578 lines
22 KiB
Python
578 lines
22 KiB
Python
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#!/usr/bin/env python3
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"""
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TradingBot V1.6 MT5 Profitability Analyzer
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Holt Trade-Historie aus MT5 und berechnet echte Performance
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"""
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import MetaTrader5 as mt
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import json
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import glob
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from datetime import datetime, timedelta
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from collections import defaultdict
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import keyring as kr
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class MT5ProfitabilityAnalyzer:
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"""Analysiert echte Trading-Performance aus MT5"""
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def __init__(self, strategy_name="TradingBot_V1.6", symbol="XAUUSD"):
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self.strategy_name = strategy_name
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self.symbol = symbol
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self.json_trades = []
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self.mt5_deals = []
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self.mt5_positions = []
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self.matched_trades = []
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self.stats = {}
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def connect_mt5(self):
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"""Verbindet zu MT5"""
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print("🔌 Verbinde zu MT5...")
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if not mt.initialize():
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print(f"❌ MT5 Initialisierung fehlgeschlagen: {mt.last_error()}")
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return False
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# Login (wie im Bot)
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login = 10800246
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server = 'VantageInternational-Demo'
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password = kr.get_password(server, str(login))
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if not mt.login(login, password, server):
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print(f"❌ MT5 Login fehlgeschlagen: {mt.last_error()}")
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return False
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account_info = mt.account_info()
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if account_info:
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print(f"✅ MT5 verbunden")
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print(f" Account: {account_info.login}")
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print(f" Balance: ${account_info.balance:.2f}")
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print(f" Equity: ${account_info.equity:.2f}")
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print(f" Profit: ${account_info.profit:.2f}")
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return True
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def load_json_trades(self, json_pattern="trade_performance_v16_XAUUSD_*.json"):
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"""Lädt JSON Trade-Daten"""
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print(f"\n📂 Lade JSON Trade-Daten...")
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json_files = glob.glob(json_pattern)
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if not json_files:
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print(f"❌ Keine JSON-Files gefunden")
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return False
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all_trades = []
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for file in json_files:
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try:
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with open(file, 'r') as f:
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data = json.load(f)
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all_trades.extend(data)
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except Exception as e:
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print(f"⚠️ Fehler beim Laden von {file}: {e}")
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if not all_trades:
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return False
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self.json_trades = all_trades
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print(f"✅ {len(all_trades)} JSON Trades geladen")
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return True
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def fetch_mt5_history(self, days_back=30):
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"""Holt Trade-Historie aus MT5"""
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print(f"\n📊 Hole MT5 Trade-Historie (letzte {days_back} Tage)...")
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# Zeitraum
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date_to = datetime.now()
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date_from = date_to - timedelta(days=days_back)
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# Hole ALLE Deals für das Symbol (nicht nur mit Comment)
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all_deals = mt.history_deals_get(date_from, date_to, symbol=self.symbol)
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if all_deals is None:
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print(f"❌ Keine Deals gefunden: {mt.last_error()}")
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return False
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print(f"📊 {len(all_deals)} Total Deals für {self.symbol} gefunden")
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# Filtere nach Strategy
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strategy_deals = [
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deal for deal in all_deals
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if self.strategy_name in deal.comment
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]
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# Zähle Entry vs Exit Deals
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entry_deals = [d for d in strategy_deals if d.entry == 0] # IN
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exit_deals = [d for d in strategy_deals if d.entry == 1] # OUT
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self.mt5_deals = strategy_deals
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print(f"✅ {len(strategy_deals)} Strategy Deals gefunden:")
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print(f" - {len(entry_deals)} Entry Deals (entry=0)")
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print(f" - {len(exit_deals)} Exit Deals (entry=1)")
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# Wenn keine Exit Deals, dann sind Positionen noch offen ODER
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# sie wurden per SL/TP geschlossen (anderer Comment?)
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if len(exit_deals) == 0:
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print(f"\n⚠️ KEINE Exit Deals gefunden!")
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print(f" Das bedeutet: Positionen wurden per SL/TP geschlossen,")
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print(f" aber Exit-Deals haben anderen Comment (nicht '{self.strategy_name}')")
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print(f"\n🔍 Prüfe alle Deals für Position-IDs...")
