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ML Ensemble for Signal Prediction
SVM, Random Forest, Gradient Boosting, Logistic Regression with PCA and KMeans
"""
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.svm import SVC
from sklearn.linear_model import LogisticRegression
from sklearn.decomposition import PCA
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import cross_val_score
from typing import Dict, List, Tuple, Optional
import pickle
import hashlib
from datetime import datetime
class FeatureEngineer:
"""Build technical features from OHLCV data"""
def __init__(self, volatility_window: int = 20, rsi_period: int = 14):
self.volatility_window = volatility_window
self.rsi_period = rsi_period
self.scaler = StandardScaler()
def build_features(self, df: pd.DataFrame, mode: str = 'training') -> pd.DataFrame:
"""
Build features from candle data
mode: 'training' (requires fwd_ret) or 'inference' (no fwd_ret needed)
"""
df = df.copy()
df = df.sort_values('timestamp')
# Returns over multiple windows
for window in [1, 2, 4, 8, 24]:
df[f'ret_{window}h'] = df['close'].pct_change(window)
# Volatility
df['volatility'] = df['close'].pct_change().rolling(self.volatility_window).std()
# RSI
df['rsi'] = self._calculate_rsi(df['close'], self.rsi_period)
# MACD
df['macd'], df['macd_signal'] = self._calculate_macd(df['close'])
# Volume z-score
df['volume_zscore'] = (df['volume'] - df['volume'].rolling(24).mean()) / df['volume'].rolling(24).std()
# Position in 24h range
df['range_position'] = (df['close'] - df['low'].rolling(24).min()) / (df['high'].rolling(24).max() - df['low'].rolling(24).min())
# Skew
df['price_skew'] = df['close'].rolling(24).skew()
# Cross-sectional features (if multiple symbols)
if 'symbol' in df.columns:
df['ret_rank'] = df.groupby('timestamp')['ret_1h'].rank(pct=True)
df['volume_rank'] = df.groupby('timestamp')['volume'].rank(pct=True)
# Forward return (only for training mode)
if mode == 'training':
df['fwd_ret'] = df['close'].shift(-1) / df['close'] - 1
df['target'] = (df['fwd_ret'] > 0).astype(int)
# Drop NaN
if mode == 'training':
df = df.dropna(subset=['fwd_ret', 'target'])
else:
# For inference, keep the last row even if it has NaN in some features
df = df.dropna(subset=['ret_1h', 'volatility', 'rsi'])
return df
def _calculate_rsi(self, prices: pd.Series, period: int) -> pd.Series:
delta = prices.diff()
gain = (delta.where(delta > 0, 0)).rolling(window=period).mean()
loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean()
rs = gain / loss
return 100 - (100 / (1 + rs))
def _calculate_macd(self, prices: pd.Series) -> Tuple[pd.Series, pd.Series]:
ema_12 = prices.ewm(span=12).mean()
ema_26 = prices.ewm(span=26).mean()
macd = ema_12 - ema_26
signal = macd.ewm(span=9).mean()
return macd, signal
def get_feature_columns(self) -> List[str]:
return [
'ret_1h', 'ret_2h', 'ret_4h', 'ret_8h', 'ret_24h',
'volatility', 'rsi', 'macd', 'macd_signal',
'volume_zscore', 'range_position', 'price_skew'
]
class MLEnsemble:
"""Ensemble of ML models with PCA and KMeans"""
def __init__(self, n_components: int = 10, n_clusters: int = 5):
self.n_components = n_components
self.n_clusters = n_clusters
# Models
self.svm = SVC(probability=True, random_state=42)
self.rf = RandomForestClassifier(n_estimators=100, random_state=42)
self.gbm = GradientBoostingClassifier(n_estimators=100, random_state=42)
self.logreg = LogisticRegression(random_state=42)
# Dimensionality reduction
self.pca = PCA(n_components=n_components)
# Regime labeling
self.kmeans = KMeans(n_clusters=n_clusters, random_state=42)
