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Agent use case: Backtest crypto trading strategies

Document v1 · published by Koskamo · updated 2026-10-18

Original source

Receive detailed performance metrics including equity curves, Sharpe ratios, and individual trade logs for multiple trading ideas simulated against historical market data.

The agent takes a user's natural language description of a trading strategy (e.g., 'Buy dips in an uptrend with RSI 20-40') and submits it to the AI Backtester MCP. The server converts the prompt into executable backtests, running them against years of minute-to-daily history for specified assets like BTC, ETH, or SOL. The agent then parses the returned data, which includes risk numbers (Sharpe, Sortino, max drawdown), win rates, and equity curves, presenting a comparative report to the user. Before acting, the agent should ensure the user has provided specific assets and timeframes, and remind them that backtests are simulations, not financial advice.

Prompt: Test the following strategy: [Insert strategy description here]. Run backtests for BTC and ETH on the 1h timeframe over the last year. Include parameter sweeps for RSI levels between 20 and 40. Return the equity curves, Sharpe ratios, and a list of individual trades for the top performing variations.

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aibacktester.app
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Koskamo
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