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# Sammle alle Position IDs aus Entry Deals
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entry_position_ids = set(d.position_id for d in entry_deals)
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# Suche ALLE Deals mit diesen Position IDs (auch ohne Strategy Comment)
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all_position_deals = [
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d for d in all_deals
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if d.position_id in entry_position_ids
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]
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print(f"✅ {len(all_position_deals)} Deals für diese Position-IDs gefunden")
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# Verwende ALLE Deals für diese Positionen
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self.mt5_deals = all_position_deals
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# Neu zählen
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entry_deals = [d for d in all_position_deals if d.entry == 0]
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exit_deals = [d for d in all_position_deals if d.entry == 1]
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print(f" - {len(entry_deals)} Entry Deals")
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print(f" - {len(exit_deals)} Exit Deals")
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# Hole auch geschlossene Positionen
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positions_history = mt.history_orders_get(date_from, date_to)
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if positions_history:
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strategy_positions = [
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pos for pos in positions_history
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if pos.symbol == self.symbol and self.strategy_name in pos.comment
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]
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self.mt5_positions = strategy_positions
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print(f"✅ {len(strategy_positions)} Order-Historie-Einträge gefunden")
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return True
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def match_trades(self):
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"""Matched JSON Entry-Daten mit MT5 Exit-Daten"""
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print(f"\n🔗 Matche JSON Entries mit MT5 Exits...")
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matched = []
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unmatched_json = []
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# Gruppiere Deals nach Position ID
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deals_by_position = defaultdict(list)
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for deal in self.mt5_deals:
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deals_by_position[deal.position_id].append(deal)
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# Debug: Zeige erste Deals
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print(f"\n🔍 Debug: Erste 3 Deals:")
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for i, deal in enumerate(self.mt5_deals[:3]):
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print(f" Deal {i+1}:")
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print(f" ticket: {deal.ticket}")
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print(f" position_id: {deal.position_id}")
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print(f" entry: {deal.entry}")
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print(f" type: {deal.type}")
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print(f" price: {deal.price}")
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print(f" profit: {deal.profit}")
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# Extrahiere Deal-IDs aus JSON
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for json_trade in self.json_trades:
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order_str = json_trade.get('order_result', '')
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# Extrahiere Deal ID
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import re
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deal_match = re.search(r'deal=(\d+)', order_str)
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if not deal_match:
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unmatched_json.append(json_trade)
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continue
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entry_deal_id = int(deal_match.group(1))
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# Finde Entry Deal in MT5
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entry_deal = None
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for deal in self.mt5_deals:
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if deal.ticket == entry_deal_id:
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entry_deal = deal
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break
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if not entry_deal:
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unmatched_json.append(json_trade)
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continue
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# Finde zugehörigen Exit Deal
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# Ein Exit Deal hat:
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# - Gleiche position_id
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# - Andere ticket ID
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# - entry = 1 (OUT) statt 0 (IN)
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# - Späterer Zeitstempel
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position_id = entry_deal.position_id
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position_deals = deals_by_position[position_id]
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exit_deal = None
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if len(position_deals) >= 2:
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# Sortiere nach Zeit
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sorted_deals = sorted(position_deals, key=lambda d: d.time)
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# Entry sollte erster sein, Exit zweiter
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for deal in sorted_deals:
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if deal.ticket != entry_deal_id and deal.time > entry_deal.time:
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exit_deal = deal
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break
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# Erstelle Match-Entry
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match_entry = {
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'json_trade': json_trade,
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'entry_deal': entry_deal,
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'exit_deal': exit_deal,
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'is_closed': exit_deal is not None,
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'entry_time': datetime.fromtimestamp(entry_deal.time),
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'entry_price': entry_deal.price,
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'entry_volume': entry_deal.volume,
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}
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if exit_deal:
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match_entry.update({
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'exit_time': datetime.fromtimestamp(exit_deal.time),
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'exit_price': exit_deal.price,
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'profit': exit_deal.profit,
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'commission': exit_deal.commission,
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'swap': exit_deal.swap,
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'net_profit': exit_deal.profit + exit_deal.commission + exit_deal.swap,
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'hold_time_hours': (datetime.fromtimestamp(exit_deal.time) -
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datetime.fromtimestamp(entry_deal.time)).total_seconds() / 3600,
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'pips': abs(exit_deal.price - entry_deal.price),
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'is_winner': exit_deal.profit > 0,
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})
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matched.append(match_entry)
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self.matched_trades = matched
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print(f"\n✅ {len(matched)} Trades gematched")
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print(f" - {sum(1 for m in matched if m['is_closed'])} geschlossen")
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print(f" - {sum(1 for m in matched if not m['is_closed'])} noch offen")
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if unmatched_json:
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print(f"⚠️ {len(unmatched_json)} JSON Trades konnten nicht gematched werden")
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return True
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def calculate_performance_metrics(self):
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"""Berechnet umfassende Performance-Metriken"""
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print(f"\n📈 Berechne Performance-Metriken...")
