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Tail-Risk-Toolkit

by John-Amal
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Python
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    TDQS

    A4.2/5.0

    Scored across 6 tools

    Disambiguation5/5

    Each tool targets a distinct stage of tail-risk analysis: data loading, threshold diagnostics, GPD fitting, VaR/ES estimation, return levels, and backtesting. Even the related VaR/ES and return-level tools are clearly separated by phrasing (confidence level vs. return period).

    Naming Consistency4/5

    All tools share a consistent tailrisk_ prefix and use snake_case, making the family instantly recognizable. However, the second part mixes verb-object forms like fit_gpd and backtest_var with noun phrases like var_es and threshold_stability, so the naming is not perfectly uniform.

    Tool Count5/5

    Six tools is well-scoped for a specialized tail-risk toolkit. Each tool contributes a necessary part of the workflow without redundancy or bloat.

    Completeness5/5

    The toolkit covers the full practical workflow: loading data, assessing threshold stability, fitting the GPD, computing VaR/ES and return levels, and backtesting the VaR model. There are no obvious dead ends or missing core operations for the stated domain.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues

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