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607,103 tools. Updated 2026-09-24 13:15

"A guide to backtesting for financial analysis and strategy development" matching MCP tools:

  • Retrieve the JSON schema defining the strategy document structure for condition_tree and indicators. Use this to guide manual strategy composition before validation.
    MIT
  • Retrieve strategy signals for securities, filtered by symbol, strategy, catalyst, and time range, with pagination support.
    Apache 2.0
  • Register a trading strategy by providing its JSON definition. Validates and saves the strategy, enabling subsequent backtesting and optimization.
    Apache 2.0
  • Detect lookahead bias in strategy code before backtesting. Scans the AST for negative shifts, forward indexing, and future-dated attributes to flag common backtest-invalidating patterns.
    MIT

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  • Turn TradeDroid code into a trading strategy by setting name, market, timeframe, and description for automated backtesting and deployment.
    MIT
  • Retrieve detailed information about Freqtrade strategy callback methods, including full signatures, parameter types, return types, and documentation to assist in strategy development.
    GPL 3.0
  • Check for available upgrades, breaking changes, and migration guide in one call. Provide your current version to receive a complete upgrade analysis.
    MIT
  • Access the full usage guide with examples and best practices. Use it first to plan codebase analysis, navigation, or auditing workflows.
    MIT
  • Analyze zoning regulations and development constraints for U.S. properties to assess buildability and identify key restrictions before planning projects.
    MIT
  • Analyze any GTM strategy document using CRAFT principles to get a 0-100 score, dimension assessment, gap analysis, and improvement recommendations.
    MIT
  • Fetch Brazilian economic time series (SELIC, CDI, IPCA, USD PTAX) over a date range and get all data points plus summary stats for trend analysis, indexation, and FX backtesting.
    MIT
  • Analyze a company's financial health, profitability, growth, and cash flow, and receive derived signals on margin trends, financial strength, and growth trajectory. Ideal for fundamental analysis.
    MIT
  • Load structured workflow instructions to guide feature development from idea to implementation, following the Requirements → Design → Tasks → Implementation sequence for spec-driven software development.
    GPL 3.0
  • Access daily OHLCV price history with buy/sell flow data for crypto backtesting and trend analysis. Supports up to 7 years of data across multiple exchanges.
    MIT
  • Run a parameter sweep over strategy variations to find top performers, ranking by metrics like Sharpe ratio for optimized backtesting.
    MIT