""" Validateur IA - Efficacité détection CREUX_REBOUND Analyse la performance du pattern CREUX_REBOUND et valide la qualité de la détection """ import json import os from datetime import datetime from collections import defaultdict import statistics class CreuxReboundValidator: """Validateur de performance du pattern CREUX_REBOUND""" def __init__(self): self.script_dir = os.path.dirname(os.path.abspath(__file__)) self.history_file = os.path.join(self.script_dir, 'trade_history.json') self.trades = [] self.stats = { 'total_trades': 0, 'wins': 0, 'losses': 0, 'total_pnl': 0, 'total_pnl_pct': 0, 'avg_pnl_pct': 0, 'win_rate': 0, 'avg_duration_minutes': 0, 'best_trade': None, 'worst_trade': None, 'quick_exits': 0, 'take_profits': 0, 'stop_losses': 0, } def load_trades(self): """Charge l'historique des trades""" if not os.path.exists(self.history_file): print("❌ Aucun historique de trades trouvé") return False with open(self.history_file, 'r', encoding='utf-8') as f: all_trades = json.load(f) # Filtrer uniquement les trades CREUX_REBOUND self.trades = [t for t in all_trades if t.get('pattern') in ['CREUX_REBOUND', 'CREUX_REBOUND_EARLY']] if not self.trades: print("⚠️ Aucun trade CREUX_REBOUND trouvé dans l'historique") return False print(f"✅ {len(self.trades)} trade(s) CREUX_REBOUND chargé(s)") return True def analyze_trades(self): """Analyse les performances des trades CREUX_REBOUND""" if not self.trades: return wins = [] losses = [] durations = [] reasons_count = defaultdict(int) for trade in self.trades: pnl_pct = trade.get('pnl_pct', 0) pnl = trade.get('pnl', 0) reason = trade.get('reason', 'unknown') # Calculer durée try: entry_time = datetime.fromisoformat(trade['entry_time']) exit_time = datetime.fromisoformat(trade['exit_time']) duration_minutes = (exit_time - entry_time).total_seconds() / 60 durations.append(duration_minutes) except: duration_minutes = 0 # Compter raisons de sortie reasons_count[reason] += 1 # Win/Loss if pnl_pct > 0: wins.append(trade) self.stats['wins'] += 1 else: losses.append(trade) self.stats['losses'] += 1 self.stats['total_pnl'] += pnl self.stats['total_pnl_pct'] += pnl_pct # Statistiques globales self.stats['total_trades'] = len(self.trades) self.stats['win_rate'] = (self.stats['wins'] / self.stats['total_trades'] * 100) if self.stats['total_trades'] > 0 else 0 self.stats['avg_pnl_pct'] = self.stats['total_pnl_pct'] / self.stats['total_trades'] if self.stats['total_trades'] > 0 else 0 self.stats['avg_duration_minutes'] = statistics.mean(durations) if durations else 0 # Meilleur et pire trade if self.trades: self.stats['best_trade'] = max(self.trades, key=lambda x: x.get('pnl_pct', 0)) self.stats['worst_trade'] = min(self.trades, key=lambda x: x.get('pnl_pct', 0)) # Raisons de sortie self.stats['quick_exits'] = reasons_count.get('quick-exit', 0) self.stats['take_profits'] = reasons_count.get('take-profit', 0) self.stats['stop_losses'] = reasons_count.get('stop-loss', 0) return wins, losses, reasons_count def validate_detection_quality(self): """Valide la qualité de la détection CREUX_REBOUND""" print("\n" + "="*70) print("🔍 VALIDATION QUALITÉ DÉTECTION CREUX_REBOUND") print("="*70) # Critères de qualité d'un bon CREUX_REBOUND perfect_conditions = 0 good_conditions = 0 weak_conditions = 0 for trade in self.trades: symbol = trade.get('symbol', 'UNKNOWN') pnl_pct = trade.get('pnl_pct', 0) duration = 0 try: entry_time = datetime.fromisoformat(trade['entry_time']) exit_time = datetime.fromisoformat(trade['exit_time']) duration = (exit_time - entry_time).total_seconds() / 60 except: pass # Scoring de la détection score = 0 # ✅ Trade profitable if pnl_pct > 0: score += 3 # ✅ Gain significatif (>1%) if pnl_pct > 1.0: score += 2 # ✅ Take profit atteint (meilleure sortie) if trade.get('reason') == 'take-profit': score += 2 # ✅ Durée raisonnable (pas trop court, pas trop long) if 5 <= duration <= 180: # Entre 5 min et 3h score += 1 # ❌ Perte if pnl_pct < 0: score -= 3 # ❌ Stop loss (mauvaise détection) if trade.get('reason') == 'stop-loss': score -= 2 # Classification