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>

tests/conftest.py

@@ -0,0 +1,96 @@
+"""
+Pytest Configuration - Fixtures Globales.
+
+Ce fichier contient les fixtures pytest partagées par tous les tests.
+"""
+
+import pytest
+import pandas as pd
+import numpy as np
+from datetime import datetime, timedelta
+from typing import Dict
+
+from src.core.risk_manager import RiskManager
+from src.core.strategy_engine import StrategyEngine
+
+
+@pytest.fixture
+def sample_config() -> Dict:
+    """
+    Fixture de configuration de test.
+    
+    Returns:
+        Configuration complète pour tests
+    """
+    return {
+        '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': {
+                'scalping': {
+                    'risk_per_trade': 0.01,
+                    'max_trades_per_day': 50,
+                },
+                'intraday': {
+                    'risk_per_trade': 0.02,
+                    'max_trades_per_day': 20,
+                },
+            }
+        },
+        'strategy_params': {
+            'scalping_strategy': {
+                'name': 'scalping',
+                'timeframe': '5m',
+                'risk_per_trade': 0.01,
+                'max_holding_time': 1800,
+                'max_trades_per_day': 50,
+                'adaptive_params': {
+                    'bb_period': 20,
+                    'rsi_period': 14,
+                    'min_confidence': 0.65,
+                }
+            }
+        }
+    }
+
+
+@pytest.fixture
+def risk_manager(sample_config) -> RiskManager:
+    """Fixture RiskManager initialisé."""
+    rm = RiskManager()
+    rm.initialize(sample_config['risk_limits'])
+    return rm
+
+
+@pytest.fixture
+def sample_ohlcv_data() -> pd.DataFrame:
+    """Fixture de données OHLCV pour tests."""
+    dates = pd.date_range(start='2024-01-01', periods=100, freq='1H')
+    np.random.seed(42)
+    
+    base_price = 1.1000
+    returns = np.random.normal(0.0001, 0.01, 100)
+    prices = base_price * 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, 100))
+    df['low'] = df[['open', 'close']].min(axis=1) * (1 - np.random.uniform(0, 0.001, 100))
+    df['volume'] = np.random.randint(1000, 10000, 100)
+    
+    return df
+
+
+@pytest.fixture(autouse=True)
+def reset_singletons():
+    """Reset les singletons entre chaque test."""
+    RiskManager._instance = None
+    yield
+    RiskManager._instance = None