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Archetype: Simulation backtesting product

Use when​

Strategies must be exercised across historical regimes with transparent assumptions—common in allocation, lending, robotics, trading.

Common users​

  • Quant researchers validating policies
  • Product strategists forecasting outcomes
  • University capstone cohorts benchmarking ideas

Minimal MVP​

  • Scenario configuration surface
  • Deterministic simulator with seed control
  • Performance analytics + drawdown summaries
  • Exportable run artifacts

Required files​

  • product.yaml
  • product-brief.md
  • data-contract.md
  • evaluation.md
  • demo.md

Evaluation​

  • Out-of-sample stability
  • Sensitivity to parameter priors
  • Runtime cost envelopes

Reference products​

  • Stacking Sats

Common mistakes​

  • Silent look-ahead bias
  • Omitting friction (fees, latency, liquidity)
  • Toy simulators unrelated to production constraints

Agent prompt​

You are building a simulation microproduct. Document data windows, lookahead controls, friction models, random seeds, and success metrics prior to widening feature scope.