Position sizing ties together three things: how often you win (win rate), how often you lose (loss rate), and how big your wins are compared with your losses (risk reward ratio, or R:R). Once you know these, you can estimate expectancy and use the Kelly criterion to size positions. This guide explains the link between win/loss/R:R and how Kelly can inform (but not replace) your risk rules. For basics, see position size and risk reward ratio.
Win Rate, Loss Rate and R:R
Win rate is the fraction of trades that are profitable (e.g. 50% means half your trades win). Loss rate is the rest: 1 − win rate. Win rate alone does not tell you if you make money; you can win 60% of the time and still lose if your losing trades are much larger than your winners.
Risk reward ratio (R:R) compares the size of a typical win to the size of a typical loss, often in units of R (one R = one unit of risk). If you risk 1R per trade and your winners average 2R, your R:R is 1:2. Win rate and R:R together determine expectancy: the average outcome per trade in R.
Expectancy and Edge
Expectancy (per trade, in R) is:
If you lose 1R on every loss, the second term is just loss rate. If your winners average 2R, then expectancy = (win rate × 2) − (loss rate × 1). For example, 50% win rate and 2R average win: 0.5×2 − 0.5×1 = 0.5R per trade. Positive expectancy means you have an edge; the size of that edge drives how aggressively you can size (e.g. via Kelly).
The Kelly Criterion
The Kelly criterion answers: what fraction of capital should you risk per trade to maximize long-term growth, assuming you know your true win rate and win/loss ratio? The formula (in its simple form for binary outcomes) is:
where p = win rate, b = ratio of win size to loss size (e.g. 2 for 1:2 R:R). So if you win 50% and win 2R when you win and lose 1R when you lose: f* = 0.5 − 0.5/2 = 0.25. Kelly suggests risking 25% of capital per trade in that ideal case. In practice that is far too aggressive: variance and estimation error make full Kelly dangerous. Most traders use fractional Kelly (e.g. half-Kelly or less) or a fixed risk cap (e.g. 1% per trade) and use Kelly only as a sanity check.
How to Use Kelly in Practice
Use your actual win rate and average R when you win (from your journal or backtest), not a guess. Plug them into Kelly to see what the math says. Then risk a fraction of that (e.g. 25% or 50% of f*) or stick to a fixed rule like 1% per trade. Kelly is useful to spot when you might be under-sizing (positive edge but tiny risk) or over-sizing (Kelly says 5% but you risk 10%). It does not replace a simple rule like the 1% rule; it complements it by linking size to edge.
Track win rate, loss rate and R:R in your journal and in performance insights so you can update your estimates over time. Combine that with a 1% risk rule and a minimum R:R for a disciplined approach.
Frequently Asked Questions
- What is the Kelly criterion in trading?
- The Kelly criterion is a formula that suggests what fraction of capital to risk per trade to maximize long-term growth, given your win rate and win/loss size ratio. Full Kelly is often too aggressive; many traders use half-Kelly or less.
- How do win rate and R:R affect expectancy?
- Expectancy per trade = (win rate × avg win) − (loss rate × avg loss). With R:R, avg win and avg loss are in units of R. So expectancy in R = (win rate × avg R when you win) − (loss rate × 1). Higher win rate or higher R when you win improves expectancy.
- Should I use Kelly for every trade?
- Kelly assumes you know your true win rate and R:R, which in practice you only estimate. Using full Kelly is volatile; half-Kelly or a fixed fraction (e.g. 1% risk) is common. Kelly is a reference, not a mandate.
- What is loss rate?
- Loss rate is the fraction of trades that lose (1 − win rate). Together with win rate and the size of wins vs losses (R:R), it determines whether your system has positive expectancy.