existing_positions(symbol, strategy_name)
|
||||
print(f"📊 Neue Position-Anzahl: {new_info['count']}")
|
||||
|
||||
return order_result
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Trade execution failed: {e}")
|
||||
return None
|
||||
|
||||
else:
|
||||
if debug:
|
||||
print(f"\n⏸️ TRADE SKIPPED: {reason}")
|
||||
print(f"Confidence: {confidence}% | Threshold: {adaptive_threshold}%")
|
||||
print(f"Signal Quality: {signal_quality} | Regime: {market_regime['regime']}")
|
||||
print(f"Positions: {position_info['count']}/{max_positions}")
|
||||
return None
|
||||
|
||||
except ImportError:
|
||||
print("❌ Konnte bestehende Funktionen nicht importieren. Verwende vereinfachte Version.")
|
||||
return None
|
||||
except Exception as e:
|
||||
print(f"❌ Fehler in enhanced_execute_trade_v2_with_position_control: {e}")
|
||||
return None
|
||||
|
||||
def position_management_menu():
|
||||
"""
|
||||
Interaktives Menü für Position-Management
|
||||
"""
|
||||
symbol = "XAUUSD"
|
||||
strategy_name = "TradingBot_V1.4_Complete"
|
||||
|
||||
while True:
|
||||
print(f"\n🔧 POSITION MANAGEMENT MENU")
|
||||
print("=" * 40)
|
||||
print("1. Position Status anzeigen")
|
||||
print("2. Alle Positionen schließen")
|
||||
print("3. Position-Check für neuen Trade")
|
||||
print("4. Trading mit Position-Control testen")
|
||||
print("5. Exit")
|
||||
|
||||
choice = input("\nWähle Option (1-5): ").strip()
|
||||
|
||||
if choice == "1":
|
||||
get_position_summary(symbol, strategy_name)
|
||||
|
||||
elif choice == "2":
|
||||
confirm = input("❗ Alle Positionen schließen? (yes/no): ").strip().lower()
|
||||
if confirm in ['yes', 'y', 'ja', 'j']:
|
||||
close_existing_positions(symbol, strategy_name, force_close=True)
|
||||
else:
|
||||
print("❌ Abgebrochen")
|
||||
|
||||
elif choice == "3":
|
||||
has_pos, pos_info = check_existing_positions(symbol, strategy_name)
|
||||
if has_pos:
|
||||
print(f"🛑 {pos_info['count']} Position(en) aktiv - KEIN neuer Trade möglich")
|
||||
else:
|
||||
print("✅ Keine Positionen - neuer Trade möglich")
|
||||
|
||||
elif choice == "4":
|
||||
print("🧪 Testing Trading mit Position Control...")
|
||||
result = enhanced_execute_trade_v2_with_position_control(
|
||||
symbol=symbol,
|
||||
strategy_name=strategy_name,
|
||||
debug=True
|
||||
)
|
||||
if result:
|
||||
print(f"✅ Test-Trade ausgeführt: {result}")
|
||||
else:
|
||||
print("⏸️ Kein Test-Trade ausgeführt")
|
||||
|
||||
elif choice == "5":
|
||||
print("👋 Exit Position Management")
|
||||
break
|
||||
|
||||
else:
|
||||
print("❌ Ungültige Option")
|
||||
|
||||
if __name__ == "__main__":
|
||||
# Teste Position Control
|
||||
symbol = "XAUUSD"
|
||||
strategy_name = "TradingBot_V1.4_Complete"
|
||||
|
||||
print("🔍 TESTING POSITION CONTROL")
|
||||
print("=" * 40)
|
||||
|
||||
# Check current positions
|
||||
get_position_summary(symbol, strategy_name)
|
||||
|
||||
# Start interactive menu
|
||||
position_management_menu()
|
||||
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-11068
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@@ -1,2 +0,0 @@
|
||||
[ZoneTransfer]
|
||||
ZoneId=3
|
||||
File diff suppressed because it is too large.
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File diff suppressed because it is too large.
Load diff
@@ -1,680 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# TradingBot V1.4 - Complete Version\n",
|
||||
"\n",
|
||||
"## 🚀 Optimierte Entry Signal Logik\n",
|
||||
"\n",
|
||||
"### Hauptverbesserungen:\n",
|
||||
"1. **Adaptive Confidence Threshold** - Automatische Anpassung an Marktbedingungen\n",
|
||||
"2. **Market Regime Detection** - Erkennung von Trending/Ranging/Volatile Märkten\n",
|
||||
"3. **Entry Timing Optimization** - Pullback-basierte Entries\n",
|
||||
"4. **Risk-Adjusted Signal Strength** - Kombiniert Confidence mit Trend-Stärke\n",
|
||||
"5. **Performance Monitoring** - Automatisches Tracking"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Imports\n",
|
||||
"import pandas as pd\n",
|
||||
"import numpy as np\n",
|
||||
"import MetaTrader5 as mt\n",
|
||||
"import pandas_ta as ta\n",
|
||||
"from scipy.signal import savgol_filter, find_peaks\n",
|
||||
"from sklearn.linear_model import LinearRegression\n",
|
||||
"from tabulate import tabulate\n",
|
||||
"from datetime import datetime, timedelta\n",
|
||||
"import json\n",
|
||||
"import keyring as kr\n",
|
||||
"\n",
|
||||
"print(\"✅ All imports successful\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# MT5 Login\n",
|
||||
"mt.initialize()\n",
|
||||
"login = 10800246\n",
|
||||
"server = 'VantageInternational-Demo'\n",
|
||||
"password = kr.get_password(server, str(login))\n",
|
||||
"login_result = mt.login(login, password, server)\n",
|
||||
"print(f\"Login successful: {login_result}\")\n",
|
||||
"\n",
|
||||
"# Trading Parameter\n",
|
||||
"symbol = \"XAUUSD\"\n",
|
||||
"strategy_name = \"TradingBot_V1.4_Complete\"\n",
|
||||
"print(f\"Symbol: {symbol}\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Helper Functions\n",
|
||||
"def get_rates(timeframe=\"h4\", count=200, symbol=\"XAUUSD\"):\n",
|
||||
" timeframes_dict = {\n",
|
||||
" \"m1\": mt.TIMEFRAME_M1, \"m5\": mt.TIMEFRAME_M5, \"m15\": mt.TIMEFRAME_M15,\n",
|
||||
" \"m30\": mt.TIMEFRAME_M30, \"h1\": mt.TIMEFRAME_H1, \"h4\": mt.TIMEFRAME_H4, \"d1\": mt.TIMEFRAME_D1\n",
|
||||
" }\n",
|
||||
" try:\n",
|
||||
" rates = mt.copy_rates_from_pos(symbol, timeframes_dict[timeframe], 0, count)\n",
|
||||
" if rates is None: return None\n",
|
||||
" df = pd.DataFrame(rates)\n",
|
||||
" df['time'] = pd.to_datetime(df['time'], unit='s')\n",
|
||||
" df.set_index('time', inplace=True)\n",
|
||||
" df['atr'] = ta.atr(df['high'], df['low'], df['close'], length=14)\n",
|
||||
" return df\n",
|
||||
" except Exception as e:\n",
|
||||
" print(f\"Error getting rates: {e}\")\n",
|
||||
" return None\n",
|
||||
"\n",
|
||||
"def check_risk_limits(symbol, volume=None, order_type=\"buy\", max_risk_per_trade=0.01):\n",
|
||||
" try:\n",
|
||||
" account_info = mt.account_info()\n",
|
||||
" if not account_info: return False\n",
|
||||
" balance, equity = account_info.balance, account_info.equity\n",
|
||||
" if equity < balance * 0.8: return False\n",
|
||||
" return True\n",
|
||||
" except: return False\n",
|
||||
"\n",
|
||||
"def market_order(symbol, volume, order_type, stoploss=None, take_profit=None, deviation=20):\n",
|
||||
" try:\n",
|
||||
" price_dict = {'buy': mt.symbol_info_tick(symbol).ask, 'sell': mt.symbol_info_tick(symbol).bid}\n",
|
||||
" order_type_dict = {'buy': mt.ORDER_TYPE_BUY, 'sell': mt.ORDER_TYPE_SELL}\n",
|
||||
" request = {\n",
|
||||
" \"action\": mt.TRADE_ACTION_DEAL, \"symbol\": symbol, \"volume\": volume,\n",
|
||||
" \"type\": order_type_dict[order_type], \"price\": price_dict[order_type],\n",
|
||||
" \"sl\": stoploss, \"tp\": take_profit, \"deviation\": deviation,\n",
|
||||
" \"magic\": 234000, \"comment\": strategy_name, \"type_time\": mt.ORDER_TIME_GTC,\n",
|
||||
" \"type_filling\": mt.ORDER_FILLING_IOC\n",
|
||||
" }\n",
|
||||
" return mt.order_send(request)\n",
|
||||
" except Exception as e:\n",
|
||||
" print(f\"Error in market order: {e}\")\n",
|
||||
" return None\n",
|
||||
"\n",
|
||||
"print(\"✅ Helper functions defined\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Market Regime Detection\n",
|
||||
"def detect_market_regime(df, lookback=50):\n",
|
||||
" try:\n",
|
||||
" adx_data = ta.adx(df['high'], df['low'], df['close'], length=14)\n",
|
||||
" adx = adx_data['ADX_14'].iloc[-1] if adx_data is not None and 'ADX_14' in adx_data.columns else 25.0\n",
|
||||
" \n",
|
||||
" try:\n",
|
||||
" bb = ta.bbands(df['close'], length=20)\n",
|
||||
" if bb is not None and len(bb.columns) >= 3:\n",
|
||||
" bb_cols = bb.columns.tolist()\n",
|
||||
" bb_width = ((bb[bb_cols[0]] - bb[bb_cols[2]]) / bb[bb_cols[1]] * 100).iloc[-lookback:].mean()\n",
|
||||
" else: bb_width = 4.0\n",
|
||||
" except: bb_width = 4.0\n",
|
||||
" \n",
|
||||
" price_range = df['high'].iloc[-lookback:].max() - df['low'].iloc[-lookback:].min()\n",
|
||||
" atr_avg = df['atr'].iloc[-lookback:].mean()\n",
|
||||
" range_ratio = price_range / (atr_avg * lookback) if atr_avg > 0 else 1.0\n",
|
||||
" vol_cluster = df['atr'].iloc[-10:].std() / df['atr'].iloc[-50:].mean() if len(df) >= 50 else 1.0\n",
|
||||
" \n",
|
||||
" if adx > 25 and range_ratio > 1.5:\n",
|
||||
" regime, strength = 'trending', min(100, adx * 2)\n",
|
||||
" elif vol_cluster > 1.5:\n",
|
||||
" regime, strength = 'volatile', min(100, vol_cluster * 50)\n",
|
||||
" else:\n",
|
||||
" regime, strength = 'ranging', max(0, 100 - adx * 2)\n",
|
||||
" \n",
|
||||
" return {'regime': regime, 'strength': strength, 'adx': adx, 'bb_width': bb_width, 'range_ratio': range_ratio, 'vol_cluster': vol_cluster}\n",
|
||||
" except Exception as e:\n",
|
||||
" return {'regime': 'ranging', 'strength': 50, 'adx': 20, 'bb_width': 4.0, 'range_ratio': 1.0, 'vol_cluster': 1.0}\n",
|
||||
"\n",
|
||||
"print(\"✅ Market Regime Detection defined\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Adaptive Confidence System\n",
|
||||
"def calculate_adaptive_confidence_threshold(regime_info, base_confidence=70):\n",
|
||||
" regime = regime_info['regime']\n",
|
||||
" adx = regime_info['adx']\n",
|
||||
" \n",
|
||||
" if regime == 'trending':\n",
|
||||
" return max(60, base_confidence - 15) if adx > 30 else base_confidence - 10\n",
|
||||
" elif regime == 'ranging':\n",
|
||||
" return base_confidence + 15\n",
|
||||
" elif regime == 'volatile':\n",
|
||||
" return base_confidence + 20\n",
|
||||
" return base_confidence\n",
|
||||
"\n",
|
||||
"print(\"✅ Adaptive Confidence System defined\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Enhanced Trend Analysis\n",
|
||||
"def get_enhanced_trend(timeframe=\"H4\", lookback=150, symbol=\"XAUUSD\"):\n",
|
||||
" tf_map = {\"D1\": \"d1\", \"H4\": \"h4\", \"H1\": \"h1\", \"M30\": \"m30\", \"M15\": \"m15\", \"M5\": \"m5\"}\n",
|
||||
" tf = tf_map.get(timeframe, timeframe.lower())\n",
|
||||
" \n",
|
||||
" try:\n",
|
||||
" df = get_rates(tf, lookback, symbol)\n",
|
||||
" if df is None or len(df) < 50: return None\n",
|
||||
" \n",
|
||||
" df['close_smooth'] = savgol_filter(df['close'], min(15, len(df)//10), 3)\n",
|
||||
" X = np.arange(len(df)).reshape(-1, 1)\n",
|
||||
" y = df['close_smooth'].values\n",
|
||||
" model = LinearRegression().fit(X, y)\n",
|
||||
" slope = model.coef_[0]\n",
|
||||
" \n",
|
||||
" regime_info = detect_market_regime(df.iloc[-50:])\n",
|
||||
" base_threshold = df['atr'].iloc[-1] * 0.0001\n",
|
||||
" \n",
|
||||
" if regime_info['regime'] == 'trending':\n",
|
||||
" slope_threshold = base_threshold * 0.7\n",
|
||||
" elif regime_info['regime'] == 'ranging':\n",
|
||||
" slope_threshold = base_threshold * 1.5\n",
|
||||
" else:\n",
|
||||
" slope_threshold = base_threshold * 1.2\n",
|
||||
" \n",
|
||||
" trend = \"uptrend\" if slope > slope_threshold else \"downtrend\" if slope < -slope_threshold else \"sideways\"\n",
|
||||
" trend_strength = abs(slope) / slope_threshold if slope_threshold > 0 else 0\n",
|
||||
" \n",
|
||||
" return {\n",
|
||||
" \"trend\": trend, \"slope\": slope, \"slope_threshold\": slope_threshold,\n",
|
||||
" \"trend_strength\": trend_strength, \"atr\": df['atr'].iloc[-1],\n",
|
||||
" \"price\": df['close'].iloc[-1], \"regime_info\": regime_info\n",
|
||||
" }\n",
|
||||
" except Exception as e:\n",
|
||||
" print(f\"Error in get_enhanced_trend: {e}\")\n",
|
||||
" return None\n",
|
||||
"\n",
|
||||
"print(\"✅ Enhanced Trend Analysis defined\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Extended Top-Down Analysis V2\n",
|
||||
"def extended_top_down_v2(symbol=\"XAUUSD\", lookback=150):\n",
|
||||
" timeframes = [\"D1\", \"H4\", \"H1\", \"M30\", \"M15\", \"M5\"]\n",
|
||||
" trend_info = {}\n",
|
||||
" \n",
|
||||
" for tf in timeframes:\n",
|
||||
" trend_info[tf] = get_enhanced_trend(tf, lookback, symbol)\n",
|
||||
" if trend_info[tf] is None:\n",
|
||||
" print(f\"⚠️ Keine Daten für {tf}\")\n",
|
||||
" return None\n",
|
||||
" \n",
|
||||
" main_regime = trend_info[\"H4\"][\"regime_info\"]\n",
|
||||
" adaptive_confidence_threshold = calculate_adaptive_confidence_threshold(main_regime)\n",
|
||||
" \n",
|
||||
" # Standard-Trend (D1 + H4)\n",
|
||||
" d1_trend = trend_info[\"D1\"][\"trend\"]\n",
|
||||
" h4_trend = trend_info[\"H4\"][\"trend\"]\n",
|
||||
" d1_strength = trend_info[\"D1\"][\"trend_strength\"]\n",
|
||||
" h4_strength = trend_info[\"H4\"][\"trend_strength\"]\n",
|
||||
" \n",
|
||||
" if d1_trend == h4_trend and d1_trend != \"sideways\":\n",
|
||||
" standard_trend = d1_trend\n",
|
||||
" standard_strength = (d1_strength * 0.6 + h4_strength * 0.4)\n",
|
||||
" elif d1_strength > h4_strength * 1.5:\n",
|
||||
" standard_trend = d1_trend\n",
|
||||
" standard_strength = d1_strength * 0.8\n",
|
||||
" elif h4_strength > d1_strength * 1.5:\n",
|
||||
" standard_trend = h4_trend\n",
|
||||
" standard_strength = h4_strength * 0.8\n",
|
||||
" else:\n",
|
||||
" standard_trend = \"sideways\"\n",
|
||||
" standard_strength = 0\n",
|
||||
" \n",
|
||||
" # Fast-Trend\n",
|
||||
" fast_timeframes = [\"H1\", \"M30\", \"M15\", \"M5\"]\n",
|
||||
" fast_trends = [trend_info[tf][\"trend\"] for tf in fast_timeframes]\n",
|
||||
" fast_strengths = [trend_info[tf][\"trend_strength\"] for tf in fast_timeframes]\n",
|
||||
" \n",
|
||||
" required_alignment = 2 if main_regime['regime'] == 'trending' else 3\n",
|
||||
" \n",
|
||||
" trend_counts = {'uptrend': 0, 'downtrend': 0, 'sideways': 0}\n",
|
||||
" weighted_strengths = {'uptrend': 0, 'downtrend': 0}\n",
|
||||
" weights = [1.0, 0.8, 0.6, 0.4]\n",
|
||||
" \n",
|
||||
" for i, (trend, strength) in enumerate(zip(fast_trends, fast_strengths)):\n",
|
||||
" trend_counts[trend] += 1\n",
|
||||
" if trend != 'sideways':\n",
|
||||
" weighted_strengths[trend] += strength * weights[i]\n",
|
||||
" \n",
|
||||
" max_count = max(trend_counts['uptrend'], trend_counts['downtrend'])\n",
|
||||
" if max_count >= required_alignment:\n",
|
||||
" if trend_counts['uptrend'] > trend_counts['downtrend']:\n",
|
||||
" fast_trend = \"uptrend\"\n",
|
||||
" elif trend_counts['downtrend'] > trend_counts['uptrend']:\n",
|
||||
" fast_trend = \"downtrend\"\n",
|
||||
" else:\n",
|
||||
" fast_trend = \"uptrend\" if weighted_strengths['uptrend'] > weighted_strengths['downtrend'] else \"downtrend\"\n",
|
||||
" else:\n",
|
||||
" fast_trend = \"sideways\"\n",
|
||||
" \n",
|
||||
" # Top-Down-Trend\n",
|
||||
" if standard_trend == fast_trend and standard_trend != \"sideways\":\n",
|
||||
" top_down_trend = standard_trend\n",
|
||||
" combined_strength = (standard_strength + weighted_strengths.get(fast_trend, 0)) / 2\n",
|
||||
" else:\n",
|
||||
" top_down_trend = \"sideways\"\n",
|
||||
" combined_strength = 0\n",
|
||||
" \n",
|
||||
" # Confidence Calculation\n",
|
||||
" weights = {\"D1\": 2.5, \"H4\": 2.0, \"H1\": 1.5, \"M30\": 1.0, \"M15\": 0.8, \"M5\": 0.6}\n",
|
||||
" \n",
|
||||
" weighted_matching = sum(\n",
|
||||
" weights[tf] * trend_info[tf][\"trend_strength\"] \n",
|
||||
" for tf in timeframes\n",
|
||||
" if trend_info[tf][\"trend\"] == top_down_trend and trend_info[tf][\"trend\"] != \"sideways\"\n",
|
||||
" )\n",
|
||||
" \n",
|
||||
" weighted_total = sum(\n",
|
||||
" weights[tf] * trend_info[tf][\"trend_strength\"]\n",
|
||||
" for tf in timeframes\n",
|
||||
" if trend_info[tf][\"trend\"] != \"sideways\"\n",
|
||||
" )\n",
|
||||
" \n",
|
||||
" confidence = round((weighted_matching / weighted_total) * 100, 2) if weighted_total > 0 else 0.0\n",
|
||||
" \n",
|
||||
" # Risk-Adjusted Signal Strength\n",
|
||||
" atr = trend_info[\"M5\"][\"atr\"]\n",
|
||||
" rrr = 2.5\n",
|
||||
" risk_adjusted_strength = confidence * combined_strength * min(2.0, rrr)\n",
|
||||
" \n",
|
||||
" # Entry Signal\n",
|
||||
" entry_signal = 0\n",
|
||||
" signal_quality = \"none\"\n",
|
||||
" \n",
|
||||
" if (top_down_trend != \"sideways\" and \n",
|
||||
" confidence >= adaptive_confidence_threshold and\n",
|
||||
" risk_adjusted_strength >= 100):\n",
|
||||
" \n",
|
||||
" entry_signal = 1 if top_down_trend == \"uptrend\" else -1\n",
|
||||
" \n",
|
||||
" if confidence >= 85 and risk_adjusted_strength >= 150:\n",
|
||||
" signal_quality = \"excellent\"\n",
|
||||
" elif confidence >= 75 and risk_adjusted_strength >= 120:\n",
|
||||
" signal_quality = \"good\"\n",
|
||||
" else:\n",
|
||||
" signal_quality = \"fair\"\n",
|
||||
" \n",
|
||||
" # Debug Output\n",
|
||||
" debug_data = []\n",
|
||||
" for tf in timeframes:\n",
|
||||
" info = trend_info[tf]\n",
|
||||
" debug_data.append([tf, info[\"trend\"], f\"{info['trend_strength']:.2f}\", \n",
|
||||
" f\"{info['atr']:.4f}\", f\"{info['slope']:.6f}\", f\"{info['price']:.2f}\"])\n",
|
||||
" \n",
|
||||
" print(f\"📊 Enhanced Trend-Analyse für {symbol}\")\n",
|
||||
" print(f\"🎯 Market Regime: {main_regime['regime'].upper()} (Strength: {main_regime['strength']:.0f}%)\")\n",
|
||||
" print(f\"🎚️ Adaptive Confidence Threshold: {adaptive_confidence_threshold}%\")\n",
|
||||
" print(tabulate(debug_data, headers=[\"TF\", \"Trend\", \"Strength\", \"ATR\", \"Slope\", \"Price\"], tablefmt=\"psql\"))\n",
|
||||
" print(f\"➡️ Standard-Trend: {standard_trend} (Strength: {standard_strength:.2f})\")\n",
|
||||
" print(f\"➡️ Fast-Trend: {fast_trend}\")\n",
|
||||
" print(f\"➡️ Top-Down-Trend: {top_down_trend}\")\n",
|
||||
" print(f\"➡️ Confidence: {confidence}% (Threshold: {adaptive_confidence_threshold}%)\")\n",
|
||||
" print(f\"➡️ Risk-Adjusted Strength: {risk_adjusted_strength:.1f}\")\n",
|
||||
" print(f\"➡️ Signal Quality: {signal_quality.upper()}\")\n",
|
||||
" \n",
|
||||
" return {\n",
|
||||
" \"symbol\": symbol, \"trend_info\": trend_info, \"market_regime\": main_regime,\n",
|
||||
" \"standard_trend\": standard_trend, \"fast_trend\": fast_trend, \"top_down_trend\": top_down_trend,\n",
|
||||
" \"confidence\": confidence, \"adaptive_threshold\": adaptive_confidence_threshold,\n",
|
||||
" \"risk_adjusted_strength\": risk_adjusted_strength, \"entry_signal\": entry_signal,\n",
|
||||
" \"signal_quality\": signal_quality, \"combined_strength\": combined_strength\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
"print(\"✅ Extended Top-Down V2 defined\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Entry Timing Optimization\n",
|
||||
"def check_pullback_entry(symbol, signal_info, timeframe=\"M5\"):\n",
|
||||
" if signal_info[\"entry_signal\"] == 0:\n",
|
||||
" return False, \"No base signal\"\n",
|
||||
" \n",
|
||||
" try:\n",
|
||||
" df = get_rates(timeframe.lower(), 50, symbol)\n",
|
||||
" if df is None or len(df) < 20:\n",
|
||||
" return False, \"Insufficient data\"\n",
|
||||
" \n",
|
||||
" df['ema21'] = df['close'].ewm(span=21).mean()\n",
|
||||
" df['ema50'] = df['close'].ewm(span=50).mean()\n",
|
||||
" \n",
|
||||
" current_price = df['close'].iloc[-1]\n",
|
||||
" ema21 = df['ema21'].iloc[-1]\n",
|
||||
" ema50 = df['ema50'].iloc[-1]\n",
|
||||
" signal_direction = signal_info[\"entry_signal\"]\n",
|
||||
" \n",
|
||||
" if signal_direction == 1: # Long\n",
|
||||
" if current_price <= ema21 * 1.002 and ema21 > ema50:\n",
|
||||
" return True, \"Pullback to EMA21 for Long\"\n",
|
||||
" elif current_price <= ema21 * 0.998:\n",
|
||||
" return True, \"Below EMA21 - Good Long Entry\"\n",
|
||||
" elif signal_direction == -1: # Short\n",
|
||||
" if current_price >= ema21 * 0.998 and ema21 < ema50:\n",
|
||||
" return True, \"Pullback to EMA21 for Short\"\n",
|
||||
" elif current_price >= ema21 * 1.002:\n",
|
||||
" return True, \"Above EMA21 - Good Short Entry\"\n",
|
||||
" \n",
|
||||
" return False, \"Waiting for better entry timing\"\n",
|
||||
" except Exception as e:\n",
|
||||
" return True, \"Using immediate entry (fallback)\"\n",
|
||||
"\n",
|
||||
"print(\"✅ Entry Timing Optimization defined\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Enhanced Execute Trade Function\n",
|
||||
"def execute_trade_v2(\n",
|
||||
" symbol=\"XAUUSD\",\n",
|
||||
" atr_mult=1.5,\n",
|
||||
" base_confidence=70,\n",
|
||||
" max_risk_per_trade=0.01,\n",
|
||||
" risk_filter=True,\n",
|
||||
" min_atr=0.0010,\n",
|
||||
" use_pullback_entry=True,\n",
|
||||
" debug=True\n",
|
||||
"):\n",
|
||||
" signal_info = extended_top_down_v2(symbol)\n",
|
||||
" if signal_info is None:\n",
|
||||
" print(\"❌ Signal-Analyse fehlgeschlagen\")\n",
|
||||
" return None\n",
|
||||
" \n",
|
||||
" entry_signal = signal_info[\"entry_signal\"]\n",
|
||||
" confidence = signal_info[\"confidence\"]\n",
|
||||
" adaptive_threshold = signal_info[\"adaptive_threshold\"]\n",
|
||||
" signal_quality = signal_info[\"signal_quality\"]\n",
|
||||
" market_regime = signal_info[\"market_regime\"]\n",
|
||||
" \n",
|
||||
" m5_info = signal_info[\"trend_info\"][\"M5\"]\n",
|
||||
" price = m5_info[\"price\"]\n",
|
||||
" atr = m5_info[\"atr\"]\n",
|
||||
" \n",
|
||||
" reason = \"\"\n",
|
||||
" \n",
|
||||
" if confidence < adaptive_threshold:\n",
|
||||
" reason = f\"Confidence {confidence}% < threshold {adaptive_threshold}%\"\n",
|
||||
" elif entry_signal == 0:\n",
|
||||
" reason = f\"No entry signal (Trend: {signal_info['top_down_trend']})\"\n",
|
||||
" elif price is None or atr is None:\n",
|
||||
" reason = \"Price/ATR not available\"\n",
|
||||
" elif risk_filter and atr < min_atr:\n",
|
||||
" reason = f\"ATR {atr:.5f} < min_atr {min_atr}\"\n",
|
||||
" elif signal_quality == \"none\":\n",
|
||||
" reason = \"Signal quality insufficient\"\n",
|
||||
" else:\n",
|
||||
" if use_pullback_entry:\n",
|
||||
" pullback_ok, pullback_reason = check_pullback_entry(symbol, signal_info)\n",
|
||||
" if not pullback_ok:\n",
|
||||
" reason = f\"Entry timing: {pullback_reason}\"\n",
|
||||
" \n",
|
||||
" if not reason:\n",
|
||||
" risk_ok = check_risk_limits(symbol, max_risk_per_trade=max_risk_per_trade)\n",
|
||||
" if not risk_ok:\n",
|
||||
" reason = \"Risk limits exceeded\"\n",
|
||||
" \n",
|
||||
" if not reason:\n",
|
||||
" regime_mult = 1.0\n",
|
||||
" if market_regime['regime'] == 'volatile':\n",
|
||||
" regime_mult = 1.3\n",
|
||||
" elif market_regime['regime'] == 'ranging':\n",
|
||||
" regime_mult = 0.8\n",
|
||||
" \n",
|
||||
" adjusted_atr_mult = atr_mult * regime_mult\n",
|
||||
" \n",
|
||||
" if entry_signal == 1:\n",
|
||||
" stop_loss = price - adjusted_atr_mult * atr\n",
|
||||
" take_profit = price + adjusted_atr_mult * atr * 2.5\n",
|
||||
" else:\n",
|
||||
" stop_loss = price + adjusted_atr_mult * atr\n",
|
||||
" take_profit = price - adjusted_atr_mult * atr * 2.5\n",
|
||||
" \n",
|
||||
" account_info = mt.account_info()\n",
|
||||
" if account_info:\n",
|
||||
" balance = account_info.balance\n",
|
||||
" risk_amount = balance * max_risk_per_trade\n",
|
||||
" if symbol == \"XAUUSD\":\n",
|
||||
" volume = min(0.1, max(0.01, risk_amount / (adjusted_atr_mult * atr * 100)))\n",
|
||||
" else:\n",
|
||||
" volume = 0.01\n",
|
||||
" else:\n",
|
||||
" volume = 0.01\n",
|
||||
" \n",
|
||||
" print(f\"🚀 ENHANCED TRADE EXECUTION\")\n",
|
||||
" print(f\"Symbol: {symbol}\")\n",
|
||||
" print(f\"Direction: {'LONG' if entry_signal == 1 else 'SHORT'}\")\n",
|
||||
" print(f\"Price: {price:.5f}\")\n",
|
||||
" print(f\"Volume: {volume:.2f}\")\n",
|
||||
" print(f\"Stop Loss: {stop_loss:.5f}\")\n",
|
||||
" print(f\"Take Profit: {take_profit:.5f}\")\n",
|
||||
" print(f\"Confidence: {confidence}% (Threshold: {adaptive_threshold}%)\")\n",
|
||||
" print(f\"Signal Quality: {signal_quality.upper()}\")\n",
|
||||
" print(f\"Market Regime: {market_regime['regime'].upper()}\")\n",
|
||||
" \n",
|
||||
" try:\n",
|
||||
" order_result = market_order(\n",
|
||||
" symbol=symbol,\n",
|
||||
" volume=volume,\n",
|
||||
" order_type=\"buy\" if entry_signal == 1 else \"sell\",\n",
|
||||
" stoploss=stop_loss,\n",
|
||||
" take_profit=take_profit\n",
|
||||
" )\n",
|
||||
" return order_result\n",
|
||||
" except Exception as e:\n",
|
||||
" print(f\"❌ Trade execution failed: {e}\")\n",
|
||||
" return None\n",
|
||||
" else:\n",
|
||||
" if debug:\n",
|
||||
" print(f\"⏸️ TRADE SKIPPED: {reason}\")\n",
|
||||
" print(f\"Confidence: {confidence}% | Threshold: {adaptive_threshold}%\")\n",
|
||||
" print(f\"Signal Quality: {signal_quality} | Regime: {market_regime['regime']}\")\n",
|
||||
" return None\n",
|
||||
"\n",
|
||||
"print(\"✅ Enhanced Execute Trade defined\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Test der optimierten Funktionen\n",
|
||||
"print(\"🔍 Testing Market Regime Detection...\")\n",
|
||||
"df_test = get_rates(\"h4\", 100, symbol)\n",
|
||||
"if df_test is not None:\n",
|
||||
" regime = detect_market_regime(df_test)\n",
|
||||
" print(f\"Regime: {regime['regime'].upper()}\")\n",
|
||||
" print(f\"Strength: {regime['strength']:.1f}%\")\n",
|
||||
" print(f\"ADX: {regime['adx']:.1f}\")\n",
|
||||
" \n",
|
||||
" adaptive_threshold = calculate_adaptive_confidence_threshold(regime)\n",
|
||||
" print(f\"Adaptive Threshold: {adaptive_threshold}% (vs 80% fixed)\")\n",
|
||||
"else:\n",
|
||||
" print(\"❌ Could not get test data\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Test Enhanced Top-Down Analysis\n",
|
||||
"print(\"🔍 Testing Enhanced Top-Down Analysis...\")\n",
|
||||
"signal_result = extended_top_down_v2(symbol)\n",
|
||||
"\n",
|
||||
"if signal_result:\n",
|
||||
" print(f\"🎯 SIGNAL SUMMARY:\")\n",
|
||||
" print(f\"Entry Signal: {signal_result['entry_signal']}\")\n",
|
||||
" print(f\"Confidence: {signal_result['confidence']}%\")\n",
|
||||
" print(f\"Adaptive Threshold: {signal_result['adaptive_threshold']}%\")\n",
|
||||
" print(f\"Signal Quality: {signal_result['signal_quality'].upper()}\")\n",
|
||||
" print(f\"Market Regime: {signal_result['market_regime']['regime'].upper()}\")\n",
|
||||
" print(f\"Risk-Adjusted Strength: {signal_result['risk_adjusted_strength']:.1f}\")\n",
|
||||
" \n",
|
||||
" if signal_result['entry_signal'] != 0:\n",
|
||||
" direction = \"LONG\" if signal_result['entry_signal'] == 1 else \"SHORT\"\n",
|
||||
" print(f\"🚀 TRADING SIGNAL: {direction}\")\n",
|
||||
" else:\n",
|
||||
" print(f\"⏸️ NO TRADING SIGNAL\")\n",
|
||||
"else:\n",
|
||||
" print(\"❌ Signal analysis failed\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Trading Configuration\n",
|
||||
"TRADING_CONFIG = {\n",
|
||||
" 'symbol': symbol,\n",
|
||||
" 'atr_mult': 1.5,\n",
|
||||
" 'base_confidence': 70,\n",
|
||||
" 'max_risk_per_trade': 0.01,\n",
|
||||
" 'risk_filter': True,\n",
|
||||
" 'min_atr': 0.0010,\n",
|
||||
" 'use_pullback_entry': True,\n",
|
||||
" 'debug': True\n",
|
||||
"}\n",
|
||||
"\n",
|
||||
"print(\"⚙️ Trading Configuration:\")\n",
|
||||
"for key, value in TRADING_CONFIG.items():\n",
|
||||
" print(f\" {key}: {value}\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Test Trading Execution\n",
|
||||
"def test_trading():\n",
|
||||
" print(\"🚀 Testing Trade Execution...\")\n",
|
||||
" try:\n",
|
||||
" result = execute_trade_v2(**TRADING_CONFIG)\n",
|
||||
" if result:\n",
|
||||
" print(\"✅ Trade executed successfully!\")\n",
|
||||
" return result\n",
|
||||
" else:\n",
|
||||
" print(\"⏸️ No trade executed\")\n",
|
||||
" return None\n",
|
||||
" except Exception as e:\n",
|
||||
" print(f\"❌ Error: {e}\")\n",
|
||||
" return None\n",
|
||||
"\n",
|
||||
"test_result = test_trading()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Status Check\n",
|
||||
"def check_bot_status():\n",
|
||||
" print(\"🔍 Trading Bot Status:\")\n",
|
||||
" print(f\" MT5 Connection: {'✅' if mt.terminal_info() else '❌'}\")\n",
|
||||
" \n",
|
||||
" try:\n",
|
||||
" signal_info = extended_top_down_v2(symbol)\n",
|
||||
" if signal_info:\n",
|
||||
" print(f\" Current Signal: {signal_info['entry_signal']}\")\n",
|
||||
" print(f\" Confidence: {signal_info['confidence']}%\")\n",
|
||||
" print(f\" Market Regime: {signal_info['market_regime']['regime'].upper()}\")\n",
|
||||
" print(f\" Signal Quality: {signal_info['signal_quality'].upper()}\")\n",
|
||||
" except Exception as e:\n",
|
||||
" print(f\" Signal Check: ❌ Error: {e}\")\n",
|
||||
"\n",
|
||||
"check_bot_status()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 📝 Zusammenfassung\n",
|
||||
"\n",
|
||||
"### ✅ **TradingBot V1.4 Complete ist bereit!**\n",
|
||||
"\n",
|
||||
"**Hauptfunktionen:**\n",
|
||||
"- `extended_top_down_v2()` - Optimierte Signal-Analyse\n",
|
||||
"- `execute_trade_v2()` - Verbesserte Trade-Ausführung\n",
|
||||
"- `detect_market_regime()` - Marktregime-Erkennung\n",
|
||||
"\n",
|
||||
"**Nächste Schritte:**\n",
|
||||
"1. Teste die Funktionen im Demo-Modus\n",
|
||||
"2. Überwache Performance für 1-2 Wochen\n",
|
||||
"3. Optimiere Parameter basierend auf Ergebnissen\n",
|
||||
"4. Bei Erfolg: Live-Trading aktivieren"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
File diff suppressed because it is too large.
Load diff
@@ -1,2 +0,0 @@
|
||||
[ZoneTransfer]
|
||||
ZoneId=3
|
||||
File diff suppressed because it is too large.
Load diff
@@ -1,2 +0,0 @@
|
||||
[ZoneTransfer]
|
||||
ZoneId=3
|
||||
File diff suppressed because it is too large.
Load diff
@@ -1,753 +0,0 @@
|
||||
"""
|
||||
TradingBot V1.4 - Fixed Version
|
||||
Korrigierte Entry Signal Logik ohne externe Abhängigkeiten
|
||||
|
||||
Alle notwendigen Funktionen sind integriert.
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import MetaTrader5 as mt
|
||||
import pandas_ta as ta
|
||||
from scipy.signal import savgol_filter, find_peaks
|
||||
from sklearn.linear_model import LinearRegression
|
||||
from tabulate import tabulate
|
||||
from datetime import datetime, timedelta
|
||||
import json
|
||||
|
||||
# =============================================================================
|
||||
# HILFSFUNKTIONEN (aus deiner bestehenden V1.3)
|
||||
# =============================================================================
|
||||
|
||||
def get_rates(timeframe="h4", count=200, symbol="XAUUSD"):
|
||||
"""
|
||||
Holt Kursdaten von MT5
|
||||
"""
|
||||
timeframes_dict = {
|
||||
"m1": mt.TIMEFRAME_M1,
|
||||
"m5": mt.TIMEFRAME_M5,
|
||||
"m15": mt.TIMEFRAME_M15,
|
||||
"m30": mt.TIMEFRAME_M30,
|
||||
"h1": mt.TIMEFRAME_H1,
|
||||
"h4": mt.TIMEFRAME_H4,
|
||||
"d1": mt.TIMEFRAME_D1
|
||||
}
|
||||
|
||||
try:
|
||||
rates = mt.copy_rates_from_pos(symbol, timeframes_dict[timeframe], 0, count)
|
||||
if rates is None:
|
||||
return None
|
||||
|
||||
df = pd.DataFrame(rates)
|
||||
df['time'] = pd.to_datetime(df['time'], unit='s')
|
||||
df.set_index('time', inplace=True)
|
||||
|
||||
# ATR hinzufügen
|
||||
df['atr'] = ta.atr(df['high'], df['low'], df['close'], length=14)
|
||||
|
||||
return df
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error getting rates: {e}")
|
||||
return None
|
||||
|
||||
def check_risk_limits(symbol, volume=None, order_type="buy", max_risk_per_trade=0.01):
|
||||
"""
|
||||
Prüft alle Risikolimits (vereinfachte Version)
|
||||
"""
|
||||
try:
|
||||
account_info = mt.account_info()
|
||||
if not account_info:
|
||||
print("⚠️ Kontodaten nicht verfügbar.")
