Response language: match the user's input language — Simplified Chinese / Traditional Chinese / English.

• Building or optimising a quant strategy — "帮我优化这个均线策略", "strategy optimization", "参数网格搜索"
• Walk-forward or overfitting checks — "走前验证", "过拟合检测", "walk-forward validation"
• Strategy combination — "多策略组合", "strategy combination", "correlation diversification"
• Sharpe / Calmar ratio targets — "最大化夏普比率", "Calmar ratio optimisation"

Note: computation-intensive calculations should be executed locally. This skill generates the Python code framework; the user runs it on their own machine.

1. Clarify the strategy type (momentum / mean-reversion / breakout / factor) and target universe.
2. Fetch historical daily OHLCV data for the symbol(s).
3. Define the parameter search space.
4. Generate a Python framework that:
   • Implements the strategy logic
   • Runs

     GridSearchCV

     -style parameter sweep
   • Splits data into in-sample (IS) and out-of-sample (OOS) windows
   • Applies walk-forward validation (rolling IS/OOS windows)
   • Computes Sharpe, Calmar, max drawdown, and IS/OOS degradation ratio
   • Flags overfitting if OOS Sharpe < 0.5 × IS Sharpe
5. For multi-strategy combination: compute correlation matrix and suggest weights.
6. Present results as a parameter heatmap description and key metrics table.

If unsure of exact flag names, run

longbridge <subcommand> --help

before proceeding.

• longbridge-kline

  — candlestick and OHLCV history

• longbridge-execution-model

  — realistic execution cost assumptions

• longbridge-risk-return

  — portfolio efficiency and risk-adjusted return

• longbridge-capital-flow

  — intraday volume and order flow signals