claude

quant-research

Quantitative research metrics: SOTA evaluation for range bars, Sharpe ratios with daily aggregation, ML prediction quality (IC, autocorrelation), crypto-specific considerations

2 on GitHubby terrylicav34.14.0MITupdated todaytrading
/

Plugin quant-research from marketplace cc-skills in terrylica/cc-skills. Commands read from the repo's manifest — today.

What it installs

Skills (8)

  • adaptive-wfo-epoch

    Adaptive epoch selection for Walk-Forward Optimization. TRIGGERS - WFO epoch, epoch selection, WFE optimization, overfitting epochs.

  • backtesting-py-oracle

    backtesting.py configuration for SQL oracle validation and range bar pattern backtesting. Use when running backtesting.py against.

  • evolutionary-metric-ranking

    Multi-objective evolutionary optimization for per-metric percentile cutoffs and intersection-based config selection.

  • exchange-session-detector

    DST-aware exchange session detection using exchange_calendars. Holiday & lunch break handling. TRIGGERS - exchange session, trading hours

  • odb-microstructure-forensics

    Investigate why ODB bars are oversized, have zero duration, or show anomalous price ranges by forensically analyzing Parquet.

  • opendeviation-eval-metrics

    evaluating open deviation bar signal quality, computing Sharpe ratios with non-IID bars, running PSR/MinTRL statistical tests, or assessing ODB strategy performance.

  • sharpe-ratio-non-iid-corrections

    Sharpe ratio inference for ONE strategy under non-Normal, serially correlated returns. Implements the ρ-corrected (AR(1), skew, kurtosis) Sharpe variance of López de Prado, Lipton & Zoonekynd (2026, Eq 2-3) and the single-test tools built on it: PSR, MinTRL, critical Sharpe ratio and test power. Num

  • zigzag-pattern-classifier

    ZigZag swing pattern classification for algorithmic trading. UP-DOWN and UP-DOWN-UP patterns. TRIGGERS - zigzag, swing classification

View source on GitHub

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