|
||||
return False
|
||||
|
||||
# Einfache Checks - erweitere nach Bedarf
|
||||
balance = account_info.balance
|
||||
equity = account_info.equity
|
||||
|
||||
# Basis-Risiko-Check
|
||||
if equity < balance * 0.8: # Mehr als 20% Verlust
|
||||
print("⚠️ Drawdown-Limit erreicht")
|
||||
return False
|
||||
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error in risk check: {e}")
|
||||
return False
|
||||
|
||||
def market_order(symbol, volume, order_type, stoploss=None, take_profit=None, deviation=20):
|
||||
"""
|
||||
Führt Market Order aus (vereinfachte Version)
|
||||
"""
|
||||
try:
|
||||
price_dict = {
|
||||
'buy': mt.symbol_info_tick(symbol).ask,
|
||||
'sell': mt.symbol_info_tick(symbol).bid
|
||||
}
|
||||
|
||||
order_type_dict = {
|
||||
'buy': mt.ORDER_TYPE_BUY,
|
||||
'sell': mt.ORDER_TYPE_SELL
|
||||
}
|
||||
|
||||
request = {
|
||||
"action": mt.TRADE_ACTION_DEAL,
|
||||
"symbol": symbol,
|
||||
"volume": volume,
|
||||
"type": order_type_dict[order_type],
|
||||
"price": price_dict[order_type],
|
||||
"sl": stoploss,
|
||||
"tp": take_profit,
|
||||
"deviation": deviation,
|
||||
"magic": 234000,
|
||||
"comment": "TradingBot_V1.4_Optimized",
|
||||
"type_time": mt.ORDER_TIME_GTC,
|
||||
"type_filling": mt.ORDER_FILLING_IOC,
|
||||
}
|
||||
|
||||
result = mt.order_send(request)
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error in market order: {e}")
|
||||
return None
|
||||
|
||||
# =============================================================================
|
||||
# 1. MARKET REGIME DETECTION
|
||||
# =============================================================================
|
||||
|
||||
def detect_market_regime(df, lookback=50):
|
||||
"""
|
||||
Erkennt das aktuelle Marktregime (Trending vs. Ranging)
|
||||
"""
|
||||
try:
|
||||
# ADX für Trendstärke
|
||||
adx_data = ta.adx(df['high'], df['low'], df['close'], length=14)
|
||||
if adx_data is None or 'ADX_14' not in adx_data.columns:
|
||||
# Fallback: einfache ADX-Berechnung
|
||||
adx = 25.0 # Standardwert
|
||||
else:
|
||||
adx = adx_data['ADX_14'].iloc[-1] if not pd.isna(adx_data['ADX_14'].iloc[-1]) else 25.0
|
||||
|
||||
# Bollinger Band Squeeze für Ranging Markets
|
||||
try:
|
||||
bb = ta.bbands(df['close'], length=20)
|
||||
if bb is not None and len(bb.columns) >= 3:
|
||||
bb_cols = bb.columns.tolist()
|
||||
bb_upper = bb[bb_cols[0]] # Oberes Band
|
||||
bb_middle = bb[bb_cols[1]] # Mittleres Band
|
||||
bb_lower = bb[bb_cols[2]] # Unteres Band
|
||||
bb_width = ((bb_upper - bb_lower) / bb_middle * 100).iloc[-lookback:].mean()
|
||||
else:
|
||||
bb_width = 4.0 # Standardwert
|
||||
except:
|
||||
bb_width = 4.0
|
||||
|
||||
# Price Action Analysis
|
||||
price_range = (df['high'].iloc[-lookback:].max() - df['low'].iloc[-lookback:].min())
|
||||
atr_avg = df['atr'].iloc[-lookback:].mean()
|
||||
range_ratio = price_range / (atr_avg * lookback) if atr_avg > 0 else 1.0
|
||||
|
||||
# Volatility Cluster Detection
|
||||
vol_cluster = df['atr'].iloc[-10:].std() / df['atr'].iloc[-50:].mean() if len(df) >= 50 else 1.0
|
||||
|
||||
# Regime-Bestimmung
|
||||
if adx > 25 and range_ratio > 1.5:
|
||||
regime = 'trending'
|
||||
strength = min(100, adx * 2)
|
||||
elif vol_cluster > 1.5:
|
||||
regime = 'volatile'
|
||||
strength = min(100, vol_cluster * 50)
|
||||
else:
|
||||
regime = 'ranging'
|
||||
strength = max(0, 100 - adx * 2)
|
||||
|
||||
return {
|
||||
'regime': regime,
|
||||
'strength': strength,
|
||||
'adx': adx,
|
||||
'bb_width': bb_width,
|
||||
'range_ratio': range_ratio,
|
||||
'vol_cluster': vol_cluster
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error in regime detection: {e}")
|
||||
# Fallback
|
||||
return {
|
||||
'regime': 'ranging',
|
||||
'strength': 50,
|
||||
'adx': 20,
|
||||
'bb_width': 4.0,
|
||||
'range_ratio': 1.0,
|
||||
'vol_cluster': 1.0
|
||||
}
|
||||
|
||||
# =============================================================================
|
||||
# 2. ADAPTIVE CONFIDENCE SYSTEM
|
||||
# =============================================================================
|
||||
|
||||
def calculate_adaptive_confidence_threshold(regime_info, base_confidence=70):
|
||||
"""
|
||||
Berechnet adaptive Confidence-Schwelle basierend auf Marktregime
|
||||
"""
|
||||
regime = regime_info['regime']
|
||||
strength = regime_info['strength']
|
||||
adx = regime_info['adx']
|
||||
|
||||
if regime == 'trending':
|
||||
# In Trending Markets: niedrigere Schwelle bei starken Trends
|
||||
if adx > 30:
|
||||
return max(60, base_confidence - 15)
|
||||
else:
|
||||
return base_confidence - 10
|
||||
|
||||
elif regime == 'ranging':
|
||||
# In Ranging Markets: höhere Schwelle für mehr Selektivität
|
||||
return base_confidence + 15
|
||||
|
||||
elif regime == 'volatile':
|
||||
# In Volatile Markets: deutlich höhere Schwelle
|
||||
return base_confidence + 20
|
||||
|
||||
return base_confidence
|
||||
|
||||
# =============================================================================
|
||||
# 3. ENHANCED TREND ANALYSIS
|
||||
# =============================================================================
|
||||
|
||||
def get_enhanced_trend(timeframe="H4", lookback=150, symbol="XAUUSD"):
|
||||
"""
|
||||
Verbesserte Trend-Analyse mit Regime-Awareness
|
||||
"""
|
||||
# Timeframe mapping
|
||||
tf_map = {"D1": "d1", "H4": "h4", "H1": "h1", "M30": "m30", "M15": "m15", "M5": "m5"}
|
||||
tf = tf_map.get(timeframe, timeframe.lower())
|
||||
|
||||
try:
|
||||
df = get_rates(tf, lookback, symbol)
|
||||
if df is None or len(df) < 50:
|
||||
return None
|
||||
|
||||
# Bestehende Trend-Logik
|
||||
df['close_smooth'] = savgol_filter(df['close'], min(15, len(df)//10), 3)
|
||||
|
||||
# Linear Regression
|
||||
X = np.arange(len(df)).reshape(-1, 1)
|
||||
y = df['close_smooth'].values
|
||||
model = LinearRegression().fit(X, y)
|
||||
slope = model.coef_[0]
|
||||
|
||||
# Market Regime Detection
|
||||
regime_info = detect_market_regime(df.iloc[-50:])
|
||||
|
||||
# Adaptive Slope Threshold basierend auf Regime
|
||||
base_threshold = df['atr'].iloc[-1] * 0.0001
|
||||
|
||||
if regime_info['regime'] == 'trending':
|
||||
slope_threshold = base_threshold * 0.7 # Niedrigere Schwelle in Trends
|
||||
elif regime_info['regime'] == 'ranging':
|
||||
slope_threshold = base_threshold * 1.5 # Höhere Schwelle in Ranges
|
||||
else: # volatile
|
||||
slope_threshold = base_threshold * 1.2
|
||||
|
||||
# Trend bestimmen
|
||||
if slope > slope_threshold:
|
||||
trend = "uptrend"
|
||||
elif slope < -slope_threshold:
|
||||
trend = "downtrend"
|
||||
else:
|
||||
trend = "sideways"
|
||||
|
||||
# Enhanced Trend Strength
|
||||
trend_strength = abs(slope) / slope_threshold if slope_threshold > 0 else 0
|
||||
|
||||
return {
|
||||
"trend": trend,
|
||||
"slope": slope,
|
||||
"slope_threshold": slope_threshold,
|
||||
"trend_strength": trend_strength,
|
||||
"atr": df['atr'].iloc[-1],
|
||||
"price": df['close'].iloc[-1],
|
||||
"regime_info": regime_info
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error in get_enhanced_trend: {e}")
|
||||
return None
|
||||
|
||||
# =============================================================================
|
||||
# 4. OPTIMIZED TOP-DOWN ANALYSIS
|
||||
# =============================================================================
|
||||
|
||||
def extended_top_down_v2(symbol="XAUUSD", lookback=150):
|
||||
"""
|
||||
Optimierte Top-Down-Analyse mit adaptiven Parametern
|
||||
"""
|
||||
|
||||
timeframes = ["D1", "H4", "H1", "M30", "M15", "M5"]
|
||||
trend_info = {}
|
||||
|
||||
# 1. Alle Timeframes analysieren
|
||||
for tf in timeframes:
|
||||
trend_info[tf] = get_enhanced_trend(tf, lookback, symbol)
|
||||
if trend_info[tf] is None:
|
||||
print(f"⚠️ Keine Daten für {tf}")
|
||||
return None
|
||||
|
||||
# 2. Market Regime aus H4 bestimmen (repräsentativ)
|
||||
main_regime = trend_info["H4"]["regime_info"]
|
||||
|
||||
# 3. Adaptive Confidence Threshold
|
||||
adaptive_confidence_threshold = calculate_adaptive_confidence_threshold(main_regime)
|
||||
|
||||
# 4. Enhanced Standard-Trend (D1 + H4 mit Gewichtung)
|
||||
d1_trend = trend_info["D1"]["trend"]
|
||||
h4_trend = trend_info["H4"]["trend"]
|
||||
d1_strength = trend_info["D1"]["trend_strength"]
|
||||
h4_strength = trend_info["H4"]["trend_strength"]
|
||||
|
||||
# Gewichteter Standard-Trend
|
||||
if d1_trend == h4_trend and d1_trend != "sideways":
|
||||
standard_trend = d1_trend
|
||||
standard_strength = (d1_strength * 0.6 + h4_strength * 0.4)
|
||||
elif d1_strength > h4_strength * 1.5: # D1 deutlich stärker
|
||||
standard_trend = d1_trend
|
||||
standard_strength = d1_strength * 0.8
|
||||
elif h4_strength > d1_strength * 1.5: # H4 deutlich stärker
|
||||
standard_trend = h4_trend
|
||||
standard_strength = h4_strength * 0.8
|
||||
else:
|
||||
standard_trend = "sideways"
|
||||
standard_strength = 0
|
||||
|
||||
# 5. Enhanced Fast-Trend mit Regime-Awareness
|
||||
fast_timeframes = ["H1", "M30", "M15", "M5"]
|
||||
fast_trends = [trend_info[tf]["trend"] for tf in fast_timeframes]
|
||||
fast_strengths = [trend_info[tf]["trend_strength"] for tf in fast_timeframes]
|
||||
|
||||
# Regime-abhängige Fast-Trend Logik
|
||||
if main_regime['regime'] == 'trending':
|
||||
# In Trends: 2 von 4 TFs reichen
|
||||
required_alignment = 2
|
||||
else:
|
||||
# In Ranging/Volatile: 3 von 4 TFs erforderlich
|
||||
required_alignment = 3
|
||||
|
||||
trend_counts = {'uptrend': 0, 'downtrend': 0, 'sideways': 0}
|
||||
weighted_strengths = {'uptrend': 0, 'downtrend': 0}
|
||||
|
||||
weights = [1.0, 0.8, 0.6, 0.4] # H1, M30, M15, M5
|
||||
|
||||
for i, (trend, strength) in enumerate(zip(fast_trends, fast_strengths)):
|
||||
trend_counts[trend] += 1
|
||||
if trend != 'sideways':
|
||||
weighted_strengths[trend] += strength * weights[i]
|
||||
|
||||
# Fast-Trend bestimmen
|
||||
max_count = max(trend_counts['uptrend'], trend_counts['downtrend'])
|
||||
if max_count >= required_alignment:
|
||||
if trend_counts['uptrend'] > trend_counts['downtrend']:
|
||||
fast_trend = "uptrend"
|
||||
elif trend_counts['downtrend'] > trend_counts['uptrend']:
|
||||
fast_trend = "downtrend"
|
||||
else:
|
||||
# Bei Gleichstand: Stärke entscheidet
|
||||
if weighted_strengths['uptrend'] > weighted_strengths['downtrend']:
|
||||
fast_trend = "uptrend"
|
||||
else:
|
||||
fast_trend = "downtrend"
|
||||
else:
|
||||
fast_trend = "sideways"
|
||||
|
||||
# 6. Top-Down-Trend Bestimmung
|
||||
if standard_trend == fast_trend and standard_trend != "sideways":
|
||||
top_down_trend = standard_trend
|
||||
combined_strength = (standard_strength + weighted_strengths.get(fast_trend, 0)) / 2
|
||||
else:
|
||||
top_down_trend = "sideways"
|
||||
combined_strength = 0
|
||||
|
||||
# 7. Enhanced Confidence Calculation
|
||||
weights = {"D1": 2.5, "H4": 2.0, "H1": 1.5, "M30": 1.0, "M15": 0.8, "M5": 0.6}
|
||||
|
||||
weighted_matching = sum(
|
||||
weights[tf] * trend_info[tf]["trend_strength"]
|
||||
for tf in timeframes
|
||||
if trend_info[tf]["trend"] == top_down_trend and trend_info[tf]["trend"] != "sideways"
|
||||
)
|
||||
|
||||
weighted_total = sum(
|
||||
weights[tf] * trend_info[tf]["trend_strength"]
|
||||
for tf in timeframes
|
||||
if trend_info[tf]["trend"] != "sideways"
|
||||
)
|
||||
|
||||
confidence = round((weighted_matching / weighted_total) * 100, 2) if weighted_total > 0 else 0.0
|
||||
|
||||
# 8. Risk-Adjusted Signal Strength
|
||||
atr = trend_info["M5"]["atr"]
|
||||
rrr = 2.5 # Risk-Reward Ratio
|
||||
|
||||
risk_adjusted_strength = confidence * combined_strength * min(2.0, rrr)
|
||||
|
||||
# 9. Entry Signal mit adaptiven Kriterien
|
||||
entry_signal = 0
|
||||
signal_quality = "none"
|
||||
|
||||
if (top_down_trend != "sideways" and
|
||||
confidence >= adaptive_confidence_threshold and
|
||||
risk_adjusted_strength >= 100):
|
||||
|
||||
entry_signal = 1 if top_down_trend == "uptrend" else -1
|
||||
|
||||
# Signal Quality Assessment
|
||||
if confidence >= 85 and risk_adjusted_strength >= 150:
|
||||
signal_quality = "excellent"
|
||||
elif confidence >= 75 and risk_adjusted_strength >= 120:
|
||||
signal_quality = "good"
|
||||
else:
|
||||
signal_quality = "fair"
|
||||
|
||||
# 10. Enhanced Debug Output
|
||||
debug_data = []
|
||||
for tf in timeframes:
|
||||
info = trend_info[tf]
|
||||
debug_data.append([
|
||||
tf,
|
||||
info["trend"],
|
||||
f"{info['trend_strength']:.2f}",
|
||||
f"{info['atr']:.4f}",
|
||||
f"{info['slope']:.6f}",
|
||||
f"{info['price']:.2f}"
|
||||
])
|
||||
|
||||
print(f"\n📊 Enhanced Trend-Analyse für {symbol}")
|
||||
print(f"🎯 Market Regime: {main_regime['regime'].upper()} (Strength: {main_regime['strength']:.0f}%)")
|
||||
print(f"🎚️ Adaptive Confidence Threshold: {adaptive_confidence_threshold}%")
|
||||
print()
|
||||
print(tabulate(debug_data, headers=["TF", "Trend", "Strength", "ATR", "Slope", "Price"], tablefmt="psql"))
|
||||
print(f"\n➡️ Standard-Trend: {standard_trend} (Strength: {standard_strength:.2f})")
|
||||
print(f"➡️ Fast-Trend: {fast_trend}")
|
||||
print(f"➡️ Top-Down-Trend: {top_down_trend}")
|
||||
print(f"➡️ Confidence: {confidence}% (Threshold: {adaptive_confidence_threshold}%)")
|
||||
print(f"➡️ Risk-Adjusted Strength: {risk_adjusted_strength:.1f}")
|
||||
print(f"➡️ Signal Quality: {signal_quality.upper()}")
|
||||
|
||||
return {
|
||||
"symbol": symbol,
|
||||
"trend_info": trend_info,
|
||||
"market_regime": main_regime,
|
||||
"standard_trend": standard_trend,
|
||||
"fast_trend": fast_trend,
|
||||
"top_down_trend": top_down_trend,
|
||||
"confidence": confidence,
|
||||
"adaptive_threshold": adaptive_confidence_threshold,
|
||||
"risk_adjusted_strength": risk_adjusted_strength,
|
||||
"entry_signal": entry_signal,
|
||||
"signal_quality": signal_quality,
|
||||
"combined_strength": combined_strength
|
||||
}
|
||||
|
||||
# =============================================================================
|
||||
# 5. ENTRY TIMING OPTIMIZATION
|
||||
# =============================================================================
|
||||
|
||||
def check_pullback_entry(symbol, signal_info, timeframe="M5"):
|
||||
"""
|
||||
Prüft optimale Entry-Timing durch Pullback-Analyse
|
||||
"""
|
||||
if signal_info["entry_signal"] == 0:
|
||||
return False, "No base signal"
|
||||
|
||||
try:
|
||||
df = get_rates(timeframe.lower(), 50, symbol)
|
||||
|
||||
if df is None or len(df) < 20:
|
||||
return False, "Insufficient data"
|
||||
|
||||
# EMAs für Pullback-Erkennung
|
||||
df['ema21'] = df['close'].ewm(span=21).mean()
|
||||
df['ema50'] = df['close'].ewm(span=50).mean()
|
||||
|
||||
current_price = df['close'].iloc[-1]
|
||||
ema21 = df['ema21'].iloc[-1]
|
||||
ema50 = df['ema50'].iloc[-1]
|
||||
|
||||
signal_direction = signal_info["entry_signal"]
|
||||
|
||||
if signal_direction == 1: # Long Signal
|
||||
# Pullback zu EMA21 oder Support
|
||||
if current_price <= ema21 * 1.002 and ema21 > ema50:
|
||||
return True, "Pullback to EMA21 for Long"
|
||||
elif current_price <= ema21 * 0.998:
|
||||
return True, "Below EMA21 - Good Long Entry"
|
||||
|
||||
elif signal_direction == -1: # Short Signal
|
||||
# Pullback zu EMA21 oder Resistance
|
||||
if current_price >= ema21 * 0.998 and ema21 < ema50:
|
||||
return True, "Pullback to EMA21 for Short"
|
||||
elif current_price >= ema21 * 1.002:
|
||||
return True, "Above EMA21 - Good Short Entry"
|
||||
|
||||
return False, "Waiting for better entry timing"
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error in pullback check: {e}")
|
||||
return True, "Using immediate entry (fallback)"
|
||||
|
||||
# =============================================================================
|
||||
# 6. ENHANCED EXECUTE TRADE FUNCTION
|
||||
# =============================================================================
|
||||
|
||||
def execute_trade_v2(
|
||||
symbol="XAUUSD",
|
||||
atr_mult=1.5,
|
||||
base_confidence=70,
|
||||
max_risk_per_trade=0.01,
|
||||
risk_filter=True,
|
||||
min_atr=0.0010,
|
||||
use_pullback_entry=True,
|
||||
debug=True
|
||||
):
|
||||
"""
|
||||
Optimierte Trade-Ausführung mit allen Verbesserungen
|
||||
"""
|
||||
|
||||
# 1. Enhanced Signal Analysis
|
||||
signal_info = extended_top_down_v2(symbol)
|
||||
if signal_info is None:
|
||||
print("❌ Signal-Analyse fehlgeschlagen")
|
||||
return None
|
||||
|
||||
entry_signal = signal_info["entry_signal"]
|
||||
confidence = signal_info["confidence"]
|
||||
adaptive_threshold = signal_info["adaptive_threshold"]
|
||||
signal_quality = signal_info["signal_quality"]
|
||||
market_regime = signal_info["market_regime"]
|
||||
|
||||
# 2. Get Price/ATR from M5
|
||||
m5_info = signal_info["trend_info"]["M5"]
|
||||
price = m5_info["price"]
|
||||
atr = m5_info["atr"]
|
||||
|
||||
# 3. Enhanced Pre-checks
|
||||
reason = ""
|
||||
|
||||
if confidence < adaptive_threshold:
|
||||
reason = f"Confidence {confidence}% < adaptive threshold {adaptive_threshold}%"
|
||||
elif entry_signal == 0:
|
||||
reason = f"No entry signal (Trend: {signal_info['top_down_trend']}, Regime: {market_regime['regime']})"
|
||||
elif price is None or atr is None:
|
||||
reason = "Price/ATR not available"
|
||||
elif risk_filter and atr < min_atr:
|
||||
reason = f"ATR {atr:.5f} < min_atr {min_atr}"
|
||||
elif signal_quality == "none":
|
||||
reason = "Signal quality insufficient"
|
||||
else:
|
||||
# 4. Entry Timing Check
|
||||
if use_pullback_entry:
|
||||
pullback_ok, pullback_reason = check_pullback_entry(symbol, signal_info)
|
||||
if not pullback_ok:
|
||||
reason = f"Entry timing: {pullback_reason}"
|
||||
|
||||
if not reason:
|
||||
# 5. Enhanced Risk Checks
|
||||
risk_ok = check_risk_limits(symbol, max_risk_per_trade=max_risk_per_trade)
|
||||
if not risk_ok:
|
||||
reason = "Risk limits exceeded"
|
||||
|
||||
# 6. Execute Trade if all checks pass
|
||||
if not reason:
|
||||
# Enhanced SL/TP calculation based on regime
|
||||
regime_mult = 1.0
|
||||
if market_regime['regime'] == 'volatile':
|
||||
regime_mult = 1.3 # Wider stops in volatile markets
|
||||
elif market_regime['regime'] == 'ranging':
|
||||
regime_mult = 0.8 # Tighter stops in ranging markets
|
||||
|
||||
adjusted_atr_mult = atr_mult * regime_mult
|
||||
|
||||
if entry_signal == 1: # Long
|
||||
stop_loss = price - adjusted_atr_mult * atr
|
||||
take_profit = price + adjusted_atr_mult * atr * 2.5 # Better RRR
|
||||
else: # Short
|
||||
stop_loss = price + adjusted_atr_mult * atr
|
||||
take_profit = price - adjusted_atr_mult * atr * 2.5
|
||||
|
||||
# Dynamic Position Sizing (vereinfacht)
|
||||
stop_distance = adjusted_atr_mult * atr
|
||||
account_info = mt.account_info()
|
||||
if account_info:
|
||||
balance = account_info.balance
|
||||
risk_amount = balance * max_risk_per_trade
|
||||
# Vereinfachte Volumen-Berechnung
|
||||
if symbol == "XAUUSD":
|
||||
volume = min(0.1, max(0.01, risk_amount / (stop_distance * 100)))
|
||||
else:
|
||||
volume = 0.01 # Standard für andere Symbole
|
||||
else:
|
||||
volume = 0.01
|
||||
|
||||
# Log Enhanced Trade Info
|
||||
print(f"\n🚀 ENHANCED TRADE EXECUTION")
|
||||
print(f"Symbol: {symbol}")
|
||||
print(f"Direction: {'LONG' if entry_signal == 1 else 'SHORT'}")
|
||||
print(f"Price: {price:.5f}")
|
||||
print(f"Volume: {volume:.2f}")
|
||||
print(f"Stop Loss: {stop_loss:.5f}")
|
||||
print(f"Take Profit: {take_profit:.5f}")
|
||||
print(f"Confidence: {confidence}% (Threshold: {adaptive_threshold}%)")
|
||||
print(f"Signal Quality: {signal_quality.upper()}")
|
||||
print(f"Market Regime: {market_regime['regime'].upper()}")
|
||||
print(f"ATR Multiplier: {adjusted_atr_mult:.2f} (Base: {atr_mult})")
|
||||
print(f"Risk per Trade: {max_risk_per_trade*100:.1f}%")
|
||||
|
||||
# Execute the actual trade
|
||||
try:
|
||||
order_result = market_order(
|
||||
symbol=symbol,
|
||||
volume=volume,
|
||||
order_type="buy" if entry_signal == 1 else "sell",
|
||||
stoploss=stop_loss,
|
||||
take_profit=take_profit
|
||||
)
|
||||
|
||||
# Log Trade to Performance Monitor
|
||||
log_trade_performance(signal_info, order_result)
|
||||
|
||||
return order_result
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Trade execution failed: {e}")
|
||||
return None
|
||||
|
||||
else:
|
||||
if debug:
|
||||
print(f"\n⏸️ TRADE SKIPPED: {reason}")
|
||||
print(f"Confidence: {confidence}% | Threshold: {adaptive_threshold}%")
|
||||
print(f"Signal Quality: {signal_quality} | Regime: {market_regime['regime']}")
|
||||
return None
|
||||
|
||||
# =============================================================================
|
||||
# 7. PERFORMANCE MONITORING
|
||||
# =============================================================================
|
||||
|
||||
def log_trade_performance(signal_info, order_result):
|
||||
"""
|
||||
Loggt Trade-Performance für Analyse und Optimierung
|
||||
"""
|
||||
trade_data = {
|
||||
'timestamp': datetime.now().isoformat(),
|
||||
'symbol': signal_info['symbol'],
|
||||
'entry_signal': signal_info['entry_signal'],
|
||||
'confidence': signal_info['confidence'],
|
||||
'adaptive_threshold': signal_info['adaptive_threshold'],
|
||||
'signal_quality': signal_info['signal_quality'],
|
||||
'market_regime': signal_info['market_regime']['regime'],
|
||||
'regime_strength': signal_info['market_regime']['strength'],
|
||||
'risk_adjusted_strength': signal_info['risk_adjusted_strength'],
|
||||
'order_result': str(order_result) if order_result else None
|
||||
}
|
||||
|
||||
# Save to JSON file for analysis
|
||||
try:
|
||||
filename = f"trade_performance_{signal_info['symbol']}_{datetime.now().strftime('%Y%m')}.json"
|
||||
|
||||
try:
|
||||
with open(filename, 'r') as f:
|
||||
data = json.load(f)
|
||||
except FileNotFoundError:
|
||||
data = []
|
||||
|
||||
data.append(trade_data)
|
||||
|
||||
with open(filename, 'w') as f:
|
||||
json.dump(data, f, indent=2)
|
||||
|
||||
except Exception as e:
|
||||
print(f"Warning: Could not log performance data: {e}")
|
||||
|
||||
def analyze_performance(symbol="XAUUSD", days_back=30):
|
||||
"""
|
||||
Analysiert Performance der letzten Trades
|
||||
"""
|
||||
try:
|
||||
filename = f"trade_performance_{symbol}_{datetime.now().strftime('%Y%m')}.json"
|
||||
|
||||
with open(filename, 'r') as f:
|
||||
data = json.load(f)
|
||||
|
||||
# Filter last X days
|
||||
cutoff = datetime.now() - timedelta(days=days_back)
|
||||
recent_trades = [
|
||||
trade for trade in data
|
||||
if datetime.fromisoformat(trade['timestamp']) > cutoff
|
||||
]
|
||||
|
||||
if not recent_trades:
|
||||
print(f"No trades found in last {days_back} days")
|
||||
return
|
||||
|
||||
# Analysis
|
||||
total_trades = len(recent_trades)
|
||||
by_regime = {}
|
||||
by_confidence = {'high': 0, 'medium': 0, 'low': 0}
|
||||
by_quality = {}
|
||||
|
||||
for trade in recent_trades:
|
||||
# By regime
|
||||
regime = trade['market_regime']
|
||||
by_regime[regime] = by_regime.get(regime, 0) + 1
|
||||
|
||||
# By confidence
|
||||
conf = trade['confidence']
|
||||
if conf >= 85:
|
||||
by_confidence['high'] += 1
|
||||
elif conf >= 75:
|
||||
by_confidence['medium'] += 1
|
||||
else:
|
||||
by_confidence['low'] += 1
|
||||
|
||||
# By quality
|
||||
quality = trade['signal_quality']
|
||||
by_quality[quality] = by_quality.get(quality, 0) + 1
|
||||
|
||||
print(f"\n📊 PERFORMANCE ANALYSIS - Last {days_back} days")
|
||||
print(f"Total Trades: {total_trades}")
|
||||
print(f"\nBy Market Regime:")
|
||||
for regime, count in by_regime.items():
|
||||
print(f" {regime.upper()}: {count} ({count/total_trades*100:.1f}%)")
|
||||
|
||||
print(f"\nBy Confidence Level:")
|
||||
for level, count in by_confidence.items():
|
||||
print(f" {level.upper()}: {count} ({count/total_trades*100:.1f}%)")
|
||||
|
||||
print(f"\nBy Signal Quality:")
|
||||
for quality, count in by_quality.items():
|
||||
print(f" {quality.upper()}: {count} ({count/total_trades*100:.1f}%)")
|
||||
|
||||
except Exception as e:
|
||||
print(f"Could not analyze performance: {e}")
|
||||
|
||||
# =============================================================================
|
||||
# 8. QUICK SETUP FUNCTION
|
||||
# =============================================================================
|
||||
|
||||
def setup_optimized_trading():
|
||||
"""
|
||||
Quick setup für optimiertes Trading
|
||||
"""
|
||||
print("🚀 Setting up Optimized Trading Bot V1.4 - Fixed Version")
|
||||
print("\nKey Improvements:")
|
||||
print("✅ Adaptive confidence thresholds")
|
||||
print("✅ Market regime detection")
|
||||
print("✅ Enhanced entry timing")
|
||||
print("✅ Risk-adjusted signal strength")
|
||||
print("✅ Performance monitoring")
|
||||
print("✅ Fixed import dependencies")
|
||||
print("\nReady to trade with enhanced logic!")
|
||||
|
||||
if __name__ == "__main__":
|
||||
setup_optimized_trading()
|
||||
@@ -1,849 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# TradingBot V1.4 - Fixed Integration\n",
|
||||
"\n",
|
||||
"## ✅ **Korrigierte Version ohne externe Abhängigkeiten**\n",
|
||||
"\n",
|
||||
"Diese Version behebt das Import-Problem und integriert alle notwendigen Funktionen direkt."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 1. Standard Imports"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Standard Imports aus deiner V1.3\n",
|
||||
"import pandas as pd\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import mplfinance as mpf\n",
|
||||
"import keyring as kr\n",
|
||||
"import MetaTrader5 as mt\n",
|
||||
"import requests\n",
|
||||
"import re\n",
|
||||
"from time import sleep\n",
|
||||
"import sqlite3 as db\n",
|
||||
"import pandas_ta as ta\n",
|
||||
"import numpy as np\n",
|
||||
"from sklearn.linear_model import LinearRegression\n",
|
||||
"from scipy.signal import savgol_filter, find_peaks\n",
|
||||
"from tabulate import tabulate\n",
|
||||
"from ta.trend import ADXIndicator, EMAIndicator\n",
|
||||
"from ta.momentum import RSIIndicator\n",
|
||||
"from talib import CDLHAMMER, CDLSHOOTINGSTAR\n",
|
||||
"from datetime import datetime, timedelta\n",
|
||||
"import json"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 2. Login & Setup (unverändert)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Login zu MT5\n",
|
||||
"mt.initialize()\n",
|
||||
" \n",
|
||||
"login = 10800246\n",
|
||||
"server = 'VantageInternational-Demo'\n",
|
||||
"password = kr.get_password(server, str(login))\n",
|
||||
"\n",
|
||||
"login_result = mt.login(login, password, server)\n",
|
||||
"print(f\"Login successful: {login_result}\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Trading Parameter\n",
|
||||
"symbol = \"XAUUSD\"\n",
|
||||
"pause_trading = 0\n",
|
||||
"strategy_name = \"TradingBot_V1.4_Fixed\"\n",
|
||||
"\n",
|
||||
"print(f\"Symbol: {symbol}\")\n",
|
||||
"print(f\"Strategy: {strategy_name}\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 3. Integrierte Hilfsfunktionen"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def get_rates(timeframe=\"h4\", count=200, symbol=\"XAUUSD\"):\n",
|
||||
" \"\"\"\n",
|
||||
" Holt Kursdaten von MT5\n",
|
||||
" \"\"\"\n",
|
||||
" timeframes_dict = {\n",
|
||||
" \"m1\": mt.TIMEFRAME_M1,\n",
|
||||
" \"m5\": mt.TIMEFRAME_M5,\n",
|
||||
" \"m15\": mt.TIMEFRAME_M15,\n",
|
||||
" \"m30\": mt.TIMEFRAME_M30,\n",
|
||||
" \"h1\": mt.TIMEFRAME_H1,\n",
|
||||
" \"h4\": mt.TIMEFRAME_H4,\n",
|
||||
" \"d1\": mt.TIMEFRAME_D1\n",
|
||||
" }\n",
|
||||
" \n",
|
||||
" try:\n",
|
||||
" rates = mt.copy_rates_from_pos(symbol, timeframes_dict[timeframe], 0, count)\n",
|
||||
" if rates is None:\n",
|
||||
" return None\n",
|
||||
" \n",
|
||||
" df = pd.DataFrame(rates)\n",
|
||||
" df['time'] = pd.to_datetime(df['time'], unit='s')\n",
|
||||
" df.set_index('time', inplace=True)\n",
|
||||
" \n",
|
||||
" # ATR hinzufügen\n",
|
||||
" df['atr'] = ta.atr(df['high'], df['low'], df['close'], length=14)\n",
|
||||
" \n",
|
||||
" return df\n",
|
||||
" \n",
|
||||
" except Exception as e:\n",
|
||||
" print(f\"Error getting rates: {e}\")\n",
|
||||
" return None\n",
|
||||
"\n",
|
||||
"# Test der get_rates Funktion\n",
|
||||
"test_data = get_rates(\"h4\", 50, symbol)\n",
|
||||
"if test_data is not None:\n",
|
||||
" print(f\"✅ get_rates working - got {len(test_data)} bars\")\n",
|
||||
" print(f\"Latest price: {test_data['close'].iloc[-1]:.5f}\")\n",
|
||||
"else:\n",
|
||||
" print(\"❌ get_rates failed\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def check_risk_limits(symbol, volume=None, order_type=\"buy\", max_risk_per_trade=0.01):\n",
|
||||
" \"\"\"\n",
|
||||
" Prüft alle Risikolimits (vereinfachte Version)\n",
|
||||
" \"\"\"\n",
|
||||
" try:\n",
|
||||
" account_info = mt.account_info()\n",
|
||||
" if not account_info:\n",
|
||||
" print(\"⚠️ Kontodaten nicht verfügbar.\")\n",
|
||||
" return False\n",
|
||||
" \n",
|
||||
" # Einfache Checks\n",
|
||||
" balance = account_info.balance\n",
|
||||
" equity = account_info.equity\n",
|
||||
" \n",
|
||||
" # Basis-Risiko-Check\n",
|
||||
" if equity < balance * 0.8: # Mehr als 20% Verlust\n",
|
||||
" print(\"⚠️ Drawdown-Limit erreicht\")\n",
|
||||
" return False\n",
|
||||
" \n",
|
||||
" return True\n",
|
||||
" \n",
|
||||
" except Exception as e:\n",
|
||||
" print(f\"Error in risk check: {e}\")\n",
|
||||
" return False\n",
|
||||
"\n",
|
||||
"def market_order(symbol, volume, order_type, stoploss=None, take_profit=None, deviation=20):\n",
|
||||
" \"\"\"\n",
|
||||
" Führt Market Order aus (vereinfachte Version)\n",
|
||||
" \"\"\"\n",
|
||||
" try:\n",
|
||||
" price_dict = {\n",
|
||||
" 'buy': mt.symbol_info_tick(symbol).ask,\n",
|
||||
" 'sell': mt.symbol_info_tick(symbol).bid\n",
|
||||
" }\n",
|
||||
" \n",
|
||||
" order_type_dict = {\n",
|
||||
" 'buy': mt.ORDER_TYPE_BUY,\n",
|
||||
" 'sell': mt.ORDER_TYPE_SELL\n",
|
||||
" }\n",
|
||||
" \n",
|
||||
" request = {\n",
|
||||
" \"action\": mt.TRADE_ACTION_DEAL,\n",
|
||||
" \"symbol\": symbol,\n",
|
||||
" \"volume\": volume,\n",
|
||||
" \"type\": order_type_dict[order_type],\n",
|
||||
" \"price\": price_dict[order_type],\n",
|
||||
" \"sl\": stoploss,\n",
|
||||
" \"tp\": take_profit,\n",
|
||||
" \"deviation\": deviation,\n",
|
||||
" \"magic\": 234000,\n",
|
||||
" \"comment\": strategy_name,\n",
|
||||
" \"type_time\": mt.ORDER_TIME_GTC,\n",
|
||||
" \"type_filling\": mt.ORDER_FILLING_IOC,\n",
|
||||
" }\n",
|
||||
" \n",
|
||||
" result = mt.order_send(request)\n",
|
||||
" return result\n",
|
||||
" \n",
|
||||
" except Exception as e:\n",
|
||||
" print(f\"Error in market order: {e}\")\n",
|
||||
" return None\n",
|
||||
"\n",
|
||||
"print(\"✅ Hilfsfunktionen definiert\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 4. Market Regime Detection"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def detect_market_regime(df, lookback=50):\n",
|
||||
" \"\"\"\n",
|
||||
" Erkennt das aktuelle Marktregime (Trending vs. Ranging)\n",
|
||||
" \"\"\"\n",
|
||||
" try:\n",
|
||||
" # ADX für Trendstärke\n",
|
||||
" adx_data = ta.adx(df['high'], df['low'], df['close'], length=14)\n",
|
||||
" if adx_data is None or 'ADX_14' not in adx_data.columns:\n",
|
||||
" adx = 25.0 # Standardwert\n",
|
||||
" else:\n",
|
||||
" adx = adx_data['ADX_14'].iloc[-1] if not pd.isna(adx_data['ADX_14'].iloc[-1]) else 25.0\n",
|
||||
" \n",
|
||||
" # Bollinger Band Squeeze für Ranging Markets\n",
|
||||
" try:\n",
|
||||
" bb = ta.bbands(df['close'], length=20)\n",
|
||||
" if bb is not None and len(bb.columns) >= 3:\n",
|
||||
" bb_cols = bb.columns.tolist()\n",
|
||||
" bb_upper = bb[bb_cols[0]] # Oberes Band\n",
|
||||
" bb_middle = bb[bb_cols[1]] # Mittleres Band\n",
|
||||
" bb_lower = bb[bb_cols[2]] # Unteres Band\n",
|
||||
" bb_width = ((bb_upper - bb_lower) / bb_middle * 100).iloc[-lookback:].mean()\n",
|
||||
" else:\n",
|
||||
" bb_width = 4.0 # Standardwert\n",
|
||||
" except:\n",
|
||||
" bb_width = 4.0\n",
|
||||
" \n",
|
||||
" # Price Action Analysis\n",
|
||||
" price_range = (df['high'].iloc[-lookback:].max() - df['low'].iloc[-lookback:].min())\n",
|
||||
" atr_avg = df['atr'].iloc[-lookback:].mean()\n",
|
||||
" range_ratio = price_range / (atr_avg * lookback) if atr_avg > 0 else 1.0\n",
|
||||
" \n",
|
||||
" # Volatility Cluster Detection\n",
|
||||
" vol_cluster = df['atr'].iloc[-10:].std() / df['atr'].iloc[-50:].mean() if len(df) >= 50 else 1.0\n",
|
||||
" \n",
|
||||
" # Regime-Bestimmung\n",
|
||||
" if adx > 25 and range_ratio > 1.5:\n",
|
||||
" regime = 'trending'\n",
|
||||
" strength = min(100, adx * 2)\n",
|
||||
" elif vol_cluster > 1.5:\n",
|
||||
" regime = 'volatile'\n",
|
||||
" strength = min(100, vol_cluster * 50)\n",
|
||||
" else:\n",
|
||||
" regime = 'ranging'\n",
|
||||
" strength = max(0, 100 - adx * 2)\n",
|
||||
" \n",
|
||||
" return {\n",
|
||||
" 'regime': regime,\n",
|
||||
" 'strength': strength,\n",
|
||||
" 'adx': adx,\n",
|
||||
" 'bb_width': bb_width,\n",
|
||||
" 'range_ratio': range_ratio,\n",
|
||||
" 'vol_cluster': vol_cluster\n",
|
||||
" }\n",
|
||||
" \n",
|
||||
" except Exception as e:\n",
|
||||
" print(f\"Error in regime detection: {e}\")\n",
|
||||
" # Fallback\n",
|
||||
" return {\n",
|
||||
" 'regime': 'ranging',\n",
|
||||
" 'strength': 50,\n",
|
||||
" 'adx': 20,\n",
|
||||
" 'bb_width': 4.0,\n",
|
||||
" 'range_ratio': 1.0,\n",
|
||||
" 'vol_cluster': 1.0\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
"# Test Market Regime Detection\n",
|
||||
"if test_data is not None:\n",
|
||||
" regime = detect_market_regime(test_data)\n",
|
||||
" print(\"\\n🎯 Market Regime Test:\")\n",
|
||||
" print(f\"Regime: {regime['regime'].upper()}\")\n",
|
||||
" print(f\"Strength: {regime['strength']:.1f}%\")\n",
|
||||
" print(f\"ADX: {regime['adx']:.1f}\")\n",
|
||||
" print(f\"Volatility Cluster: {regime['vol_cluster']:.2f}\")\n",
|
||||
"else:\n",
|
||||
" print(\"❌ Cannot test regime detection without data\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 5. Adaptive Confidence System"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def calculate_adaptive_confidence_threshold(regime_info, base_confidence=70):\n",
|
||||
" \"\"\"\n",
|
||||
" Berechnet adaptive Confidence-Schwelle basierend auf Marktregime\n",
|
||||
" \"\"\"\n",
|
||||
" regime = regime_info['regime']\n",
|
||||
" strength = regime_info['strength']\n",
|
||||
" adx = regime_info['adx']\n",
|
||||
" \n",
|
||||
" if regime == 'trending':\n",
|
||||
" # In Trending Markets: niedrigere Schwelle bei starken Trends\n",
|
||||
" if adx > 30:\n",
|
||||
" return max(60, base_confidence - 15)\n",
|
||||
" else:\n",
|
||||
" return base_confidence - 10\n",
|
||||
" \n",
|
||||
" elif regime == 'ranging':\n",
|
||||
" # In Ranging Markets: höhere Schwelle für mehr Selektivität\n",
|
||||
" return base_confidence + 15\n",
|
||||
" \n",
|
||||
" elif regime == 'volatile':\n",
|
||||
" # In Volatile Markets: deutlich höhere Schwelle\n",
|
||||
" return base_confidence + 20\n",
|
||||
" \n",
|
||||
" return base_confidence\n",
|
||||
"\n",
|
||||
"# Test Adaptive Confidence\n",
|
||||
"if test_data is not None:\n",
|
||||
" regime = detect_market_regime(test_data)\n",
|
||||
" adaptive_threshold = calculate_adaptive_confidence_threshold(regime)\n",
|
||||
" print(f\"\\n🎚️ Adaptive Confidence Test:\")\n",
|
||||
" print(f\"Base Confidence: 70%\")\n",
|
||||
" print(f\"Regime: {regime['regime'].upper()}\")\n",
|
||||
" print(f\"Adaptive Threshold: {adaptive_threshold}%\")\n",
|
||||
" print(f\"Adjustment: {adaptive_threshold - 70:+d}%\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 6. Enhanced Trend Analysis"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def get_enhanced_trend(timeframe=\"H4\", lookback=150, symbol=\"XAUUSD\"):\n",
|
||||
" \"\"\"\n",
|
||||
" Verbesserte Trend-Analyse mit Regime-Awareness\n",
|
||||
" \"\"\"\n",
|
||||
" # Timeframe mapping\n",
|
||||
" tf_map = {\"D1\": \"d1\", \"H4\": \"h4\", \"H1\": \"h1\", \"M30\": \"m30\", \"M15\": \"m15\", \"M5\": \"m5\"}\n",
|
||||
" tf = tf_map.get(timeframe, timeframe.lower())\n",
|
||||
" \n",
|
||||
" try:\n",
|
||||
" df = get_rates(tf, lookback, symbol)\n",
|
||||
" if df is None or len(df) < 50:\n",
|
||||
" return None\n",
|
||||
" \n",
|
||||
" # Bestehende Trend-Logik\n",
|
||||
" df['close_smooth'] = savgol_filter(df['close'], min(15, len(df)//10), 3)\n",
|
||||
" \n",
|
||||
" # Linear Regression\n",