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closed_trades = [t for t in self.matched_trades if t['is_closed']]
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if not closed_trades:
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print("⚠️ Keine geschlossenen Trades gefunden!")
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return False
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# Basic Stats
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total_closed = len(closed_trades)
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winners = [t for t in closed_trades if t['is_winner']]
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losers = [t for t in closed_trades if not t['is_winner']]
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win_count = len(winners)
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loss_count = len(losers)
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win_rate = (win_count / total_closed * 100) if total_closed > 0 else 0
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# P&L
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total_profit = sum(t['profit'] for t in closed_trades)
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total_commission = sum(t['commission'] for t in closed_trades)
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total_swap = sum(t['swap'] for t in closed_trades)
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net_profit = sum(t['net_profit'] for t in closed_trades)
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gross_profit = sum(t['profit'] for t in winners) if winners else 0
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gross_loss = abs(sum(t['profit'] for t in losers)) if losers else 0
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profit_factor = (gross_profit / gross_loss) if gross_loss > 0 else float('inf')
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# Average Trade
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avg_win = (sum(t['profit'] for t in winners) / win_count) if winners else 0
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avg_loss = (sum(t['profit'] for t in losers) / loss_count) if losers else 0
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avg_trade = net_profit / total_closed
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# Hold Time
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avg_hold_time = sum(t['hold_time_hours'] for t in closed_trades) / total_closed
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# Expectancy
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expectancy = (win_rate/100 * avg_win) + ((1 - win_rate/100) * avg_loss)
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# Drawdown Analyse
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cumulative_profits = []
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running_profit = 0
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for trade in sorted(closed_trades, key=lambda x: x['exit_time']):
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running_profit += trade['net_profit']
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cumulative_profits.append(running_profit)
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peak = cumulative_profits[0]
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max_drawdown = 0
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drawdown_pct = 0
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for profit in cumulative_profits:
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if profit > peak:
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peak = profit
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drawdown = peak - profit
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if drawdown > max_drawdown:
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max_drawdown = drawdown
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drawdown_pct = (drawdown / peak * 100) if peak > 0 else 0
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# Session Analysis
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session_performance = defaultdict(lambda: {'count': 0, 'profit': 0, 'wins': 0})
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for trade in closed_trades:
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session = trade['json_trade'].get('session', 'unknown')
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session_performance[session]['count'] += 1
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session_performance[session]['profit'] += trade['net_profit']
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if trade['is_winner']:
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session_performance[session]['wins'] += 1
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# Regime Analysis
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regime_performance = defaultdict(lambda: {'count': 0, 'profit': 0, 'wins': 0})
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for trade in closed_trades:
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regime = trade['json_trade'].get('market_regime', 'unknown')
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regime_performance[regime]['count'] += 1
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regime_performance[regime]['profit'] += trade['net_profit']
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if trade['is_winner']:
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regime_performance[regime]['wins'] += 1
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# Quality Analysis
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quality_performance = defaultdict(lambda: {'count': 0, 'profit': 0, 'wins': 0})
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for trade in closed_trades:
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quality = trade['json_trade'].get('signal_quality', 'unknown')
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quality_performance[quality]['count'] += 1
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quality_performance[quality]['profit'] += trade['net_profit']
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if trade['is_winner']:
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quality_performance[quality]['wins'] += 1
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# Best/Worst Trades
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best_trade = max(closed_trades, key=lambda x: x['profit'])
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worst_trade = min(closed_trades, key=lambda x: x['profit'])
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self.stats = {
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'total_closed': total_closed,
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'win_count': win_count,
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'loss_count': loss_count,
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'win_rate': win_rate,
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'total_profit': total_profit,
|
||
|
|
'total_commission': total_commission,
|
||
|
|
'total_swap': total_swap,
|
||
|
|
'net_profit': net_profit,
|
||
|
|
'gross_profit': gross_profit,
|
||
|
|
'gross_loss': gross_loss,
|
||
|
|
'profit_factor': profit_factor,
|
||
|
|
'avg_win': avg_win,
|
||
|
|
'avg_loss': avg_loss,
|
||
|
|
'avg_trade': avg_trade,
|
||
|
|
'avg_hold_time': avg_hold_time,
|
||
|
|
'expectancy': expectancy,
|
||
|
|
'max_drawdown': max_drawdown,
|
||
|
|
'max_drawdown_pct': drawdown_pct,
|
||
|
|
'session_performance': dict(session_performance),
|
||
|
|
'regime_performance': dict(regime_performance),
|
||
|
|
'quality_performance': dict(quality_performance),
|
||
|
|
'best_trade': {
|
||
|
|
'profit': best_trade['profit'],
|
||
|
|
'entry_time': best_trade['entry_time'],
|
||
|
|
'session': best_trade['json_trade'].get('session'),
|
||
|
|
},
|
||
|
|
'worst_trade': {
|
||
|
|
'profit': worst_trade['profit'],
|
||
|
|
'entry_time': worst_trade['entry_time'],
|
||
|
|
'session': worst_trade['json_trade'].get('session'),
|
||
|
|
},
|
||
|
|
'cumulative_profits': cumulative_profits,
|
||
|
|
}
|
||
|
|
|
||
|
|
print("✅ Performance-Metriken berechnet")
|
||
|
|
return True
|
||
|
|
|
||
|
|
def print_profitability_report(self):
|
||
|
|
"""Druckt umfassenden Profitabilitäts-Report"""
|
||
|
|
stats = self.stats
|
||
|
|
|
||
|
|
print("\n" + "="*70)
|
||
|
|
print("💰 TRADINGBOT V1.6 - PROFITABILITY REPORT")
|
||
|
|
print("="*70)
|
||
|
|
|
||
|
|
# Profitability Status
|
||
|
|
is_profitable = stats['net_profit'] > 0
