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>
This commit is contained in:
Tika
1
tests/unit/__init__.py
Normal file
1
tests/unit/__init__.py
Normal file
@@ -0,0 +1 @@
|
||||
"""Tests unitaires."""
|
||||
205
tests/unit/test_data_validator.py
Normal file
205
tests/unit/test_data_validator.py
Normal file
@@ -0,0 +1,205 @@
|
||||
"""
|
||||
Tests Unitaires - DataValidator.
|
||||
|
||||
Tests de validation et nettoyage des données.
|
||||
"""
|
||||
|
||||
import pytest
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
|
||||
from src.data.data_validator import DataValidator
|
||||
|
||||
|
||||
class TestDataValidation:
|
||||
"""Tests de validation des données."""
|
||||
|
||||
def test_validate_valid_data(self, sample_ohlcv_data):
|
||||
"""Test validation de données valides."""
|
||||
validator = DataValidator()
|
||||
|
||||
is_valid, errors = validator.validate(sample_ohlcv_data)
|
||||
|
||||
assert is_valid is True
|
||||
assert len(errors) == 0
|
||||
|
||||
def test_validate_empty_dataframe(self):
|
||||
"""Test rejet DataFrame vide."""
|
||||
validator = DataValidator()
|
||||
|
||||
df = pd.DataFrame()
|
||||
is_valid, errors = validator.validate(df)
|
||||
|
||||
assert is_valid is False
|
||||
assert len(errors) > 0
|
||||
assert 'empty' in errors[0].lower()
|
||||
|
||||
def test_validate_missing_columns(self):
|
||||
"""Test rejet si colonnes manquantes."""
|
||||
validator = DataValidator()
|
||||
|
||||
df = pd.DataFrame({
|
||||
'open': [1.1, 1.2],
|
||||
'close': [1.15, 1.25]
|
||||
# Manque high, low, volume
|
||||
})
|
||||
|
||||
is_valid, errors = validator.validate(df)
|
||||
|
||||
assert is_valid is False
|
||||
assert any('missing columns' in e.lower() for e in errors)
|
||||
|
||||
def test_validate_price_inconsistency(self):
|
||||
"""Test détection incohérences de prix."""
|
||||
validator = DataValidator()
|
||||
|
||||
df = pd.DataFrame({
|
||||
'open': [1.1, 1.2, 1.3],
|
||||
'high': [1.15, 1.25, 1.35],
|
||||
'low': [1.2, 1.3, 1.4], # Low > High (invalide)
|
||||
'close': [1.12, 1.22, 1.32],
|
||||
'volume': [1000, 2000, 3000]
|
||||
})
|
||||
|
||||
is_valid, errors = validator.validate(df)
|
||||
|
||||
assert is_valid is False
|
||||
assert any('high < low' in e.lower() for e in errors)
|
||||
|
||||
def test_validate_excessive_missing_values(self):
|
||||
"""Test rejet si trop de valeurs manquantes."""
|
||||
validator = DataValidator(config={'max_missing_pct': 0.05})
|
||||
|
||||
df = pd.DataFrame({
|
||||
'open': [1.1, np.nan, 1.3, np.nan, 1.5] * 10,
|
||||
'high': [1.15, 1.25, np.nan, 1.45, 1.55] * 10,
|
||||
'low': [1.05, 1.15, 1.25, np.nan, 1.45] * 10,
|
||||
'close': [1.12, 1.22, 1.32, 1.42, np.nan] * 10,
|
||||
'volume': [1000] * 50
|
||||
})
|
||||
|
||||
is_valid, errors = validator.validate(df)
|
||||
|
||||
assert is_valid is False
|
||||
assert any('missing values' in e.lower() for e in errors)
|
||||
|
||||
|
||||
class TestDataCleaning:
|
||||
"""Tests de nettoyage des données."""
|
||||
|
||||
def test_clean_removes_duplicates(self):
|
||||
"""Test suppression des doublons."""
|
||||
validator = DataValidator()
|
||||
|
||||
dates = pd.date_range('2024-01-01', periods=10, freq='1H')
|
||||
df = pd.DataFrame({
|
||||
'open': [1.1] * 10,
|
||||
'high': [1.15] * 10,
|
||||
'low': [1.05] * 10,
|
||||
'close': [1.12] * 10,
|
||||
'volume': [1000] * 10
|
||||
}, index=dates)
|
||||
|
||||
# Ajouter doublon
|
||||
df = pd.concat([df, df.iloc[[5]]])
|
||||
|
||||
assert len(df) == 11
|
||||
|
||||
df_clean = validator.clean(df)
|
||||
|
||||
assert len(df_clean) == 10
|
||||
|
||||
def test_clean_sorts_chronologically(self):
|
||||
"""Test tri chronologique."""
|
||||
validator = DataValidator()
|
||||
|
||||
dates = pd.date_range('2024-01-01', periods=10, freq='1H')
|
||||
df = pd.DataFrame({
|
||||
'open': [1.1] * 10,
|
||||
'high': [1.15] * 10,
|
||||
'low': [1.05] * 10,
|
||||
'close': [1.12] * 10,
|
||||
'volume': [1000] * 10
|
||||
}, index=dates)
|
||||
|
||||
# Mélanger l'ordre
|
||||
df = df.sample(frac=1)
|
||||
|
||||
df_clean = validator.clean(df)
|
||||
|
||||
assert df_clean.index.is_monotonic_increasing
|
||||
|
||||
def test_clean_interpolates_missing_values(self):
|
||||
"""Test interpolation valeurs manquantes."""
|
||||
validator = DataValidator()
|
||||
|
||||
df = pd.DataFrame({
|
||||
'open': [1.1, np.nan, 1.3, 1.4, 1.5],
|
||||
'high': [1.15, 1.25, np.nan, 1.45, 1.55],
|
||||
'low': [1.05, 1.15, 1.25, np.nan, 1.45],
|
||||
'close': [1.12, 1.22, 1.32, 1.42, 1.52],
|
||||
'volume': [1000, 2000, 3000, 4000, 5000]
|
||||
})
|
||||
|
||||
df_clean = validator.clean(df)
|
||||
|
||||
# Vérifier que les NaN sont interpolés
|
||||
assert df_clean['open'].isna().sum() == 0
|
||||
assert df_clean['high'].isna().sum() == 0
|
||||
assert df_clean['low'].isna().sum() == 0
|
||||
|
||||
def test_clean_fixes_price_inconsistencies(self):
|
||||
"""Test correction incohérences de prix."""
|
||||
validator = DataValidator()
|
||||
|
||||
df = pd.DataFrame({
|
||||
'open': [1.1, 1.2, 1.3],
|
||||
'high': [1.05, 1.15, 1.25], # High < Open (invalide)
|
||||
'low': [1.15, 1.25, 1.35], # Low > Open (invalide)
|
||||
'close': [1.12, 1.22, 1.32],
|
||||
'volume': [1000, 2000, 3000]
|
||||
})
|
||||
|
||||
df_clean = validator.clean(df)
|
||||
|
||||
# Vérifier cohérence
|
||||
assert (df_clean['high'] >= df_clean['low']).all()
|
||||
assert (df_clean['high'] >= df_clean['open']).all()
|
||||
assert (df_clean['high'] >= df_clean['close']).all()
|
||||
assert (df_clean['low'] <= df_clean['open']).all()
|
||||
assert (df_clean['low'] <= df_clean['close']).all()
|
||||
|
||||
|
||||
class TestDataQualityReport:
|
||||
"""Tests du rapport de qualité."""
|
||||
|
||||
def test_generate_quality_report(self, sample_ohlcv_data):
|
||||
"""Test génération rapport de qualité."""
|
||||
validator = DataValidator()
|
||||
|
||||
report = validator.get_data_quality_report(sample_ohlcv_data)
|
||||
|
||||
assert 'total_rows' in report
|
||||
assert 'date_range' in report
|
||||
assert 'missing_values' in report
|
||||
assert 'is_valid' in report
|
||||
assert 'price_stats' in report
|
||||
|
||||
assert report['total_rows'] == len(sample_ohlcv_data)
|
||||
assert report['is_valid'] is True
|
||||
|
||||
def test_report_includes_statistics(self, sample_ohlcv_data):
|
||||
"""Test inclusion statistiques dans rapport."""
|
||||
validator = DataValidator()
|
||||
|
||||
report = validator.get_data_quality_report(sample_ohlcv_data)
|
||||
|
||||
price_stats = report['price_stats']
|
||||
|
||||
assert 'mean_close' in price_stats
|
||||
assert 'std_close' in price_stats
|
||||
assert 'min_close' in price_stats
|
||||
assert 'max_close' in price_stats
|
||||
|
||||
assert price_stats['mean_close'] > 0
|
||||
assert price_stats['std_close'] > 0
|
||||
1
tests/unit/test_ml/__init__.py
Normal file
1
tests/unit/test_ml/__init__.py
Normal file
@@ -0,0 +1 @@
|
||||
"""Tests unitaires pour le module ML."""