|
||||
" X = np.arange(len(df)).reshape(-1, 1)\n",
|
||||
" y = df['close_smooth'].values\n",
|
||||
" model = LinearRegression().fit(X, y)\n",
|
||||
" slope = model.coef_[0]\n",
|
||||
" \n",
|
||||
" # Market Regime Detection\n",
|
||||
" regime_info = detect_market_regime(df.iloc[-50:])\n",
|
||||
" \n",
|
||||
" # Adaptive Slope Threshold basierend auf Regime\n",
|
||||
" base_threshold = df['atr'].iloc[-1] * 0.0001\n",
|
||||
" \n",
|
||||
" if regime_info['regime'] == 'trending':\n",
|
||||
" slope_threshold = base_threshold * 0.7 # Niedrigere Schwelle in Trends\n",
|
||||
" elif regime_info['regime'] == 'ranging':\n",
|
||||
" slope_threshold = base_threshold * 1.5 # Höhere Schwelle in Ranges\n",
|
||||
" else: # volatile\n",
|
||||
" slope_threshold = base_threshold * 1.2\n",
|
||||
" \n",
|
||||
" # Trend bestimmen\n",
|
||||
" if slope > slope_threshold:\n",
|
||||
" trend = \"uptrend\"\n",
|
||||
" elif slope < -slope_threshold:\n",
|
||||
" trend = \"downtrend\"\n",
|
||||
" else:\n",
|
||||
" trend = \"sideways\"\n",
|
||||
" \n",
|
||||
" # Enhanced Trend Strength\n",
|
||||
" trend_strength = abs(slope) / slope_threshold if slope_threshold > 0 else 0\n",
|
||||
" \n",
|
||||
" return {\n",
|
||||
" \"trend\": trend,\n",
|
||||
" \"slope\": slope,\n",
|
||||
" \"slope_threshold\": slope_threshold,\n",
|
||||
" \"trend_strength\": trend_strength,\n",
|
||||
" \"atr\": df['atr'].iloc[-1],\n",
|
||||
" \"price\": df['close'].iloc[-1],\n",
|
||||
" \"regime_info\": regime_info\n",
|
||||
" }\n",
|
||||
" \n",
|
||||
" except Exception as e:\n",
|
||||
" print(f\"Error in get_enhanced_trend: {e}\")\n",
|
||||
" return None\n",
|
||||
"\n",
|
||||
"# Test Enhanced Trend Analysis\n",
|
||||
"print(\"\\n🔍 Testing Enhanced Trend Analysis...\")\n",
|
||||
"trend_test = get_enhanced_trend(\"H4\", 100, symbol)\n",
|
||||
"if trend_test:\n",
|
||||
" print(f\"✅ Trend: {trend_test['trend'].upper()}\")\n",
|
||||
" print(f\"✅ Strength: {trend_test['trend_strength']:.2f}\")\n",
|
||||
" print(f\"✅ Price: {trend_test['price']:.5f}\")\n",
|
||||
" print(f\"✅ ATR: {trend_test['atr']:.5f}\")\n",
|
||||
" print(f\"✅ Regime: {trend_test['regime_info']['regime'].upper()}\")\n",
|
||||
"else:\n",
|
||||
" print(\"❌ Enhanced trend analysis failed\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 7. Optimized Top-Down Analysis"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def extended_top_down_v2(symbol=\"XAUUSD\", lookback=150):\n",
|
||||
" \"\"\"\n",
|
||||
" Optimierte Top-Down-Analyse mit adaptiven Parametern\n",
|
||||
" \"\"\"\n",
|
||||
" \n",
|
||||
" timeframes = [\"D1\", \"H4\", \"H1\", \"M30\", \"M15\", \"M5\"]\n",
|
||||
" trend_info = {}\n",
|
||||
" \n",
|
||||
" print(f\"🔍 Analyzing {symbol} across {len(timeframes)} timeframes...\")\n",
|
||||
" \n",
|
||||
" # 1. Alle Timeframes analysieren\n",
|
||||
" for tf in timeframes:\n",
|
||||
" print(f\" Analyzing {tf}...\")\n",
|
||||
" trend_info[tf] = get_enhanced_trend(tf, lookback, symbol)\n",
|
||||
" if trend_info[tf] is None:\n",
|
||||
" print(f\"⚠️ Keine Daten für {tf}\")\n",
|
||||
" return None\n",
|
||||
" \n",
|
||||
" # 2. Market Regime aus H4 bestimmen (repräsentativ)\n",
|
||||
" main_regime = trend_info[\"H4\"][\"regime_info\"]\n",
|
||||
" \n",
|
||||
" # 3. Adaptive Confidence Threshold\n",
|
||||
" adaptive_confidence_threshold = calculate_adaptive_confidence_threshold(main_regime)\n",
|
||||
" \n",
|
||||
" # 4. Enhanced Standard-Trend (D1 + H4 mit Gewichtung)\n",
|
||||
" d1_trend = trend_info[\"D1\"][\"trend\"]\n",
|
||||
" h4_trend = trend_info[\"H4\"][\"trend\"]\n",
|
||||
" d1_strength = trend_info[\"D1\"][\"trend_strength\"]\n",
|
||||
" h4_strength = trend_info[\"H4\"][\"trend_strength\"]\n",
|
||||
" \n",
|
||||
" # Gewichteter Standard-Trend\n",
|
||||
" if d1_trend == h4_trend and d1_trend != \"sideways\":\n",
|
||||
" standard_trend = d1_trend\n",
|
||||
" standard_strength = (d1_strength * 0.6 + h4_strength * 0.4)\n",
|
||||
" elif d1_strength > h4_strength * 1.5: # D1 deutlich stärker\n",
|
||||
" standard_trend = d1_trend\n",
|
||||
" standard_strength = d1_strength * 0.8\n",
|
||||
" elif h4_strength > d1_strength * 1.5: # H4 deutlich stärker\n",
|
||||
" standard_trend = h4_trend\n",
|
||||
" standard_strength = h4_strength * 0.8\n",
|
||||
" else:\n",
|
||||
" standard_trend = \"sideways\"\n",
|
||||
" standard_strength = 0\n",
|
||||
" \n",
|
||||
" # 5. Enhanced Fast-Trend mit Regime-Awareness\n",
|
||||
" fast_timeframes = [\"H1\", \"M30\", \"M15\", \"M5\"]\n",
|
||||
" fast_trends = [trend_info[tf][\"trend\"] for tf in fast_timeframes]\n",
|
||||
" fast_strengths = [trend_info[tf][\"trend_strength\"] for tf in fast_timeframes]\n",
|
||||
" \n",
|
||||
" # Regime-abhängige Fast-Trend Logik\n",
|
||||
" if main_regime['regime'] == 'trending':\n",
|
||||
" # In Trends: 2 von 4 TFs reichen\n",
|
||||
" required_alignment = 2\n",
|
||||
" else:\n",
|
||||
" # In Ranging/Volatile: 3 von 4 TFs erforderlich\n",
|
||||
" required_alignment = 3\n",
|
||||
" \n",
|
||||
" trend_counts = {'uptrend': 0, 'downtrend': 0, 'sideways': 0}\n",
|
||||
" weighted_strengths = {'uptrend': 0, 'downtrend': 0}\n",
|
||||
" \n",
|
||||
" weights = [1.0, 0.8, 0.6, 0.4] # H1, M30, M15, M5\n",
|
||||
" \n",
|
||||
" for i, (trend, strength) in enumerate(zip(fast_trends, fast_strengths)):\n",
|
||||
" trend_counts[trend] += 1\n",
|
||||
" if trend != 'sideways':\n",
|
||||
" weighted_strengths[trend] += strength * weights[i]\n",
|
||||
" \n",
|
||||
" # Fast-Trend bestimmen\n",
|
||||
" max_count = max(trend_counts['uptrend'], trend_counts['downtrend'])\n",
|
||||
" if max_count >= required_alignment:\n",
|
||||
" if trend_counts['uptrend'] > trend_counts['downtrend']:\n",
|
||||
" fast_trend = \"uptrend\"\n",
|
||||
" elif trend_counts['downtrend'] > trend_counts['uptrend']:\n",
|
||||
" fast_trend = \"downtrend\"\n",
|
||||
" else:\n",
|
||||
" # Bei Gleichstand: Stärke entscheidet\n",
|
||||
" if weighted_strengths['uptrend'] > weighted_strengths['downtrend']:\n",
|
||||
" fast_trend = \"uptrend\"\n",
|
||||
" else:\n",
|
||||
" fast_trend = \"downtrend\"\n",
|
||||
" else:\n",
|
||||
" fast_trend = \"sideways\"\n",
|
||||
" \n",
|
||||
" # 6. Top-Down-Trend Bestimmung\n",
|
||||
" if standard_trend == fast_trend and standard_trend != \"sideways\":\n",
|
||||
" top_down_trend = standard_trend\n",
|
||||
" combined_strength = (standard_strength + weighted_strengths.get(fast_trend, 0)) / 2\n",
|
||||
" else:\n",
|
||||
" top_down_trend = \"sideways\"\n",
|
||||
" combined_strength = 0\n",
|
||||
" \n",
|
||||
" # 7. Enhanced Confidence Calculation\n",
|
||||
" weights = {\"D1\": 2.5, \"H4\": 2.0, \"H1\": 1.5, \"M30\": 1.0, \"M15\": 0.8, \"M5\": 0.6}\n",
|
||||
" \n",
|
||||
" weighted_matching = sum(\n",
|
||||
" weights[tf] * trend_info[tf][\"trend_strength\"] \n",
|
||||
" for tf in timeframes\n",
|
||||
" if trend_info[tf][\"trend\"] == top_down_trend and trend_info[tf][\"trend\"] != \"sideways\"\n",
|
||||
" )\n",
|
||||
" \n",
|
||||
" weighted_total = sum(\n",
|
||||
" weights[tf] * trend_info[tf][\"trend_strength\"]\n",
|
||||
" for tf in timeframes\n",
|
||||
" if trend_info[tf][\"trend\"] != \"sideways\"\n",
|
||||
" )\n",
|
||||
" \n",
|
||||
" confidence = round((weighted_matching / weighted_total) * 100, 2) if weighted_total > 0 else 0.0\n",
|
||||
" \n",
|
||||
" # 8. Risk-Adjusted Signal Strength\n",
|
||||
" atr = trend_info[\"M5\"][\"atr\"]\n",
|
||||
" rrr = 2.5 # Risk-Reward Ratio\n",
|
||||
" \n",
|
||||
" risk_adjusted_strength = confidence * combined_strength * min(2.0, rrr)\n",
|
||||
" \n",
|
||||
" # 9. Entry Signal mit adaptiven Kriterien\n",
|
||||
" entry_signal = 0\n",
|
||||
" signal_quality = \"none\"\n",
|
||||
" \n",
|
||||
" if (top_down_trend != \"sideways\" and \n",
|
||||
" confidence >= adaptive_confidence_threshold and\n",
|
||||
" risk_adjusted_strength >= 100):\n",
|
||||
" \n",
|
||||
" entry_signal = 1 if top_down_trend == \"uptrend\" else -1\n",
|
||||
" \n",
|
||||
" # Signal Quality Assessment\n",
|
||||
" if confidence >= 85 and risk_adjusted_strength >= 150:\n",
|
||||
" signal_quality = \"excellent\"\n",
|
||||
" elif confidence >= 75 and risk_adjusted_strength >= 120:\n",
|
||||
" signal_quality = \"good\"\n",
|
||||
" else:\n",
|
||||
" signal_quality = \"fair\"\n",
|
||||
" \n",
|
||||
" # 10. Enhanced Debug Output\n",
|
||||
" debug_data = []\n",
|
||||
" for tf in timeframes:\n",
|
||||
" info = trend_info[tf]\n",
|
||||
" debug_data.append([\n",
|
||||
" tf,\n",
|
||||
" info[\"trend\"],\n",
|
||||
" f\"{info['trend_strength']:.2f}\",\n",
|
||||
" f\"{info['atr']:.4f}\",\n",
|
||||
" f\"{info['slope']:.6f}\",\n",
|
||||
" f\"{info['price']:.2f}\"\n",
|
||||
" ])\n",
|
||||
" \n",
|
||||
" print(f\"\\n📊 Enhanced Trend-Analyse für {symbol}\")\n",
|
||||
" print(f\"🎯 Market Regime: {main_regime['regime'].upper()} (Strength: {main_regime['strength']:.0f}%)\")\n",
|
||||
" print(f\"🎚️ Adaptive Confidence Threshold: {adaptive_confidence_threshold}%\")\n",
|
||||
" print()\n",
|
||||
" print(tabulate(debug_data, headers=[\"TF\", \"Trend\", \"Strength\", \"ATR\", \"Slope\", \"Price\"], tablefmt=\"psql\"))\n",
|
||||
" print(f\"\\n➡️ Standard-Trend: {standard_trend} (Strength: {standard_strength:.2f})\")\n",
|
||||
" print(f\"➡️ Fast-Trend: {fast_trend}\")\n",
|
||||
" print(f\"➡️ Top-Down-Trend: {top_down_trend}\")\n",
|
||||
" print(f\"➡️ Confidence: {confidence}% (Threshold: {adaptive_confidence_threshold}%)\")\n",
|
||||
" print(f\"➡️ Risk-Adjusted Strength: {risk_adjusted_strength:.1f}\")\n",
|
||||
" print(f\"➡️ Signal Quality: {signal_quality.upper()}\")\n",
|
||||
" \n",
|
||||
" return {\n",
|
||||
" \"symbol\": symbol,\n",
|
||||
" \"trend_info\": trend_info,\n",
|
||||
" \"market_regime\": main_regime,\n",
|
||||
" \"standard_trend\": standard_trend,\n",
|
||||
" \"fast_trend\": fast_trend,\n",
|
||||
" \"top_down_trend\": top_down_trend,\n",
|
||||
" \"confidence\": confidence,\n",
|
||||
" \"adaptive_threshold\": adaptive_confidence_threshold,\n",
|
||||
" \"risk_adjusted_strength\": risk_adjusted_strength,\n",
|
||||
" \"entry_signal\": entry_signal,\n",
|
||||
" \"signal_quality\": signal_quality,\n",
|
||||
" \"combined_strength\": combined_strength\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
"print(\"✅ Extended Top-Down V2 function defined\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 8. Test der optimierten Signallogik"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Test Enhanced Top-Down Analysis\n",
|
||||
"print(\"🔍 Testing Enhanced Top-Down Analysis...\\n\")\n",
|
||||
"signal_result = extended_top_down_v2(symbol)\n",
|
||||
"\n",
|
||||
"if signal_result:\n",
|
||||
" print(f\"\\n🎯 SIGNAL SUMMARY:\")\n",
|
||||
" print(f\"Entry Signal: {signal_result['entry_signal']}\")\n",
|
||||
" print(f\"Confidence: {signal_result['confidence']}%\")\n",
|
||||
" print(f\"Adaptive Threshold: {signal_result['adaptive_threshold']}%\")\n",
|
||||
" print(f\"Signal Quality: {signal_result['signal_quality'].upper()}\")\n",
|
||||
" print(f\"Market Regime: {signal_result['market_regime']['regime'].upper()}\")\n",
|
||||
" print(f\"Risk-Adjusted Strength: {signal_result['risk_adjusted_strength']:.1f}\")\n",
|
||||
" \n",
|
||||
" # Zusätzliche Analyse\n",
|
||||
" if signal_result['entry_signal'] != 0:\n",
|
||||
" direction = \"LONG\" if signal_result['entry_signal'] == 1 else \"SHORT\"\n",
|
||||
" print(f\"\\n🚀 TRADING SIGNAL: {direction}\")\n",
|
||||
" print(f\"Quality: {signal_result['signal_quality'].upper()}\")\n",
|
||||
" print(f\"Regime-optimized for: {signal_result['market_regime']['regime'].upper()} market\")\n",
|
||||
" else:\n",
|
||||
" print(f\"\\n⏸️ NO TRADING SIGNAL\")\n",
|
||||
" print(f\"Reason: Confidence {signal_result['confidence']}% < Threshold {signal_result['adaptive_threshold']}%\")\n",
|
||||
"else:\n",
|
||||
" print(\"❌ Signal analysis failed\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 9. Vergleich mit ursprünglicher Logik"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def compare_signal_logic():\n",
|
||||
" \"\"\"\n",
|
||||
" Vergleicht V1.3 vs V1.4 Signallogik\n",
|
||||
" \"\"\"\n",
|
||||
" if signal_result:\n",
|
||||
" print(\"\\n📊 VERGLEICH V1.3 vs V1.4\")\n",
|
||||
" print(\"=\" * 50)\n",
|
||||
" \n",
|
||||
" # V1.3 hätte feste 80% Schwelle\n",
|
||||
" v13_threshold = 80\n",
|
||||
" v14_threshold = signal_result['adaptive_threshold']\n",
|
||||
" current_confidence = signal_result['confidence']\n",
|
||||
" \n",
|
||||
" # V1.3 Signal\n",
|
||||
" v13_signal = 1 if (signal_result['top_down_trend'] == 'uptrend' and current_confidence >= v13_threshold) else (\n",
|
||||
" -1 if (signal_result['top_down_trend'] == 'downtrend' and current_confidence >= v13_threshold) else 0)\n",
|
||||
" \n",
|
||||
" # V1.4 Signal\n",
|
||||
" v14_signal = signal_result['entry_signal']\n",
|
||||
" \n",
|
||||
" comparison_data = [\n",
|
||||
" [\"Metric\", \"V1.3 (Old)\", \"V1.4 (New)\", \"Improvement\"],\n",
|
||||
" [\"Confidence Threshold\", f\"{v13_threshold}%\", f\"{v14_threshold}%\", f\"{v14_threshold-v13_threshold:+d}%\"],\n",
|
||||
" [\"Current Confidence\", f\"{current_confidence}%\", f\"{current_confidence}%\", \"Same\"],\n",
|
||||
" [\"Entry Signal\", f\"{v13_signal}\", f\"{v14_signal}\", \"✅\" if v14_signal != 0 else \"⏸️\"],\n",
|
||||
" [\"Market Regime\", \"Not considered\", signal_result['market_regime']['regime'].upper(), \"✅ New\"],\n",
|
||||
" [\"Signal Quality\", \"Binary\", signal_result['signal_quality'].upper(), \"✅ Graded\"],\n",
|
||||
" [\"Risk-Adj. Strength\", \"Not calculated\", f\"{signal_result['risk_adjusted_strength']:.1f}\", \"✅ New\"]\n",
|
||||
" ]\n",
|
||||
" \n",
|
||||
" print(tabulate(comparison_data, headers=\"firstrow\", tablefmt=\"psql\"))\n",
|
||||
" \n",
|
||||
" # Fazit\n",
|
||||
" if v13_signal != v14_signal:\n",
|
||||
" if v14_signal != 0 and v13_signal == 0:\n",
|
||||
" print(\"\\n✅ V1.4 ADVANTAGE: Signal erkannt, den V1.3 verpasst hätte!\")\n",
|
||||
" print(f\" Grund: Adaptive Schwelle {v14_threshold}% vs. feste {v13_threshold}%\")\n",
|
||||
" elif v13_signal != 0 and v14_signal == 0:\n",
|
||||
" print(\"\\n🛡️ V1.4 ADVANTAGE: Riskantes Signal gefiltert!\")\n",
|
||||
" print(f\" Grund: {signal_result['market_regime']['regime'].upper()} market requires higher confidence\")\n",
|
||||
" else:\n",
|
||||
" print(\"\\n🤝 SAME RESULT: Both versions agree on signal\")\n",
|
||||
" \n",
|
||||
" print(f\"\\n🎯 V1.4 Benefits:\")\n",
|
||||
" print(f\" • Regime-aware threshold: {v14_threshold}% (vs fixed 80%)\")\n",
|
||||
" print(f\" • Quality assessment: {signal_result['signal_quality'].upper()}\")\n",
|
||||
" print(f\" • Risk-adjusted strength: {signal_result['risk_adjusted_strength']:.1f}\")\n",
|
||||
" print(f\" • Market regime: {signal_result['market_regime']['regime'].upper()}\")\n",
|
||||
"\n",
|
||||
"compare_signal_logic()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 10. Quick Test ohne vollständige Analyse"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Für schnelle Tests ohne vollständige Multi-Timeframe Analyse\n",
|
||||
"def quick_regime_test(symbol=\"XAUUSD\"):\n",
|
||||
" \"\"\"\n",
|
||||
" Schneller Test der Regime-Erkennung\n",
|
||||
" \"\"\"\n",
|
||||
" print(f\"⚡ Quick Regime Test for {symbol}\")\n",
|
||||
" \n",
|
||||
" # Hole H4 Daten\n",
|
||||
" df = get_rates(\"h4\", 100, symbol)\n",
|
||||
" if df is None:\n",
|
||||
" print(\"❌ No data available\")\n",
|
||||
" return\n",
|
||||
" \n",
|
||||
" # Regime-Erkennung\n",
|
||||
" regime = detect_market_regime(df)\n",
|
||||
" adaptive_threshold = calculate_adaptive_confidence_threshold(regime)\n",
|
||||
" \n",
|
||||
" print(f\"\\n📊 Current Market Conditions:\")\n",
|
||||
" print(f\" Regime: {regime['regime'].upper()}\")\n",
|
||||
" print(f\" Strength: {regime['strength']:.1f}%\")\n",
|
||||
" print(f\" ADX: {regime['adx']:.1f}\")\n",
|
||||
" print(f\" Adaptive Threshold: {adaptive_threshold}% (vs 80% fixed)\")\n",
|
||||
" print(f\" Current Price: {df['close'].iloc[-1]:.5f}\")\n",
|
||||
" \n",
|
||||
" # Trading-Empfehlung basierend auf Regime\n",
|
||||
" if regime['regime'] == 'trending':\n",
|
||||
" print(f\"\\n🎯 TRENDING MARKET - Good for trend-following strategies\")\n",
|
||||
" print(f\" • Lower confidence threshold: {adaptive_threshold}%\")\n",
|
||||
" print(f\" • Expect stronger directional moves\")\n",
|
||||
" elif regime['regime'] == 'ranging':\n",
|
||||
" print(f\"\\n📊 RANGING MARKET - Be more selective\")\n",
|
||||
" print(f\" • Higher confidence threshold: {adaptive_threshold}%\")\n",
|
||||
" print(f\" • Look for range breakouts\")\n",
|
||||
" else: # volatile\n",
|
||||
" print(f\"\\n⚡ VOLATILE MARKET - Use wider stops\")\n",
|
||||
" print(f\" • Highest confidence threshold: {adaptive_threshold}%\")\n",
|
||||
" print(f\" • Expect choppy price action\")\n",
|
||||
"\n",
|
||||
"# Führe Quick Test aus\n",
|
||||
"quick_regime_test(symbol)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 11. Status & Next Steps"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"\\n🎉 TradingBot V1.4 - Fixed Version Ready!\")\n",
|
||||
"print(\"=\"*50)\n",
|
||||
"print(\"\\n✅ Successfully Fixed:\")\n",
|
||||
"print(\" • Removed external dependencies\")\n",
|
||||
"print(\" • Integrated all helper functions\")\n",
|
||||
"print(\" • Fixed import errors\")\n",
|
||||
"print(\" • Tested all components\")\n",
|
||||
"\n",
|
||||
"print(\"\\n🚀 Key Features Working:\")\n",
|
||||
"print(\" • Market Regime Detection\")\n",
|
||||
"print(\" • Adaptive Confidence Thresholds\")\n",
|
||||
"print(\" • Enhanced Trend Analysis\")\n",
|
||||
"print(\" • Multi-Timeframe Signal Logic\")\n",
|
||||
"print(\" • Risk-Adjusted Signal Strength\")\n",
|
||||
"\n",
|
||||
"print(\"\\n📋 Next Steps:\")\n",
|
||||
"print(\" 1. Run extended_top_down_v2() for full analysis\")\n",
|
||||
"print(\" 2. Test with different market conditions\")\n",
|
||||
"print(\" 3. Compare results with your V1.3\")\n",
|
||||
"print(\" 4. Implement automated trading loop\")\n",
|
||||
"print(\" 5. Monitor performance in demo mode\")\n",
|
||||
"\n",
|
||||
"print(\"\\n🎯 Ready for Testing!\")\n",
|
||||
"print(f\" Current Symbol: {symbol}\")\n",
|
||||
"print(f\" MT5 Connected: {'✅' if mt.terminal_info() else '❌'}\")\n",
|
||||
"print(f\" Strategy: {strategy_name}\")"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -1,569 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# TradingBot V1.4 - Optimized Integration\n",
|
||||
"\n",
|
||||
"## Verbesserungen gegenüber V1.3:\n",
|
||||
"\n",
|
||||
"### 🎯 **Hauptverbesserungen:**\n",
|
||||
"1. **Adaptive Confidence Threshold** - Automatische Anpassung an Marktbedingungen\n",
|
||||
"2. **Market Regime Detection** - Erkennung von Trending/Ranging/Volatile Märkten\n",
|
||||
"3. **Entry Timing Optimization** - Pullback-basierte Entries für bessere R/R\n",
|
||||
"4. **Risk-Adjusted Signal Strength** - Kombiniert Confidence mit Trend-Stärke\n",
|
||||
"5. **Performance Monitoring** - Automatisches Tracking der Signal-Performance\n",
|
||||
"\n",
|
||||
"### 📊 **Technische Verbesserungen:**\n",
|
||||
"- Regime-abhängige Parameter-Anpassung\n",
|
||||
"- Gewichtete Trend-Stärke-Berechnung\n",
|
||||
"- Adaptive ATR-Multiplikatoren\n",
|
||||
"- Enhanced Debug-Output mit Regime-Info"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 1. Import optimierte Funktionen"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Import der optimierten Funktionen\n",
|
||||
"from TradingBot_V1.4_Optimized import (\n",
|
||||
" detect_market_regime,\n",
|
||||
" calculate_adaptive_confidence_threshold,\n",
|
||||
" get_enhanced_trend,\n",
|
||||
" extended_top_down_v2,\n",
|
||||
" check_pullback_entry,\n",
|
||||
" execute_trade_v2,\n",
|
||||
" log_trade_performance,\n",
|
||||
" analyze_performance,\n",
|
||||
" setup_optimized_trading\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"# Bestehende Imports aus V1.3\n",
|
||||
"import pandas as pd\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import mplfinance as mpf\n",
|
||||
"import keyring as kr\n",
|
||||
"import MetaTrader5 as mt\n",
|
||||
"import requests\n",
|
||||
"import re\n",
|
||||
"from time import sleep\n",
|
||||
"import sqlite3 as db\n",
|
||||
"import pandas_ta as ta\n",
|
||||
"import numpy as np\n",
|
||||
"from sklearn.linear_model import LinearRegression\n",
|
||||
"from scipy.signal import savgol_filter, find_peaks\n",
|
||||
"from tabulate import tabulate\n",
|
||||
"from ta.trend import ADXIndicator, EMAIndicator\n",
|
||||
"from ta.momentum import RSIIndicator\n",
|
||||
"from talib import CDLHAMMER, CDLSHOOTINGSTAR"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 2. Setup & Login (unverändert)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Setup optimiertes Trading\n",
|
||||
"setup_optimized_trading()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Login zu MT5 (unverändert)\n",
|
||||
"mt.initialize()\n",
|
||||
" \n",
|
||||
"login = 10800246\n",
|
||||
"server = 'VantageInternational-Demo'\n",
|
||||
"password = kr.get_password(server, str(login))\n",
|
||||
"\n",
|
||||
"mt.login(login, password, server)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Projekt Setup (unverändert)\n",
|
||||
"project = \"trading-\" + server[-4::1]\n",
|
||||
"project = project.lower()\n",
|
||||
"print(f\"Project: {project}\")\n",
|
||||
"\n",
|
||||
"# Trading Parameter\n",
|
||||
"symbol = \"XAUUSD\"\n",
|
||||
"pause_trading = 0\n",
|
||||
"strategy_name = \"TradingBot_V1.4_Optimized\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 3. Bestehende Hilfsfunktionen (unverändert)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Hier würden deine bestehenden Funktionen stehen:\n",
|
||||
"# - get_rates()\n",
|
||||
"# - market_order()\n",
|
||||
"# - check_risk_limits()\n",
|
||||
"# etc.\n",
|
||||
"\n",
|
||||
"# Beispiel get_rates Funktion (falls nicht vorhanden):\n",
|
||||
"def get_rates(timeframe=\"h4\", count=200):\n",
|
||||
" \"\"\"\n",
|
||||
" Holt Kursdaten von MT5\n",
|
||||
" \"\"\"\n",
|
||||
" timeframes_dict = {\n",
|
||||
" \"m1\": mt.TIMEFRAME_M1,\n",
|
||||
" \"m5\": mt.TIMEFRAME_M5,\n",
|
||||
" \"m15\": mt.TIMEFRAME_M15,\n",
|
||||
" \"m30\": mt.TIMEFRAME_M30,\n",
|
||||
" \"h1\": mt.TIMEFRAME_H1,\n",
|
||||
" \"h4\": mt.TIMEFRAME_H4,\n",
|
||||
" \"d1\": mt.TIMEFRAME_D1\n",
|
||||
" }\n",
|
||||
" \n",
|
||||
" try:\n",
|
||||
" rates = mt.copy_rates_from_pos(symbol, timeframes_dict[timeframe], 0, count)\n",
|
||||
" if rates is None:\n",
|
||||
" return None\n",
|
||||
" \n",
|
||||
" df = pd.DataFrame(rates)\n",
|
||||
" df['time'] = pd.to_datetime(df['time'], unit='s')\n",
|
||||
" df.set_index('time', inplace=True)\n",
|
||||
" \n",
|
||||
" # ATR hinzufügen\n",
|
||||
" df['atr'] = ta.atr(df['high'], df['low'], df['close'], length=14)\n",
|
||||
" \n",
|
||||
" return df\n",
|
||||
" \n",
|
||||
" except Exception as e:\n",
|
||||
" print(f\"Error getting rates: {e}\")\n",
|
||||
" return None"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 4. Test der optimierten Funktionen"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Test Market Regime Detection\n",
|
||||
"df_test = get_rates(\"h4\", 100)\n",
|
||||
"if df_test is not None:\n",
|
||||
" regime = detect_market_regime(df_test)\n",
|
||||
" print(\"🎯 Market Regime Analysis:\")\n",
|
||||
" print(f\"Regime: {regime['regime'].upper()}\")\n",
|
||||
" print(f\"Strength: {regime['strength']:.1f}%\")\n",
|
||||
" print(f\"ADX: {regime['adx']:.1f}\")\n",
|
||||
" print(f\"Volatility Cluster: {regime['vol_cluster']:.2f}\")\n",
|
||||
"else:\n",
|
||||
" print(\"❌ Could not get test data\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Test Enhanced Top-Down Analysis\n",
|
||||
"print(\"🔍 Testing Enhanced Top-Down Analysis...\\n\")\n",
|
||||
"signal_result = extended_top_down_v2(symbol)\n",
|
||||
"\n",
|
||||
"if signal_result:\n",
|
||||
" print(f\"\\n🎯 SIGNAL SUMMARY:\")\n",
|
||||
" print(f\"Entry Signal: {signal_result['entry_signal']}\")\n",
|
||||
" print(f\"Confidence: {signal_result['confidence']}%\")\n",
|
||||
" print(f\"Adaptive Threshold: {signal_result['adaptive_threshold']}%\")\n",
|
||||
" print(f\"Signal Quality: {signal_result['signal_quality'].upper()}\")\n",
|
||||
" print(f\"Market Regime: {signal_result['market_regime']['regime'].upper()}\")\n",
|
||||
" print(f\"Risk-Adjusted Strength: {signal_result['risk_adjusted_strength']:.1f}\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 5. Optimierte Trade-Ausführung"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Konfiguration für optimierten Trading Bot\n",
|
||||
"OPTIMIZED_CONFIG = {\n",
|
||||
" 'symbol': symbol,\n",
|
||||
" 'atr_mult': 1.5,\n",
|
||||
" 'base_confidence': 70, # Wird automatisch angepasst\n",
|
||||
" 'max_risk_per_trade': 0.01,\n",
|
||||
" 'risk_filter': True,\n",
|
||||
" 'min_atr': 0.0010,\n",
|
||||
" 'use_pullback_entry': True,\n",
|
||||
" 'debug': True\n",
|
||||
"}\n",
|
||||
"\n",
|
||||
"print(\"⚙️ Optimized Trading Configuration:\")\n",
|
||||
"for key, value in OPTIMIZED_CONFIG.items():\n",
|
||||
" print(f\" {key}: {value}\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Test der optimierten Trade-Funktion\n",
|
||||
"def test_optimized_trading():\n",
|
||||
" \"\"\"\n",
|
||||
" Testet die optimierte Trading-Logik\n",
|
||||
" \"\"\"\n",
|
||||
" print(\"🚀 Testing Optimized Trade Execution...\\n\")\n",
|
||||
" \n",
|
||||
" try:\n",
|
||||
" result = execute_trade_v2(**OPTIMIZED_CONFIG)\n",
|
||||
" \n",
|
||||
" if result:\n",
|
||||
" print(\"✅ Trade executed successfully!\")\n",
|
||||
" return result\n",
|
||||
" else:\n",
|
||||
" print(\"⏸️ No trade executed (conditions not met)\")\n",
|
||||
" return None\n",
|
||||
" \n",
|
||||
" except Exception as e:\n",
|
||||
" print(f\"❌ Error in trade execution: {e}\")\n",
|
||||
" return None\n",
|
||||
"\n",
|
||||
"# Führe Test aus\n",
|
||||
"test_result = test_optimized_trading()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 6. Performance Monitoring"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Aktuelle Performance analysieren\n",
|
||||
"print(\"📊 Analyzing Recent Performance...\\n\")\n",
|
||||
"analyze_performance(symbol, days_back=7) # Letzte 7 Tage"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 7. Automatisierung mit optimierter Logik"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Wrapper-Funktion für Scheduler\n",
|
||||
"def optimized_trading_job():\n",
|
||||
" \"\"\"\n",
|
||||
" Hauptfunktion für automatisierten Trading mit optimierter Logik\n",
|
||||
" \"\"\"\n",
|
||||
" try:\n",
|
||||
" print(f\"\\n⏰ {pd.Timestamp.now().strftime('%Y-%m-%d %H:%M:%S')} - Running Optimized Trading Check\")\n",
|
||||
" \n",
|
||||
" # Führe optimierte Trade-Analyse aus\n",
|
||||
" result = execute_trade_v2(**OPTIMIZED_CONFIG)\n",
|
||||
" \n",
|
||||
" if result:\n",
|
||||
" print(\"✅ Trade executed with optimized logic!\")\n",
|
||||
" else:\n",
|
||||
" print(\"⏸️ No trade - waiting for better conditions\")\n",
|
||||
" \n",
|
||||
" # Performance-Update alle 6 Stunden\n",
|
||||
" current_hour = pd.Timestamp.now().hour\n",
|
||||
" if current_hour % 6 == 0: # 0, 6, 12, 18 Uhr\n",
|
||||
" analyze_performance(symbol, days_back=1)\n",
|
||||
" \n",
|
||||
" except Exception as e:\n",
|
||||
" print(f\"❌ Error in optimized trading job: {e}\")\n",
|
||||
"\n",
|
||||
"# Test der Job-Funktion\n",
|
||||
"optimized_trading_job()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 8. Scheduler Setup mit optimierter Logik"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from apscheduler.schedulers.background import BackgroundScheduler\n",
|
||||
"\n",
|
||||
"# Scheduler für optimierten Trading Bot\n",
|
||||
"optimized_scheduler = BackgroundScheduler()\n",
|
||||
"\n",
|
||||
"# Hinzufügen des optimierten Trading Jobs\n",
|
||||
"# Läuft alle 5 Minuten während der Handelszeiten\n",
|
||||
"optimized_scheduler.add_job(\n",
|
||||
" optimized_trading_job, \n",
|
||||
" 'cron', \n",
|
||||
" year=\"*\", \n",
|
||||
" month=\"*\", \n",
|
||||
" day_of_week=\"mon,tue,wed,thu,fri\", \n",
|
||||
" hour='0-23', \n",
|
||||
" minute='*/5',\n",
|
||||
" id='optimized_trading'\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"print(\"⚙️ Optimized Scheduler configured:\")\n",
|
||||
"print(\" - Trading checks every 5 minutes\")\n",
|
||||
" print(\" - Monday to Friday, 24 hours\")\n",
|
||||
"print(\" - Enhanced signal logic active\")\n",
|
||||
"print(\" - Performance monitoring included\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Scheduler starten\n",
|
||||
"print(\"🚀 Starting Optimized Trading Bot...\")\n",
|
||||
"optimized_scheduler.start()\n",
|
||||
"print(\"✅ Optimized Trading Bot is now running!\")\n",
|
||||
"print(\"\\n📋 Active Jobs:\")\n",
|
||||
"for job in optimized_scheduler.get_jobs():\n",
|
||||
" print(f\" - {job.id}: {job.next_run_time}\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 9. Monitoring & Control"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Status Check\n",
|
||||
"def check_optimized_bot_status():\n",
|
||||
" \"\"\"\n",
|
||||
" Überprüft den Status des optimierten Trading Bots\n",
|
||||
" \"\"\"\n",
|
||||
" print(\"🔍 Optimized Trading Bot Status:\")\n",
|
||||
" print(f\" MT5 Connection: {'✅' if mt.terminal_info() else '❌'}\")\n",
|
||||
" print(f\" Scheduler Running: {'✅' if optimized_scheduler.running else '❌'}\")\n",
|
||||
" print(f\" Active Jobs: {len(optimized_scheduler.get_jobs())}\")\n",
|
||||
" \n",
|
||||
" # Aktuelle Signal-Info\n",
|
||||
" try:\n",
|
||||
" signal_info = extended_top_down_v2(symbol)\n",
|
||||
" if signal_info:\n",
|
||||
" print(f\" Current Signal: {signal_info['entry_signal']}\")\n",
|
||||
" print(f\" Confidence: {signal_info['confidence']}%\")\n",
|
||||
" print(f\" Market Regime: {signal_info['market_regime']['regime'].upper()}\")\n",
|
||||
" print(f\" Signal Quality: {signal_info['signal_quality'].upper()}\")\n",
|
||||
" except Exception as e:\n",
|
||||
" print(f\" Signal Check: ❌ Error: {e}\")\n",
|
||||
"\n",
|
||||
"check_optimized_bot_status()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 10. Vergleich V1.3 vs V1.4"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def compare_versions():\n",
|
||||
" \"\"\"\n",
|
||||
" Vergleicht die alte und neue Signallogik\n",
|
||||
" \"\"\"\n",
|
||||
" print(\"📊 COMPARISON: V1.3 vs V1.4\")\n",
|
||||
" print(\"=\" * 50)\n",
|
||||
" \n",
|
||||
" comparison_data = [\n",
|
||||
" [\"Feature\", \"V1.3\", \"V1.4\"],\n",
|
||||
" [\"Confidence Threshold\", \"Fixed 80%\", \"Adaptive 60-90%\"],\n",
|
||||
" [\"Market Regime\", \"Not considered\", \"Active detection\"],\n",
|
||||
" [\"Entry Timing\", \"Immediate\", \"Pullback optimization\"],\n",
|
||||
" [\"Signal Strength\", \"Basic confidence\", \"Risk-adjusted strength\"],\n",
|
||||
" [\"Performance Tracking\", \"Manual\", \"Automatic logging\"],\n",
|
||||
" [\"Parameter Adaptation\", \"Static\", \"Dynamic (regime-based)\"],\n",
|
||||
" [\"Risk Management\", \"Basic\", \"Enhanced (regime-aware)\"],\n",
|
||||
" [\"Signal Quality\", \"Binary\", \"Graded (excellent/good/fair)\"],\n",
|
||||
" [\"Trend Strength\", \"Not weighted\", \"Weighted by timeframe\"]\n",
|
||||
" ]\n",
|
||||
" \n",
|
||||
" print(tabulate(comparison_data, headers=\"firstrow\", tablefmt=\"psql\"))\n",
|
||||
" \n",
|
||||
" print(\"\\n🎯 Key Improvements in V1.4:\")\n",
|
||||
" improvements = [\n",
|
||||
" \"🔄 Adapts to market conditions automatically\",\n",
|
||||
" \"⏰ Better entry timing reduces risk\",\n",
|
||||
" \"📊 Comprehensive performance tracking\",\n",
|
||||
" \"🎚️ Dynamic parameter adjustment\",\n",
|
||||
" \"🔍 Enhanced signal quality assessment\",\n",
|
||||
" \"⚖️ Risk-adjusted position sizing\"\n",
|
||||
" ]\n",
|
||||
" \n",
|
||||
" for improvement in improvements:\n",
|
||||
" print(f\" {improvement}\")\n",
|
||||
"\n",
|
||||
"compare_versions()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 11. Control Panel"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Stoppe alle Jobs\n",
|
||||
"optimized_scheduler.remove_all_jobs()\n",
|
||||
"print(\"⏹️ All jobs removed\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Scheduler herunterfahren\n",
|
||||
"optimized_scheduler.shutdown()\n",
|
||||
"print(\"🔴 Optimized Trading Bot stopped\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 📝 Zusammenfassung der Optimierungen\n",
|
||||
"\n",
|
||||
"### ✅ **Implementierte Verbesserungen:**\n",
|
||||
"\n",
|
||||
"1. **🎯 Adaptive Confidence Threshold**\n",
|
||||
" - Automatische Anpassung an Marktregime\n",
|
||||
" - Trending: 60-75% | Ranging: 85% | Volatile: 90%\n",
|
||||
"\n",
|
||||
"2. **📊 Market Regime Detection**\n",
|
||||
" - ADX-basierte Trendstärke-Analyse\n",
|
||||
" - Bollinger Band Width für Ranging-Erkennung\n",
|
||||
" - Volatility Cluster Detection\n",
|
||||
"\n",
|
||||
"3. **⏰ Entry Timing Optimization**\n",
|
||||
" - Pullback zu EMA21 für bessere R/R\n",
|
||||
" - Regime-abhängige Entry-Kriterien\n",
|
||||
"\n",
|
||||
"4. **💪 Risk-Adjusted Signal Strength**\n",
|
||||
" - Kombiniert Confidence × Trend-Stärke × RRR\n",
|
||||
" - Gewichtete Timeframe-Analyse\n",
|
||||
"\n",
|
||||
"5. **📈 Performance Monitoring**\n",
|
||||
" - Automatisches Logging aller Trades\n",
|
||||
" - Regime-basierte Performance-Analyse\n",
|
||||
" - JSON-Export für weitere Analyse\n",
|
||||
"\n",
|
||||
"### 🎚️ **Nächste Schritte:**\n",
|
||||
"1. Teste den Bot im Demo-Modus für 1-2 Wochen\n",
|
||||
"2. Analysiere die Performance-Logs\n",
|
||||
"3. Optimiere Parameter basierend auf Ergebnissen\n",
|
||||
"4. Bei positiven Ergebnissen: Live-Trading aktivieren"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -1,633 +0,0 @@
|
||||
"""
|
||||
TradingBot V1.4 - Optimized Version
|
||||
Verbesserte Entry Signal Logik basierend auf Analyse-Empfehlungen
|
||||
|
||||
Hauptverbesserungen:
|
||||
1. Adaptive Confidence Threshold
|
||||
2. Market Regime Detection
|
||||
3. Entry Timing Optimization
|
||||
4. Risk-Adjusted Signal Strength
|
||||
5. Performance Monitoring
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import MetaTrader5 as mt
|
||||
import pandas_ta as ta
|
||||
from scipy.signal import savgol_filter, find_peaks
|
||||
from sklearn.linear_model import LinearRegression
|
||||
from tabulate import tabulate
|
||||
from datetime import datetime, timedelta
|
||||
import json
|
||||
|
||||
# =============================================================================
|
||||
# 1. MARKET REGIME DETECTION
|
||||
# =============================================================================
|
||||
|
||||
def detect_market_regime(df, lookback=50):
|
||||
"""
|
||||
Erkennt das aktuelle Marktregime (Trending vs. Ranging)
|
||||
|
||||
Returns:
|
||||
- regime: 'trending', 'ranging', 'volatile'
|
||||
- strength: 0-100 (Stärke des Regimes)
|
||||
"""
|
||||
|
||||
# ADX für Trendstärke
|
||||
adx = ta.adx(df['high'], df['low'], df['close'], length=14)['ADX_14'].iloc[-1]
|
||||
|
||||
# Bollinger Band Squeeze für Ranging Markets
|
||||
bb = ta.bbands(df['close'], length=20)
|
||||
bb_width = ((bb['BBU_20_2.0'] - bb['BBL_20_2.0']) / bb['BBM_20_2.0'] * 100).iloc[-lookback:].mean()
|
||||
|
||||
# Price Action Analysis
|
||||
price_range = (df['high'].iloc[-lookback:].max() - df['low'].iloc[-lookback:].min())
|
||||
atr_avg = df['atr'].iloc[-lookback:].mean()
|
||||
range_ratio = price_range / (atr_avg * lookback)
|
||||
|
||||
# Volatility Cluster Detection
|
||||
vol_cluster = df['atr'].iloc[-10:].std() / df['atr'].iloc[-50:].mean()
|
||||
|
||||
# Regime-Bestimmung
|
||||
if adx > 25 and range_ratio > 1.5:
|
||||
regime = 'trending'
|
||||
strength = min(100, adx * 2)
|
||||
elif vol_cluster > 1.5:
|
||||
regime = 'volatile'
|
||||
strength = min(100, vol_cluster * 50)
|
||||
else:
|
||||
regime = 'ranging'
|
||||
strength = max(0, 100 - adx * 2)
|
||||
|
||||
return {
|
||||
'regime': regime,
|
||||
'strength': strength,
|
||||
'adx': adx,
|
||||
'bb_width': bb_width,
|
||||
'range_ratio': range_ratio,
|
||||
'vol_cluster': vol_cluster
|
||||
}
|
||||
|
||||
# =============================================================================
|
||||
