MONTE CARLO LAB

Strategy and portfolio validation through statistical simulation — simulations do not predict the future

MODEL ESTIMATES10,000 simulations

Resamples individual trades with replacement to generate synthetic equity curves

DRAWDOWN DISTRIBUTION — WHY HISTORICAL MAX DD IS NOT ENOUGH

Historical maximum drawdown reflects what happened in the past. Monte Carlo simulation shows the range of drawdowns that could occur in the future given the same trade distribution. The 95th percentile is a more realistic planning figure.

Historical Max DD

8.4%

MC Median Max DD

11.2%

MC 95th Percentile

18.7%

OUTCOME DISTRIBUTIONS

P54.2%Worst 5% of outcomes
P257.8%Below-median outcome
P5011.2%Median expected outcome
P7514.6%Above-median outcome
P9518.7%Best 5% of outcomes

P50 is the median — half of simulations produced better outcomes, half produced worse. P5 and P95 represent the tails of the distribution.

RISK OF RUIN ANALYSIS

MODEL ESTIMATE

Estimated probability of reaching specified drawdown levels. This is a model estimate based on assumed trade independence, win probability, and payoff distribution. It is not a certainty.

10% DD
34%
20% DD
12%
30% DD
4%
50% DD
1%
Capital Floor
0%

Model Inputs

Capital$100,000
Risk per trade1%
Win probability58%
Capital floor$70,000

Historical max drawdown (8.4%) is likely lower than future worst-case

Monte Carlo median max drawdown (11.2%) is the more realistic planning figure

Correlation shocks not modeled in this run — use Stress Lab for portfolio-level scenarios

SIMULATION ASSUMPTIONS

Bootstrap resampling preserves individual trade characteristics

Trade order is randomized — sequence risk is modeled

Slippage perturbation: ±0.2 pips around 0.4-pip assumption

Simulations do not predict the future

Model dependence: LOW MODEL DEPENDENCE