strategy-translator

What This Skill Does

The strategy-translator is a specialized OpenClaw agent skill designed to bridge the gap between disparate quantitative trading environments. Trading strategies are often siloed in proprietary languages like Pine Script (TradingView) or legacy C++ backtesters. This skill standardizes the logic extraction and conversion process, enabling users to port complex strategies into modern, Python-based frameworks such as NautilusTrader, VectorBT, or custom research environments. It ensures that the mathematical intent of an algorithm is preserved while adapting to the specific execution, data handling, and event-driven architectures of the target platform. By focusing on reproducible outputs, it transforms messy, platform-specific code into modular, production-ready research assets.

Installation

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

clawhub install openclaw/skills/skills/ahuserious/strategy-translator

Ensure your local environment is configured with the necessary dependencies for your target framework (e.g., pandas, numpy, or specific SDKs for NautilusTrader) prior to execution.

Use Cases

• Pine Script Migration: Automatically convert legacy indicator-based TradingView strategies into Python scripts suitable for backtesting on historical data using pandas or specialized libraries.
• Framework Porting: Rewrite institutional strategies from proprietary C++ or Java systems into clean, readable Python code compliant with the NautilusTrader event-driven engine.
• Research Standardization: Standardize data ingestion and feature engineering pipelines for strategies moving from sandbox prototyping to live paper trading environments.
• Algorithm Auditing: Deconstruct complex strategies into discrete logic blocks to identify potential look-ahead bias or overfitting risks that may have been obscured by the original implementation.

Example Prompts

• "Translate this Pine Script RSI-Bollinger Band crossover strategy into a NautilusTrader strategy class. Ensure you account for stop-loss and take-profit logic defined in the source."
• "Analyze my current Python backtest code and refactor it to use vectorized operations for performance, then provide a checklist for potential data leakage during the signal generation phase."
• "I have a strategy logic description. Please generate a Python scaffold using the VectorBT framework, including setup for 1-minute crypto data and a 3-month walk-forward validation block."

Tips & Limitations

• Verification is Mandatory: The skill generates code sketches; always validate output against your own unit tests. Financial logic can behave unexpectedly when translated between frameworks.
• Data Handling: Be explicit about your data structure requirements. If the source uses Tick-level data and the target uses OHLCV, specify how you want downsampling handled.
• Risk Awareness: The tool provides code, not financial advice. Always perform Monte Carlo simulations and sanity checks on your strategy output before deploying capital.
• Context is King: The more detail you provide about your objective (e.g., latency constraints, specific API limits), the more precise the code generation will be.