if score >= 5: perfect_conditions += 1 elif score >= 2: good_conditions += 1 else: weak_conditions += 1 total = len(self.trades) perfect_pct = (perfect_conditions / total * 100) if total > 0 else 0 good_pct = (good_conditions / total * 100) if total > 0 else 0 weak_pct = (weak_conditions / total * 100) if total > 0 else 0 print(f"\n📊 QUALITÉ DES DÉTECTIONS:") print(f" 🌟 Excellentes: {perfect_conditions}/{total} ({perfect_pct:.1f}%)") print(f" ✅ Bonnes: {good_conditions}/{total} ({good_pct:.1f}%)") print(f" ⚠️ Faibles: {weak_conditions}/{total} ({weak_pct:.1f}%)") # Évaluation globale if perfect_pct >= 60: print(f"\n🎯 ÉVALUATION: EXCELLENT - Le pattern détecte très bien les creux!") elif perfect_pct + good_pct >= 70: print(f"\n✅ ÉVALUATION: BON - Le pattern fonctionne correctement") elif perfect_pct + good_pct >= 50: print(f"\n⚠️ ÉVALUATION: MOYEN - Amélioration possible sur les conditions") else: print(f"\n❌ ÉVALUATION: FAIBLE - Les conditions doivent être revues") def generate_report(self): """Génère un rapport détaillé""" wins, losses, reasons = self.analyze_trades() print("\n" + "="*70) print("📈 RAPPORT D'ANALYSE - PATTERN CREUX_REBOUND") print("="*70) print(f"\n📊 STATISTIQUES GLOBALES:") print(f" Total trades: {self.stats['total_trades']}") print(f" Wins: {self.stats['wins']} ({self.stats['win_rate']:.1f}%)") print(f" Losses: {self.stats['losses']}") print(f" P&L Total: {self.stats['total_pnl']:+.2f} USDT") print(f" P&L Total %: {self.stats['total_pnl_pct']:+.2f}%") print(f" P&L Moyen: {self.stats['avg_pnl_pct']:+.2f}%") print(f" Durée moyenne: {self.stats['avg_duration_minutes']:.0f} minutes") print(f"\n💰 RAISONS DE SORTIE:") print(f" Take Profit: {self.stats['take_profits']} ({self.stats['take_profits']/self.stats['total_trades']*100:.1f}%)") print(f" Quick Exit: {self.stats['quick_exits']} ({self.stats['quick_exits']/self.stats['total_trades']*100:.1f}%)") print(f" Stop Loss: {self.stats['stop_losses']} ({self.stats['stop_losses']/self.stats['total_trades']*100:.1f}%)") if self.stats['best_trade']: best = self.stats['best_trade'] print(f"\n🏆 MEILLEUR TRADE:") print(f" Symbol: {best['symbol']}") print(f" P&L: {best['pnl_pct']:+.2f}%") print(f" Sortie: {best['reason']}") print(f" Entrée: {best['entry_time']}") if self.stats['worst_trade']: worst = self.stats['worst_trade'] print(f"\n💔 PIRE TRADE:") print(f" Symbol: {worst['symbol']}") print(f" P&L: {worst['pnl_pct']:+.2f}%") print(f" Sortie: {worst['reason']}") print(f" Entrée: {worst['entry_time']}") # Validation qualité self.validate_detection_quality() # Recommandations print("\n" + "="*70) print("💡 RECOMMANDATIONS:") print("="*70) if self.stats['win_rate'] < 50: print(" ⚠️ Win rate < 50% - Renforcer conditions d'entrée (RSI, momentum)") if self.stats['stop_losses'] > self.stats['take_profits']: print(" ⚠️ Plus de stop-loss que de take-profit - Revoir SL/TP ou conditions") if self.stats['avg_pnl_pct'] < 0: print(" ❌ P&L moyen négatif - Pattern non rentable, révision nécessaire") elif self.stats['avg_pnl_pct'] > 0.5: print(" ✅ P&L moyen positif - Pattern rentable, continuer optimisation") if self.stats['avg_duration_minutes'] < 5: print(" ⚠️ Durée moyenne < 5min - Trades trop rapides, risque de faux signaux") elif self.stats['avg_duration_minutes'] > 180: print(" ⚠️ Durée moyenne > 3h - Positions trop longues, risque de retournement") print("\n" + "="*70) def run(self): """Exécute la validation complète""" print("\n🤖 VALIDATEUR IA - PATTERN CREUX_REBOUND") print("="*70) if not self.load_trades(): print("\n⏸️ Validation impossible - Attendez quelques trades pour analyse") return self.generate_report() # Sauvegarder rapport report_file = os.path.join(self.script_dir, 'creux_rebound_validation.json') with open(report_file, 'w', encoding='utf-8') as f: json.dump({ 'timestamp': datetime.now().isoformat(), 'stats': self.stats, 'total_trades': len(self.trades) }, f, indent=2) print(f"\n📄 Rapport sauvegardé: creux_rebound_validation.json") print("="*70 + "\n") if __name__ == '__main__': validator = CreuxReboundValidator() validator.run()