Expectancy — does your strategy really earn?
Most beginners judge a trading strategy by one number: win rate. "I have a seventy percent hit rate, so the system works" goes the typical forum boast. The trouble is that win rate on its own says nothing about whether equity actually grows. A strategy with a seventy percent win rate can lose money steadily, and a strategy with a thirty percent win rate can compound it. What separates the two is a single figure called expectancy, which is the average profit or loss generated by one trade once every winner and every loser has been added up. This article explains what expectancy really is, how to compute it and why it, not raw accuracy, decides long-term profitability.
What expectancy really is
Expectancy is the average profit or loss generated by a single trade over a long horizon, measured across a sample of at least one hundred trades. The formula is mathematically simple and can be stated in one sentence: expectancy equals the probability of winning multiplied by the average win, minus the probability of losing multiplied by the average loss. The result is a concrete amount of money — "each trade earns about fifty euros on average" or "each trade loses about eight euros on average."
Win rate on its own says nothing about profitability until you also know the ratio between the average win and the average loss. A trader with seven winners out of every ten, but with an average loss three times the size of the average win, loses money as inevitably as someone opening positions at random. A hundred trades is a sample large enough for short-term swings to cancel each other out, leaving pure arithmetic behind. Everything reduces to one number that combines both pieces of information into a single decimal.
Why win rate on its own can mislead
The cleanest way to understand this is to compare three hypothetical strategies with different combinations of hit rate and average win-to-loss ratio. Treat all three as running on the same ten thousand euro account, trading the same EUR/USD pair.
The conclusion is brutally counter-intuitive. A seventy percent win rate sounds impressive in marketing copy, but when the average loss is twice the size of the average win, five euros per trade is a borderline number that operating costs eat within the first few weeks. This is why experienced traders do not ask a beginner about win rate; they ask about expectancy. More on how to build a systemic edge sits in the companion piece on discovering a trader's edge; a longer treatment of the related concept lives in the ForexMechanics glossary entry on the risk-reward ratio.
R-multiples — Van Tharp's universal yardstick
Expressing expectancy in euros or dollars has one drawback — the result depends on the size of the account. A trader with five thousand euros and a trader with five hundred thousand euros can run an identical strategy, yet their expectancies in cash will differ by a factor of one hundred. To get around this, Van Tharp introduced in his book Trade Your Way to Financial Freedom the concept of the R-multiple, the unit of risk per trade. One R equals the amount you risk on a single position, typically one or two percent of capital. The expectancy formula in R-multiples reads exactly the same as the one in cash, except that the averages are now in R rather than in euros.
A concrete hypothetical example: a strategy with a fifty percent win rate, an average win of two R and an average loss of one R. Half of two R gives one R of statistical profit, half of one R gives half an R of statistical loss. The difference works out to half an R per trade — each position earns, on average, half a unit of risk. This figure is independent of account size. How to set the size of a single R sensibly is covered in the companion piece on the one-percent rule of position sizing.
"Expectancy tells you how much you will make on average for each dollar you risk over the long run. There is nothing more important you can learn about your strategy from your trading journal." — Van K. Tharp, Trade Your Way to Financial Freedom, McGraw-Hill, 2007.
Expectancy thresholds — what each number means in practice
The formula on its own does not answer whether a given result is excellent, mediocre or disastrous. Industry practice has settled on a fairly consistent set of thresholds for expectancy expressed in R-multiples.
For perspective: even the legendary Renaissance Technologies Medallion fund has historically operated around zero point four R of expectancy — applied with enormous capital across thousands of trades a day, it produces multi-decade annual returns in the tens of percent. A retail trader operating at lower frequency and with higher unit costs should aim for at least zero point three R; below that line, costs start eating the statistical edge faster than the strategy can build it. Related is the question of maximum drawdown — even a system with healthy expectancy passes through periodic equity drops that must be tolerable both psychologically and financially.
An honest caveat — what expectancy does not promise
The entire framework above rests on one assumption that markets do not guarantee: that the distribution of wins and losses observed in the past will look broadly similar in the future. An expectancy calculated from the last one hundred trades is a good extrapolation only as long as the market regime stays put — as long as volatility, the correlations between pairs and your broker's spread profile all stay in the same band. In practice, regimes shift regularly. A long phase of low volatility gives way to turbulence, correlations break in a single week, brokers raise commissions. Experienced traders therefore recompute expectancy every few dozen trades and treat a sharp drop as a signal that the strategy needs review. Practical tools sit in the guide on trading statistics and key metrics.
Sample size — when a calculated expectancy becomes trustworthy
An expectancy calculated from ten trades has no diagnostic value whatsoever. Ten outcomes is a sample small enough that pure luck can generate a streak of nine wins out of ten.
- Thirty trades offer a first rough indication, but with a confidence interval of roughly plus or minus fifty percent — a calculated 0.3R expectancy could really be anything between 0.15R and 0.45R.
- One hundred trades form the first solid basis for inference. The confidence interval narrows to about plus or minus twenty percent.
- Five hundred trades tighten the confidence interval to around plus or minus eight percent — the sample size at which professional traders make strategic decisions about scaling capital.
