Monte Carlo Simulation

Monte Carlo simulation tests your strategy's robustness by running thousands of randomized variations. If a strategy only works with trades in the exact order they occurred, it's fragile. Monte Carlo reveals how it performs under different conditions.

This is one of the most common reasons strategies fail in live trading — see Why Strategies Fail for the full picture.

What Makes AlgoChef's Monte Carlo Different

Most Monte Carlo tools ask one question: "What is my net profit at the 95th percentile?"

AlgoChef asks a fundamentally different question: "Does my strategy's composite quality score stay stable across thousands of randomized scenarios?"

Here's why that distinction matters. Net profit at a single confidence level tells you one number about one outcome. It says nothing about whether your strategy's risk profile, statistical reliability, or overall quality holds up under randomization — or collapses.

AlgoChef's Monte Carlo recalculates the full CSI (Casey Score Index) — and all three of its components (Profitability Score, Risk Score, and Confidence Score) — for every single simulation run, across all five methods. Each run produces a complete multi-dimensional quality assessment, not just a P&L figure.

The result: AlgoChef measures the variability and stability of your strategy's composite score across thousands of scenarios. If the CSI and its components remain stable across simulations, the strategy is marked as robust. If they collapse or vary wildly, the strategy is fragile — regardless of what the net profit line shows.

This gives you confidence intervals across 20+ metrics spanning all three scoring dimensions, displayed as gauges — the same visual system as the Strategy Analyzer — so you can immediately see where your strategy holds up and where it doesn't.

Simulation Methods

AlgoChef offers five simulation methods, each testing a different aspect of robustness:

1. Shuffle (Fisher-Yates)

Randomly reorders your trades. Tests whether the strategy's profitability depends on the specific sequence of wins and losses.

Best for: Checking if results are order-dependent.

2. Bootstrap (With Replacement)

Randomly samples trades from your history, allowing the same trade to appear multiple times. Simulates "what if I had more/fewer of certain types of trades."

Best for: Estimating the range of possible outcomes with your trade distribution.

3. Block Bootstrap

Preserves consecutive trade sequences (streaks) while shuffling blocks. Maintains the temporal structure of winning and losing streaks.

Best for: Strategies where streaks matter (mean-reversion or momentum systems).

4. Parametric

Generates synthetic trades based on your strategy's statistical profile (mean, standard deviation, skewness). Creates entirely new trade sequences.

Best for: Testing if the statistical edge holds in synthetic scenarios.

5. Stress+ (Shuffle + Noise)

Shuffles trades AND adds random noise to each trade's P&L. The most aggressive test.

Best for: Worst-case scenario analysis with degraded trade quality.

Tabs

Full History

Runs simulations across all your trades and provides a robustness grade. Shows confidence intervals (50%, 75%, 90%, 95%, 99%) for key metrics like net profit and max drawdown.

IS/OOS Validation

Splits your data into In-Sample and Out-of-Sample periods, then runs thousands of simulations on each. Compares the distributions to check if your strategy's edge persists out-of-sample.

Capital Calculator

Determines how much starting capital you need to survive worst-case drawdowns. Uses Risk of Ruin (RoR) analysis to calculate the probability of hitting various drawdown levels.

Answer questions like:

• "What starting capital do I need for a 1% chance of 30% drawdown?"
• "What's my risk of ruin with $50,000 starting capital?"

Stress Testing

Six predefined stress scenarios that degrade your strategy's metrics:

Reading the Results

Confidence Intervals