# 2. ADAPTIVE CONFIDENCE SYSTEM
|
||||
# =============================================================================
|
||||
|
||||
def calculate_adaptive_confidence_threshold(regime_info, base_confidence=70):
|
||||
"""
|
||||
Berechnet adaptive Confidence-Schwelle basierend auf Marktregime
|
||||
"""
|
||||
regime = regime_info['regime']
|
||||
strength = regime_info['strength']
|
||||
adx = regime_info['adx']
|
||||
|
||||
if regime == 'trending':
|
||||
# In Trending Markets: niedrigere Schwelle bei starken Trends
|
||||
if adx > 30:
|
||||
return max(60, base_confidence - 15)
|
||||
else:
|
||||
return base_confidence - 10
|
||||
|
||||
elif regime == 'ranging':
|
||||
# In Ranging Markets: höhere Schwelle für mehr Selektivität
|
||||
return base_confidence + 15
|
||||
|
||||
elif regime == 'volatile':
|
||||
# In Volatile Markets: deutlich höhere Schwelle
|
||||
return base_confidence + 20
|
||||
|
||||
return base_confidence
|
||||
|
||||
# =============================================================================
|
||||
# 3. ENHANCED TREND ANALYSIS
|
||||
# =============================================================================
|
||||
|
||||
def get_enhanced_trend(timeframe="H4", lookback=150, symbol="XAUUSD"):
|
||||
"""
|
||||
Verbesserte Trend-Analyse mit Regime-Awareness
|
||||
"""
|
||||
from your_existing_functions import get_rates # Import your existing function
|
||||
|
||||
# Timeframe mapping
|
||||
tf_map = {"D1": "d1", "H4": "h4", "H1": "h1", "M30": "m30", "M15": "m15", "M5": "m5"}
|
||||
tf = tf_map.get(timeframe, timeframe.lower())
|
||||
|
||||
try:
|
||||
df = get_rates(tf, lookback)
|
||||
if df is None or len(df) < 50:
|
||||
return None
|
||||
|
||||
# Bestehende Trend-Logik
|
||||
df['close_smooth'] = savgol_filter(df['close'], min(15, len(df)//10), 3)
|
||||
df['atr'] = ta.atr(df['high'], df['low'], df['close'], length=14)
|
||||
|
||||
# Linear Regression
|
||||
X = np.arange(len(df)).reshape(-1, 1)
|
||||
y = df['close_smooth'].values
|
||||
model = LinearRegression().fit(X, y)
|
||||
slope = model.coef_[0]
|
||||
|
||||
# Market Regime Detection
|
||||
regime_info = detect_market_regime(df.iloc[-50:])
|
||||
|
||||
# Adaptive Slope Threshold basierend auf Regime
|
||||
base_threshold = df['atr'].iloc[-1] * 0.0001
|
||||
|
||||
if regime_info['regime'] == 'trending':
|
||||
slope_threshold = base_threshold * 0.7 # Niedrigere Schwelle in Trends
|
||||
elif regime_info['regime'] == 'ranging':
|
||||
slope_threshold = base_threshold * 1.5 # Höhere Schwelle in Ranges
|
||||
else: # volatile
|
||||
slope_threshold = base_threshold * 1.2
|
||||
|
||||
# Trend bestimmen
|
||||
if slope > slope_threshold:
|
||||
trend = "uptrend"
|
||||
elif slope < -slope_threshold:
|
||||
trend = "downtrend"
|
||||
else:
|
||||
trend = "sideways"
|
||||
|
||||
# Enhanced Trend Strength
|
||||
trend_strength = abs(slope) / slope_threshold if slope_threshold > 0 else 0
|
||||
|
||||
return {
|
||||
"trend": trend,
|
||||
"slope": slope,
|
||||
"slope_threshold": slope_threshold,
|
||||
"trend_strength": trend_strength,
|
||||
"atr": df['atr'].iloc[-1],
|
||||
"price": df['close'].iloc[-1],
|
||||
"regime_info": regime_info
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error in get_enhanced_trend: {e}")
|
||||
return None
|
||||
|
||||
# =============================================================================
|
||||
# 4. OPTIMIZED TOP-DOWN ANALYSIS
|
||||
# =============================================================================
|
||||
|
||||
def extended_top_down_v2(symbol="XAUUSD", lookback=150):
|
||||
"""
|
||||
Optimierte Top-Down-Analyse mit adaptiven Parametern
|
||||
"""
|
||||
|
||||
timeframes = ["D1", "H4", "H1", "M30", "M15", "M5"]
|
||||
trend_info = {}
|
||||
|
||||
# 1. Alle Timeframes analysieren
|
||||
for tf in timeframes:
|
||||
trend_info[tf] = get_enhanced_trend(tf, lookback, symbol)
|
||||
if trend_info[tf] is None:
|
||||
print(f"⚠️ Keine Daten für {tf}")
|
||||
return None
|
||||
|
||||
# 2. Market Regime aus H4 bestimmen (repräsentativ)
|
||||
main_regime = trend_info["H4"]["regime_info"]
|
||||
|
||||
# 3. Adaptive Confidence Threshold
|
||||
adaptive_confidence_threshold = calculate_adaptive_confidence_threshold(main_regime)
|
||||
|
||||
# 4. Enhanced Standard-Trend (D1 + H4 mit Gewichtung)
|
||||
d1_trend = trend_info["D1"]["trend"]
|
||||
h4_trend = trend_info["H4"]["trend"]
|
||||
d1_strength = trend_info["D1"]["trend_strength"]
|
||||
h4_strength = trend_info["H4"]["trend_strength"]
|
||||
|
||||
# Gewichteter Standard-Trend
|
||||
if d1_trend == h4_trend and d1_trend != "sideways":
|
||||
standard_trend = d1_trend
|
||||
standard_strength = (d1_strength * 0.6 + h4_strength * 0.4)
|
||||
elif d1_strength > h4_strength * 1.5: # D1 deutlich stärker
|
||||
standard_trend = d1_trend
|
||||
standard_strength = d1_strength * 0.8
|
||||
elif h4_strength > d1_strength * 1.5: # H4 deutlich stärker
|
||||
standard_trend = h4_trend
|
||||
standard_strength = h4_strength * 0.8
|
||||
else:
|
||||
standard_trend = "sideways"
|
||||
standard_strength = 0
|
||||
|
||||
# 5. Enhanced Fast-Trend mit Regime-Awareness
|
||||
fast_timeframes = ["H1", "M30", "M15", "M5"]
|
||||
fast_trends = [trend_info[tf]["trend"] for tf in fast_timeframes]
|
||||
fast_strengths = [trend_info[tf]["trend_strength"] for tf in fast_timeframes]
|
||||
|
||||
# Regime-abhängige Fast-Trend Logik
|
||||
if main_regime['regime'] == 'trending':
|
||||
# In Trends: 2 von 4 TFs reichen
|
||||
required_alignment = 2
|
||||
else:
|
||||
# In Ranging/Volatile: 3 von 4 TFs erforderlich
|
||||
required_alignment = 3
|
||||
|
||||
trend_counts = {'uptrend': 0, 'downtrend': 0, 'sideways': 0}
|
||||
weighted_strengths = {'uptrend': 0, 'downtrend': 0}
|
||||
|
||||
weights = [1.0, 0.8, 0.6, 0.4] # H1, M30, M15, M5
|
||||
|
||||
for i, (trend, strength) in enumerate(zip(fast_trends, fast_strengths)):
|
||||
trend_counts[trend] += 1
|
||||
if trend != 'sideways':
|
||||
weighted_strengths[trend] += strength * weights[i]
|
||||
|
||||
# Fast-Trend bestimmen
|
||||
max_count = max(trend_counts['uptrend'], trend_counts['downtrend'])
|
||||
if max_count >= required_alignment:
|
||||
if trend_counts['uptrend'] > trend_counts['downtrend']:
|
||||
fast_trend = "uptrend"
|
||||
elif trend_counts['downtrend'] > trend_counts['uptrend']:
|
||||
fast_trend = "downtrend"
|
||||
else:
|
||||
# Bei Gleichstand: Stärke entscheidet
|
||||
if weighted_strengths['uptrend'] > weighted_strengths['downtrend']:
|
||||
fast_trend = "uptrend"
|
||||
else:
|
||||
fast_trend = "downtrend"
|
||||
else:
|
||||
fast_trend = "sideways"
|
||||
|
||||
# 6. Top-Down-Trend Bestimmung
|
||||
if standard_trend == fast_trend and standard_trend != "sideways":
|
||||
top_down_trend = standard_trend
|
||||
combined_strength = (standard_strength + weighted_strengths[fast_trend]) / 2
|
||||
else:
|
||||
top_down_trend = "sideways"
|
||||
combined_strength = 0
|
||||
|
||||
# 7. Enhanced Confidence Calculation
|
||||
weights = {"D1": 2.5, "H4": 2.0, "H1": 1.5, "M30": 1.0, "M15": 0.8, "M5": 0.6}
|
||||
|
||||
weighted_matching = sum(
|
||||
weights[tf] * trend_info[tf]["trend_strength"]
|
||||
for tf in timeframes
|
||||
if trend_info[tf]["trend"] == top_down_trend and trend_info[tf]["trend"] != "sideways"
|
||||
)
|
||||
|
||||
weighted_total = sum(
|
||||
weights[tf] * trend_info[tf]["trend_strength"]
|
||||
for tf in timeframes
|
||||
if trend_info[tf]["trend"] != "sideways"
|
||||
)
|
||||
|
||||
confidence = round((weighted_matching / weighted_total) * 100, 2) if weighted_total > 0 else 0.0
|
||||
|
||||
# 8. Risk-Adjusted Signal Strength
|
||||
atr = trend_info["M5"]["atr"]
|
||||
rrr = 2.5 # Risk-Reward Ratio
|
||||
|
||||
risk_adjusted_strength = confidence * combined_strength * min(2.0, rrr)
|
||||
|
||||
# 9. Entry Signal mit adaptiven Kriterien
|
||||
entry_signal = 0
|
||||
signal_quality = "none"
|
||||
|
||||
if (top_down_trend != "sideways" and
|
||||
confidence >= adaptive_confidence_threshold and
|
||||
risk_adjusted_strength >= 100):
|
||||
|
||||
entry_signal = 1 if top_down_trend == "uptrend" else -1
|
||||
|
||||
# Signal Quality Assessment
|
||||
if confidence >= 85 and risk_adjusted_strength >= 150:
|
||||
signal_quality = "excellent"
|
||||
elif confidence >= 75 and risk_adjusted_strength >= 120:
|
||||
signal_quality = "good"
|
||||
else:
|
||||
signal_quality = "fair"
|
||||
|
||||
# 10. Enhanced Debug Output
|
||||
debug_data = []
|
||||
for tf in timeframes:
|
||||
info = trend_info[tf]
|
||||
debug_data.append([
|
||||
tf,
|
||||
info["trend"],
|
||||
f"{info['trend_strength']:.2f}",
|
||||
f"{info['atr']:.4f}",
|
||||
f"{info['slope']:.6f}",
|
||||
f"{info['price']:.2f}"
|
||||
])
|
||||
|
||||
print(f"\n📊 Enhanced Trend-Analyse für {symbol}")
|
||||
print(f"🎯 Market Regime: {main_regime['regime'].upper()} (Strength: {main_regime['strength']:.0f}%)")
|
||||
print(f"🎚️ Adaptive Confidence Threshold: {adaptive_confidence_threshold}%")
|
||||
print()
|
||||
print(tabulate(debug_data, headers=["TF", "Trend", "Strength", "ATR", "Slope", "Price"], tablefmt="psql"))
|
||||
print(f"\n➡️ Standard-Trend: {standard_trend} (Strength: {standard_strength:.2f})")
|
||||
print(f"➡️ Fast-Trend: {fast_trend}")
|
||||
print(f"➡️ Top-Down-Trend: {top_down_trend}")
|
||||
print(f"➡️ Confidence: {confidence}% (Threshold: {adaptive_confidence_threshold}%)")
|
||||
print(f"➡️ Risk-Adjusted Strength: {risk_adjusted_strength:.1f}")
|
||||
print(f"➡️ Signal Quality: {signal_quality.upper()}")
|
||||
|
||||
return {
|
||||
"symbol": symbol,
|
||||
"trend_info": trend_info,
|
||||
"market_regime": main_regime,
|
||||
"standard_trend": standard_trend,
|
||||
"fast_trend": fast_trend,
|
||||
"top_down_trend": top_down_trend,
|
||||
"confidence": confidence,
|
||||
"adaptive_threshold": adaptive_confidence_threshold,
|
||||
"risk_adjusted_strength": risk_adjusted_strength,
|
||||
"entry_signal": entry_signal,
|
||||
"signal_quality": signal_quality,
|
||||
"combined_strength": combined_strength
|
||||
}
|
||||
|
||||
# =============================================================================
|
||||
# 5. ENTRY TIMING OPTIMIZATION
|
||||
# =============================================================================
|
||||
|
||||
def check_pullback_entry(symbol, signal_info, timeframe="M5"):
|
||||
"""
|
||||
Prüft optimale Entry-Timing durch Pullback-Analyse
|
||||
"""
|
||||
if signal_info["entry_signal"] == 0:
|
||||
return False, "No base signal"
|
||||
|
||||
try:
|
||||
from your_existing_functions import get_rates # Import your existing function
|
||||
df = get_rates(timeframe.lower(), 50)
|
||||
|
||||
if df is None or len(df) < 20:
|
||||
return False, "Insufficient data"
|
||||
|
||||
# EMAs für Pullback-Erkennung
|
||||
df['ema21'] = df['close'].ewm(span=21).mean()
|
||||
df['ema50'] = df['close'].ewm(span=50).mean()
|
||||
|
||||
current_price = df['close'].iloc[-1]
|
||||
ema21 = df['ema21'].iloc[-1]
|
||||
ema50 = df['ema50'].iloc[-1]
|
||||
|
||||
signal_direction = signal_info["entry_signal"]
|
||||
|
||||
if signal_direction == 1: # Long Signal
|
||||
# Pullback zu EMA21 oder Support
|
||||
if current_price <= ema21 * 1.002 and ema21 > ema50:
|
||||
return True, "Pullback to EMA21 for Long"
|
||||
elif current_price <= ema21 * 0.998:
|
||||
return True, "Below EMA21 - Good Long Entry"
|
||||
|
||||
elif signal_direction == -1: # Short Signal
|
||||
# Pullback zu EMA21 oder Resistance
|
||||
if current_price >= ema21 * 0.998 and ema21 < ema50:
|
||||
return True, "Pullback to EMA21 for Short"
|
||||
elif current_price >= ema21 * 1.002:
|
||||
return True, "Above EMA21 - Good Short Entry"
|
||||
|
||||
return False, "Waiting for better entry timing"
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error in pullback check: {e}")
|
||||
return True, "Using immediate entry (fallback)"
|
||||
|
||||
# =============================================================================
|
||||
# 6. ENHANCED EXECUTE TRADE FUNCTION
|
||||
# =============================================================================
|
||||
|
||||
def execute_trade_v2(
|
||||
symbol="XAUUSD",
|
||||
atr_mult=1.5,
|
||||
base_confidence=70,
|
||||
max_risk_per_trade=0.01,
|
||||
risk_filter=True,
|
||||
min_atr=0.0010,
|
||||
use_pullback_entry=True,
|
||||
debug=True
|
||||
):
|
||||
"""
|
||||
Optimierte Trade-Ausführung mit allen Verbesserungen
|
||||
"""
|
||||
|
||||
# 1. Enhanced Signal Analysis
|
||||
signal_info = extended_top_down_v2(symbol)
|
||||
if signal_info is None:
|
||||
print("❌ Signal-Analyse fehlgeschlagen")
|
||||
return None
|
||||
|
||||
entry_signal = signal_info["entry_signal"]
|
||||
confidence = signal_info["confidence"]
|
||||
adaptive_threshold = signal_info["adaptive_threshold"]
|
||||
signal_quality = signal_info["signal_quality"]
|
||||
market_regime = signal_info["market_regime"]
|
||||
|
||||
# 2. Get Price/ATR from M5
|
||||
m5_info = signal_info["trend_info"]["M5"]
|
||||
price = m5_info["price"]
|
||||
atr = m5_info["atr"]
|
||||
|
||||
# 3. Enhanced Pre-checks
|
||||
reason = ""
|
||||
|
||||
if confidence < adaptive_threshold:
|
||||
reason = f"Confidence {confidence}% < adaptive threshold {adaptive_threshold}%"
|
||||
elif entry_signal == 0:
|
||||
reason = f"No entry signal (Trend: {signal_info['top_down_trend']}, Regime: {market_regime['regime']})"
|
||||
elif price is None or atr is None:
|
||||
reason = "Price/ATR not available"
|
||||
elif risk_filter and atr < min_atr:
|
||||
reason = f"ATR {atr:.5f} < min_atr {min_atr}"
|
||||
elif signal_quality == "none":
|
||||
reason = "Signal quality insufficient"
|
||||
else:
|
||||
# 4. Entry Timing Check
|
||||
if use_pullback_entry:
|
||||
pullback_ok, pullback_reason = check_pullback_entry(symbol, signal_info)
|
||||
if not pullback_ok:
|
||||
reason = f"Entry timing: {pullback_reason}"
|
||||
|
||||
if not reason:
|
||||
# 5. Enhanced Risk Checks
|
||||
from your_existing_functions import check_risk_limits # Import your existing function
|
||||
risk_ok = check_risk_limits(symbol, max_risk_per_trade=max_risk_per_trade)
|
||||
if not risk_ok:
|
||||
reason = "Risk limits exceeded"
|
||||
|
||||
# 6. Execute Trade if all checks pass
|
||||
if not reason:
|
||||
# Enhanced SL/TP calculation based on regime
|
||||
regime_mult = 1.0
|
||||
if market_regime['regime'] == 'volatile':
|
||||
regime_mult = 1.3 # Wider stops in volatile markets
|
||||
elif market_regime['regime'] == 'ranging':
|
||||
regime_mult = 0.8 # Tighter stops in ranging markets
|
||||
|
||||
adjusted_atr_mult = atr_mult * regime_mult
|
||||
|
||||
if entry_signal == 1: # Long
|
||||
stop_loss = price - adjusted_atr_mult * atr
|
||||
take_profit = price + adjusted_atr_mult * atr * 2.5 # Better RRR
|
||||
else: # Short
|
||||
stop_loss = price + adjusted_atr_mult * atr
|
||||
take_profit = price - adjusted_atr_mult * atr * 2.5
|
||||
|
||||
# Dynamic Position Sizing
|
||||
stop_distance = adjusted_atr_mult * atr
|
||||
risk_amount = max_risk_per_trade * 10000 # Assuming account balance
|
||||
volume = min(0.1, risk_amount / (stop_distance * 100000)) # Simplified calculation
|
||||
|
||||
# Log Enhanced Trade Info
|
||||
print(f"\n🚀 ENHANCED TRADE EXECUTION")
|
||||
print(f"Symbol: {symbol}")
|
||||
print(f"Direction: {'LONG' if entry_signal == 1 else 'SHORT'}")
|
||||
print(f"Price: {price:.5f}")
|
||||
print(f"Volume: {volume:.2f}")
|
||||
print(f"Stop Loss: {stop_loss:.5f}")
|
||||
print(f"Take Profit: {take_profit:.5f}")
|
||||
print(f"Confidence: {confidence}% (Threshold: {adaptive_threshold}%)")
|
||||
print(f"Signal Quality: {signal_quality.upper()}")
|
||||
print(f"Market Regime: {market_regime['regime'].upper()}")
|
||||
print(f"ATR Multiplier: {adjusted_atr_mult:.2f} (Base: {atr_mult})")
|
||||
print(f"Risk per Trade: {max_risk_per_trade*100:.1f}%")
|
||||
|
||||
# Execute the actual trade
|
||||
try:
|
||||
from your_existing_functions import market_order # Import your existing function
|
||||
order_result = market_order(
|
||||
symbol=symbol,
|
||||
volume=volume,
|
||||
order_type="buy" if entry_signal == 1 else "sell",
|
||||
stoploss=stop_loss,
|
||||
take_profit=take_profit
|
||||
)
|
||||
|
||||
# Log Trade to Performance Monitor
|
||||
log_trade_performance(signal_info, order_result)
|
||||
|
||||
return order_result
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Trade execution failed: {e}")
|
||||
return None
|
||||
|
||||
else:
|
||||
if debug:
|
||||
print(f"\n⏸️ TRADE SKIPPED: {reason}")
|
||||
print(f"Confidence: {confidence}% | Threshold: {adaptive_threshold}%")
|
||||
print(f"Signal Quality: {signal_quality} | Regime: {market_regime['regime']}")
|
||||
return None
|
||||
|
||||
# =============================================================================
|
||||
# 7. PERFORMANCE MONITORING
|
||||
# =============================================================================
|
||||
|
||||
def log_trade_performance(signal_info, order_result):
|
||||
"""
|
||||
Loggt Trade-Performance für Analyse und Optimierung
|
||||
"""
|
||||
trade_data = {
|
||||
'timestamp': datetime.now().isoformat(),
|
||||
'symbol': signal_info['symbol'],
|
||||
'entry_signal': signal_info['entry_signal'],
|
||||
'confidence': signal_info['confidence'],
|
||||
'adaptive_threshold': signal_info['adaptive_threshold'],
|
||||
'signal_quality': signal_info['signal_quality'],
|
||||
'market_regime': signal_info['market_regime']['regime'],
|
||||
'regime_strength': signal_info['market_regime']['strength'],
|
||||
'risk_adjusted_strength': signal_info['risk_adjusted_strength'],
|
||||
'order_result': str(order_result) if order_result else None
|
||||
}
|
||||
|
||||
# Save to JSON file for analysis
|
||||
try:
|
||||
filename = f"trade_performance_{signal_info['symbol']}_{datetime.now().strftime('%Y%m')}.json"
|
||||
|
||||
try:
|
||||
with open(filename, 'r') as f:
|
||||
data = json.load(f)
|
||||
except FileNotFoundError:
|
||||
data = []
|
||||
|
||||
data.append(trade_data)
|
||||
|
||||
with open(filename, 'w') as f:
|
||||
json.dump(data, f, indent=2)
|
||||
|
||||
except Exception as e:
|
||||
print(f"Warning: Could not log performance data: {e}")
|
||||
|
||||
def analyze_performance(symbol="XAUUSD", days_back=30):
|
||||
"""
|
||||
Analysiert Performance der letzten Trades
|
||||
"""
|
||||
try:
|
||||
filename = f"trade_performance_{symbol}_{datetime.now().strftime('%Y%m')}.json"
|
||||
|
||||
with open(filename, 'r') as f:
|
||||
data = json.load(f)
|
||||
|
||||
# Filter last X days
|
||||
cutoff = datetime.now() - timedelta(days=days_back)
|
||||
recent_trades = [
|
||||
trade for trade in data
|
||||
if datetime.fromisoformat(trade['timestamp']) > cutoff
|
||||
]
|
||||
|
||||
if not recent_trades:
|
||||
print(f"No trades found in last {days_back} days")
|
||||
return
|
||||
|
||||
# Analysis
|
||||
total_trades = len(recent_trades)
|
||||
by_regime = {}
|
||||
by_confidence = {'high': 0, 'medium': 0, 'low': 0}
|
||||
by_quality = {}
|
||||
|
||||
for trade in recent_trades:
|
||||
# By regime
|
||||
regime = trade['market_regime']
|
||||
by_regime[regime] = by_regime.get(regime, 0) + 1
|
||||
|
||||
# By confidence
|
||||
conf = trade['confidence']
|
||||
if conf >= 85:
|
||||
by_confidence['high'] += 1
|
||||
elif conf >= 75:
|
||||
by_confidence['medium'] += 1
|
||||
else:
|
||||
by_confidence['low'] += 1
|
||||
|
||||
# By quality
|
||||
quality = trade['signal_quality']
|
||||
by_quality[quality] = by_quality.get(quality, 0) + 1
|
||||
|
||||
print(f"\n📊 PERFORMANCE ANALYSIS - Last {days_back} days")
|
||||
print(f"Total Trades: {total_trades}")
|
||||
print(f"\nBy Market Regime:")
|
||||
for regime, count in by_regime.items():
|
||||
print(f" {regime.upper()}: {count} ({count/total_trades*100:.1f}%)")
|
||||
|
||||
print(f"\nBy Confidence Level:")
|
||||
for level, count in by_confidence.items():
|
||||
print(f" {level.upper()}: {count} ({count/total_trades*100:.1f}%)")
|
||||
|
||||
print(f"\nBy Signal Quality:")
|
||||
for quality, count in by_quality.items():
|
||||
print(f" {quality.upper()}: {count} ({count/total_trades*100:.1f}%)")
|
||||
|
||||
except Exception as e:
|
||||
print(f"Could not analyze performance: {e}")
|
||||
|
||||
# =============================================================================
|
||||
# 8. QUICK SETUP FUNCTION
|
||||
# =============================================================================
|
||||
|
||||
def setup_optimized_trading():
|
||||
"""
|
||||
Quick setup für optimiertes Trading
|
||||
"""
|
||||
print("🚀 Setting up Optimized Trading Bot V1.4")
|
||||
print("\nKey Improvements:")
|
||||
print("✅ Adaptive confidence thresholds")
|
||||
print("✅ Market regime detection")
|
||||
print("✅ Enhanced entry timing")
|
||||
print("✅ Risk-adjusted signal strength")
|
||||
print("✅ Performance monitoring")
|
||||
print("\nReady to trade with enhanced logic!")
|
||||
|
||||
if __name__ == "__main__":
|
||||
setup_optimized_trading()
|
||||
File diff suppressed because it is too large.
Load diff
@@ -1,394 +0,0 @@
|
||||
"""
|
||||
TradingBot V1.4 - Relaxed Version
|
||||
Weniger restriktive Parameter für mehr Trading-Signale
|
||||
|
||||
Hauptänderungen:
|
||||
- Niedrigere Confidence-Schwellen
|
||||
- Weniger strenge Signal-Quality-Filter
|
||||
- Relaxed Risk-Adjusted Strength
|
||||
"""
|
||||
|
||||
# Import der Fixed Version als Basis
|
||||
from TradingBot_V1.4_Fixed import *
|
||||
|
||||
def calculate_adaptive_confidence_threshold_relaxed(regime_info, base_confidence=60):
|
||||
"""
|
||||
Relaxed Version: Niedrigere Schwellen für mehr Signale
|
||||
"""
|
||||
regime = regime_info['regime']
|
||||
strength = regime_info['strength']
|
||||
adx = regime_info['adx']
|
||||
|
||||
if regime == 'trending':
|
||||
# In Trending Markets: noch niedrigere Schwelle
|
||||
if adx > 30:
|
||||
return max(50, base_confidence - 20) # War -15
|
||||
else:
|
||||
return base_confidence - 15 # War -10
|
||||
|
||||
elif regime == 'ranging':
|
||||
# In Ranging Markets: weniger streng
|
||||
return base_confidence + 10 # War +15
|
||||
|
||||
elif regime == 'volatile':
|
||||
# In Volatile Markets: weniger restriktiv
|
||||
return base_confidence + 15 # War +20
|
||||
|
||||
return base_confidence
|
||||
|
||||
def extended_top_down_v2_relaxed(symbol="XAUUSD", lookback=150):
|
||||
"""
|
||||
Relaxed Version der Top-Down-Analyse für mehr Trading-Signale
|
||||
"""
|
||||
|
||||
timeframes = ["D1", "H4", "H1", "M30", "M15", "M5"]
|
||||
trend_info = {}
|
||||
|
||||
print(f"🔍 Analyzing {symbol} with RELAXED parameters...")
|
||||
|
||||
# 1. Alle Timeframes analysieren
|
||||
for tf in timeframes:
|
||||
trend_info[tf] = get_enhanced_trend(tf, lookback, symbol)
|
||||
if trend_info[tf] is None:
|
||||
print(f"⚠️ Keine Daten für {tf}")
|
||||
return None
|
||||
|
||||
# 2. Market Regime aus H4 bestimmen
|
||||
main_regime = trend_info["H4"]["regime_info"]
|
||||
|
||||
# 3. RELAXED Adaptive Confidence Threshold
|
||||
adaptive_confidence_threshold = calculate_adaptive_confidence_threshold_relaxed(main_regime)
|
||||
|
||||
# 4. Standard-Trend (gleich wie vorher)
|
||||
d1_trend = trend_info["D1"]["trend"]
|
||||
h4_trend = trend_info["H4"]["trend"]
|
||||
d1_strength = trend_info["D1"]["trend_strength"]
|
||||
h4_strength = trend_info["H4"]["trend_strength"]
|
||||
|
||||
if d1_trend == h4_trend and d1_trend != "sideways":
|
||||
standard_trend = d1_trend
|
||||
standard_strength = (d1_strength * 0.6 + h4_strength * 0.4)
|
||||
elif d1_strength > h4_strength * 1.5:
|
||||
standard_trend = d1_trend
|
||||
standard_strength = d1_strength * 0.8
|
||||
elif h4_strength > d1_strength * 1.5:
|
||||
standard_trend = h4_trend
|
||||
standard_strength = h4_strength * 0.8
|
||||
else:
|
||||
standard_trend = "sideways"
|
||||
standard_strength = 0
|
||||
|
||||
# 5. RELAXED Fast-Trend (weniger TFs erforderlich)
|
||||
fast_timeframes = ["H1", "M30", "M15", "M5"]
|
||||
fast_trends = [trend_info[tf]["trend"] for tf in fast_timeframes]
|
||||
fast_strengths = [trend_info[tf]["trend_strength"] for tf in fast_timeframes]
|
||||
|
||||
# RELAXED: Immer nur 2 von 4 TFs erforderlich (statt regime-abhängig)
|
||||
required_alignment = 2
|
||||
|
||||
trend_counts = {'uptrend': 0, 'downtrend': 0, 'sideways': 0}
|
||||
weighted_strengths = {'uptrend': 0, 'downtrend': 0}
|
||||
|
||||
weights = [1.0, 0.8, 0.6, 0.4]
|
||||
|
||||
for i, (trend, strength) in enumerate(zip(fast_trends, fast_strengths)):
|
||||
trend_counts[trend] += 1
|
||||
if trend != 'sideways':
|
||||
weighted_strengths[trend] += strength * weights[i]
|
||||
|
||||
max_count = max(trend_counts['uptrend'], trend_counts['downtrend'])
|
||||
if max_count >= required_alignment:
|
||||
if trend_counts['uptrend'] > trend_counts['downtrend']:
|
||||
fast_trend = "uptrend"
|
||||
elif trend_counts['downtrend'] > trend_counts['uptrend']:
|
||||
fast_trend = "downtrend"
|
||||
else:
|
||||
if weighted_strengths['uptrend'] > weighted_strengths['downtrend']:
|
||||
fast_trend = "uptrend"
|
||||
else:
|
||||
fast_trend = "downtrend"
|
||||
else:
|
||||
fast_trend = "sideways"
|
||||
|
||||
# 6. Top-Down-Trend Bestimmung
|
||||
if standard_trend == fast_trend and standard_trend != "sideways":
|
||||
top_down_trend = standard_trend
|
||||
combined_strength = (standard_strength + weighted_strengths.get(fast_trend, 0)) / 2
|
||||
else:
|
||||
top_down_trend = "sideways"
|
||||
combined_strength = 0
|
||||
|
||||
# 7. Enhanced Confidence Calculation (gleich)
|
||||
weights = {"D1": 2.5, "H4": 2.0, "H1": 1.5, "M30": 1.0, "M15": 0.8, "M5": 0.6}
|
||||
|
||||
weighted_matching = sum(
|
||||
weights[tf] * trend_info[tf]["trend_strength"]
|
||||
for tf in timeframes
|
||||
if trend_info[tf]["trend"] == top_down_trend and trend_info[tf]["trend"] != "sideways"
|
||||
)
|
||||
|
||||
weighted_total = sum(
|
||||
weights[tf] * trend_info[tf]["trend_strength"]
|
||||
for tf in timeframes
|
||||
if trend_info[tf]["trend"] != "sideways"
|
||||
)
|
||||
|
||||
confidence = round((weighted_matching / weighted_total) * 100, 2) if weighted_total > 0 else 0.0
|
||||
|
||||
# 8. RELAXED Risk-Adjusted Signal Strength
|
||||
atr = trend_info["M5"]["atr"]
|
||||
rrr = 2.5
|
||||
|
||||
risk_adjusted_strength = confidence * combined_strength * min(2.0, rrr)
|
||||
|
||||
# 9. RELAXED Entry Signal (niedrigere Schwelle)
|
||||
entry_signal = 0
|
||||
signal_quality = "none"
|
||||
|
||||
# RELAXED: Niedrigere Schwelle für risk_adjusted_strength
|
||||
min_strength = 80 # War 100
|
||||
|
||||
if (top_down_trend != "sideways" and
|
||||
confidence >= adaptive_confidence_threshold and
|
||||
risk_adjusted_strength >= min_strength):
|
||||
|
||||
entry_signal = 1 if top_down_trend == "uptrend" else -1
|
||||
|
||||
# RELAXED Signal Quality (niedrigere Schwellen)
|
||||
if confidence >= 80 and risk_adjusted_strength >= 130: # War 85/150
|
||||
signal_quality = "excellent"
|
||||
elif confidence >= 70 and risk_adjusted_strength >= 100: # War 75/120
|
||||
signal_quality = "good"
|
||||
else:
|
||||
signal_quality = "fair"
|
||||
|
||||
# 10. Debug Output
|
||||
debug_data = []
|
||||
for tf in timeframes:
|
||||
info = trend_info[tf]
|
||||
debug_data.append([
|
||||
tf,
|
||||
info["trend"],
|
||||
f"{info['trend_strength']:.2f}",
|
||||
f"{info['atr']:.4f}",
|
||||
f"{info['slope']:.6f}",
|
||||
f"{info['price']:.2f}"
|
||||
])
|
||||
|
||||
print(f"\n📊 RELAXED Trend-Analyse für {symbol}")
|
||||
print(f"🎯 Market Regime: {main_regime['regime'].upper()} (Strength: {main_regime['strength']:.0f}%)")
|
||||
print(f"🎚️ RELAXED Adaptive Threshold: {adaptive_confidence_threshold}% (vs standard V1.4)")
|
||||
print()
|
||||
print(tabulate(debug_data, headers=["TF", "Trend", "Strength", "ATR", "Slope", "Price"], tablefmt="psql"))
|
||||
print(f"\n➡️ Standard-Trend: {standard_trend} (Strength: {standard_strength:.2f})")
|
||||
print(f"➡️ Fast-Trend: {fast_trend}")
|
||||
print(f"➡️ Top-Down-Trend: {top_down_trend}")
|
||||
print(f"➡️ Confidence: {confidence}% (RELAXED Threshold: {adaptive_confidence_threshold}%)")
|
||||
print(f"➡️ Risk-Adjusted Strength: {risk_adjusted_strength:.1f} (Min: {min_strength})")
|
||||
print(f"➡️ Signal Quality: {signal_quality.upper()}")
|
||||
|
||||
# Vergleich mit Standard V1.4
|
||||
standard_threshold = calculate_adaptive_confidence_threshold(main_regime)
|
||||
print(f"\n🔄 RELAXED vs STANDARD V1.4:")
|
||||
print(f" Standard V1.4 threshold: {standard_threshold}%")
|
||||
print(f" Relaxed V1.4 threshold: {adaptive_confidence_threshold}%")
|
||||
print(f" Difference: {adaptive_confidence_threshold - standard_threshold:+d}% (more lenient)")
|
||||
|
||||
return {
|
||||
"symbol": symbol,
|
||||
"trend_info": trend_info,
|
||||
"market_regime": main_regime,
|
||||
"standard_trend": standard_trend,
|
||||
"fast_trend": fast_trend,
|
||||
"top_down_trend": top_down_trend,
|
||||
"confidence": confidence,
|
||||
"adaptive_threshold": adaptive_confidence_threshold,
|
||||
"risk_adjusted_strength": risk_adjusted_strength,
|
||||
"entry_signal": entry_signal,
|
||||
"signal_quality": signal_quality,
|
||||
"combined_strength": combined_strength,
|
||||
"min_strength_used": min_strength
|
||||
}
|
||||
|
||||
def execute_trade_v2_relaxed(
|
||||
symbol="XAUUSD",
|
||||
atr_mult=1.5,
|
||||
base_confidence=60, # Niedriger als 70
|
||||
max_risk_per_trade=0.01,
|
||||
risk_filter=True,
|
||||
min_atr=0.0008, # Niedriger als 0.0010
|
||||
use_pullback_entry=False, # DISABLED für mehr Signale
|
||||
debug=True
|
||||
):
|
||||
"""
|
||||
RELAXED Trade-Ausführung mit weniger restriktiven Parametern
|
||||
"""
|
||||
|
||||
# 1. RELAXED Signal Analysis
|
||||
signal_info = extended_top_down_v2_relaxed(symbol)
|
||||
if signal_info is None:
|
||||
print("❌ Signal-Analyse fehlgeschlagen")
|
||||
return None
|
||||
|
||||
entry_signal = signal_info["entry_signal"]
|
||||
confidence = signal_info["confidence"]
|
||||
adaptive_threshold = signal_info["adaptive_threshold"]
|
||||
signal_quality = signal_info["signal_quality"]
|
||||
market_regime = signal_info["market_regime"]
|
||||
|
||||
# 2. Get Price/ATR from M5
|
||||
m5_info = signal_info["trend_info"]["M5"]
|
||||
price = m5_info["price"]
|
||||
atr = m5_info["atr"]
|
||||
|
||||
# 3. RELAXED Pre-checks
|
||||
reason = ""
|
||||
|
||||
if confidence < adaptive_threshold:
|
||||
reason = f"Confidence {confidence}% < relaxed threshold {adaptive_threshold}%"
|
||||
elif entry_signal == 0:
|
||||
reason = f"No entry signal (Trend: {signal_info['top_down_trend']}, Regime: {market_regime['regime']})"
|
||||
elif price is None or atr is None:
|
||||
reason = "Price/ATR not available"
|
||||
elif risk_filter and atr < min_atr:
|
||||
reason = f"ATR {atr:.5f} < min_atr {min_atr}"
|
||||
else:
|
||||
# 4. SKIP Entry Timing Check (use_pullback_entry=False)
|
||||
|
||||
# 5. RELAXED Risk Checks
|
||||
risk_ok = check_risk_limits(symbol, max_risk_per_trade=max_risk_per_trade)
|
||||
if not risk_ok:
|
||||
reason = "Risk limits exceeded"
|
||||
|
||||
# 6. Execute Trade if all checks pass
|
||||
if not reason:
|
||||
# Standard SL/TP calculation
|
||||
regime_mult = 1.0
|
||||
if market_regime['regime'] == 'volatile':
|
||||
regime_mult = 1.2 # Weniger drastisch als 1.3
|
||||
elif market_regime['regime'] == 'ranging':
|
||||
regime_mult = 0.9 # Weniger drastisch als 0.8
|
||||
|
||||
adjusted_atr_mult = atr_mult * regime_mult
|
||||
|
||||
if entry_signal == 1: # Long
|
||||
stop_loss = price - adjusted_atr_mult * atr
|
||||
take_profit = price + adjusted_atr_mult * atr * 2.5
|
||||
else: # Short
|
||||
stop_loss = price + adjusted_atr_mult * atr
|
||||
take_profit = price - adjusted_atr_mult * atr * 2.5
|
||||
|
||||
# Position Sizing
|
||||
account_info = mt.account_info()
|
||||
if account_info:
|
||||
balance = account_info.balance
|
||||
risk_amount = balance * max_risk_per_trade
|
||||
if symbol == "XAUUSD":
|
||||
volume = min(0.1, max(0.01, risk_amount / (adjusted_atr_mult * atr * 100)))
|
||||
else:
|
||||
volume = 0.01
|
||||
else:
|
||||
volume = 0.01
|
||||
|
||||
# Log Enhanced Trade Info
|
||||
print(f"\n🚀 RELAXED TRADE EXECUTION")
|
||||
print(f"Symbol: {symbol}")
|
||||
print(f"Direction: {'LONG' if entry_signal == 1 else 'SHORT'}")
|
||||
print(f"Price: {price:.5f}")
|
||||
print(f"Volume: {volume:.2f}")
|
||||
print(f"Stop Loss: {stop_loss:.5f}")
|
||||
print(f"Take Profit: {take_profit:.5f}")
|
||||
print(f"Confidence: {confidence}% (RELAXED Threshold: {adaptive_threshold}%)")
|
||||
print(f"Signal Quality: {signal_quality.upper()}")
|
||||
print(f"Market Regime: {market_regime['regime'].upper()}")
|
||||
print(f"Pullback Entry: DISABLED (immediate entry)")
|
||||
print(f"Risk per Trade: {max_risk_per_trade*100:.1f}%")
|
||||
|
||||
# Execute trade
|
||||
try:
|
||||
order_result = market_order(
|
||||
symbol=symbol,
|
||||
volume=volume,
|
||||
order_type="buy" if entry_signal == 1 else "sell",
|
||||
stoploss=stop_loss,
|
||||
take_profit=take_profit
|
||||
)
|
||||
|
||||
log_trade_performance(signal_info, order_result)
|
||||
return order_result
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Trade execution failed: {e}")
|
||||
return None
|
||||
|
||||
else:
|
||||
if debug:
|
||||
print(f"\n⏸️ RELAXED TRADE SKIPPED: {reason}")
|
||||
print(f"Confidence: {confidence}% | Relaxed Threshold: {adaptive_threshold}%")
|
||||
print(f"Signal Quality: {signal_quality} | Regime: {market_regime['regime']}")
|
||||
return None
|
||||
|
||||
def compare_all_versions(symbol="XAUUSD"):
|
||||
"""
|
||||
Vergleicht alle Versionen: V1.3, V1.4 Standard, V1.4 Relaxed
|
||||
"""
|
||||
print("🔄 COMPARING ALL VERSIONS")
|
||||
print("=" * 40)
|
||||
|
||||
try:
|
||||
# V1.4 Standard
|
||||
signal_std = extended_top_down_v2(symbol, lookback=100)
|
||||
|
||||
# V1.4 Relaxed
|
||||
signal_rel = extended_top_down_v2_relaxed(symbol, lookback=100)
|
||||
|
||||
if signal_std and signal_rel:
|
||||
comparison_data = [
|
||||
["Version", "Confidence", "Threshold", "Signal", "Quality", "Would Trade"],
|
||||
["V1.3 (simulated)", f"{signal_std['confidence']:.1f}%", "80%",
|
||||
"1" if signal_std['confidence'] >= 80 and signal_std['top_down_trend'] != 'sideways' else "0",
|
||||
"N/A", "✅" if signal_std['confidence'] >= 80 and signal_std['top_down_trend'] != 'sideways' else "❌"],
|
||||
["V1.4 Standard", f"{signal_std['confidence']:.1f}%", f"{signal_std['adaptive_threshold']}%",
|
||||
str(signal_std['entry_signal']), signal_std['signal_quality'],
|
||||
"✅" if signal_std['entry_signal'] != 0 else "❌"],
|
||||
["V1.4 Relaxed", f"{signal_rel['confidence']:.1f}%", f"{signal_rel['adaptive_threshold']}%",
|
||||
str(signal_rel['entry_signal']), signal_rel['signal_quality'],
|
||||
"✅" if signal_rel['entry_signal'] != 0 else "❌"]
|
||||
]
|
||||
|
||||
print(tabulate(comparison_data, headers="firstrow", tablefmt="psql"))
|
||||
|
||||
# Empfehlung
|
||||
trading_count = sum([
|
||||
1 if signal_std['confidence'] >= 80 and signal_std['top_down_trend'] != 'sideways' else 0,
|
||||
1 if signal_std['entry_signal'] != 0 else 0,
|
||||
1 if signal_rel['entry_signal'] != 0 else 0
|
||||
])
|
||||
|
||||
print(f"\n📊 TRADING SIGNALS: {trading_count}/3 versions would trade")
|
||||
|
||||
if trading_count == 0:
|
||||
print("🛑 NO VERSION would trade - market conditions not suitable")
|
||||
elif trading_count == 1:
|
||||
print("⚡ Only one version trades - high selectivity")
|
||||
elif trading_count == 2:
|
||||
print("🎯 Two versions agree - moderate confidence")
|
||||
else:
|
||||
print("🚀 All versions agree - strong signal!")
|
||||
|
||||
return signal_rel
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error in comparison: {e}")
|
||||
return None
|
||||
|
||||
if __name__ == "__main__":
|
||||
print("🚀 TradingBot V1.4 - RELAXED Version Ready!")
|
||||
print("Less restrictive parameters for more trading signals")
|
||||
print("\nKey changes:")
|
||||
print("• Lower confidence thresholds")
|
||||
print("• Disabled pullback entry timing")
|
||||
print("• Lower risk-adjusted strength requirement")
|
||||
print("• More lenient signal quality assessment")
|
||||
print("\nUse: extended_top_down_v2_relaxed() for analysis")
|
||||
print("Use: execute_trade_v2_relaxed() for trading")
|
||||
File diff suppressed because it is too large.
Load diff
-2060
File diff suppressed because it is too large.
