da30ef19ed
Architecture Docker (8 services), FastAPI, TimescaleDB, Redis, Streamlit. Stratégies : scalping, intraday, swing. MLEngine + RegimeDetector (HMM). BacktestEngine + WalkForwardAnalyzer + Optuna optimizer. Routes API complètes dont /optimize async. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
149 lines
4.3 KiB
Python
149 lines
4.3 KiB
Python
"""
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Exemple Simple - Premier Backtest.
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Cet exemple montre comment:
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1. Configurer le système
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2. Charger une stratégie
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3. Lancer un backtest
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4. Analyser les résultats
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"""
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import asyncio
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import sys
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from pathlib import Path
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from datetime import datetime, timedelta
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# Ajouter src au path
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sys.path.insert(0, str(Path(__file__).parent.parent))
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from src.core.risk_manager import RiskManager
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from src.core.strategy_engine import StrategyEngine
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from src.backtesting.backtest_engine import BacktestEngine
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from src.utils.logger import setup_logger
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async def main():
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"""Fonction principale."""
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# 1. Setup logging
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setup_logger(level='INFO')
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print("=" * 60)
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print("EXEMPLE SIMPLE - PREMIER BACKTEST")
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print("=" * 60)
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# 2. Configuration
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config = {
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'risk_limits': {
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'initial_capital': 10000.0,
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'global_limits': {
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'max_portfolio_risk': 0.05,
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'max_position_size': 0.10,
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'max_drawdown': 0.15,
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'max_daily_loss': 0.03,
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'max_correlation': 0.7,
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},
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'strategy_limits': {
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'intraday': {
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'risk_per_trade': 0.02,
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'max_trades_per_day': 20,
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}
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}
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},
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'strategy_params': {
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'intraday_strategy': {
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'name': 'intraday',
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'timeframe': '1h',
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'risk_per_trade': 0.02,
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'max_holding_time': 28800,
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'max_trades_per_day': 20,
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'adaptive_params': {
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'ema_fast': 9,
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'ema_slow': 21,
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'ema_trend': 50,
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'adx_threshold': 25,
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}
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}
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},
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'backtesting_config': {
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'transaction_costs': {
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'commission_pct': 0.0001,
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'slippage_pct': 0.0005,
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'spread_pct': 0.0002,
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}
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}
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}
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# 3. Initialiser Risk Manager
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print("\n📊 Initialisation du Risk Manager...")
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risk_manager = RiskManager()
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risk_manager.initialize(config['risk_limits'])
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# 4. Initialiser Strategy Engine
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print("🎯 Initialisation du Strategy Engine...")
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strategy_engine = StrategyEngine(
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config=config['strategy_params'],
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risk_manager=risk_manager
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)
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# 5. Charger stratégie Intraday
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print("📈 Chargement de la stratégie Intraday...")
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await strategy_engine.load_strategy('intraday')
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# 6. Créer Backtest Engine
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print("🔄 Création du Backtest Engine...")
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backtest_engine = BacktestEngine(
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strategy_engine=strategy_engine,
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config=config
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)
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# 7. Lancer backtest
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print("\n🚀 Lancement du backtest...")
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print("Symbole: EURUSD")
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print("Période: 6 mois")
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print("Capital initial: $10,000")
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results = await backtest_engine.run(
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symbols=['EURUSD'],
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period='6m',
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initial_capital=10000.0
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)
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# 8. Afficher résultats
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if results:
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print("\n" + "=" * 60)
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print("RÉSULTATS DU BACKTEST")
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print("=" * 60)
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metrics = results['metrics']
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print(f"\n📈 PERFORMANCE")
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print(f"Return Total: {metrics['total_return']:>10.2%}")
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print(f"Sharpe Ratio: {metrics['sharpe_ratio']:>10.2f}")
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print(f"Max Drawdown: {metrics['max_drawdown']:>10.2%}")
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print(f"\n💼 TRADING")
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print(f"Total Trades: {metrics['total_trades']:>10}")
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print(f"Win Rate: {metrics['win_rate']:>10.2%}")
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print(f"Profit Factor: {metrics['profit_factor']:>10.2f}")
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print("\n" + "=" * 60)
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# 9. Validation
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if results['is_valid']:
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print("✅ STRATÉGIE VALIDE pour paper trading!")
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print("\nProchaine étape: Lancer paper trading pendant 30 jours")
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else:
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print("❌ STRATÉGIE NON VALIDE")
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print("\nActions recommandées:")
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print("1. Optimiser les paramètres")
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print("2. Tester sur différentes périodes")
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print("3. Analyser les trades perdants")
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else:
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print("\n❌ Backtest échoué - Vérifier les logs")
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if __name__ == '__main__':
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asyncio.run(main())
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