longbridge-strategy-optimizer

longbridge-strategy-optimizer

Quantitative strategy generation and optimisation framework — grid search, walk-forward validation, and overfitting detection.

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

When to use

Trigger on prompts asking for:

• 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.

Workflow

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

If unsure of exact flag names, run longbridge <subcommand> --help before proceeding.

CLI

# Daily OHLCV — backtest data (up to 500 trading days)
longbridge kline <SYMBOL> --period day --count 500 --format json

Output structure

STRATEGY OPTIMISATION REPORT — <SYMBOL>  <Date>

STRATEGY: <Name / Type>
Universe:  <SYMBOL>
Period:    <start> – <end>  (xxx days)

PARAMETER SEARCH
Parameter       Range         Step    Best Value
fast_ma         5–50          5       xx
slow_ma         20–200        10      xxx
stop_loss       0.5%–5%       0.5%    x.x%

BEST RESULT (IN-SAMPLE)
Sharpe:  x.xx   Calmar:  x.xx   Max DD:  -xx.x%
CAGR:    xx.x%  Win Rate: xx.x%  Trades: xxx

OUT-OF-SAMPLE VALIDATION
Sharpe:  x.xx   Calmar:  x.xx   Max DD:  -xx.x%
IS/OOS Degradation: xx%  → [Acceptable | Possible Overfit | Overfit]

WALK-FORWARD SUMMARY
Window 1: IS Sharpe x.xx → OOS Sharpe x.xx
Window 2: IS Sharpe x.xx → OOS Sharpe x.xx
...

PYTHON CODE FRAMEWORK
<generated Python code>

Error handling

| Situation | 简体回复 | 繁體回復 | English reply | |-----------|---------|---------|---------------| | Symbol not found | 未找到该代码，请确认市场和格式。 | 找不到該代碼，請確認市場和格式。 | Symbol not found — verify exchange and ticker. | | Insufficient history | 历史数据不足，回测结果可靠性下降。 | 歷史數據不足，回測結果可靠性下降。 | Insufficient history — backtest reliability is reduced. | | command not found: longbridge | 请安装 longbridge-terminal 或通过 MCP 连接。 | 請安裝 longbridge-terminal 或透過 MCP 連線。 | Install longbridge-terminal or connect via MCP. | | not logged in | 请运行 longbridge auth login。 | 請執行 longbridge auth login。 | Run longbridge auth login. |

MCP fallback

When the CLI is unavailable, fall back to the MCP server. Discover available tools from the MCP server's tool list at runtime.

Related skills

• 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

File layout

skills/longbridge-strategy-optimizer/
└── SKILL.md