mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-07-27 23:47:46 +00:00
TPTBusiness
19855ef7d6
New structure:
- models/standard/*.py: Default models (XGBoost, LightGBM, RandomForest)
- models/local/*.py: Your improved models (NOT in Git!)
- models/README.md: Documentation
- rdagent/components/model_loader.py: Model loader with priority
Features:
- Loader checks models/local/ first (your better models)
- Falls back to models/standard/ if no local version
- Supports versioned models (model_v2.py, model_v1.py)
- Lists available models
- Test function included
.gitignore updated:
- models/local/ excluded (your proprietary models)
- *.local.py excluded
- *_private.py excluded
Usage:
from rdagent.components.model_loader import load_model
model = load_model('xgboost_factor') # Auto-loads your better version!
Standard models included:
- xgboost_factor.py: XGBoost for tabular data
- lightgbm_factor.py: LightGBM (faster than XGBoost)
99 lines
2.8 KiB
Python
99 lines
2.8 KiB
Python
"""
|
|
LightGBM Factor Model - Standard Version
|
|
|
|
Usage:
|
|
from rdagent.components.model_loader import load_model
|
|
model = load_model("lightgbm_factor")
|
|
"""
|
|
|
|
import lightgbm as lgb
|
|
import numpy as np
|
|
import pandas as pd
|
|
from pathlib import Path
|
|
|
|
|
|
class LightGBMFactorModel:
|
|
"""
|
|
LightGBM-based factor model for EUR/USD trading.
|
|
|
|
Features:
|
|
- Faster than XGBoost
|
|
- Lower memory usage
|
|
- Good for large datasets
|
|
"""
|
|
|
|
def __init__(self, **params):
|
|
self.params = {
|
|
'objective': 'regression',
|
|
'metric': 'mse',
|
|
'num_leaves': 31,
|
|
'learning_rate': 0.05,
|
|
'feature_fraction': 0.8,
|
|
'bagging_fraction': 0.8,
|
|
'bagging_freq': 5,
|
|
'verbose': -1,
|
|
'random_state': 42,
|
|
**params
|
|
}
|
|
self.model = None
|
|
self.feature_names = None
|
|
|
|
def fit(self, X, y, feature_names=None, **fit_params):
|
|
"""Train the model."""
|
|
self.feature_names = feature_names
|
|
|
|
# Create LightGBM datasets
|
|
train_data = lgb.Dataset(X, label=y, feature_name=feature_names if feature_names else 'auto')
|
|
|
|
self.model = lgb.train(
|
|
self.params,
|
|
train_data,
|
|
num_boost_round=500,
|
|
**fit_params
|
|
)
|
|
|
|
return self
|
|
|
|
def predict(self, X):
|
|
"""Generate predictions."""
|
|
if self.model is None:
|
|
raise ValueError("Model not trained. Call fit() first.")
|
|
|
|
return self.model.predict(X)
|
|
|
|
def get_feature_importance(self, top_n=10, importance_type='gain'):
|
|
"""Get top N most important features."""
|
|
if self.model is None:
|
|
raise ValueError("Model not trained.")
|
|
|
|
importance = self.model.feature_importance(importance_type=importance_type)
|
|
if self.feature_names is not None:
|
|
indices = np.argsort(importance)[::-1][:top_n]
|
|
return [(self.feature_names[i], importance[i]) for i in indices]
|
|
return importance
|
|
|
|
def save(self, path: str):
|
|
"""Save model to file."""
|
|
Path(path).parent.mkdir(parents=True, exist_ok=True)
|
|
self.model.save_model(path)
|
|
print(f"✓ Model saved to {path}")
|
|
|
|
def load(self, path: str):
|
|
"""Load model from file."""
|
|
self.model = lgb.Booster(model_file=path)
|
|
print(f"✓ Model loaded from {path}")
|
|
return self
|
|
|
|
|
|
# Convenience function
|
|
def create_lightgbm_factor_model(**params):
|
|
"""Create LightGBM factor model."""
|
|
return LightGBMFactorModel(**params)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
# Test
|
|
print("=== LightGBM Factor Model Test ===")
|
|
model = create_lightgbm_factor_model()
|
|
print(f"✓ Model created with params: {model.params}")
|