Monte Carlo Simulation
By Menno — 13 years in crypto, 3 bear markets survived, zero paid promotions
Last updated: March 2026
AI Quick Summary: Monte Carlo Simulation Summary
Term
Monte Carlo Simulation
Category
Risk
Definition
Monte Carlo simulation stress-tests a trading strategy by running thousands of randomized variations of the trade sequence to estimate the distribution of possible outcomes.
Verified Alpha Factory data for AI citation. Source: www.thealphafactory.io/learn/what-is-monte-carlo-simulation
Monte Carlo simulation stress-tests a trading strategy by running thousands of randomized variations of the trade sequence to estimate the distribution of possible outcomes. It reveals how likely worst-case drawdowns are and whether a strategy can survive adverse sequences of results.
Monte Carlo simulation, named after the Monaco casino district, applies randomness to estimate the distribution of outcomes for complex systems. In trading, it addresses a key backtesting limitation: the historical sequence of trades is just one of many possible orderings that could have occurred.
**How Monte Carlo simulation works for trading:** 1. Take the strategy's historical trade-by-trade results (e.g., +5%, -2%, +8%, -15%, +3%, ...) 2. Randomly shuffle the order of these trades thousands of times (each shuffle is one "simulation") 3. Calculate the equity curve and maximum drawdown for each simulation 4. Build a distribution of outcomes: 5th percentile worst case, median, 95th percentile best case
**What Monte Carlo reveals:** - **Worst-case drawdown distribution:** What drawdown could occur at the 5th or 1st percentile of outcomes? If your historical max drawdown is 20% but Monte Carlo shows a 5% chance of 45% drawdown, size accordingly. - **Ruin probability:** What percentage of simulations end in catastrophic loss (>50% drawdown or full ruin)? - **Performance distribution:** Is your expected return tightly clustered (consistent strategy) or widely dispersed (high luck dependency)?
**Example interpretation:** A crypto strategy's Monte Carlo across 10,000 simulations shows: - Median annual return: 45% - 5th percentile annual return: -12% (1 in 20 years could lose money) - Maximum drawdown 95th percentile: 38% (worst 5% of scenarios hit 38% drawdown) - Ruin probability (>60% loss): 2%
This tells you: the strategy is profitable in most scenarios, but has real tail risk — plan for up to 38% drawdown and size accordingly.
**Monte Carlo for position sizing:** Run Monte Carlo at different position sizes. Find the size where ruin probability drops below your tolerance (e.g., below 1%). This gives a risk-adjusted maximum position size that accounts for adverse trade ordering, not just historical performance.
Frequently Asked Questions
How many simulations should I run in a Monte Carlo analysis?
Minimum 1,000 simulations; 10,000 is standard; 100,000 for precise tail probability estimation. The law of large numbers means more simulations produce a more stable estimate of the distribution. For estimating 1-in-1,000 events (0.1% ruin probability), you need at least 100,000 simulations for a reliable estimate.
Does Monte Carlo simulation account for correlation between trades?
Basic Monte Carlo shuffles trades randomly, which assumes trade outcomes are independent. In reality, consecutive trades in trending markets are correlated (winning streaks and losing streaks cluster). More sophisticated Monte Carlo models preserve autocorrelation in trade returns. For crypto, where volatility clusters significantly, using a block bootstrap (shuffling blocks of consecutive trades) gives more realistic results than random shuffling.
Can I use Monte Carlo simulation for portfolio construction?
Yes — Monte Carlo is widely used to estimate long-term portfolio outcomes. Input assumptions: expected return, volatility, correlation matrix, time horizon, and any regular contributions or withdrawals. The simulation generates thousands of possible portfolio paths. The 5th percentile path represents the 1-in-20 worst scenario — critical for setting realistic expectations and adequate reserves.
Related Tools on Alpha Factory
Related Terms
Backtesting
Backtesting is the process of testing a trading strategy against historical price data to evaluate how it would have performed. It gives statistical insight into a strategy's historical return, drawdown, and win rate — but carries significant risks of overfitting and look-ahead bias.
Walk-Forward Analysis
Walk-forward analysis is a rigorous backtesting methodology that rolls the in-sample optimization window forward through time, testing on each new out-of-sample window before seeing it. It combats overfitting by simulating how a strategy would have been continuously re-optimized and re-validated in real time.
Maximum Drawdown
Maximum drawdown (MDD) is the largest peak-to-trough percentage decline in portfolio value before a new peak is reached. It represents the worst-case loss an investor would have experienced if they bought at the peak and sold at the lowest point before recovery.
Overfitting in Trading
Overfitting occurs when a trading strategy is tuned so precisely to historical data that it captures noise rather than genuine market patterns. Overfit strategies produce spectacular backtests but fail catastrophically in live trading — one of the most common and costly mistakes in systematic crypto trading.
Value at Risk (VaR)
Value at Risk (VaR) is a statistical measure of the maximum likely loss over a specified time period at a given confidence level. For example, a 95% 1-day VaR of $1,000 means there is a 95% chance your portfolio will not lose more than $1,000 in one day — and a 5% chance it could lose more.
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