|
||
|
|
status_emoji = "✅" if is_profitable else "❌"
|
||
|
|
status_text = "PROFITABEL" if is_profitable else "NICHT PROFITABEL"
|
||
|
|
|
||
|
|
print(f"\n{status_emoji} STATUS: {status_text}")
|
||
|
|
print(f" Net Profit: ${stats['net_profit']:.2f}")
|
||
|
|
|
||
|
|
print(f"\n📊 TRADE STATISTICS:")
|
||
|
|
print(f" Total Closed Trades: {stats['total_closed']}")
|
||
|
|
print(f" Winners: {stats['win_count']} ({stats['win_rate']:.1f}%)")
|
||
|
|
print(f" Losers: {stats['loss_count']} ({100-stats['win_rate']:.1f}%)")
|
||
|
|
|
||
|
|
print(f"\n💵 PROFIT & LOSS:")
|
||
|
|
print(f" Gross Profit: ${stats['gross_profit']:.2f}")
|
||
|
|
print(f" Gross Loss: ${stats['gross_loss']:.2f}")
|
||
|
|
print(f" Total Commission: ${stats['total_commission']:.2f}")
|
||
|
|
print(f" Total Swap: ${stats['total_swap']:.2f}")
|
||
|
|
print(f" Net Profit: ${stats['net_profit']:.2f}")
|
||
|
|
|
||
|
|
print(f"\n📈 PERFORMANCE METRICS:")
|
||
|
|
pf_display = f"{stats['profit_factor']:.2f}" if stats['profit_factor'] != float('inf') else "∞"
|
||
|
|
print(f" Profit Factor: {pf_display}")
|
||
|
|
print(f" Average Win: ${stats['avg_win']:.2f}")
|
||
|
|
print(f" Average Loss: ${stats['avg_loss']:.2f}")
|
||
|
|
print(f" Average Trade: ${stats['avg_trade']:.2f}")
|
||
|
|
print(f" Expectancy: ${stats['expectancy']:.2f}")
|
||
|
|
|
||
|
|
print(f"\n⏱️ TIMING:")
|
||
|
|
print(f" Avg Hold Time: {stats['avg_hold_time']:.1f} hours")
|
||
|
|
|
||
|
|
print(f"\n📉 RISK METRICS:")
|
||
|
|
print(f" Max Drawdown: ${stats['max_drawdown']:.2f} ({stats['max_drawdown_pct']:.1f}%)")
|
||
|
|
|
||
|
|
print(f"\n🏆 BEST TRADE:")
|
||
|
|
best = stats['best_trade']
|
||
|
|
print(f" Profit: ${best['profit']:.2f}")
|
||
|
|
print(f" Time: {best['entry_time'].strftime('%Y-%m-%d %H:%M')}")
|
||
|
|
print(f" Session: {best['session']}")
|
||
|
|
|
||
|
|
print(f"\n💔 WORST TRADE:")
|
||
|
|
worst = stats['worst_trade']
|
||
|
|
print(f" Loss: ${worst['profit']:.2f}")
|
||
|
|
print(f" Time: {worst['entry_time'].strftime('%Y-%m-%d %H:%M')}")
|
||
|
|
print(f" Session: {worst['session']}")
|
||
|
|
|
||
|
|
print(f"\n🌍 SESSION PERFORMANCE:")
|
||
|
|
for session in ['asian', 'london', 'overlap', 'ny']:
|
||
|
|
if session in stats['session_performance']:
|
||
|
|
perf = stats['session_performance'][session]
|
||
|
|
win_rate = (perf['wins'] / perf['count'] * 100) if perf['count'] > 0 else 0
|
||
|
|
profit_emoji = "✅" if perf['profit'] > 0 else "❌"
|
||
|
|
print(f" {session.capitalize():8s}: {perf['count']:3d} trades | "
|
||
|
|
f"${perf['profit']:7.2f} | Win Rate: {win_rate:5.1f}% {profit_emoji}")
|
||
|
|
|
||
|
|
print(f"\n📈 REGIME PERFORMANCE:")
|
||
|
|
for regime, perf in stats['regime_performance'].items():
|
||
|
|
win_rate = (perf['wins'] / perf['count'] * 100) if perf['count'] > 0 else 0
|
||
|
|
profit_emoji = "✅" if perf['profit'] > 0 else "❌"
|
||
|
|
print(f" {regime.capitalize():10s}: {perf['count']:3d} trades | "
|
||
|
|
f"${perf['profit']:7.2f} | Win Rate: {win_rate:5.1f}% {profit_emoji}")
|
||
|
|
|
||
|
|
print(f"\n🎯 SIGNAL QUALITY PERFORMANCE:")
|
||
|
|
for quality in ['excellent', 'good', 'fair']:
|
||
|
|
if quality in stats['quality_performance']:
|
||
|
|
perf = stats['quality_performance'][quality]
|
||
|
|
win_rate = (perf['wins'] / perf['count'] * 100) if perf['count'] > 0 else 0
|
||
|
|
profit_emoji = "✅" if perf['profit'] > 0 else "❌"
|
||
|
|
print(f" {quality.capitalize():10s}: {perf['count']:3d} trades | "
|
||
|
|
f"${perf['profit']:7.2f} | Win Rate: {win_rate:5.1f}% {profit_emoji}")
|
||
|
|
|
||
|
|
print("\n" + "="*70)
|
||
|
|
|
||
|
|
# Interpretation
|
||
|
|
print("\n💡 INTERPRETATION:")
|
||
|
|
|
||
|
|
if is_profitable:
|
||
|
|
print(" ✅ Die Strategie ist profitabel!")
|
||
|
|
if stats['win_rate'] >= 50:
|
||
|
|
print(" ✅ Gute Win-Rate")
|
||
|
|
else:
|
||
|
|
print(" ⚠️ Win-Rate unter 50% - Strategie profitiert von großen Wins")
|
||
|
|
|
||
|
|
if stats['profit_factor'] >= 2.0:
|
||
|
|
print(" ✅ Exzellenter Profit Factor (>=2.0)")
|
||
|
|
elif stats['profit_factor'] >= 1.5:
|
||
|
|
print(" ✅ Guter Profit Factor (>=1.5)")
|
||
|
|
else:
|
||
|
|
print(" ⚠️ Profit Factor könnte besser sein")
|
||
|
|
else:
|
||
|
|
print(" ❌ Die Strategie ist derzeit nicht profitabel")
|
||
|
|
print(" ⚠️ Optimierung notwendig!")