Load diff
@@ -1,498 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# 🚨 Trading Bot Fix - Warum handelt er nicht mehr?\n",
|
||||
"\n",
|
||||
"## Systematische Diagnose und Lösungen"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Imports\n",
|
||||
"import pandas as pd\n",
|
||||
"import numpy as np\n",
|
||||
"import MetaTrader5 as mt\n",
|
||||
"import pandas_ta as ta\n",
|
||||
"from scipy.signal import savgol_filter\n",
|
||||
"from sklearn.linear_model import LinearRegression\n",
|
||||
"from tabulate import tabulate\n",
|
||||
"from datetime import datetime\n",
|
||||
"\n",
|
||||
"# Deine bestehenden Funktionen\n",
|
||||
"try:\n",
|
||||
" from TradingBot_V1.4_Fixed import extended_top_down_v2, get_rates, detect_market_regime\n",
|
||||
" print(\"✅ V1.4 functions imported successfully\")\n",
|
||||
"except ImportError as e:\n",
|
||||
" print(f\"❌ Import error: {e}\")\n",
|
||||
" print(\"Continuing with basic diagnosis...\")\n",
|
||||
"\n",
|
||||
"symbol = \"XAUUSD\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 1. 🔍 Grundlegende Diagnose"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"🔍 TRADING BOT DIAGNOSIS\")\n",
|
||||
"print(\"=\" * 50)\n",
|
||||
"\n",
|
||||
"issues_found = []\n",
|
||||
"\n",
|
||||
"# MT5 Connection Check\n",
|
||||
"print(\"\\n1️⃣ MT5 Connection Check:\")\n",
|
||||
"terminal_info = mt.terminal_info()\n",
|
||||
"if terminal_info:\n",
|
||||
" print(\" ✅ MT5 connected\")\n",
|
||||
" print(f\" Company: {terminal_info.company}\")\n",
|
||||
" print(f\" Connected: {terminal_info.connected}\")\n",
|
||||
" print(f\" Trade allowed: {terminal_info.trade_allowed}\")\n",
|
||||
" if not terminal_info.trade_allowed:\n",
|
||||
" issues_found.append(\"Trading not allowed in terminal\")\n",
|
||||
"else:\n",
|
||||
" print(\" ❌ MT5 not connected\")\n",
|
||||
" issues_found.append(\"MT5 connection failed\")\n",
|
||||
"\n",
|
||||
"# Account Info Check\n",
|
||||
"print(\"\\n2️⃣ Account Info Check:\")\n",
|
||||
"account_info = mt.account_info()\n",
|
||||
"if account_info:\n",
|
||||
" print(f\" ✅ Account: {account_info.login}\")\n",
|
||||
" print(f\" Balance: {account_info.balance}\")\n",
|
||||
" print(f\" Equity: {account_info.equity}\")\n",
|
||||
" print(f\" Trade allowed: {account_info.trade_allowed}\")\n",
|
||||
" if not account_info.trade_allowed:\n",
|
||||
" issues_found.append(\"Trading not allowed on account\")\n",
|
||||
"else:\n",
|
||||
" print(\" ❌ Cannot get account info\")\n",
|
||||
" issues_found.append(\"Account info unavailable\")\n",
|
||||
"\n",
|
||||
"# Symbol Info Check\n",
|
||||
"print(f\"\\n3️⃣ Symbol Info Check ({symbol}):\")\n",
|
||||
"symbol_info = mt.symbol_info(symbol)\n",
|
||||
"if symbol_info:\n",
|
||||
" print(f\" ✅ Symbol exists: {symbol_info.name}\")\n",
|
||||
" print(f\" Trade mode: {symbol_info.trade_mode}\")\n",
|
||||
" print(f\" Min volume: {symbol_info.volume_min}\")\n",
|
||||
" if symbol_info.trade_mode == 0:\n",
|
||||
" issues_found.append(f\"Trading disabled for {symbol}\")\n",
|
||||
"else:\n",
|
||||
" print(f\" ❌ Symbol {symbol} not found\")\n",
|
||||
" issues_found.append(f\"Symbol {symbol} not available\")\n",
|
||||
"\n",
|
||||
"# Current Price Check\n",
|
||||
"print(\"\\n4️⃣ Price Data Check:\")\n",
|
||||
"tick = mt.symbol_info_tick(symbol)\n",
|
||||
"if tick:\n",
|
||||
" print(f\" ✅ Current price: Bid={tick.bid}, Ask={tick.ask}\")\n",
|
||||
" spread = tick.ask - tick.bid\n",
|
||||
" print(f\" Spread: {spread:.5f}\")\n",
|
||||
" if spread > 0.01:\n",
|
||||
" issues_found.append(f\"High spread: {spread:.5f}\")\n",
|
||||
"else:\n",
|
||||
" print(\" ❌ No current price data\")\n",
|
||||
" issues_found.append(\"No price data available\")\n",
|
||||
"\n",
|
||||
"print(f\"\\n📋 Issues Found: {len(issues_found)}\")\n",
|
||||
"for i, issue in enumerate(issues_found, 1):\n",
|
||||
" print(f\" {i}. {issue}\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 2. 📊 Signal-Analyse: Warum kein Trade?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Teste deine optimierte V1.4 Logik\n",
|
||||
"print(\"🔍 SIGNAL ANALYSIS - Why no trading?\")\n",
|
||||
"print(\"=\" * 50)\n",
|
||||
"\n",
|
||||
"try:\n",
|
||||
" # Hole Signal-Info\n",
|
||||
" signal_info = extended_top_down_v2(symbol, lookback=100)\n",
|
||||
" \n",
|
||||
" if signal_info:\n",
|
||||
" print(f\"\\n📊 CURRENT SIGNAL STATUS:\")\n",
|
||||
" print(f\"Entry Signal: {signal_info['entry_signal']}\")\n",
|
||||
" print(f\"Confidence: {signal_info['confidence']:.1f}%\")\n",
|
||||
" print(f\"Adaptive Threshold: {signal_info['adaptive_threshold']:.1f}%\")\n",
|
||||
" print(f\"Signal Quality: {signal_info['signal_quality'].upper()}\")\n",
|
||||
" print(f\"Market Regime: {signal_info['market_regime']['regime'].upper()}\")\n",
|
||||
" print(f\"Top-Down Trend: {signal_info['top_down_trend'].upper()}\")\n",
|
||||
" print(f\"Risk-Adjusted Strength: {signal_info['risk_adjusted_strength']:.1f}\")\n",
|
||||
" \n",
|
||||
" # Analyse warum kein Signal\n",
|
||||
" print(f\"\\n🔍 WHY NO TRADING SIGNAL:\")\n",
|
||||
" \n",
|
||||
" reasons = []\n",
|
||||
" \n",
|
||||
" if signal_info['top_down_trend'] == 'sideways':\n",
|
||||
" reasons.append(\"❌ No clear trend direction (sideways market)\")\n",
|
||||
" \n",
|
||||
" if signal_info['confidence'] < signal_info['adaptive_threshold']:\n",
|
||||
" deficit = signal_info['adaptive_threshold'] - signal_info['confidence']\n",
|
||||
" reasons.append(f\"❌ Confidence too low: {signal_info['confidence']:.1f}% < {signal_info['adaptive_threshold']:.1f}% (need {deficit:.1f}% more)\")\n",
|
||||
" \n",
|
||||
" if signal_info['risk_adjusted_strength'] < 100:\n",
|
||||
" reasons.append(f\"❌ Risk-adjusted strength too low: {signal_info['risk_adjusted_strength']:.1f} < 100\")\n",
|
||||
" \n",
|
||||
" if signal_info['signal_quality'] == 'none':\n",
|
||||
" reasons.append(\"❌ Signal quality insufficient\")\n",
|
||||
" \n",
|
||||
" if reasons:\n",
|
||||
" for reason in reasons:\n",
|
||||
" print(f\" {reason}\")\n",
|
||||
" else:\n",
|
||||
" print(\" ✅ All signal criteria met - check other filters\")\n",
|
||||
" \n",
|
||||
" # Vergleich mit V1.3\n",
|
||||
" print(f\"\\n🔄 V1.3 vs V1.4 COMPARISON:\")\n",
|
||||
" v13_threshold = 80\n",
|
||||
" v13_would_trade = (\n",
|
||||
" signal_info['top_down_trend'] != 'sideways' and \n",
|
||||
" signal_info['confidence'] >= v13_threshold\n",
|
||||
" )\n",
|
||||
" v14_trades = signal_info['entry_signal'] != 0\n",
|
||||
" \n",
|
||||
" print(f\" V1.3 would trade: {'✅' if v13_would_trade else '❌'} (fixed 80% threshold)\")\n",
|
||||
" print(f\" V1.4 trades: {'✅' if v14_trades else '❌'} (adaptive {signal_info['adaptive_threshold']:.1f}% threshold)\")\n",
|
||||
" \n",
|
||||
" if v13_would_trade and not v14_trades:\n",
|
||||
" print(f\" 🛡️ V1.4 is MORE SELECTIVE in {signal_info['market_regime']['regime'].upper()} market\")\n",
|
||||
" elif not v13_would_trade and v14_trades:\n",
|
||||
" print(f\" 🚀 V1.4 found opportunity V1.3 missed!\")\n",
|
||||
" \n",
|
||||
" else:\n",
|
||||
" print(\"❌ Could not get signal info\")\n",
|
||||
" \n",
|
||||
"except Exception as e:\n",
|
||||
" print(f\"❌ Error in signal analysis: {e}\")\n",
|
||||
" print(\"Will try simplified analysis...\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 3. 🔧 Quick Fixes"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"🔧 QUICK FIXES TO TRY\")\n",
|
||||
"print(\"=\" * 30)\n",
|
||||
"\n",
|
||||
"# Fix 1: Temporär niedrigere Schwelle\n",
|
||||
"print(\"\\n1️⃣ LOWER THRESHOLD TEMPORARILY:\")\n",
|
||||
"print(\" Copy this code to make V1.4 less selective:\")\n",
|
||||
"print(\"\")\n",
|
||||
"quick_fix_1 = '''def quick_fix_lower_threshold(signal_info):\n",
|
||||
" # Reduziere adaptive Schwelle um 15%\n",
|
||||
" original_threshold = signal_info['adaptive_threshold']\n",
|
||||
" new_threshold = max(60, original_threshold - 15)\n",
|
||||
" \n",
|
||||
" print(f\"Original threshold: {original_threshold}%\")\n",
|
||||
" print(f\"New threshold: {new_threshold}%\")\n",
|
||||
" \n",
|
||||
" if signal_info['confidence'] >= new_threshold:\n",
|
||||
" print(\"✅ Would trade with lower threshold!\")\n",
|
||||
" return True\n",
|
||||
" else:\n",
|
||||
" print(f\"❌ Still need {new_threshold - signal_info['confidence']:.1f}% more confidence\")\n",
|
||||
" return False'''\n",
|
||||
"\n",
|
||||
"print(quick_fix_1)\n",
|
||||
"\n",
|
||||
"# Fix 2: Bypass Pullback Entry\n",
|
||||
"print(\"\\n\\n2️⃣ BYPASS PULLBACK ENTRY:\")\n",
|
||||
"print(\" Set: use_pullback_entry = False\")\n",
|
||||
"print(\" This allows immediate entries instead of waiting for pullbacks\")\n",
|
||||
"\n",
|
||||
"# Fix 3: Allow Fair Quality\n",
|
||||
"print(\"\\n3️⃣ ALLOW 'FAIR' SIGNAL QUALITY:\")\n",
|
||||
"print(\" Modify signal quality check to accept 'fair' signals\")\n",
|
||||
"\n",
|
||||
"# Fix 4: Emergency V1.3 Mode\n",
|
||||
"print(\"\\n4️⃣ EMERGENCY V1.3 MODE:\")\n",
|
||||
"print(\" Use simplified logic similar to your original bot\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 4. 🚨 Emergency V1.3-Style Trading"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def emergency_trading_signal(symbol=\"XAUUSD\"):\n",
|
||||
" \"\"\"\n",
|
||||
" Vereinfachte V1.3-ähnliche Logik als Fallback\n",
|
||||
" \"\"\"\n",
|
||||
" print(\"🚨 EMERGENCY V1.3-STYLE TRADING\")\n",
|
||||
" print(\"=\" * 40)\n",
|
||||
" \n",
|
||||
" try:\n",
|
||||
" # Einfache get_rates Funktion\n",
|
||||
" def simple_get_rates(tf, count):\n",
|
||||
" tf_map = {\"h4\": mt.TIMEFRAME_H4, \"m5\": mt.TIMEFRAME_M5}\n",
|
||||
" rates = mt.copy_rates_from_pos(symbol, tf_map[tf], 0, count)\n",
|
||||
" if rates is None:\n",
|
||||
" return None\n",
|
||||
" df = pd.DataFrame(rates)\n",
|
||||
" df['atr'] = ta.atr(df['high'], df['low'], df['close'], length=14)\n",
|
||||
" return df\n",
|
||||
" \n",
|
||||
" # Hole H4 und M5 Daten\n",
|
||||
" h4_df = simple_get_rates(\"h4\", 150)\n",
|
||||
" m5_df = simple_get_rates(\"m5\", 100)\n",
|
||||
" \n",
|
||||
" if h4_df is None or m5_df is None:\n",
|
||||
" return 0, \"No data available\"\n",
|
||||
" \n",
|
||||
" print(f\"Got H4 data: {len(h4_df)} bars\")\n",
|
||||
" print(f\"Got M5 data: {len(m5_df)} bars\")\n",
|
||||
" \n",
|
||||
" # Vereinfachte Trend-Analyse H4\n",
|
||||
" h4_df['close_smooth'] = savgol_filter(h4_df['close'], 15, 3)\n",
|
||||
" X = np.arange(len(h4_df)).reshape(-1, 1)\n",
|
||||
" y = h4_df['close_smooth'].values\n",
|
||||
" model = LinearRegression().fit(X, y)\n",
|
||||
" slope_h4 = model.coef_[0]\n",
|
||||
" \n",
|
||||
" atr_h4 = h4_df['atr'].iloc[-1]\n",
|
||||
" threshold_h4 = atr_h4 * 0.0001\n",
|
||||
" \n",
|
||||
" if slope_h4 > threshold_h4:\n",
|
||||
" h4_trend = \"uptrend\"\n",
|
||||
" elif slope_h4 < -threshold_h4:\n",
|
||||
" h4_trend = \"downtrend\"\n",
|
||||
" else:\n",
|
||||
" h4_trend = \"sideways\"\n",
|
||||
" \n",
|
||||
" print(f\"H4 Trend: {h4_trend} (slope: {slope_h4:.6f})\")\n",
|
||||
" \n",
|
||||
" # Vereinfachte Trend-Analyse M5\n",
|
||||
" m5_df['close_smooth'] = savgol_filter(m5_df['close'], 15, 3)\n",
|
||||
" X_m5 = np.arange(len(m5_df)).reshape(-1, 1)\n",
|
||||
" y_m5 = m5_df['close_smooth'].values\n",
|
||||
" model_m5 = LinearRegression().fit(X_m5, y_m5)\n",
|
||||
" slope_m5 = model_m5.coef_[0]\n",
|
||||
" \n",
|
||||
" atr_m5 = m5_df['atr'].iloc[-1]\n",
|
||||
" threshold_m5 = atr_m5 * 0.0001\n",
|
||||
" \n",
|
||||
" if slope_m5 > threshold_m5:\n",
|
||||
" m5_trend = \"uptrend\"\n",
|
||||
" elif slope_m5 < -threshold_m5:\n",
|
||||
" m5_trend = \"downtrend\"\n",
|
||||
" else:\n",
|
||||
" m5_trend = \"sideways\"\n",
|
||||
" \n",
|
||||
" print(f\"M5 Trend: {m5_trend} (slope: {slope_m5:.6f})\")\n",
|
||||
" \n",
|
||||
" # Vereinfachte Confidence\n",
|
||||
" if h4_trend == m5_trend and h4_trend != \"sideways\":\n",
|
||||
" confidence = 85 # Hoch wenn aligned\n",
|
||||
" signal = 1 if h4_trend == \"uptrend\" else -1\n",
|
||||
" trend_agreement = \"✅ ALIGNED\"\n",
|
||||
" else:\n",
|
||||
" confidence = 45 # Niedrig wenn nicht aligned\n",
|
||||
" signal = 0\n",
|
||||
" trend_agreement = \"❌ NOT ALIGNED\"\n",
|
||||
" \n",
|
||||
" print(f\"Trend Agreement: {trend_agreement}\")\n",
|
||||
" print(f\"Confidence: {confidence}%\")\n",
|
||||
" \n",
|
||||
" # V1.3-ähnliche niedrige Schwelle\n",
|
||||
" emergency_threshold = 70\n",
|
||||
" \n",
|
||||
" print(f\"Emergency Threshold: {emergency_threshold}%\")\n",
|
||||
" \n",
|
||||
" if confidence >= emergency_threshold and signal != 0:\n",
|
||||
" direction = \"LONG\" if signal == 1 else \"SHORT\"\n",
|
||||
" return signal, f\"🚀 EMERGENCY {direction} SIGNAL! Confidence: {confidence}%\"\n",
|
||||
" else:\n",
|
||||
" return 0, f\"❌ No emergency signal: confidence {confidence}% < {emergency_threshold}%\"\n",
|
||||
" \n",
|
||||
" except Exception as e:\n",
|
||||
" return 0, f\"Error in emergency analysis: {e}\"\n",
|
||||
"\n",
|
||||
"# Test Emergency Signal\n",
|
||||
"emergency_signal, emergency_reason = emergency_trading_signal(symbol)\n",
|
||||
"print(f\"\\n🎯 EMERGENCY RESULT:\")\n",
|
||||
"print(f\"Signal: {emergency_signal}\")\n",
|
||||
"print(f\"Reason: {emergency_reason}\")\n",
|
||||
"\n",
|
||||
"if emergency_signal != 0:\n",
|
||||
" print(\"\\n🚀 EMERGENCY SIGNAL DETECTED!\")\n",
|
||||
" print(\"You could use this as a fallback trading signal.\")\n",
|
||||
"else:\n",
|
||||
" print(\"\\n⏸️ Even emergency logic shows no signal\")\n",
|
||||
" print(\"Market conditions may genuinely not be suitable for trading.\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 5. 🔄 Test mit deiner ursprünglichen V1.3 Funktion"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"🔄 TESTING YOUR ORIGINAL V1.3 LOGIC\")\n",
|
||||
"print(\"=\" * 40)\n",
|
||||
"\n",
|
||||
"# Hier würdest du deine ursprüngliche extended_top_down() Funktion aus V1.3 aufrufen\n",
|
||||
"print(\"To test your original logic, run:\")\n",
|
||||
"print(\"\")\n",
|
||||
"print(\"# Copy your original extended_top_down() function here\")\n",
|
||||
"print(\"# Then run:\")\n",
|
||||
"print(\"original_result = extended_top_down(symbol)\")\n",
|
||||
"print(\"print(f'Original V1.3 result: {original_result}')\")\n",
|
||||
"print(\"\")\n",
|
||||
"print(\"This will show you if your V1.3 logic would still trade.\")\n",
|
||||
"print(\"If V1.3 trades but V1.4 doesn't, we know V1.4 is too restrictive.\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 6. 🎯 Immediate Solutions"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"🎯 IMMEDIATE SOLUTIONS TO GET TRADING AGAIN\")\n",
|
||||
"print(\"=\" * 50)\n",
|
||||
"\n",
|
||||
"solutions = [\n",
|
||||
" \"1️⃣ QUICK FIX - Lower Adaptive Threshold:\",\n",
|
||||
" \" In execute_trade_v2(), change:\",\n",
|
||||
" \" adaptive_threshold = signal_info['adaptive_threshold'] - 15\",\n",
|
||||
" \"\",\n",
|
||||
" \"2️⃣ DISABLE PULLBACK ENTRY:\",\n",
|
||||
" \" Set: use_pullback_entry = False\",\n",
|
||||
" \" This removes the timing filter\",\n",
|
||||
" \"\",\n",
|
||||
" \"3️⃣ ACCEPT FAIR SIGNALS:\",\n",
|
||||
" \" Allow signal_quality = 'fair' to trade\",\n",
|
||||
" \"\",\n",
|
||||
" \"4️⃣ EMERGENCY MODE:\",\n",
|
||||
" \" Use the emergency_trading_signal() function above\",\n",
|
||||
" \" as your main trading logic temporarily\",\n",
|
||||
" \"\",\n",
|
||||
" \"5️⃣ HYBRID APPROACH:\",\n",
|
||||
" \" If V1.4 says no trade, check emergency signal\",\n",
|
||||
" \" If emergency signal exists, trade with smaller volume\",\n",
|
||||
" \"\",\n",
|
||||
" \"6️⃣ REVERT TO V1.3:\",\n",
|
||||
" \" Temporarily go back to your original bot\",\n",
|
||||
" \" while we fine-tune V1.4 parameters\"\n",
|
||||
"]\n",
|
||||
"\n",
|
||||
"for solution in solutions:\n",
|
||||
" print(solution)\n",
|
||||
"\n",
|
||||
"print(\"\\n\" + \"=\" * 50)\n",
|
||||
"print(\"🚨 MOST LIKELY ISSUE: V1.4 is TOO SELECTIVE\")\n",
|
||||
"print(\"V1.4 was designed to be more careful, which means fewer trades.\")\n",
|
||||
"print(\"This is actually GOOD for avoiding bad trades, but may feel like 'not working'.\")\n",
|
||||
"print(\"\")\n",
|
||||
"print(\"💡 RECOMMENDATION:\")\n",
|
||||
"print(\"1. Try the emergency signal above\")\n",
|
||||
"print(\"2. If it works, gradually tune V1.4 to be less restrictive\")\n",
|
||||
"print(\"3. Compare results over 1-2 weeks to see which performs better\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 7. 📊 Parameter Tuning"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Zeige aktuelle Parameter und Tuning-Optionen\n",
|
||||
"print(\"📊 PARAMETER TUNING OPTIONS\")\n",
|
||||
"print(\"=\" * 35)\n",
|
||||
"\n",
|
||||
"tuning_options = [\n",
|
||||
" \"Current V1.4 Parameters (too restrictive?):\",\n",
|
||||
" \"• Base confidence: 70%\",\n",
|
||||
" \"• Trending adjustment: -10 to -15%\",\n",
|
||||
" \"• Ranging adjustment: +15%\",\n",
|
||||
" \"• Volatile adjustment: +20%\",\n",
|
||||
" \"• Min risk-adjusted strength: 100\",\n",
|
||||
" \"\",\n",
|
||||
" \"Suggested Relaxed Parameters:\",\n",
|
||||
" \"• Base confidence: 60%\",\n",
|
||||
" \"• Trending adjustment: -15 to -20%\",\n",
|
||||
" \"• Ranging adjustment: +10%\",\n",
|
||||
" \"• Volatile adjustment: +15%\",\n",
|
||||
" \"• Min risk-adjusted strength: 80\",\n",
|
||||
" \"\",\n",
|
||||
" \"Very Relaxed (V1.3-like):\",\n",
|
||||
" \"• Fixed confidence: 75%\",\n",
|
||||
" \"• No regime adjustments\",\n",
|
||||
" \"• No signal quality filter\",\n",
|
||||
" \"• No pullback entry timing\"\n",
|
||||
"]\n",
|
||||
"\n",
|
||||
"for option in tuning_options:\n",
|
||||
" print(option)"
|
||||
]
|
||||
}\n",
|
||||
],\n",
|
||||
"metadata": {\n",
|
||||
"kernelspec": {\n",
|
||||
\"display_name\": \"Python 3\",\n",
|
||||
\"language\": \"python\",\n",
|
||||
\"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}
|
||||
Vendored
BIN
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BIN
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BIN
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BIN
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@@ -0,0 +1,324 @@
|
||||
# 🚀 TradingBot V1.8 - Aggressive Mode (Nur NY Session)
|
||||
|
||||
**Deployment Datum:** 26. November 2025
|
||||
**Status:** ✅ Aktiv auf VPS
|
||||
**Strategie:** Nur NY Session (13:00-21:00 UTC)
|
||||
|
||||
---
|
||||
|
||||
## 📊 Warum dieser Wechsel?
|
||||
|
||||
### Performance-Analyse nach 2 Wochen (V1.7):
|
||||
|
||||
| Session | Trades | Profit | Win-Rate | Entscheidung |
|
||||
|---------|--------|--------|----------|--------------|
|
||||
| **NY** | 21 | **+$660** | **47.6%** | ✅ **NUR DIESE aktiviert!** |
|
||||
| Overlap | 44 | -$208 | 27.3% | ❌ Deaktiviert |
|
||||
| Asian | 43 | +$202 | 30.2% | ❌ Deaktiviert |
|
||||
| London | 26 | +$79 | 30.8% | ❌ Deaktiviert |
|
||||
|
||||
### 🎯 Key Findings:
|
||||
|
||||
1. **NY Session dominiert:**
|
||||
- 90% des Gesamt-Profits ($660 von $733)
|
||||
- 47.6% Win-Rate (beste Session!)
|
||||
- $31.43 Profit pro Trade (höchste Effizienz)
|
||||
|
||||
2. **Overlap bleibt problematisch:**
|
||||
- Kumuliert -$208 über 2 Wochen
|
||||
- Win-Rate nur 27.3%
|
||||
- Trotz Erholung in Woche 2 negativ
|
||||
|
||||
3. **Asian & London mittelmäßig:**
|
||||
- Win-Rates ~30% (zu niedrig)
|
||||
- Zwar positiv, aber nicht profitabel genug
|
||||
- Fressen durch Verluste die Gewinne auf
|
||||
|
||||
---
|
||||
|
||||
## ⚙️ V1.8 Konfiguration
|
||||
|
||||
### Session Filter:
|
||||
```python
|
||||
'enabled_sessions': {
|
||||
'asian': False, # ❌ DEAKTIVIERT
|
||||
'london': False, # ❌ DEAKTIVIERT
|
||||
'overlap': False, # ❌ DEAKTIVIERT
|
||||
'ny': True, # ✅ NUR NY AKTIV (13:00-21:00 UTC)
|
||||
}
|
||||
```
|
||||
|
||||
### Trading Parameter (unverändert):
|
||||
```python
|
||||
'base_confidence': 60, # Confidence Threshold
|
||||
'atr_mult': 1.5, # ATR Multiplikator
|
||||
'max_risk_per_trade': 0.01, # 1% Risk pro Trade
|
||||
'min_atr': 0.0008, # Minimum ATR
|
||||
'risk_filter': True, # ATR Risk Filter aktiv
|
||||
'use_pullback_entry': False # Kein Pullback Entry
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 📈 Erwartete Performance (V1.8)
|
||||
|
||||
### Basierend auf 2-Wochen-Daten:
|
||||
|
||||
| Metrik | V1.7 (2 Wochen) | V1.8 Erwartung | Verbesserung |
|
||||
|--------|-----------------|----------------|--------------|
|
||||
| **Trades** | 134 | ~21 pro Woche | -84% (sehr selektiv!) |
|
||||
| **Win-Rate** | 32.09% | **~48%** | +15.91% ✅ |
|
||||
| **Net Profit** | $733 | ~$660 | -10% ⚠️ |
|
||||
| **Profit/Trade** | $5.47 | **$31.43** | +475% ✅✅ |
|
||||
| **Max Drawdown** | 81% | **<50%** (erwartet) | -31% ✅ |
|
||||
| **Profit Factor** | 1.162 | **>1.5** (erwartet) | +29% ✅ |
|
||||
|
||||
### 🎯 Trade-off Analyse:
|
||||
|
||||
**✅ Vorteile:**
|
||||
- **Höchste Win-Rate** (~48% statt 32%)
|
||||
- **Beste Profit-Effizienz** ($31/Trade statt $5/Trade)
|
||||
- **Minimaler Drawdown** (weniger Trades = weniger Risiko)
|
||||
- **Höchste Qualität** (nur beste Trading-Session)
|
||||
- **Weniger Stress** (nur 8h pro Tag aktiv)
|
||||
|
||||
**⚠️ Nachteile:**
|
||||
- **Weniger Trades** (~21 statt 134 pro 2 Wochen)
|
||||
- **Weniger Profit-Chancen** (verpasst Asian/London/Overlap)
|
||||
- **Absoluter Profit evtl. niedriger** (aber pro Trade VIEL höher!)
|
||||
|
||||
---
|
||||
|
||||
## 🕐 Trading Zeiten (V1.8)
|
||||
|
||||
### NY Session:
|
||||
- **Start:** 13:00 UTC (14:00 MEZ / 15:00 MESZ)
|
||||
- **Ende:** 21:00 UTC (22:00 MEZ / 23:00 MESZ)
|
||||
- **Dauer:** 8 Stunden
|
||||
- **Adaptive Intervalle:** 5/15/30 Minuten (basierend auf Volatilität)
|
||||
|
||||
### Blockierte Zeiten:
|
||||
- **Asian (00:00-08:00 UTC):** ❌ Keine Trades
|
||||
- **London (08:00-16:00 UTC):** ❌ Keine Trades (außer NY Overlap)
|
||||
- **Overlap (13:00-16:00 UTC):** ❌ Keine Trades (in V1.8 deaktiviert)
|
||||
|
||||
**Log-Ausgabe bei blockierten Sessions:**
|
||||
```
|
||||
⏸️ Trading SKIP: Session blocked: Asian has +$202, but only 30.2% win-rate
|
||||
⏸️ Trading SKIP: Session blocked: London has +$79, but only 30.8% win-rate
|
||||
⏸️ Trading SKIP: Session blocked: Overlap has -$208 cumulative, 27.3% win-rate
|
||||
```
|
||||
|
||||
**Log-Ausgabe bei NY Session:**
|
||||
```
|
||||
✅ Session: NY - NY allowed: +$660 profit, 47.6% win-rate (BEST!)
|
||||
📊 Confidence Threshold: 60%
|
||||
⏱️ Intervall: 15 min
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 📋 Monitoring Checklist (nächste 1-2 Wochen)
|
||||
|
||||
### Täglich prüfen:
|
||||
|
||||
- [ ] Bot läuft auf VPS (keine Crashes)
|
||||
- [ ] Nur NY Session Trades im Log
|
||||
- [ ] Keine Asian/London/Overlap Trades
|
||||
- [ ] Confidence Threshold bei 60%
|
||||
- [ ] Log zeigt: `⏸️ Trading SKIP` für andere Sessions
|
||||
|
||||
### Nach 1 Woche (ca. 3. Dezember):
|
||||
|
||||
```bash
|
||||
python analyze_mt5_profitability.py
|
||||
```
|
||||
|
||||
**Prüfe:**
|
||||
1. **Win-Rate:** Sollte >40% sein (idealerweise 45-50%)
|
||||
2. **Profit Factor:** Sollte >1.4 sein
|
||||
3. **Max Drawdown:** Sollte <60% sein
|
||||
4. **NY Trades:** Sollte ~10-12 Trades sein
|
||||
5. **Andere Sessions:** Sollten 0 neue Trades sein
|
||||
|
||||
### Nach 2 Wochen (ca. 10. Dezember):
|
||||
|
||||
**Entscheidung:**
|
||||
|
||||
#### Szenario A: Win-Rate >45%
|
||||
```
|
||||
✅ V1.8 ist ein ERFOLG!
|
||||
→ Behalte Aggressive Mode
|
||||
→ Optional: Confidence auf 70% erhöhen für noch höhere Qualität
|
||||
```
|
||||
|
||||
#### Szenario B: Win-Rate 40-45%
|
||||
```
|
||||
✅ V1.8 funktioniert gut
|
||||
→ Behalte Aggressive Mode
|
||||
→ Monitor weitere 2 Wochen
|
||||
```
|
||||
|
||||
#### Szenario C: Win-Rate 35-40%
|
||||
```
|
||||
⚠️ Schlechter als erwartet
|
||||
→ Teste Option C: NY + Overlap + Confidence 75%
|
||||
```
|
||||
|
||||
#### Szenario D: Win-Rate <35%
|
||||
```
|
||||
❌ Aggressive Mode funktioniert nicht
|
||||
→ Zurück zu V1.7 (NY + Overlap)
|
||||
→ ODER: NY + Asian + London (alle positiven Sessions)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 🔧 Schnell-Anpassungen (falls nötig)
|
||||
|
||||
### Alle Sessions wieder aktivieren:
|
||||
```python
|
||||
# In session_filter_patch.py
|
||||
'enabled_sessions': {
|
||||
'asian': True,
|
||||
'london': True,
|
||||
'overlap': True,
|
||||
'ny': True,
|
||||
}
|
||||
```
|
||||
|
||||
### Nur NY + Overlap (zurück zu V1.7):
|
||||
```python
|
||||
'enabled_sessions': {
|
||||
'asian': False,
|
||||
'london': False,
|
||||
'overlap': True, # ✅
|
||||
'ny': True,
|
||||
}
|
||||
```
|
||||
|
||||
### NY + alle positiven Sessions:
|
||||
```python
|
||||
'enabled_sessions': {
|
||||
'asian': True, # ✅ +$202
|
||||
'london': True, # ✅ +$79
|
||||
'overlap': False, # ❌ -$208
|
||||
'ny': True, # ✅ +$660
|
||||
}
|
||||
```
|
||||
|
||||
### Confidence erhöhen (falls Win-Rate gut):
|
||||
```python
|
||||
'base_confidence': 70, # von 60 auf 70
|
||||
# ODER
|
||||
'base_confidence': 75, # für ultra-konservativ
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 📊 Performance-Vergleich
|
||||
|
||||
### V1.6 (Original - ALLE Sessions):
|
||||
- Trades: 134 (2 Wochen)
|
||||
- Win-Rate: 32.09%
|
||||
- Net Profit: $733
|
||||
- Profit/Trade: $5.47
|
||||
- Max Drawdown: 81%
|
||||
|
||||
### V1.7 (NY + Overlap):
|
||||
- Trades: 65 (2 Wochen, aktive Sessions)
|
||||
- Win-Rate: 32.09%
|
||||
- Net Profit: $733
|
||||
- Profit/Trade: $11.28
|
||||
- Max Drawdown: 81%
|
||||
|
||||
### V1.8 (Nur NY - JETZT):
|
||||
- Trades: ~21 (2 Wochen, erwartet)
|
||||
- Win-Rate: **~48%** (erwartet)
|
||||
- Net Profit: ~$660
|
||||
- Profit/Trade: **$31.43**
|
||||
- Max Drawdown: **<50%** (erwartet)
|
||||
|
||||
---
|
||||
|
||||
## 🎯 Erfolgs-Kriterien für V1.8
|
||||
|
||||
### Must-Have (Minimum):
|
||||
- ✅ Win-Rate >40%
|
||||
- ✅ Profit Factor >1.3
|
||||
- ✅ Max Drawdown <60%
|
||||
- ✅ Profit/Trade >$25
|
||||
|
||||
### Nice-to-Have (Ziel):
|
||||
- 🎯 Win-Rate >45%
|
||||
- 🎯 Profit Factor >1.5
|
||||
- 🎯 Max Drawdown <50%
|
||||
- 🎯 Profit/Trade >$30
|
||||
|
||||
### Excellent (Best Case):
|
||||
- 🏆 Win-Rate >48%
|
||||
- 🏆 Profit Factor >1.7
|
||||
- 🏆 Max Drawdown <40%
|
||||
- 🏆 Profit/Trade >$35
|
||||
|
||||
---
|
||||
|
||||
## 🔄 Nächste Phasen (Roadmap)
|
||||
|
||||
### Phase 2A (nach V1.8 Validierung):
|
||||
**Falls V1.8 erfolgreich (Win-Rate >45%):**
|
||||
- Confidence Threshold auf 70-75% erhöhen
|
||||
- Erwartung: Win-Rate >50%, weniger Trades, höchste Qualität
|
||||
|
||||
### Phase 2B (Infrastructure):
|
||||
- SQLite Migration für besseres Datenmanagement
|
||||
- Telegram Notifications für Mobile Monitoring
|
||||
- Automatische Performance-Reports
|
||||
|
||||
### Phase 3 (Advanced):
|
||||
- Backtesting Framework für historische Analyse
|
||||
- ML Signal Filter (Random Forest / XGBoost)
|
||||
- Multi-Symbol Support (XAUUSD + andere Paare)
|
||||
|
||||
---
|
||||
|
||||
## 📚 Dokumentation
|
||||
|
||||
- **Diese Datei:** V1.8 Aggressive Mode Dokumentation
|
||||
- [V1.7_SESSION_FILTER_AKTIVIERT.md](V1.7_SESSION_FILTER_AKTIVIERT.md) - Vorherige Version
|
||||
- [CONFIDENCE_THRESHOLD_GUIDE.md](CONFIDENCE_THRESHOLD_GUIDE.md) - Threshold Anleitung
|
||||
- [SESSION_FILTER_INSTALLATION.md](SESSION_FILTER_INSTALLATION.md) - Installation Guide
|
||||
- [session_filter_patch.py](session_filter_patch.py) - Config Datei
|
||||
|
||||
---
|
||||
|
||||
## ✅ Deployment Checklist
|
||||
|
||||
- [x] Config angepasst (nur NY Session)
|
||||
- [x] File auf VPS kopiert
|
||||
- [x] Bot neu gestartet
|
||||
- [x] Log prüfen: Nur NY Trades
|
||||
- [ ] Nach 1 Woche: Performance analysieren
|
||||
- [ ] Nach 2 Wochen: Entscheidung für nächste Phase
|
||||
|
||||
---
|
||||
|
||||
## 🎲 Zusammenfassung
|
||||
|
||||
**TradingBot V1.8** ist jetzt im **Ultra-Selektiven Modus**:
|
||||
- Nur die **beste Trading-Session** (NY)
|
||||
- Höchste **Win-Rate** (~48%)
|
||||
- Beste **Profit-Effizienz** ($31/Trade)
|
||||
- Minimales **Risiko** (weniger Drawdown)
|
||||
|
||||
**Trade-off:**
|
||||
Weniger Trades = weniger absolute Profit-Chancen, ABER: Jeder Trade hat deutlich höhere Erfolgswahrscheinlichkeit!
|
||||
|
||||
**Philosophie:**
|
||||
"Qualität über Quantität" - Lieber 10 gute Trades als 100 mittelmäßige!
|
||||
|
||||
---
|
||||
|
||||
**Viel Erfolg mit V1.8! 🚀**
|
||||
|
||||
Melde dich in 1-2 Wochen mit den Performance-Daten!
|
||||
@@ -1,75 +0,0 @@
|
||||
{
|
||||
"trading_config": {
|
||||
"symbol": "XAUUSD",
|
||||
"base_confidence": 70,
|
||||
"atr_multiplier": 1.5,
|
||||
"max_risk_per_trade": 0.01,
|
||||
"min_atr": 0.0010,
|
||||
"use_pullback_entry": true,
|
||||
"strategy_name": "TradingBot_V1.4_Optimized"
|
||||
},
|
||||
|
||||
"regime_thresholds": {
|
||||
"trending": {
|
||||
"min_adx": 25,
|
||||
"confidence_adjustment": -10,
|
||||
"atr_mult_adjustment": 0.0
|
||||
},
|
||||
"ranging": {
|
||||
"max_adx": 20,
|
||||
"confidence_adjustment": 15,
|
||||
"atr_mult_adjustment": -0.2
|
||||
},
|
||||
"volatile": {
|
||||
"vol_cluster_threshold": 1.5,
|
||||
"confidence_adjustment": 20,
|
||||
"atr_mult_adjustment": 0.3
|
||||
}
|
||||
},
|
||||
|
||||
"timeframe_weights": {
|
||||
"D1": 2.5,
|
||||
"H4": 2.0,
|
||||
"H1": 1.5,
|
||||
"M30": 1.0,
|
||||
"M15": 0.8,
|
||||
"M5": 0.6
|
||||
},
|
||||
|
||||
"signal_quality_thresholds": {
|
||||
"excellent": {
|
||||
"min_confidence": 85,
|
||||
"min_risk_adjusted_strength": 150
|
||||
},
|
||||
"good": {
|
||||
"min_confidence": 75,
|
||||
"min_risk_adjusted_strength": 120
|
||||
},
|
||||
"fair": {
|
||||
"min_confidence": 60,
|
||||
"min_risk_adjusted_strength": 100
|
||||
}
|
||||
},
|
||||
|
||||
"risk_management": {
|
||||
"max_daily_loss": 0.05,
|
||||
"max_drawdown": 0.10,
|
||||
"max_positions": 3,
|
||||
"spread_filter": true,
|
||||
"max_spread": 3.0
|
||||
},
|
||||
|
||||
"schedule_settings": {
|
||||
"check_interval_minutes": 5,
|
||||
"trading_days": ["mon", "tue", "wed", "thu", "fri"],
|
||||
"trading_hours": "0-23",
|
||||
"performance_analysis_hours": [0, 6, 12, 18]
|
||||
},
|
||||
|
||||
"debug_settings": {
|
||||
"verbose_logging": true,
|
||||
"save_performance_logs": true,
|
||||
"print_regime_info": true,
|
||||
"log_skipped_trades": true
|
||||
}
|
||||
}
|
||||
@@ -1,542 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 28,
|
||||
"id": "02ce9da2",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import keyring as kr\n",
|
||||
"import MetaTrader5 as mt\n",
|
||||
"import requests\n",
|
||||
"import re\n",
|
||||
"from time import sleep\n",
|
||||
"from time import strftime\n",
|
||||
"import datetime as dt\n",
|
||||
"import sqlite3 as db\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "ffcf5c1a",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"True"
|
||||
]
|
||||
},
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# login to your Trading Account - sign up in the description\n",
|
||||
"mt.initialize()\n",
|
||||
" \n",
|
||||
"login = 10800246\n",
|
||||
"server = 'VantageInternational-Demo'\n",
|
||||
"password = kr.get_password(server, str(login))\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"mt.login(login, password, server)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "d94de78e",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"symbols = ['BTCUSD', 'ETHUSD', \n",
|
||||
" 'XRPUSD', 'XAUUSD', 'EURUSD', 'EURNZD']"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"id": "f225d16e",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"SymbolInfo(custom=False, chart_mode=0, select=True, visible=True, session_deals=0, session_buy_orders=0, session_sell_orders=0, volume=0, volumehigh=0, volumelow=0, time=1747583136, digits=2, spread=1603, spread_float=True, ticks_bookdepth=0, trade_calc_mode=5, trade_mode=4, start_time=0, expiration_time=0, trade_stops_level=0, trade_freeze_level=0, trade_exemode=2, swap_mode=5, swap_rollover3days=5, margin_hedged_use_leg=False, expiration_mode=15, filling_mode=2, order_mode=63, order_gtc_mode=0, option_mode=0, option_right=0, bid=103854.84, bidhigh=104085.24, bidlow=102892.04, ask=103870.87, askhigh=104101.27, asklow=102908.07, last=0.0, lasthigh=0.0, lastlow=0.0, volume_real=0.0, volumehigh_real=0.0, volumelow_real=0.0, option_strike=0.0, point=0.01, trade_tick_value=0.01, trade_tick_value_profit=0.01, trade_tick_value_loss=0.01, trade_tick_size=0.01, trade_contract_size=1.0, trade_accrued_interest=0.0, trade_face_value=0.0, trade_liquidity_rate=0.0, volume_min=0.01, volume_max=100.0, volume_step=0.01, volume_limit=0.0, swap_long=-19.0, swap_short=10.0, margin_initial=0.0, margin_maintenance=0.0, session_volume=0.0, session_turnover=0.0, session_interest=0.0, session_buy_orders_volume=0.0, session_sell_orders_volume=0.0, session_open=103363.54, session_close=103363.54, session_aw=0.0, session_price_settlement=0.0, session_price_limit_min=0.0, session_price_limit_max=0.0, margin_hedged=0.0, price_change=0.4753, price_volatility=0.0, price_theoretical=0.0, price_greeks_delta=0.0, price_greeks_theta=0.0, price_greeks_gamma=0.0, price_greeks_vega=0.0, price_greeks_rho=0.0, price_greeks_omega=0.0, price_sensitivity=0.0, basis='', category='', currency_base='BTC', currency_profit='USD', currency_margin='BTC', bank='', description='Bitcoin', exchange='', formula='', isin='', name='BTCUSD', page='', path='Crypto Currency\\\\Crypto Major\\\\BTCUSD')"
|
||||
]
|
||||
},
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"mt.symbol_info('BTCUSD')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "329ff8fe",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"datetime.datetime(2025, 5, 18, 21, 59, 2, 465512)"
|
||||
]
|
||||
},
|
||||
"execution_count": 65,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"current_date_and_time = dt.datetime.now()\n",
|
||||
"format = '%Y-%m-%d %H:%M:%S'\n",
|
||||
"current_date_and_time.strftime(format)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "5f56252c",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 150,
|
||||
"id": "3ce21edb",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"{'XAUUSD': 0.97, 'EURUSD': 0.87, 'EURNZD': 0.47, 'ETHUSD': -0.0, 'BTCUSD': -1.65, 'XRPUSD': -3.22}\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'BTCUSD': -1.65,\n",
|
||||
" 'ETHUSD': -0.0,\n",
|
||||
" 'XRPUSD': -3.22,\n",
|
||||
" 'XAUUSD': 0.97,\n",
|
||||
" 'EURUSD': 0.87,\n",
|
||||
" 'EURNZD': 0.47}"
|
||||
]
|
||||
},
|
||||
"execution_count": 150,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"pricemovement = {}\n",
|
||||
"for s in symbols:\n",
|
||||
" items = mt.symbol_info(s)\n",
|
||||
" pricemovement[s] = round(items.price_change,2)\n",
|
||||
" #print(s, round(items.price_change,2))\n",
|
||||
"\n",
|
||||
"sorted_pricemovement = sorted(pricemovement.items(), key=lambda x:x[1], reverse=True)\n",
|
||||
"converted_dict = dict(sorted_pricemovement)\n",
|
||||
"\n",
|
||||
"print(converted_dict)\n",
|
||||
"\n",
|
||||
"pricemovement"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "b30f529c",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 68,
|
||||
"id": "f174bc75",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"0\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"con = db.connect(\"trading.db\")\n",
|
||||
"print(con.total_changes)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 69,
|
||||
"id": "f2ff9f72",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"cursor = con.cursor()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 70,
|
||||
"id": "0b4facf6",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<sqlite3.Cursor at 0x268bf0321c0>"
|
||||
]
|
||||
},
|
||||
"execution_count": 70,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"cursor.execute(\"Create Table If Not Exists pricemovment (date TEXT,symbol TEXT, movement INTEGER)\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 151,
|
||||
"id": "107514d6",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"BTCUSD -1.65\n",
|
||||
"ETHUSD -0.0\n",
|
||||
"XRPUSD -3.22\n",
|
||||
"XAUUSD 0.97\n",
|
||||
"EURUSD 0.87\n",
|
||||
"EURNZD 0.47\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"for key, value in pricemovement.items():\n",
|
||||
" print(key, value)\n",
|
||||
" current_date_and_time = dt.datetime.now()\n",
|
||||
" format = '%Y-%m-%d %H:%M:%S'\n",
|
||||
" current_date_and_time.strftime(format)\n",
|
||||
" cursor.execute(\"Insert into pricemovment values(?, ?, ?)\", (current_date_and_time, key, value))\n",
|
||||
" con.commit()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 152,
|
||||
"id": "b99a7d5f",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"BTCUSD\n",
|
||||
"[('2025-05-18 22:02:41.439814', 'BTCUSD', 0.13), ('2025-05-18 22:06:10.375867', 'BTCUSD', 0.2), ('2025-05-18 22:42:44.244148', 'BTCUSD', 0.65), ('2025-05-18 22:57:00.481991', 'BTCUSD', 0.65)]\n",
|
||||
"ETHUSD\n",
|
||||
"[('2025-05-18 22:02:41.441815', 'ETHUSD', -5.19), ('2025-05-18 22:06:10.379865', 'ETHUSD', -4.86), ('2025-05-18 22:42:44.249142', 'ETHUSD', -3.92), ('2025-05-18 22:57:00.485990', 'ETHUSD', -4.05)]\n",
|
||||
"XRPUSD\n",
|
||||
"[('2025-05-18 22:02:41.441815', 'XRPUSD', -0.63), ('2025-05-18 22:06:10.382865', 'XRPUSD', -0.37), ('2025-05-18 22:42:44.252143', 'XRPUSD', 0.45), ('2025-05-18 22:57:00.487996', 'XRPUSD', 0.45)]\n",
|
||||
"XAUUSD\n",
|
||||
"[('2025-05-18 22:02:41.441815', 'XAUUSD', -1.21), ('2025-05-18 22:06:10.384868', 'XAUUSD', -1.21), ('2025-05-18 22:42:44.255142', 'XAUUSD', -1.21), ('2025-05-18 22:57:00.491343', 'XAUUSD', -1.21)]\n",
|
||||
"EURUSD\n",
|
||||
"[('2025-05-18 22:02:41.441815', 'EURUSD', -0.2), ('2025-05-18 22:06:10.387868', 'EURUSD', -0.2), ('2025-05-18 22:42:44.258146', 'EURUSD', -0.2), ('2025-05-18 22:57:00.493342', 'EURUSD', -0.2)]\n",
|
||||
"EURNZD\n",
|
||||
"[('2025-05-18 22:02:41.441815', 'EURNZD', -0.29), ('2025-05-18 22:06:10.426110', 'EURNZD', -0.29), ('2025-05-18 22:42:44.262143', 'EURNZD', -0.29), ('2025-05-18 22:57:00.496339', 'EURNZD', -0.29)]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"for s in symbols:\n",
|
||||
" print(s)\n",
|
||||
" cursor.execute(f\"Select * from pricemovment where date like '2025-05-18%' and symbol = '{s}'\")\n",
|
||||
" inhalt = cursor.fetchall()\n",
|
||||
" print(inhalt)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 155,
|
||||
"id": "9b8fc8e9",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>date</th>\n",
|
||||
" <th>symbol</th>\n",
|
||||
" <th>percentage</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>2025-05-19 14:42:14.686479</td>\n",
|
||||
" <td>BTCUSD</td>\n",
|
||||
" <td>-1.72</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>2025-05-19 14:42:14.735008</td>\n",
|
||||
" <td>ETHUSD</td>\n",
|
||||
" <td>-0.04</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>2025-05-19 14:42:14.738010</td>\n",
|
||||
" <td>XRPUSD</td>\n",
|
||||
" <td>-3.05</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>2025-05-19 14:42:14.741009</td>\n",
|
||||
" <td>XAUUSD</td>\n",
|
||||
" <td>1.30</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>2025-05-19 14:42:14.743007</td>\n",
|
||||
" <td>EURUSD</td>\n",
|
||||
" <td>0.92</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>5</th>\n",
|
||||
" <td>2025-05-19 14:42:14.746007</td>\n",
|
||||
" <td>EURNZD</td>\n",
|
||||
" <td>0.43</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>6</th>\n",
|
||||
" <td>2025-05-19 14:57:55.112334</td>\n",
|
||||
" <td>BTCUSD</td>\n",
|
||||
" <td>-1.65</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>7</th>\n",
|
||||
" <td>2025-05-19 14:57:55.116333</td>\n",
|
||||
" <td>ETHUSD</td>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>8</th>\n",
|
||||
" <td>2025-05-19 14:57:55.124334</td>\n",
|
||||
" <td>XRPUSD</td>\n",
|
||||
" <td>-3.22</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>9</th>\n",
|
||||
" <td>2025-05-19 14:57:55.128902</td>\n",
|
||||
" <td>XAUUSD</td>\n",
|
||||
" <td>0.97</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>10</th>\n",
|
||||
" <td>2025-05-19 14:57:55.186314</td>\n",
|
||||
" <td>EURUSD</td>\n",
|
||||
" <td>0.87</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>11</th>\n",
|
||||
" <td>2025-05-19 14:57:55.202850</td>\n",
|
||||
" <td>EURNZD</td>\n",
|
||||
" <td>0.47</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" date symbol percentage\n",
|
||||
"0 2025-05-19 14:42:14.686479 BTCUSD -1.72\n",
|
||||
"1 2025-05-19 14:42:14.735008 ETHUSD -0.04\n",
|
||||
"2 2025-05-19 14:42:14.738010 XRPUSD -3.05\n",
|
||||
"3 2025-05-19 14:42:14.741009 XAUUSD 1.30\n",
|
||||
"4 2025-05-19 14:42:14.743007 EURUSD 0.92\n",
|
||||
"5 2025-05-19 14:42:14.746007 EURNZD 0.43\n",
|
||||
"6 2025-05-19 14:57:55.112334 BTCUSD -1.65\n",
|
||||
"7 2025-05-19 14:57:55.116333 ETHUSD 0.00\n",
|
||||
"8 2025-05-19 14:57:55.124334 XRPUSD -3.22\n",
|
||||
"9 2025-05-19 14:57:55.128902 XAUUSD 0.97\n",
|
||||
"10 2025-05-19 14:57:55.186314 EURUSD 0.87\n",
|
||||
"11 2025-05-19 14:57:55.202850 EURNZD 0.47"
|
||||
]
|
||||
},
|
||||
"execution_count": 155,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"cursor.execute(\"Select * from pricemovment where date like '2025-05-19%'\")\n",
|
||||
"inhalt = cursor.fetchall()\n",
|
||||
"inhalt = pd.DataFrame(inhalt, columns=['date', 'symbol', 'percentage'])\n",
|
||||
"\n",
|
||||
"for s in symbols:\n",
|
||||
" x = inhalt.loc[inhalt['symbol'] == s].percentage.diff()\n",
|
||||
" pd.concat([inhalt, x])\n",
|
||||
" \n",
|
||||
"inhalt\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 187,
|
||||
"id": "6f27e0ac",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[1.3, 0.97]"
|
||||
]
|
||||
},
|
||||
"execution_count": 187,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"list = inhalt.loc[inhalt['symbol'] == 'XAUUSD'].percentage.to_list()\n",
|
||||
"list\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "31180731",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"cursor.execute(\"Insert into pricemovement values(?, ?, ?)\", ())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "e5425f12",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 59,
|
||||
"id": "1422339e",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<sqlite3.Cursor at 0x268bee8ab40>"
|
||||
]
|
||||
},
|
||||
"execution_count": 59,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"cursor.execute('Drop Table pricemovment')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 74,
|
||||
"id": "3d94a299",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"con.commit()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "bf1487ac",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"cursor.execute('INSERT INTO pricemovement VALUES(1, datetime('now'))'"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "ce546c4d",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"con.close()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "354653fc",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "base",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@@ -1,630 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "6d202eb3",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Import Libaries"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "5d0e0f92",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"import pandas_ta as ta\n",
|
||||
"import MetaTrader5 as mt\n",
|
||||
"import keyring as kr"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "de2abb26",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Login"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "51d9b448",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"True"
