# How much to bet: Kelly, risk of ruin and *time in the market*

Author: Jiří Fabšic · Jul 6, 2026 · Category: Risk
Canonical URL: https://www.binaryfintech.com/en/blog/position-sizing-kelly/

> Position sizing decides whether an account survives: the Kelly criterion and its formula, why fractional Kelly is the practice, sizing for the left tail of the distribution — and why to measure market exposure (time in the market).

## TL;DR

- **What decides an account's survival is often not the entry signal but the position size.** The same strategy with double the sizing is not twice as good — it can be the road to ruin.
- **The Kelly criterion** gives the mathematical optimum of bet size. In practice you bet a fraction of it — the parameters are only estimates, and overbetting the optimum is worse than underbetting it.
- **Return is not the only axis.** Measure the share of time in the market (market exposure) too: capital the strategy is not holding in a position can work elsewhere — and that changes which strategy is “better”.
- **Risk of ruin is monitored and has guardrails.** Extreme scenarios and losing-streak lengths are known in advance from walk-forward and Monte Carlo — a strategy that crosses them is switched off. Cutting losses follows rules defined upfront.

Take two traders, give them **the same strategy** with the same signals — and let each choose just one number: what percentage of capital to bet per trade. In five years, one multiplies the account, the other ends at zero. No difference in logic, no difference in data; only bet size. Position sizing is the most underrated decision in systematic trading — and the only one entirely under your control.

## Kelly: the mathematics of the optimal bet

The question “how much to bet” was settled mathematically in 1956 by John L. Kelly at Bell Labs — curiously, in a paper about information transmission, not markets. For a repeated bet with win probability W and a ratio of average win to average loss R, the optimal fraction of capital is:

f* = W − (1 − W) / R

Example: a strategy wins 55 % of the time (W = 0.55) and the average win is 1.5× the average loss (R = 1.5). Then f* = 0.55 − 0.45/1.5 = 0.25 — Kelly says bet 25 % of capital. This f* maximizes the **long-run geometric growth rate** — no other constant fraction compounds capital faster. Edward Thorp carried the formula into practice: first beating blackjack with it, then Wall Street.

## Why nobody sane bets full Kelly

The Kelly curve has an unpleasant shape. To the left of the optimum you grow more slowly, but more calmly. **To the right of the optimum growth declines — and beyond twice f* you lose money in the long run even with a winning strategy.** An overbet winning wager is a losing wager; volatility eats more than the edge earns.

*Figure: Left of f* you grow more slowly but more safely. Beyond ~2× f*, even a winning strategy loses. That is why practice bets a fraction.*

And there is a second, more practical problem: **you do not know W or R.** You only have estimates from a backtest — and as we wrote [about overfitting](https://www.binaryfintech.com/en/blog/overfitting/), estimates from history tend to be systematically inflated. A Kelly computed from embellished numbers sits to the right of the true optimum — precisely in the zone where winning turns into losing. Hence the craftsman's rule: **bet a fraction of Kelly.** Half Kelly keeps roughly three quarters of the growth rate at roughly half the volatility — and, above all, creates a cushion against estimation error. Quarter Kelly is common for strategies with a short history.

## Size for the left tail, not the average

Kelly assumes you know the distribution of outcomes. But markets have [fat tails](https://www.binaryfintech.com/en/blog/black-swans-fat-tails/) — the extreme day arrives more often than the textbook allows. The practical consequence for sizing: **position size is dimensioned by the left tail of the distribution, not by the average trade.** The question is not “how much do I usually make”, but “what happens to the account when the fifth percentile arrives — and do I survive it?” Ruin is an absorbing state: zero does not compound. That is exactly why we read results as [Monte Carlo distributions](https://www.binaryfintech.com/en/blog/monte-carlo-simulation/) — sizing is set against the worst-case boundary we see there.

## Guardrails set in advance: when a strategy is switched off

Risk of ruin, moreover, is not a one-off calculation at deployment — **it is monitored continuously.** Every strategy carries, from [walk-forward](https://www.binaryfintech.com/en/blog/walk-forward-analysis/) and [Monte Carlo analysis](https://www.binaryfintech.com/en/blog/monte-carlo-simulation/), its extreme scenarios known in advance, good and bad alike: we know what its best and worst predicted paths look like, and both must be acceptable for production before the strategy is given its first money. The same goes for **losing streaks** — how many consecutive losing trades still sit within the distribution is known upfront.

Then a hard rule applies: **a strategy that crosses its pre-set guardrails is switched off.** Nobody gets to live in the belief that the situation will turn around — it does not perform, it ends. We treat risk management strictly and programmatically: cutting a loss follows rules defined in advance, not in the heat of the moment. At the trade level this means that essentially every isolated trade has its own stop loss; and where it makes more sense, the whole bet is the stop loss — the position is sized so that even its full loss is acceptable, and respected, in advance.

## The second axis: time in the market

Annual return is only half the story. The other half: **what share of time did the strategy need to hold capital in a position to earn it?** This metric is called market exposure — the share of time “at work” — and we watch it in the results as closely as the return itself. Capital in a position is occupied; capital out of a position is a *free resource*.

A rhetorical question: which is better — strategy A, earning **50 % a year while in the market 60 % of the time**, or strategy B, earning **20 % a year while in the market only 50 % of the time**?

Standalone arithmetic is clear: A earns more per unit of exposure (50/60 ≈ 0.83 % per percent of time in the market, B only 20/50 = 0.40). If you may run only one, take A. But the question has a second layer: **strategy B leaves capital free half the time.** If an uncorrelated strategy C exists to fill those gaps, the portfolio B+C can compound a higher return with a smoother equity curve than A achieves alone — because the same pool of money works two independent sources of profit. So the honest answer is: *it depends on whether you are building a strategy or a portfolio.* And composing strategies over one pool of capital — running them concurrently — deserves its own article: [Strategy portfolios: one pool of money](https://www.binaryfintech.com/en/blog/strategy-portfolios/).

## How we work with it

Sizing here is not a gut number: position size leans on the distribution of outcomes (drawdown percentiles from Monte Carlo, not averages), a fractional approach to Kelly is the default, and **market exposure is reported with every test** — because without it, returns cannot be compared honestly and a portfolio cannot be composed honestly. The closing rule: signals decide whether you make money; **sizing decides whether you are still there when it happens.**

**Reading:** J. L. Kelly (1956): [A New Interpretation of Information Rate](https://www.princeton.edu/~wbialek/rome/refs/kelly_56.pdf) (original paper) · overview: [Kelly criterion](https://en.wikipedia.org/wiki/Kelly_criterion). Related articles: [Strategy portfolios](https://www.binaryfintech.com/en/blog/strategy-portfolios/), [Monte Carlo](https://www.binaryfintech.com/en/blog/monte-carlo-simulation/), [Black swans and Fat Tony](https://www.binaryfintech.com/en/blog/black-swans-fat-tails/). The full journey from idea to production: [the strategy testing protocol](https://www.binaryfintech.com/en/blog/strategy-testing-protocol/).