|
||||
523
tests/unit/test_ml/test_feature_engineering.py
Normal file
523
tests/unit/test_ml/test_feature_engineering.py
Normal file
@@ -0,0 +1,523 @@
|
||||
"""
|
||||
Tests Unitaires - FeatureEngineering.
|
||||
|
||||
Tests de la création de features pour ML.
|
||||
"""
|
||||
|
||||
import pytest
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
from datetime import datetime, timedelta
|
||||
|
||||
from src.ml.feature_engineering import FeatureEngineering
|
||||
|
||||
|
||||
class TestFeatureEngineeringInitialization:
|
||||
"""Tests d'initialisation."""
|
||||
|
||||
def test_initialization_default(self):
|
||||
"""Test initialisation par défaut."""
|
||||
fe = FeatureEngineering()
|
||||
|
||||
assert fe.config == {}
|
||||
assert len(fe.feature_names) == 0
|
||||
|
||||
def test_initialization_with_config(self):
|
||||
"""Test initialisation avec config."""
|
||||
config = {'param1': 'value1'}
|
||||
fe = FeatureEngineering(config)
|
||||
|
||||
assert fe.config == config
|
||||
|
||||
|
||||
class TestFeatureCreation:
|
||||
"""Tests de création de features."""
|
||||
|
||||
@pytest.fixture
|
||||
def sample_data(self):
|
||||
"""Génère des données de test."""
|
||||
dates = pd.date_range(start='2024-01-01', periods=300, freq='1H')
|
||||
|
||||
np.random.seed(42)
|
||||
returns = np.random.normal(0.0001, 0.01, 300)
|
||||
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, 300))
|
||||
df['low'] = df[['open', 'close']].min(axis=1) * (1 - np.random.uniform(0, 0.001, 300))
|
||||
df['volume'] = np.random.randint(1000, 10000, 300)
|
||||
|
||||
return df
|
||||
|
||||
def test_create_all_features(self, sample_data):
|
||||
"""Test création de toutes les features."""
|
||||
fe = FeatureEngineering()
|
||||
|
||||
features_df = fe.create_all_features(sample_data)
|
||||
|
||||
assert isinstance(features_df, pd.DataFrame)
|
||||
assert len(features_df) > 0
|
||||
assert len(fe.feature_names) > 0
|
||||
|
||||
def test_features_count(self, sample_data):
|
||||
"""Test que le nombre de features est correct."""
|
||||
fe = FeatureEngineering()
|
||||
|
||||
features_df = fe.create_all_features(sample_data)
|
||||
|
||||
# Devrait créer 100+ features
|
||||
assert len(fe.feature_names) >= 100
|
||||
|
||||
def test_no_nan_in_features(self, sample_data):
|
||||
"""Test qu'il n'y a pas de NaN dans les features."""
|
||||
fe = FeatureEngineering()
|
||||
|
||||
features_df = fe.create_all_features(sample_data)
|
||||
|
||||
# Après dropna, ne devrait pas y avoir de NaN
|
||||
assert features_df.isna().sum().sum() == 0
|
||||
|
||||
|
||||
class TestPriceFeatures:
|
||||
"""Tests des features basées sur les prix."""
|
||||
|
||||
@pytest.fixture
|
||||
def sample_data(self):
|
||||
"""Génère des données de test."""
|
||||
dates = pd.date_range(start='2024-01-01', periods=300, freq='1H')
|
||||
|
||||
np.random.seed(42)
|
||||
returns = np.random.normal(0.0001, 0.01, 300)
|
||||
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) * 1.001
|
||||
df['low'] = df[['open', 'close']].min(axis=1) * 0.999
|
||||
df['volume'] = np.random.randint(1000, 10000, 300)
|
||||
|
||||
return df
|
||||
|
||||
def test_price_features_created(self, sample_data):
|
||||
"""Test que les features de prix sont créées."""
|
||||
fe = FeatureEngineering()
|
||||
|
||||
df = fe._create_price_features(sample_data.copy())
|
||||
|
||||
assert 'returns' in df.columns
|
||||
assert 'log_returns' in df.columns
|
||||
assert 'high_low_ratio' in df.columns
|
||||
assert 'close_open_ratio' in df.columns
|
||||
assert 'price_position' in df.columns
|
||||
|
||||
def test_returns_calculation(self, sample_data):
|
||||
"""Test calcul des returns."""
|
||||
fe = FeatureEngineering()
|
||||
|
||||
df = fe._create_price_features(sample_data.copy())
|
||||
|
||||
# Vérifier que returns est calculé correctement
|
||||
expected_returns = sample_data['close'].pct_change()
|
||||
pd.testing.assert_series_equal(
|
||||
df['returns'].dropna(),
|
||||
expected_returns.dropna(),
|
||||
check_names=False
|
||||
)
|
||||
|
||||
def test_price_position_range(self, sample_data):
|
||||
"""Test que price_position est entre 0 et 1."""
|
||||
fe = FeatureEngineering()
|
||||
|
||||
df = fe._create_price_features(sample_data.copy())
|
||||
|
||||
price_pos = df['price_position'].dropna()
|
||||
assert (price_pos >= 0).all()
|
||||
assert (price_pos <= 1).all()
|
||||
|
||||
|
||||
class TestTechnicalIndicators:
|
||||
"""Tests des indicateurs techniques."""
|
||||
|
||||
@pytest.fixture
|
||||
def sample_data(self):
|
||||
"""Génère des données de test."""
|
||||
dates = pd.date_range(start='2024-01-01', periods=300, freq='1H')
|
||||
|
||||
np.random.seed(42)
|
||||
returns = np.random.normal(0.0001, 0.01, 300)
|
||||
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) * 1.001
|
||||
df['low'] = df[['open', 'close']].min(axis=1) * 0.999
|
||||
df['volume'] = np.random.randint(1000, 10000, 300)
|
||||
|
||||
return df
|
||||
|
||||
def test_moving_averages_created(self, sample_data):
|
||||
"""Test création des moyennes mobiles."""
|
||||
fe = FeatureEngineering()
|
||||
|
||||
df = fe._create_technical_indicators(sample_data.copy())
|
||||
|
||||
# Vérifier SMA
|
||||
for period in [5, 10, 20, 50, 100, 200]:
|
||||
assert f'sma_{period}' in df.columns
|
||||
assert f'ema_{period}' in df.columns
|
||||
|
||||
def test_rsi_calculation(self, sample_data):
|
||||
"""Test calcul RSI."""
|
||||
fe = FeatureEngineering()
|
||||
|
||||
df = fe._create_technical_indicators(sample_data.copy())
|
||||
|
||||
# Vérifier RSI
|
||||
for period in [7, 14, 21]:
|
||||
assert f'rsi_{period}' in df.columns
|
||||
|
||||
# RSI devrait être entre 0 et 100
|
||||
rsi = df[f'rsi_{period}'].dropna()
|
||||
assert (rsi >= 0).all()
|
||||
assert (rsi <= 100).all()
|
||||
|
||||
def test_macd_calculation(self, sample_data):
|
||||
"""Test calcul MACD."""
|
||||
fe = FeatureEngineering()
|
||||
|
||||
df = fe._create_technical_indicators(sample_data.copy())
|
||||
|
||||
assert 'macd' in df.columns
|
||||
assert 'macd_signal' in df.columns
|
||||
assert 'macd_hist' in df.columns
|
||||
|
||||
def test_bollinger_bands(self, sample_data):
|
||||
"""Test calcul Bollinger Bands."""
|
||||
fe = FeatureEngineering()
|
||||
|
||||
df = fe._create_technical_indicators(sample_data.copy())
|
||||
|
||||
for period in [20, 50]:
|
||||
assert f'bb_upper_{period}' in df.columns
|
||||
assert f'bb_middle_{period}' in df.columns
|
||||
assert f'bb_lower_{period}' in df.columns
|
||||
assert f'bb_width_{period}' in df.columns
|
||||
assert f'bb_position_{period}' in df.columns
|
||||
|
||||
# Vérifier ordre: upper > middle > lower
|
||||
upper = df[f'bb_upper_{period}'].dropna()
|
||||
middle = df[f'bb_middle_{period}'].dropna()
|
||||
lower = df[f'bb_lower_{period}'].dropna()
|
||||
|
||||
assert (upper >= middle).all()
|
||||
assert (middle >= lower).all()
|
||||
|
||||
def test_atr_calculation(self, sample_data):
|
||||
"""Test calcul ATR."""
|
||||
fe = FeatureEngineering()
|
||||
|
||||
df = fe._create_technical_indicators(sample_data.copy())
|
||||
|
||||
for period in [7, 14, 21]:
|
||||
assert f'atr_{period}' in df.columns
|
||||
|
||||
# ATR devrait être positif
|
||||
atr = df[f'atr_{period}'].dropna()
|
||||
assert (atr > 0).all()
|
||||
|
||||
|
||||
class TestStatisticalFeatures:
|
||||
"""Tests des features statistiques."""
|
||||
|
||||
@pytest.fixture
|
||||
def sample_data(self):
|
||||
"""Génère des données de test."""
