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Calculator

Kelly Criterion Calculator

Fractional Kelly bet-size calculator: full/half/quarter Kelly, Monte Carlo drawdown simulation, ruin probability, per-trade cap. Browser-only. Free.

Inputs
Form inputs / CSV
Runtime
Instant
Privacy
Client-side · no upload
API key
Not required

Education · Not investment advice. BaFin/EU framework. Past performance does not indicate future results. Editorial standards Sponsor disclosure Corrections

Inputs

55.0%
1.50×
10.0%
500
500
50%

Bet size

6.25%

quarter Kelly · within cap · Strong edge

Raw Kelly: 25.00%  ·  Fractional: 6.25%

Bankroll paths (Monte Carlo)

Median = line · 5–95% band = shaded

What this tool computes

Kelly fraction is the bet size that maximizes expected long-run log-growth of bankroll, given a known win rate and win/loss ratio. Fractional Kelly (half, quarter, eighth) reduces that size — a common practical choice because real-world win rates are estimated, not known, and full Kelly is unforgiving of overestimates.

The Monte Carlo simulator then runs N independent paths of the same strategy to show the distribution of outcomes — not just the expected value. The shaded band is the 5–95% confidence band. The ruin rate is the fraction of paths that touched the ruin threshold at least once.

How to use

Step-by-step

  1. 1

    Enter win probability p (decimal 0-1) and win/loss ratio b (avg win / avg loss). These are the only required inputs — no historical data upload needed.

  2. 2

    Pick a Kelly fraction (full / half / quarter / eighth). Quarter-Kelly is a sensible default for real-world strategies where p and b are estimated from data.

  3. 3

    Set a single-trade absolute cap (e.g., 5% of bankroll max). This bounds the worst case even if the formula recommends more.

  4. 4

    Run the Monte Carlo. Read median ending bankroll, 5th-percentile ending bankroll, max drawdown, and ruin rate together — high median with high ruin rate means the strategy gambles for growth.

  5. 5

    Re-run with a smaller Kelly fraction if ruin rate is non-zero. Re-run with longer horizon (more trades) to see how the distribution tightens.

For agents

Use in an agent

Same math, same result shape as the UI above — as a static ES module. No HTTP request, no auth, no rate limit.

import { compute } from "https://aifinhub.io/engines/kelly-sizer.js";

Contract: /contracts/kelly-sizer.json Full agent guide →

Questions people ask next

FAQ

Why does Kelly recommend a fraction of bankroll, not a fixed dollar amount?

Kelly's 1956 derivation maximizes the long-run expected log-growth of bankroll. Fixed dollars don't compound efficiently because winning bets aren't pressed and losing streaks don't shrink the bet size. Fractional sizing keeps geometric growth optimal; the cost is that drawdowns can be severe at full Kelly.

Should I bet full Kelly?

No, not when you estimated p and b from data. Full Kelly assumes you know the true win rate and win/loss ratio. With estimation error, full Kelly often turns into over-betting and ruinous drawdowns. Half-Kelly (0.5×) or quarter-Kelly (0.25×) is the standard real-world choice — Thorp and MacLean argue this in print.

What's the ruin rate output mean?

It's the fraction of simulated paths where bankroll fell below the threshold you set (typically 50% of starting capital). Ruin rate is sensitive to Kelly fraction, edge size, and number of trades. A non-zero ruin rate at quarter-Kelly is a sign your estimated edge is too thin or your trade count is too high.

Why does the simulator use Gaussian noise on outcomes?

Real strategies don't have flat $1 wins and $b losses — there's a distribution around the average. The simulator multiplies outcomes by (1 + 0.1·N(0,1)), floored at 0.1, to inject mild variance. This gives more realistic drawdown distributions than a deterministic model. Heavy-tailed strategies (e.g., option selling) need a smaller Kelly fraction than this Gaussian-noise floor implies.

Does Kelly work for trading?

Kelly was originally formulated for repeated independent bets with known parameters — closer to blackjack than markets. Trading violates several Kelly assumptions: edges drift (regime change), trades correlate (sector exposure), and outcome distributions have fat tails. Treat Kelly as a sizing ceiling, not a target. Most professional traders run far below Kelly-optimal.

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