Monte Carlo Trading Simulator

Visualize how variance affects your trading outcomes

How to use this simulator:
1 Enter your Win Rate (historical accuracy), Reward-to-Risk Ratio (average win ÷ average loss), and Risk per Trade
2 Click Run Simulation to execute 1,000 Monte Carlo iterations of your strategy
3 Analyze the equity curves to see how luck/variance creates different outcomes from the same strategy

Strategy Parameters

Expected Value per Trade: +0.50R
Optimal Kelly: 25% (Half: 12.5%)

Equity Curves (50 of 1,000 simulations)

Ready to simulate
Profitable paths
Losing paths
Median outcome

Simulation Results (1,000 iterations)

Median Final
$15,892
+58.9%
Best Case (95th)
$28,445
+184.5%
Worst Case (5th)
$7,234
-27.7%
Avg Max Drawdown
-18.3%
from peak
Probability of Profit
78.4%
ended above starting
Prob. of Ruin (50%)
2.1%
hit 50% drawdown
Worst Drawdown Seen
-42.7%
across all simulations
Strategy Assessment:

Your strategy has positive expectancy (+0.50R per trade). With 2% risk per trade, you have a healthy balance between growth and risk. The 78% probability of profit after 100 trades shows this is a robust strategy—but notice how some paths still lose money due to variance.

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What is Monte Carlo Simulation in Trading?

Monte Carlo simulation is a computational technique that uses random sampling to understand the range of possible outcomes for a trading strategy. Named after the famous Monaco casino, it acknowledges that trading (like gambling) involves randomness—even with an edge, individual outcomes are uncertain.

This simulator runs 1,000 independent simulations of your strategy. Each simulation randomly determines whether each trade wins or loses based on your win rate, then calculates the resulting equity curve. By visualizing 50 of these paths, you can see how different "luck sequences" create vastly different outcomes—even with identical strategy parameters.

Why Variance Matters More Than You Think

Most traders underestimate variance. Consider a 55% win rate with 1.5:1 R:R—a clearly profitable strategy with +0.325R expected value per trade. Yet over 100 trades:

  • ~12% of the time, you'll experience 8+ losing trades in a row
  • ~5% of the time, you'll be underwater after 100 trades
  • Maximum drawdowns of 30-40% are common, even for winning strategies

Understanding this helps you: (1) size positions appropriately, (2) avoid abandoning good strategies during drawdowns, and (3) set realistic expectations for your trading journal.

Key Metrics Explained

Metric What It Means Why It Matters
Expectancy Average R earned per trade Must be positive for profitability
Kelly % Mathematically optimal risk % Maximizes growth; use half-Kelly for safety
Prob. of Ruin Chance of hitting 50% drawdown Should be under 5% for sustainable trading
Max Drawdown Largest peak-to-trough decline Can you psychologically handle this?
Median Outcome Middle result (50th percentile) More realistic than average (not skewed by outliers)

Frequently Asked Questions

A Monte Carlo simulation runs thousands of random scenarios based on your trading parameters (win rate, risk-reward ratio) to show the full range of possible outcomes. Each simulation randomly decides whether trades win or lose according to your probabilities. This reveals how variance/luck affects results—showing that even profitable strategies can temporarily lose money, and losing strategies can temporarily appear profitable.

Your strategy is mathematically profitable if it has positive expectancy. Calculate it as: Expectancy = (Win Rate × R:R) - (Loss Rate × 1). For example, 50% win rate with 2:1 R:R = (0.50 × 2) - (0.50 × 1) = +0.50R per trade. This means on average, you gain 0.5 times your risk amount per trade. The Monte Carlo simulation shows how this expectancy translates to real-world outcomes with variance.

Probability of ruin is the statistical chance that a trader will lose a critical percentage of their account (typically defined as 50%) before recovering. Even strategies with positive expectancy can have significant ruin risk if position sizing is too aggressive. A 5% or lower probability of ruin is generally considered acceptable. This simulator calculates ruin probability by counting how many of the 1,000 simulations hit a 50% drawdown.

The Kelly Criterion calculates the theoretically optimal position size to maximize long-term growth: Kelly% = (Win% × R:R - Loss%) / R:R. However, full Kelly is extremely volatile and most professional traders use "half Kelly" or less. For example, with 50% win rate and 2:1 R:R, Kelly = 25%, so half Kelly = 12.5%. If Kelly comes out negative, your strategy has negative expectancy and shouldn't be traded.

Due to variance (randomness), profitable strategies can experience extended losing streaks. A 60% win rate strategy has a 0.4^10 = 0.01% chance of losing 10 trades in a row, and a 6.5% chance of losing 5 in a row. Over 100 trades, you're almost guaranteed to hit at least one 5-loss streak. The Monte Carlo visualization shows this—some "unlucky" paths lose money even when the strategy is mathematically sound. This is why proper risk management is essential.

Professional traders typically risk 1-2% per trade. Higher risk (3-5%) accelerates both gains AND losses, leading to larger drawdowns. Use the Monte Carlo simulator to see how different risk levels affect your outcomes. A good rule: your probability of ruin should be under 5%, and you should be psychologically comfortable with the maximum drawdown shown. If 2% gives 30% drawdowns, dropping to 1% would roughly halve that to 15%.

Monte Carlo simulations are statistically sound but depend on accurate inputs. The simulation assumes: (1) constant win rate and R:R over time, (2) independent trades (no correlation), and (3) fixed position sizing rules. Real trading has changing market conditions, so think of results as "if your parameters hold, this is the range of outcomes." The more trades you simulate, the more the median converges to expected value—but short-term variance remains significant.

This tool runs 1,000 simulations, which provides statistically reliable results. Running more simulations gives more precise percentile estimates but 1,000 is generally sufficient for trading analysis. The 50 equity curves displayed are randomly sampled from all 1,000 simulations to give a visual representation without cluttering the chart.

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