|
||||
dates = pd.date_range(start='2024-01-01', periods=300, freq='1H')
|
||||
|
||||
np.random.seed(42)
|
||||
returns = np.random.normal(0.0001, 0.01, 300)
|
||||
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) * 1.001
|
||||
df['low'] = df[['open', 'close']].min(axis=1) * 0.999
|
||||
df['volume'] = np.random.randint(1000, 10000, 300)
|
||||
|
||||
return df
|
||||
|
||||
def test_statistical_features_created(self, sample_data):
|
||||
"""Test création features statistiques."""
|
||||
fe = FeatureEngineering()
|
||||
|
||||
df = fe._create_statistical_features(sample_data.copy())
|
||||
|
||||
for period in [10, 20, 50]:
|
||||
assert f'mean_{period}' in df.columns
|
||||
assert f'std_{period}' in df.columns
|
||||
assert f'skew_{period}' in df.columns
|
||||
assert f'kurt_{period}' in df.columns
|
||||
assert f'zscore_{period}' in df.columns
|
||||
|
||||
def test_zscore_calculation(self, sample_data):
|
||||
"""Test calcul z-score."""
|
||||
fe = FeatureEngineering()
|
||||
|
||||
df = fe._create_statistical_features(sample_data.copy())
|
||||
|
||||
# Z-score devrait avoir moyenne ~0 et std ~1
|
||||
zscore = df['zscore_20'].dropna()
|
||||
assert abs(zscore.mean()) < 0.5
|
||||
assert abs(zscore.std() - 1.0) < 0.5
|
||||
|
||||
|
||||
class TestVolatilityFeatures:
|
||||
"""Tests des features de volatilité."""
|
||||
|
||||
@pytest.fixture
|
||||
def sample_data(self):
|
||||
"""Génère des données de test."""
|
||||
dates = pd.date_range(start='2024-01-01', periods=300, freq='1H')
|
||||
|
||||
np.random.seed(42)
|
||||
returns = np.random.normal(0.0001, 0.01, 300)
|
||||
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) * 1.001
|
||||
df['low'] = df[['open', 'close']].min(axis=1) * 0.999
|
||||
df['volume'] = np.random.randint(1000, 10000, 300)
|
||||
|
||||
return df
|
||||
|
||||
def test_volatility_features_created(self, sample_data):
|
||||
"""Test création features volatilité."""
|
||||
fe = FeatureEngineering()
|
||||
|
||||
# Ajouter returns d'abord
|
||||
df = sample_data.copy()
|
||||
df['returns'] = df['close'].pct_change()
|
||||
|
||||
df = fe._create_volatility_features(df)
|
||||
|
||||
for period in [10, 20, 50]:
|
||||
assert f'volatility_{period}' in df.columns
|
||||
|
||||
assert 'parkinson_vol' in df.columns
|
||||
assert 'gk_vol' in df.columns
|
||||
assert 'vol_ratio' in df.columns
|
||||
|
||||
def test_volatility_positive(self, sample_data):
|
||||
"""Test que la volatilité est positive."""
|
||||
fe = FeatureEngineering()
|
||||
|
||||
df = sample_data.copy()
|
||||
df['returns'] = df['close'].pct_change()
|
||||
|
||||
df = fe._create_volatility_features(df)
|
||||
|
||||
vol = df['volatility_20'].dropna()
|
||||
assert (vol > 0).all()
|
||||
|
||||
|
||||
class TestVolumeFeatures:
|
||||
"""Tests des features de volume."""
|
||||
|
||||
@pytest.fixture
|
||||
def sample_data(self):
|
||||
"""Génère des données de test."""
|
||||
dates = pd.date_range(start='2024-01-01', periods=300, freq='1H')
|
||||
|
||||
np.random.seed(42)
|
||||
returns = np.random.normal(0.0001, 0.01, 300)
|
||||
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) * 1.001
|
||||
df['low'] = df[['open', 'close']].min(axis=1) * 0.999
|
||||
df['volume'] = np.random.randint(1000, 10000, 300)
|
||||
|
||||
return df
|
||||
|
||||
def test_volume_features_created(self, sample_data):
|
||||
"""Test création features volume."""
|
||||
fe = FeatureEngineering()
|
||||
|
||||
df = fe._create_volume_features(sample_data.copy())
|
||||
|
||||
for period in [5, 10, 20]:
|
||||
assert f'volume_ma_{period}' in df.columns
|
||||
|
||||
assert 'volume_ratio' in df.columns
|
||||
assert 'volume_change' in df.columns
|
||||
assert 'obv' in df.columns
|
||||
assert 'vwap' in df.columns
|
||||
|
||||
|
||||
class TestTimeFeatures:
|
||||
"""Tests des features temporelles."""
|
||||
|
||||
@pytest.fixture
|
||||
def sample_data(self):
|
||||
"""Génère des données de test avec index datetime."""
|
||||
dates = pd.date_range(start='2024-01-01', periods=300, freq='1H')
|
||||
|
||||
np.random.seed(42)
|
||||
returns = np.random.normal(0.0001, 0.01, 300)
|
||||
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) * 1.001
|
||||
df['low'] = df[['open', 'close']].min(axis=1) * 0.999
|
||||
df['volume'] = np.random.randint(1000, 10000, 300)
|
||||
|
||||
return df
|
||||
|
||||
def test_time_features_created(self, sample_data):
|
||||
"""Test création features temporelles."""
|
||||
fe = FeatureEngineering()
|
||||
|
||||
df = fe._create_time_features(sample_data.copy())
|
||||
|
||||
assert 'hour' in df.columns
|
||||
assert 'hour_sin' in df.columns
|
||||
assert 'hour_cos' in df.columns
|
||||
assert 'day_of_week' in df.columns
|
||||
assert 'dow_sin' in df.columns
|
||||
assert 'dow_cos' in df.columns
|
||||
assert 'month' in df.columns
|
||||
assert 'month_sin' in df.columns
|
||||
assert 'month_cos' in df.columns
|
||||
|
||||
def test_cyclic_encoding_range(self, sample_data):
|
||||
"""Test que l'encodage cyclique est dans [-1, 1]."""
|
||||
fe = FeatureEngineering()
|
||||
|
||||
df = fe._create_time_features(sample_data.copy())
|
||||
|
||||
for col in ['hour_sin', 'hour_cos', 'dow_sin', 'dow_cos', 'month_sin', 'month_cos']:
|
||||
values = df[col].dropna()
|
||||
assert (values >= -1).all()
|
||||
assert (values <= 1).all()
|
||||
|
||||
|
||||
class TestFeatureImportance:
|
||||
"""Tests de feature importance."""
|
||||
|
||||
@pytest.fixture
|
||||
def sample_features(self):
|
||||
"""Génère des features de test."""
|
||||
np.random.seed(42)
|
||||
|
||||
n_samples = 1000
|
||||
n_features = 20
|
||||
|
||||
features = pd.DataFrame(
|
||||
np.random.randn(n_samples, n_features),
|
||||
columns=[f'feature_{i}' for i in range(n_features)]
|
||||
)
|
||||
|
||||
return features
|
||||
|
||||
@pytest.fixture
|
||||
def sample_target(self):
|
||||
"""Génère une target de test."""
|
||||
np.random.seed(42)
|
||||
return pd.Series(np.random.randn(1000))
|
||||
|
||||
def test_get_feature_importance(self, sample_features, sample_target):
|
||||
"""Test calcul feature importance."""
|
||||
fe = FeatureEngineering()
|
||||
|
||||
importance = fe.get_feature_importance(
|
||||
sample_features,
|
||||
sample_target,
|
||||
method='mutual_info'
|
||||
)
|
||||
|
||||
assert isinstance(importance, pd.DataFrame)
|
||||
assert 'feature' in importance.columns
|
||||
assert 'importance' in importance.columns
|
||||
assert len(importance) == len(sample_features.columns)
|
||||
|
||||
def test_select_top_features(self, sample_features, sample_target):
|
||||
"""Test sélection top features."""
|
||||
fe = FeatureEngineering()
|
||||
|
||||
top_features = fe.select_top_features(
|
||||
sample_features,
|
||||
sample_target,
|
||||
n_features=10
|
||||
)
|
||||
|
||||
assert isinstance(top_features, list)
|
||||
assert len(top_features) == 10
|
||||
assert all(f in sample_features.columns for f in top_features)
|
||||
|
||||
|
||||
class TestFeatureEngineeringIntegration:
|
||||
"""Tests d'intégration."""
|
||||
|
||||
@pytest.fixture
|
||||
def sample_data(self):
|
||||
"""Génère des données de test."""
|
||||
dates = pd.date_range(start='2024-01-01', periods=500, freq='1H')
|
||||
|
||||
np.random.seed(42)
|
||||
returns = np.random.normal(0.0001, 0.01, 500)
|
||||
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, 500))
|
||||
df['low'] = df[['open', 'close']].min(axis=1) * (1 - np.random.uniform(0, 0.001, 500))
|
||||
df['volume'] = np.random.randint(1000, 10000, 500)
|
||||
|
||||
return df
|
||||
|
||||
def test_full_workflow(self, sample_data):
|
||||
"""Test workflow complet."""
