Monte Carlo Simulator — Trading Strategy Stress Test
Run a Monte Carlo simulation on any trading strategy. Enter your win rate, average win/loss, and risk per trade to see the probability of profit, ruin, and the range of possible outcomes across hundreds of simulated paths.
Starting account size
Probability of winning each trade
Win/Loss Ratio (R-multiple): 1.50R
Sets the ratio with Average Loss
Sets the ratio with Average Win
Only the ratio counts — 6% / 4% and 3% / 2% give the same result: each losing trade costs Risk per Trade of the balance, each winning trade the ratio times that.
Sets the size: % of balance lost on a losing trade
Number of trades in each run
Monte Carlo iterations
Median Final Balance
$13,573
Median return +35.73%
Probability of Profit
80.60%
Probability of Ruin (ends <50% of start)
0.40%
P5 (Worst 5%)
$6,943
P10
$8,682
P25
$10,855
P75
$18,283
P90
$21,220
P95 (Best 5%)
$24,628
Mean
$14,551
Break-Even Win Rate
40.00%
EV per Trade
+$37.50
Median Max Drawdown
25.50%
* Uses percent-of-balance position sizing with a deterministic simulation seed. Results are consistent but may differ from real trading due to market conditions.
Last reviewed · How we check our calculators
How to Use the Monte Carlo Trading Simulator
Enter your trading system's key parameters: initial account balance, win rate (percentage of trades won historically), average win percentage and average loss percentage per trade, and the percentage of your account risked per trade (e.g. 1%). The two averages only set the win/loss ratio (the R-multiple shown above them: 6% / 4% is 1.5R, and so is 3% / 2%); risk per trade sets the size — a losing trade costs that share of the balance, a winning one the ratio times it. Set the number of trades per simulation run and the number of simulations (Monte Carlo iterations — more gives more accurate statistics).
The simulation reruns as you type and shows the full distribution of outcomes: the probability of ending in profit, probability of ruin (ending the run below half the starting balance), the median and mean final balances, and the 5th/25th/75th/95th percentile outcomes. The simulation uses percent-of-balance position sizing, meaning position sizes grow or shrink with your equity curve — a more realistic model than fixed-dollar sizing.
Use this tool to stress-test your strategy before live trading, compare different risk-per-trade settings, and understand the realistic range of outcomes — not just the average.
The Formula
The Monte Carlo simulator runs N independent simulations of T trades each, using your strategy's parameters. On each trade, a pseudo-random outcome determines win or loss, and position sizing follows a percent-of-current-balance model.
Position Sizing (Percent of Balance)
- Position Size = Balance × (RiskPerTrade% ÷ AvgLoss%)
- A loss always removes exactly RiskPerTrade% from the current balance
- A win adds: Balance × RiskPerTrade% × (AvgWin% ÷ AvgLoss%)
This models the R-multiple approach used by professional traders, where risk is defined as a fixed percentage of equity regardless of position size.
Break-Even Win Rate
- Break-Even Win Rate = AvgLoss% ÷ (AvgWin% + AvgLoss%) × 100
Expected Value per Trade
- EV = (WinRate × WinAmount) − (LossRate × LossAmount)
Probability of Profit
- P(profit) = (Simulations ending above InitialBalance) ÷ TotalSimulations × 100
Maximum Drawdown (per simulation)
- MaxDD = (Peak − Trough) ÷ Peak × 100
The median maximum drawdown across all simulations is reported — a more robust estimate than a single-run drawdown because it accounts for the full range of possible equity paths.
Practical Examples
Example 1 — A 1.5R Swing System at 3% Risk (the Calculator's Defaults)
A swing trader wins 45% of trades; winners average 6% and losers 4% (a 1.5R win/loss ratio), and each trade risks 3% of the account. 100 trades per simulation, 500 iterations.
