mirror of
https://github.com/NicolasBohn/NexQuant.git
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TPTBusiness
cbe1c52e00
Rename all source files, scripts, tests, documentation, and configuration from Predix/predix to NexQuant/nexquant across the entire codebase.
290 lines
8.9 KiB
Python
290 lines
8.9 KiB
Python
"""
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NexQuant Backtesting Test Fixtures
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Wiederverwendbare Test-Daten und Fixtures für alle Backtesting-Tests
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"""
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import pytest
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import numpy as np
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import pandas as pd
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import tempfile
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import os
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from pathlib import Path
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from datetime import datetime, timedelta
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# Importiere die zu testenden Klassen
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import sys
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sys.path.insert(0, str(Path(__file__).parent.parent.parent))
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from rdagent.components.backtesting.backtest_engine import BacktestMetrics, FactorBacktester
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from rdagent.components.backtesting.results_db import ResultsDatabase
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from rdagent.components.backtesting.risk_management import (
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CorrelationAnalyzer, PortfolioOptimizer, AdvancedRiskManager
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)
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# =============================================================================
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# FIXTURES FÜR BACKTEST METRICS
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# =============================================================================
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@pytest.fixture
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def sample_factor_data():
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"""Normale Faktor-Daten für Standard-Tests"""
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np.random.seed(42)
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n = 252
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dates = pd.date_range(start='2024-01-01', periods=n, freq='B')
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factor_values = pd.Series(np.random.randn(n), index=dates, name='factor')
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forward_returns = pd.Series(np.random.randn(n) * 0.01 + 0.0001, index=dates, name='fwd_ret')
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return factor_values, forward_returns
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@pytest.fixture
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def sample_returns_data():
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"""Returns-Daten für Sharpe und Drawdown Tests"""
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np.random.seed(42)
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n = 252
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dates = pd.date_range(start='2024-01-01', periods=n, freq='B')
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returns = pd.Series(np.random.randn(n) * 0.01 + 0.0005, index=dates)
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equity = (1 + returns).cumprod()
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return returns, equity
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@pytest.fixture
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def backtest_metrics():
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"""BacktestMetrics Instanz mit Standard-Parametern"""
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return BacktestMetrics(risk_free_rate=0.02)
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# =============================================================================
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# FIXTURES FÜR EDGE CASES
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# =============================================================================
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@pytest.fixture
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def empty_data():
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"""Leere Daten für Edge-Case Tests"""
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return pd.Series([], dtype=float), pd.Series([], dtype=float)
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@pytest.fixture
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def nan_data():
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"""Daten mit vielen NaN-Werten"""
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np.random.seed(42)
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n = 100
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dates = pd.date_range(start='2024-01-01', periods=n, freq='B')
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factor = pd.Series([np.nan] * 50 + list(np.random.randn(50)), index=dates)
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fwd_ret = pd.Series(list(np.random.randn(50)) + [np.nan] * 50, index=dates)
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return factor, fwd_ret
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@pytest.fixture
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def insufficient_data():
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"""Zu wenig Daten (< 10 Punkte)"""
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np.random.seed(42)
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n = 5
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dates = pd.date_range(start='2024-01-01', periods=n, freq='B')
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factor = pd.Series(np.random.randn(n), index=dates)
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fwd_ret = pd.Series(np.random.randn(n), index=dates)
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return factor, fwd_ret
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@pytest.fixture
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def extreme_values_data():
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"""Daten mit Extremwerten"""
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np.random.seed(42)
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n = 252
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dates = pd.date_range(start='2024-01-01', periods=n, freq='B')
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factor = pd.Series(np.random.randn(n), index=dates)
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factor.iloc[50] = 1000 # Extremwert
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factor.iloc[100] = -1000 # Extremwert negativ
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fwd_ret = pd.Series(np.random.randn(n) * 0.01, index=dates)
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return factor, fwd_ret
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@pytest.fixture
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def constant_data():
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"""Konstante Daten (Std = 0)"""
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n = 252
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dates = pd.date_range(start='2024-01-01', periods=n, freq='B')
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factor = pd.Series([1.0] * n, index=dates)
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fwd_ret = pd.Series([0.001] * n, index=dates)
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return factor, fwd_ret
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# =============================================================================
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# FIXTURES FÜR DATABASE TESTS
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# =============================================================================
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@pytest.fixture
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def temp_db_path():
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"""Temporäre Datenbank für Tests"""
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with tempfile.TemporaryDirectory() as tmpdir:
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db_path = os.path.join(tmpdir, 'test_backtest.db')
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yield db_path
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@pytest.fixture
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def results_database(temp_db_path):
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"""ResultsDatabase Instanz mit temporärer DB"""
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db = ResultsDatabase(db_path=temp_db_path)
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yield db
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db.close()
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@pytest.fixture
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def populated_database(results_database):
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"""Datenbank mit Test-Daten befüllt"""
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db = results_database
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# Faktoren hinzufügen
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db.add_factor("Momentum", "price_based")
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db.add_factor("MeanReversion", "price_based")
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db.add_factor("Volatility", "risk_based")
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db.add_factor("Volume", "volume_based")