|
||||
fe = FeatureEngineering()
|
||||
|
||||
# 1. Créer toutes les features
|
||||
features_df = fe.create_all_features(sample_data)
|
||||
|
||||
assert len(features_df) > 0
|
||||
assert len(fe.feature_names) >= 100
|
||||
|
||||
# 2. Vérifier pas de NaN
|
||||
assert features_df.isna().sum().sum() == 0
|
||||
|
||||
# 3. Créer target
|
||||
target = features_df['returns'].shift(-1).dropna()
|
||||
features_for_ml = features_df.iloc[:-1]
|
||||
|
||||
# 4. Feature importance
|
||||
importance = fe.get_feature_importance(
|
||||
features_for_ml[fe.feature_names],
|
||||
target,
|
||||
method='correlation'
|
||||
)
|
||||
|
||||
assert len(importance) > 0
|
||||
|
||||
# 5. Sélectionner top features
|
||||
top_features = fe.select_top_features(
|
||||
features_for_ml[fe.feature_names],
|
||||
target,
|
||||
n_features=50
|
||||
)
|
||||
|
||||
assert len(top_features) == 50
|
||||
473
tests/unit/test_ml/test_regime_detector.py
Normal file
473
tests/unit/test_ml/test_regime_detector.py
Normal file
@@ -0,0 +1,473 @@
|
||||
"""
|
||||
Tests Unitaires - RegimeDetector.
|
||||
|
||||
Tests de la détection de régimes de marché avec HMM.
|
||||
"""
|
||||
|
||||
import pytest
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
from datetime import datetime, timedelta
|
||||
|
||||
from src.ml.regime_detector import RegimeDetector
|
||||
|
||||
|
||||
class TestRegimeDetectorInitialization:
|
||||
"""Tests d'initialisation du RegimeDetector."""
|
||||
|
||||
def test_initialization_default(self):
|
||||
"""Test initialisation avec paramètres par défaut."""
|
||||
detector = RegimeDetector()
|
||||
|
||||
assert detector.n_regimes == 4
|
||||
assert detector.random_state == 42
|
||||
assert detector.is_fitted is False
|
||||
assert len(detector.feature_names) == 0
|
||||
|
||||
def test_initialization_custom_regimes(self):
|
||||
"""Test initialisation avec nombre de régimes personnalisé."""
|
||||
detector = RegimeDetector(n_regimes=3)
|
||||
|
||||
assert detector.n_regimes == 3
|
||||
|
||||
def test_regime_names_defined(self):
|
||||
"""Test que les noms de régimes sont définis."""
|
||||
detector = RegimeDetector()
|
||||
|
||||
assert len(detector.REGIME_NAMES) == 4
|
||||
assert 'Trending Up' in detector.REGIME_NAMES.values()
|
||||
assert 'Trending Down' in detector.REGIME_NAMES.values()
|
||||
assert 'Ranging' in detector.REGIME_NAMES.values()
|
||||
assert 'High Volatility' in detector.REGIME_NAMES.values()
|
||||
|
||||
|
||||
class TestRegimeDetectorFitting:
|
||||
"""Tests d'entraînement du modèle."""
|
||||
|
||||
@pytest.fixture
|
||||
def sample_data(self):
|
||||
"""Génère des données de test."""
|
||||
dates = pd.date_range(start='2024-01-01', periods=200, freq='1H')
|
||||
|
||||
np.random.seed(42)
|
||||
returns = np.random.normal(0.0001, 0.01, 200)
|
||||
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) * 1.001
|
||||
df['low'] = df[['open', 'close']].min(axis=1) * 0.999
|
||||
df['volume'] = np.random.randint(1000, 10000, 200)
|
||||
|
||||
return df
|
||||
|
||||
def test_fit_success(self, sample_data):
|
||||
"""Test entraînement réussi."""
|
||||
detector = RegimeDetector()
|
||||
|
||||
detector.fit(sample_data)
|
||||
|
||||
assert detector.is_fitted is True
|
||||
assert len(detector.feature_names) > 0
|
||||
|
||||
def test_fit_creates_features(self, sample_data):
|
||||
"""Test que fit crée les features."""
|
||||
detector = RegimeDetector()
|
||||
|
||||
detector.fit(sample_data)
|
||||
|
||||
# Vérifier que les features attendues sont créées
|
||||
expected_features = ['returns', 'volatility', 'trend', 'range', 'volume_change', 'momentum']
|
||||
|
||||
for feature in expected_features:
|
||||
assert feature in detector.feature_names
|
||||
|
||||
def test_fit_with_insufficient_data(self):
|
||||
"""Test avec données insuffisantes."""
|
||||
detector = RegimeDetector()
|
||||
|
||||
# Données trop courtes
|
||||
dates = pd.date_range(start='2024-01-01', periods=10, freq='1H')
|
||||
df = pd.DataFrame({
|
||||
'close': np.random.randn(10),
|
||||
'open': np.random.randn(10),
|
||||
'high': np.random.randn(10),
|
||||
'low': np.random.randn(10),
|
||||
'volume': np.random.randint(1000, 10000, 10)
|
||||
}, index=dates)
|
||||
|
||||
# Devrait lever une erreur ou gérer gracieusement
|
||||
try:
|
||||
detector.fit(df)
|
||||
# Si pas d'erreur, vérifier que le modèle n'est pas fitted
|
||||
# ou qu'il y a un warning
|
||||
except Exception as e:
|
||||
# Acceptable
|
||||
pass
|
||||
|
||||
|
||||
class TestRegimeDetectorPrediction:
|
||||
"""Tests de prédiction des régimes."""
|
||||
|
||||
@pytest.fixture
|
||||
def fitted_detector(self, sample_data):
|
||||
"""Retourne un détecteur entraîné."""
|
||||
detector = RegimeDetector()
|
||||
detector.fit(sample_data)
|
||||
return detector
|
||||
|
||||
@pytest.fixture
|
||||
def sample_data(self):
|
||||
"""Génère des données de test."""
|
||||
dates = pd.date_range(start='2024-01-01', periods=200, freq='1H')
|
||||
|
||||
np.random.seed(42)
|
||||
returns = np.random.normal(0.0001, 0.01, 200)
|
||||
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) * 1.001
|
||||
df['low'] = df[['open', 'close']].min(axis=1) * 0.999
|
||||
df['volume'] = np.random.randint(1000, 10000, 200)
|
||||
|
||||
return df
|
||||
|
||||
def test_predict_regime_returns_array(self, fitted_detector, sample_data):
|
||||
"""Test que predict_regime retourne un array."""
|
||||
regimes = fitted_detector.predict_regime(sample_data)
|
||||
|
||||
assert isinstance(regimes, np.ndarray)
|
||||
assert len(regimes) > 0
|
||||
|
||||
def test_predict_regime_values_valid(self, fitted_detector, sample_data):
|
||||
"""Test que les régimes prédits sont valides."""
|
||||
regimes = fitted_detector.predict_regime(sample_data)
|
||||
|
||||
# Tous les régimes doivent être entre 0 et n_regimes-1
|
||||
assert (regimes >= 0).all()
|
||||
assert (regimes < fitted_detector.n_regimes).all()
|
||||
|
||||
def test_predict_current_regime(self, fitted_detector, sample_data):
|
||||
"""Test prédiction du régime actuel."""
|
||||
current_regime = fitted_detector.predict_current_regime(sample_data)
|
||||
|
||||
assert isinstance(current_regime, (int, np.integer))
|
||||
assert 0 <= current_regime < fitted_detector.n_regimes
|
||||
|
||||
def test_predict_without_fitting(self, sample_data):
|
||||
"""Test prédiction sans entraînement préalable."""
|
||||
detector = RegimeDetector()
|
||||
|
||||
with pytest.raises(ValueError, match="not fitted"):
|
||||
detector.predict_regime(sample_data)
|
||||
|
||||
def test_get_regime_probabilities(self, fitted_detector, sample_data):
|
||||
"""Test obtention des probabilités."""
|
||||
probabilities = fitted_detector.get_regime_probabilities(sample_data)
|
||||
|
||||
assert isinstance(probabilities, np.ndarray)
|
||||
assert probabilities.shape[1] == fitted_detector.n_regimes
|
||||
|
||||
# Vérifier que les probabilités somment à 1
|
||||
prob_sums = probabilities.sum(axis=1)
|
||||
np.testing.assert_array_almost_equal(prob_sums, np.ones(len(prob_sums)), decimal=5)
|
||||
|
||||
|
||||
class TestRegimeDetectorStatistics:
|
||||
"""Tests des statistiques de régimes."""
|
||||
|
||||
@pytest.fixture
|
||||
def fitted_detector(self, sample_data):
|
||||
"""Retourne un détecteur entraîné."""
|
||||
detector = RegimeDetector()
|
||||
detector.fit(sample_data)
|
||||
return detector
|
||||
|
||||
@pytest.fixture
|
||||
def sample_data(self):
|
||||
"""Génère des données de test."""
