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958d6b7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 | """Multi-Asset Alpha Model - Predicts expected returns using LSTM, Transformer, and XGBoost ensemble."""
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
from sklearn.ensemble import GradientBoostingRegressor
from typing import Dict, Tuple, Optional
import warnings
warnings.filterwarnings('ignore')
class AlphaDataset(Dataset):
"""PyTorch dataset for alpha model training"""
def __init__(self, X: np.ndarray, y: np.ndarray):
self.X = torch.FloatTensor(X)
self.y = torch.FloatTensor(y).unsqueeze(1)
def __len__(self):
return len(self.X)
def __getitem__(self, idx):
return self.X[idx], self.y[idx]
class LSTMAlpha(nn.Module):
"""LSTM-based alpha model"""
def __init__(self, input_size: int, hidden_size: int = 128,
num_layers: int = 2, dropout: float = 0.2):
super().__init__()
self.lstm = nn.LSTM(
input_size, hidden_size, num_layers,
batch_first=True, dropout=dropout if num_layers > 1 else 0
)
self.dropout = nn.Dropout(dropout)
self.fc1 = nn.Linear(hidden_size, 64)
self.fc2 = nn.Linear(64, 1)
self.relu = nn.ReLU()
def forward(self, x):
out, _ = self.lstm(x)
out = self.dropout(out[:, -1, :])
out = self.relu(self.fc1(out))
return self.fc2(out)
class TransformerAlpha(nn.Module):
"""Transformer-based alpha model"""
def __init__(self, input_size: int, d_model: int = 128,
nhead: int = 4, num_layers: int = 2, dropout: float = 0.2):
super().__init__()
self.input_proj = nn.Linear(input_size, d_model)
encoder_layer = nn.TransformerEncoderLayer(
d_model=d_model, nhead=nhead,
dim_feedforward=d_model*4, dropout=dropout,
batch_first=True
)
self.transformer = nn.TransformerEncoder(encoder_layer, num_layers)
self.dropout = nn.Dropout(dropout)
self.fc1 = nn.Linear(d_model, 64)
self.fc2 = nn.Linear(64, 1)
self.relu = nn.ReLU()
def forward(self, x):
x = self.input_proj(x)
out = self.transformer(x)
out = self.dropout(out.mean(dim=1))
out = self.relu(self.fc1(out))
return self.fc2(out)
class XGBoostAlpha:
"""XGBoost-based alpha model (using sklearn GradientBoosting)"""
def __init__(self, max_depth: int = 6, learning_rate: float = 0.05,
n_estimators: int = 200):
self.model = GradientBoostingRegressor(
max_depth=max_depth,
learning_rate=learning_rate,
n_estimators=n_estimators,
subsample=0.8,
random_state=42
)
def fit(self, X: np.ndarray, y: np.ndarray):
"""X should be flattened: (n_samples, lookback * features)"""
n_samples = X.shape[0]
X_flat = X.reshape(n_samples, -1)
self.model.fit(X_flat, y)
return self
def predict(self, X: np.ndarray) -> np.ndarray:
n_samples = X.shape[0]
X_flat = X.reshape(n_samples, -1)
return self.model.predict(X_flat)
def feature_importances(self) -> np.ndarray:
return self.model.feature_importances_
class AlphaEnsemble:
"""Ensemble of LSTM, Transformer, and XGBoost alpha models"""
def __init__(self, input_size: int, seq_len: int,
lstm_hidden: int = 128, lstm_layers: int = 2,
trans_d_model: int = 128, trans_nhead: int = 4, trans_layers: int = 2,
xgb_depth: int = 6, xgb_lr: float = 0.05, xgb_estimators: int = 200,
weights: Optional[Dict[str, float]] = None,
device: str = 'cpu'):
self.device = torch.device(device)
self.seq_len = seq_len
self.input_size = input_size
# Models
self.lstm = LSTMAlpha(input_size, lstm_hidden, lstm_layers).to(self.device)
self.transformer = TransformerAlpha(input_size, trans_d_model,
trans_nhead, trans_layers).to(self.device)
self.xgboost = XGBoostAlpha(xgb_depth, xgb_lr, xgb_estimators)
# Weights
self.weights = weights or {'lstm': 0.3, 'transformer': 0.3, 'xgboost': 0.4}
self.is_fitted = False
self.ic_history = []
self.feature_drift_history = []
def fit(self, X_train: np.ndarray, y_train: np.ndarray,
X_val: Optional[np.ndarray] = None, y_val: Optional[np.ndarray] = None,
epochs: int = 50, batch_size: int = 64, lr: float = 1e-4) -> Dict:
"""Train all models"""
# Train LSTM
print("Training LSTM alpha model...")
