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Initial commit — Trading AI Secure project complet

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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>
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2026-03-08 17:38:09 +00:00
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# 📚 Exemples - Trading AI Secure
## 🎯 Vue d'ensemble
Ce dossier contient des exemples pratiques pour démarrer rapidement avec Trading AI Secure.
---
## 📁 Exemples Disponibles
### 1. simple_backtest.py
**Premier backtest simple**
Montre comment :
- Configurer le système
- Charger une stratégie
- Lancer un backtest
- Analyser les résultats
**Usage** :
```bash
python examples/simple_backtest.py
```
**Résultat attendu** :
```
============================================================
RÉSULTATS DU BACKTEST
============================================================
📈 PERFORMANCE
Return Total: 12.50%
Sharpe Ratio: 1.85
Max Drawdown: 8.20%
💼 TRADING
Total Trades: 125
Win Rate: 57.60%
Profit Factor: 1.45
============================================================
✅ STRATÉGIE VALIDE pour paper trading!
```
---
## 🚀 Exemples à Créer
### 2. multi_strategy_backtest.py (À créer)
Backtest avec plusieurs stratégies simultanées.
### 3. parameter_optimization.py (À créer)
Optimisation des paramètres avec Optuna.
### 4. walk_forward_analysis.py (À créer)
Walk-forward analysis pour éviter overfitting.
### 5. paper_trading_example.py (À créer)
Exemple de paper trading temps réel.
### 6. custom_strategy.py (À créer)
Comment créer une stratégie personnalisée.
---
## 📖 Guide d'Utilisation
### Prérequis
```bash
# Installer dépendances
pip install -r requirements.txt
# Configurer
cp config/*.example.yaml config/
# Éditer config/*.yaml
```
### Lancer un Exemple
```bash
# Exemple simple
python examples/simple_backtest.py
# Avec logs détaillés
python examples/simple_backtest.py --log-level DEBUG
```
---
## 🎓 Apprendre par l'Exemple
### Workflow Recommandé
1. **Commencer par simple_backtest.py**
- Comprendre le flow de base
- Voir les résultats
2. **Modifier les paramètres**
- Changer la stratégie
- Ajuster le capital
- Tester différentes périodes
3. **Créer votre propre exemple**
- Copier un exemple existant
- Adapter à vos besoins
---
## 💡 Conseils
### Pour Débutants
✅ Commencer avec `simple_backtest.py`
✅ Lire les commentaires dans le code
✅ Expérimenter avec différents paramètres
✅ Consulter la documentation complète
### Pour Avancés
✅ Créer stratégies personnalisées
✅ Optimiser paramètres
✅ Combiner plusieurs stratégies
✅ Implémenter walk-forward analysis
---
## 🐛 Debugging
### Problèmes Courants
**Erreur : ModuleNotFoundError**
```bash
# Solution
pip install -r requirements.txt
```
**Erreur : Configuration manquante**
```bash
# Solution
cp config/*.example.yaml config/
```
**Backtest ne génère pas de trades**
```bash
# Vérifier :
1. Données suffisantes (> 100 barres)
2. Paramètres stratégie corrects
3. Logs pour voir les signaux
```
---
## 📚 Ressources
- [Documentation Complète](../docs/)
- [Guide Stratégies](../docs/STRATEGY_GUIDE.md)
- [Guide Backtesting](../docs/BACKTESTING_GUIDE.md)
- [API Reference](../docs/API_REFERENCE.md)
---
**Bon apprentissage ! 🚀**

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"""
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())

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