#!/usr/bin/env python3 """ Teste différentes combinaisons de martingale pour trouver la meilleure """ from martingale_backtester import MartingaleBacktester from martingale_strategy import MartingaleStrategy import copy print("="*80) print("🔬 TEST DE MULTIPLES COMBINAISONS MARTINGALE") print("="*80) # Fetch data une seule fois backtester = MartingaleBacktester( initial_capital=1000.0, trading_fee_pct=0.0005, timeout_minutes=60 ) print("\n📥 Téléchargement données...") df = backtester.fetch_binance_data('BTCUSDT', '1m', 1000) if df is None: print("❌ Erreur téléchargement") exit(1) print(f"✅ {len(df)} bougies chargées\n") # ============ CONFIGURATIONS À TESTER ============ configs = [ { 'name': '1. ORIGINAL (base)', 'base_position_size': 10.0, 'martingale_multiplier': 2, # x2 classique 'base_leverage': 100, 'max_leverage': 1000, 'stop_loss_pct': 0.003, 'take_profit_ratio': 3.0, 'min_signal_score': 0.6, 'reverse_signals': False }, { 'name': '2. TRIPLE Martingale (x3)', 'base_position_size': 10.0, 'martingale_multiplier': 3, # x3 au lieu de x2 'base_leverage': 100, 'max_leverage': 1000, 'stop_loss_pct': 0.003, 'take_profit_ratio': 3.0, 'min_signal_score': 0.6, 'reverse_signals': False }, { 'name': '3. TRIPLE Leverage', 'base_position_size': 10.0, 'martingale_multiplier': 2, 'base_leverage': 300, # 3x plus de leverage 'max_leverage': 1000, 'stop_loss_pct': 0.003, 'take_profit_ratio': 3.0, 'min_signal_score': 0.6, 'reverse_signals': False }, { 'name': '4. TRIPLE Position Size', 'base_position_size': 30.0, # 3x la mise 'martingale_multiplier': 2, 'base_leverage': 100, 'max_leverage': 1000, 'stop_loss_pct': 0.003, 'take_profit_ratio': 3.0, 'min_signal_score': 0.6, 'reverse_signals': False }, { 'name': '5. INVERSE Signals', 'base_position_size': 10.0, 'martingale_multiplier': 2, 'base_leverage': 100, 'max_leverage': 1000, 'stop_loss_pct': 0.003, 'take_profit_ratio': 3.0, 'min_signal_score': 0.6, 'reverse_signals': True # LONG→SHORT, SHORT→LONG }, { 'name': '6. CONSERVATIVE (leverage réduit)', 'base_position_size': 10.0, 'martingale_multiplier': 2, 'base_leverage': 50, # 2x moins 'max_leverage': 500, # 2x moins 'stop_loss_pct': 0.005, # Plus large 'take_profit_ratio': 5.0, # Plus large 'min_signal_score': 0.6, 'reverse_signals': False }, { 'name': '7. AGGRESSIVE (TP large)', 'base_position_size': 10.0, 'martingale_multiplier': 2, 'base_leverage': 100, 'max_leverage': 1000, 'stop_loss_pct': 0.002, # Plus serré 'take_profit_ratio': 8.0, # Très large! 'min_signal_score': 0.6, 'reverse_signals': False }, { 'name': '8. HYBRID (x3 + leverage réduit)', 'base_position_size': 10.0, 'martingale_multiplier': 3, # x3 'base_leverage': 50, # Réduit 'max_leverage': 500, # Réduit 'stop_loss_pct': 0.004, 'take_profit_ratio': 5.0, 'min_signal_score': 0.6, 'reverse_signals': False }, { 'name': '9. FIBONACCI (1.5x)', 'base_position_size': 10.0, 'martingale_multiplier': 1.5, # Fibonacci-like 'base_leverage': 100, 'max_leverage': 1000, 'stop_loss_pct': 0.003, 'take_profit_ratio': 3.0, 'min_signal_score': 0.6, 'reverse_signals': False }, { 'name': '10. ANTI-MARTINGALE (réduit après loss)', 'base_position_size': 10.0, 'martingale_multiplier': 0.5, # RÉDUIT après perte! 