Back

Outcome Dispersion / Fat-tail Demo

New Lab

See why low win-rate, high-R systems have much wider outcome variance (fat tails) than high win-rate systems even when the underlying edge is identical.

How to use this Outcome Dispersion Simulator

Adjust win rate, average win, average loss, risk per trade, and total trades. Visualize outcome dispersion and see how variance affects equity curves in low-win-rate, high-reward-to-risk setups over a large series of trades.

Parameters

Account Capital (₹)
Risk Per Trade (%)
Constant System Edge
R
Trades per Run
qty
Simulations per Win Rate
runs
Simulation Seed

Same inputs + same seed = same result. Change seed to test a different path sample.

Outcome Variance Spread (Best vs. Worst P&L)

Difference between best and worst simulation outcomes over 200 trades with a 0.5R constant edge.

₹0
₹0
Win Rate Sweep (10% ➔ 90%)
The Uncertainty Premium: Notice how taller bars concentrate on the left (low win-rates). Systems with high risk-to-reward ratios but low win rates (e.g. 20% win-rate with 4.5R) have extreme final outcome variance due to streaks, even if they hold the exact same positive edge as a 70% win-rate system.
Educational Use Only

Educational only. Not financial advice. Results are simplified estimates based on the inputs you provide. They do not include brokerage, taxes, slippage, liquidity, bid-ask spread, margin rules, dividends, early exercise, exchange-specific contract rules, or emotional execution mistakes. Use this as a learning and planning tool, not as a trade recommendation.

Outcome Dispersion / Fat-tail Demo Guide

This tool shows how two systems with the same expectancy can feel very different. Low-win-rate, high-R systems often produce wider outcome dispersion and more emotionally difficult paths than high-win-rate systems with the same average edge.

How to use this tool

  1. 1Enter account capital and risk per trade so the result is shown in rupee terms.
  2. 2Choose a constant system edge. The tool adjusts R:R across win rates to keep expectancy similar.
  3. 3Run the simulation with a fixed seed to compare the same scenario repeatedly.
  4. 4Compare low win-rate and high win-rate systems by ending balance spread, not only expectancy.
  5. 5Use the tool to understand psychological variance, not to choose a strategy only by return.

Key metrics

Required R:R

The average win size required to maintain the selected expectancy at that win rate.

How to use it: Shows why low win-rate systems need much larger winners.

Outcome Spread

Difference between best and worst simulated ending balances.

How to use it: Wider spread means more uncertainty and more psychological pressure.

Median Ending Balance

Middle ending balance across simulations.

How to use it: Use this instead of best case. Best case can be emotionally misleading.

Worst Ending Balance

Worst simulated ending balance in the sample.

How to use it: Use this as a stress warning, not a guaranteed worst case.

Formulas used

Required R:R For Constant Edge

R = (Edge + Loss Rate) ÷ Win Rate

If expectancy is fixed, lower win rate requires larger average winners.

Trade P&L

Win = Risk Amount × R, Loss = −Risk Amount

Risk amount comes from account capital and risk percentage.

Common mistakes to avoid

  • • Do not call this a proof of statistical fat tails. It is an educational outcome-dispersion simulation.
  • • Do not choose a low-win-rate strategy only because the average edge is positive.
  • • Do not ignore long losing streaks and emotional pressure.