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