|
||
|
|
|
||
|
|
print("="*70)
|
||
|
|
|
||
|
|
def save_results(self, output_file='profitability_analysis.json'):
|
||
|
|
"""Speichert Ergebnisse als JSON"""
|
||
|
|
print(f"\n💾 Speichere Ergebnisse...")
|
||
|
|
|
||
|
|
results = {
|
||
|
|
'analysis_date': datetime.now().isoformat(),
|
||
|
|
'strategy_name': self.strategy_name,
|
||
|
|
'symbol': self.symbol,
|
||
|
|
'statistics': {
|
||
|
|
k: v for k, v in self.stats.items()
|
||
|
|
if k not in ['best_trade', 'worst_trade', 'cumulative_profits']
|
||
|
|
},
|
||
|
|
'best_trade': {
|
||
|
|
'profit': self.stats['best_trade']['profit'],
|
||
|
|
'entry_time': self.stats['best_trade']['entry_time'].isoformat(),
|
||
|
|
'session': self.stats['best_trade']['session'],
|
||
|
|
},
|
||
|
|
'worst_trade': {
|
||
|
|
'profit': self.stats['worst_trade']['profit'],
|
||
|
|
'entry_time': self.stats['worst_trade']['entry_time'].isoformat(),
|
||
|
|
'session': self.stats['worst_trade']['session'],
|
||
|
|
},
|
||
|
|
}
|
||
|
|
|
||
|
|
with open(output_file, 'w') as f:
|
||
|
|
json.dump(results, f, indent=2)
|
||
|
|
|
||
|
|
print(f"✅ Ergebnisse gespeichert: {output_file}")
|
||
|
|
|
||
|
|
def disconnect_mt5(self):
|
||
|
|
"""Trennt MT5 Verbindung"""
|
||
|
|
mt.shutdown()
|
||
|
|
print("✅ MT5 Verbindung getrennt")
|
||
|
|
|
||
|
|
|
||
|
|
def main():
|
||
|
|
"""Haupt-Analyse"""
|
||
|
|
print("="*70)
|
||
|
|
print("💰 TradingBot V1.6 - MT5 Profitability Analyzer")
|
||
|
|
print("="*70)
|
||
|
|
|
||
|
|
analyzer = MT5ProfitabilityAnalyzer()
|
||
|
|
|
||
|
|
# 1. Connect MT5
|
||
|
|
if not analyzer.connect_mt5():
|
||
|
|
return
|
||
|
|
|
||
|
|
# 2. Load JSON Trades
|
||
|
|
if not analyzer.load_json_trades():
|
||
|
|
analyzer.disconnect_mt5()
|
||
|
|
return
|
||
|
|
|
||
|
|
# 3. Fetch MT5 History
|
||
|
|
if not analyzer.fetch_mt5_history(days_back=30):
|
||
|
|
analyzer.disconnect_mt5()
|
||
|
|
return
|
||
|
|
|
||
|
|
# 4. Match Trades
|
||
|
|
if not analyzer.match_trades():
|
||
|
|
analyzer.disconnect_mt5()
|
||
|
|
return
|
||
|
|
|
||
|
|
# 5. Calculate Performance
|
||
|
|
if not analyzer.calculate_performance_metrics():
|
||
|
|
analyzer.disconnect_mt5()
|
||
|
|
return
|
||
|
|
|
||
|
|
# 6. Print Report
|
||
|
|
analyzer.print_profitability_report()
|
||
|
|
|
||
|
|
# 7. Save Results
|
||
|
|
analyzer.save_results()
|
||
|
|
|
||
|
|
# 8. Disconnect
|
||
|
|
analyzer.disconnect_mt5()
|
||
|
|
|
||
|
|
print("\n" + "="*70)
|
||
|
|
print("✅ ANALYSE ABGESCHLOSSEN")
|
||
|
|
print("="*70)
|
||
|
|
print("\n📂 Generierte Files:")
|
||
|
|
print(" - profitability_analysis.json")
|
||
|
|
print("\n🎯 Nächste Schritte basierend auf Ergebnis:")
|
||
|
|
print(" • Falls profitabel → SQLite + Telegram + Scaling")
|
||
|
|
print(" • Falls nicht profitabel → Parameter-Optimierung + Backtesting")
|
||
|
|
print("="*70)
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
main()
|