Position sizing: how much to risk per trade, and why Kelly is a ceiling not a target

By John, builder of Wise Apple · Updated August 27, 2026

Your strategy has a real edge — say 55% winners at 1.5-to-1. Sized at 2% risk per trade it makes a modest, steady return. Sized at 25% it makes a spectacular one right up until a normal losing streak removes most of the account. Same rules, same trades, same edge.

Sizing is the part of a strategy that most backtests get for free — a fixed lot, a fixed dollar amount, whatever the tool defaults to — and it is the part that decides whether an edge survives contact with a losing streak. A recurring theme on r/algotrading is a system that worked for months and then lost a year's gains in a day, and when the details come out the entry rule is usually fine. The size was wrong.

This guide covers the four sizing methods that matter, with the Kelly formula worked through so you can check it, why full Kelly is reckless even when it is correct, what volatility scaling does that the others cannot, and why the drawdown number to plan around is its length, not its depth.

Fixed fraction and fixed risk

The simplest method is a fixed fraction of equity per position — 10% of the account in each trade, say. It compounds naturally and needs nothing but the account balance. Its weakness is that it ignores how far the trade can go against you: 10% in a sleepy utility and 10% in a small-cap biotech are not the same bet.

Fixed risk fixes that. You decide how much of the account you will lose if the stop is hit — 1%, 2% — and work backwards to the position size from the stop distance. Van Tharp's Trade Your Way to Financial Freedom (1998) built a whole vocabulary on this: the amount at risk is one R, every outcome is measured in R-multiples, and a strategy's expectancy is its average R per trade. A wide stop means a small position; a tight stop means a large one; the loss on a stop-out is the same either way.

Fixed risk needs a stop to exist, which is one reason the exit rule and the sizing rule cannot be designed separately. It also needs the stop to be honoured — a gap through the stop costs more than 1R, and a backtest that fills every stop at its price is quietly optimistic about that.

The Kelly criterion, worked through

John Kelly's 1956 paper, "A New Interpretation of Information Rate", gave the fraction of a bankroll that maximises long-run growth for a repeated bet with known odds. For a simple bet that wins with probability p, pays b times the stake when it wins, and loses the stake otherwise, the fraction is:

f = p − (1 − p) / b

Take the strategy from the opening: 55% winners, average win 1.5 times the average loss. Then p = 0.55, b = 1.5, and:

f = 0.55 − 0.45 / 1.5 = 0.55 − 0.30 = 0.25

Kelly says risk 25% of the account per trade. That number is mathematically correct for those inputs and practically insane, for three reasons.

1. The inputs are estimates. You do not know p is 0.55; you measured 0.55 over some number of trades, with an error bar. Over 100 trades that bar is roughly ±10 points — see the sample-size guide. Run the formula at p = 0.45 and the Kelly fraction is 0.45 − 0.55/1.5 ≈ 0.08. At p = 0.40 it is negative: do not bet. A sizing rule that swings from 25% to "stay out" inside the error bar of its own inputs is not a rule.
2. Kelly maximises growth, not comfort. Full-Kelly sizing on a true edge still produces drawdowns that most people would abandon the strategy during. The growth is only realised if you keep betting through them.
3. Trades are not independent coin flips. Losses cluster. Regimes exist. The formula assumes each bet is a fresh draw from the same distribution, and markets do not cooperate.

Edward Thorp, who applied Kelly to blackjack and then to markets, made the practical recommendation in "The Kelly Criterion in Blackjack, Sports Betting, and the Stock Market" (2006): bet a fraction of Kelly. Half-Kelly gives up about a quarter of the long-run growth rate in exchange for roughly halving the size of the drawdowns, and it is far more forgiving when the estimated edge is wrong. Many practitioners go lower still. The useful reading of Kelly is as a ceiling: a size above it is certain to be too much, and a size well below it is where the estimation error lives comfortably.

Volatility scaling

Fixed risk sizes each trade from its stop. Volatility scaling sizes each position from how much the instrument moves. Robert Carver's Systematic Trading (2015) and Leveraged Trading (2019) lay it out: choose a target volatility for the position — the amount you want a typical day or week to move your account — measure the instrument's recent volatility, and set the size so the two match. A coin that moves 5% a day gets a fifth of the size of one that moves 1%.

