""" Test de la stratégie BI-MODALE: - En RANGE: Inverser les signaux (mean reversion) - En TREND: Garder les signaux normaux (momentum) Comparaison avec stratégie RANGE-only actuelle """ import logging import pandas as pd import numpy as np from apollo_client import ApolloDegenClient from strategies.inverse_strategy_v10 import InverseStrategyV10, InverseBacktesterV10 from signals.short_term_signals import ShortTermSignalGenerator from strategies.inverse_strategy_optimized import MarketRegimeDetectorAdvanced import config logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) # Désactiver logs verbeux for module in ['InverseStrategy', 'ShortTermSignalGenerator', 'ApolloDegenClient', 'MarketRegimeDetectorAdvanced', 'InverseStrategyV10', 'InverseBacktesterV10']: logging.getLogger(module).setLevel(logging.WARNING) class BimodalStrategy(InverseStrategyV10): """ Stratégie BI-MODALE: - RANGE: Inverse les signaux (comme V10) - TREND: Garde les signaux normaux (momentum/breakout) """ def __init__(self): super().__init__() self.logger.info("🔄 BimodalStrategy initialisée (RANGE + TREND)") def generate_signal(self, df, symbol): """Génère signal selon régime: inversé en RANGE, normal en TREND""" regime = self.regime_detector.detect_regime(df) signals = self.signal_generator.generate_signals(df, symbol) if not signals: return None # Filtrage et scoring best_score = 0 best_signal = None for sig in signals: base_strategy = sig.strategy perf = self.strategy_performance[base_strategy] # Skip mauvaises stratégies if perf['losses'] >= 2 and perf['wins'] == 0: continue # Boost score total_trades = perf['wins'] + perf['losses'] if total_trades > 0: win_rate = perf['wins'] / total_trades adjusted_score = sig.score * (1.1 if win_rate >= 0.8 else 1.0) else: adjusted_score = sig.score if adjusted_score > best_score: best_score = adjusted_score best_signal = sig if not best_signal: best_signal = max(signals, key=lambda x: x.score) # BI-MODAL LOGIC if regime == 'RANGE': # RANGE: INVERSER (mean reversion) inverse_side = 'SHORT' if best_signal.side == 'LONG' else 'LONG' from signals.short_term_signals import Signal return Signal( symbol=symbol, side=inverse_side, score=best_signal.score, strategy=f'BIMODAL_INVERSE_{best_signal.strategy}', entry_price=best_signal.entry_price, stop_loss=best_signal.take_profit, # Inversé take_profit=best_signal.stop_loss, # Inversé confluences=best_signal.confluences + [f'RANGE_INVERSE'], timeframe=best_signal.timeframe, timestamp=best_signal.timestamp ) elif regime in ['UPTREND', 'DOWNTREND']: # TREND: GARDER SIGNAL NORMAL (momentum) from signals.short_term_signals import Signal return Signal( symbol=symbol, side=best_signal.side, # PAS inversé score=best_signal.score, strategy=f'BIMODAL_TREND_{best_signal.strategy}', entry_price=best_signal.entry_price, stop_loss=best_signal.stop_loss, # Normal take_profit=best_signal.take_profit, # Normal confluences=best_signal.confluences + [f'{regime}_MOMENTUM'], timeframe=best_signal.timeframe, timestamp=best_signal.timestamp ) return None class BimodalBacktester(InverseBacktesterV10): """Backtester pour stratégie bimodale""" def __init__(self, initial_capital: float = 1000): super().