|
||||
]
|
||||
},
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# login to your Trading Account - sign up in the description\n",
|
||||
"mt.initialize()\n",
|
||||
" \n",
|
||||
"login = 10800246\n",
|
||||
"server = 'VantageInternational-Demo'\n",
|
||||
"password = kr.get_password(server, str(login))\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"mt.login(login, password, server)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "c92d8dee",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'trading-demo'"
|
||||
]
|
||||
},
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"project = \"trading-\" + server[-4::1]\n",
|
||||
"project = project.lower()\n",
|
||||
"project"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "16e26255",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"symbols = ['XAUUSD']"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "20de0d64",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"periods_dict = {\n",
|
||||
" 'BTCUSD' : ['h1', 'm30', 'm15', 'm5', 'm1'],\n",
|
||||
" \n",
|
||||
" 'ETHUSD' : ['h1', 'm30', 'm15', 'm5', 'm1'],\n",
|
||||
"\n",
|
||||
" \n",
|
||||
" 'XRPUSD': ['h1', 'm30', 'm15', 'm5', 'm1'],\n",
|
||||
" \n",
|
||||
" 'XAUUSD': [ 'm5'],\n",
|
||||
"\n",
|
||||
" \n",
|
||||
" 'EURUSD': ['h1', 'm30', 'm15', 'm5', 'm1'],\n",
|
||||
"\n",
|
||||
"\n",
|
||||
" 'EURNZD': ['m5', 'm2', 'm1'],\n",
|
||||
"\n",
|
||||
"}"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"id": "e65dfd75",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"symbol = 'XAUUSD'"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "874862b3",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 107,
|
||||
"id": "95f9b002",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def get_rates():\n",
|
||||
"\n",
|
||||
" ohlc = mt.copy_rates_from_pos(symbol, mt.TIMEFRAME_M5, 0, 200)\n",
|
||||
" df = pd.DataFrame(ohlc)\n",
|
||||
" df['time']=pd.to_datetime(df['time'], unit='s')\n",
|
||||
"\n",
|
||||
" #df = df[[\"time\",\"open\",\"high\",\"low\",\"close\"]]\n",
|
||||
"\n",
|
||||
" df[\"open\"] = df.open.astype(float)\n",
|
||||
" df[\"high\"] = df.high.astype(float)\n",
|
||||
" df[\"low\"] = df.low.astype(float)\n",
|
||||
" df[\"close\"] = df.close.astype(float)\n",
|
||||
"\n",
|
||||
" ## Take the rolling atr so the yaxis doesn't shake too much \n",
|
||||
" df[\"atr\"] = ta.atr(high=df.high, low=df.low, close=df.close)\n",
|
||||
" df[\"atr\"] = df.atr.rolling(window=30).mean()\n",
|
||||
"\n",
|
||||
"\n",
|
||||
" df.set_index(\"time\", inplace = True)\n",
|
||||
"\n",
|
||||
" return df"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "42dd19c7",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Plotting Price Data"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"id": "17a837df",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%matplotlib inline\n",
|
||||
"\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"from matplotlib.animation import FuncAnimation\n",
|
||||
"from scipy.signal import savgol_filter\n",
|
||||
"from scipy.signal import find_peaks\n",
|
||||
"from IPython import display\n",
|
||||
"from IPython.display import HTML\n",
|
||||
"pd.set_option('mode.chained_assignment', None)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"id": "be326702",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 640x480 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"#df2 = df.iloc[0:500]\n",
|
||||
"df2 = df\n",
|
||||
"\n",
|
||||
"fig, ax = plt.subplots()\n",
|
||||
"plt.xticks(rotation=-30)\n",
|
||||
"price, = ax.plot(df2.index, df2.close, c='grey', lw=2, alpha=0.5, zorder=5)\n",
|
||||
"\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"id": "e27cba63",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 640x480 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"#df2 = df.iloc[0:500]\n",
|
||||
"df2 = df\n",
|
||||
"\n",
|
||||
"df2[\"close_smooth\"] = savgol_filter(df2.close, 49, 5)\n",
|
||||
"\n",
|
||||
"fig, ax = plt.subplots()\n",
|
||||
"plt.xticks(rotation=-30)\n",
|
||||
"price, = ax.plot(df2.index, df2.close, c='grey', lw=2, alpha=0.5, zorder=5)\n",
|
||||
"price_smooth, = ax.plot(df2.index, df2.close_smooth, c='b', lw=2, zorder=5)\n",
|
||||
"\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "b00d9b70",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Detecting Extrema"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 112,
|
||||
"id": "2c1a99ec",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 640x480 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"downtrend\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"#df2 = df.iloc[0:500]\n",
|
||||
"df2 = get_rates()\n",
|
||||
"\n",
|
||||
"df2[\"close_smooth\"] = savgol_filter(df2.close, 49, 5)\n",
|
||||
"\n",
|
||||
"fig, ax = plt.subplots()\n",
|
||||
"plt.xticks(rotation=-30)\n",
|
||||
"price, = ax.plot(df2.index, df2.close, c='grey', lw=2, alpha=0.5, zorder=5)\n",
|
||||
"price_smooth, = ax.plot(df2.index, df2.close_smooth, c='b', lw=2, zorder=5)\n",
|
||||
"\n",
|
||||
"atr = df2.atr.iloc[-1] # all the first atrs are NaN\n",
|
||||
"\n",
|
||||
"peaks_idx, _ = find_peaks(df2.close_smooth, distance = 15, \n",
|
||||
" width = 3, prominence=atr)\n",
|
||||
"\n",
|
||||
"troughs_idx, _ = find_peaks(-1*df2.close_smooth, distance = 15, \n",
|
||||
" width = 3, prominence=atr)\n",
|
||||
"\n",
|
||||
"peaks, = ax.plot(df2.index[peaks_idx], df2.close_smooth.iloc[peaks_idx], \\\n",
|
||||
" c=\"r\", linestyle='None', markersize = 10.0, marker = \"o\", zorder=10)\n",
|
||||
"\n",
|
||||
"troughs, = ax.plot(df2.index[troughs_idx], df2.close_smooth.iloc[troughs_idx], \\\n",
|
||||
" c=\"g\", linestyle='None', markersize = 10.0, marker = \"o\", zorder=10)\n",
|
||||
"\n",
|
||||
"plt.show()\n",
|
||||
"\n",
|
||||
"#print(peaks_idx[-1], troughs_idx[-1])\n",
|
||||
"\n",
|
||||
"if peaks_idx[-1] > troughs_idx[-1]:\n",
|
||||
" print(\"downtrend\")\n",
|
||||
"else:\n",
|
||||
" print(\"uptrend\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "3865822f",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Finding Runs"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 89,
|
||||
"id": "5a190d91",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"[21] [85] Line2D(_child2) Line2D(_child3)\n",
|
||||
"1 0\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 640x480 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df2 = df.iloc[0:100]\n",
|
||||
"#df2 = df.tail(50)\n",
|
||||
"\n",
|
||||
"df2[\"close_smooth\"] = savgol_filter(df2.close, 49, 5)\n",
|
||||
"\n",
|
||||
"fig, ax = plt.subplots()\n",
|
||||
"plt.xticks(rotation=-30)\n",
|
||||
"plt.xticks(rotation=-30)\n",
|
||||
"price, = ax.plot(df2.index, df2.close, c='grey', lw=2, alpha=0.5, zorder=5)\n",
|
||||
"price_smooth, = ax.plot(df2.index, df2.close_smooth, c='b', lw=2, zorder=5)\n",
|
||||
"\n",
|
||||
"atr = df2.atr.iloc[-1] # all the first atrs are NaN\n",
|
||||
"\n",
|
||||
"peaks_idx, _ = find_peaks(df2.close_smooth, distance = 15, \n",
|
||||
" width = 3, prominence=atr)\n",
|
||||
"\n",
|
||||
"troughs_idx, _ = find_peaks(-1*df2.close_smooth, distance = 15, \n",
|
||||
" width = 3, prominence=atr)\n",
|
||||
"\n",
|
||||
"peaks, = ax.plot(df2.index[peaks_idx], df2.close_smooth.iloc[peaks_idx], \\\n",
|
||||
" c=\"r\", linestyle='None', markersize = 10.0, marker = \"o\", zorder=10)\n",
|
||||
"\n",
|
||||
"troughs, = ax.plot(df2.index[troughs_idx], df2.close_smooth.iloc[troughs_idx], \\\n",
|
||||
" c=\"g\", linestyle='None', markersize = 10.0, marker = \"o\", zorder=10)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"print(peaks_idx, troughs_idx, peaks, troughs)\n",
|
||||
"\n",
|
||||
"up_run_length = 0\n",
|
||||
"up_run = True\n",
|
||||
"down_run_length = 0\n",
|
||||
"while up_run:\n",
|
||||
" if 2 + up_run_length > len(peaks_idx) or 2 + up_run_length > len(troughs_idx):\n",
|
||||
" break\n",
|
||||
" if df2.close_smooth.iloc[peaks_idx[-1 - up_run_length]] > df2.close_smooth.iloc[peaks_idx[-2 - up_run_length]] and \\\n",
|
||||
" df2.close_smooth.iloc[troughs_idx[-1 - up_run_length]] > df2.close_smooth.iloc[troughs_idx[-2 - up_run_length]]:\n",
|
||||
" up_run_length += 1\n",
|
||||
" else:\n",
|
||||
" up_run = False \n",
|
||||
"\n",
|
||||
"# down_run_length = 0\n",
|
||||
"# down_run = True\n",
|
||||
"# while down_run:\n",
|
||||
"# if 2 + down_run_length > len(peaks_idx) or 2 + down_run_length > len(troughs_idx):\n",
|
||||
"# break\n",
|
||||
" \n",
|
||||
"# if df2.close_smooth.iloc[peaks_idx[-1 - down_run_length]] < df2.close_smooth.iloc[peaks_idx[-2 - down_run_length]] and \\\n",
|
||||
"# df2.close_smooth.iloc[troughs_idx[-1 - down_run_length]] < + df2.close_smooth.iloc[troughs_idx[-2 - down_run_length]]:\n",
|
||||
"# down_run_length += 1\n",
|
||||
"# else:\n",
|
||||
"# down_run = False\n",
|
||||
" \n",
|
||||
"\n",
|
||||
"if up_run_length > 0:\n",
|
||||
" ax.set_facecolor((150/255, 255/255, 159/255, 0.3))\n",
|
||||
" pause_trading = 0\n",
|
||||
"else:# down_run_length > 0:\n",
|
||||
" ax.set_facecolor((255/255, 255/255, 80/255, 0.3))\n",
|
||||
" pause_trading = 1\n",
|
||||
" \n",
|
||||
"print(pause_trading, up_run_length)\n",
|
||||
"\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 55,
|
||||
"id": "6b7b9586",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#if df2.close_smooth.iloc[peaks_idx[-1 - down_run_length]] < df2.close_smooth.iloc[peaks_idx[-2 - down_run_length]] :\n",
|
||||
"#\\\n",
|
||||
"if df2.close_smooth.iloc[troughs_idx[-1 - down_run_length]] < df2.close_smooth.iloc[troughs_idx[-1 - down_run_length]]:\n",
|
||||
" print(True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "564deae7",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"!pip install ffmpeg-python "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "414f56a7",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 31,
|
||||
"id": "e8e6c15e",
|
||||
"metadata": {
|
||||
"scrolled": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"c:\\ProgramData\\anaconda3\\Lib\\site-packages\\matplotlib\\animation.py:1740: UserWarning: Can not start iterating the frames for the initial draw. This can be caused by passing in a 0 length sequence for *frames*.\n",
|
||||
"\n",
|
||||
"If you passed *frames* as a generator it may be exhausted due to a previous display or save.\n",
|
||||
" warnings.warn(\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"ename": "RuntimeError",
|
||||
"evalue": "Requested MovieWriter (ffmpeg) not available",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[1;31mRuntimeError\u001b[0m Traceback (most recent call last)",
|
||||
"Cell \u001b[1;32mIn[31], line 85\u001b[0m\n\u001b[0;32m 81\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m price, price_smooth, peaks, troughs\n\u001b[0;32m 84\u001b[0m anim \u001b[38;5;241m=\u001b[39m FuncAnimation(fig, animate, frames\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mlen\u001b[39m(df)\u001b[38;5;241m-\u001b[39mbars_in_frame, interval\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m40\u001b[39m, blit\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m---> 85\u001b[0m video \u001b[38;5;241m=\u001b[39m HTML(anim\u001b[38;5;241m.\u001b[39mto_html5_video())\n\u001b[0;32m 86\u001b[0m display\u001b[38;5;241m.\u001b[39mdisplay(video)\n",
|
||||
"File \u001b[1;32mc:\\ProgramData\\anaconda3\\Lib\\site-packages\\matplotlib\\animation.py:1284\u001b[0m, in \u001b[0;36mAnimation.to_html5_video\u001b[1;34m(self, embed_limit)\u001b[0m\n\u001b[0;32m 1281\u001b[0m path \u001b[38;5;241m=\u001b[39m Path(tmpdir, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtemp.m4v\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 1282\u001b[0m \u001b[38;5;66;03m# We create a writer manually so that we can get the\u001b[39;00m\n\u001b[0;32m 1283\u001b[0m \u001b[38;5;66;03m# appropriate size for the tag\u001b[39;00m\n\u001b[1;32m-> 1284\u001b[0m Writer \u001b[38;5;241m=\u001b[39m writers[mpl\u001b[38;5;241m.\u001b[39mrcParams[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124manimation.writer\u001b[39m\u001b[38;5;124m'\u001b[39m]]\n\u001b[0;32m 1285\u001b[0m writer \u001b[38;5;241m=\u001b[39m Writer(codec\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mh264\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m 1286\u001b[0m bitrate\u001b[38;5;241m=\u001b[39mmpl\u001b[38;5;241m.\u001b[39mrcParams[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124manimation.bitrate\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[0;32m 1287\u001b[0m fps\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1000.\u001b[39m \u001b[38;5;241m/\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_interval)\n\u001b[0;32m 1288\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msave(\u001b[38;5;28mstr\u001b[39m(path), writer\u001b[38;5;241m=\u001b[39mwriter)\n",
|
||||
"File \u001b[1;32mc:\\ProgramData\\anaconda3\\Lib\\site-packages\\matplotlib\\animation.py:148\u001b[0m, in \u001b[0;36mMovieWriterRegistry.__getitem__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m 146\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mis_available(name):\n\u001b[0;32m 147\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_registered[name]\n\u001b[1;32m--> 148\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRequested MovieWriter (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m) not available\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
|
||||
"\u001b[1;31mRuntimeError\u001b[0m: Requested MovieWriter (ffmpeg) not available"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 640x480 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import matplotlib\n",
|
||||
"matplotlib.rcParams['animation.embed_limit'] = 2**128\n",
|
||||
"\n",
|
||||
"bars_in_frame = 300\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"fig, ax = plt.subplots()\n",
|
||||
"\n",
|
||||
"# increase video quality\n",
|
||||
"#fig, ax = plt.subplots(figsize=(8,8), dpi=300)\n",
|
||||
"\n",
|
||||
"price, = ax.plot([], c='grey', lw=3, alpha=0.5, zorder=5)\n",
|
||||
"price_smooth, = ax.plot([], c='b', lw=5, zorder=5)\n",
|
||||
"peaks, = ax.plot([], c=\"r\", linestyle='None', markersize = 15.0, marker = \"o\", zorder=10)\n",
|
||||
"troughs, = ax.plot([], c=\"g\", linestyle='None', markersize = 15.0, marker = \"o\", zorder=10)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"## Might turn the yaxis off, can be annoying\n",
|
||||
"ax.set_ylim(15000,20000)\n",
|
||||
"ax.set_xlim(0,bars_in_frame)\n",
|
||||
"\n",
|
||||
"def animate(frame):\n",
|
||||
" \n",
|
||||
" frames_behind = 30\n",
|
||||
"\n",
|
||||
" df2 = df.iloc[-len(df) + frame + 30:frame+bars_in_frame + 30]\n",
|
||||
" df2[\"close_smooth\"] = savgol_filter(df2.close, 49, 5)\n",
|
||||
"\n",
|
||||
" df2 = df2.iloc[frames_behind:]\n",
|
||||
" \n",
|
||||
" x_coords = [x for x in range(len(df2.close_smooth))]\n",
|
||||
" \n",
|
||||
" price.set_data((x_coords, df2.close))\n",
|
||||
" price_smooth.set_data((x_coords, df2.close_smooth))\n",
|
||||
" \n",
|
||||
" first_atr = df2.atr.iloc[0]\n",
|
||||
" \n",
|
||||
" ax.set_ylim(df2.close_smooth.min() - 10*first_atr, df2.close_smooth.max() + 10*first_atr)\n",
|
||||
" \n",
|
||||
" peaks_idx, _ = find_peaks(df2.close_smooth, distance = 15, \n",
|
||||
" width = 3, prominence=first_atr)\n",
|
||||
"\n",
|
||||
" troughs_idx, _ = find_peaks(-1*df2.close_smooth, distance = 15, \n",
|
||||
" width = 3, prominence=first_atr)\n",
|
||||
" \n",
|
||||
" up_run_length = 0\n",
|
||||
" up_run = True\n",
|
||||
" while up_run:\n",
|
||||
" if 2 + up_run_length > len(peaks_idx) or 2 + up_run_length > len(troughs_idx):\n",
|
||||
" break\n",
|
||||
"\n",
|
||||
" if df2.close_smooth.iloc[peaks_idx[-1 - up_run_length]] > df2.close_smooth.iloc[peaks_idx[-2 - up_run_length]] and \\\n",
|
||||
" df2.close_smooth.iloc[troughs_idx[-1 - up_run_length]] > df2.close_smooth.iloc[troughs_idx[-2 - up_run_length]]:\n",
|
||||
" up_run_length += 1\n",
|
||||
" else:\n",
|
||||
" up_run = False\n",
|
||||
"\n",
|
||||
" down_run_length = 0\n",
|
||||
" down_run = True\n",
|
||||
" while down_run:\n",
|
||||
" if 2 + down_run_length > len(peaks_idx) or 2 + down_run_length > len(troughs_idx):\n",
|
||||
" break\n",
|
||||
" \n",
|
||||
" if df2.close_smooth.iloc[peaks_idx[-1 - down_run_length]] < df2.close_smooth.iloc[peaks_idx[-2 - down_run_length]] and \\\n",
|
||||
" df2.close_smooth.iloc[troughs_idx[-1 - down_run_length]] < + df2.close_smooth.iloc[troughs_idx[-2 - down_run_length]]:\n",
|
||||
" down_run_length += 1\n",
|
||||
" else:\n",
|
||||
" down_run = False\n",
|
||||
" \n",
|
||||
" peaks.set_data((peaks_idx, df2.close_smooth.iloc[peaks_idx]))\n",
|
||||
" troughs.set_data((troughs_idx, df2.close_smooth.iloc[troughs_idx]))\n",
|
||||
" \n",
|
||||
" if up_run_length > 0:\n",
|
||||
" \n",
|
||||
" ax.set_facecolor((150/255, 255/255, 159/255, 0.3))\n",
|
||||
" elif down_run_length > 0:\n",
|
||||
" ax.set_facecolor((255/255, 150/255, 150/255, 0.3))\n",
|
||||
" else:\n",
|
||||
" ax.set_facecolor(\"white\")\n",
|
||||
" \n",
|
||||
" return price, price_smooth, peaks, troughs\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"anim = FuncAnimation(fig, animate, frames=len(df)-bars_in_frame, interval=40, blit=True)\n",
|
||||
"video = HTML(anim.to_html5_video())\n",
|
||||
"display.display(video)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "87748618",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Saving our animation"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "8d808048",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"with open('video-hd.html', 'w') as f:\n",
|
||||
" f.write(video.data)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "21838944",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "base",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@@ -1,2 +0,0 @@
|
||||
[ZoneTransfer]
|
||||
ZoneId=3
|
||||
@@ -0,0 +1,322 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
📥 JSON Trade History → SQLite Database Importer
|
||||
Importiert alte Trade-Daten aus JSON-Dateien in die trading_bot.db
|
||||
"""
|
||||
|
||||
import json
|
||||
import sqlite3
|
||||
import os
|
||||
from datetime import datetime
|
||||
import glob
|
||||
|
||||
# ==========================================
|
||||
# CONFIGURATION
|
||||
# ==========================================
|
||||
|
||||
DB_PATH = "trading_bot.db"
|
||||
JSON_PATTERN = "trade_performance_*.json" # Alle JSON-Dateien mit diesem Muster
|
||||
|
||||
# ==========================================
|
||||
# DATABASE CONNECTION
|
||||
# ==========================================
|
||||
|
||||
def get_db_connection():
|
||||
"""Verbindung zur Datenbank herstellen"""
|
||||
conn = sqlite3.connect(DB_PATH)
|
||||
conn.row_factory = sqlite3.Row
|
||||
return conn
|
||||
|
||||
# ==========================================
|
||||
# IMPORT FUNCTIONS
|
||||
# ==========================================
|
||||
|
||||
def parse_trade_data(trade, index=0):
|
||||
"""
|
||||
Konvertiert JSON Trade-Daten ins Datenbank-Format
|
||||
"""
|
||||
# Generate unique ticket from timestamp + index (für historische Daten ohne echtes Ticket)
|
||||
timestamp = trade.get('timestamp', '')
|
||||
ticket = hash(timestamp + str(index)) % 1000000000 # Unique ticket generieren
|
||||
|
||||
# Basis-Daten
|
||||
data = {
|
||||
'ticket': ticket,
|
||||
'position_id': 0, # Nicht in JSON vorhanden
|
||||
'symbol': trade.get('symbol', 'XAUUSD'),
|
||||
'strategy_name': 'TradingBot_' + trade.get('version', 'V1.6'),
|
||||
'type': 'BUY' if trade.get('entry_signal') == 1 else 'SELL' if trade.get('entry_signal') == -1 else 'UNKNOWN',
|
||||
'volume': 0.01, # Default, nicht in JSON
|
||||
'entry_price': 0.0, # Nicht in JSON
|
||||
'exit_price': None,
|
||||
'sl_price': None,
|
||||
'tp_price': None,
|
||||
'entry_time': trade.get('timestamp'),
|
||||
'exit_time': None,
|
||||
'duration_hours': None,
|
||||
'session': trade.get('session', 'unknown'),
|
||||
'regime': trade.get('market_regime', 'unknown'),
|
||||
'quality': trade.get('signal_quality', 'unknown'),
|
||||
'confidence': trade.get('confidence', 0.0),
|
||||
'timeframe_alignment': trade.get('timeframe_alignment', 2),
|
||||
'profit': None, # Nicht in JSON
|
||||
'commission': 0.0,
|
||||
'swap': 0.0,
|
||||
'net_profit': None,
|
||||
'profit_pct': None,
|
||||
'risk_amount': trade.get('risk_amount'),
|
||||
'risk_pct': trade.get('risk_pct', 0.01),
|
||||
'rr_ratio': None,
|
||||
'status': 'historical', # Markiere als historische Daten
|
||||
'exit_reason': 'imported_from_json'
|
||||
}
|
||||
|
||||
return data
|
||||
|
||||
def import_json_file(filepath, conn):
|
||||
"""
|
||||
Importiert eine JSON-Datei in die Datenbank
|
||||
"""
|
||||
print(f"\n📄 Processing: {os.path.basename(filepath)}")
|
||||
|
||||
try:
|
||||
with open(filepath, 'r') as f:
|
||||
trades = json.load(f)
|
||||
|
||||
if not isinstance(trades, list):
|
||||
print(f" ⚠️ Skipped: Not a list of trades")
|
||||
return 0
|
||||
|
||||
imported_count = 0
|
||||
skipped_count = 0
|
||||
|
||||
cursor = conn.cursor()
|
||||
|
||||
for i, trade in enumerate(trades):
|
||||
try:
|
||||
# Parse trade data (mit Index für unique ticket)
|
||||
data = parse_trade_data(trade, index=i)
|
||||
|
||||
# Check if already exists (by timestamp + symbol)
|
||||
cursor.execute("""
|
||||
SELECT id FROM trades
|
||||
WHERE entry_time = ? AND symbol = ?
|
||||
""", (data['entry_time'], data['symbol']))
|
||||
|
||||
if cursor.fetchone():
|
||||
skipped_count += 1
|
||||
continue
|
||||
|
||||
# Insert into database
|
||||
cursor.execute("""
|
||||
INSERT INTO trades (
|
||||
ticket, position_id, symbol, strategy_name, type, volume,
|
||||
entry_price, exit_price, sl_price, tp_price,
|
||||
entry_time, exit_time, duration_hours,
|
||||
session, regime, quality, confidence, timeframe_alignment,
|
||||
profit, commission, swap, net_profit, profit_pct,
|
||||
risk_amount, risk_pct, rr_ratio,
|
||||
status, exit_reason
|
||||
) VALUES (
|
||||
?, ?, ?, ?, ?, ?,
|
||||
?, ?, ?, ?,
|
||||
?, ?, ?,
|
||||
?, ?, ?, ?, ?,
|
||||
?, ?, ?, ?, ?,
|
||||
?, ?, ?,
|
||||
?, ?
|
||||
)
|
||||
""", (
|
||||
data['ticket'], data['position_id'], data['symbol'],
|
||||
data['strategy_name'], data['type'], data['volume'],
|
||||
data['entry_price'], data['exit_price'], data['sl_price'], data['tp_price'],
|
||||
data['entry_time'], data['exit_time'], data['duration_hours'],
|
||||
data['session'], data['regime'], data['quality'],
|
||||
data['confidence'], data['timeframe_alignment'],
|
||||
data['profit'], data['commission'], data['swap'],
|
||||
data['net_profit'], data['profit_pct'],
|
||||
data['risk_amount'], data['risk_pct'], data['rr_ratio'],
|
||||
data['status'], data['exit_reason']
|
||||
))
|
||||
|
||||
imported_count += 1
|
||||
|
||||
except Exception as e:
|
||||
print(f" ⚠️ Error importing trade: {e}")
|
||||
continue
|
||||
|
||||
conn.commit()
|
||||
|
||||
print(f" ✅ Imported: {imported_count} trades")
|
||||
print(f" ⏭️ Skipped: {skipped_count} trades (duplicates)")
|
||||
|
||||
return imported_count
|
||||
|
||||
except Exception as e:
|
||||
print(f" ❌ Error reading file: {e}")
|
||||
return 0
|
||||
|
||||
def import_all_json_files():
|
||||
"""
|
||||
Importiert alle JSON-Dateien im aktuellen Verzeichnis
|
||||
"""
|
||||
print("=" * 80)
|
||||
print("📥 JSON TO DATABASE IMPORTER")
|
||||
print("=" * 80)
|
||||
|
||||
# Finde alle JSON-Dateien
|
||||
json_files = glob.glob(JSON_PATTERN)
|
||||
|
||||
if not json_files:
|
||||
print(f"\n⚠️ No JSON files found matching pattern: {JSON_PATTERN}")
|
||||
print("\nSearching for any trade_performance*.json files...")
|
||||
json_files = glob.glob("trade_performance*.json")
|
||||
|
||||
if not json_files:
|
||||
print("\n❌ No JSON files found!")
|
||||
print("\nMake sure you have JSON files in this directory:")
|
||||
print(f" {os.getcwd()}")
|
||||
return
|
||||
|
||||
print(f"\n📊 Found {len(json_files)} JSON file(s):")
|
||||
for f in json_files:
|
||||
print(f" • {os.path.basename(f)}")
|
||||
|
||||
# Verbinde zur Datenbank
|
||||
print(f"\n🔌 Connecting to database: {DB_PATH}")
|
||||
|
||||
if not os.path.exists(DB_PATH):
|
||||
print(f"\n❌ Database not found: {DB_PATH}")
|
||||
print("\n💡 Please run the Trading Bot first to create the database!")
|
||||
return
|
||||
|
||||
conn = get_db_connection()
|
||||
|
||||
# Importiere jede Datei
|
||||
total_imported = 0
|
||||
|
||||
for json_file in json_files:
|
||||
imported = import_json_file(json_file, conn)
|
||||
total_imported += imported
|
||||
|
||||
conn.close()
|
||||
|
||||
# Summary
|
||||
print("\n" + "=" * 80)
|
||||
print("📊 IMPORT SUMMARY")
|
||||
print("=" * 80)
|
||||
print(f"Files processed: {len(json_files)}")
|
||||
print(f"Total trades imported: {total_imported}")
|
||||
print(f"\n✅ Import complete!")
|
||||
|
||||
# Zeige Statistik
|
||||
show_database_stats()
|
||||
|
||||
def show_database_stats():
|
||||
"""
|
||||
Zeigt Statistiken der importierten Daten
|
||||
"""
|
||||
conn = get_db_connection()
|
||||
cursor = conn.cursor()
|
||||
|
||||
print("\n" + "=" * 80)
|
||||
print("📈 DATABASE STATISTICS")
|
||||
print("=" * 80)
|
||||
|
||||
# Total trades
|
||||
cursor.execute("SELECT COUNT(*) FROM trades")
|
||||
total = cursor.fetchone()[0]
|
||||
print(f"\nTotal trades in database: {total}")
|
||||
|
||||
# By session
|
||||
cursor.execute("""
|
||||
SELECT session, COUNT(*) as count
|
||||
FROM trades
|
||||
GROUP BY session
|
||||
ORDER BY count DESC
|
||||
""")
|
||||
|
||||
print("\nTrades by Session:")
|
||||
for row in cursor.fetchall():
|
||||
session = row[0] or 'unknown'
|
||||
count = row[1]
|
||||
pct = (count / total * 100) if total > 0 else 0
|
||||
print(f" {session:10s}: {count:4d} ({pct:5.1f}%)")
|
||||
|
||||
# By version
|
||||
cursor.execute("""
|
||||
SELECT strategy_name, COUNT(*) as count
|
||||
FROM trades
|
||||
GROUP BY strategy_name
|
||||
ORDER BY count DESC
|
||||
""")
|
||||
|
||||
print("\nTrades by Version:")
|
||||
for row in cursor.fetchall():
|
||||
version = row[0]
|
||||
count = row[1]
|
||||
print(f" {version:20s}: {count:4d}")
|
||||
|
||||
# Date range
|
||||
cursor.execute("""
|
||||
SELECT
|
||||
MIN(entry_time) as first_trade,
|
||||
MAX(entry_time) as last_trade
|
||||
FROM trades
|
||||
""")
|
||||
|
||||
row = cursor.fetchone()
|
||||
if row[0]:
|
||||
print(f"\nDate Range:")
|
||||
print(f" First trade: {row[0]}")
|
||||
print(f" Last trade: {row[1]}")
|
||||
|
||||
conn.close()
|
||||
|
||||
print("\n" + "=" * 80)
|
||||
|
||||
# ==========================================
|
||||
# MANUAL IMPORT FUNCTION
|
||||
# ==========================================
|
||||
|
||||
def import_specific_file(filepath):
|
||||
"""
|
||||
Importiert eine spezifische JSON-Datei
|
||||
|
||||
Usage:
|
||||
python import_json_to_db.py <filepath>
|
||||
"""
|
||||
print("=" * 80)
|
||||
print("📥 MANUAL JSON IMPORT")
|
||||
print("=" * 80)
|
||||
|
||||
if not os.path.exists(filepath):
|
||||
print(f"\n❌ File not found: {filepath}")
|
||||
return
|
||||
|
||||
if not os.path.exists(DB_PATH):
|
||||
print(f"\n❌ Database not found: {DB_PATH}")
|
||||
print("\n💡 Please run the Trading Bot first to create the database!")
|
||||
return
|
||||
|
||||
conn = get_db_connection()
|
||||
imported = import_json_file(filepath, conn)
|
||||
conn.close()
|
||||
|
||||
print(f"\n✅ Import complete! Imported {imported} trades.")
|
||||
show_database_stats()
|
||||
|
||||
# ==========================================
|
||||
# MAIN
|
||||
# ==========================================
|
||||
|
||||
if __name__ == "__main__":
|
||||
import sys
|
||||
|
||||
if len(sys.argv) > 1:
|
||||
# Manual import of specific file
|
||||
filepath = sys.argv[1]
|
||||
import_specific_file(filepath)
|
||||
else:
|
||||
# Auto-import all JSON files
|
||||
import_all_json_files()
|
||||
@@ -0,0 +1,531 @@
|
||||
"""
|
||||
🔧 INFRASTRUCTURE PATCH - SQLite + Telegram Integration
|
||||
Verbindet TradingBot mit Database Logging und Telegram Notifications
|
||||
|
||||
INTEGRATION:
|
||||
- Automatisches DB Logging bei Trade Entry/Exit
|
||||
- Telegram Notifications für alle Events
|
||||
- Performance Tracking
|
||||
- Error Handling
|
||||
|
||||
USAGE:
|
||||
1. Import am Anfang des Notebooks
|
||||
2. Initialize Infrastructure
|
||||
3. Wrapper um execute_trade und Position Management
|
||||
"""
|
||||
|
||||
from trading_database import TradingDatabase
|
||||
from telegram_notifier import TelegramNotifier, load_telegram_config
|
||||
from datetime import datetime
|
||||
from typing import Dict, Optional, Callable
|
||||
import json
|
||||
import os
|
||||
|
||||
|
||||
class TradingInfrastructure:
|
||||
"""
|
||||
Infrastruktur-Manager für Database + Telegram Integration
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
db_path: str = "trading_bot.db",
|
||||
telegram_config: Dict = None,
|
||||
enable_telegram: bool = True,
|
||||
enable_database: bool = True
|
||||
):
|
||||
"""
|
||||
Initialize Infrastructure
|
||||
|
||||
Args:
|
||||
db_path: Path to SQLite database
|
||||
telegram_config: Dict with bot_token and chat_id
|
||||
enable_telegram: Enable Telegram notifications
|
||||
enable_database: Enable database logging
|
||||
"""
|
||||
self.enable_telegram = enable_telegram
|
||||
self.enable_database = enable_database
|
||||
|
||||
# Initialize Database
|
||||
if self.enable_database:
|
||||
try:
|
||||
self.db = TradingDatabase(db_path)
|
||||
print(f"✅ Database initialized: {db_path}")
|
||||
except Exception as e:
|
||||
print(f"❌ Database initialization failed: {e}")
|
||||
self.enable_database = False
|
||||
self.db = None
|
||||
else:
|
||||
self.db = None
|
||||
|
||||
# Initialize Telegram
|
||||
if self.enable_telegram:
|
||||
try:
|
||||
if telegram_config is None:
|
||||
telegram_config = load_telegram_config()
|
||||
|
||||
if telegram_config:
|
||||
self.telegram = TelegramNotifier(
|
||||
bot_token=telegram_config['bot_token'],
|
||||
chat_id=telegram_config['chat_id']
|
||||
)
|
||||
print("✅ Telegram notifications enabled")
|
||||
else:
|
||||
print("⚠️ No Telegram config found, notifications disabled")
|
||||
self.enable_telegram = False
|
||||
self.telegram = None
|
||||
except Exception as e:
|
||||
print(f"❌ Telegram initialization failed: {e}")
|
||||
self.enable_telegram = False
|
||||
self.telegram = None
|
||||
else:
|
||||
self.telegram = None
|
||||
|
||||
|
||||
# ==========================================
|
||||
# TRADE LOGGING
|
||||
# ==========================================
|
||||
|
||||
def log_trade_entry(self, trade_data: Dict):
|
||||
"""
|
||||
Log trade entry to database and send notification
|
||||
|
||||
Args:
|
||||
trade_data: Dictionary with trade information
|
||||
"""
|
||||
# Log to database
|
||||
if self.enable_database and self.db:
|
||||
try:
|
||||
self.db.log_trade_entry(trade_data)
|
||||
except Exception as e:
|
||||
print(f"⚠️ Database logging failed: {e}")
|
||||
|
||||
# Send Telegram notification
|
||||
if self.enable_telegram and self.telegram:
|
||||
try:
|
||||
self.telegram.notify_trade_entry(trade_data)
|
||||
except Exception as e:
|
||||
print(f"⚠️ Telegram notification failed: {e}")
|
||||
|
||||
|
||||
def log_trade_exit(self, ticket: int, exit_data: Dict):
|
||||
"""
|
||||
Log trade exit to database and send notification
|
||||
|
||||
Args:
|
||||
ticket: MT5 ticket number
|
||||
exit_data: Dictionary with exit information
|
||||
"""
|
||||
# Update database
|
||||
if self.enable_database and self.db:
|
||||
try:
|
||||
self.db.update_trade_exit(ticket, exit_data)
|
||||
except Exception as e:
|
||||
print(f"⚠️ Database update failed: {e}")
|
||||
|
||||
# Send Telegram notification
|
||||
if self.enable_telegram and self.telegram:
|
||||
try:
|
||||
# Get full trade data from database
|
||||
if self.db:
|
||||
trades = self.db.cursor.execute(
|
||||
"SELECT * FROM trades WHERE ticket = ?",
|
||||
(ticket,)
|
||||
).fetchone()
|
||||
|
||||
if trades:
|
||||
trade_dict = dict(trades)
|
||||
trade_dict.update(exit_data)
|
||||
self.telegram.notify_trade_exit(trade_dict)
|
||||
else:
|
||||
# Fallback if no database
|
||||
self.telegram.notify_trade_exit(exit_data)
|
||||
|
||||
except Exception as e:
|
||||
print(f"⚠️ Telegram notification failed: {e}")
|
||||
|
||||
|
||||
# ==========================================
|
||||
# WRAPPER FUNCTIONS
|
||||
# ==========================================
|
||||
|
||||
def create_monitored_execute_trade(self, original_execute_func: Callable):
|
||||
"""
|
||||
Create a wrapper around execute_trade function that logs to infrastructure
|
||||
|
||||
Args:
|
||||
original_execute_func: Original execute_trade_v2_adaptive function
|
||||
|
||||
Returns:
|
||||
Wrapped function with logging
|
||||
"""
|
||||
|
||||
def wrapped_execute_trade(*args, **kwargs):
|
||||
"""
|
||||
Wrapped execute trade with infrastructure logging
|
||||
"""
|
||||
try:
|
||||
# Call original function
|
||||
result = original_execute_func(*args, **kwargs)
|
||||
|
||||
# If trade was executed, result should contain trade info
|
||||
# This depends on your execute_trade implementation
|
||||
# You might need to modify execute_trade to return trade data
|
||||
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
# Log error
|
||||
if self.enable_telegram and self.telegram:
|
||||
self.telegram.send_error_alert(
|
||||
str(e),
|
||||
f"Error in execute_trade for {kwargs.get('symbol', 'unknown')}"
|
||||
)
|
||||
raise
|
||||
|
||||
return wrapped_execute_trade
|
||||
|
||||
|
||||
def extract_trade_data_from_mt5(self, position, strategy_name: str, session: str,
|
||||
confidence: float = None, quality: str = None) -> Dict:
|
||||
"""
|
||||
Extract trade data from MT5 position for logging
|
||||
|
||||
Args:
|
||||
position: MT5 position object
|
||||
strategy_name: Strategy name
|
||||
session: Trading session
|
||||
confidence: Signal confidence
|
||||
quality: Signal quality
|
||||
|
||||
Returns:
|
||||
Dictionary with trade data
|
||||
"""
|
||||
return {
|
||||
'ticket': position.ticket,
|
||||
'position_id': position.identifier,
|
||||
'symbol': position.symbol,
|
||||
'strategy_name': strategy_name,
|
||||
'type': 'BUY' if position.type == 0 else 'SELL',
|
||||
'volume': position.volume,
|
||||
'entry_price': position.price_open,
|
||||
'sl_price': position.sl,
|
||||
'tp_price': position.tp,
|
||||
'entry_time': datetime.fromtimestamp(position.time).strftime('%Y-%m-%d %H:%M:%S'),
|
||||
'session': session,
|
||||
'confidence': confidence,
|
||||
'quality': quality,
|
||||
'risk_pct': 0.01, # From config
|
||||
'status': 'open'
|
||||
}
|
||||
|
||||
|
||||
# ==========================================
|
||||
# STATUS & MONITORING
|
||||
# ==========================================
|
||||
|
||||
def send_bot_started(self, config: Dict):
|
||||
"""
|
||||
Send bot started notification
|
||||
|
||||
Args:
|
||||
config: Bot configuration
|
||||
"""
|
||||
if self.enable_telegram and self.telegram:
|
||||
try:
|
||||
self.telegram.send_bot_started(config)
|
||||
except Exception as e:
|
||||
print(f"⚠️ Telegram notification failed: {e}")
|
||||
|
||||
|
||||
def send_bot_stopped(self, reason: str = "Manual stop"):
|
||||
"""
|
||||
Send bot stopped notification
|
||||
|
||||
Args:
|
||||
reason: Stop reason
|
||||
"""
|
||||
if self.enable_telegram and self.telegram:
|
||||
try:
|
||||
self.telegram.send_bot_stopped(reason)
|
||||
except Exception as e:
|
||||
print(f"⚠️ Telegram notification failed: {e}")
|
||||
|
||||
|
||||
def send_daily_report(self):
|
||||
"""Send daily performance report"""
|
||||
if not (self.enable_database and self.enable_telegram):
|
||||
return
|
||||
|
||||
try:
|
||||
stats = self.db.get_daily_summary()
|
||||
self.telegram.send_daily_report(stats)
|
||||
except Exception as e:
|
||||
print(f"⚠️ Daily report failed: {e}")
|
||||
|
||||
|
||||
def send_weekly_report(self):
|
||||
"""Send weekly performance report"""
|
||||
if not (self.enable_database and self.enable_telegram):
|
||||
return
|
||||
|
||||
try:
|
||||
stats = self.db.get_overall_statistics(days=7)
|
||||
session_perf = self.db.get_session_performance(days=7)
|
||||
self.telegram.send_weekly_report(stats, session_perf)
|
||||
except Exception as e:
|
||||
print(f"⚠️ Weekly report failed: {e}")
|
||||
|
||||
|
||||
def log_bot_status(self, status: str, config: Dict = None, error_message: str = None):
|
||||
"""
|
||||
Log bot status to database
|
||||
|
||||
Args:
|
||||
status: 'running', 'stopped', 'error'
|
||||
config: Optional bot configuration
|
||||
error_message: Optional error message
|
||||
"""
|
||||
if not (self.enable_database and self.db):
|
||||
return
|
||||
|
||||
try:
|
||||
status_data = {
|
||||
'status': status,
|
||||
'version': config.get('version', 'V1.8') if config else 'V1.8',
|
||||
'active_sessions': [s for s, enabled in config.get('enabled_sessions', {}).items() if enabled] if config else [],
|
||||
'confidence_threshold': config.get('base_confidence', 60) if config else 60,
|
||||
'error_message': error_message
|
||||
}
|
||||
|
||||
self.db.log_bot_status(status_data)
|
||||
|
||||
except Exception as e:
|
||||
print(f"⚠️ Status logging failed: {e}")
|
||||
|
||||
|
||||
# ==========================================
|
||||
# MIGRATION & UTILITIES
|
||||
# ==========================================
|
||||
|
||||
def migrate_json_files(self, json_folder: str = "."):
|
||||
"""
|
||||
Migrate all JSON performance files to SQLite
|
||||
|
||||
Args:
|
||||
json_folder: Folder containing JSON files
|
||||
"""
|
||||
if not (self.enable_database and self.db):
|
||||
print("❌ Database not enabled")
|
||||
return
|
||||
|
||||
import glob
|
||||
|
||||
json_files = glob.glob(os.path.join(json_folder, "trade_performance_*.json"))
|
||||
|
||||
if not json_files:
|
||||
print("⚠️ No JSON files found")
|
||||
return
|
||||
|
||||
print(f"📁 Found {len(json_files)} JSON files")
|
||||
|
||||
for json_file in json_files:
|
||||
print(f"\n🔄 Migrating: {json_file}")
|
||||
try:
|
||||
self.db.migrate_from_json(json_file)
|
||||
except Exception as e:
|
||||
print(f"❌ Migration failed for {json_file}: {e}")
|
||||
|
||||
print("\n✅ Migration complete")
|
||||
|
||||
|
||||
def get_performance_summary(self, days: int = 7) -> Dict:
|
||||
"""
|
||||
Get performance summary
|
||||
|
||||
Args:
|
||||
days: Number of days to analyze
|
||||
|
||||
Returns:
|
||||
Dictionary with statistics
|
||||
"""
|
||||
if not (self.enable_database and self.db):
|
||||
return {}
|
||||
|
||||
try:
|
||||
stats = self.db.get_overall_statistics(days=days)
|
||||
session_perf = self.db.get_session_performance(days=days)
|
||||
|
||||
return {
|
||||
'overall': stats,
|
||||
'sessions': session_perf
|
||||
}
|
||||
except Exception as e:
|
||||
print(f"⚠️ Performance summary failed: {e}")
|
||||
return {}
|
||||
|
||||
|
||||
def close(self):
|
||||
"""Close all connections"""
|
||||
if self.db:
|
||||
self.db.close()
|
||||
|
||||
|
||||
def __enter__(self):
|
||||
"""Context manager entry"""
|
||||
return self
|
||||
|
||||
|
||||
def __exit__(self, exc_type, exc_val, exc_tb):
|
||||
"""Context manager exit"""
|
||||
self.close()
|
||||
|
||||
|
||||
# ==========================================
|
||||
# SCHEDULER INTEGRATION
|
||||
# ==========================================
|
||||
|
||||
def create_scheduled_reports(infrastructure: TradingInfrastructure, scheduler):
|
||||
"""
|
||||
Add scheduled reports to APScheduler
|
||||
|
||||
Args:
|
||||
infrastructure: TradingInfrastructure instance
|
||||
scheduler: APScheduler instance
|
||||
"""
|
||||
# Daily report at 22:00 UTC
|
||||
scheduler.add_job(
|
||||
infrastructure.send_daily_report,
|
||||
'cron',
|
||||
hour=22,
|
||||
minute=0,
|
||||
id='daily_report',
|
||||
replace_existing=True
|
||||
)
|
||||
|
||||
# Weekly report on Sunday at 23:00 UTC
|
||||
scheduler.add_job(
|
||||
infrastructure.send_weekly_report,
|
||||
'cron',
|
||||
day_of_week='sun',
|
||||
hour=23,
|
||||
minute=0,
|
||||
id='weekly_report',
|
||||
replace_existing=True
|
||||
)
|
||||
|
||||
print("✅ Scheduled reports added:")
|
||||
print(" 📊 Daily report: 22:00 UTC")
|
||||
print(" 📈 Weekly report: Sunday 23:00 UTC")
|
||||
|
||||
|
||||
# ==========================================
|
||||
# INTEGRATION HELPER
|
||||
# ==========================================
|
||||
|
||||
def create_infrastructure_config() -> Dict:
|
||||
"""
|
||||
Create infrastructure configuration
|
||||
|
||||
Returns:
|
||||
Dictionary with configuration
|
||||
"""
|
||||
return {
|
||||
'database': {
|
||||
'enabled': True,
|
||||
'path': 'trading_bot.db'
|
||||
},
|
||||
'telegram': {
|
||||
'enabled': True,
|
||||
'notifications': {
|
||||
'trade_entry': True,
|
||||
'trade_exit': True,
|
||||
'daily_report': True,
|
||||
'weekly_report': True,
|
||||
'error_alerts': True
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
# ==========================================
|
||||
# USAGE EXAMPLE
|
||||
# ==========================================
|
||||
|
||||
if __name__ == "__main__":
|
||||
print("="*70)
|
||||
print("🔧 INFRASTRUCTURE PATCH - Setup Check")
|
||||
print("="*70)
|
||||
|
||||
# Initialize infrastructure
|
||||
infra = TradingInfrastructure(
|
||||
db_path="trading_bot.db",
|
||||
enable_telegram=False # Disable for testing
|
||||
)
|
||||
|
||||
print("\n📊 Checking database...")
|
||||
if infra.db:
|
||||
stats = infra.db.get_overall_statistics()
|
||||
print(f" Total Trades: {stats.get('total_trades', 0)}")
|
||||
print(f" Win Rate: {stats.get('win_rate', 0)}%")
|
||||
|
||||
print("\n" + "="*70)
|
||||
print("✅ Infrastructure ready!")
|
||||
print("="*70)
|
||||
|
||||
print("""
|
||||
📋 Integration Steps:
|
||||
|
||||
1. Configure Telegram (optional):
|
||||
- Run: python telegram_notifier.py
|
||||
- Follow setup instructions
|
||||
- Create telegram_config.json
|
||||
|
||||
2. In your Notebook, add at the beginning:
|
||||
```python
|
||||
from infrastructure_patch import TradingInfrastructure, create_scheduled_reports
|
||||
from session_filter_patch import SESSION_WHITELIST_CONFIG
|
||||
|
||||
# Initialize infrastructure
|
||||
infra = TradingInfrastructure(
|
||||
db_path="trading_bot.db",
|
||||
enable_telegram=True,
|
||||
enable_database=True
|
||||
)
|
||||
|
||||
# Send bot started notification
|
||||
infra.send_bot_started(SESSION_WHITELIST_CONFIG)
|
||||
|
||||
# Add scheduled reports to your scheduler
|
||||
create_scheduled_reports(infra, scheduler)
|
||||
```
|
||||
|
||||
3. When opening a trade, log it:
|
||||
```python
|
||||
# After position opened
|
||||
trade_data = infra.extract_trade_data_from_mt5(
|
||||
position, strategy_name, session, confidence, quality
|
||||
)
|
||||
infra.log_trade_entry(trade_data)
|
||||
```
|
||||
|
||||
4. When closing a trade, log exit:
|
||||
```python
|
||||
# After position closed
|
||||
exit_data = {
|
||||
'exit_price': close_price,
|
||||
'exit_time': datetime.now(),
|
||||
'profit': profit,
|
||||
'net_profit': net_profit,
|
||||
'exit_reason': 'tp' or 'sl'
|
||||
}
|
||||
infra.log_trade_exit(ticket, exit_data)
|
||||
```
|
||||
|
||||
5. Optional - Migrate existing JSON data:
|
||||
```python
|
||||
infra.migrate_json_files(".")
|
||||
```
|
||||
""")
|
||||
|
||||
infra.close()
|
||||
-1963
File diff suppressed because it is too large.
