Post-backtest robustness sections for Report (Monte Carlo, parameter sensitivity, regime classification)

Expected Behavior

Feature request, not a bug — mapping to the template below.

Lean's Report should offer built-in robustness checks that answer "how much can I trust this backtest result?" Specifically, three opt-in post-backtest sections:

1. Monte Carlo — resample the trade sequence to produce confidence bands (5th/50th/95th percentile) for CAGR, max drawdown, and Sharpe.
2. Parameter sensitivity — re-run the backtest with perturbed parameters (±5/10/20%) and report the sensitivity envelope.
3. Dynamic regime classification — partition returns into bull/bear/sideways/high-vol/low-vol regimes and report per-regime performance.

Actual Behavior

Report currently provides descriptive statistics only (Sharpe, Sortino, drawdown, PSR, hardcoded crisis events, rolling metrics). There is no built-in way to test whether a result is robust to trade ordering, parameter perturbation, or market regime.

Potential Solution

Monte Carlo and regime classification run purely post-backtest on the existing trade log and equity curve — no engine changes, opt-in via config, new sections in Report/template.html. Parameter sensitivity is heavier (it re-runs backtests) and could reuse the existing optimization infrastructure (OptimizationParameter, ParameterSet) or a lighter standalone wrapper.

Asking for design direction before I start: would you accept these, and as one PR or landed separately? My suggestion is to ship Monte Carlo first as a standalone, lowest-risk PR, then discuss the other two.

I'll implement with full unit tests and a regression algorithm.

Prior art: quantstrat (R) has trade-return Monte Carlo; Backtrader has had community MC requests for years with no implementation; Zipline and Freqtrade have none. vectorbt offers resampling and parameter sweeps, so it isn't unprecedented in Python — but it isn't standard.

Checklist

• I have completely filled out this template
• I have confirmed that this issue exists on the current master branch
• I have confirmed that this is not a duplicate issue by searching issues

Start with Report and Report/template.html, then inspect the existing optimization infrastructure around OptimizationParameter and ParameterSet. Clarify whether Monte Carlo, parameter sensitivity, and regime classification should be separate opt-in sections or a staged change. Done means the agreed sections are implemented with opt-in behavior, full unit tests, and a regression algorithm as described.