DeepAlpha is an open-source Python toolkit for time-series prediction. LightGBM ensemble, SHAP feature selection, and walk-forward validation — all in one pip install.
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Python
Every component follows academic best practices. No shortcuts, no lookahead bias, no black boxes.
Multi-model consensus system that combines multiple gradient-boosted trees for robust predictions with automatic hyperparameter tuning.
Automatic feature pruning using SHAP values. Keeps only the features that actually matter, reducing noise and improving generalization.
Advanced labeling technique that creates well-defined classification targets using upper, lower, and time-expiry boundaries for superior signal quality.
Expanding-window cross-validation that respects temporal order. No data leakage, no lookahead bias — just honest evaluation.
Secondary model that filters false positives from the primary model, dramatically improving precision on out-of-sample data.
No GPU required, no TensorFlow, no PyTorch. Runs on any machine with Python and scikit-learn. Fast training on CPU.
Simple API, powerful internals. Configure your model, fit your data, and get predictions with confidence scores.
From installation to inference in minutes, not days.
One command, zero configuration headaches. Works on Linux, macOS, and Windows.
pip install deepalpha-freqai
Set your timeframe, feature count, and validation parameters. Sensible defaults included.
config = {"features": 50}
Get ML-powered predictions with confidence scores. Walk-forward validated, out-of-sample tested.
model.predict(X_test)
All results are walk-forward validated on out-of-sample data. No cherry-picked windows.
Model accuracy on out-of-sample test sets using walk-forward validation
Engineered features automatically ranked and pruned via SHAP importance
Walk-forward cross-validation folds to prevent overfitting and data leakage
Walk-forward validation prevents overfitting. Past model performance does not guarantee future results.
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