""" Analyse détaillée de la stratégie V10 - Performance hebdomadaire sur le dernier mois - Projection de profits réalistes - Paramètres de la stratégie """ import pandas as pd import numpy as np from datetime import datetime, timedelta import json from strategies.inverse_strategy_v10 import InverseStrategyV10, InverseBacktesterV10 import requests def get_binance_klines(symbol='BTCUSDT', interval='1m', days=30): """Récupère les données historiques depuis Binance""" url = "https://api.binance.com/api/v3/klines" end_time = int(datetime.now().timestamp() * 1000) start_time = int((datetime.now() - timedelta(days=days)).timestamp() * 1000) all_data = [] current_start = start_time print(f"📥 Récupération {days} jours de données {symbol} (1m)...") while current_start < end_time: params = { 'symbol': symbol, 'interval': interval, 'startTime': current_start, 'limit': 1000 } response = requests.get(url, params=params) data = response.json() if not data: break all_data.extend(data) current_start = data[-1][0] + 60000 # +1 minute print(f" {len(all_data)} chandeliers récupérés...", end='\r') print(f"\n✅ {len(all_data)} chandeliers récupérés") # Convertir en DataFrame df = pd.DataFrame(all_data, columns=[ 'timestamp', 'open', 'high', 'low', 'close', 'volume', 'close_time', 'quote_volume', 'trades', 'taker_buy_base', 'taker_buy_quote', 'ignore' ]) df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms') for col in ['open', 'high', 'low', 'close', 'volume']: df[col] = df[col].astype(float) return df[['timestamp', 'open', 'high', 'low', 'close', 'volume']] def backtest_weekly(df, initial_capital=1000): """Backtest semaine par semaine""" # Diviser en semaines df['week'] = df['timestamp'].dt.isocalendar().week df['year'] = df['timestamp'].dt.year weeks = df.groupby(['year', 'week']) results = [] cumulative_capital = initial_capital print(f"\n{'='*80}") print(f"📊 BACKTEST HEBDOMADAIRE - Capital initial: ${initial_capital:,.0f}") print(f"{'='*80}\n") for (year, week), week_data in weeks: if len(week_data) < 500: # Skip semaines incomplètes continue week_start = week_data['timestamp'].min() week_end = week_data['timestamp'].max() # Backtest sur cette semaine backtester = InverseBacktesterV10(initial_capital=cumulative_capital) result = backtester.backtest(week_data.reset_index(drop=True), 'BTCUSDT') if result: week_return = result['total_return'] week_trades = len(result['trades']) week_wr = result['win_rate'] * 100 new_capital = result['final_capital'] profit = new_capital - cumulative_capital results.append({ 'year': year, 'week': week, 'start': week_start, 'end': week_end, 'trades': week_trades, 'win_rate': week_wr, 'return_pct': week_return, 'profit_usd': profit, 'capital_before': cumulative_capital, 'capital_after': new_capital }) # Afficher status = "✅" if week_return > 0 else "❌" print(f"{status} Semaine {week:02d}/{year} ({week_start.strftime('%d/%m')} - {week_end.strftime('%d/%m')})") print(f" Trades: {week_trades:2d} | WR: {week_wr:5.1f}% | " f"Return: {week_return:+7.2f}% | Profit: ${profit:+8.2f}") print(f" Capital: ${cumulative_capital:,.2f} → ${new_capital:,.2f}") print() # Mettre à jour capital cumulatif cumulative_capital = new_capital return results, cumulative_capital def analyze_strategy_params(): """Analyse les paramètres de la stratégie V10""" print(f"\n{'='*80}") print(f"⚙️ PARAMÈTRES STRATÉGIE V10 INVERSE") print(f"{'='*80}\n") print("📋 PRINCIPE:") print(" • Détecte marchés en RANGE (faible volatilité)") print(" • INVERSE les signaux classiques (contrarian)") print(" • Utilise 3 stratégies: RSI, MACD, Bollinger") print() print("📊 DÉTECTION RANGE:") print(" • ATR < 0.3% (volatilité faible)") print(" • Touches répétées support/résistance (2+ de chaque)") print(" • Lookback: 100 chandeliers") print() print("⚡ LEVERAGE DYNAMIQUE:") print(" • Signal PARFAIT (score 1.0 + 4 