Minute-Level Mean-Reversion Strategy: Design, Backtest, Validation, and Significance

Context

You are given minute-level OHLCV data (open, high, low, close, volume) for a single equity over 180 regular trading days. Assume the data are split-adjusted and cover regular trading hours (no overnight trading).

Task

Design and implement a simple mean-reversion strategy and evaluate it rigorously.

1. Specify the complete strategy specification:
   • Entry/exit rules based on a mean-reversion signal.
   • Risk controls (e.g., time stop, stop-loss, daily stop, position caps, no-trade windows).
   • Position sizing (e.g., volatility targeting) with explicit formulas/parameters.
   • Transaction costs and slippage model.
2. Implement a reproducible backtest that outputs at minimum:
   • Total and per-day PnL, win rate, average trade PnL, max drawdown, realized volatility, Sharpe ratio, and turnover.
3. Perform out-of-sample validation using a hold-out split or walk-forward (rolling) validation. If you tune parameters, do so only on in-sample windows and freeze them for the test windows.
4. Discuss overfitting risks and apply a suitable statistical test (e.g., bootstrap-based White’s Reality Check or similar) to assess whether performance is statistically significant given data-snooping.

Deliver a clear, step-by-step solution with code or pseudocode, including assumptions and guardrails to avoid look-ahead and survivorship biases.