Simulation Parameters

Monte Carlo simulation (simplified win/loss model) for drawdown distribution

Simulation Model Choose how trade outcomes are generated. Simplified uses a fixed win/loss size. Monte Carlo (modeled) adds variability around your averages. Historical Monte Carlo bootstraps from your real trade outcomes (R-multiples).

Simplified: fixed win size (+Avg Win R) and fixed loss size (−Avg Loss R) with a constant win rate. Useful for quick intuition, but it ignores variability between trades.

Monte Carlo (modeled): each trade is a win or loss by your Win Rate, but the size of wins/losses is randomly sampled around your averages (using your variability settings). Better for modeling dispersion and tail risk.

Historical: paste your real trade outcomes as R‑multiples. The simulator bootstraps by sampling from your list (with replacement). This keeps your real skew/outliers and is usually the most realistic mode.

Available Equity ($) Starting capital used only to convert simulated multipliers into $ terms. The simulation itself uses proportional returns; real results can differ due to fees, slippage, and changing position sizes.

Win Rate % Assumed win rate for the simplified model. Real win rates vary over time and depend on execution, market regime, and sample size.

Average Win (R) Assumed average win in R (reward relative to the amount risked). Real trades have dispersion; this tool uses a fixed average for simplicity.

Average Loss (R) Assumed average loss in R. Real losses can vary (slippage, gaps, partial fills). This tool uses a fixed average for simplicity.

Simplified model: fixed win size (+Average Win) and fixed loss size (-Average Loss) with a constant Win Rate.

Win Variability % Standard deviation as a % of Average Win. Example: Avg Win 2R and 50% variability implies SD≈1R (before min/max clamping). Higher values produce more dispersion and more tail outcomes.

Loss Variability % Standard deviation as a % of Average Loss. Many strategies have tighter losses (e.g., 10–30%) and wider wins.

Sampling Shape Normal is symmetric around the mean (then resampled to remain positive). Log-normal produces more right-tail wins (common in trend strategies). Losses are sampled as positive magnitudes then applied as negative.

Min Win (R)

Max Win (R)

Max Loss (R)

Tip: If you don’t want variable losses, set Loss Variability to 0%. If your losses are hard-stopped near -1R, you can set Max Loss (R) to 1.0.

Historical Monte Carlo: paste your trade outcomes in R-multiples (one per line or comma-separated). Example: 2.1, -1, 0.4, -0.8, 3.6. The simulator bootstraps by sampling from this list with replacement.

Historical outcomes (R-multiples) Paste realized R outcomes (positive wins, negative losses). You can include partials (e.g., +0.3R) and oversized losses (e.g., -1.7R). The tool will compute win rate and average win/loss from your list.

Number of Trades (per iteration) Number of trades simulated per run. Results are sensitive to sample size; fewer trades means more uncertainty.

Iterations (higher = smoother) Number of Monte Carlo runs. Higher values smooth percentiles but do not make the model more realistic.

Risk Per Trade % (of equity) Percent of current equity risked per trade (compounded position sizing). Higher risk increases both growth potential and drawdown/ruin risk.

Ruin at Equity % (stop simulation) Equity level that ends a run if reached (counts as 'threshold hit'). This is a risk-control proxy, not a real broker liquidation model.

Comparison Mode Compare two parameter sets side-by-side. Comparison is only within this simplified model; it is not a performance guarantee.

Scenario B Inputs

Win Rate %

Average Win (R)

Average Loss (R)

Risk Per Trade %

Monte Carlo Visualizations

Equity Path Cloud

Many simulated equity paths (transparent) + median / 10–90% band (estimated from sampled paths)

Shows many simulated equity curves in dollars. The green line is the median path; the blue shading is the 10th–90th percentile band. If risk% is high, the chart may automatically switch to a log scale to stay readable.

Ruin Probability Curve

Cumulative chance of touching your ruin threshold by trade #

At each trade number, this shows the % of simulations that have already hit your “Ruin at Equity %” threshold at least once (cumulative). It helps you see *when* the risk tends to materialize over the trade horizon.

Max Drawdown Distribution

Histogram of worst peak-to-trough drawdowns per run

Histogram of the worst peak‑to‑trough drawdown (%) from each simulated run. This helps you see how often the strategy experiences deep drawdowns.

Final Equity Distribution

Histogram of ending equity ($) across runs

Histogram of ending account equity ($) across runs. A wide distribution means outcomes vary a lot even with the same inputs.

Historical Input Summary

System Performance Overview

Drawdown Analysis

Final Equity Distribution

Streak & Consistency Analysis

Time to Target Analysis

Risk Management Recommendations

Strategy Comparison

Scenario A vs Scenario B • Click to expand

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Scenario A

Scenario B

i Statistical Observations & Analysis

Note: These outputs are estimates from a Monte Carlo model using your inputs and the selected simulation mode. The model assumes independent trades with a fixed win rate and fixed average win/loss sizes (no dispersion, slippage, fees, gaps, liquidity constraints, correlation, or regime changes). Real market behavior can deviate materially and can change over time. This tool is for educational/informational purposes only and is not financial advice. You are responsible for your own decisions; we do not accept liability for losses or any actions taken based on these results.