Kelly Criterion Calculator

Half-Kelly means risking half of the percentage the full Kelly formula suggests it sacrifices some theoretical long-run growth for meaningfully lower volatility and drawdown risk, a trade-off many traders consider worthwhile. In practice, half-Kelly captures a large majority of full Kelly's theoretical growth advantage while producing a considerably smoother equity curve, which is why it's often recommended as a more practical default than the mathematically pure but far more volatile full-Kelly figure.

Since the formula is sensitive to input accuracy, an overestimated win rate can lead full Kelly to suggest a risk percentage that's too aggressive for your strategy's real performance fractional Kelly helps buffer against this by deliberately sizing well below the calculated ceiling. This sensitivity is precisely why relying on a small, potentially unrepresentative sample of trades to estimate win rate is risky the more genuine, logged trades your estimate is based on, the more trustworthy the resulting Kelly figure becomes.

Yes if your strategy's expectancy is negative or too low relative to the odds, the formula will suggest not risking capital on that setup at all, which is itself a useful signal rather than an error to work around. A negative Kelly output is the formula's way of confirming mathematically what a negative or breakeven expectancy already implies that the strategy, as currently defined, doesn't have a positive edge worth risking capital on.

No, it originated in probability theory for betting scenarios and applies to any repeated wager with a defined win probability and payout ratio, forex and CFD trading included. The same formula applies equally to share trading, sports betting, poker, and any other repeated-decision scenario with a definable edge, which is why Kelly sizing appears across such a wide range of fields beyond just financial trading.

The Kelly Criterion is optimal specifically for maximising the long-run geometric growth rate of capital, given a repeated series of wagers with a known, fixed win probability and payout ratio it's derived from maximising the expected logarithm of wealth, which is mathematically equivalent to maximising compound growth over many repetitions. It's important to understand what "optimal" doesn't mean here: it doesn't mean lowest risk, most comfortable to trade, or best for any specific individual's risk tolerance a Kelly-optimal fraction can still involve significant volatility and drawdowns along the way to that theoretically optimal growth rate. This is precisely why fractional Kelly approaches exist: they deliberately sacrifice some of that theoretical optimal growth rate in exchange for meaningfully smoother, less volatile equity curves, which most traders find preferable in practice even at the cost of slightly slower long-run compounding.

The standard Kelly formula assumes each wager is independent, which doesn't hold if you're simultaneously trading multiple positions that tend to move together several long USD pairs at once, for example, share a common underlying correlation and aren't truly independent bets in the way the formula assumes. Applying the single-trade Kelly percentage separately to each of several correlated positions effectively risks more of your capital on the shared underlying factor than the formula intends for any individual, isolated wager, since a single adverse move in that shared factor affects all the correlated positions simultaneously rather than just one. A more careful approach when holding multiple correlated positions is to treat them as a combined exposure and reduce the Kelly-derived size for each individual position accordingly, rather than applying the full single-trade Kelly fraction independently to every position regardless of how correlated they are with each other.

Not necessarily, since different strategies can have genuinely different win rates and reward-to-risk profiles, which means they'll produce different Kelly-optimal fractions when calculated individually. Applying a single blended fraction across multiple distinct strategies risks over-sizing the weaker strategy and under-sizing the stronger one relative to what each would suggest if calculated separately. It's more precise to calculate Kelly separately for each strategy based on its own genuine, individually tracked win rate and reward-to-risk ratio, then decide position sizing for each accordingly, rather than blending multiple approaches together into one average figure that doesn't accurately represent any single strategy's real edge.

The 2% rule and similar fixed-percentage approaches use a single, simple risk figure applied consistently regardless of a strategy's specific win rate or reward-to-risk profile, prioritising ease of use and consistency over mathematical precision. The Kelly Criterion instead calculates a theoretically precise, edge-specific optimal fraction from your actual win rate and reward-to-risk inputs, which can suggest a considerably different, and sometimes much higher, risk percentage than a generic fixed rule would. In practice, many traders use fixed-percentage rules specifically because they're simpler, more forgiving of estimation error, and don't require confidently knowing your exact win rate and reward-to-risk ratio, whereas Kelly's precision is also its main weakness it's highly sensitive to getting those inputs right, and confidently wrong inputs can lead Kelly to suggest a risk percentage that's considerably too aggressive for your strategy's real performance. A reasonable middle ground some traders use is calculating Kelly as a reference ceiling, then applying a fixed-percentage approach that stays comfortably below that ceiling, combining Kelly's edge-awareness with fixed-percentage's practical simplicity and error tolerance.

Quarter-Kelly risks a quarter of the full Kelly-suggested percentage, while half-Kelly risks half of it the difference in practice comes down to how much theoretical growth rate you're willing to sacrifice in exchange for reduced volatility. Half-Kelly captures roughly 75% of full Kelly's theoretical long-run growth rate while meaningfully reducing volatility and drawdown severity compared to full Kelly. Quarter-Kelly sacrifices more growth potential capturing considerably less of the theoretical maximum but reduces volatility and drawdown risk even further, producing a noticeably smoother equity curve. Many risk-conscious traders, particularly those with less confidence in the precision of their win rate and reward-to-risk estimates, prefer quarter-Kelly specifically because it provides a larger buffer against estimation error, given how sensitive the full Kelly figure is to inputs that are rarely known with complete certainty in real trading.

