Monte Carlo Simulator — Trading Strategy Stress Test

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%). Set the number of trades per simulation run and the number of simulations (Monte Carlo iterations — more gives more accurate statistics).

Click Run Monte Carlo Simulation to see the full distribution of outcomes: the probability of ending in profit, probability of ruin (losing 50% or more), 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 Monte Carlo simulator runs N independent simulations of Ttrades 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.

Example 1 — Consistent Profitable System

A forex day trader with a 55% win rate, 5% average win, 3% average loss, risking 1% per trade, runs 100 trades per simulation over 500 iterations.

• Break-even win rate = 37.5% (well below 55% ✓)
• EV per trade = 55% × +1.67% − 45% × −1% ≈ +0.47% per trade
• Probability of profit after 100 trades: typically 75–85%
• Median return: +30–50% depending on streak clustering

Even with a clear edge, the 5th percentile outcome may be below break-even — demonstrating why risk management matters even for profitable systems.

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 ≈ +0.025% — 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: 50–55%
• 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.