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554,162 tools. Updated 2026-09-12 21:51

"A tool for backtesting trading strategies on historical data and analyzing performance metrics" matching MCP tools:

  • Retrieves historical candlestick data for technical analysis, identifying support/resistance levels, calculating indicators, and backtesting strategies.
    MIT
  • Analyze historical price data for technical analysis and trading decisions. Calculate indicators, identify support/resistance levels, and backtest strategies using candlestick data.
    -
  • Retrieve aggregated health status, performance metrics, alerts, and historical trends for the backtesting system to monitor system health and performance.
    MIT
  • Retrieve current stock metrics for any public company, including price, market cap, performance, and financials from verified official sources.
    MIT

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  • Run several trading strategies on identical data and view side-by-side comparisons, with support for benchmarks and adjustable result detail.
    MIT
  • Retrieve OHLCV candlestick data for a trading pair to analyze price trends, create charts, compute indicators, or backtest strategies.
    Go
    MIT
  • Run a historical backtest to evaluate trading strategies using OHLCV data or signal series, with configurable execution settings and detailed performance metrics.
    MIT
  • Run historical backtests of trading rules on real market data to evaluate performance, monthly returns, drawdown, turnover, and transaction costs.
    MIT
  • Retrieve trading strategies with filters for symbol, type, status, and time range, plus pagination. Check strategy status, monitor running strategies, or review completed ones.
    MIT
  • Retrieve raw performance metrics for any tool, including invocation counts, p50/p95/p99 latency, error rate, and uptime. Use these figures to diagnose performance issues and guide optimization.
    MIT
  • Retrieve historical college football betting lines from multiple books, including spread, over/under, and moneyline, for backtesting strategies.
    MIT
  • Fetch historical mark price candles for derivatives to evaluate margin and liquidation risk, backtest strategies, and compare against trading price candles.
    MIT
  • Compute market metrics including volume averages, volatility, SMAs, and trend direction for in-depth technical analysis of trading patterns beyond basic quotes.
    MIT
  • Evaluate predictive query performance by comparing predictions with known historical labels, returning metrics for classification, regression, or link prediction tasks.
    MIT
  • Retrieve historical weather data for past dates from 2010 onward, including daily summaries and hourly breakdowns, to analyze past conditions or verify weather events.
    MIT
  • Backtest trading strategies (RSI, MACD, EMA cross, etc.) on historical market data to evaluate performance with metrics like returns, drawdown, and trade logs.
    MIT
  • Retrieve historical VPS performance metrics including CPU, memory, disk, network, and uptime to monitor resource utilization over time.
    MIT