Strategy Monte Carlo Lab.
A profitable backtest can still fail because trades arrive in the wrong order. Resample your actual trade outcomes thousands of times to estimate plausible drawdowns, losing streaks, breach risk and future outcome ranges.
Load the historical outcome distribution.
Upload a TradingView Strategy Tester CSV, a generic trade log, or use a simple manual win/loss model. For CSV analysis, the lab samples your actual realized trade P/L values rather than assuming every winner and loser is identical.
CSV import
Choose a closed-trade CSV. TradingView exports are detected automatically and only completed Exit rows are used so trade P/L is not counted twice.
Manual model
Useful when you only know headline strategy statistics. This is intentionally simpler than sampling a real trade log.
Manual simulations use a binary fixed-payoff model: each trade is either the entered average win or average loss. Use a CSV when you want the actual dispersion of wins and losses represented.
Define the simulation.
Choose whether you want to isolate sequence risk, resample the historical distribution, or preserve some short-term clustering. Then set the future trade horizon and the drawdown threshold you care about.
Bootstrap is not a forecast. It assumes the historical outcome distribution remains informative and, in standard bootstrap mode, treats trades as independently resampleable. Block bootstrap preserves some local clustering but still cannot reproduce the full state logic of a strategy.
Sequence shuffle answers a narrower question. It keeps the chosen historical outcomes and changes only their order. It is useful for understanding sequence risk, but it does not simulate what would happen if strategy rules were modified.
Drawdown here is realized trade-to-trade peak-to-trough P/L. It does not reconstruct intratrade equity excursions unless those are already represented as individual outcomes in the source data.