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db.add_factor("ML_Factor", "ml_based")
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# Backtest-Ergebnisse hinzufügen
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db.add_backtest("Momentum", {
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'ic': 0.08, 'sharpe_ratio': 1.5, 'annualized_return': 0.12,
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'max_drawdown': -0.08, 'win_rate': 0.55
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})
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db.add_backtest("MeanReversion", {
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'ic': 0.05, 'sharpe_ratio': 1.2, 'annualized_return': 0.08,
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'max_drawdown': -0.05, 'win_rate': 0.52
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})
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db.add_backtest("Volatility", {
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'ic': -0.03, 'sharpe_ratio': 0.8, 'annualized_return': 0.04,
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'max_drawdown': -0.03, 'win_rate': 0.48
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})
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db.add_backtest("ML_Factor", {
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'ic': 0.12, 'sharpe_ratio': 2.1, 'annualized_return': 0.18,
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'max_drawdown': -0.10, 'win_rate': 0.60
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})
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# Loop-Ergebnisse hinzufügen
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db.add_loop(1, 4, 6, 0.08, "completed")
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db.add_loop(2, 5, 5, 0.10, "completed")
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db.add_loop(3, 3, 7, 0.05, "completed")
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return db
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# =============================================================================
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# FIXTURES FÜR RISK MANAGEMENT TESTS
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# =============================================================================
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@pytest.fixture
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def sample_returns_matrix():
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"""Returns-Matrix für Korrelations-Analyse"""
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np.random.seed(42)
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n = 252
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dates = pd.date_range(start='2024-01-01', periods=n, freq='B')
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columns = ['Mom', 'MeanRev', 'Vol', 'Volu', 'ML']
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# Erzeuge korrelierte Returns
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data = np.random.randn(n, 5)
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data[:, 0] = data[:, 1] * 0.3 + data[:, 0] * 0.7 # Mom korreliert mit MeanRev
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data[:, 3] = data[:, 2] * 0.5 + data[:, 3] * 0.5 # Volu korreliert mit Vol
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return pd.DataFrame(data, columns=columns, index=dates)
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@pytest.fixture
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def correlation_analyzer():
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"""CorrelationAnalyzer Instanz"""
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return CorrelationAnalyzer(lookback=60)
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@pytest.fixture
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def portfolio_optimizer():
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"""PortfolioOptimizer Instanz"""
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return PortfolioOptimizer()
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@pytest.fixture
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def sample_expected_returns():
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"""Erwartete Returns für Portfolio-Optimierung"""
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return pd.Series({
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'Mom': 0.10, 'MeanRev': 0.08, 'Vol': 0.06,
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'Volu': 0.07, 'ML': 0.12
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})
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@pytest.fixture
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def sample_covariance_matrix(sample_returns_matrix):
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"""Kovarianz-Matrix aus Returns"""
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return sample_returns_matrix.cov() * 252
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@pytest.fixture
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def risk_manager():
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"""AdvancedRiskManager Instanz"""
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return AdvancedRiskManager(max_pos=0.2, max_lev=5.0, max_dd=0.20)
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@pytest.fixture
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def sample_weights():
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"""Test-Gewichtungen"""
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return np.array([0.25, 0.20, 0.15, 0.20, 0.20])
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# =============================================================================
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# FIXTURES FÜR BACKTESTER
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# =============================================================================
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@pytest.fixture
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def factor_backtester():
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"""FactorBacktester Instanz mit temporärem Output-Verzeichnis"""
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with tempfile.TemporaryDirectory() as tmpdir:
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backtester = FactorBacktester()
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backtester.results_path = Path(tmpdir)
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yield backtester
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# =============================================================================
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# ZUSÄTZLICHE HILFS-FIXTURES
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# =============================================================================
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@pytest.fixture
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def realistic_market_data():
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"""Realistischere Markt-Daten mit typischen Eigenschaften"""
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np.random.seed(42)
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n = 504 # 2 Jahre
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dates = pd.date_range(start='2023-01-01', periods=n, freq='B')
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# Faktor mit etwas Autokorrelation (wie echte Faktoren)
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factor = pd.Series(index=dates)
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factor.iloc[0] = 0
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for i in range(1, n):
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factor.iloc[i] = 0.3 * factor.iloc[i-1] + np.random.randn() * 0.7
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# Forward Returns mit leichtem positiven Drift
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fwd_ret = pd.Series(np.random.randn(n) * 0.015 + 0.0002, index=dates)
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# Füge einige Ausreißer hinzu (wie bei echten Marktdaten)
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fwd_ret.iloc[50] = -0.05 # Crash-Tag
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fwd_ret.iloc[150] = 0.04 # Rally-Tag
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return factor, fwd_ret
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@pytest.fixture
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def zero_variance_returns():
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"""Returns mit Varianz = 0 (für Edge-Case Tests)"""
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n = 100
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dates = pd.date_range(start='2024-01-01', periods=n, freq='B')
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returns = pd.Series([0.001] * n, index=dates)
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equity = (1 + returns).cumprod()
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return returns, equity
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@pytest.fixture
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def negative_equity_data():
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"""Equity-Daten mit Drawdowns"""
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np.random.seed(42)
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n = 252
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dates = pd.date_range(start='2024-01-01', periods=n, freq='B')
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# Erzeuge Equity mit signifikantem Drawdown
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returns = pd.Series(np.random.randn(n) * 0.02, index=dates)
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returns.iloc[50:80] = -0.03 # Drawdown-Periode
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equity = (1 + returns).cumprod()
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return returns, equity
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