What problem does it solve?
This Skill helps you design, implement, and evaluate quant trading strategies so you can validate factor ideas through backtesting instead of relying on intuition alone.
Core Features & Use Cases
- Factor construction: Build stock selection factors such as value, growth, momentum, quality, and volatility indicators.
- Strategy coding: Write Python-based trading strategy logic and integrate it with common backtesting frameworks (e.g., backtrader, vnpy, 聚宽).
- Backtest and optimization: Interpret performance metrics (annualized return, max drawdown, Sharpe, win rate) and improve strategies using parameter tuning, risk controls, and position management.
Example use case: You want to test whether a value factor combined with a volatility filter improves risk-adjusted returns for a specific universe over a defined period.
Quick Start
Tell the AI to help you design a value-factor strategy, implement it in Python for your chosen backtesting framework, and analyze the resulting Sharpe ratio and maximum drawdown.
