Parse user intent to design and backtest quantitative trading strategies.
npx skills add https://github.com/GGwujun/SigmX --skill strategy-generate-ggwujun
Please help me install this Agent Skill. Skill: strategy-generate Source: https://github.com/GGwujun/SigmX/tree/main/agent/src/skills/strategy-generate Command: npx skills add https://github.com/GGwujun/SigmX --skill strategy-generate-ggwujun
Create, modify, and optimize quantitative trading strategies, then backtest and evaluate them.
Instruct the system to generate a backtest-ready strategy by specifying instruments and date range, then implement config.json and signal_engine.py and run the backtest for results.
High-intent search queries and answers about installing and using this skill.
To generate and backtest a quantitative trading strategy, you specify the target instruments and date range, which produces a runnable config.json and signal_engine.py, then runs the built-in backtest tool to return performance metrics.
A signal engine in quantitative backtesting is a Python module (signal_engine.py) that defines trading logic and adheres to the SignalEngine contract, ensuring the generated strategy integrates seamlessly with the backtest workflow.
Yes, you can backtest a dual-EMA strategy on AAPL by instructing the system with the instrument and date range, such as 2024-01-01 to 2024-12-31, which parses the intent to generate and run the backtest-ready project.
You configure a backtest run using config.json by defining the extracted instruments and time ranges, which allows the quantitative workflow to parse the parameters and execute the backtest tool to generate evaluation metrics.
When modifying quantitative trading strategies, limitations include ensuring the output code strictly adheres to the SignalEngine contract and passes syntax validation before the built-in backtest tool can successfully execute and generate metrics.
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