Einstein Research — Backtest Engine

What This Skill Does

The Einstein Research — Backtest Engine is a sophisticated, programmatic framework designed for quantitative trading analysis within the OpenClaw ecosystem. Unlike generic strategy platforms, this engine allows users to perform rigorous, rule-based simulations on historical market data using either yfinance or local CSV imports. It evaluates trading logic—ranging from momentum and mean-reversion to signal-based factor models—and provides actionable performance diagnostics. The engine excels at handling complex scenarios, including out-of-sample testing, walk-forward optimization, and transaction cost modeling, ensuring that strategy results reflect realistic market friction.

Installation

To integrate this engine into your local OpenClaw environment, execute the following command in your terminal:

clawhub install openclaw/skills/skills/clawdiri-ai/einstein-research-backtest-engine-dv

Ensure that your environment has internet access if you plan on fetching live historical data via yfinance, or ensure your local directory structure is correctly mapped if using CSV files.

Use Cases

• Strategy Validation: Verifying that a specific trading hypothesis (e.g., 'buy when volume spikes 2x and price hits 52-week high') actually yields profit in historical market regimes.
• Risk Profiling: Quantifying the risk-adjusted returns of a portfolio strategy using metrics like the Sortino, Calmar, and Sharpe ratios before deploying capital.
• Out-of-Sample Testing: Using the engine to test strategies against periods of data the model has never seen, a critical step to prevent overfitting.
• Automated Reporting: Quickly generating comprehensive JSON or Markdown performance reports for internal audit or strategy documentation.

Example Prompts

1. "Run a backtest for my strategy defined in momentum_v1.yaml using S&P 500 data from 2020 to 2023 with a 0.05% transaction cost applied."
2. "Perform a walk-forward optimization on the trend-following strategy located in my project folder and generate the full performance metrics report."
3. "Evaluate the performance of the mean-reversion strategy in strategy.yaml. Split the data at 2022-06-01 to ensure out-of-sample validation."

Tips & Limitations

• Strategy Definitions: Ensure your strategy.yaml files strictly follow the backtest-engine/v1 schema to avoid parsing errors during execution.
• Overfitting: Be wary of excessive parameter optimization. Always utilize the out-of-sample split feature to ensure your strategy isn't just 'memorizing' past market noise.
• Costs: Always model transaction costs. Even a small fee can significantly degrade the perceived performance of high-frequency strategies.
• Data Quality: If using CSV files, ensure the time-series data is clean and contains no gaps, as the engine's day-by-day iteration depends on continuous date indexing.