ML for Trading

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1. data data Public

   Market data acquisition, storage, and update workflows for machine learning for trading.

   Python 46 22

2. diagnostic diagnostic Public

   Signal diagnostics, statistical validation, and backtest evaluation for quantitative trading workflows.

   Python 30 19

3. backtest backtest Public

   Event-driven backtesting for quantitative strategies with configurable execution, accounting, risk, and framework-parity validation.

   Python 25 20

4. engineer engineer Public

   Feature engineering, labeling, alternative bars, and leakage-safe datasets for financial ML.

   Python 21 16

5. live live Public

   Live trading runtime for ML4T strategies with broker integrations, risk checks, and shadow mode.

   Python 20 16

6. models models Public

   Finance-specific latent-factor, asset prediction, and portfolio-learning models.

   Python 14 11