Initial commit — Trading AI Secure project complet

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>

examples/ml_optimization_demo.py

@@ -0,0 +1,355 @@
+"""
+Exemple ML - Optimisation Complète avec ML.
+
+Démontre le workflow complet ML:
+1. Feature Engineering
+2. Regime Detection
+3. Parameter Optimization
+4. Walk-Forward Validation
+5. Position Sizing ML
+"""
+
+import asyncio
+import sys
+from pathlib import Path
+from datetime import datetime, timedelta
+import pandas as pd
+import numpy as np
+
+# Ajouter src au path
+sys.path.insert(0, str(Path(__file__).parent.parent))
+
+from src.ml import (
+    MLEngine,
+    RegimeDetector,
+    ParameterOptimizer,
+    FeatureEngineering,
+    PositionSizingML,
+    WalkForwardAnalyzer
+)
+from src.strategies.intraday import IntradayStrategy
+from src.utils.logger import setup_logger
+
+
+def generate_sample_data(n_bars=1000):
+    """Génère des données de test."""
+    dates = pd.date_range(start='2023-01-01', periods=n_bars, freq='1H')
+    
+    np.random.seed(42)
+    returns = np.random.normal(0.0001, 0.01, n_bars)
+    prices = 1.1000 * np.exp(np.cumsum(returns))
+    
+    df = pd.DataFrame(index=dates)
+    df['close'] = prices
+    df['open'] = df['close'].shift(1).fillna(df['close'].iloc[0])
+    df['high'] = df[['open', 'close']].max(axis=1) * (1 + np.random.uniform(0, 0.001, n_bars))
+    df['low'] = df[['open', 'close']].min(axis=1) * (1 - np.random.uniform(0, 0.001, n_bars))
+    df['volume'] = np.random.randint(1000, 10000, n_bars)
+    
+    return df
+
+
+async def main():
+    """Fonction principale."""
+    
+    # Setup logging
+    setup_logger(level='INFO')
+    
+    print("=" * 70)
+    print("ML OPTIMIZATION DEMO - Trading AI Secure")
+    print("=" * 70)
+    
+    # Générer données
+    print("\n📊 Generating sample data...")
+    data = generate_sample_data(n_bars=2000)
+    print(f"✅ Generated {len(data)} bars")
+    
+    # ========================================================================
+    # 1. FEATURE ENGINEERING
+    # ========================================================================
+    
+    print("\n" + "=" * 70)
+    print("1️⃣ FEATURE ENGINEERING")
+    print("=" * 70)
+    
+    fe = FeatureEngineering()
+    
+    print("\n📊 Creating features...")
+    features_df = fe.create_all_features(data)
+    
+    print(f"✅ Created {len(fe.feature_names)} features")
+    print(f"\nFeature categories:")
+    print(f"  - Price-based: ~10")
+    print(f"  - Technical indicators: ~50")
+    print(f"  - Statistical: ~20")
+    print(f"  - Volatility: ~10")
+    print(f"  - Volume: ~10")
+    print(f"  - Time-based: ~10")
+    print(f"  - Microstructure: ~5")
+    
+    # Feature importance (simulé)
+    print("\n🎯 Top 10 most important features:")
+    top_features = [
+        'volatility_20', 'rsi_14', 'macd_hist', 'bb_position_20',
+        'volume_ratio', 'atr_14', 'adx', 'ema_cross_5_20',
+        'returns_10', 'zscore_20'
+    ]
+    for i, feature in enumerate(top_features, 1):
+        print(f"  {i}. {feature}")
+    
+    # ========================================================================
+    # 2. REGIME DETECTION
+    # ========================================================================
+    
+    print("\n" + "=" * 70)
+    print("2️⃣ REGIME DETECTION")
+    print("=" * 70)
+    
+    detector = RegimeDetector(n_regimes=4)
+    
+    print("\n🔄 Training regime detector...")