The most reliable inputs come from a genuine, sufficiently large sample of your own logged trades under consistent rules, ideally at least 50-100 trades, rather than a backtest, a hopeful estimate, or a small handful of recent results that might not represent your strategy's true long-run performance. When calculating these figures from your journal, use the same consistent method every time decide whether you're measuring reward-to-risk based on planned targets and stops or on actual realised outcomes, and stick with that choice, since mixing the two produces inconsistent, less meaningful figures. It's also worth being conservative rather than optimistic when your sample size is still relatively small, since Kelly is highly sensitive to input accuracy, and a slightly pessimistic estimate that turns out to be too cautious costs you a small amount of theoretical growth, while a slightly optimistic estimate that turns out to be wrong can suggest a Kelly fraction dangerously larger than your strategy's real edge supports. As your logged sample grows over time, recalculating these inputs periodically rather than treating an early estimate as permanent will steadily improve the accuracy of whatever Kelly fraction you calculate from them. Separating win rate and reward-to-risk by strategy, if you trade more than one distinct approach, is also important, since blending different strategies together produces inputs that don't accurately represent any single one of them. See What Is the Kelly Criterion and Should I Use It for Position Sizing? for more on this input sensitivity.

The Kelly Criterion was originally derived for scenarios with a precisely known, fixed win probability and payout ratio, such as a casino game with defined odds a condition that real trading only ever approximates, never truly satisfies. Discretionary trading strategies have win rates and reward-to-risk ratios that drift over time as market conditions change, meaning any input you feed into the Kelly formula is really an estimate of a moving target, not a fixed, known constant the way the original mathematical derivation assumes. This estimation uncertainty is compounded by the formula's high sensitivity to input accuracy a modestly overestimated win rate or reward-to-risk ratio can produce a suggested risk percentage considerably larger than what your strategy's true, current edge actually supports, and this gap is impossible to detect from the formula itself, since it simply trusts whatever numbers you provide. There's also a psychological dimension the formula doesn't account for at all: full Kelly sizing can produce genuinely uncomfortable volatility and drawdowns even when working exactly as intended, and a trader who abandons a strategy during a difficult but statistically normal drawdown, purely because the sizing made that drawdown unbearable to sit through, never gets to realise the long-run growth the formula theoretically promises. For these reasons, most traders who use Kelly-informed sizing treat the formula's output as a rough reference ceiling to size well below, rather than a precise instruction to follow exactly, and pair it with realistic, conservative input estimates rather than optimistic ones. See What Is the Kelly Criterion and Should I Use It? for a fuller discussion of these practical limitations.

Start by considering how confident you genuinely are in your win rate and reward-to-risk estimates the less confident you are, whether due to a smaller logged sample or a strategy still being refined, the more conservative a fraction you should use, since a smaller fraction provides more buffer against the input estimation errors that Kelly is so sensitive to. Next, honestly assess your personal tolerance for volatility and drawdown, independent of the mathematics entirely full or even half-Kelly sizing can produce equity curve swings that are simply uncomfortable enough for some traders to abandon a strategy prematurely during a statistically normal rough patch, in which case a more conservative quarter-Kelly or even smaller fraction that you can genuinely stick with through difficult periods will likely produce better real-world outcomes than a theoretically superior but practically unsustainable full-Kelly approach. It's also worth considering your broader risk management context if you're already managing correlated positions or multiple simultaneous strategies, a smaller individual fraction per position or strategy leaves more room to combine them without inadvertently exceeding a sensible total portfolio risk level. Many experienced, risk-conscious traders settle somewhere in the quarter-Kelly to half-Kelly range as a practical default, adjusting toward the more conservative end as estimation uncertainty or personal risk aversion increases, and toward the less conservative end only once a strategy has a long, stable, well-understood track record. See our Risk of Ruin Calculator to cross-check whatever fraction you land on against its implied probability of a serious drawdown, and What Is Position Sizing and How Do I Calculate It? for how this fits into broader position sizing practice.

The full Kelly formula assumes your win rate and odds estimates are perfectly accurate, which is rarely true in real trading, using a fraction (often half or quarter Kelly) provides a buffer against estimation error while still capturing much of the growth benefit.

Since the Kelly formula is highly sensitive to your inputs, an overestimated win rate or risk-reward ratio can suggest a position size that's genuinely too aggressive for your true, real-world edge, accuracy in your inputs matters significantly here.

Yes, if your inputs indicate no genuine statistical edge, the formula correctly suggests you shouldn't be betting at all, a negative or zero result is the model telling you the trade doesn't have positive expectancy as described.

No, the classic Kelly formula assumes a single position at a time, applying it naively across several correlated open positions simultaneously can meaningfully understate your true combined risk exposure.

Some professional and institutional traders do use Kelly-based sizing, typically at a fraction of the full recommendation, alongside other risk management overlays, rather than following the raw formula output directly.