ML for Trading

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1. data data Public

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

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2. diagnostic diagnostic Public

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

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3. backtest backtest Public

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

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4. engineer engineer Public

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

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5. skills skills Public

   Agent skills for Machine Learning for Algorithmic Trading: leakage-safe quant ML, backtesting, validation, and autonomous research workflows.

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6. live live Public

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

   Python 21 18