|
||||
dates = pd.date_range(start='2024-01-01', periods=200, freq='1H')
|
||||
|
||||
np.random.seed(42)
|
||||
returns = np.random.normal(0.0001, 0.01, 200)
|
||||
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) * 1.001
|
||||
df['low'] = df[['open', 'close']].min(axis=1) * 0.999
|
||||
df['volume'] = np.random.randint(1000, 10000, 200)
|
||||
|
||||
return df
|
||||
|
||||
def test_get_regime_name(self, fitted_detector):
|
||||
"""Test récupération du nom d'un régime."""
|
||||
for regime in range(fitted_detector.n_regimes):
|
||||
name = fitted_detector.get_regime_name(regime)
|
||||
assert isinstance(name, str)
|
||||
assert len(name) > 0
|
||||
|
||||
def test_get_regime_statistics(self, fitted_detector, sample_data):
|
||||
"""Test calcul des statistiques."""
|
||||
stats = fitted_detector.get_regime_statistics(sample_data)
|
||||
|
||||
assert 'regime_counts' in stats
|
||||
assert 'regime_percentages' in stats
|
||||
assert 'current_regime' in stats
|
||||
assert 'current_regime_name' in stats
|
||||
|
||||
# Vérifier que les pourcentages somment à 1
|
||||
total_pct = sum(stats['regime_percentages'].values())
|
||||
assert abs(total_pct - 1.0) < 0.01
|
||||
|
||||
|
||||
class TestRegimeDetectorAdaptation:
|
||||
"""Tests d'adaptation des paramètres."""
|
||||
|
||||
def test_adapt_strategy_parameters(self):
|
||||
"""Test adaptation des paramètres selon le régime."""
|
||||
detector = RegimeDetector()
|
||||
|
||||
base_params = {
|
||||
'min_confidence': 0.6,
|
||||
'risk_per_trade': 0.02
|
||||
}
|
||||
|
||||
# Tester pour chaque régime
|
||||
for regime in range(4):
|
||||
adapted = detector.adapt_strategy_parameters(regime, base_params)
|
||||
|
||||
assert 'min_confidence' in adapted
|
||||
assert 'risk_per_trade' in adapted
|
||||
|
||||
# Les paramètres doivent être modifiés
|
||||
assert adapted != base_params
|
||||
|
||||
def test_adapt_trending_up(self):
|
||||
"""Test adaptation pour régime Trending Up."""
|
||||
detector = RegimeDetector()
|
||||
|
||||
base_params = {
|
||||
'min_confidence': 0.6,
|
||||
'risk_per_trade': 0.02
|
||||
}
|
||||
|
||||
adapted = detector.adapt_strategy_parameters(0, base_params) # 0 = Trending Up
|
||||
|
||||
# Devrait être plus agressif
|
||||
assert adapted['min_confidence'] < base_params['min_confidence']
|
||||
assert adapted['risk_per_trade'] > base_params['risk_per_trade']
|
||||
|
||||
def test_adapt_high_volatility(self):
|
||||
"""Test adaptation pour régime High Volatility."""
|
||||
detector = RegimeDetector()
|
||||
|
||||
base_params = {
|
||||
'min_confidence': 0.6,
|
||||
'risk_per_trade': 0.02
|
||||
}
|
||||
|
||||
adapted = detector.adapt_strategy_parameters(3, base_params) # 3 = High Volatility
|
||||
|
||||
# Devrait être plus conservateur
|
||||
assert adapted['min_confidence'] > base_params['min_confidence']
|
||||
assert adapted['risk_per_trade'] < base_params['risk_per_trade']
|
||||
|
||||
def test_should_trade_in_regime(self):
|
||||
"""Test décision de trading selon régime."""
|
||||
detector = RegimeDetector()
|
||||
|
||||
# Scalping devrait trader en Ranging
|
||||
assert detector.should_trade_in_regime(2, 'scalping') is True
|
||||
|
||||
# Scalping ne devrait pas trader en High Volatility
|
||||
assert detector.should_trade_in_regime(3, 'scalping') is False
|
||||
|
||||
# Intraday devrait trader en Trending
|
||||
assert detector.should_trade_in_regime(0, 'intraday') is True
|
||||
assert detector.should_trade_in_regime(1, 'intraday') is True
|
||||
|
||||
# Intraday ne devrait pas trader en Ranging
|
||||
assert detector.should_trade_in_regime(2, 'intraday') is False
|
||||
|
||||
|
||||
class TestRegimeDetectorFeatures:
|
||||
"""Tests de calcul des features."""
|
||||
|
||||
@pytest.fixture
|
||||
def sample_data(self):
|
||||
"""Génère des données de test."""
|
||||
dates = pd.date_range(start='2024-01-01', periods=200, freq='1H')
|
||||
|
||||
np.random.seed(42)
|
||||
returns = np.random.normal(0.0001, 0.01, 200)
|
||||
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) * 1.001
|
||||
df['low'] = df[['open', 'close']].min(axis=1) * 0.999
|
||||
df['volume'] = np.random.randint(1000, 10000, 200)
|
||||
|
||||
return df
|
||||
|
||||
def test_calculate_features(self, sample_data):
|
||||
"""Test calcul des features."""
|
||||
detector = RegimeDetector()
|
||||
|
||||
features = detector._calculate_features(sample_data)
|
||||
|
||||
assert isinstance(features, pd.DataFrame)
|
||||
assert len(features) > 0
|
||||
|
||||
# Vérifier présence des features
|
||||
expected_features = ['returns', 'volatility', 'trend', 'range', 'volume_change', 'momentum']
|
||||
|
||||
for feature in expected_features:
|
||||
assert feature in features.columns
|
||||
|
||||
def test_features_no_nan(self, sample_data):
|
||||
"""Test que les features n'ont pas de NaN après nettoyage."""
|
||||
detector = RegimeDetector()
|
||||
|
||||
features = detector._calculate_features(sample_data)
|
||||
|
||||
# Après dropna, ne devrait pas y avoir de NaN
|
||||
assert features.isna().sum().sum() == 0
|
||||
|
||||
def test_normalize_features(self):
|
||||
"""Test normalisation des features."""
|
||||
detector = RegimeDetector()
|
||||
|
||||
# Créer features test
|
||||
X = np.random.randn(100, 6)
|
||||
|
||||
X_normalized = detector._normalize_features(X)
|
||||
|
||||
# Vérifier que la moyenne est proche de 0 et std proche de 1
|
||||
assert abs(X_normalized.mean()) < 0.1
|
||||
assert abs(X_normalized.std() - 1.0) < 0.1
|
||||
|
||||
|
||||
class TestRegimeDetectorEdgeCases:
|
||||
"""Tests des cas limites."""
|
||||
|
||||
def test_with_missing_columns(self):
|
||||
"""Test avec colonnes manquantes."""
|
||||
detector = RegimeDetector()
|
||||
|
||||
# DataFrame incomplet
|
||||
df = pd.DataFrame({
|
||||
'close': np.random.randn(100)
|
||||
# Manque open, high, low, volume
|
||||
})
|
||||
|
||||
with pytest.raises(KeyError):
|
||||
detector.fit(df)
|
||||
|
||||
def test_with_constant_prices(self):
|
||||
"""Test avec prix constants."""
|
||||
detector = RegimeDetector()
|
||||
|
||||
dates = pd.date_range(start='2024-01-01', periods=200, freq='1H')
|
||||
|
||||
df = pd.DataFrame(index=dates)
|
||||
df['close'] = 1.1000 # Prix constant
|
||||
df['open'] = 1.1000
|
||||
df['high'] = 1.1000
|
||||
df['low'] = 1.1000
|
||||
df['volume'] = 1000
|
||||
|
||||
# Devrait gérer gracieusement (ou lever erreur appropriée)
|
||||
try:
|
||||
detector.fit(df)
|
||||
# Si réussit, vérifier que le modèle est fitted
|
||||
assert detector.is_fitted
|
||||
except Exception:
|
||||
# Acceptable si erreur appropriée
|
||||
pass
|
||||
|
||||
def test_regime_name_invalid(self):
|
||||
"""Test avec numéro de régime invalide."""
|
||||
detector = RegimeDetector()
|
||||
|
||||
# Régime hors limites
|
||||
name = detector.get_regime_name(999)
|
||||
|
||||
# Devrait retourner un nom par défaut
|
||||
assert 'Regime' in name
|
||||
|
||||
|
||||
class TestRegimeDetectorIntegration:
|
||||
"""Tests d'intégration."""
|
||||
|
||||
@pytest.fixture
|
||||
def sample_data(self):
|
||||
"""Génère des données de test."""
|
||||
dates = pd.date_range(start='2024-01-01', periods=500, freq='1H')
|
||||
|
||||
np.random.seed(42)
|
||||
|
||||
# Créer différents régimes
|
||||
regimes = []
|
||||
prices = []
|
||||
base_price = 1.1000
|
||||
|
||||
for i in range(500):
|
||||
if i < 125: # Trending Up
|
||||
regime = 0
|
||||
price = base_price * (1 + i * 0.0001)
|
||||
elif i < 250: # Trending Down
|
||||
regime = 1
|
||||
price = base_price * (1 - (i - 125) * 0.0001)
|
||||
elif i < 375: # Ranging
|
||||
regime = 2
|
||||
price = base_price + 0.001 * np.sin(i / 10)
|
||||
else: # High Volatility
|
||||
regime = 3
|
||||
price = base_price + 0.01 * np.random.randn()
|
||||
|
||||
regimes.append(regime)
|
||||
prices.append(price)
|
||||
|
||||
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) * 1.001
|
||||
df['low'] = df[['open', 'close']].min(axis=1) * 0.999
|
||||
df['volume'] = np.random.randint(1000, 10000, 500)
|
||||
|
||||
return df
|
||||
|
||||
def test_full_workflow(self, sample_data):
|
||||
"""Test workflow complet."""