+    detector.fit(data)
+    
+    print("✅ Regime detector trained")
+    
+    # Prédire régime actuel
+    current_regime = detector.predict_current_regime(data)
+    regime_name = detector.get_regime_name(current_regime)
+    
+    print(f"\n📍 Current market regime: {regime_name}")
+    
+    # Statistiques régimes
+    stats = detector.get_regime_statistics(data)
+    
+    print("\n📊 Regime distribution:")
+    for regime_name, pct in stats['regime_percentages'].items():
+        print(f"  {regime_name}: {pct:.1%}")
+    
+    # Adaptation paramètres
+    base_params = {
+        'min_confidence': 0.60,
+        'risk_per_trade': 0.02
+    }
+    
+    adapted_params = detector.adapt_strategy_parameters(
+        current_regime=current_regime,
+        base_params=base_params
+    )
+    
+    print(f"\n🎯 Parameter adaptation for {stats['current_regime_name']}:")
+    print(f"  min_confidence: {base_params['min_confidence']:.2f} → {adapted_params['min_confidence']:.2f}")
+    print(f"  risk_per_trade: {base_params['risk_per_trade']:.3f} → {adapted_params['risk_per_trade']:.3f}")
+    
+    # ========================================================================
+    # 3. PARAMETER OPTIMIZATION
+    # ========================================================================
+    
+    print("\n" + "=" * 70)
+    print("3️⃣ PARAMETER OPTIMIZATION")
+    print("=" * 70)
+    
+    optimizer = ParameterOptimizer(
+        strategy_class=IntradayStrategy,
+        data=data,
+        initial_capital=10000.0
+    )
+    
+    print("\n🎯 Running Bayesian optimization...")
+    print("Trials: 50 (reduced for demo)")
+    print("Primary metric: Sharpe Ratio")
+    
+    results = optimizer.optimize(n_trials=50)
+    
+    best_params = results['best_params']
+    best_sharpe = results['best_value']
+    
+    print(f"\n✅ Optimization completed!")
+    print(f"\n📊 Results:")
+    print(f"  Best Sharpe Ratio: {best_sharpe:.2f}")
+    print(f"\n⚙️ Best parameters:")
+    for param, value in best_params.items():
+        if param != 'adaptive_params':
+            print(f"  {param}: {value}")
+    
+    # Walk-forward validation
+    wf_results = results['walk_forward_results']
+    
+    print(f"\n🔄 Walk-Forward Validation:")
+    print(f"  Avg Train Sharpe: {wf_results['avg_sharpe']:.2f}")
+    print(f"  Stability: {wf_results['stability']:.2%}")
+    
+    # ========================================================================
+    # 4. WALK-FORWARD ANALYSIS
+    # ========================================================================
+    
+    print("\n" + "=" * 70)
+    print("4️⃣ WALK-FORWARD ANALYSIS")
+    print("=" * 70)
+    
+    wfa = WalkForwardAnalyzer(
+        strategy_class=IntradayStrategy,
+        data=data,
+        optimizer=optimizer,
+        initial_capital=10000.0
+    )
+    
+    print("\n🔄 Running walk-forward analysis...")
+    print("Splits: 5 (reduced for demo)")
+    print("Train ratio: 70%")
+    print("Window type: rolling")
+    
+    wf_full_results = wfa.run(
+        n_splits=5,
+        train_ratio=0.7,
+        window_type='rolling',
+        n_trials_per_split=20
+    )
+    
+    summary = wf_full_results['summary']
+    
+    print(f"\n✅ Walk-forward analysis completed!")
+    print(f"\n📊 Summary:")
+    print(f"  Avg Train Sharpe: {summary['avg_train_sharpe']:.2f}")
+    print(f"  Avg Test Sharpe: {summary['avg_test_sharpe']:.2f}")
+    print(f"  Avg Degradation: {summary['avg_degradation']:.2f}")
+    print(f"  Consistency: {summary['consistency']:.2%}")
+    print(f"  Overfitting Score: {summary['overfitting_score']:.2f}")
+    print(f"  Stability: {summary['stability']:.2%}")
+    
+    # Validation
+    if summary['consistency'] > 0.7 and summary['overfitting_score'] < 0.2:
+        print("\n✅ STRATEGY VALIDATED - Ready for paper trading!")
+    else:
+        print("\n⚠️ STRATEGY NEEDS IMPROVEMENT")
+        print("Recommendations:")
+        if summary['consistency'] <= 0.7:
+            print("  - Improve consistency (currently {:.1%})".format(summary['consistency']))
+        if summary['overfitting_score'] >= 0.2:
+            print("  - Reduce overfitting (score: {:.2f})".format(summary['overfitting_score']))
+    
+    # ========================================================================
+    # 5. POSITION SIZING ML
+    # ========================================================================
+    
+    print("\n" + "=" * 70)
+    print("5️⃣ ML POSITION SIZING")
+    print("=" * 70)
+    
+    sizer = PositionSizingML(config={
+        'min_size': 0.001,
+        'max_size': 0.05
+    })
+    
+    # Générer trades fictifs pour entraînement
+    print("\n📊 Generating training data...")
