Simulation Framework

Our Monte Carlo engine runs thousands of randomized trade sequences using base performance statistics extracted from historical or forward data. Each simulation respects prop-firm-specific parameters such as daily drawdown limits, trailing equity, and static account caps.

We model trade outcome variance rather than trying to predict specific trades. This reveals expectancy ranges, streak probabilities, and the likelihood of breaching firm limits before payout.

Inputs and Assumptions

• Trade data: real or modeled statistics (win rate, average win/loss, reward:risk ratio).
• Account parameters: fixed prop-firm drawdown rules and scaling thresholds.
• Sampling: Monte Carlo resampling with replacement, up to 10,000 iterations per scenario.
• Termination: simulation stops on payout, failure, or max trade count.

Output Metrics

• Probability of rule breach before target.
• Distribution of cumulative P&L and equity paths.
• Expected return vs. drawdown trade-off curve.
• Sharpe and Sortino ratios computed per run.

Interpretation

The goal isn't to predict a single outcome, but to expose the range of plausible futures. Seeing 1,000 simulated equity curves teaches more about risk than any single backtest can. It highlights where edges are robust — and where luck still dominates.

Transparency & Reproducibility

All parameter files and result sets are published in CSV format. You can re-run our experiments locally or build upon them. We believe transparency beats marketing — every number you see here can be traced to source data.