|
||||
detector = RegimeDetector(n_regimes=4)
|
||||
|
||||
# 1. Fit
|
||||
detector.fit(sample_data)
|
||||
assert detector.is_fitted
|
||||
|
||||
# 2. Predict
|
||||
regimes = detector.predict_regime(sample_data)
|
||||
assert len(regimes) > 0
|
||||
|
||||
# 3. Current regime
|
||||
current = detector.predict_current_regime(sample_data)
|
||||
assert 0 <= current < 4
|
||||
|
||||
# 4. Statistics
|
||||
stats = detector.get_regime_statistics(sample_data)
|
||||
assert 'current_regime' in stats
|
||||
|
||||
# 5. Adaptation
|
||||
adapted = detector.adapt_strategy_parameters(current, {'min_confidence': 0.6})
|
||||
assert 'min_confidence' in adapted
|
||||
|
||||
# 6. Should trade
|
||||
should_trade = detector.should_trade_in_regime(current, 'intraday')
|
||||
assert isinstance(should_trade, bool)
|
||||
314
tests/unit/test_risk_manager.py
Normal file
314
tests/unit/test_risk_manager.py
Normal file
@@ -0,0 +1,314 @@
|
||||
"""
|
||||
Tests Unitaires - RiskManager.
|
||||
|
||||
Tests complets du Risk Manager incluant:
|
||||
- Pattern Singleton
|
||||
- Validation pré-trade
|
||||
- Gestion positions
|
||||
- Métriques de risque
|
||||
- Circuit breakers
|
||||
"""
|
||||
|
||||
import pytest
|
||||
from datetime import datetime
|
||||
|
||||
from src.core.risk_manager import RiskManager, Position, RiskMetrics
|
||||
|
||||
|
||||
class TestRiskManagerSingleton:
|
||||
"""Tests du pattern Singleton."""
|
||||
|
||||
def test_singleton_same_instance(self):
|
||||
"""Vérifie que deux appels retournent la même instance."""
|
||||
rm1 = RiskManager()
|
||||
rm2 = RiskManager()
|
||||
|
||||
assert rm1 is rm2
|
||||
|
||||
def test_singleton_shared_state(self, risk_manager):
|
||||
"""Vérifie que l'état est partagé entre instances."""
|
||||
rm1 = risk_manager
|
||||
rm1.portfolio_value = 15000.0
|
||||
|
||||
rm2 = RiskManager()
|
||||
|
||||
assert rm2.portfolio_value == 15000.0
|
||||
|
||||
|
||||
class TestRiskManagerInitialization:
|
||||
"""Tests d'initialisation."""
|
||||
|
||||
def test_initialize_with_config(self, sample_config):
|
||||
"""Vérifie l'initialisation avec configuration."""
|
||||
rm = RiskManager()
|
||||
rm.initialize(sample_config['risk_limits'])
|
||||
|
||||
assert rm.initial_capital == 10000.0
|
||||
assert rm.portfolio_value == 10000.0
|
||||
assert rm.peak_value == 10000.0
|
||||
assert len(rm.positions) == 0
|
||||
|
||||
def test_config_loaded_correctly(self, risk_manager, sample_config):
|
||||
"""Vérifie que la configuration est chargée."""
|
||||
assert risk_manager.config == sample_config['risk_limits']
|
||||
|
||||
|
||||
class TestTradeValidation:
|
||||
"""Tests de validation pré-trade."""
|
||||
|
||||
def test_validate_trade_success(self, risk_manager):
|
||||
"""Test validation d'un trade valide."""
|
||||
is_valid, error = risk_manager.validate_trade(
|
||||
symbol='EURUSD',
|
||||
quantity=1000,
|
||||
price=1.1000,
|
||||
stop_loss=1.0950,
|
||||
take_profit=1.1100,
|
||||
strategy='intraday'
|
||||
)
|
||||
|
||||
assert is_valid is True
|
||||
assert error is None
|
||||
|
||||
def test_validate_trade_no_stop_loss(self, risk_manager):
|
||||
"""Test rejet si pas de stop-loss."""
|
||||
is_valid, error = risk_manager.validate_trade(
|
||||
symbol='EURUSD',
|
||||
quantity=1000,
|
||||
price=1.1000,
|
||||
stop_loss=None,
|
||||
take_profit=1.1100,
|
||||
strategy='intraday'
|
||||
)
|
||||
|
||||
assert is_valid is False
|
||||
assert 'stop-loss' in error.lower()
|
||||
|
||||
def test_validate_trade_excessive_risk(self, risk_manager):
|
||||
"""Test rejet si risque trop élevé."""
|
||||
is_valid, error = risk_manager.validate_trade(
|
||||
symbol='EURUSD',
|
||||
quantity=100000, # Très grande position
|
||||
price=1.1000,
|
||||
stop_loss=1.0000, # Stop très loin
|
||||
take_profit=1.2000,
|
||||
strategy='intraday'
|
||||
)
|
||||
|
||||
assert is_valid is False
|
||||
assert 'risk' in error.lower()
|
||||
|
||||
def test_validate_trade_position_too_large(self, risk_manager):
|
||||
"""Test rejet si position trop grande."""
|
||||
is_valid, error = risk_manager.validate_trade(
|
||||
symbol='EURUSD',
|
||||
quantity=20000, # > 10% du portfolio
|
||||
price=1.1000,
|
||||
stop_loss=1.0950,
|
||||
take_profit=1.1100,
|
||||
strategy='intraday'
|
||||
)
|
||||
|
||||
assert is_valid is False
|
||||
assert 'position size' in error.lower()
|
||||
|
||||
def test_validate_trade_bad_risk_reward(self, risk_manager):
|
||||
"""Test rejet si R:R ratio insuffisant."""
|
||||
is_valid, error = risk_manager.validate_trade(
|
||||
symbol='EURUSD',
|
||||
quantity=1000,
|
||||
price=1.1000,
|
||||
stop_loss=1.0950, # 50 pips risk
|
||||
take_profit=1.1020, # 20 pips reward (R:R = 0.4)
|
||||
strategy='intraday'
|
||||
)
|
||||
|
||||
assert is_valid is False
|
||||
assert 'risk/reward' in error.lower()
|
||||
|
||||
|
||||
class TestPositionManagement:
|
||||
"""Tests de gestion des positions."""
|
||||
|
||||
def test_add_position(self, risk_manager):
|
||||
"""Test ajout d'une position."""
|
||||
position = Position(
|
||||
symbol='EURUSD',
|
||||
quantity=1000,
|
||||
entry_price=1.1000,
|
||||
current_price=1.1000,
|
||||
stop_loss=1.0950,
|
||||
take_profit=1.1100,
|
||||
strategy='intraday',
|
||||
entry_time=datetime.now(),
|
||||
unrealized_pnl=0.0,
|
||||
risk_amount=50.0
|
||||
)
|
||||
|
||||
risk_manager.add_position(position)
|
||||
|
||||
assert 'EURUSD' in risk_manager.positions
|
||||
assert risk_manager.positions['EURUSD'] == position
|
||||
assert risk_manager.total_trades == 1
|
||||
|
||||
def test_update_position(self, risk_manager):
|
||||
"""Test mise à jour d'une position."""
|
||||
position = Position(
|
||||
symbol='EURUSD',
|
||||
quantity=1000,
|
||||
entry_price=1.1000,
|
||||
current_price=1.1000,
|
||||
stop_loss=1.0950,
|
||||
take_profit=1.1100,
|
||||
strategy='intraday',
|
||||
entry_time=datetime.now(),
|
||||
unrealized_pnl=0.0,
|
||||
risk_amount=50.0
|
||||
)
|
||||
|
||||
risk_manager.add_position(position)
|
||||
risk_manager.update_position('EURUSD', 1.1050)
|
||||
|
||||
assert risk_manager.positions['EURUSD'].current_price == 1.1050
|
||||
assert risk_manager.positions['EURUSD'].unrealized_pnl == 50.0
|
||||
|
||||
def test_close_position_profit(self, risk_manager):
|
||||
"""Test fermeture position avec profit."""
