cbazzaandClaude Sonnet 4.6
dd2611eaef
Files were edited but not staged in earlier commits: - adaptive_rhythm_manager.py: mt→mt5, pytz→timezone, get_volatility_level, shutdown() - check_market_regime.py: ADX_THRESHOLD, Wilder EWM, try/finally, UTC timestamp, sys import - check_system_status.py: remove duplicate cursor.execute - drawdown_protection.py: float(inf), persist pause state, DB save_setting, Markdown fix - performance_analysis.py: KeyError export fix, profit factor, drawdown positive, SQL filter - performance_analysis_simple.py: fromisoformat, numeric bin sort, profit factor - trading_dashboard.py: st.rerun(), session_state auto-refresh, pathlib DB path, errors=coerce Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
441 lines
13 KiB
Python
441 lines
13 KiB
Python
#!/usr/bin/env python3
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"""
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📊 Trading Bot Dashboard - Streamlit
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Real-time monitoring für V1.8 Trading Bot
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"""
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import streamlit as st
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import sqlite3
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import pandas as pd
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from datetime import datetime, timedelta
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import plotly.express as px
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import plotly.graph_objects as go
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from pathlib import Path
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# ==========================================
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# PAGE CONFIG
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# ==========================================
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st.set_page_config(
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page_title="Trading Bot Dashboard",
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page_icon="📊",
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layout="wide"
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)
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# ==========================================
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# DATABASE CONNECTION
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# ==========================================
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DB_PATH = Path(__file__).parent / "trading_bot.db"
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@st.cache_resource
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def get_connection():
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if not DB_PATH.exists():
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st.error(f"Database not found: {DB_PATH}")
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st.stop()
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return sqlite3.connect(str(DB_PATH), check_same_thread=False)
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conn = get_connection()
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# ==========================================
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# HEADER
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# ==========================================
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st.title("📊 Trading Bot Dashboard V1.8")
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st.markdown("---")
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# Controls
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col1, col2, col3 = st.columns([1, 1, 2])
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with col1:
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if st.button("🔄 Refresh Data"):
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st.cache_data.clear()
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st.rerun()
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with col2:
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auto_refresh = st.toggle("Auto-refresh (30s)")
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with col3:
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trade_filter = st.selectbox(
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"📊 Filter Trades:",
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["All Trades", "Live Trades Only", "Historical Only"],
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index=1 # Default to "Live Trades Only"
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)
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if auto_refresh:
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if "last_refresh" not in st.session_state:
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st.session_state.last_refresh = datetime.now()
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elapsed = (datetime.now() - st.session_state.last_refresh).total_seconds()
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if elapsed >= 30:
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st.session_state.last_refresh = datetime.now()
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st.rerun()
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else:
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st.markdown(f"*Auto-refresh in {30 - int(elapsed)}s...*")
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# ==========================================
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# LOAD DATA
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# ==========================================
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@st.cache_data(ttl=30)
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def load_all_trades():
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query = """
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SELECT
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ticket, position_id, symbol, strategy_name, type, volume,
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entry_price, sl_price, tp_price, entry_time, exit_time,
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session, regime, quality, confidence, timeframe_alignment,
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risk_amount, risk_pct, net_profit, profit_pct, rr_ratio,
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status, exit_reason
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FROM trades
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ORDER BY entry_time DESC
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"""
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try:
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return pd.read_sql_query(query, conn)
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except Exception as e:
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st.error(f"Error loading trades: {e}")
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return pd.DataFrame()
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@st.cache_data(ttl=30)
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def load_bot_status():
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try:
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return pd.read_sql_query("SELECT * FROM bot_status ORDER BY timestamp DESC LIMIT 1", conn)
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except Exception as e:
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st.error(f"Error loading bot status: {e}")
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return pd.DataFrame()
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# Load data
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df_trades_raw = load_all_trades()
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df_status = load_bot_status()
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# ==========================================
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# APPLY TRADE FILTER
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# ==========================================
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if trade_filter == "Live Trades Only":
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df_trades = df_trades_raw[df_trades_raw['status'] != 'historical'].copy()
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st.info(f"📊 Showing **Live Trades Only** (excluding {(df_trades_raw['status'] == 'historical').sum()} historical imports)")
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elif trade_filter == "Historical Only":
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df_trades = df_trades_raw[df_trades_raw['status'] == 'historical'].copy()
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st.info(f"📚 Showing **Historical Trades Only** ({len(df_trades)} trades)")
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else: # All Trades
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df_trades = df_trades_raw.copy()
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live_count = (df_trades['status'] != 'historical').sum()
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hist_count = (df_trades['status'] == 'historical').sum()
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st.info(f"📊 Showing **All Trades** ({live_count} live + {hist_count} historical)")
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# ==========================================
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# TOP METRICS
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# ==========================================
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st.subheader("📈 Key Metrics")
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col1, col2, col3, col4, col5 = st.columns(5)
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total_trades = len(df_trades)
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closed_trades = len(df_trades[df_trades['status'] == 'closed'])
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open_trades = len(df_trades[df_trades['status'] == 'open'])
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if closed_trades > 0:
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winning_trades = len(df_trades[(df_trades['status'] == 'closed') & (df_trades['net_profit'] > 0)])
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win_rate = (winning_trades / closed_trades) * 100
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total_profit = df_trades[df_trades['status'] == 'closed']['net_profit'].sum()
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else:
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win_rate = 0
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total_profit = 0
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with col1:
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st.metric("Total Trades", total_trades)
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with col2:
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st.metric("Open Positions", open_trades)
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with col3:
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st.metric("Win Rate", f"{win_rate:.1f}%")
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with col4:
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profit_color = "normal" if total_profit >= 0 else "inverse"
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st.metric("Total Profit", f"${total_profit:.2f}", delta=None)
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with col5:
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if closed_trades > 0:
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avg_profit = total_profit / closed_trades
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st.metric("Avg Profit/Trade", f"${avg_profit:.2f}")
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else:
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st.metric("Avg Profit/Trade", "N/A")
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st.markdown("---")
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# ==========================================
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# SESSION FILTER CHECK
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# ==========================================
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st.subheader("🎯 Session Filter Status")
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# Calculate session distribution
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session_counts = df_trades['session'].value_counts()
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col1, col2 = st.columns([1, 2])
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with col1:
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st.markdown("#### Session Distribution")
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for session in ['ny', 'london', 'asian', 'overlap']:
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count = session_counts.get(session, 0)
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pct = (count / total_trades * 100) if total_trades > 0 else 0
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if session == 'ny':
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if count == total_trades:
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st.success(f"✅ NY: {count} ({pct:.1f}%) - PERFECT!")
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else:
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st.warning(f"⚠️ NY: {count} ({pct:.1f}%)")
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else:
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if count > 0:
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st.error(f"❌ {session.upper()}: {count} ({pct:.1f}%) - VIOLATION!")
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else:
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st.success(f"✅ {session.upper()}: {count} (0%) - Blocked")
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with col2:
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st.markdown("#### Verdict")
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ny_count = session_counts.get('ny', 0)
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violation_count = total_trades - ny_count
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if total_trades == 0:
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st.info("ℹ️ No trades yet - waiting for data...")
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elif violation_count == 0:
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st.success("✅ **SESSION FILTER WORKING PERFECTLY!**")
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st.markdown("All trades are in NY session (13:00-21:00 UTC)")
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else:
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st.error(f"❌ **SESSION FILTER NOT WORKING!**")
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st.markdown(f"**{violation_count} trades** ({violation_count/total_trades*100:.1f}%) outside NY session!")
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st.markdown("**Action Required:** See FIX_DUPLICATE_SCHEDULER.md")
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st.markdown("---")
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# ==========================================
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# HOURLY DISTRIBUTION
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# ==========================================
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st.subheader("⏰ Trades by Hour (UTC)")
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if not df_trades.empty:
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# Extract hour from entry_time (handle both ISO8601 and standard format)
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df_trades['hour_utc'] = pd.to_datetime(df_trades['entry_time'], errors='coerce').dt.hour
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# Count trades by hour
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hourly_dist = df_trades.groupby('hour_utc').size().reset_index(name='count')
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# Create visualization
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fig = go.Figure()
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# Add bars
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colors = ['red' if (h < 13 or h >= 21) else 'green' for h in hourly_dist['hour_utc']]
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fig.add_trace(go.Bar(
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x=hourly_dist['hour_utc'],
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y=hourly_dist['count'],
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marker_color=colors,
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text=hourly_dist['count'],
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textposition='outside'
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))
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# Add NY session marker
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fig.add_vrect(x0=13, x1=21, fillcolor="green", opacity=0.1, layer="below", line_width=0)
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fig.add_annotation(x=17, y=hourly_dist['count'].max(), text="NY Session (13-21 UTC)", showarrow=False)
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fig.update_layout(
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xaxis_title="Hour (UTC)",
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yaxis_title="Number of Trades",
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height=400,
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showlegend=False,
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xaxis=dict(dtick=1, range=[-0.5, 23.5])
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)
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st.plotly_chart(fig, use_container_width=True)
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# Check for violations
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violations = df_trades[(df_trades['hour_utc'] < 13) | (df_trades['hour_utc'] >= 21)]
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if not violations.empty:
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st.error(f"⚠️ **{len(violations)} trades outside NY hours detected!**")
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with st.expander("Show violation details"):
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st.dataframe(violations[['ticket', 'session', 'entry_time', 'hour_utc', 'type', 'net_profit']])
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else:
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st.info("No trades to display")
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st.markdown("---")
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# ==========================================
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# SESSION PERFORMANCE
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# ==========================================
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st.subheader("📊 Performance by Session")
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if closed_trades > 0:
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session_perf = df_trades[df_trades['status'] == 'closed'].groupby('session').agg({
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'ticket': 'count',
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'net_profit': ['sum', 'mean']
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}).round(2)
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session_perf.columns = ['Trades', 'Total Profit', 'Avg Profit']
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# Calculate win rate per session
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win_rates = []
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for session in session_perf.index:
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session_trades = df_trades[(df_trades['status'] == 'closed') & (df_trades['session'] == session)]
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wins = len(session_trades[session_trades['net_profit'] > 0])
