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Alternatives to noisefloor

  • A
    license
    A
    quality
    A
    maintenance
    Public MCP research for AI finance and trading agents: funding, bank risk and liquidity evidence. Companion SDK: persistent Python MCP sessions and Python/JS source-status streams. Setup: https://beepboop2025.github.io/financial-evidence-skills/distribution/ai-trading-tools.html. No data API key. Keep source dates, rights and gaps. No live quotes or execution.
    2
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    209 PyPI
    1
    MIT

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  • A
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    Enables MCP clients such as Claude Desktop, Cursor, and Windsurf to run two-sample Welch's t-tests that compute Satterthwaite degrees of freedom and two-tailed p-values for A/B test comparisons. It also exposes zero-dependency statistical primitives including Holt linear forecasting, Z-score/IQR anomaly detection, gradient-descent regression, and power-iteration PCA, all using only the Python standard library.
    7
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  • A
    license
    A
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    Eval-integrity statistics for AI benchmark claims — multiple-testing correction, power/MDE for model gaps, judge-bias and leaderboard-rank checks. Catches a benchmark number that won't survive a second look.
    9
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  • A
    license
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    quality
    B
    maintenance
    Enables zero-dependency statistical analysis of numerical and time-series streams, flagging outliers via standard Z-score, modified median absolute deviation (MAD), and Tukey IQR fences. Also supports trend forecasting with Holt linear smoothing, multivariate gradient-descent regression, Welch's t-test hypothesis testing, and Power Iteration PCA dimensionality reduction through a native MCP stdio interface.
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  • A
    license
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    D
    maintenance
    Checks whether a trading backtest survives its own statistics: deflated Sharpe, multiple-testing correction against a best-of-N-noise benchmark, minimum track record length, and fill realism. Takes no market data and no API keys, and cannot recommend a trade — it only reports that a result is weaker than claimed or not yet provable.
    MIT
  • A
    license
    Not graded
    quality
    B
    maintenance
    Enables agents to run statistics and data-science computations over numerical streams using only the Python standard library, including Z-score/IQR anomaly detection, Holt linear forecasting, Welch's t-test hypothesis evaluation, gradient-descent multivariate regression, and power-iteration SVD/PCA dimensionality reduction. Exposes these capabilities over JSON-RPC 2.0 stdio so clients like Claude Desktop, Cursor, and Windsurf can project high-dimensional feature vectors into principal components without any external dependencies.
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TDQS

A4.4/5.0

Scored across 10 tools

Disambiguation4/5

The change-detection tools (did_it_change, real_or_sampling, market_assessment, spectral_assessment) share a review-queue theme, but descriptions explicitly state when to choose each based on input type and purpose. Other tools (ab_test, forecast_next, score_forecasts, narrative_triage, dyson_reference, which_metrics_matter) are clearly distinct. The need to carefully read the change-assessment family's conditions keeps it from a 5.

Naming Consistency4/5

All tool names use snake_case and are descriptive, which is a consistent convention. However, they mix grammatical forms: noun phrases (market_assessment, spectral_assessment), verb phrases (forecast_next, score_forecasts), and question/idiom phrases (which_metrics_matter, did_it_change). This is a minor deviation from a uniform verb_noun pattern.

Tool Count5/5

The 10 tools cover distinct analytical functions—A/B testing, change detection, sampling-artifact checks, forecasting and backtesting, market/narrative/spectral assessment, evidence ranking, and synthetic reference. Each tool earns its place with no obvious redundancy, fitting well within the 3–15 range.

Completeness4/5

The toolkit covers core signal-vs-noise workflows: change detection, artifact checks, forecasting and backtesting, market/narrative/spectral assessment, A/B testing, and evidence ranking. Minor gaps exist (e.g., no generic continuous two-sample test, no multivariate change detection for non-market series), but agents can work around them with existing tools.

Maintenance

ActivityMaintained
ResponsivenessNo issues