Monte Carlo simulation is a powerful technique that runs thousands of random scenarios to help traders understand the range of possible outcomes. Named after the famous Monaco casino, this method uses randomness to solve problems that would be impossible to calculate directly. For traders, it answers critical questions about drawdowns, profitability, and worst-case scenarios.
What is Monte Carlo Simulation?
Monte Carlo simulation generates thousands of possible future outcomes by randomly sampling from your historical trading data. Instead of looking at just one sequence of trades, you see thousands of possible sequences, revealing the full range of what could happen.
Core Concept: Your historical trades happened in one specific order. Monte Carlo shuffles them into thousands of different orders to show all possible paths your equity could have taken - and might take in the future.
Why Traders Need Monte Carlo
Your actual trading results represent just one path through probability space. Monte Carlo reveals:
- Worst-case drawdowns: How bad could losses get if trades occurred in an unlucky sequence?
- Probability of ruin: What are the chances of losing a devastating percentage?
- Realistic expectations: What range of returns should you actually expect?
- Strategy robustness: Is your edge real or just luck?
Basic Monte Carlo Process
Here is how Monte Carlo simulation works for trading:
Step 1: Collect Trade Data
Gather your historical trades. You need at least 30-50 trades for meaningful results, though 100+ is better.
Example: 100 trades with varying profits and losses
Step 2: Run Simulations
Randomly reorder your trades thousands of times. Each reordering creates a different equity curve.
Example: Run 10,000 simulations, each shuffling the same 100 trades into different sequences
Step 3: Analyze Results
Calculate statistics across all simulations: median return, worst drawdown at various confidence levels, probability of hitting goals or ruin.
Detailed Calculation Example
Let us walk through a practical example with real numbers.
Historical Performance (50 trades):
- Starting capital: $50,000
- Win rate: 60% (30 winners, 20 losers)
- Average win: $800
- Average loss: $500
- Total profit: $14,000 (28% return)
- Actual max drawdown experienced: -8%
Monte Carlo Results (10,000 simulations):
- Median ending balance: $64,000 (28% return - same expected value)
- 5th percentile ending balance: $58,500 (17% return)
- 95th percentile ending balance: $69,500 (39% return)
- Worst simulation ending balance: $52,000 (4% return)
- Best simulation ending balance: $76,000 (52% return)
Drawdown Analysis:
- Median max drawdown: -11%
- 95th percentile max drawdown: -18%
- Worst-case max drawdown: -26%
This reveals that while you experienced an 8% drawdown, there was significant risk of much larger drawdowns given the same trades in a different order.
Interpreting Confidence Levels
Monte Carlo results are typically presented at different confidence levels:
| Confidence | Meaning | Use Case |
|---|---|---|
| 50% (median) | Half of outcomes better, half worse | Realistic expectation |
| 95% | 19 out of 20 outcomes better | Conservative planning |
| 99% | 99 out of 100 outcomes better | Worst-case planning |
Probability of Ruin Calculation
One of the most valuable Monte Carlo outputs is probability of ruin - the chance of hitting a devastating drawdown level.
Example calculation:
- Run 10,000 simulations
- Count simulations where drawdown exceeded 30%
- Result: 312 out of 10,000
- Probability of 30%+ drawdown: 3.12%
Use this to set appropriate position sizes. If 3.12% probability of 30% drawdown is too high, reduce position sizes and rerun the simulation.
Monte Carlo for Strategy Validation
Monte Carlo can help determine if your strategy has a real edge or if results were just luck.
Statistical Significance Test: If 95% of Monte Carlo simulations show positive returns, your strategy likely has a real edge. If only 60% show positive returns, the results might be due to luck.
Validation Example:
- Run 10,000 simulations of your 100 trades
- 9,847 simulations ended profitable
- Probability of positive return: 98.47%
- Conclusion: Strategy likely has genuine edge
Advanced: Parametric Monte Carlo
Instead of just shuffling actual trades, advanced Monte Carlo generates new synthetic trades based on your statistics:
- Calculate win rate, average win, average loss, and standard deviations
- Generate random trades following these distributions
- Run thousands of simulations with synthetic data
This approach can generate unlimited scenarios and accounts for trades outside your historical range.
Common Monte Carlo Applications
Setting Stop Losses
Run Monte Carlo to find the drawdown level that is only exceeded 5% of the time. Set your maximum drawdown stop at that level.
Position Sizing
Adjust position sizes until Monte Carlo shows acceptable drawdown probabilities at your desired confidence level.
Goal Setting
Determine realistic profit targets by looking at median outcomes rather than best-case scenarios.
Risk Budgeting
Allocate capital across strategies based on their Monte Carlo risk profiles.
Limitations to Consider
Monte Carlo is powerful but has important limitations:
- Assumes trade independence: Real trades may be correlated (winning streaks, losing streaks)
- Based on historical data: Future market conditions may differ
- Sensitive to sample size: Need sufficient trades for reliable results
- Does not predict black swans: Extreme events outside historical experience are not captured
Build Your Trade History
Pro Trader Dashboard automatically tracks all your trades, building the historical dataset you need for Monte Carlo analysis.
Practical Implementation Tips
- Minimum data: Use at least 50 trades, preferably 100+
- Run many simulations: 10,000 is standard, 100,000 for critical decisions
- Update regularly: Rerun analysis as you accumulate more trades
- Use conservative estimates: Plan for 95th percentile drawdowns, not median
- Consider correlation: If trades cluster (streaks), standard Monte Carlo underestimates risk
Summary
Monte Carlo simulation is an essential tool for understanding trading risk. By running thousands of scenarios based on your actual trading data, you gain insight into the full range of possible outcomes - from best case to worst case. Use it to validate your strategy, set realistic expectations, determine appropriate position sizes, and plan for drawdowns. While it has limitations, Monte Carlo provides far better risk assessment than looking at a single historical equity curve.
Learn more about Value at Risk or probability of profit.