'base_leverage': 100, 'max_leverage': 1000, 'stop_loss_pct': 0.003, 'take_profit_ratio': 3.0, 'min_signal_score': 0.6, 'reverse_signals': False } ] # ============ TESTER CHAQUE CONFIG ============ results_summary = [] for i, config in enumerate(configs, 1): print(f"\n{'='*80}") print(f"🧪 TEST {i}/{len(configs)}: {config['name']}") print(f"{'='*80}") # Créer stratégie modifiée class ModifiedMartingale(MartingaleStrategy): def __init__(self, multiplier, reverse, **kwargs): super().__init__(**kwargs) self.multiplier = multiplier self.reverse_signals = reverse def get_martingale_multiplier(self): """Override pour multiplier custom""" return int(self.multiplier ** self.current_level) def generate_signal(self, df): """Override pour inverser signaux si nécessaire""" signal = super().generate_signal(df) if signal and self.reverse_signals: # Inverser LONG ↔ SHORT signal.side = 'SHORT' if signal.side == 'LONG' else 'LONG' # Inverser SL/TP signal.stop_loss, signal.take_profit = signal.take_profit, signal.stop_loss return signal strategy = ModifiedMartingale( multiplier=config['martingale_multiplier'], reverse=config['reverse_signals'], base_position_size=config['base_position_size'], max_martingale_level=5, base_leverage=config['base_leverage'], max_leverage=config['max_leverage'], stop_loss_pct=config['stop_loss_pct'], take_profit_ratio=config['take_profit_ratio'], min_signal_score=config['min_signal_score'] ) # Backtest result = backtester.run_backtest(df, 'BTCUSDT', strategy) if result: results_summary.append({ 'name': config['name'], 'roi': result.total_return_pct, 'win_rate': result.win_rate, 'profit_factor': result.profit_factor, 'trades': result.total_trades, 'max_dd': result.max_drawdown_pct, 'final_capital': result.final_capital }) else: results_summary.append({ 'name': config['name'], 'roi': 0, 'win_rate': 0, 'profit_factor': 0, 'trades': 0, 'max_dd': 0, 'final_capital': 1000 }) # ============ COMPARAISON FINALE ============ print("\n" + "="*80) print("🏆 CLASSEMENT DES STRATÉGIES") print("="*80) # Trier par ROI results_summary.sort(key=lambda x: x['roi'], reverse=True) print(f"\n{'RANG':<5} {'STRATÉGIE':<35} {'ROI %':>10} {'WR %':>8} {'PF':>6} {'Trades':>7} {'MaxDD %':>9}") print("-"*80) for rank, res in enumerate(results_summary, 1): emoji = "🥇" if rank == 1 else "🥈" if rank == 2 else "🥉" if rank == 3 else f"{rank:2}" print(f"{emoji:<5} {res['name']:<35} {res['roi']:>9.2f}% {res['win_rate']*100:>7.1f}% " f"{res['profit_factor']:>5.2f} {res['trades']:>7} {res['max_dd']:>8.2f}%") # Meilleure stratégie best = results_summary[0] print(f"\n{'='*80}") print(f"🏆 MEILLEURE STRATÉGIE: {best['name']}") print(f"{'='*80}") print(f" ROI: {best['roi']:+.2f}%") print(f" Win Rate: {best['win_rate']*100:.1f}%") print(f" Profit Factor: {best['profit_factor']:.2f}") print(f" Trades: {best['trades']}") print(f" Max DD: {best['max_dd']:.2f}%") print(f" Capital Final: ${best['final_capital']:.2f}") print(f"{'='*80}") # Sauvegarder résultats import json with open('combination_test_results.json', 'w') as f: json.dump(results_summary, f, indent=2) print("\n💾 Résultats sauvegardés: combination_test_results.json")