Read Me First — Monte Carlo Results

The Monte Carlo Results dashboard is where a large experiment becomes interpretable. Instead of focusing on one simulated path, it shows the distribution of strategy and benchmark outcomes, path-level drawdowns, regime effects, and research provenance.

What this lab is designed to do

The Monte Carlo Results dashboard is where a large experiment becomes interpretable. Instead of focusing on one simulated path, it shows the distribution of strategy and benchmark outcomes, path-level drawdowns, regime effects, and research provenance.

Key controls and inputs

• Run / regime. Confirm which strategy, market regime, seed, and experiment produced the dashboard.
• Displayed paths. The spaghetti plot usually shows only a visual subset; statistical summaries should use the full run.
• Strategy equity and benchmark/control equity. These should be paired so path luck affects both sides similarly.
• Terminal excess return. Positive means the strategy finished ahead of its matched control on that path; negative means it lagged.
• Maximum drawdown distribution. This reveals whether risk reduction is consistent or concentrated in a few paths.
• Regime effect and confidence intervals. Use uncertainty intervals to judge whether observed differences are large relative to Monte Carlo variation.

What the outputs mean

• Spaghetti plot. Use it to see path diversity and timing, not to count winners by eye.
• Terminal return distribution. Shows frequency, magnitude, skew, and tails of strategy-vs-control outcomes.
• Drawdown distribution. A strategy can have a similar average return but materially different downside path behavior.
• Regime mean excess return. Helps identify whether an apparent edge is conditional on one market state.
• 95% confidence interval. A narrow interval around zero suggests little measurable edge under the model; a wide interval suggests the experiment may need more paths or has inherently variable outcomes.
• Research provenance. Always verify parameters and engine metadata before citing a result.

A good first experiment

1. Open the dashboard immediately after a known experiment so provenance is clear.
2. Check the strategy and matched benchmark names first.
3. Look at the terminal excess-return distribution before the average. Ask how often the strategy wins and what the losing tail looks like.
4. Compare drawdown distributions and effective exposure.
5. Inspect regime-specific effects and confidence intervals.
6. Repeat the experiment with a different seed or larger path count if the conclusion depends on a small mean difference.

How to interpret the result

Do not judge the strategy from one path, one seed, or one favorable market environment. Read return, drawdown, exposure, trade frequency, and benchmark-relative performance together. A result is more credible when it persists across reasonable parameter changes and when the comparison benchmark has similar economic exposure.

Important assumptions and limitations

• Monte Carlo paths are generated from the chosen model; more paths reduce sampling noise but do not remove model risk.
• Confidence intervals quantify simulation uncertainty under the model, not all real-world uncertainty.
• Charts can visually exaggerate small differences depending on scale; use numerical summaries as well.
• A strong mean can coexist with an unfavorable tail or low win rate.
• Benchmark mismatch can invalidate an otherwise precise comparison.

How this complements backtesting

Historical backtests show the realized path that actually occurred. Monte Carlo results show a distribution of plausible modeled paths. Use the dashboard to understand path dependence and tail behavior, then compare those findings with historical episodes to see whether the model is producing economically plausible behavior.