lstm_metrics = self._train_nn(self.lstm, X_train, y_train,
X_val, y_val, epochs, batch_size, lr)
# Train Transformer
print("Training Transformer alpha model...")
trans_metrics = self._train_nn(self.transformer, X_train, y_train,
X_val, y_val, epochs, batch_size, lr)
# Train XGBoost
print("Training XGBoost alpha model...")
self.xgboost.fit(X_train, y_train)
xgb_pred = self.xgboost.predict(X_val) if X_val is not None else None
xgb_ic = self._compute_ic(xgb_pred, y_val) if xgb_pred is not None else None
self.is_fitted = True
return {
'lstm': lstm_metrics,
'transformer': trans_metrics,
'xgboost': {'ic': xgb_ic}
}
def _train_nn(self, model: nn.Module, X_train: np.ndarray, y_train: np.ndarray,
X_val: Optional[np.ndarray], y_val: Optional[np.ndarray],
epochs: int, batch_size: int, lr: float) -> Dict:
"""Train a neural network model"""
train_dataset = AlphaDataset(X_train, y_train)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
optimizer = torch.optim.Adam(model.parameters(), lr=lr)
criterion = nn.MSELoss()
metrics = {'train_loss': [], 'val_loss': [], 'val_ic': []}
for epoch in range(epochs):
model.train()
epoch_loss = 0
for X_batch, y_batch in train_loader:
X_batch, y_batch = X_batch.to(self.device), y_batch.to(self.device)
optimizer.zero_grad()
pred = model(X_batch)
loss = criterion(pred, y_batch)
loss.backward()
optimizer.step()
epoch_loss += loss.item()
metrics['train_loss'].append(epoch_loss / len(train_loader))
# Validation
if X_val is not None and y_val is not None:
model.eval()
with torch.no_grad():
X_val_t = torch.FloatTensor(X_val).to(self.device)
val_pred = model(X_val_t).cpu().numpy().flatten()
val_loss = np.mean((val_pred - y_val) ** 2)
val_ic = self._compute_ic(val_pred, y_val)
metrics['val_loss'].append(val_loss)
metrics['val_ic'].append(val_ic)
if epoch % 10 == 0:
print(f" Epoch {epoch}: train_loss={metrics['train_loss'][-1]:.6f}, "
f"val_loss={val_loss:.6f}, val_ic={val_ic:.4f}")
return metrics
def predict(self, X: np.ndarray) -> np.ndarray:
"""Generate ensemble predictions"""
if not self.is_fitted:
raise ValueError("Models must be fitted before prediction")
# LSTM prediction
self.lstm.eval()
with torch.no_grad():
X_t = torch.FloatTensor(X).to(self.device)
lstm_pred = self.lstm(X_t).cpu().numpy().flatten()
# Transformer prediction
self.transformer.eval()
with torch.no_grad():
trans_pred = self.transformer(X_t).cpu().numpy().flatten()
# XGBoost prediction
xgb_pred = self.xgboost.predict(X)
# Weighted ensemble
ensemble = (self.weights['lstm'] * lstm_pred +
self.weights['transformer'] * trans_pred +
self.weights['xgboost'] * xgb_pred)
return ensemble
def _compute_ic(self, pred: np.ndarray, actual: np.ndarray) -> float:
"""Compute Information Coefficient (rank correlation)"""
if pred is None or len(pred) < 10:
return 0.0
mask = ~(np.isnan(pred) | np.isnan(actual))
if mask.sum() < 10:
return 0.0
from scipy.stats import spearmanr
ic, _ = spearmanr(pred[mask], actual[mask])
return ic if not np.isnan(ic) else 0.0
def track_ic(self, pred: np.ndarray, actual: np.ndarray):
"""Track IC over time"""
ic = self._compute_ic(pred, actual)
self.ic_history.append(ic)
return ic
def track_feature_drift(self, X_current: np.ndarray, X_reference: np.ndarray):
"""Track feature importance drift using XGBoost"""
current_imp = self.xgboost.feature_importances()
# Fit reference model
ref_model = XGBoostAlpha()
ref_model.fit(X_reference, np.zeros(len(X_reference)))
ref_imp = ref_model.feature_importances()
# JS divergence between importance distributions
drift = np.sum(np.abs(current_imp - ref_imp))
self.feature_drift_history.append(drift)
return drift
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