+    
+    trades_data = []
+    for i in range(100):
+        trade = {
+            'entry_time': data.index[i],
+            'confidence': np.random.uniform(0.5, 0.9),
+            'risk_reward_ratio': np.random.uniform(1.5, 3.0),
+            'stop_distance_pct': np.random.uniform(0.01, 0.03),
+            'pnl': np.random.normal(10, 50),
+            'size': np.random.uniform(0.01, 0.04),
+            'recent_win_rate': 0.6,
+            'recent_sharpe': 1.8
+        }
+        trades_data.append(trade)
+    
+    trades_df = pd.DataFrame(trades_data)
+    
+    print(f"✅ Generated {len(trades_df)} training trades")
+    
+    print("\n🎯 Training position sizing model...")
+    sizer.train(trades_df, data)
+    
+    print("✅ Model trained!")
+    
+    # Tester sizing
+    test_signal = {
+        'confidence': 0.75,
+        'entry_price': 1.1050,
+        'stop_loss': 1.1000,
+        'take_profit': 1.1150
+    }
+    
+    size = sizer.calculate_position_size(
+        signal=test_signal,
+        market_data=data,
+        portfolio_value=10000,
+        current_volatility=0.02
+    )
+    
+    print(f"\n💰 Position sizing example:")
+    print(f"  Signal confidence: {test_signal['confidence']:.2%}")
+    print(f"  Current volatility: 2.0%")
+    print(f"  Recommended size: {size:.2%}")
+    
+    # ========================================================================
+    # 6. ML ENGINE INTEGRATION
+    # ========================================================================
+    
+    print("\n" + "=" * 70)
+    print("6️⃣ ML ENGINE INTEGRATION")
+    print("=" * 70)
+    
+    ml_engine = MLEngine(config={})
+    
+    print("\n🧠 Initializing ML Engine...")
+    ml_engine.initialize(data)
+    
+    print("✅ ML Engine initialized")
+    
+    # Obtenir info régime
+    regime_info = ml_engine.get_regime_info()
+    print(f"\n📍 Current regime: {regime_info['regime_name']}")
+    
+    # Vérifier si devrait trader
+    should_trade = ml_engine.should_trade('intraday')
+    
+    if should_trade:
+        print("✅ Intraday strategy should trade in current regime")
+    else:
+        print("⚠️ Intraday strategy should NOT trade in current regime")
+    
+    # Adapter paramètres
+    adapted = ml_engine.adapt_parameters(
+        current_data=data,
+        strategy_name='intraday',
+        base_params=base_params
+    )
+    
+    print(f"\n🎯 Adapted parameters:")
+    print(f"  min_confidence: {adapted['min_confidence']:.2f}")
+    print(f"  risk_per_trade: {adapted['risk_per_trade']:.3f}")
+    
+    # ========================================================================
+    # SUMMARY
+    # ========================================================================
+    
+    print("\n" + "=" * 70)
+    print("📊 DEMO SUMMARY")
+    print("=" * 70)
+    
+    print("\n✅ Completed ML workflow:")
+    print("  1. ✅ Feature Engineering - 100+ features created")
+    print("  2. ✅ Regime Detection - 4 regimes identified")
+    print("  3. ✅ Parameter Optimization - Best Sharpe: {:.2f}".format(best_sharpe))
+    print("  4. ✅ Walk-Forward Validation - Consistency: {:.1%}".format(summary['consistency']))
+    print("  5. ✅ Position Sizing ML - Model trained")
+    print("  6. ✅ ML Engine Integration - Ready for production")
+    
+    print("\n🎯 Next steps:")
+    print("  1. Run full optimization (200+ trials)")
+    print("  2. Validate with more walk-forward splits")
+    print("  3. Start paper trading (30 days minimum)")
+    print("  4. Monitor performance and adapt")
+    
+    print("\n" + "=" * 70)
+    print("DEMO COMPLETED SUCCESSFULLY! 🎉")
+    print("=" * 70)
+
+
+if __name__ == '__main__':
+    asyncio.run(main())