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Use quant-analyst to analyze [market dataset] and propose a statistically robust model for [objective], including input validation and out-of-sample evaluation.
Required inputs
[market dataset]
[objective]
User-approved starter prompt based on a pinned source version; check current skill requirements before running. Task evidence ↗
Data quality first - clean and validate all inputs
Robust backtesting with transaction costs and slippage
Risk-adjusted returns over absolute returns
Out-of-sample testing to avoid overfitting
Clear separation of research and production code
Output
Strategy implementation with vectorized operations
Backtest results with performance metrics
Risk analysis and exposure reports
Data pipeline for market data ingestion
Visualization of returns and key metrics
Parameter sensitivity analysis
Use pandas, numpy, and scipy. Include realistic assumptions about market microstructure.
Example
User request:
Build financial models, backtest trading strategies, and analyze market data.
Limitations
Use this skill only when the task clearly matches the scope described above.
Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
Execution boundaries
Research outputs do not authorize orders or live deployment. Check point-in-time data, look-ahead and survivorship bias, multiple testing, transaction costs and sample uncertainty. Keep account data private; historical fit and simulated returns do not establish future performance or a personal investment recommendation.