|
||||
position = Position(
|
||||
symbol='EURUSD',
|
||||
quantity=1000,
|
||||
entry_price=1.1000,
|
||||
current_price=1.1000,
|
||||
stop_loss=1.0950,
|
||||
take_profit=1.1100,
|
||||
strategy='intraday',
|
||||
entry_time=datetime.now(),
|
||||
unrealized_pnl=0.0,
|
||||
risk_amount=50.0
|
||||
)
|
||||
|
||||
risk_manager.add_position(position)
|
||||
initial_value = risk_manager.portfolio_value
|
||||
|
||||
pnl = risk_manager.close_position('EURUSD', 1.1100, 'take_profit')
|
||||
|
||||
assert pnl == 100.0
|
||||
assert 'EURUSD' not in risk_manager.positions
|
||||
assert risk_manager.portfolio_value == initial_value + 100.0
|
||||
assert risk_manager.winning_trades == 1
|
||||
|
||||
def test_close_position_loss(self, risk_manager):
|
||||
"""Test fermeture position avec perte."""
|
||||
position = Position(
|
||||
symbol='EURUSD',
|
||||
quantity=1000,
|
||||
entry_price=1.1000,
|
||||
current_price=1.1000,
|
||||
stop_loss=1.0950,
|
||||
take_profit=1.1100,
|
||||
strategy='intraday',
|
||||
entry_time=datetime.now(),
|
||||
unrealized_pnl=0.0,
|
||||
risk_amount=50.0
|
||||
)
|
||||
|
||||
risk_manager.add_position(position)
|
||||
initial_value = risk_manager.portfolio_value
|
||||
|
||||
pnl = risk_manager.close_position('EURUSD', 1.0950, 'stop_loss')
|
||||
|
||||
assert pnl == -50.0
|
||||
assert risk_manager.portfolio_value == initial_value - 50.0
|
||||
assert risk_manager.losing_trades == 1
|
||||
|
||||
|
||||
class TestRiskMetrics:
|
||||
"""Tests des métriques de risque."""
|
||||
|
||||
def test_get_risk_metrics(self, risk_manager):
|
||||
"""Test calcul des métriques de risque."""
|
||||
metrics = risk_manager.get_risk_metrics()
|
||||
|
||||
assert isinstance(metrics, RiskMetrics)
|
||||
assert metrics.total_risk >= 0
|
||||
assert metrics.current_drawdown >= 0
|
||||
assert 0 <= metrics.risk_utilization <= 1
|
||||
|
||||
def test_calculate_drawdown(self, risk_manager):
|
||||
"""Test calcul du drawdown."""
|
||||
risk_manager.peak_value = 12000.0
|
||||
risk_manager.portfolio_value = 10800.0
|
||||
|
||||
dd = risk_manager._calculate_current_drawdown()
|
||||
|
||||
assert dd == 0.10 # 10% drawdown
|
||||
|
||||
def test_calculate_var(self, risk_manager):
|
||||
"""Test calcul VaR."""
|
||||
# Ajouter historique de P&L
|
||||
risk_manager.pnl_history = [100, -50, 75, -30, 120, -80, 90, -40, 110, -60] * 3
|
||||
|
||||
var = risk_manager._calculate_var(confidence=0.95)
|
||||
|
||||
assert var > 0
|
||||
|
||||
|
||||
class TestCircuitBreakers:
|
||||
"""Tests des circuit breakers."""
|
||||
|
||||
def test_halt_on_max_drawdown(self, risk_manager):
|
||||
"""Test arrêt si drawdown maximum atteint."""
|
||||
risk_manager.peak_value = 10000.0
|
||||
risk_manager.portfolio_value = 8400.0 # 16% drawdown
|
||||
|
||||
risk_manager.check_circuit_breakers()
|
||||
|
||||
assert risk_manager.trading_halted is True
|
||||
assert 'drawdown' in risk_manager.halt_reason.lower()
|
||||
|
||||
def test_halt_on_daily_loss(self, risk_manager):
|
||||
"""Test arrêt si perte journalière excessive."""
|
||||
# Simuler grosse perte journalière
|
||||
risk_manager.portfolio_value = 10000.0
|
||||
risk_manager.daily_trades = [
|
||||
{'time': datetime.now(), 'strategy': 'test'}
|
||||
]
|
||||
risk_manager.pnl_history = [-400] # -4% en un jour
|
||||
|
||||
risk_manager.check_circuit_breakers()
|
||||
|
||||
assert risk_manager.trading_halted is True
|
||||
|
||||
def test_resume_trading(self, risk_manager):
|
||||
"""Test reprise du trading."""
|
||||
risk_manager.halt_trading("Test halt")
|
||||
|
||||
assert risk_manager.trading_halted is True
|
||||
|
||||
risk_manager.resume_trading()
|
||||
|
||||
assert risk_manager.trading_halted is False
|
||||
assert risk_manager.halt_reason is None
|
||||
|
||||
|
||||
class TestStatistics:
|
||||
"""Tests des statistiques."""
|
||||
|
||||
def test_get_statistics(self, risk_manager):
|
||||
"""Test récupération des statistiques."""
|
||||
stats = risk_manager.get_statistics()
|
||||
|
||||
assert 'portfolio_value' in stats
|
||||
assert 'total_return' in stats
|
||||
assert 'win_rate' in stats
|
||||
assert 'total_trades' in stats
|
||||
|
||||
def test_win_rate_calculation(self, risk_manager):
|
||||
"""Test calcul du win rate."""
|
||||
risk_manager.winning_trades = 6
|
||||
risk_manager.losing_trades = 4
|
||||
risk_manager.total_trades = 10
|
||||
|
||||
stats = risk_manager.get_statistics()
|
||||
|
||||
assert stats['win_rate'] == 0.6
|
||||
318
tests/unit/test_strategies.py
Normal file
318
tests/unit/test_strategies.py
Normal file
@@ -0,0 +1,318 @@
|
||||
"""
|
||||
Tests Unitaires - Strategies.
|
||||
|
||||
Tests des stratégies de trading:
|
||||
- BaseStrategy
|
||||
- ScalpingStrategy
|
||||
- IntradayStrategy
|
||||
- SwingStrategy
|
||||
"""
|
||||
|
||||
import pytest
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
|
||||
from src.strategies.base_strategy import BaseStrategy, Signal
|
||||
from src.strategies.scalping.scalping_strategy import ScalpingStrategy
|
||||
from src.strategies.intraday.intraday_strategy import IntradayStrategy
|
||||
from src.strategies.swing.swing_strategy import SwingStrategy
|
||||
|
||||
|
||||
class TestBaseStrategy:
|
||||
"""Tests de la classe BaseStrategy."""
|
||||
|
||||
def test_cannot_instantiate_abstract_class(self):
|
||||
"""Vérifie qu'on ne peut pas instancier BaseStrategy directement."""
|
||||
with pytest.raises(TypeError):
|
||||
BaseStrategy({})
|
||||
|
||||
def test_position_sizing_kelly(self, sample_config):
|
||||
"""Test calcul position sizing avec Kelly Criterion."""
|
||||
strategy = ScalpingStrategy(sample_config['strategy_params']['scalping_strategy'])
|
||||
|
||||
# Simuler historique
|
||||
strategy.win_rate = 0.6
|
||||
strategy.avg_win = 100
|
||||
strategy.avg_loss = -50
|
||||
|
||||
signal = Signal(
|
||||
symbol='EURUSD',
|
||||
direction='LONG',
|
||||
entry_price=1.1000,
|
||||
stop_loss=1.0950,
|
||||
take_profit=1.1100,
|
||||
confidence=0.8,
|
||||
timestamp=pd.Timestamp.now(),
|
||||
strategy='scalping',
|
||||
metadata={}
|
||||
)
|
||||
|
||||
position_size = strategy.calculate_position_size(
|
||||
signal=signal,
|
||||
portfolio_value=10000,
|
||||
current_volatility=0.02
|
||||
)
|
||||
|
||||
assert position_size > 0
|
||||
assert position_size < 10000 # Pas plus que le portfolio
|
||||
|
||||
|
||||
class TestScalpingStrategy:
|
||||
"""Tests de la stratégie Scalping."""
|
||||
|
||||
def test_initialization(self, sample_config):
|
||||
"""Test initialisation de la stratégie."""
|
||||
strategy = ScalpingStrategy(sample_config['strategy_params']['scalping_strategy'])
|
||||
|
||||
assert strategy.name == 'scalping'
|
||||
assert 'bb_period' in strategy.parameters
|
||||
assert 'rsi_period' in strategy.parameters
|
||||
|
||||
def test_calculate_indicators(self, sample_config, sample_ohlcv_data):
|
||||
"""Test calcul des indicateurs."""
|
||||
strategy = ScalpingStrategy(sample_config['strategy_params']['scalping_strategy'])
|
||||
|
||||
df = strategy.calculate_indicators(sample_ohlcv_data)
|
||||
|
||||
# Vérifier que tous les indicateurs sont présents
|
||||
assert 'bb_upper' in df.columns
|
||||
assert 'bb_lower' in df.columns
|
||||
assert 'bb_position' in df.columns
|
||||
assert 'rsi' in df.columns
|
||||
assert 'macd' in df.columns
|
||||
assert 'macd_hist' in df.columns
|
||||
assert 'atr' in df.columns
|
||||
|
||||
def test_analyze_generates_signal(self, sample_config, sample_ohlcv_data):
|
||||
"""Test génération de signal."""