- One thousand trades and above reduce the margin of error to roughly plus or minus five percent — lets you compare strategies with surgical precision.
The practical implication is unambiguous. Never scale up position size or commit fresh capital to a strategy whose expectancy you have computed from fewer than one hundred trades. Beginners fall into this trap relentlessly: after ten winners in a row they raise leverage, move from demo to live, add capital and then lose all of it on the first serious losing streak. The broader framework for managing risk at thresholds like these sits in the companion guide on risk management basics.
What to do tomorrow
The theory is clear, but its real value appears only once you translate it into your own journal. Below are four concrete steps worth taking over the coming week, to move from the question "is my strategy actually earning?" to a numerical answer expressed in R-multiples.
- Open your trade journal and select the last one hundred closed positions. If you have fewer than that, the first conclusion is already in: you are not collecting enough data to evaluate anything. Count four numbers carefully — how many trades were winners, how many were losers, what the average winner produced, what the average loser cost — and plug them into the formula directly.
- Convert the result into R-multiples. Sum the amounts risked on every trade, divide by the number of positions and you have the average value of one R for that period. Then express the average win and the average loss in R rather than in euros, and recompute expectancy in that unit. A number in R is comparable across accounts and across time periods in a way that a number in euros simply is not.
- Compare the result with the thresholds above and make one concrete decision. If expectancy is below zero, stop live trading and go back to a demo account or to a paper backtest on historical data. If it sits between zero and 0.1R, work on setup selection or cut frequency rather than adding size. Only when expectancy clears 0.3R should you cautiously consider scaling capital.
- Add a permanent reminder to your journal to recompute expectancy every fifty trades. Market regimes drift enough that a once-calculated number loses its meaning within a few months. Regular recomputation lets you spot early the moment a strategy stops working under new conditions, before a string of losses eats a meaningful share of your capital.
Related reading: trader's risk management basics — the systemic framework into which expectancy fits; the one-percent rule — how to size a single R against the account; trading statistics in practice — which metrics should be recomputed alongside expectancy.
Sources & bibliography
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Van K. Tharp Trade Your Way to Financial Freedom (2nd ed.) · rozdział o expectancy i krotnościach R — fundament współczesnego myślenia o systemowej przewadze www.mheducation.com ↗
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ESMA Product intervention measures relating to CFDs · badanie ESMA, na którym opiera się obowiązkowe ostrzeżenie 74–89% strat na rachunkach detalicznych CFD — kontekst dla wymagań rzetelnej diagnostyki strategii www.esma.europa.eu ↗
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Bank for International Settlements OTC foreign exchange turnover in April 2022 (Triennial Survey) · dane o skali rynku — 7,5 bln USD obrotu dziennie — kontekst dla dyskusji o reżimach zmienności i stabilności rozkładu wyników www.bis.org ↗
Frequently asked
What is expectancy in trading?
Expectancy is the average profit or loss generated by a single trade over a long horizon, measured across a sample of at least one hundred trades. It is computed as the probability of winning multiplied by the average win, minus the probability of losing multiplied by the average loss. The result gives a concrete number of money or R-multiples: "each trade earns roughly fifty euros on average" or "each trade loses about eight euros on average." Without this number a trader cannot tell whether the strategy carries a real edge — win rate considered in isolation, divorced from average sizes, leads to faulty conclusions about system quality.
How does the calculation look on concrete numbers?
Comparing three hypothetical strategies shows why win rate alone misleads. Strategy A, with seventy percent winners, an average win of fifty euros and an average loss of one hundred euros, yields an expectancy of about five euros per trade — borderline, eaten by costs within weeks. Strategy B, with fifty percent winners, an average win of one hundred euros and an average loss of fifty euros, gives twenty-five euros per trade — five times more, despite the lower hit rate. Strategy C, with sixty percent winners, an average win of eighty euros and an average loss of one hundred and twenty euros, returns zero, a clean break-even. The takeaway is that win rate considered apart from average sizes leads to wrong decisions.
What are Van Tharp R-multiples?
The R-multiple is a unit of risk per trade, introduced by Van Tharp so that expectancies become comparable across accounts of different sizes. One R equals the amount you risk on a single position, typically one or two percent of capital. If the average win is two R, the average loss one R and the hit rate fifty percent, expectancy works out to half an R per trade. The figure is independent of account size, so a retail trader with a five-thousand-euro account and a fund running a billion-dollar book can compare expectancies on the same scale. Professionals tend to say "my expectancy is zero point three R," because R applies everywhere while a currency figure applies only to one specific account.
What expectancy thresholds count as good?
Practical thresholds expressed in R-multiples look like this. Expectancy above zero point five R per trade signals an excellent strategy with a durable market edge. Values between zero point three R and zero point five R indicate a solid, robust strategy — the typical range achieved by experienced retail traders. Between zero point one R and zero point three R you have a marginal edge that is sensitive to rising costs. A value close to zero means break-even — in its current form the system does not cover operating costs. Any negative value indicates a system that loses money regardless of how good individual months look. A retail trader should aim for at least zero point three R.