Load diff
@@ -1,199 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Import Libaries"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import keyring as kr"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Save Pwd to keyring\n",
|
||||
"\n",
|
||||
"- **servicename** – The name of the service i.e to a certain organization with which the person is related to.\n",
|
||||
"- **username** – Username of the person.\n",
|
||||
"- **password** – Password associated with that username.\n",
|
||||
"\n",
|
||||
"```python\n",
|
||||
"kr.set_password(\"servicename\",\"username\",\"pwd\")\n",
|
||||
"```\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Live Acccount\n",
|
||||
"login = 10676507\n",
|
||||
"password = '5l5^hbEL'\n",
|
||||
"server = 'VantageInternational-Live 4'"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Demo Acccount\n",
|
||||
"login = 10800246\n",
|
||||
"password = '%dN!Zu96'\n",
|
||||
"server = 'VantageInternational-Demo'"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"kr.set_password(server, str(login), password)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Get Password\n",
|
||||
"\n",
|
||||
"To retrieve the saved password we will use the get_password() method. It takes two parameters.\n",
|
||||
"\n",
|
||||
"- Servicename\n",
|
||||
"- Username\n",
|
||||
"\n",
|
||||
"```python\n",
|
||||
"print(kr.get_password(\"Servicename\",\"Username\"))\n",
|
||||
"```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"5l5^hbEL\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print(kr.get_password(server,str(login)))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Retrievieng Credentials"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Username : 10676507\n",
|
||||
"Password : 5l5^hbEL\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"cred = kr.get_credential(server,\"\") \n",
|
||||
" \n",
|
||||
"print(\"Username : \",cred.username) \n",
|
||||
"print(\"Password : \",cred.password)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Delete from Keyring\n",
|
||||
"\n",
|
||||
"To delete a password we will use the delete_password() method. It also takes two parameters:\n",
|
||||
"\n",
|
||||
"- Servicename\n",
|
||||
"- Username\n",
|
||||
"\n",
|
||||
"```python\n",
|
||||
"kr.delete_password(\"Servicename\", \"Username\") \n",
|
||||
"```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"kr.delete_password(server, str(login)) "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"None\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print(kr.get_password(server, str(login)))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "base",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -0,0 +1,59 @@
|
||||
======================================================================
|
||||
TRADINGBOT V1.6 - DETAILED PERFORMANCE REPORT
|
||||
======================================================================
|
||||
|
||||
Generated: 2025-11-13 01:07:34
|
||||
|
||||
ZEITRAUM
|
||||
----------------------------------------------------------------------
|
||||
Start: 2025-10-31 00:43
|
||||
Ende: 2025-11-13 00:45
|
||||
Tage: 14 Tage
|
||||
Total Trades: 112
|
||||
Trades/Tag: 8.00
|
||||
|
||||
SESSION VERTEILUNG
|
||||
----------------------------------------------------------------------
|
||||
Asian : 44 ( 39.29%)
|
||||
London : 27 ( 24.11%)
|
||||
Ny : 13 ( 11.61%)
|
||||
Overlap : 28 ( 25.00%)
|
||||
|
||||
MARKET REGIME
|
||||
----------------------------------------------------------------------
|
||||
Ranging : 112 (100.00%)
|
||||
|
||||
ADAPTIVE INTERVALS
|
||||
----------------------------------------------------------------------
|
||||
5 Min: 13 ( 11.61%)
|
||||
15 Min: 63 ( 56.25%)
|
||||
30 Min: 36 ( 32.14%)
|
||||
|
||||
HOURLY DISTRIBUTION (Top 10)
|
||||
----------------------------------------------------------------------
|
||||
14:00 UTC: 11 ( 9.82%)
|
||||
15:00 UTC: 11 ( 9.82%)
|
||||
09:00 UTC: 9 ( 8.04%)
|
||||
02:00 UTC: 8 ( 7.14%)
|
||||
13:00 UTC: 7 ( 6.25%)
|
||||
03:00 UTC: 6 ( 5.36%)
|
||||
16:00 UTC: 6 ( 5.36%)
|
||||
04:00 UTC: 5 ( 4.46%)
|
||||
06:00 UTC: 5 ( 4.46%)
|
||||
11:00 UTC: 5 ( 4.46%)
|
||||
|
||||
WICHTIGE ERKENNTNISSE
|
||||
----------------------------------------------------------------------
|
||||
• Aktivste Session: ASIAN (44 trades)
|
||||
• Häufigstes Regime: RANGING (112 trades)
|
||||
• Meist genutztes Interval: 15 min (63 trades)
|
||||
• Durchschnittliche Confidence: 85.0%
|
||||
• Durchschnittliches Risk/Reward: 2.45
|
||||
|
||||
WARNUNG
|
||||
----------------------------------------------------------------------
|
||||
Dieser Report zeigt nur Signal-Generierung und Entry-Daten.
|
||||
Für echte Profitabilitäts-Bewertung werden Exit-Daten
|
||||
und P&L aus MT5 benötigt.
|
||||
|
||||
======================================================================
|
||||
File diff suppressed because it is too large.
Load diff
File diff suppressed because it is too large.
Load diff
@@ -0,0 +1,288 @@
|
||||
"""
|
||||
🎯 SESSION FILTER PATCH - TradingBot V1.6 to V1.7
|
||||
Optimiert Trading basierend auf Performance-Analyse
|
||||
|
||||
ÄNDERUNGEN:
|
||||
1. Deaktiviert Asian Session (25.6% Win-Rate, -$199 Verlust)
|
||||
2. Aktiviert nur NY + Overlap (beste Performance)
|
||||
3. Optional: London Session (Break-Even)
|
||||
|
||||
PERFORMANCE-IMPACT:
|
||||
- Asian ausschalten: -$199 eliminiert
|
||||
- Nur NY+Overlap: +$581 (49% mehr Profit)
|
||||
- Win-Rate: 30.9% → ~38%
|
||||
"""
|
||||
|
||||
# ==========================================
|
||||
# SESSION WHITELIST CONFIGURATION
|
||||
# ==========================================
|
||||
|
||||
SESSION_WHITELIST_CONFIG = {
|
||||
# Welche Sessions erlauben?
|
||||
'enabled_sessions': {
|
||||
'asian': False, # ❌ DEAKTIVIERT: +$202, aber nur 30.2% Win-Rate
|
||||
'london': False, # ❌ DEAKTIVIERT: +$79, aber nur 30.8% Win-Rate
|
||||
'overlap': False, # ❌ DEAKTIVIERT: -$208 kumuliert (2 Wochen), 27.3% Win-Rate
|
||||
'ny': True, # ✅ NUR NY AKTIV: +$660, 47.6% Win-Rate (BESTE!)
|
||||
},
|
||||
|
||||
# Trading Parameter
|
||||
'base_confidence': 60, # 🎯 CONFIDENCE THRESHOLD (60=relaxed, 70=balanced, 75=konservativ, 80=sehr strikt)
|
||||
'atr_mult': 1.5, # ATR Multiplikator für SL/TP
|
||||
'max_risk_per_trade': 0.01, # Max Risk pro Trade (1%)
|
||||
'min_atr': 0.0008, # Minimum ATR für Risk Filter
|
||||
|
||||
# Trading Optionen
|
||||
'risk_filter': True, # ATR-basierter Risk Filter
|
||||
'use_pullback_entry': False, # Pullback Entry Strategie
|
||||
|
||||
# Alternativ: Aggressive Mode (nur NY)
|
||||
'aggressive_mode': False, # ⚠️ Nicht nötig - already configured via enabled_sessions
|
||||
|
||||
# Alternativ: Conservative Mode (NY + Overlap + London)
|
||||
'conservative_mode': False, # ⚠️ Nicht aktiv
|
||||
|
||||
# Debug-Modus
|
||||
'debug': True,
|
||||
}
|
||||
|
||||
|
||||
# ==========================================
|
||||
# SESSION FILTER FUNCTION
|
||||
# ==========================================
|
||||
|
||||
def is_session_allowed(session_name, config=SESSION_WHITELIST_CONFIG):
|
||||
"""
|
||||
Prüft ob Trading in aktueller Session erlaubt ist
|
||||
|
||||
Args:
|
||||
session_name: 'asian', 'london', 'overlap', 'ny'
|
||||
config: Configuration Dictionary
|
||||
|
||||
Returns:
|
||||
(allowed: bool, reason: str)
|
||||
"""
|
||||
|
||||
# Aggressive Mode: Nur NY
|
||||
if config['aggressive_mode']:
|
||||
allowed = session_name == 'ny'
|
||||
reason = f"Aggressive Mode: Only NY session" if not allowed else "NY session - best performance"
|
||||
return allowed, reason
|
||||
|
||||
# Conservative Mode: Alle außer Asian
|
||||
if config['conservative_mode']:
|
||||
allowed = session_name != 'asian'
|
||||
reason = f"Conservative Mode: Asian disabled" if not allowed else f"{session_name.upper()} session allowed"
|
||||
return allowed, reason
|
||||
|
||||
# Standard: Whitelist-basiert
|
||||
allowed = config['enabled_sessions'].get(session_name, False)
|
||||
|
||||
if not allowed:
|
||||
reasons = {
|
||||
'asian': "Session blocked: Asian has -$199 loss, 25.6% win-rate",
|
||||
'london': "Session blocked: London is break-even, 29.6% win-rate",
|
||||
'overlap': "Session blocked: Not in whitelist",
|
||||
'ny': "Session blocked: Not in whitelist",
|
||||
}
|
||||
reason = reasons.get(session_name, f"Session {session_name} not in whitelist")
|
||||
else:
|
||||
performance = {
|
||||
'ny': "+$372 profit, 50.0% win-rate (BEST!)",
|
||||
'overlap': "+$209 profit, 32.1% win-rate (GOOD)",
|
||||
'london': "+$7 profit, 29.6% win-rate (Break-even)",
|
||||
}
|
||||
reason = f"{session_name.upper()} allowed: {performance.get(session_name, 'In whitelist')}"
|
||||
|
||||
return allowed, reason
|
||||
|
||||
|
||||
# ==========================================
|
||||
# SESSION FILTER WRAPPER
|
||||
# ==========================================
|
||||
|
||||
def create_session_filtered_check(
|
||||
rhythm_manager,
|
||||
execute_func,
|
||||
symbol,
|
||||
strategy_name,
|
||||
max_positions,
|
||||
logger,
|
||||
datetime,
|
||||
config=None
|
||||
):
|
||||
"""
|
||||
Factory-Funktion die eine gefilterte Trading-Check-Funktion erstellt
|
||||
|
||||
Args:
|
||||
rhythm_manager: AdaptiveRhythmManager Instanz
|
||||
execute_func: execute_trade_v2_adaptive Funktion
|
||||
symbol: Trading Symbol (z.B. "XAUUSD")
|
||||
strategy_name: Strategy Name
|
||||
max_positions: Max Positionen
|
||||
logger: Logger Instanz
|
||||
datetime: datetime module
|
||||
config: Optional custom config, sonst SESSION_WHITELIST_CONFIG
|
||||
|
||||
Returns:
|
||||
Gefilterte adaptive_trading_check Funktion
|
||||
"""
|
||||
if config is None:
|
||||
config = SESSION_WHITELIST_CONFIG
|
||||
|
||||
# Hole Trading-Parameter aus Config
|
||||
confidence_threshold = config.get('base_confidence', 60)
|
||||
atr_mult = config.get('atr_mult', 1.5)
|
||||
max_risk = config.get('max_risk_per_trade', 0.01)
|
||||
risk_filter = config.get('risk_filter', True)
|
||||
min_atr = config.get('min_atr', 0.0008)
|
||||
use_pullback = config.get('use_pullback_entry', False)
|
||||
|
||||
def adaptive_trading_check_filtered():
|
||||
"""
|
||||
🆕 V1.7: Session-gefilterte Trading Check Funktion
|
||||
"""
|
||||
try:
|
||||
# 1. Prüfe aktuelle Session
|
||||
session = rhythm_manager.get_current_session()
|
||||
allowed, reason = is_session_allowed(session, config)
|
||||
|
||||
if not allowed:
|
||||
if config['debug']:
|
||||
logger.info(f"⏸️ Trading SKIP: {reason}")
|
||||
return
|
||||
|
||||
# 2. Berechne optimales Intervall
|
||||
optimal_interval = rhythm_manager.calculate_optimal_interval()
|
||||
current_minute = datetime.now().minute
|
||||
|
||||
# 3. Trading nur zu berechneten Zeitpunkten
|
||||
if current_minute % optimal_interval == 0:
|
||||
logger.info(f"\n⏰ {datetime.now().strftime('%Y-%m-%d %H:%M:%S')} - ADAPTIVE Check")
|
||||
logger.info(f"✅ Session: {session.upper()} - {reason}")
|
||||
logger.info(f"📊 Confidence Threshold: {confidence_threshold}%")
|
||||
logger.info(f"⏱️ Intervall: {optimal_interval} min")
|
||||
|
||||
# Führe Trading aus mit Parametern aus Config
|
||||
execute_func(
|
||||
symbol=symbol,
|
||||
atr_mult=atr_mult,
|
||||
base_confidence=confidence_threshold,
|
||||
max_risk_per_trade=max_risk,
|
||||
risk_filter=risk_filter,
|
||||
min_atr=min_atr,
|
||||
use_pullback_entry=use_pullback,
|
||||
max_positions=max_positions,
|
||||
strategy_name=strategy_name,
|
||||
debug=True
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Fehler im Session-Filtered Trading Check: {e}")
|
||||
|
||||
return adaptive_trading_check_filtered
|
||||
|
||||
|
||||
# ==========================================
|
||||
# USAGE INSTRUCTIONS
|
||||
# ==========================================
|
||||
|
||||
"""
|
||||
📋 ANLEITUNG - Wie den Patch anwenden:
|
||||
|
||||
SCHRITT 1: Dieses File ins gleiche Verzeichnis wie das Notebook kopieren
|
||||
|
||||
SCHRITT 2: In Cell 24 (Adaptive Scheduler) folgendes ändern:
|
||||
|
||||
VORHER:
|
||||
```python
|
||||
def adaptive_trading_check():
|
||||
try:
|
||||
optimal_interval = rhythm_manager.calculate_optimal_interval()
|
||||
...
|
||||
```
|
||||
|
||||
NACHHER:
|
||||
```python
|
||||
# Importiere Session Filter
|
||||
from session_filter_patch import (
|
||||
adaptive_trading_check_with_session_filter,
|
||||
SESSION_WHITELIST_CONFIG,
|
||||
is_session_allowed
|
||||
)
|
||||
|
||||
# Alias für Kompatibilität
|
||||
adaptive_trading_check = adaptive_trading_check_with_session_filter
|
||||
```
|
||||
|
||||
SCHRITT 3: Scheduler neu starten
|
||||
|
||||
SCHRITT 4: Teste mit verschiedenen Modi:
|
||||
|
||||
# Standard Mode (NY + Overlap)
|
||||
SESSION_WHITELIST_CONFIG['enabled_sessions'] = {
|
||||
'asian': False, 'london': False,
|
||||
'overlap': True, 'ny': True
|
||||
}
|
||||
|
||||
# Aggressive Mode (nur NY, 50% Win-Rate!)
|
||||
SESSION_WHITELIST_CONFIG['aggressive_mode'] = True
|
||||
|
||||
# Conservative Mode (alles außer Asian)
|
||||
SESSION_WHITELIST_CONFIG['conservative_mode'] = True
|
||||
|
||||
SCHRITT 5: Monitor Performance für 1-2 Wochen
|
||||
|
||||
"""
|
||||
|
||||
|
||||
# ==========================================
|
||||
# TESTING
|
||||
# ==========================================
|
||||
|
||||
if __name__ == "__main__":
|
||||
print("="*70)
|
||||
print("🧪 SESSION FILTER TESTS")
|
||||
print("="*70)
|
||||
|
||||
sessions = ['asian', 'london', 'overlap', 'ny']
|
||||
|
||||
print("\n📊 STANDARD MODE (NY + Overlap):")
|
||||
print("-" * 70)
|
||||
for session in sessions:
|
||||
allowed, reason = is_session_allowed(session)
|
||||
emoji = "✅" if allowed else "❌"
|
||||
print(f"{emoji} {session.upper():8s}: {reason}")
|
||||
|
||||
print("\n📊 AGGRESSIVE MODE (nur NY):")
|
||||
print("-" * 70)
|
||||
test_config = SESSION_WHITELIST_CONFIG.copy()
|
||||
test_config['aggressive_mode'] = True
|
||||
for session in sessions:
|
||||
allowed, reason = is_session_allowed(session, test_config)
|
||||
emoji = "✅" if allowed else "❌"
|
||||
print(f"{emoji} {session.upper():8s}: {reason}")
|
||||
|
||||
print("\n📊 CONSERVATIVE MODE (alles außer Asian):")
|
||||
print("-" * 70)
|
||||
test_config = SESSION_WHITELIST_CONFIG.copy()
|
||||
test_config['conservative_mode'] = True
|
||||
test_config['aggressive_mode'] = False
|
||||
for session in sessions:
|
||||
allowed, reason = is_session_allowed(session, test_config)
|
||||
emoji = "✅" if allowed else "❌"
|
||||
print(f"{emoji} {session.upper():8s}: {reason}")
|
||||
|
||||
print("\n" + "="*70)
|
||||
print("✅ Tests complete")
|
||||
print("="*70)
|
||||
|
||||
print("\n💡 ERWARTETE PERFORMANCE-VERBESSERUNG:")
|
||||
print(" Standard Mode (NY + Overlap):")
|
||||
print(" • Trades: 40 statt 110 (-64%)")
|
||||
print(" • Profit: +$581 statt +$389 (+49%)")
|
||||
print(" • Win-Rate: ~38% statt 30.9%")
|
||||
print("\n Aggressive Mode (nur NY):")
|
||||
print(" • Trades: 12 statt 110 (-89%)")
|
||||
print(" • Profit: +$372 statt +$389 (-4%, aber 50% Win-Rate!)")
|
||||
print(" • Win-Rate: 50% statt 30.9% (+19.1%)")
|
||||
@@ -0,0 +1,13 @@
|
||||
{
|
||||
"bot_token": "YOUR_BOT_TOKEN_HERE",
|
||||
"chat_id": "YOUR_CHAT_ID_HERE",
|
||||
"notifications": {
|
||||
"trade_entry": true,
|
||||
"trade_exit": true,
|
||||
"daily_report": true,
|
||||
"weekly_report": true,
|
||||
"error_alerts": true
|
||||
},
|
||||
"daily_report_time": "22:00",
|
||||
"weekly_report_day": "Sunday"
|
||||
}
|
||||
@@ -0,0 +1,551 @@
|
||||
"""
|
||||
📱 TELEGRAM NOTIFIER - Mobile Trading Notifications
|
||||
Sendet Trade Updates, Performance Reports und Alerts
|
||||
|
||||
FEATURES:
|
||||
- Trade Entry/Exit Notifications
|
||||
- Daily/Weekly Performance Reports
|
||||
- Error Alerts
|
||||
- Bot Status Updates
|
||||
- Custom Commands (/status, /report)
|
||||
"""
|
||||
|
||||
import requests
|
||||
import json
|
||||
from datetime import datetime, timedelta
|
||||
from typing import Dict, List, Optional
|
||||
import os
|
||||
|
||||
|
||||
class TelegramNotifier:
|
||||
"""
|
||||
Telegram Bot Integration für Trading Notifications
|
||||
"""
|
||||
|
||||
def __init__(self, bot_token: str, chat_id: str):
|
||||
"""
|
||||
Initialize Telegram Bot
|
||||
|
||||
Args:
|
||||
bot_token: Telegram Bot Token (von @BotFather)
|
||||
chat_id: Telegram Chat ID (deine User ID)
|
||||
"""
|
||||
self.bot_token = bot_token
|
||||
self.chat_id = chat_id
|
||||
self.base_url = f"https://api.telegram.org/bot{bot_token}"
|
||||
|
||||
# Test connection
|
||||
self._test_connection()
|
||||
|
||||
|
||||
def _test_connection(self):
|
||||
"""Test Telegram API connection"""
|
||||
try:
|
||||
response = requests.get(f"{self.base_url}/getMe", timeout=5)
|
||||
if response.status_code == 200:
|
||||
bot_info = response.json()
|
||||
print(f"✅ Telegram Bot connected: @{bot_info['result']['username']}")
|
||||
else:
|
||||
print(f"⚠️ Telegram connection issue: {response.status_code}")
|
||||
except Exception as e:
|
||||
print(f"❌ Telegram connection failed: {e}")
|
||||
|
||||
|
||||
def send_message(self, text: str, parse_mode: str = "Markdown") -> bool:
|
||||
"""
|
||||
Send a text message to Telegram
|
||||
|
||||
Args:
|
||||
text: Message text (supports Markdown)
|
||||
parse_mode: 'Markdown' or 'HTML'
|
||||
|
||||
Returns:
|
||||
True if successful, False otherwise
|
||||
"""
|
||||
try:
|
||||
url = f"{self.base_url}/sendMessage"
|
||||
payload = {
|
||||
'chat_id': self.chat_id,
|
||||
'text': text,
|
||||
'parse_mode': parse_mode,
|
||||
'disable_web_page_preview': True
|
||||
}
|
||||
|
||||
response = requests.post(url, json=payload, timeout=10)
|
||||
|
||||
if response.status_code == 200:
|
||||
return True
|
||||
else:
|
||||
print(f"⚠️ Telegram send failed: {response.status_code}")
|
||||
return False
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Telegram error: {e}")
|
||||
return False
|
||||
|
||||
|
||||
# ==========================================
|
||||
# TRADE NOTIFICATIONS
|
||||
# ==========================================
|
||||
|
||||
def notify_trade_entry(self, trade_data: Dict):
|
||||
"""
|
||||
Notify about new trade entry
|
||||
|
||||
Args:
|
||||
trade_data: Dictionary with trade information
|
||||
"""
|
||||
type_emoji = "🟢" if trade_data['type'] == 'BUY' else "🔴"
|
||||
session_emoji = self._get_session_emoji(trade_data.get('session', 'unknown'))
|
||||
|
||||
message = f"""
|
||||
{type_emoji} *Trade Opened*
|
||||
|
||||
*Symbol:* {trade_data['symbol']}
|
||||
*Type:* {trade_data['type']}
|
||||
*Entry:* {trade_data['entry_price']}
|
||||
|
||||
*SL:* {trade_data.get('sl_price', 'N/A')} | *TP:* {trade_data.get('tp_price', 'N/A')}
|
||||
|
||||
*Risk:* ${trade_data.get('risk_amount', 0):.2f} ({trade_data.get('risk_pct', 0)*100:.1f}%)
|
||||
*Volume:* {trade_data.get('volume', 0)} lots
|
||||
|
||||
{session_emoji} *Session:* {trade_data.get('session', 'unknown').upper()}
|
||||
📊 *Confidence:* {trade_data.get('confidence', 0):.1f}%
|
||||
🎯 *Quality:* {trade_data.get('quality', 'unknown').upper()}
|
||||
|
||||
⏰ {datetime.now().strftime('%Y-%m-%d %H:%M:%S')} UTC
|
||||
"""
|
||||
self.send_message(message)
|
||||
|
||||
|
||||
def notify_trade_exit(self, trade_data: Dict):
|
||||
"""
|
||||
Notify about trade exit
|
||||
|
||||
Args:
|
||||
trade_data: Dictionary with trade and exit information
|
||||
"""
|
||||
profit = trade_data.get('net_profit', 0)
|
||||
profit_emoji = "✅" if profit > 0 else "❌"
|
||||
type_emoji = "🟢" if trade_data['type'] == 'BUY' else "🔴"
|
||||
|
||||
# Format profit/loss
|
||||
profit_text = f"+${profit:.2f}" if profit > 0 else f"${profit:.2f}"
|
||||
|
||||
message = f"""
|
||||
{profit_emoji} *Trade Closed* {type_emoji}
|
||||
|
||||
*Symbol:* {trade_data['symbol']}
|
||||
*Type:* {trade_data['type']}
|
||||
|
||||
*Entry:* {trade_data['entry_price']}
|
||||
*Exit:* {trade_data.get('exit_price', 'N/A')}
|
||||
|
||||
*Profit:* {profit_text}
|
||||
*Duration:* {trade_data.get('duration_hours', 0):.1f}h
|
||||
|
||||
*Exit Reason:* {trade_data.get('exit_reason', 'unknown').upper()}
|
||||
*R:R Ratio:* {trade_data.get('rr_ratio', 0):.2f}
|
||||
|
||||
⏰ {datetime.now().strftime('%Y-%m-%d %H:%M:%S')} UTC
|
||||
"""
|
||||
self.send_message(message)
|
||||
|
||||
|
||||
# ==========================================
|
||||
# PERFORMANCE REPORTS
|
||||
# ==========================================
|
||||
|
||||
def send_daily_report(self, stats: Dict):
|
||||
"""
|
||||
Send daily performance summary
|
||||
|
||||
Args:
|
||||
stats: Dictionary with daily statistics
|
||||
"""
|
||||
win_rate = stats.get('win_rate', 0)
|
||||
net_profit = stats.get('net_profit', 0)
|
||||
|
||||
# Emojis based on performance
|
||||
profit_emoji = "💰" if net_profit > 0 else "📉"
|
||||
wr_emoji = "🎯" if win_rate >= 50 else "⚠️" if win_rate >= 40 else "🔴"
|
||||
|
||||
message = f"""
|
||||
📊 *Daily Trading Report*
|
||||
📅 {datetime.now().strftime('%Y-%m-%d')}
|
||||
|
||||
━━━━━━━━━━━━━━━━━━━━
|
||||
|
||||
*Trades:* {stats.get('total_trades', 0)}
|
||||
*Wins:* {stats.get('wins', 0)} | *Losses:* {stats.get('losses', 0)}
|
||||
{wr_emoji} *Win Rate:* {win_rate:.1f}%
|
||||
|
||||
{profit_emoji} *Net Profit:* ${net_profit:.2f}
|
||||
*Gross Profit:* ${stats.get('gross_profit', 0):.2f}
|
||||
*Gross Loss:* ${stats.get('gross_loss', 0):.2f}
|
||||
|
||||
*Avg Trade:* ${stats.get('avg_trade', 0):.2f}
|
||||
|
||||
━━━━━━━━━━━━━━━━━━━━
|
||||
✅ Bot Status: Running
|
||||
"""
|
||||
self.send_message(message)
|
||||
|
||||
|
||||
def send_weekly_report(self, stats: Dict, session_perf: Dict = None):
|
||||
"""
|
||||
Send weekly performance summary
|
||||
|
||||
Args:
|
||||
stats: Dictionary with weekly statistics
|
||||
session_perf: Optional session performance breakdown
|
||||
"""
|
||||
win_rate = stats.get('win_rate', 0)
|
||||
net_profit = stats.get('net_profit', 0)
|
||||
profit_factor = stats.get('profit_factor', 0)
|
||||
|
||||
# Emojis
|
||||
profit_emoji = "💰" if net_profit > 0 else "📉"
|
||||
pf_emoji = "🟢" if profit_factor >= 1.5 else "🟡" if profit_factor >= 1.2 else "🔴"
|
||||
|
||||
message = f"""
|
||||
📈 *Weekly Trading Report*
|
||||
📅 {(datetime.now() - timedelta(days=7)).strftime('%Y-%m-%d')} to {datetime.now().strftime('%Y-%m-%d')}
|
||||
|
||||
━━━━━━━━━━━━━━━━━━━━
|
||||
|
||||
📊 *Overall Performance*
|
||||
*Total Trades:* {stats.get('total_trades', 0)}
|
||||
*Wins:* {stats.get('wins', 0)} | *Losses:* {stats.get('losses', 0)}
|
||||
*Win Rate:* {win_rate:.1f}%
|
||||
|
||||
{profit_emoji} *Net Profit:* ${net_profit:.2f}
|
||||
{pf_emoji} *Profit Factor:* {profit_factor:.2f}
|
||||
*Avg Trade:* ${stats.get('avg_trade', 0):.2f}
|
||||
|
||||
━━━━━━━━━━━━━━━━━━━━
|
||||
"""
|
||||
|
||||
# Add session breakdown if available
|
||||
if session_perf:
|
||||
message += "\n📍 *Session Performance*\n\n"
|
||||
|
||||
# Sort sessions by profit
|
||||
sorted_sessions = sorted(
|
||||
session_perf.items(),
|
||||
key=lambda x: x[1].get('total_profit', 0),
|
||||
reverse=True
|
||||
)
|
||||
|
||||
for session, data in sorted_sessions:
|
||||
session_emoji = self._get_session_emoji(session)
|
||||
profit = data.get('total_profit', 0)
|
||||
wr = data.get('win_rate', 0)
|
||||
|
||||
profit_text = f"+${profit:.2f}" if profit > 0 else f"${profit:.2f}"
|
||||
status = "✅" if profit > 0 else "❌"
|
||||
|
||||
message += f"{session_emoji} *{session.upper()}:* {profit_text} ({wr:.1f}% WR) {status}\n"
|
||||
|
||||
message += "\n━━━━━━━━━━━━━━━━━━━━\n✅ Bot Status: Running"
|
||||
|
||||
self.send_message(message)
|
||||
|
||||
|
||||
# ==========================================
|
||||
# ALERTS & STATUS
|
||||
# ==========================================
|
||||
|
||||
def send_error_alert(self, error_message: str, context: str = ""):
|
||||
"""
|
||||
Send error alert
|
||||
|
||||
Args:
|
||||
error_message: Error description
|
||||
context: Additional context
|
||||
"""
|
||||
message = f"""
|
||||
🚨 *ERROR ALERT*
|
||||
|
||||
*Time:* {datetime.now().strftime('%Y-%m-%d %H:%M:%S')} UTC
|
||||
|
||||
*Error:* {error_message}
|
||||
|
||||
{f'*Context:* {context}' if context else ''}
|
||||
|
||||
⚠️ Please check the bot!
|
||||
"""
|
||||
self.send_message(message)
|
||||
|
||||
|
||||
def send_bot_started(self, config: Dict):
|
||||
"""
|
||||
Notify when bot starts
|
||||
|
||||
Args:
|
||||
config: Bot configuration
|
||||
"""
|
||||
active_sessions = [s for s, enabled in config.get('enabled_sessions', {}).items() if enabled]
|
||||
|
||||
message = f"""
|
||||
🚀 *TradingBot Started*
|
||||
|
||||
*Version:* {config.get('version', 'V1.8')}
|
||||
*Time:* {datetime.now().strftime('%Y-%m-%d %H:%M:%S')} UTC
|
||||
|
||||
⚙️ *Configuration:*
|
||||
*Active Sessions:* {', '.join(s.upper() for s in active_sessions)}
|
||||
*Confidence Threshold:* {config.get('base_confidence', 60)}%
|
||||
*Max Risk/Trade:* {config.get('max_risk_per_trade', 0.01)*100:.1f}%
|
||||
|
||||
✅ Bot is now monitoring the market
|
||||
"""
|
||||
self.send_message(message)
|
||||
|
||||
|
||||
def send_bot_stopped(self, reason: str = "Manual stop"):
|
||||
"""
|
||||
Notify when bot stops
|
||||
|
||||
Args:
|
||||
reason: Stop reason
|
||||
"""
|
||||
message = f"""
|
||||
⏸️ *TradingBot Stopped*
|
||||
|
||||
*Time:* {datetime.now().strftime('%Y-%m-%d %H:%M:%S')} UTC
|
||||
*Reason:* {reason}
|
||||
|
||||
ℹ️ Bot has stopped trading
|
||||
"""
|
||||
self.send_message(message)
|
||||
|
||||
|
||||
def send_session_blocked(self, session: str, reason: str):
|
||||
"""
|
||||
Notify when a trading session is blocked
|
||||
|
||||
Args:
|
||||
session: Session name
|
||||
reason: Block reason
|
||||
"""
|
||||
session_emoji = self._get_session_emoji(session)
|
||||
|
||||
message = f"""
|
||||
⏸️ *Trading Skipped*
|
||||
|
||||
{session_emoji} *Session:* {session.upper()}
|
||||
*Reason:* {reason}
|
||||
|
||||
💡 Bot is waiting for allowed session
|
||||
"""
|
||||
# Only send if debug mode (optional - can be noisy)
|
||||
# self.send_message(message)
|
||||
|
||||
|
||||
# ==========================================
|
||||
# BOT COMMANDS (Optional - for interactive use)
|
||||
# ==========================================
|
||||
|
||||
def handle_command(self, command: str, db) -> str:
|
||||
"""
|
||||
Handle Telegram bot commands
|
||||
|
||||
Args:
|
||||
command: Command string (e.g., '/status', '/report')
|
||||
db: TradingDatabase instance
|
||||
|
||||
Returns:
|
||||
Response message
|
||||
"""
|
||||
if command == '/status':
|
||||
return self._cmd_status(db)
|
||||
|
||||
elif command == '/report':
|
||||
return self._cmd_report(db)
|
||||
|
||||
elif command == '/today':
|
||||
return self._cmd_today(db)
|
||||
|
||||
elif command == '/help':
|
||||
return self._cmd_help()
|
||||
|
||||
else:
|
||||
return "Unknown command. Use /help for available commands."
|
||||
|
||||
|
||||
def _cmd_status(self, db) -> str:
|
||||
"""Get bot status"""
|
||||
open_positions = db.get_open_positions()
|
||||
stats = db.get_overall_statistics(days=7)
|
||||
|
||||
return f"""
|
||||
📊 *Bot Status*
|
||||
|
||||
*Open Positions:* {len(open_positions)}
|
||||
*7-Day Win Rate:* {stats.get('win_rate', 0):.1f}%
|
||||
*7-Day Profit:* ${stats.get('net_profit', 0):.2f}
|
||||
|
||||
✅ Bot is running
|
||||
"""
|
||||
|
||||
|
||||
def _cmd_report(self, db) -> str:
|
||||
"""Get performance report"""
|
||||
stats = db.get_overall_statistics(days=7)
|
||||
self.send_weekly_report(stats)
|
||||
return "Report sent!"
|
||||
|
||||
|
||||
def _cmd_today(self, db) -> str:
|
||||
"""Get today's summary"""
|
||||
stats = db.get_daily_summary()
|
||||
self.send_daily_report(stats)
|
||||
return "Today's report sent!"
|
||||
|
||||
|
||||
def _cmd_help(self) -> str:
|
||||
"""Get help text"""
|
||||
return """
|
||||
📚 *Available Commands*
|
||||
|
||||
/status - Bot status & open positions
|
||||
/report - Weekly performance report
|
||||
/today - Today's summary
|
||||
/help - Show this help
|
||||
|
||||
Bot will automatically send:
|
||||
• Trade entry/exit notifications
|
||||
• Daily reports (22:00 UTC)
|
||||
• Error alerts
|
||||
"""
|
||||
|
||||
|
||||
# ==========================================
|
||||
# HELPER FUNCTIONS
|
||||
# ==========================================
|
||||
|
||||
def _get_session_emoji(self, session: str) -> str:
|
||||
"""Get emoji for session"""
|
||||
emojis = {
|
||||
'asian': '🌙',
|
||||
'london': '🇬🇧',
|
||||
'overlap': '🔄',
|
||||
'ny': '🇺🇸'
|
||||
}
|
||||
return emojis.get(session.lower(), '❓')
|
||||
|
||||
|
||||
# ==========================================
|
||||
# CONFIGURATION
|
||||
# ==========================================
|
||||
|
||||
def load_telegram_config() -> Dict:
|
||||
"""
|
||||
Load Telegram configuration from file or environment
|
||||
|
||||
Returns:
|
||||
Dictionary with bot_token and chat_id
|
||||
"""
|
||||
# Try to load from config file
|
||||
config_file = "telegram_config.json"
|
||||
|
||||
if os.path.exists(config_file):
|
||||
with open(config_file, 'r') as f:
|
||||
return json.load(f)
|
||||
|
||||
# Try environment variables
|
||||
bot_token = os.getenv('TELEGRAM_BOT_TOKEN')
|
||||
chat_id = os.getenv('TELEGRAM_CHAT_ID')
|
||||
|
||||
if bot_token and chat_id:
|
||||
return {
|
||||
'bot_token': bot_token,
|
||||
'chat_id': chat_id
|
||||
}
|
||||
|
||||
# Return empty config if nothing found
|
||||
return {}
|
||||
|
||||
|
||||
def create_telegram_config_template():
|
||||
"""Create template configuration file"""
|
||||
template = {
|
||||
"bot_token": "YOUR_BOT_TOKEN_HERE",
|
||||
"chat_id": "YOUR_CHAT_ID_HERE",
|
||||
"notifications": {
|
||||
"trade_entry": True,
|
||||
"trade_exit": True,
|
||||
"daily_report": True,
|
||||
"weekly_report": True,
|
||||
"error_alerts": True
|
||||
},
|
||||
"daily_report_time": "22:00", # UTC
|
||||
"weekly_report_day": "Sunday"
|
||||
}
|
||||
|
||||
with open('telegram_config_template.json', 'w') as f:
|
||||
json.dump(template, f, indent=2)
|
||||
|
||||
print("✅ Created telegram_config_template.json")
|
||||
print("📝 Edit this file and rename to telegram_config.json")
|
||||
|
||||
|
||||
# ==========================================
|
||||
# USAGE EXAMPLE
|
||||
# ==========================================
|
||||
|
||||
if __name__ == "__main__":
|
||||
print("="*70)
|
||||
print("📱 TELEGRAM NOTIFIER - Setup")
|
||||
print("="*70)
|
||||
|
||||
# Create config template
|
||||
create_telegram_config_template()
|
||||
|
||||
print("""
|
||||
📋 Setup Instructions:
|
||||
|
||||
1. Create Telegram Bot:
|
||||
- Open Telegram and search for @BotFather
|
||||
- Send /newbot and follow instructions
|
||||
- Copy the Bot Token
|
||||
|
||||
2. Get your Chat ID:
|
||||
- Search for @userinfobot in Telegram
|
||||
- Send /start
|
||||
- Copy your User ID
|
||||
|
||||
3. Configure:
|
||||
- Edit telegram_config_template.json
|
||||
- Add your bot_token and chat_id
|
||||
- Rename to telegram_config.json
|
||||
|
||||
4. Test:
|
||||
- Run this script again to test connection
|
||||
|
||||
Example Usage:
|
||||
```python
|
||||
from telegram_notifier import TelegramNotifier
|
||||
|
||||
# Initialize
|
||||
notifier = TelegramNotifier(
|
||||
bot_token='YOUR_BOT_TOKEN',
|
||||
chat_id='YOUR_CHAT_ID'
|
||||
)
|
||||
|
||||
# Send trade notification
|
||||
notifier.notify_trade_entry({
|
||||
'symbol': 'XAUUSD',
|
||||
'type': 'BUY',
|
||||
'entry_price': 2650.00,
|
||||
'sl_price': 2645.00,
|
||||
'tp_price': 2660.00,
|
||||
'session': 'ny',
|
||||
'confidence': 75.5
|
||||
})
|
||||
```
|
||||
""")
|
||||
|
||||
print("="*70)
|
||||
@@ -0,0 +1,434 @@
|
||||
[
|
||||
{
|
||||
"timestamp": "2025-10-31T00:43:54.044909",
|
||||
"version": "V1.6_Adaptive_Complete",
|
||||
"symbol": "XAUUSD",
|
||||
"entry_signal": 1,
|
||||
"confidence": 78.8,
|
||||
"adaptive_threshold": 70,
|
||||
"signal_quality": "good",
|
||||
"market_regime": "ranging",
|
||||
"regime_strength": 37.20677415534778,
|
||||
"risk_adjusted_strength": 47620.067708887604,
|
||||
"adaptive_interval": 15,
|
||||
"session": "asian",
|
||||
"relaxed_features": {
|
||||
"pullback_entry_disabled": true,
|
||||
"lower_confidence_threshold": true,
|
||||
"lower_min_strength": true,
|
||||
"fixed_tf_alignment": true
|
||||
},
|
||||
"adaptive_features": {
|
||||
"adaptive_rhythm": true,
|
||||
"session_aware": true,
|
||||
"volatility_based": true
|
||||
},
|
||||
"position_control_active": true,
|
||||
"order_result": "OrderSendResult(retcode=10009, deal=435070787, order=478265698, volume=0.07, price=4032.7, bid=0.0, ask=0.0, comment='Request executed', request_id=2258304401, retcode_external=0, request=TradeRequest(action=1, magic=234000, order=0, symbol='XAUUSD', volume=0.07, price=4032.81, stoplimit=0.0, sl=4025.0112621236303, tp=4051.676844690925, deviation=20, type=0, type_filling=1, type_time=0, expiration=0, comment='TradingBot_V1.6', position=0, position_by=0))"
|
||||
},
|
||||
{
|
||||
"timestamp": "2025-10-31T02:00:00.811779",
|
||||
"version": "V1.6_Adaptive_Complete",
|
||||
"symbol": "XAUUSD",
|
||||
"entry_signal": 1,
|
||||
"confidence": 79.06,
|
||||
"adaptive_threshold": 70,
|
||||
"signal_quality": "good",
|
||||
"market_regime": "ranging",
|
||||
"regime_strength": 41.618593353525405,
|
||||
"risk_adjusted_strength": 59305.554223185194,
|
||||
"adaptive_interval": 15,
|
||||
"session": "asian",
|
||||
"relaxed_features": {
|
||||
"pullback_entry_disabled": true,
|
||||
"lower_confidence_threshold": true,
|
||||
"lower_min_strength": true,
|
||||
"fixed_tf_alignment": true
|
||||
},
|
||||
"adaptive_features": {
|
||||
"adaptive_rhythm": true,
|
||||
"session_aware": true,
|
||||
"volatility_based": true
|
||||
},
|
||||
"position_control_active": true,
|
||||
"order_result": "OrderSendResult(retcode=10009, deal=435184360, order=478391992, volume=0.09, price=4025.19, bid=0.0, ask=0.0, comment='Request executed', request_id=2258304402, retcode_external=0, request=TradeRequest(action=1, magic=234000, order=0, symbol='XAUUSD', volume=0.09, price=4025.32, stoplimit=0.0, sl=4018.93281627515, tp=4040.5879593121244, deviation=20, type=0, type_filling=1, type_time=0, expiration=0, comment='TradingBot_V1.6', position=0, position_by=0))"
|
||||
},
|
||||
{
|
||||
"timestamp": "2025-10-31T03:00:09.446976",
|
||||
"version": "V1.6_Adaptive_Complete",
|
||||
"symbol": "XAUUSD",
|
||||
"entry_signal": 1,
|
||||
"confidence": 78.88,
|
||||
"adaptive_threshold": 70,
|
||||
"signal_quality": "good",
|
||||
"market_regime": "ranging",
|
||||
"regime_strength": 41.618593353525405,
|
||||
"risk_adjusted_strength": 57477.635686851994,
|
||||
"adaptive_interval": 15,
|
||||
"session": "asian",
|
||||
"relaxed_features": {
|
||||
"pullback_entry_disabled": true,
|
||||
"lower_confidence_threshold": true,
|
||||
"lower_min_strength": true,
|
||||
"fixed_tf_alignment": true
|
||||
},
|
||||
"adaptive_features": {
|
||||
"adaptive_rhythm": true,
|
||||
"session_aware": true,
|
||||
"volatility_based": true
|
||||
},
|
||||
"position_control_active": true,
|
||||
"order_result": "OrderSendResult(retcode=10009, deal=435297036, order=478512868, volume=0.08, price=4018.08, bid=0.0, ask=0.0, comment='Request executed', request_id=2258304403, retcode_external=0, request=TradeRequest(action=1, magic=234000, order=0, symbol='XAUUSD', volume=0.08, price=4018.54, stoplimit=0.0, sl=4011.338532716335, tp=4035.8786682091622, deviation=20, type=0, type_filling=1, type_time=0, expiration=0, comment='TradingBot_V1.6', position=0, position_by=0))"
|
||||
},
|
||||
{
|
||||
"timestamp": "2025-10-31T03:15:02.774892",
|
||||
"version": "V1.6_Adaptive_Complete",
|
||||
"symbol": "XAUUSD",
|
||||
"entry_signal": 1,
|
||||
"confidence": 78.79,
|
||||
"adaptive_threshold": 70,
|
||||
"signal_quality": "good",
|
||||
"market_regime": "ranging",
|
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|
||||
"order_result": "OrderSendResult(retcode=10009, deal=437066084, order=480409859, volume=0.05, price=3979.04, bid=0.0, ask=0.0, comment='Request executed', request_id=2258304419, retcode_external=0, request=TradeRequest(action=1, magic=234000, order=0, symbol='XAUUSD', volume=0.05, price=3979.26, stoplimit=0.0, sl=3969.2344213705546, tp=4003.6589465736133, deviation=20, type=0, type_filling=1, type_time=0, expiration=0, comment='TradingBot_V1.6', position=0, position_by=0))"
|
||||
}
|
||||
]
|
||||
File diff suppressed because it is too large.
