backtester
Professional backtesting framework for trading strategies. Tests SMA crossover, RSI, MACD, Bollinger Bands, and custom strategies on historical data. Generates equity curves, drawdown analysis, and performance metrics.
Install via CLI (Recommended)
clawhub install openclaw/skills/skills/1477009639zw-blip/betabacktestrWhat This Skill Does
The Beta Backtester is a professional-grade quantitative analysis engine designed for the OpenClaw AI ecosystem. It allows users to rigorously stress-test trading strategies against historical OHLCV data across stocks, cryptocurrencies, and forex markets. Beyond simple performance tracking, the skill calculates critical financial metrics including Sharpe and Sortino ratios, Max Drawdown, and Win Rates, providing a transparent view of risk-adjusted returns. It supports pre-built algorithmic strategies like SMA Crossover, RSI, MACD, and Bollinger Bands, while also allowing advanced users to inject custom entry and exit logic to suit specific trading styles.
Installation
To integrate this skill into your OpenClaw environment, execute the following command in your terminal:
clawhub install openclaw/skills/skills/1477009639zw-blip/betabacktestr
Ensure your local environment meets the minimum requirements of Python 3.8+ and has the necessary dependencies (pandas, numpy, matplotlib) which are typically auto-configured by the installation script.
Use Cases
- Strategy Validation: Prove the viability of a new technical analysis setup before deploying capital.
- Parameter Optimization: Determine the ideal look-back periods for indicators to maximize profitability.
- Risk Management: Analyze historical volatility to set appropriate stop-loss levels.
- Comparison: Run two variations of the same strategy side-by-side to determine which handles market downturns more gracefully.
Example Prompts
- "Run a backtest for the SMA crossover strategy on TSLA data for the last 2 years and provide a summary of the Sharpe ratio."
- "Compare the performance of RSI(14) versus a MACD crossover strategy using BTC-USD daily data from 2021 to 2023."
- "Show me the drawdown analysis for a custom momentum strategy on AAPL and list the worst 5 trades identified."
Tips & Limitations
To get the most accurate results, prioritize using high-quality OHLCV data. While the tool defaults to Yahoo Finance, importing clean CSV data often yields more reliable results for backtesting. Remember that this tool is for research only. The disclaimer is paramount: backtested results, even when positive, do not guarantee future performance. Market conditions change, and "overfitting"—where a strategy is tuned too specifically to past data—can lead to poor live performance. Always paper-trade your strategies for a significant period before committing real capital.
Metadata
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Find the right skillPaste this into your clawhub.json to enable this plugin.
{
"plugins": {
"official-1477009639zw-blip-betabacktestr": {
"enabled": true,
"auto_update": true
}
}
}Tags(AI)
Flags: file-read, file-write, external-api, code-execution
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