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alpha-decay

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Farxida / prediction-market-ml

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End-to-end ML system for prediction market trading — 521K markets, 78 features, 7 model architectures, walk-forward validation, live VPS A/B across 7 configs. Honest research-stop on alpha decay (NO-GO verdict). AFML methodology: Purged K-Fold, Deflated Sharpe Ratio, meta-labeling, focal loss.

data-science machine-learning time-series pytorch lightgbm ab-testing quantitative-finance prediction-markets backtesting kelly-criterion paper-trading mlops finbert alpha-decay meta-labeling deflated-sharpe-ratio polymarket walk-forward-validation purged-kfold
  • Updated Apr 28, 2026
  • Python

GDSimpson3 / Polynomial-regression-001-impl

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Multi term Polynomial Regression with Learnable Exponents and Coefficients (+L1 Regularisation for Term Pruning & Coefficient/Exponent Based Feature augmentation)

automatic-differentiation mse pruning gradient-descent polynomial-regression from-scratch normalization non-linear-regression non-linear-optimization l1-regularization dynamic-regression lasso-regularization dynamic-pruning alpha-decay function-estimation 2-degree-polynomial 3-degree-polynomial exponent-clamping power-law-models n-degree-polynomial
  • Updated Dec 30, 2025
  • Jupyter Notebook

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