Load diff
@@ -0,0 +1,418 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
📊 Trading Bot Dashboard - Streamlit
|
||||
Real-time monitoring für V1.8 Trading Bot
|
||||
"""
|
||||
|
||||
import streamlit as st
|
||||
import sqlite3
|
||||
import pandas as pd
|
||||
from datetime import datetime, timedelta
|
||||
import plotly.express as px
|
||||
import plotly.graph_objects as go
|
||||
|
||||
# ==========================================
|
||||
# PAGE CONFIG
|
||||
# ==========================================
|
||||
|
||||
st.set_page_config(
|
||||
page_title="Trading Bot Dashboard",
|
||||
page_icon="📊",
|
||||
layout="wide"
|
||||
)
|
||||
|
||||
# ==========================================
|
||||
# DATABASE CONNECTION
|
||||
# ==========================================
|
||||
|
||||
@st.cache_resource
|
||||
def get_connection():
|
||||
return sqlite3.connect("trading_bot.db", check_same_thread=False)
|
||||
|
||||
conn = get_connection()
|
||||
|
||||
# ==========================================
|
||||
# HEADER
|
||||
# ==========================================
|
||||
|
||||
st.title("📊 Trading Bot Dashboard V1.8")
|
||||
st.markdown("---")
|
||||
|
||||
# Refresh button
|
||||
col1, col2, col3 = st.columns([1, 1, 4])
|
||||
with col1:
|
||||
if st.button("🔄 Refresh Data"):
|
||||
st.cache_data.clear()
|
||||
st.experimental_rerun()
|
||||
|
||||
with col2:
|
||||
auto_refresh = st.checkbox("Auto-refresh (30s)")
|
||||
|
||||
if auto_refresh:
|
||||
st.markdown("*Auto-refreshing every 30 seconds...*")
|
||||
import time
|
||||
time.sleep(30)
|
||||
st.experimental_rerun()
|
||||
|
||||
# ==========================================
|
||||
# LOAD DATA
|
||||
# ==========================================
|
||||
|
||||
@st.cache_data(ttl=30)
|
||||
def load_all_trades():
|
||||
query = """
|
||||
SELECT
|
||||
ticket,
|
||||
position_id,
|
||||
symbol,
|
||||
strategy_name,
|
||||
type,
|
||||
volume,
|
||||
entry_price,
|
||||
sl_price,
|
||||
tp_price,
|
||||
entry_time,
|
||||
exit_time,
|
||||
session,
|
||||
regime,
|
||||
quality,
|
||||
confidence,
|
||||
timeframe_alignment,
|
||||
risk_amount,
|
||||
risk_pct,
|
||||
net_profit,
|
||||
profit_pct,
|
||||
rr_ratio,
|
||||
status,
|
||||
exit_reason
|
||||
FROM trades
|
||||
ORDER BY entry_time DESC
|
||||
"""
|
||||
return pd.read_sql_query(query, conn)
|
||||
|
||||
@st.cache_data(ttl=30)
|
||||
def load_bot_status():
|
||||
query = "SELECT * FROM bot_status ORDER BY timestamp DESC LIMIT 1"
|
||||
return pd.read_sql_query(query, conn)
|
||||
|
||||
# Load data
|
||||
df_trades = load_all_trades()
|
||||
df_status = load_bot_status()
|
||||
|
||||
# ==========================================
|
||||
# TOP METRICS
|
||||
# ==========================================
|
||||
|
||||
st.subheader("📈 Key Metrics")
|
||||
|
||||
col1, col2, col3, col4, col5 = st.columns(5)
|
||||
|
||||
total_trades = len(df_trades)
|
||||
closed_trades = len(df_trades[df_trades['status'] == 'closed'])
|
||||
open_trades = len(df_trades[df_trades['status'] == 'open'])
|
||||
|
||||
if closed_trades > 0:
|
||||
winning_trades = len(df_trades[(df_trades['status'] == 'closed') & (df_trades['net_profit'] > 0)])
|
||||
win_rate = (winning_trades / closed_trades) * 100
|
||||
total_profit = df_trades[df_trades['status'] == 'closed']['net_profit'].sum()
|
||||
else:
|
||||
win_rate = 0
|
||||
total_profit = 0
|
||||
|
||||
with col1:
|
||||
st.metric("Total Trades", total_trades)
|
||||
|
||||
with col2:
|
||||
st.metric("Open Positions", open_trades)
|
||||
|
||||
with col3:
|
||||
st.metric("Win Rate", f"{win_rate:.1f}%")
|
||||
|
||||
with col4:
|
||||
profit_color = "normal" if total_profit >= 0 else "inverse"
|
||||
st.metric("Total Profit", f"${total_profit:.2f}", delta=None)
|
||||
|
||||
with col5:
|
||||
if closed_trades > 0:
|
||||
avg_profit = total_profit / closed_trades
|
||||
st.metric("Avg Profit/Trade", f"${avg_profit:.2f}")
|
||||
else:
|
||||
st.metric("Avg Profit/Trade", "N/A")
|
||||
|
||||
st.markdown("---")
|
||||
|
||||
# ==========================================
|
||||
# SESSION FILTER CHECK
|
||||
# ==========================================
|
||||
|
||||
st.subheader("🎯 Session Filter Status")
|
||||
|
||||
# Calculate session distribution
|
||||
session_counts = df_trades['session'].value_counts()
|
||||
|
||||
col1, col2 = st.columns([1, 2])
|
||||
|
||||
with col1:
|
||||
st.markdown("#### Session Distribution")
|
||||
|
||||
for session in ['ny', 'london', 'asian', 'overlap']:
|
||||
count = session_counts.get(session, 0)
|
||||
pct = (count / total_trades * 100) if total_trades > 0 else 0
|
||||
|
||||
if session == 'ny':
|
||||
if count == total_trades:
|
||||
st.success(f"✅ NY: {count} ({pct:.1f}%) - PERFECT!")
|
||||
else:
|
||||
st.warning(f"⚠️ NY: {count} ({pct:.1f}%)")
|
||||
else:
|
||||
if count > 0:
|
||||
st.error(f"❌ {session.upper()}: {count} ({pct:.1f}%) - VIOLATION!")
|
||||
else:
|
||||
st.success(f"✅ {session.upper()}: {count} (0%) - Blocked")
|
||||
|
||||
with col2:
|
||||
st.markdown("#### Verdict")
|
||||
|
||||
ny_count = session_counts.get('ny', 0)
|
||||
violation_count = total_trades - ny_count
|
||||
|
||||
if total_trades == 0:
|
||||
st.info("ℹ️ No trades yet - waiting for data...")
|
||||
elif violation_count == 0:
|
||||
st.success("✅ **SESSION FILTER WORKING PERFECTLY!**")
|
||||
st.markdown("All trades are in NY session (13:00-21:00 UTC)")
|
||||
else:
|
||||
st.error(f"❌ **SESSION FILTER NOT WORKING!**")
|
||||
st.markdown(f"**{violation_count} trades** ({violation_count/total_trades*100:.1f}%) outside NY session!")
|
||||
st.markdown("**Action Required:** See FIX_DUPLICATE_SCHEDULER.md")
|
||||
|
||||
st.markdown("---")
|
||||
|
||||
# ==========================================
|
||||
# HOURLY DISTRIBUTION
|
||||
# ==========================================
|
||||
|
||||
st.subheader("⏰ Trades by Hour (UTC)")
|
||||
|
||||
if not df_trades.empty:
|
||||
# Extract hour from entry_time
|
||||
df_trades['hour_utc'] = pd.to_datetime(df_trades['entry_time']).dt.hour
|
||||
|
||||
# Count trades by hour
|
||||
hourly_dist = df_trades.groupby('hour_utc').size().reset_index(name='count')
|
||||
|
||||
# Create visualization
|
||||
fig = go.Figure()
|
||||
|
||||
# Add bars
|
||||
colors = ['red' if (h < 13 or h >= 21) else 'green' for h in hourly_dist['hour_utc']]
|
||||
|
||||
fig.add_trace(go.Bar(
|
||||
x=hourly_dist['hour_utc'],
|
||||
y=hourly_dist['count'],
|
||||
marker_color=colors,
|
||||
text=hourly_dist['count'],
|
||||
textposition='outside'
|
||||
))
|
||||
|
||||
# Add NY session marker
|
||||
fig.add_vrect(x0=13, x1=21, fillcolor="green", opacity=0.1, layer="below", line_width=0)
|
||||
fig.add_annotation(x=17, y=hourly_dist['count'].max(), text="NY Session (13-21 UTC)", showarrow=False)
|
||||
|
||||
fig.update_layout(
|
||||
xaxis_title="Hour (UTC)",
|
||||
yaxis_title="Number of Trades",
|
||||
height=400,
|
||||
showlegend=False,
|
||||
xaxis=dict(dtick=1, range=[-0.5, 23.5])
|
||||
)
|
||||
|
||||
st.plotly_chart(fig, use_container_width=True)
|
||||
|
||||
# Check for violations
|
||||
violations = df_trades[(df_trades['hour_utc'] < 13) | (df_trades['hour_utc'] >= 21)]
|
||||
if not violations.empty:
|
||||
st.error(f"⚠️ **{len(violations)} trades outside NY hours detected!**")
|
||||
with st.expander("Show violation details"):
|
||||
st.dataframe(violations[['ticket', 'session', 'entry_time', 'hour_utc', 'type', 'net_profit']])
|
||||
else:
|
||||
st.info("No trades to display")
|
||||
|
||||
st.markdown("---")
|
||||
|
||||
# ==========================================
|
||||
# SESSION PERFORMANCE
|
||||
# ==========================================
|
||||
|
||||
st.subheader("📊 Performance by Session")
|
||||
|
||||
if closed_trades > 0:
|
||||
session_perf = df_trades[df_trades['status'] == 'closed'].groupby('session').agg({
|
||||
'ticket': 'count',
|
||||
'net_profit': ['sum', 'mean']
|
||||
}).round(2)
|
||||
|
||||
session_perf.columns = ['Trades', 'Total Profit', 'Avg Profit']
|
||||
|
||||
# Calculate win rate per session
|
||||
win_rates = []
|
||||
for session in session_perf.index:
|
||||
session_trades = df_trades[(df_trades['status'] == 'closed') & (df_trades['session'] == session)]
|
||||
wins = len(session_trades[session_trades['net_profit'] > 0])
|
||||
wr = (wins / len(session_trades) * 100) if len(session_trades) > 0 else 0
|
||||
win_rates.append(wr)
|
||||
|
||||
session_perf['Win Rate %'] = win_rates
|
||||
|
||||
# Color code
|
||||
def color_sessions(row):
|
||||
if row.name == 'ny':
|
||||
return ['background-color: #90EE90'] * len(row) # Light green
|
||||
else:
|
||||
return ['background-color: #FFB6C6'] * len(row) # Light red
|
||||
|
||||
st.dataframe(session_perf.style.apply(color_sessions, axis=1), use_container_width=True)
|
||||
else:
|
||||
st.info("No closed trades yet")
|
||||
|
||||
st.markdown("---")
|
||||
|
||||
# ==========================================
|
||||
# RECENT TRADES
|
||||
# ==========================================
|
||||
|
||||
st.subheader("📋 Recent Trades (Last 20)")
|
||||
|
||||
if not df_trades.empty:
|
||||
recent = df_trades.head(20).copy()
|
||||
|
||||
# Color code session
|
||||
def highlight_session(row):
|
||||
if row['session'] == 'ny':
|
||||
return ['background-color: #90EE90'] * len(row)
|
||||
else:
|
||||
return ['background-color: #FFB6C6'] * len(row)
|
||||
|
||||
# Select columns
|
||||
display_cols = ['ticket', 'type', 'session', 'entry_time', 'confidence', 'quality', 'regime', 'status', 'net_profit']
|
||||
recent_display = recent[display_cols]
|
||||
|
||||
st.dataframe(
|
||||
recent_display.style.apply(highlight_session, axis=1),
|
||||
use_container_width=True,
|
||||
height=400
|
||||
)
|
||||
else:
|
||||
st.info("No trades yet")
|
||||
|
||||
st.markdown("---")
|
||||
|
||||
# ==========================================
|
||||
# PROFIT OVER TIME
|
||||
# ==========================================
|
||||
|
||||
st.subheader("💰 Cumulative Profit Over Time")
|
||||
|
||||
if closed_trades > 0:
|
||||
profit_timeline = df_trades[df_trades['status'] == 'closed'].copy()
|
||||
profit_timeline['exit_time'] = pd.to_datetime(profit_timeline['exit_time'])
|
||||
profit_timeline = profit_timeline.sort_values('exit_time')
|
||||
profit_timeline['cumulative_profit'] = profit_timeline['net_profit'].cumsum()
|
||||
|
||||
fig = px.line(
|
||||
profit_timeline,
|
||||
x='exit_time',
|
||||
y='cumulative_profit',
|
||||
title='Cumulative Profit',
|
||||
labels={'exit_time': 'Date', 'cumulative_profit': 'Profit ($)'}
|
||||
)
|
||||
|
||||
fig.update_traces(line_color='green' if profit_timeline['cumulative_profit'].iloc[-1] > 0 else 'red')
|
||||
fig.add_hline(y=0, line_dash="dash", line_color="gray")
|
||||
|
||||
st.plotly_chart(fig, use_container_width=True)
|
||||
else:
|
||||
st.info("No closed trades yet")
|
||||
|
||||
st.markdown("---")
|
||||
|
||||
# ==========================================
|
||||
# CONFIDENCE & QUALITY ANALYSIS
|
||||
# ==========================================
|
||||
|
||||
st.subheader("🎯 Signal Quality Analysis")
|
||||
|
||||
col1, col2 = st.columns(2)
|
||||
|
||||
with col1:
|
||||
st.markdown("#### Confidence Distribution")
|
||||
if not df_trades.empty:
|
||||
fig = px.histogram(
|
||||
df_trades,
|
||||
x='confidence',
|
||||
nbins=20,
|
||||
title='Trade Confidence Distribution'
|
||||
)
|
||||
st.plotly_chart(fig, use_container_width=True)
|
||||
else:
|
||||
st.info("No data")
|
||||
|
||||
with col2:
|
||||
st.markdown("#### Quality Breakdown")
|
||||
if not df_trades.empty:
|
||||
quality_counts = df_trades['quality'].value_counts()
|
||||
fig = px.pie(
|
||||
values=quality_counts.values,
|
||||
names=quality_counts.index,
|
||||
title='Signal Quality Distribution'
|
||||
)
|
||||
st.plotly_chart(fig, use_container_width=True)
|
||||
else:
|
||||
st.info("No data")
|
||||
|
||||
st.markdown("---")
|
||||
|
||||
# ==========================================
|
||||
# BOT STATUS
|
||||
# ==========================================
|
||||
|
||||
st.subheader("🤖 Bot Status")
|
||||
|
||||
if not df_status.empty:
|
||||
status = df_status.iloc[0]
|
||||
|
||||
col1, col2, col3 = st.columns(3)
|
||||
|
||||
with col1:
|
||||
st.markdown("**Version:**")
|
||||
st.code(status.get('version', 'N/A'))
|
||||
|
||||
with col2:
|
||||
st.markdown("**Status:**")
|
||||
bot_status = status.get('status', 'unknown')
|
||||
if bot_status == 'running':
|
||||
st.success("🟢 Running")
|
||||
elif bot_status == 'stopped':
|
||||
st.error("🔴 Stopped")
|
||||
else:
|
||||
st.warning("⚠️ Unknown")
|
||||
|
||||
with col3:
|
||||
st.markdown("**Last Update:**")
|
||||
st.code(status.get('timestamp', 'N/A'))
|
||||
|
||||
if 'config' in status and status['config']:
|
||||
with st.expander("Show Configuration"):
|
||||
st.json(status['config'])
|
||||
else:
|
||||
st.warning("No bot status available")
|
||||
|
||||
st.markdown("---")
|
||||
|
||||
# ==========================================
|
||||
# FOOTER
|
||||
# ==========================================
|
||||
|
||||
st.markdown("---")
|
||||
st.caption(f"Dashboard last updated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
|
||||
st.caption("📊 Trading Bot V1.8 - Aggressive Mode (NY Only)")
|
||||
@@ -0,0 +1,630 @@
|
||||
"""
|
||||
🗄️ TRADING DATABASE MODULE - SQLite Integration
|
||||
Ersetzt JSON Logging mit strukturierter Datenbank
|
||||
|
||||
FEATURES:
|
||||
- Trade History Tracking
|
||||
- Performance Analytics
|
||||
- Session-based Queries
|
||||
- Confidence-based Analysis
|
||||
- Easy Migration von JSON
|
||||
"""
|
||||
|
||||
import sqlite3
|
||||
import json
|
||||
from datetime import datetime, timedelta
|
||||
from typing import Dict, List, Optional, Tuple
|
||||
import os
|
||||
|
||||
|
||||
class TradingDatabase:
|
||||
"""
|
||||
SQLite Database für Trading Bot Performance Tracking
|
||||
"""
|
||||
|
||||
def __init__(self, db_path: str = "trading_bot.db"):
|
||||
"""
|
||||
Initialize Database Connection
|
||||
|
||||
Args:
|
||||
db_path: Path to SQLite database file
|
||||
"""
|
||||
self.db_path = db_path
|
||||
self.conn = None
|
||||
self.cursor = None
|
||||
self._connect()
|
||||
self._create_tables()
|
||||
|
||||
def _connect(self):
|
||||
"""Establish database connection"""
|
||||
self.conn = sqlite3.connect(self.db_path, check_same_thread=False)
|
||||
self.conn.row_factory = sqlite3.Row # Enable column access by name
|
||||
self.cursor = self.conn.cursor()
|
||||
|
||||
def _create_tables(self):
|
||||
"""Create all necessary tables"""
|
||||
|
||||
# Trades Table - Main trade history
|
||||
self.cursor.execute("""
|
||||
CREATE TABLE IF NOT EXISTS trades (
|
||||
id INTEGER PRIMARY KEY AUTOINCREMENT,
|
||||
ticket INTEGER UNIQUE,
|
||||
position_id INTEGER,
|
||||
symbol TEXT NOT NULL,
|
||||
strategy_name TEXT NOT NULL,
|
||||
|
||||
-- Trade Details
|
||||
type TEXT NOT NULL, -- 'BUY' or 'SELL'
|
||||
volume REAL NOT NULL,
|
||||
entry_price REAL NOT NULL,
|
||||
exit_price REAL,
|
||||
|
||||
-- Stops
|
||||
sl_price REAL,
|
||||
tp_price REAL,
|
||||
|
||||
-- Timing
|
||||
entry_time DATETIME NOT NULL,
|
||||
exit_time DATETIME,
|
||||
duration_hours REAL,
|
||||
|
||||
-- Session Info
|
||||
session TEXT NOT NULL, -- 'asian', 'london', 'overlap', 'ny'
|
||||
regime TEXT, -- 'trending', 'ranging'
|
||||
quality TEXT, -- 'excellent', 'good', 'medium', 'poor'
|
||||
|
||||
-- Signal Quality
|
||||
confidence REAL,
|
||||
timeframe_alignment INTEGER, -- How many timeframes aligned
|
||||
|
||||
-- Performance
|
||||
profit REAL,
|
||||
commission REAL DEFAULT 0,
|
||||
swap REAL DEFAULT 0,
|
||||
net_profit REAL,
|
||||
profit_pct REAL,
|
||||
|
||||
-- Risk Management
|
||||
risk_amount REAL,
|
||||
risk_pct REAL,
|
||||
rr_ratio REAL, -- Risk/Reward ratio
|
||||
|
||||
-- Status
|
||||
status TEXT DEFAULT 'open', -- 'open', 'closed', 'cancelled'
|
||||
exit_reason TEXT, -- 'tp', 'sl', 'manual', 'timeout'
|
||||
|
||||
-- Metadata
|
||||
created_at DATETIME DEFAULT CURRENT_TIMESTAMP,
|
||||
updated_at DATETIME DEFAULT CURRENT_TIMESTAMP
|
||||
)
|
||||
""")
|
||||
|
||||
# Performance Summary Table - Daily/Weekly aggregates
|
||||
self.cursor.execute("""
|
||||
CREATE TABLE IF NOT EXISTS performance_summary (
|
||||
id INTEGER PRIMARY KEY AUTOINCREMENT,
|
||||
date DATE NOT NULL UNIQUE,
|
||||
|
||||
-- Trade Counts
|
||||
total_trades INTEGER DEFAULT 0,
|
||||
wins INTEGER DEFAULT 0,
|
||||
losses INTEGER DEFAULT 0,
|
||||
win_rate REAL DEFAULT 0,
|
||||
|
||||
-- Profit
|
||||
gross_profit REAL DEFAULT 0,
|
||||
gross_loss REAL DEFAULT 0,
|
||||
net_profit REAL DEFAULT 0,
|
||||
profit_factor REAL DEFAULT 0,
|
||||
|
||||
-- Session Breakdown
|
||||
asian_trades INTEGER DEFAULT 0,
|
||||
london_trades INTEGER DEFAULT 0,
|
||||
overlap_trades INTEGER DEFAULT 0,
|
||||
ny_trades INTEGER DEFAULT 0,
|
||||
|
||||
asian_profit REAL DEFAULT 0,
|
||||
london_profit REAL DEFAULT 0,
|
||||
overlap_profit REAL DEFAULT 0,
|
||||
ny_profit REAL DEFAULT 0,
|
||||
|
||||
-- Risk Metrics
|
||||
max_drawdown REAL DEFAULT 0,
|
||||
avg_trade REAL DEFAULT 0,
|
||||
|
||||
created_at DATETIME DEFAULT CURRENT_TIMESTAMP
|
||||
)
|
||||
""")
|
||||
|
||||
# Bot Status Table - Health monitoring
|
||||
self.cursor.execute("""
|
||||
CREATE TABLE IF NOT EXISTS bot_status (
|
||||
id INTEGER PRIMARY KEY AUTOINCREMENT,
|
||||
timestamp DATETIME DEFAULT CURRENT_TIMESTAMP,
|
||||
|
||||
status TEXT NOT NULL, -- 'running', 'stopped', 'error'
|
||||
version TEXT,
|
||||
active_sessions TEXT, -- JSON array of enabled sessions
|
||||
confidence_threshold REAL,
|
||||
|
||||
-- Health Metrics
|
||||
uptime_hours REAL,
|
||||
last_trade_time DATETIME,
|
||||
total_positions INTEGER DEFAULT 0,
|
||||
|
||||
error_message TEXT,
|
||||
|
||||
created_at DATETIME DEFAULT CURRENT_TIMESTAMP
|
||||
)
|
||||
""")
|
||||
|
||||
# Create indexes for common queries
|
||||
self.cursor.execute("""
|
||||
CREATE INDEX IF NOT EXISTS idx_trades_entry_time
|
||||
ON trades(entry_time)
|
||||
""")
|
||||
|
||||
self.cursor.execute("""
|
||||
CREATE INDEX IF NOT EXISTS idx_trades_session
|
||||
ON trades(session)
|
||||
""")
|
||||
|
||||
self.cursor.execute("""
|
||||
CREATE INDEX IF NOT EXISTS idx_trades_status
|
||||
ON trades(status)
|
||||
""")
|
||||
|
||||
self.cursor.execute("""
|
||||
CREATE INDEX IF NOT EXISTS idx_trades_confidence
|
||||
ON trades(confidence)
|
||||
""")
|
||||
|
||||
self.conn.commit()
|
||||
|
||||
|
||||
# ==========================================
|
||||
# INSERT OPERATIONS
|
||||
# ==========================================
|
||||
|
||||
def log_trade_entry(self, trade_data: Dict) -> int:
|
||||
"""
|
||||
Log a new trade entry
|
||||
|
||||
Args:
|
||||
trade_data: Dictionary with trade information
|
||||
|
||||
Returns:
|
||||
trade_id: Database ID of inserted trade
|
||||
"""
|
||||
query = """
|
||||
INSERT INTO trades (
|
||||
ticket, position_id, symbol, strategy_name,
|
||||
type, volume, entry_price, sl_price, tp_price,
|
||||
entry_time, session, regime, quality,
|
||||
confidence, timeframe_alignment,
|
||||
risk_amount, risk_pct,
|
||||
status
|
||||
) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
|
||||
"""
|
||||
|
||||
values = (
|
||||
trade_data.get('ticket'),
|
||||
trade_data.get('position_id'),
|
||||
trade_data.get('symbol'),
|
||||
trade_data.get('strategy_name'),
|
||||
trade_data.get('type'),
|
||||
trade_data.get('volume'),
|
||||
trade_data.get('entry_price'),
|
||||
trade_data.get('sl_price'),
|
||||
trade_data.get('tp_price'),
|
||||
trade_data.get('entry_time'),
|
||||
trade_data.get('session'),
|
||||
trade_data.get('regime'),
|
||||
trade_data.get('quality'),
|
||||
trade_data.get('confidence'),
|
||||
trade_data.get('timeframe_alignment'),
|
||||
trade_data.get('risk_amount'),
|
||||
trade_data.get('risk_pct'),
|
||||
'open'
|
||||
)
|
||||
|
||||
self.cursor.execute(query, values)
|
||||
self.conn.commit()
|
||||
|
||||
return self.cursor.lastrowid
|
||||
|
||||
|
||||
def update_trade_exit(self, ticket: int, exit_data: Dict):
|
||||
"""
|
||||
Update trade with exit information
|
||||
|
||||
Args:
|
||||
ticket: MT5 ticket number
|
||||
exit_data: Dictionary with exit information
|
||||
"""
|
||||
query = """
|
||||
UPDATE trades SET
|
||||
exit_price = ?,
|
||||
exit_time = ?,
|
||||
duration_hours = ?,
|
||||
profit = ?,
|
||||
commission = ?,
|
||||
swap = ?,
|
||||
net_profit = ?,
|
||||
profit_pct = ?,
|
||||
rr_ratio = ?,
|
||||
status = 'closed',
|
||||
exit_reason = ?,
|
||||
updated_at = CURRENT_TIMESTAMP
|
||||
WHERE ticket = ?
|
||||
"""
|
||||
|
||||
values = (
|
||||
exit_data.get('exit_price'),
|
||||
exit_data.get('exit_time'),
|
||||
exit_data.get('duration_hours'),
|
||||
exit_data.get('profit'),
|
||||
exit_data.get('commission', 0),
|
||||
exit_data.get('swap', 0),
|
||||
exit_data.get('net_profit'),
|
||||
exit_data.get('profit_pct'),
|
||||
exit_data.get('rr_ratio'),
|
||||
exit_data.get('exit_reason'),
|
||||
ticket
|
||||
)
|
||||
|
||||
self.cursor.execute(query, values)
|
||||
self.conn.commit()
|
||||
|
||||
|
||||
def log_bot_status(self, status_data: Dict):
|
||||
"""
|
||||
Log bot status for health monitoring
|
||||
|
||||
Args:
|
||||
status_data: Dictionary with bot status information
|
||||
"""
|
||||
query = """
|
||||
INSERT INTO bot_status (
|
||||
status, version, active_sessions, confidence_threshold,
|
||||
uptime_hours, last_trade_time, total_positions, error_message
|
||||
) VALUES (?, ?, ?, ?, ?, ?, ?, ?)
|
||||
"""
|
||||
|
||||
values = (
|
||||
status_data.get('status', 'running'),
|
||||
status_data.get('version'),
|
||||
json.dumps(status_data.get('active_sessions', [])),
|
||||
status_data.get('confidence_threshold'),
|
||||
status_data.get('uptime_hours'),
|
||||
status_data.get('last_trade_time'),
|
||||
status_data.get('total_positions', 0),
|
||||
status_data.get('error_message')
|
||||
)
|
||||
|
||||
self.cursor.execute(query, values)
|
||||
self.conn.commit()
|
||||
|
||||
|
||||
# ==========================================
|
||||
# QUERY OPERATIONS
|
||||
# ==========================================
|
||||
|
||||
def get_recent_trades(self, limit: int = 50) -> List[Dict]:
|
||||
"""Get most recent trades"""
|
||||
query = """
|
||||
SELECT * FROM trades
|
||||
ORDER BY entry_time DESC
|
||||
LIMIT ?
|
||||
"""
|
||||
self.cursor.execute(query, (limit,))
|
||||
return [dict(row) for row in self.cursor.fetchall()]
|
||||
|
||||
|
||||
def get_open_positions(self) -> List[Dict]:
|
||||
"""Get all currently open positions"""
|
||||
query = """
|
||||
SELECT * FROM trades
|
||||
WHERE status = 'open'
|
||||
ORDER BY entry_time DESC
|
||||
"""
|
||||
self.cursor.execute(query)
|
||||
return [dict(row) for row in self.cursor.fetchall()]
|
||||
|
||||
|
||||
def get_session_performance(self, days: int = 30) -> Dict:
|
||||
"""
|
||||
Get performance breakdown by session
|
||||
|
||||
Args:
|
||||
days: Number of days to analyze
|
||||
|
||||
Returns:
|
||||
Dictionary with session statistics
|
||||
"""
|
||||
query = """
|
||||
SELECT
|
||||
session,
|
||||
COUNT(*) as trade_count,
|
||||
SUM(CASE WHEN net_profit > 0 THEN 1 ELSE 0 END) as wins,
|
||||
SUM(CASE WHEN net_profit < 0 THEN 1 ELSE 0 END) as losses,
|
||||
ROUND(AVG(CASE WHEN net_profit > 0 THEN 1.0 ELSE 0.0 END) * 100, 2) as win_rate,
|
||||
ROUND(SUM(net_profit), 2) as total_profit,
|
||||
ROUND(AVG(net_profit), 2) as avg_profit
|
||||
FROM trades
|
||||
WHERE status = 'closed'
|
||||
AND entry_time >= datetime('now', '-' || ? || ' days')
|
||||
GROUP BY session
|
||||
ORDER BY total_profit DESC
|
||||
"""
|
||||
|
||||
self.cursor.execute(query, (days,))
|
||||
results = {}
|
||||
|
||||
for row in self.cursor.fetchall():
|
||||
results[row['session']] = {
|
||||
'count': row['trade_count'],
|
||||
'wins': row['wins'],
|
||||
'losses': row['losses'],
|
||||
'win_rate': row['win_rate'],
|
||||
'total_profit': row['total_profit'],
|
||||
'avg_profit': row['avg_profit']
|
||||
}
|
||||
|
||||
return results
|
||||
|
||||
|
||||
def get_confidence_analysis(self, days: int = 30) -> Dict:
|
||||
"""
|
||||
Analyze performance by confidence levels
|
||||
|
||||
Args:
|
||||
days: Number of days to analyze
|
||||
|
||||
Returns:
|
||||
Dictionary with confidence-based statistics
|
||||
"""
|
||||
query = """
|
||||
SELECT
|
||||
CASE
|
||||
WHEN confidence >= 80 THEN '80-100'
|
||||
WHEN confidence >= 70 THEN '70-79'
|
||||
WHEN confidence >= 60 THEN '60-69'
|
||||
ELSE '<60'
|
||||
END as confidence_range,
|
||||
COUNT(*) as trade_count,
|
||||
SUM(CASE WHEN net_profit > 0 THEN 1 ELSE 0 END) as wins,
|
||||
ROUND(AVG(CASE WHEN net_profit > 0 THEN 1.0 ELSE 0.0 END) * 100, 2) as win_rate,
|
||||
ROUND(SUM(net_profit), 2) as total_profit,
|
||||
ROUND(AVG(net_profit), 2) as avg_profit
|
||||
FROM trades
|
||||
WHERE status = 'closed'
|
||||
AND confidence IS NOT NULL
|
||||
AND entry_time >= datetime('now', '-' || ? || ' days')
|
||||
GROUP BY confidence_range
|
||||
ORDER BY confidence_range DESC
|
||||
"""
|
||||
|
||||
self.cursor.execute(query, (days,))
|
||||
results = {}
|
||||
|
||||
for row in self.cursor.fetchall():
|
||||
results[row['confidence_range']] = {
|
||||
'count': row['trade_count'],
|
||||
'wins': row['wins'],
|
||||
'win_rate': row['win_rate'],
|
||||
'total_profit': row['total_profit'],
|
||||
'avg_profit': row['avg_profit']
|
||||
}
|
||||
|
||||
return results
|
||||
|
||||
|
||||
def get_daily_summary(self, date: str = None) -> Dict:
|
||||
"""
|
||||
Get daily performance summary
|
||||
|
||||
Args:
|
||||
date: Date string (YYYY-MM-DD), defaults to today
|
||||
|
||||
Returns:
|
||||
Dictionary with daily statistics
|
||||
"""
|
||||
if date is None:
|
||||
date = datetime.now().strftime('%Y-%m-%d')
|
||||
|
||||
query = """
|
||||
SELECT
|
||||
COUNT(*) as total_trades,
|
||||
SUM(CASE WHEN net_profit > 0 THEN 1 ELSE 0 END) as wins,
|
||||
SUM(CASE WHEN net_profit < 0 THEN 1 ELSE 0 END) as losses,
|
||||
ROUND(AVG(CASE WHEN net_profit > 0 THEN 1.0 ELSE 0.0 END) * 100, 2) as win_rate,
|
||||
ROUND(SUM(CASE WHEN net_profit > 0 THEN net_profit ELSE 0 END), 2) as gross_profit,
|
||||
ROUND(SUM(CASE WHEN net_profit < 0 THEN net_profit ELSE 0 END), 2) as gross_loss,
|
||||
ROUND(SUM(net_profit), 2) as net_profit,
|
||||
ROUND(AVG(net_profit), 2) as avg_trade
|
||||
FROM trades
|
||||
WHERE status = 'closed'
|
||||
AND DATE(entry_time) = ?
|
||||
"""
|
||||
|
||||
self.cursor.execute(query, (date,))
|
||||
row = self.cursor.fetchone()
|
||||
|
||||
if row and row['total_trades'] > 0:
|
||||
return dict(row)
|
||||
else:
|
||||
return {
|
||||
'total_trades': 0,
|
||||
'wins': 0,
|
||||
'losses': 0,
|
||||
'win_rate': 0,
|
||||
'gross_profit': 0,
|
||||
'gross_loss': 0,
|
||||
'net_profit': 0,
|
||||
'avg_trade': 0
|
||||
}
|
||||
|
||||
|
||||
def get_overall_statistics(self, days: int = None) -> Dict:
|
||||
"""
|
||||
Get overall performance statistics
|
||||
|
||||
Args:
|
||||
days: Number of days to analyze (None = all time)
|
||||
|
||||
Returns:
|
||||
Dictionary with comprehensive statistics
|
||||
"""
|
||||
date_filter = ""
|
||||
params = []
|
||||
|
||||
if days:
|
||||
date_filter = "AND entry_time >= datetime('now', '-' || ? || ' days')"
|
||||
params.append(days)
|
||||
|
||||
query = f"""
|
||||
SELECT
|
||||
COUNT(*) as total_trades,
|
||||
SUM(CASE WHEN net_profit > 0 THEN 1 ELSE 0 END) as wins,
|
||||
SUM(CASE WHEN net_profit < 0 THEN 1 ELSE 0 END) as losses,
|
||||
ROUND(AVG(CASE WHEN net_profit > 0 THEN 1.0 ELSE 0.0 END) * 100, 2) as win_rate,
|
||||
ROUND(SUM(CASE WHEN net_profit > 0 THEN net_profit ELSE 0 END), 2) as gross_profit,
|
||||
ROUND(SUM(CASE WHEN net_profit < 0 THEN net_profit ELSE 0 END), 2) as gross_loss,
|
||||
ROUND(SUM(net_profit), 2) as net_profit,
|
||||
ROUND(AVG(net_profit), 2) as avg_trade,
|
||||
ROUND(SUM(commission), 2) as total_commission,
|
||||
ROUND(SUM(swap), 2) as total_swap,
|
||||
ROUND(AVG(confidence), 2) as avg_confidence,
|
||||
ROUND(AVG(duration_hours), 2) as avg_duration
|
||||
FROM trades
|
||||
WHERE status = 'closed'
|
||||
{date_filter}
|
||||
"""
|
||||
|
||||
self.cursor.execute(query, params)
|
||||
row = self.cursor.fetchone()
|
||||
|
||||
stats = dict(row) if row else {}
|
||||
|
||||
# Calculate profit factor
|
||||
gross_profit = stats.get('gross_profit') or 0
|
||||
gross_loss = stats.get('gross_loss') or 0
|
||||
|
||||
if gross_loss != 0:
|
||||
stats['profit_factor'] = round(
|
||||
abs(gross_profit / gross_loss),
|
||||
2
|
||||
)
|
||||
else:
|
||||
stats['profit_factor'] = 0
|
||||
|
||||
return stats
|
||||
|
||||
|
||||
# ==========================================
|
||||
# UTILITY FUNCTIONS
|
||||
# ==========================================
|
||||
|
||||
def migrate_from_json(self, json_file: str):
|
||||
"""
|
||||
Migrate existing JSON trade data to SQLite
|
||||
|
||||
Args:
|
||||
json_file: Path to JSON performance file
|
||||
"""
|
||||
if not os.path.exists(json_file):
|
||||
print(f"❌ JSON file not found: {json_file}")
|
||||
return
|
||||
|
||||
with open(json_file, 'r') as f:
|
||||
data = json.load(f)
|
||||
|
||||
# Extract trades from JSON structure
|
||||
trades = data.get('trades', [])
|
||||
|
||||
migrated = 0
|
||||
skipped = 0
|
||||
|
||||
for trade in trades:
|
||||
try:
|
||||
# Check if trade already exists
|
||||
self.cursor.execute(
|
||||
"SELECT id FROM trades WHERE ticket = ?",
|
||||
(trade.get('ticket'),)
|
||||
)
|
||||
|
||||
if self.cursor.fetchone():
|
||||
skipped += 1
|
||||
continue
|
||||
|
||||
# Insert trade
|
||||
self.log_trade_entry(trade)
|
||||
|
||||
# If trade is closed, update exit data
|
||||
if trade.get('exit_time'):
|
||||
self.update_trade_exit(
|
||||
trade['ticket'],
|
||||
trade
|
||||
)
|
||||
|
||||
migrated += 1
|
||||
|
||||
except Exception as e:
|
||||
print(f"⚠️ Error migrating trade {trade.get('ticket')}: {e}")
|
||||
|
||||
print(f"✅ Migration complete: {migrated} trades migrated, {skipped} skipped")
|
||||
|
||||
|
||||
def close(self):
|
||||
"""Close database connection"""
|
||||
if self.conn:
|
||||
self.conn.close()
|
||||
|
||||
|
||||
def __enter__(self):
|
||||
"""Context manager entry"""
|
||||
return self
|
||||
|
||||
|
||||
def __exit__(self, exc_type, exc_val, exc_tb):
|
||||
"""Context manager exit"""
|
||||
self.close()
|
||||
|
||||
|
||||
# ==========================================
|
||||
# USAGE EXAMPLE
|
||||
# ==========================================
|
||||
|
||||
if __name__ == "__main__":
|
||||
# Initialize database
|
||||
db = TradingDatabase("trading_bot.db")
|
||||
|
||||
print("="*70)
|
||||
print("🗄️ TRADING DATABASE - System Check")
|
||||
print("="*70)
|
||||
|
||||
# Check tables
|
||||
db.cursor.execute("""
|
||||
SELECT name FROM sqlite_master
|
||||
WHERE type='table'
|
||||
ORDER BY name
|
||||
""")
|
||||
|
||||
tables = db.cursor.fetchall()
|
||||
print(f"\n📊 Tables created: {len(tables)}")
|
||||
for table in tables:
|
||||
print(f" ✅ {table['name']}")
|
||||
|
||||
# Get overall stats
|
||||
stats = db.get_overall_statistics()
|
||||
print(f"\n📈 Overall Statistics:")
|
||||
print(f" Total Trades: {stats.get('total_trades', 0)}")
|
||||
print(f" Win Rate: {stats.get('win_rate', 0)}%")
|
||||
print(f" Net Profit: ${stats.get('net_profit', 0)}")
|
||||
|
||||
db.close()
|
||||
|
||||
print("\n" + "="*70)
|
||||
print("✅ Database ready!")
|
||||
print("="*70)
|
||||
Reference in new issue
Block a user