|
||||
strategy = ScalpingStrategy(sample_config['strategy_params']['scalping_strategy'])
|
||||
|
||||
# Créer données oversold
|
||||
df = sample_ohlcv_data.copy()
|
||||
df = strategy.calculate_indicators(df)
|
||||
|
||||
signal = strategy.analyze(df)
|
||||
|
||||
# Signal peut être None ou Signal valide
|
||||
if signal is not None:
|
||||
assert isinstance(signal, Signal)
|
||||
assert signal.direction in ['LONG', 'SHORT']
|
||||
assert signal.stop_loss is not None
|
||||
assert signal.take_profit is not None
|
||||
assert 0 <= signal.confidence <= 1
|
||||
|
||||
def test_oversold_conditions(self, sample_config):
|
||||
"""Test détection conditions oversold."""
|
||||
strategy = ScalpingStrategy(sample_config['strategy_params']['scalping_strategy'])
|
||||
|
||||
# Créer données oversold artificielles
|
||||
dates = pd.date_range(start='2024-01-01', periods=100, freq='5min')
|
||||
df = pd.DataFrame(index=dates)
|
||||
|
||||
# Prix descendant puis rebond
|
||||
prices = np.linspace(1.1000, 1.0900, 100)
|
||||
df['close'] = prices
|
||||
df['open'] = df['close'].shift(1).fillna(df['close'].iloc[0])
|
||||
df['high'] = df[['open', 'close']].max(axis=1) * 1.001
|
||||
df['low'] = df[['open', 'close']].min(axis=1) * 0.999
|
||||
df['volume'] = 5000
|
||||
|
||||
df = strategy.calculate_indicators(df)
|
||||
|
||||
# Vérifier RSI oversold
|
||||
assert df['rsi'].iloc[-1] < 50 # Devrait être bas
|
||||
|
||||
|
||||
class TestIntradayStrategy:
|
||||
"""Tests de la stratégie Intraday."""
|
||||
|
||||
def test_initialization(self, sample_config):
|
||||
"""Test initialisation."""
|
||||
config = {
|
||||
'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,
|
||||
}
|
||||
}
|
||||
|
||||
strategy = IntradayStrategy(config)
|
||||
|
||||
assert strategy.name == 'intraday'
|
||||
assert strategy.parameters['ema_fast'] == 9
|
||||
assert strategy.parameters['ema_slow'] == 21
|
||||
|
||||
def test_calculate_adx(self, sample_config, sample_ohlcv_data):
|
||||
"""Test calcul ADX."""
|
||||
config = {
|
||||
'name': 'intraday',
|
||||
'timeframe': '1h',
|
||||
'adaptive_params': {
|
||||
'ema_fast': 9,
|
||||
'ema_slow': 21,
|
||||
'ema_trend': 50,
|
||||
}
|
||||
}
|
||||
|
||||
strategy = IntradayStrategy(config)
|
||||
df = strategy.calculate_indicators(sample_ohlcv_data)
|
||||
|
||||
# Vérifier ADX calculé
|
||||
assert 'adx' in df.columns
|
||||
assert 'pos_di' in df.columns
|
||||
assert 'neg_di' in df.columns
|
||||
|
||||
# ADX devrait être entre 0 et 100
|
||||
adx_values = df['adx'].dropna()
|
||||
assert (adx_values >= 0).all()
|
||||
assert (adx_values <= 100).all()
|
||||
|
||||
def test_ema_crossover_detection(self, sample_config):
|
||||
"""Test détection croisement EMA."""
|
||||
config = {
|
||||
'name': 'intraday',
|
||||
'timeframe': '1h',
|
||||
'adaptive_params': {
|
||||
'ema_fast': 9,
|
||||
'ema_slow': 21,
|
||||
'ema_trend': 50,
|
||||
'adx_threshold': 25,
|
||||
}
|
||||
}
|
||||
|
||||
strategy = IntradayStrategy(config)
|
||||
|
||||
# Créer données avec croisement
|
||||
dates = pd.date_range(start='2024-01-01', periods=100, freq='1H')
|
||||
df = pd.DataFrame(index=dates)
|
||||
|
||||
# Tendance haussière
|
||||
df['close'] = np.linspace(1.0900, 1.1100, 100)
|
||||
df['open'] = df['close'].shift(1).fillna(df['close'].iloc[0])
|
||||
df['high'] = df[['open', 'close']].max(axis=1) * 1.001
|
||||
df['low'] = df[['open', 'close']].min(axis=1) * 0.999
|
||||
df['volume'] = 5000
|
||||
|
||||
df = strategy.calculate_indicators(df)
|
||||
|
||||
# Vérifier que EMA fast > EMA slow en fin de tendance
|
||||
assert df['ema_fast'].iloc[-1] > df['ema_slow'].iloc[-1]
|
||||
|
||||
|
||||
class TestSwingStrategy:
|
||||
"""Tests de la stratégie Swing."""
|
||||
|
||||
def test_initialization(self):
|
||||
"""Test initialisation."""
|
||||
config = {
|
||||
'name': 'swing',
|
||||
'timeframe': '4h',
|
||||
'adaptive_params': {
|
||||
'sma_short': 20,
|
||||
'sma_long': 50,
|
||||
'fibonacci_lookback': 50,
|
||||
}
|
||||
}
|
||||
|
||||
strategy = SwingStrategy(config)
|
||||
|
||||
assert strategy.name == 'swing'
|
||||
assert strategy.parameters['sma_short'] == 20
|
||||
assert strategy.parameters['sma_long'] == 50
|
||||
|
||||
def test_fibonacci_levels(self, sample_ohlcv_data):
|
||||
"""Test calcul niveaux Fibonacci."""
|
||||
config = {
|
||||
'name': 'swing',
|
||||
'timeframe': '4h',
|
||||
'adaptive_params': {
|
||||
'sma_short': 20,
|
||||
'sma_long': 50,
|
||||
'fibonacci_lookback': 50,
|
||||
}
|
||||
}
|
||||
|
||||
strategy = SwingStrategy(config)
|
||||
df = strategy.calculate_indicators(sample_ohlcv_data)
|
||||
|
||||
# Vérifier niveaux Fibonacci
|
||||
assert 'fib_236' in df.columns
|
||||
assert 'fib_382' in df.columns
|
||||
assert 'fib_500' in df.columns
|
||||
assert 'fib_618' in df.columns
|
||||
assert 'fib_786' in df.columns
|
||||
|
||||
# Vérifier ordre des niveaux
|
||||
last_row = df.iloc[-1]
|
||||
assert last_row['fib_high'] >= last_row['fib_236']
|
||||
assert last_row['fib_236'] >= last_row['fib_382']
|
||||
assert last_row['fib_382'] >= last_row['fib_500']
|
||||
assert last_row['fib_500'] >= last_row['fib_618']
|
||||
assert last_row['fib_618'] >= last_row['fib_786']
|
||||
assert last_row['fib_786'] >= last_row['fib_low']
|
||||
|
||||
def test_get_strategy_info(self):
|
||||
"""Test récupération infos stratégie."""
|
||||
config = {
|
||||
'name': 'swing',
|
||||
'timeframe': '4h',
|
||||
'adaptive_params': {}
|
||||
}
|
||||
|
||||
strategy = SwingStrategy(config)
|
||||
info = strategy.get_strategy_info()
|
||||
|
||||
assert 'name' in info
|
||||
assert 'type' in info
|
||||
assert 'timeframe' in info
|
||||
assert 'indicators' in info
|
||||
assert info['type'] == 'swing'
|
||||
|
||||
|
||||
class TestSignal:
|
||||
"""Tests de la classe Signal."""
|
||||
|
||||
def test_signal_creation(self):
|
||||
"""Test création d'un signal."""
|
||||
signal = Signal(
|
||||
symbol='EURUSD',
|
||||
direction='LONG',
|
||||
entry_price=1.1000,
|
||||
stop_loss=1.0950,
|
||||
take_profit=1.1100,
|
||||
confidence=0.75,
|
||||
timestamp=pd.Timestamp.now(),
|
||||
strategy='scalping',
|
||||
metadata={'rsi': 25, 'bb_position': 0.1}
|
||||
)
|
||||
|
||||
assert signal.symbol == 'EURUSD'
|
||||
assert signal.direction == 'LONG'
|
||||
assert signal.confidence == 0.75
|
||||
assert 'rsi' in signal.metadata
|
||||
|
||||
def test_signal_risk_reward(self):
|
||||
"""Test calcul risk/reward d'un signal."""
|
||||
signal = Signal(
|
||||
symbol='EURUSD',
|
||||
direction='LONG',
|
||||
entry_price=1.1000,
|
||||
stop_loss=1.0950, # 50 pips risk
|
||||
take_profit=1.1100, # 100 pips reward
|
||||
confidence=0.75,
|
||||
timestamp=pd.Timestamp.now(),
|
||||
strategy='scalping',
|
||||
metadata={}
|
||||
)
|
||||
|
||||
risk = abs(signal.entry_price - signal.stop_loss)
|
||||
reward = abs(signal.take_profit - signal.entry_price)
|
||||
rr_ratio = reward / risk
|
||||
|
||||
assert rr_ratio == 2.0 # R:R de 2:1
|
||||
Reference in New Issue
Block a user