mirror of
https://github.com/NicolasBohn/NexQuant.git
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TPTBusiness
f01960ab55
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
"""
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LightGBM Factor Model - Standard Version
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Usage:
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from rdagent.components.model_loader import load_model
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model = load_model("lightgbm_factor")
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"""
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import lightgbm as lgb
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import numpy as np
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import pandas as pd
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from pathlib import Path
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class LightGBMFactorModel:
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"""
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LightGBM-based factor model for EUR/USD trading.
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Features:
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- Faster than XGBoost
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- Lower memory usage
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- Good for large datasets
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"""
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def __init__(self, **params):
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self.params = {
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'objective': 'regression',
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'metric': 'mse',
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'num_leaves': 31,
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'learning_rate': 0.05,
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'feature_fraction': 0.8,
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'bagging_fraction': 0.8,
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'bagging_freq': 5,
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'verbose': -1,
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'random_state': 42,
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**params
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}
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self.model = None
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self.feature_names = None
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def fit(self, X, y, feature_names=None, **fit_params):
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"""Train the model."""
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self.feature_names = feature_names
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# Create LightGBM datasets
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train_data = lgb.Dataset(X, label=y, feature_name=feature_names if feature_names else 'auto')
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self.model = lgb.train(
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self.params,
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train_data,
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num_boost_round=500,
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**fit_params
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)
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return self
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def predict(self, X):
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"""Generate predictions."""
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if self.model is None:
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raise ValueError("Model not trained. Call fit() first.")
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return self.model.predict(X)
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def get_feature_importance(self, top_n=10, importance_type='gain'):
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"""Get top N most important features."""
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if self.model is None:
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raise ValueError("Model not trained.")
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importance = self.model.feature_importance(importance_type=importance_type)
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if self.feature_names is not None:
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indices = np.argsort(importance)[::-1][:top_n]
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return [(self.feature_names[i], importance[i]) for i in indices]
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return importance
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def save(self, path: str):
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"""Save model to file."""
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Path(path).parent.mkdir(parents=True, exist_ok=True)
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self.model.save_model(path)
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print(f"✓ Model saved to {path}")
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def load(self, path: str):
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"""Load model from file."""
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self.model = lgb.Booster(model_file=path)
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print(f"✓ Model loaded from {path}")
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return self
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# Convenience function
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def create_lightgbm_factor_model(**params):
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"""Create LightGBM factor model."""
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return LightGBMFactorModel(**params)
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if __name__ == "__main__":
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# Test
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print("=== LightGBM Factor Model Test ===")
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model = create_lightgbm_factor_model()
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print(f"✓ Model created with params: {model.params}")
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