Two things fall out of this that no other method gives you.

• Instruments become comparable. A strategy applied to a calm ETF and to a volatile crypto pair makes bets of the same economic size in each, so the backtest results can be read side by side and the portfolio does not end up dominated by whatever moves most.
• Size shrinks automatically when volatility rises. Crashes are high-volatility events. A volatility-scaled position is already smaller by the time the crash is under way, because the measured volatility climbed on the way in. It is a mechanical de-risking that needs no forecast.

The costs are that it needs a volatility estimate, which lags, and that it changes size as volatility changes, which means trades to rebalance and therefore costs. It also does nothing about the estimate of the edge — a volatility-scaled bet on a strategy with no edge is a well-sized bet on nothing. Wilder's ATR from New Concepts in Technical Trading Systems (1978) is the usual volatility measure on bar data, and it serves the stop and the size at once.

The four methods side by side

In practice the methods combine: a volatility-scaled position, capped so the stop-out never exceeds a fixed R, with the R itself set well below the Kelly ceiling. Each layer handles one thing the others do not.

Drawdown: depth is the number you quote, length is the one that hurts

Every report prints maximum drawdown as a percentage. It is the wrong number to plan around, for two reasons. The first is arithmetic: recovery is not symmetric. A 20% drawdown needs a 25% gain to recover. A 33% drawdown needs 50%. A 50% drawdown needs 100%. Size is what turns a 20% drawdown into a 50% one, and the recovery it then demands is not twice as hard but four times.

The second reason is psychological, and a thread that made the point well noted that nobody talks about how long a drawdown lasts, only how deep it goes. A 15% drawdown that lasts three weeks is forgettable. A 15% drawdown that lasts fourteen months is where strategies get abandoned — usually just before they recover. Time under water is the number that predicts whether you will still be running the system when it turns. Read it off the equity curve as the longest stretch between one peak and the next, and plan your size so that stretch is one you can sit through.

Leverage multiplies the error too

Leverage is a multiplier applied to everything: the edge, the costs, the drawdown, and the error in your estimate of all three. A fifteen-times-levered strategy that survived four years did so because something — a volatility target, a hard cap, a kill rule — kept the effective exposure far below fifteen most of the time. The headline number was the maximum, not the mean. If a strategy needs leverage to beat its benchmark, compare the benchmark at the same leverage first; the buy-and-hold guide covers why a multiplier is not an edge.

The kill rule

Every sizing scheme needs a rule for when to stop. Not a stop on a trade — a stop on the strategy. A drawdown deeper than the backtest ever showed, a losing streak longer than the backtest ever showed, or a live expectancy that has sat below zero for more trades than the error bars allow: any of those is evidence that the edge has changed, and the regime guide is about telling which. Write the rule down before the first trade, with numbers. A kill rule decided during the drawdown is just panic with a spreadsheet.

Putting it together

1. Measure the edge with its error bars — win rate, payoff ratio, expectancy in R — on out-of-sample trades only.
2. Compute Kelly from the pessimistic end of the error bar, and treat it as a ceiling.
3. Choose a fixed risk per trade well under that ceiling — a small single-digit percentage is where most survivable systems live.
4. Scale the position to the instrument's volatility, capped by the fixed-risk figure.
5. Read the drawdown's length off the backtest, assume live is worse, and check you can sit through it at that size.
6. Write the kill rule down with numbers, before the first trade.

How this looks in Wise Apple

Wise Apple exposes sizing as settings rather than a fixed lot: Kelly Fraction lets you run the simulation at a fraction of the Kelly size, from a win probability and a payoff ratio you enter — take both from the out-of-sample report, never the in-sample one — and Volatility Scaling sizes each position from recent volatility so a calm ETF and a volatile coin make comparable bets. The ATR Trailing Stop supplies the stop distance the fixed-risk arithmetic needs. The report shows maximum drawdown beside the HODL benchmark, and the candle-by-candle journal lets you read the length of each drawdown off the equity curve. The arithmetic is the same as on this page; the software just runs it on every trade.