Trading Automation

BacktestBot

Vor 45 Tagen aktualisiertSetup in <1 Min.

Überblick

BacktestBot is a backtesting engine for validating and refining trading strategies using historical market data.

BacktestBot is a backtesting engine for validating and refining trading strategies using historical market data. It accepts strategies described in natural language or as structured rules (entry/exit signals, position sizing, stop losses, trailing stops) and simulates them across tick or daily OHLCV data for equities, options, futures, and crypto. Outputs include detailed performance metrics (Sharpe ratio, CAGR, max drawdown, win rate, profit factor), trade-level breakdowns, and risk analytics (value-at-risk, worst-case drawdown periods, tail risk, benchmark correlations). Use cases include strategy validation before live deployment, parameter optimization and sensitivity analysis, stress testing across market regimes, and side-by-side comparison of strategy variants. Core advantages are multi-asset, multi-frequency simulation, reproducible analytics, natural-language strategy input, and configurable caching and API authentication for scalable testing workflows.

Skill.md

So funktioniert diese Skill

SKILL.mdALPHIO / VERIFIZIERT

Backtest trading strategies against historical market data with detailed performance analytics.

What it does

BacktestBot enables you to define, test, and evaluate trading strategies using historical data, including:

Strategy definition — describe strategies in natural language or structured rules (entry/exit signals, position sizing, stop losses)
Historical simulation — run strategies against years of tick or daily data across equities, options, futures, and crypto
Performance metrics — Sharpe ratio, max drawdown, win rate, profit factor, CAGR, and trade-level breakdown
Risk analysis — value-at-risk, correlation to benchmarks, worst-case drawdown periods, and tail risk metrics
Comparison — test multiple strategy variants side-by-side and rank by risk-adjusted returns

Usage

Ask your agent to backtest strategies and analyze results:

"Backtest a mean reversion strategy on SPY using RSI below 30 as entry over the last 5 years"
"Compare buy-and-hold vs momentum rotation across the S&P 500 sectors since 2020"
"What is the max drawdown if I use a 2% trailing stop on AAPL swing trades?"
"Optimize the lookback period for my moving average crossover strategy on QQQ"

Configuration

Set the following environment variables:

BACKTESTBOT_API_KEY — API key for BacktestBot. Used to authenticate requests for historical OHLCV data, strategy simulations, and performance metrics.
BACKTESTBOT_DATA_DIR — (optional) local directory for cached historical data. Defaults to ~/.backtestbot/data.