The Backtester & Monte Carlo

The Backtester lets you test a strategy on historical data. You choose an asset (BTC, ETH, SOL...), a time period, and the backtester simulates the trades the strategy would have made.

The backtest trap: A perfect backtest guarantees NOTHING for the future. This is the overfitting problem — a strategy over-optimized for the past fails on new data. It's like a student who memorizes answers from a past exam: 100% on that exam, 0% on the next.

Monte Carlo simulation solves this. It runs 1000 simulations by randomly shuffling the order of trades (bootstrap resampling). If your strategy is robust, it will perform well in most of the 1000 parallel universes. If it only works in one specific order, that's a red flag.

The Robustness Score summarizes everything in one number. It combines: percentage of profitable simulations, return stability, and Sharpe Ratio consistency across simulations. A score > 70% indicates a reliable strategy.

Practical exercise: go to the Backtester, test CUDA Evolved on BTC, then run Monte Carlo. Compare with Buy & Hold. The robustness difference will surprise you.