""" Test de la stratégie BI-MODALE sur PLUSIEURS périodes Pour valider si trader en TREND améliore vraiment la performance """ 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 Signal, ShortTermSignalGenerator from strategies.inverse_strategy_optimized import MarketRegimeDetectorAdvanced from datetime import datetime, timedelta logging.basicConfig(level=logging.WARNING) logger = logging.getLogger(__name__) # Silence logs for module in ['InverseStrategy', 'ShortTermSignalGenerator', 'ApolloDegenClient', 'MarketRegimeDetectorAdvanced', 'InverseStrategyV10', 'InverseBacktesterV10', 'BimodalStrategy', 'BimodalBacktester']: logging.getLogger(module).setLevel(logging.ERROR) class BimodalStrategy(InverseStrategyV10): """Stratégie BI-MODALE: Inverse en RANGE, Normal en TREND""" def generate_inverse_signal(self, df, symbol): """Override pour supporter TREND + RANGE""" regime = self.regime_detector.detect_regime(df) signals = self.signal_generator.generate_signals(df, symbol) if not signals: return None # Scoring best_score = 0 best_signal = None for sig in signals: base_strategy = sig.strategy perf = self.strategy_performance[base_strategy] if perf['losses'] >= 2 and perf['wins'] == 0: continue 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 inverse_side = 'SHORT' if best_signal.side == 'LONG' else 'LONG' 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, take_profit=best_signal.stop_loss, confluences=best_signal.confluences + ['RANGE_INV'], timeframe=best_signal.timeframe, timestamp=best_signal.timestamp ) elif regime in ['UPTREND', 'DOWNTREND']: # TREND: GARDER NORMAL 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, take_profit=best_signal.take_profit, confluences=best_signal.confluences + [f'{regime}_MOM'], timeframe=best_signal.timeframe, timestamp=best_signal.timestamp ) return None class BimodalBacktester(InverseBacktesterV10): """Backtester bi-modal""" def __init__(self, initial_capital: float = 1000): super().__init__(initial_capital) self.strategy = BimodalStrategy() def test_period(df, period_name): """Test une période donnée""" results = { 'period': period_name, 'candles': len(df), 'range_only': None, 'bimodal': None } # Test RANGE ONLY try: bt_range = InverseBacktesterV10(initial_capital=1000) range_result = bt_range.backtest(df, 'BTCUSDT') if range_result: results['range_only'] = { 'return': range_result['total_return'], 'wr': range_result['win_rate'] * 100, 'trades': len(range_result['trades']), 'pf': range_result['profit_factor'] } except Exception as e: logger.error(f"Error RANGE ONLY {period_name}: {e}") # Test BIMODAL try: bt_bimodal = BimodalBacktester(initial_capital=1000) bimodal_result = bt_bimodal.backtest(df, 'BTCUSDT') if bimodal_result: # Compter trades par régime range_trades = [t for t in bimodal_result['trades'] if 'INVERSE' in t['strategy']] trend_trades = [t for t in bimodal_result['trades'] if 'TREND' in t['strategy']] results['bimodal'] = { 'return': bimodal_result['total_return'], 'wr': bimodal_result['win_rate'] * 100, 'trades': len(bimodal_result['trades']), 'pf': bimodal_result['profit_factor'], 'range_trades': len(range_trades), 'trend_trades': len(trend_trades) } except Exception as e: logger.error(f"Error BIMODAL {period_name}: {e}") return results if __name__ == '__main__': print("="*80) print("🔬 TEST MULTI-PÉRIODES - RANGE ONLY vs BI-MODAL") print("="*80) print() client = ApolloDegenClient() # Tester différentes tailles d'échantillons test_configs = [ {'limit': 500, 'name': 'Sample 1 (500 candles ~8h)'}, {'limit': 1000, 'name': 'Sample 2 (1000 candles ~17h)'}, {'limit': 1500, 'name': 'Sample 3 (1500 candles ~25h)'}, {'limit': 2000, 'name': 'Sample 4 (2000 candles ~33h)'}, {'limit': 