A professional quantitative backtesting framework for validating and optimizing trading strategies across stocks, crypto, and forex.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install openclaw-backtester
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 openclaw-backtester 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 Beta Backtester?
Beta Backtester is a specialized tool within the Openclaw Skills ecosystem designed for quantitative analysts and traders. It provides a robust environment to test technical strategies like SMA crossover, RSI, MACD, and Bollinger Bands against historical OHLCV data. By simulating real-world trading conditions—including commissions and slippage—it allows users to stress-test their logic before deploying capital.
The framework excels at iterative optimization, enabling developers to perform grid searches and parameter sweeps to find the best risk-adjusted returns. Whether you are using local SQLite databases for A-shares or Yahoo Finance for international markets, Beta Backtester ensures high-fidelity simulations with detailed performance reporting.
Beta Backtester Use Cases
- Validating new trading ideas against years of historical market data.
- Optimizing technical indicator parameters to maximize Sharpe and Sortino ratios.
- Performing walk-forward analysis to detect and prevent strategy overfitting.
- Comparing multiple strategies side-by-side via A/B testing to identify the most robust performer.
- Simulating the impact of transaction costs and slippage on overall portfolio equity.
How Beta Backtester Works
- Data Ingestion: The skill connects to a data source, such as a local SQLite database or an external API, and fetches historical price bars for the specified ticker.
- Strategy Application: It applies the selected logic (e.g., RSI reversals) to the dataset, generating entry and exit signals based on defined parameters.
- Backtest Execution: The engine iterates through the timeline, simulating trades, calculating position sizes, and deducting commissions.
- Performance Calculation: It computes key quantitative metrics including Max Drawdown, Win Rate, and the Sharpe Ratio.
- Reporting: The skill outputs a comprehensive summary and visualization of the equity curve for further analysis.
Beta Backtester Setup
Ensure you have Python 3.8+ installed. The framework will automatically handle dependencies like pandas and matplotlib upon first run.
# Standard backtest command
python3 backtest.py --strategy sma_crossover --ticker 600036 --start 2025-01-01 --end 2025-03-31
# Optimization command with custom thresholds
python3 backtest.py --strategy rsi --ticker 000001 --upper 65 --lower 35
Beta Backtester Data Schema & Taxonomy
The skill organizes data primarily through a structured SQLite schema or CSV inputs.
| Component | Format | Description |
|---|---|---|
| Input Data | OHLCV | Open, High, Low, Close, Volume bars |
| Metrics | JSON/Text | Sharpe, Sortino, Max Drawdown, Win % |
| Database | .db | Local SQLite file (e.g., a_stock_complete.db) |
| Logs | .txt | Detailed trade-by-trade entry and exit logs |
Beta Backtester Advanced Features
- Iterative Parameter Sweeping: Automatically test ranges of variables to identify peak performance zones.
- Walk-Forward Analysis: Built-in support for out-of-sample validation to ensure strategy robustness.
- Custom Strategy Hooks: A flexible Python template for implementing proprietary entry and exit logic.
- Monte Carlo Simulations: Add random noise to price data to stress-test how strategies handle market volatility.
- Multi-Asset Compatibility: Automatic handling of ticker formatting and data sourcing for global markets.
SKILL.md
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