BacktestBot is a quantitative analysis tool designed to simulate trading strategies against historical data with comprehensive performance and risk reporting.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install backtestbot
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install backtestbot using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is BacktestBot?
BacktestBot is a powerful engine for traders and developers who need to validate financial hypotheses using historical market data. As a specialized addition to your Openclaw Skills library, it allows you to define strategies in natural language or structured rules—including entry/exit signals, position sizing, and stop losses—and see how they would have performed over years of tick or daily data.
This skill eliminates the manual labor of data sourcing and simulation, providing a streamlined workflow for evaluating equities, options, futures, and crypto. By integrating these Openclaw Skills into your development environment, you can move from a trading idea to a full risk-adjusted performance report in seconds, ensuring your strategies are backed by empirical evidence before deployment.
BacktestBot Use Cases
- Simulating mean reversion strategies on specific tickers like SPY using RSI or other technical indicators.
- Benchmarking momentum-based sector rotation against a standard buy-and-hold approach.
- Evaluating the impact of different risk management parameters like trailing stops or fixed percentage drawdowns.
- Fine-tuning and optimizing lookback periods for moving average crossovers to maximize the Sharpe ratio.
How BacktestBot Works
- The user provides a strategy description or specific rules to the agent using Openclaw Skills.
- BacktestBot fetches historical OHLCV data via its API based on the requested symbols and timeframe.
- The simulation engine processes the data chronologically, executing trades according to the defined logic.
- Detailed analytics are generated, covering everything from win rates to tail risk metrics.
- A final performance report is returned to the user, facilitating side-by-side strategy comparison.
BacktestBot Setup
To start using this skill, you must provide your API credentials via environment variables.
# Set your mandatory API key
export BACKTESTBOT_API_KEY="your_api_key_here"
# Optional: Define a local directory for caching historical market data
export BACKTESTBOT_DATA_DIR="~/.backtestbot/data"
Ensure these are correctly set in your environment so that your Openclaw Skills can authenticate with the BacktestBot backend.
BacktestBot Data Schema & Taxonomy
The skill manages data across several internal formats to provide structured insights:
| Data Type | Description | storage Mapping |
|---|---|---|
| Market Data | Historical OHLCV (tick or daily) | BACKTESTBOT_DATA_DIR |
| Strategy Metadata | Signal logic and risk parameters | In-memory JSON |
| Trade Ledger | Execution history including slippage and fees | Standardized JSON Output |
| Performance Metrics | Sharpe ratio, CAGR, Max Drawdown, and VaR | Analytics Summary Report |
BacktestBot Advanced Features
- Support for multi-variant strategy comparison to rank different approaches by risk-adjusted returns.
- Advanced risk analysis including Value-at-Risk (VaR) and correlation to major benchmarks.
- Hyper-parameter optimization to identify the most effective indicators for specific market regimes.
- Local caching of historical data for faster iteration on complex Openclaw Skills workflows.
SKILL.md
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