- Break-even win rate = 4 ÷ (6 + 4) × 100 = 40% (45% is above it ✓)
- EV per trade = 45% × +4.5% − 55% × −3% ≈ +0.38% per trade
- Probability of profit after 100 trades: ≈81% of the 500 simulated paths
- Probability of ruin (ending below half the starting balance): 0.4%
- Median return: ≈ +36%; 5th–95th percentile range ≈ −31% to +146%
A real edge still ends about one run in five at a loss, and the unluckiest 5% of paths are down by roughly a third — the same system, different streaks. Lower the risk per trade to 1% and the losing paths shrink, along with the gains. Sizing for the bad paths is what risk management is for.
Example 2 — High-Frequency Scalping
Win rate: 65%, average win: 1.5%, average loss: 2.5%, risk: 0.5%, trades per run: 500.
- Break-even win rate = 2.5 ÷ (1.5 + 2.5) × 100 = 62.5%
- Edge above break-even: only 2.5 percentage points — thin margin
- EV per trade ≈ +0.02% of the account — very small, consistent gains
- Probability of ruin: low, but dependent on execution quality
High win rate scalping with negative R:R (win < loss) requires exceptional execution precision. The Monte Carlo reveals how sensitive the outcome distribution is to even small changes in win rate.
Example 3 — Underpowered System
Win rate: 45%, average win: 5%, average loss: 4%, risk: 2%.
- Break-even win rate = 4 ÷ (5 + 4) × 100 = 44.4%
- Edge: 45% − 44.4% = only 0.6% above break-even — effectively a coin flip
- Probability of profit after 100 trades (500 simulated paths): ≈54%
- Wide outcome distribution: some paths very profitable, others ruinous
This illustrates a common trap: a system that looks positive on paper can produce a nearly random outcome distribution in simulation, revealing it lacks a real statistical edge.
Frequently Asked Questions
A Monte Carlo simulation in trading runs hundreds or thousands of randomized sequences of your historical trade outcomes to estimate the range of possible results. Rather than showing a single equity curve (which is just one of many possible paths), it shows a distribution — the probability of profit, worst-case drawdowns, and the spread between good and bad luck scenarios. It was originally used in nuclear physics to model probabilistic systems, and was applied to finance to stress-test portfolios and trading strategies.
You need four statistics from your trading history: (1) win rate — the percentage of trades that closed in profit; (2) average win percentage — the average return on winning trades; (3) average loss percentage — the average loss on losing trades; (4) risk per trade — the percentage of your account you risk on each trade. These can come from a backtest, a forward-tested sample of at least 50 trades, or your trading plan's theoretical targets. Using real historical data from your own trading produces the most accurate simulation.
100 simulations give a rough picture. 500 provides solid statistical reliability for most purposes. 1,000 simulations produce stable percentile estimates that don't change much with additional iterations. For strategy validation before going live, 500 is generally sufficient. Running 100 first lets you quickly see if a strategy is viable, then 500–1,000 for final verification. Note that this calculator uses a deterministic seed — results are consistent across runs, so you always see the same statistical picture for the same inputs.
A strategy with a clear positive edge (win rate well above break-even) should show a probability of profit above 70–80% for 100-trade runs. If your probability of profit is below 60%, it suggests either your edge is very thin, your sample is small, or the system has negative expectancy. Professional systems targeting 1–2% monthly returns typically aim for probability-of-profit scores above 80% over 100 trades at 1% risk per trade. High-frequency systems may achieve 90%+ due to the law of large numbers smoothing out variance.
Probability of ruin is the percentage of simulations that end the run with the account below 50% of the starting balance. A run that dips below half and climbs back above it by the last trade does not count as ruined — the Median Max Drawdown figure shows how deep a typical path fell along the way. Losing half is considered 'ruin' in many risk management frameworks because recovering from a 50% drawdown requires a 100% gain just to break even, and most traders would stop trading or blow the account before that recovery. For strategies targeting 1% risk per trade, probability of ruin should be near zero for systems with positive EV. If ruin probability is above 10%, consider reducing risk per trade.
Sources
- Monte Carlo method (Wikipedia)
- Ralph Vince, The Mathematics of Money Management (Wiley, 1992)
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