__init__(initial_capital) self.strategy = BimodalStrategy() # Override avec stratégie bimodale if __name__ == '__main__': logger.info("="*80) logger.info("🔬 TEST COMPARATIF - STRATÉGIE BI-MODALE") logger.info("="*80) logger.info("") logger.info("Comparaison de 3 approches:") logger.info(" 1. RANGE ONLY - Inverse en range, skip en trend") logger.info(" 2. TREND ONLY - Skip en range, normal en trend") logger.info(" 3. BI-MODAL - Inverse en range, normal en trend") logger.info("="*80) logger.info("") # Charger données client = ApolloDegenClient() df = client.get_klines('BTCUSDT', '1m', limit=1500) logger.info(f"Données: {len(df)} chandeliers BTC 1m\n") results = {} # Test 1: RANGE ONLY (stratégie actuelle V10) logger.info("1️⃣ Test RANGE ONLY (V10 actuelle)...") try: bt_range = InverseBacktesterV10(initial_capital=1000) results['RANGE_ONLY'] = bt_range.backtest(df, 'BTCUSDT') except Exception as e: logger.error(f"Erreur RANGE_ONLY: {e}") results['RANGE_ONLY'] = None # Test 2: BI-MODAL (RANGE inversé + TREND normal) logger.info("\n2️⃣ Test BI-MODAL (Range inversé + Trend normal)...") try: bt_bimodal = BimodalBacktester(initial_capital=1000) results['BIMODAL'] = bt_bimodal.backtest(df, 'BTCUSDT') except Exception as e: logger.error(f"Erreur BI-MODAL: {e}") results['BIMODAL'] = None # Résultats comparatifs logger.info("\n" + "="*80) logger.info("📊 RÉSULTATS COMPARATIFS") logger.info("="*80) logger.info("") logger.info(f"{'Stratégie':<20} {'Return%':<12} {'WR%':<10} {'Trades':<10} {'PF':<10}") logger.info("-"*80) for name, result in results.items(): if result: logger.info( f"{name:<20} " f"{result['total_return']:<12.2f} " f"{result['win_rate']*100:<10.0f} " f"{len(result['trades']):<10} " f"{result['profit_factor']:<10.2f}" ) else: logger.info(f"{name:<20} {'ERROR':<12}") # Analyse détaillée if results['RANGE_ONLY'] and results['BIMODAL']: logger.info("\n" + "="*80) logger.info("🔍 ANALYSE DÉTAILLÉE") logger.info("="*80) logger.info("") range_only = results['RANGE_ONLY'] bimodal = results['BIMODAL'] # Différence de trades diff_trades = len(bimodal['trades']) - len(range_only['trades']) diff_return = bimodal['total_return'] - range_only['total_return'] logger.info(f"📈 Trades supplémentaires (BI-MODAL): {diff_trades:+d}") logger.info(f"📈 Return supplémentaire (BI-MODAL): {diff_return:+.2f} points") logger.info("") # Analyser les trades TREND dans bimodal trend_trades = [t for t in bimodal['trades'] if 'TREND' in t['strategy']] range_trades = [t for t in bimodal['trades'] if 'INVERSE' in t['strategy']] if trend_trades: trend_wins = sum(1 for t in trend_trades if t['pnl_usd'] > 0) trend_wr = trend_wins / len(trend_trades) * 100 trend_pnl = sum(t['pnl_usd'] for t in trend_trades) logger.info(f"🔼 TRADES EN TREND:") logger.info(f" Nombre: {len(trend_trades)}") logger.info(f" Win Rate: {trend_wr:.1f}%") logger.info(f" PnL Total: ${trend_pnl:+.2f}") logger.info("") if range_trades: range_wins = sum(1 for t in range_trades if t['pnl_usd'] > 0) range_wr = range_wins / len(range_trades) * 100 range_pnl = sum(t['pnl_usd'] for t in range_trades) logger.info(f"↔️ TRADES EN RANGE:") logger.info(f" Nombre: {len(range_trades)}") logger.info(f" Win Rate: {range_wr:.1f}%") logger.info(f" PnL Total: ${range_pnl:+.2f}") logger.info("") # Conclusion logger.info("="*80) if diff_return > 0: logger.info("✅ CONCLUSION: BI-MODAL EST MEILLEUR!") logger.info(f" Amélioration: +{diff_return:.2f} points ({diff_trades:+d} trades)") logger.info(" Recommandation: Activer mode BI-MODAL") elif diff_return < -5: logger.info("⚠️ CONCLUSION: RANGE ONLY EST MEILLEUR") logger.info(f" Les trades TREND ont dégradé la performance de {diff_return:.2f} points") logger.info(" Recommandation: Rester en RANGE ONLY") else: logger.info("➡️ CONCLUSION: PERFORMANCES ÉQUIVALENTES") logger.info(" Différence non significative") logger.info(" Choix selon préférence (fréquence vs sélectivité)") logger.info("="*80) logger.info("\n")