A programmatic framework for executing quantitative trading strategies against historical data to generate professional-grade performance metrics.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install einstein-research-backtest-engine-dv
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 einstein-research-backtest-engine-dv 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 Einstein Research — Backtest Engine?
Einstein Research Backtest Engine is a specialized tool within the Openclaw Skills ecosystem designed for the rigorous execution of quantitative trading strategies. Unlike methodology-focused guides, this engine is built for action, allowing developers and traders to process historical price data from sources like yfinance or local CSV files. It transforms machine-readable strategy definitions into detailed financial reports, providing a data-driven foundation for strategy validation.
By utilizing this skill, users can simulate complex trading environments including transaction cost modeling, regime-aware splits, and walk-forward optimization. It serves as the bridge between a theoretical trading idea and a verified performance profile, making it an essential component of the Openclaw Skills suite for fintech development.
Einstein Research — Backtest Engine Use Cases
- Running a full-scale backtest on a specific momentum or mean-reversion strategy.
- Testing the viability of a strategy.yaml file generated by automated edge-discovery tools.
- Generating standardized performance metrics like Sharpe and Sortino ratios for investor reporting.
- Validating trading ideas against historical market regimes to identify potential weaknesses.
- Optimizing entry and exit signals through out-of-sample testing and walk-forward analysis.
How Einstein Research — Backtest Engine Works
- Strategy Definition: The user provides a structured strategy.yaml file containing the universe, data source, and specific signal logic.
- Data Acquisition: The engine fetches historical price data via yfinance API or parses provided local CSV datasets.
- Signal Generation: The system iterates through time-series data, applying entry and exit logic to identify trade triggers.
- Trade Simulation: A virtual portfolio executes trades based on signals, accounting for specified transaction costs and slippage.
- Metric Calculation: The engine computes a comprehensive suite of KPIs including CAGR, Max Drawdown, and Profit Factor.
- Report Generation: A finalized performance report is produced in both JSON and Markdown formats for further analysis.
Einstein Research — Backtest Engine Setup
To integrate this engine into your environment, ensure you have the necessary CLI tools installed. Use the following commands to run your first backtest using Openclaw Skills standards:
# Install the engine via your package manager
npm install -g @openclaw/backtest-engine
# Run a backtest using a predefined strategy file
backtest-engine run --strategy-file path/to/strategy.yaml --costs 0.0005
Einstein Research — Backtest Engine Data Schema & Taxonomy
The Einstein Research Backtest Engine organizes its output into a structured reporting taxonomy to ensure compatibility with other Openclaw Skills:
| File Type | Description | Key Metadata |
|---|---|---|
strategy.yaml |
Input configuration | version, universe, signals, parameters |
backtest_report.md |
Human-readable summary | Equity curve, KPI table, Annual returns |
trade_log.json |
Raw execution data | Entry/Exit timestamps, PnL per trade, duration |
metrics.json |
Machine-readable KPIs | Sharpe, Sortino, Calmar, MaxDD, CAGR |
Einstein Research — Backtest Engine Advanced Features
- Walk-forward optimization to prevent overfitting and ensure strategy robustness.
- Regime-aware data splitting for training and out-of-sample testing.
- Custom transaction cost modeling to simulate realistic market slippage and commissions.
- Multi-format exports (Markdown and JSON) for seamless integration into automated research pipelines.
- Direct integration with the Openclaw Skills edge-generator for end-to-end strategy discovery and validation.
SKILL.md
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