confluences): 250x | Risk 3.0%") print(" • Signal EXCELLENT (score 1.0 OU 4 confluences): 225x | Risk 2.5%") print(" • Signal TRÈS BON (score 0.95+): 200x | Risk 2.2%") print(" • Signal BON (score 0.90+): 200x | Risk 2.0%") print() print("📈 PYRAMIDING:") print(" • Niveau 1: +0.5% profit → Ajouter 50% position initiale") print(" • Niveau 2: +1.0% profit → Ajouter 25% position initiale") print() print("🛡️ RISK MANAGEMENT:") print(" • Stop-Loss: 0.5% (250x) à 0.8% (100x)") print(" • Take-Profit: 3.5:1 ratio (R:R)") print(" • Trailing Stop: Active après +0.3%, trail à 0.15%") print() print("🎯 TRACKING PERFORMANCE:") print(" • Boost stratégies hot (WR ≥ 90%): +30% risk") print(" • Pénalité stratégies cold (WR ≤ 30%): -30% risk") print(" • Skip stratégies avec 2+ losses sans win") print() def project_profits(initial_capital, weekly_results): """Projection de profits réaliste""" if not weekly_results: return print(f"\n{'='*80}") print(f"💰 PROJECTION PROFITS RÉALISTES") print(f"{'='*80}\n") # Calculer statistiques returns = [r['return_pct'] for r in weekly_results] avg_return_week = np.mean(returns) median_return_week = np.median(returns) std_return = np.std(returns) winning_weeks = [r for r in weekly_results if r['return_pct'] > 0] win_rate_weeks = len(winning_weeks) / len(weekly_results) * 100 print(f"📊 STATISTIQUES HEBDOMADAIRES:") print(f" Semaines testées: {len(weekly_results)}") print(f" Semaines gagnantes: {len(winning_weeks)} ({win_rate_weeks:.1f}%)") print(f" Return moyen: {avg_return_week:+.2f}% / semaine") print(f" Return médian: {median_return_week:+.2f}% / semaine") print(f" Volatilité: ±{std_return:.2f}%") print() # Projections print(f"📈 PROJECTIONS (Capital initial: ${initial_capital:,.2f}):") print() scenarios = [ ("Conservative (Q1)", median_return_week * 0.5), ("Médian", median_return_week), ("Optimiste (Q3)", median_return_week * 1.5), ] periods = [ ("1 semaine", 1), ("1 mois (4 semaines)", 4), ("3 mois (12 semaines)", 12), ("6 mois (26 semaines)", 26), ("1 an (52 semaines)", 52) ] for scenario_name, weekly_return in scenarios: print(f"🎯 Scénario {scenario_name} ({weekly_return:+.2f}%/semaine):") print() for period_name, weeks in periods: # Compound return final_capital = initial_capital * ((1 + weekly_return/100) ** weeks) total_profit = final_capital - initial_capital total_return = (final_capital / initial_capital - 1) * 100 print(f" {period_name:25s}: " f"${final_capital:10,.2f} | " f"Profit: ${total_profit:+9,.2f} ({total_return:+6.1f}%)") print() # Risk warning print(f"⚠️ DISCLAIMER:") print(f" • Performances passées ≠ performances futures") print(f" • High leverage = high risk") print(f" • Toujours trader avec capital à risque uniquement") print(f" • Drawdowns possibles même avec bonne stratégie") print() if __name__ == '__main__': print("\n" + "="*80) print("📊 ANALYSE COMPLÈTE STRATÉGIE V10") print("="*80) # 1. Paramètres analyze_strategy_params() # 2. Récupérer données df = get_binance_klines(symbol='BTCUSDT', interval='1m', days=30) # 3. Backtest hebdomadaire weekly_results, final_capital = backtest_weekly(df, initial_capital=1000) # 4. Résumé if weekly_results: total_return = (final_capital / 1000 - 1) * 100 print(f"{'='*80}") print(f"📈 RÉSUMÉ GLOBAL") print(f"{'='*80}") print(f"Capital initial: $1,000.00") print(f"Capital final: ${final_capital:,.2f}") print(f"Profit total: ${final_capital - 1000:+,.2f}") print(f"Return total: {total_return:+.2f}%") print(f"{'='*80}\n") # 5. Projections project_profits(57.50, weekly_results) # Ton capital actuel # Sauvegarder résultats with open('backtest_weekly_analysis.json', 'w') as f: json.dump({ 'weekly_results': weekly_results, 'final_capital': final_capital, 'total_return': total_return }, f, indent=2, default=str) print("💾 Résultats sauvegardés: backtest_weekly_analysis.json") print("\n✅ Analyse terminée!")