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montecarlo-validator-mcp

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    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables statistical validation of trading strategy equity curves via MCP, providing probabilistic and deflated Sharpe ratios, minimum track record length, regime-conditional performance attribution, and self-attack controls.
      49 npm
      MIT
    • A
      license
      Not graded
      quality
      C
      maintenance
      Enables quant traders and researchers to test whether a strategy's edge is genuine or an artifact of testing many variants, via Probability of Backtest Overfitting, Deflated Sharpe Ratio, Minimum Backtest Length, and purged/embargoed cross-validation splits.
      MIT
    • A
      license
      Not graded
      quality
      D
      maintenance
      Local-first backtesting engine with built-in overfitting detection (PBO, deflated Sharpe, bootstrap CI, walk-forward) and a native MCP server for AI agents to validate trading strategies.
      4
      Apache 2.0
    • F
      license
      Not graded
      quality
      D
      maintenance
      Enables institutional-grade Monte Carlo risk analysis for portfolios, startups, real estate, and betting strategies using fat-tail distributions and proprietary algorithms. Provides comprehensive risk metrics including CVaR, VaR, ruin probability, and survival probability across multiple asset classes.
      1
      -
    • A
      license
      Not graded
      quality
      B
      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
      C
      maintenance
      Enables walk-forward analysis of trading strategies by computing Walk-Forward Efficiency ratios, scoring parameter surfaces for fragile curve-fit spikes versus robust plateaus, auditing whether parameters were genuinely locked after optimization, and generating correctly non-overlapping rolling in-sample/out-of-sample windows.
      MIT