Trade Simulator

Enter starting capital, risk percent, win rate and average R to run a Monte Carlo style sequence simulation.

Summary: Simulate a sequence of trades with win rate and R-multiples to project account growth and drawdowns.

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Calculator Inputs

Starting Capital ($)

Win Rate (%) help_outline

Average Winning Trade ($)

Average Losing Trade ($)

Risk Per Trade (%) help_outline

Number of Trades to Simulate

insights

Expectancy Multiplier: 1:2.0 RR

At 50% Win Rate and 1:2.0 Payoff Ratio, each simulated execution produces a theoretical mathematical expectancy of +$50.00 / trade.

analytics

Statistical Edge Profile

+16.7% Edge

Breakeven Win Rate 33.3%

Profit Factor 2.00x

Optimal Kelly Fraction 25.0%

Payoff Multiplier 1:2.0 RR

Your strategy requires a minimum 33.3% win rate to break even at current risk/reward ratios. Sizing within optimal limits mitigates sequence risk during variance clusters.

Primary Results

Deterministic calculation alongside empirical 100-run distribution bounds

Deterministic Expectancy

Assumes perfectly balanced win/loss distribution

Expected Final Capital trending_up

$15,000.00 +50.00% Net

Expected Return (ROI) percent

+50.00% Nominal Yield

Expected Max Drawdown trending_down

-8.50% -$850.00 MDD

Expected Net Profit attach_money

+$5,000.00 Expectancy: $50/trade

Monte Carlo Bounds (100 Runs)

Evaluates sequencing dispersion & streak severity

Worst Case (99th %ile)

$8,420.00

-15.80% capital impairment

Median Case (50th %ile)

$14,890.00

+48.90% capital gain

Worst Case Drawdown warning

-26.40%

Deepest run sequence

data_thresholding Sequence Risk Probabilities:

Prob. of 10% DD: 68.2% Prob. of 20% DD: 18.4% Prob. of Ruin: 0.00%

Monte Carlo Equity Trajectory Visualizer

100 stochastic permutations vs. deterministic linear trajectory

Top 5% Path ($19.2k)

Median Path ($14.9k)

Deterministic

Worst 1% Path ($8.4k)

Upper 95% Band: High upside scenario

50th Median: Convergence follows deterministic slope

Sequence Impact: Drawdown during adverse loss clusters

Sequence Risk Permutation Matrix

How order distribution variability alters drawdown depth even when final win-rate remains constant

Scenario	Sequence Pattern	Max Drawdown	Final Equity	Sequence Edge Impact

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Sequence Risk & Monte Carlo Simulation Mechanics

Your deterministic expectancy is what happens if wins and losses are evenly spaced. However, real trading involves Sequence Risk—unpredictable streaks of consecutive losses.

Why simulate?

A Monte Carlo simulator generates hundreds of random permutations of your trades based on your win rate. It proves that even a highly profitable system can suffer a massive drawdown if a cluster of losing trades hits early in the sequence.

Disclaimer: This is a simulation based on fixed historical/theoretical averages. It does not predict future market conditions, slippage, or psychological errors.

functions

The Mathematical Derivation of Expectancy

Expectancy (E) = (Win Rate × Avg Win) - (Loss Rate × Avg Loss)

Worked Calculation: With your current baseline settings of a 50% win rate ($200 win) and 50% loss rate ($100 loss):
(0.50 × $200) - (0.50 × $100) = $100 - $50 = +$50.00 per trade.
Over a deterministic sequence of simulated trades, total expected profit equals: 100 × +$50.00 = +$5,000.00.

Frequently Asked Questions

What is sequence risk and why does it cause trader failure? expand_more

Sequence risk is the danger that the timing of adverse outcomes harms your portfolio before compounding can take effect. Even if a trader has an edge with a 60% win rate, experiencing 8 consecutive losses in the first 15 trades can impair psychological discipline or trigger account stop-outs.

How many simulation runs are needed for statistical confidence? expand_more

While 100 runs offer a high-velocity snapshot of sequence variance, institutional quantitative models typically run between 500 to 1,000 runs to accurately populate the extreme 99th percentile and 1st percentile tail risks.

How does position sizing percentage impact risk of ruin? expand_more

Risk of ruin increases non-linearly. Risking 1% per trade creates negligible probability of total ruin (<0.01%), whereas scaling to 5% risk per trade can elevate ruin probability past 25% during normal negative variance streaks.

Can a positive expectancy system blow up an account? expand_more

Yes. If a strategy's bet sizing exceeds the optimal Kelly Criterion threshold or fails to adapt to volatility clustering, early sequence drawdowns can reduce capital to zero despite theoretical long-run positive mathematical expectation.

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