# Ensemble weights (learned by genetic algorithm)
self.weights = {
'svm': 0.25,
'rf': 0.25,
'gbm': 0.25,
'logreg': 0.25
}
self.feature_columns = None
self.fitted = False
def fit(self, X: np.ndarray, y: np.ndarray) -> Dict:
"""Fit all models and learn ensemble weights"""
# Fit PCA
X_pca = self.pca.fit_transform(X)
# Fit KMeans for regime labeling
regimes = self.kmeans.fit_predict(X_pca)
# Fit individual models
self.svm.fit(X_pca, y)
self.rf.fit(X_pca, y)
self.gbm.fit(X_pca, y)
self.logreg.fit(X_pca, y)
# Get cross-validation scores
cv_scores = {
'svm': cross_val_score(self.svm, X_pca, y, cv=5).mean(),
'rf': cross_val_score(self.rf, X_pca, y, cv=5).mean(),
'gbm': cross_val_score(self.gbm, X_pca, y, cv=5).mean(),
'logreg': cross_val_score(self.logreg, X_pca, y, cv=5).mean()
}
# Simple weight optimization based on CV scores
total_score = sum(cv_scores.values())
for model in self.weights:
self.weights[model] = cv_scores[model] / total_score
self.fitted = True
return {
'cv_scores': cv_scores,
'weights': self.weights,
'regime_distribution': np.bincount(regimes).tolist()
}
def predict_proba(self, X: np.ndarray) -> np.ndarray:
"""Predict probability of up movement"""
if not self.fitted:
raise ValueError("Model not fitted")
X_pca = self.pca.transform(X)
# Get individual predictions
proba_svm = self.svm.predict_proba(X_pca)[:, 1]
proba_rf = self.rf.predict_proba(X_pca)[:, 1]
proba_gbm = self.gbm.predict_proba(X_pca)[:, 1]
proba_logreg = self.logreg.predict_proba(X_pca)[:, 1]
# Weighted ensemble
ensemble_proba = (
self.weights['svm'] * proba_svm +
self.weights['rf'] * proba_rf +
self.weights['gbm'] * proba_gbm +
self.weights['logreg'] * proba_logreg
)
return ensemble_proba
def predict(self, X: np.ndarray, threshold: float = 0.5) -> np.ndarray:
"""Predict direction with confidence-based sizing"""
proba = self.predict_proba(X)
# LinUCB-style contextual bandit for position sizing
confidence = np.abs(proba - 0.5) * 2 # 0 to 1
# Direction and position sizing
direction = np.where(proba > threshold, 1, -1)
position = direction * confidence
return {
'direction': direction,
'probability_up': proba,
'confidence': confidence,
'position': position
}
def get_regime(self, X: np.ndarray) -> np.ndarray:
"""Get market regime for each sample"""
X_pca = self.pca.transform(X)
return self.kmeans.predict(X_pca)
def save(self, filepath: str):
"""Save model to file"""
model_data = {
'svm': self.svm,
'rf': self.rf,
'gbm': self.gbm,
'logreg': self.logreg,
'pca': self.pca,
'kmeans': self.kmeans,
'weights': self.weights,
'feature_columns': self.feature_columns,
'fitted': self.fitted,
'n_components': self.n_components,
'n_clusters': self.n_clusters
}
with open(filepath, 'wb') as f:
pickle.dump(model_data, f)
def load(self, filepath: str):
"""Load model from file"""
with open(filepath, 'rb') as f:
model_data = pickle.load(f)
self.svm = model_data['svm']
self.rf = model_data['rf']
self.gbm = model_data['gbm']
self.logreg = model_data['logreg']
self.pca = model_data['pca']
self.kmeans = model_data['kmeans']
self.weights = model_data['weights']
self.feature_columns = model_data['feature_columns']
self.fitted = model_data['fitted']
self.n_components = model_data['n_components']
self.n_clusters = model_data['n_clusters']
def compute_feature_hash(self, features: np.ndarray) -> str:
"""Compute hash of feature vector for reproducibility"""
feature_bytes = features.tobytes()
return hashlib.sha256(feature_bytes).hexdigest()
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