3000, 'name': 'Sample 5 (3000 candles ~2j)'}, {'limit': 5000, 'name': 'Sample 6 (5000 candles ~3.5j)'}, ] all_results = [] print("📊 Test en cours sur différentes périodes...") print() for i, config in enumerate(test_configs, 1): print(f"[{i}/6] Testing {config['name']}...", end=' ', flush=True) try: df = client.get_klines('BTCUSDT', '1m', limit=config['limit']) result = test_period(df, config['name']) all_results.append(result) print("✅") except Exception as e: print(f"❌ Error: {e}") continue # Afficher résultats print() print("="*80) print("📊 RÉSULTATS COMPARATIFS") print("="*80) print() print(f"{'Period':<30} {'Strategy':<15} {'Return%':<10} {'WR%':<8} {'Trades':<10}") print("-"*80) range_only_wins = 0 bimodal_wins = 0 ties = 0 for result in all_results: period = result['period'] # RANGE ONLY if result['range_only']: r = result['range_only'] print(f"{period:<30} {'RANGE ONLY':<15} {r['return']:<10.2f} {r['wr']:<8.0f} {r['trades']:<10}") # BIMODAL if result['bimodal']: b = result['bimodal'] print(f"{'':30} {'BI-MODAL':<15} {b['return']:<10.2f} {b['wr']:<8.0f} {b['trades']:<10}") # Compter gagnant if result['range_only']: diff = b['return'] - r['return'] if diff > 1: winner = "BI-MODAL" bimodal_wins += 1 elif diff < -1: winner = "RANGE ONLY" range_only_wins += 1 else: winner = "TIE" ties += 1 print(f"{'':30} {'→ Winner:':<15} {winner:<10} (Δ: {diff:+.2f}%)") # Afficher breakdown régime if b.get('trend_trades', 0) > 0: print(f"{'':30} {' Details:':<15} Range: {b['range_trades']}, Trend: {b['trend_trades']}") print() # Statistiques globales print("="*80) print("🏆 STATISTIQUES GLOBALES") print("="*80) print() total_tests = range_only_wins + bimodal_wins + ties print(f"Tests effectués: {total_tests}") print(f"RANGE ONLY gagne: {range_only_wins} fois ({range_only_wins/total_tests*100:.1f}%)") print(f"BI-MODAL gagne: {bimodal_wins} fois ({bimodal_wins/total_tests*100:.1f}%)") print(f"Égalité: {ties} fois ({ties/total_tests*100:.1f}%)") print() # Moyennes range_returns = [r['range_only']['return'] for r in all_results if r['range_only']] bimodal_returns = [r['bimodal']['return'] for r in all_results if r['bimodal']] if range_returns and bimodal_returns: avg_range = np.mean(range_returns) avg_bimodal = np.mean(bimodal_returns) print(f"Return moyen RANGE ONLY: {avg_range:.2f}%") print(f"Return moyen BI-MODAL: {avg_bimodal:.2f}%") print(f"Différence moyenne: {avg_bimodal - avg_range:+.2f}%") print() # Compter trades en TREND total_trend_trades = sum(r['bimodal'].get('trend_trades', 0) for r in all_results if r['bimodal']) total_range_trades = sum(r['bimodal'].get('range_trades', 0) for r in all_results if r['bimodal']) total_all_trades = total_trend_trades + total_range_trades if total_all_trades > 0: print(f"Distribution des trades BI-MODAL:") print(f" RANGE trades: {total_range_trades} ({total_range_trades/total_all_trades*100:.1f}%)") print(f" TREND trades: {total_trend_trades} ({total_trend_trades/total_all_trades*100:.1f}%)") print() # Conclusion print("="*80) print("💡 CONCLUSION") print("="*80) print() if bimodal_wins > range_only_wins + 1: print("✅ RECOMMANDATION: Activer le mode BI-MODAL") print(" La stratégie bi-modale performe mieux sur la majorité des périodes") print(" Les trades en TREND ajoutent de la valeur") elif range_only_wins > bimodal_wins + 1: print("✅ RECOMMANDATION: Garder RANGE ONLY") print(" Les trades en TREND dégradent la performance") print(" Rester focus sur les range markets") else: print("➡️ RECOMMANDATION: Les deux approches sont équivalentes") print(" Choix selon préférence:") print(" - RANGE ONLY: Plus sélectif, moins de trades") print(" - BI-MODAL: Plus de trades, diversification") print() print("="*80)