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wr = (wins / len(session_trades) * 100) if len(session_trades) > 0 else 0
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win_rates.append(wr)
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session_perf['Win Rate %'] = win_rates
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# Color code
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def color_sessions(row):
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if row.name == 'ny':
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return ['background-color: #90EE90'] * len(row) # Light green
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else:
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return ['background-color: #FFB6C6'] * len(row) # Light red
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st.dataframe(session_perf.style.apply(color_sessions, axis=1), use_container_width=True)
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else:
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st.info("No closed trades yet")
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st.markdown("---")
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# ==========================================
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# RECENT TRADES
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# ==========================================
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st.subheader("📋 Recent Trades (Last 20)")
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if not df_trades.empty:
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recent = df_trades.head(20).copy()
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# Color code session
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def highlight_session(row):
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if row['session'] == 'ny':
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return ['background-color: #90EE90'] * len(row)
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else:
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return ['background-color: #FFB6C6'] * len(row)
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# Select columns
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display_cols = ['ticket', 'type', 'session', 'entry_time', 'confidence', 'quality', 'regime', 'status', 'net_profit']
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recent_display = recent[display_cols]
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st.dataframe(
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recent_display.style.apply(highlight_session, axis=1),
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use_container_width=True,
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height=400
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)
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else:
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st.info("No trades yet")
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st.markdown("---")
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# ==========================================
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# PROFIT OVER TIME
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# ==========================================
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st.subheader("💰 Cumulative Profit Over Time")
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if closed_trades > 0:
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profit_timeline = df_trades[df_trades['status'] == 'closed'].copy()
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profit_timeline['exit_time'] = pd.to_datetime(profit_timeline['exit_time'], errors='coerce')
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profit_timeline = profit_timeline.sort_values('exit_time')
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profit_timeline['cumulative_profit'] = profit_timeline['net_profit'].cumsum()
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fig = px.line(
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profit_timeline,
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x='exit_time',
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y='cumulative_profit',
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title='Cumulative Profit',
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labels={'exit_time': 'Date', 'cumulative_profit': 'Profit ($)'}
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)
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fig.update_traces(line_color='green' if profit_timeline['cumulative_profit'].iloc[-1] > 0 else 'red')
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fig.add_hline(y=0, line_dash="dash", line_color="gray")
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st.plotly_chart(fig, use_container_width=True)
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else:
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st.info("No closed trades yet")
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st.markdown("---")
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# ==========================================
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# CONFIDENCE & QUALITY ANALYSIS
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# ==========================================
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st.subheader("🎯 Signal Quality Analysis")
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col1, col2 = st.columns(2)
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with col1:
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st.markdown("#### Confidence Distribution")
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if not df_trades.empty:
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fig = px.histogram(
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df_trades,
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x='confidence',
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nbins=20,
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title='Trade Confidence Distribution'
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)
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st.plotly_chart(fig, use_container_width=True)
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else:
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st.info("No data")
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with col2:
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st.markdown("#### Quality Breakdown")
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if not df_trades.empty:
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quality_counts = df_trades['quality'].value_counts()
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fig = px.pie(
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values=quality_counts.values,
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names=quality_counts.index,
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title='Signal Quality Distribution'
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)
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st.plotly_chart(fig, use_container_width=True)
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else:
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st.info("No data")
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st.markdown("---")
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# ==========================================
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# BOT STATUS
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# ==========================================
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st.subheader("🤖 Bot Status")
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if not df_status.empty:
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status = df_status.iloc[0]
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col1, col2, col3 = st.columns(3)
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with col1:
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st.markdown("**Version:**")
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st.code(status.get('version', 'N/A'))
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with col2:
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st.markdown("**Status:**")
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bot_status = status.get('status', 'unknown')
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if bot_status == 'running':
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st.success("🟢 Running")
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elif bot_status == 'stopped':
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st.error("🔴 Stopped")
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else:
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st.warning("⚠️ Unknown")
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with col3:
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st.markdown("**Last Update:**")
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st.code(status.get('timestamp', 'N/A'))
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if 'config' in status and status['config']:
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with st.expander("Show Configuration"):
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st.json(status['config'])
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else:
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st.warning("No bot status available")
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st.markdown("---")
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# ==========================================
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# FOOTER
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# ==========================================
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st.markdown("---")
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st.caption(f"Dashboard last updated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
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st.caption("📊 Trading Bot V1.8 - Aggressive Mode (NY Only)")
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