A Monte Carlo simulation takes the trades a strategy produced and reshuffles or resamples them thousands of times, building thousands of alternative equity curves. Because the order and selection of trades changes each run, you see the full range of outcomes the same edge could plausibly have produced — not just the single historical path.
The value is honesty about variance. A backtest shows one realised curve; Monte Carlo shows the spread around it, so you can read a median return, a worst-case drawdown band, and how often the strategy would have ended underwater. Judge a strategy by its distribution, not its single lucky or unlucky run.
Ejemplo
A strategy's single backtest shows a 12% max drawdown, but Monte Carlo reveals the 95th-percentile drawdown is 28% — a very different risk picture.
Cómo Noon Barbari usa Monte Carlo Simulation
Cada concepto aquí está implementado en la plataforma. Abre la documentación o la herramienta correspondiente para verlo en acción.
Run Monte Carlo in noonbarbari →Términos relacionados
- Riesgo
Maximum drawdown
The deepest peak-to-trough decline observed across the entire equity curve.
- Estadística
Value at Risk (VaR)
A loss threshold you won't exceed at a given confidence level over a period.
- Backtesting
Stress Test
Replaying a strategy through historically hard regimes and adversarial shocks.
- Backtesting
Walk-forward optimization
Optimize on a rolling in-sample window, validate on the next out-of-sample slice.