What Is Expectancy in Trading? The Formula and What Is Good

Expectancy in trading is the average amount you expect to win or lose per trade, expressed in dollars or in R multiples. The formula is (win rate x average win) - (loss rate x average loss). A positive expectancy means the strategy makes money on average over many trades; a negative one means it loses, no matter how good the win rate looks in isolation.

The formula, and how to read it

Expectancy = (probability of a win x average win) - (probability of a loss x average loss). Everything in that line comes from your own trade history or from a backtest, and the two probabilities always sum to 1. Because the formula weighs size against frequency, it settles the argument that win rate alone can never settle: whether being right often is worth more than being right big.

A worked example

Suppose a strategy wins 40% of the time. Its average winner is $600 and its average loser is $250. Expectancy is (0.40 x $600) - (0.60 x $250), which is $240 - $150, or $90 per trade. Over 200 trades that is roughly $18,000 in expectation, from a strategy that is wrong three times out of five. Now flip it: a strategy that wins 80% of the time with $100 winners and $500 losers has an expectancy of (0.80 x $100) - (0.20 x $500), or $80 - $100, which is negative $20 per trade. It loses money while feeling like it works, which is why so many high-win-rate systems quietly drain accounts.

Expectancy in R multiples

Most professionals express expectancy in R, where 1R is the amount risked on a trade. Stating results this way strips out position size, so a strategy with an expectancy of 0.35R makes 35 cents per dollar risked whether you are risking $50 or $5,000. It also makes strategies comparable across accounts and instruments, which raw dollar figures never are.

What is a good expectancy in trading?

A good expectancy is any positive number that survives realistic costs and a large enough sample, and for most retail strategies that lands between 0.1R and 0.5R per trade. Anything above 1R per trade should be treated as suspicious until proven across hundreds of trades and multiple market regimes, because that level of edge rarely survives contact with slippage, commissions, and the real world.

Past performance does not guarantee future results. For educational and informational purposes only. Not financial advice. Consult a licensed advisor.

Why expectancy beats win rate

Win rate answers how often you are right. Expectancy answers whether being right that often, at that size, is profitable. They frequently disagree. Trend-following strategies famously win under 40% of the time and make money because the winners run far past the losers; scalping strategies can win 85% of the time and still bleed because one bad trade erases twelve good ones. If you only track one number, track expectancy, and treat win rate as a description of how the strategy feels rather than how it performs.

The related metric worth knowing is profit factor, which divides gross profit by gross loss. Profit factor tells you the ratio of what you made to what you lost; expectancy tells you the dollar or R value of the next trade. They answer adjacent questions and it is normal to report both. What is a good profit factor covers the benchmarks there, and what is a good win rate explains the break-even math that connects the two.

What quietly destroys expectancy

The formula is simple; keeping the inputs honest is not. Four things routinely turn a positive backtested expectancy into a negative live one.

1. Costs left out. Commissions, spreads, and slippage are charged per trade, so they hit high-frequency strategies hardest. A 0.15R edge on a strategy trading twice a week can vanish entirely once realistic costs are charged.
2. A sample too small to mean anything. Thirty trades tells you almost nothing. Expectancy is an average, and averages from small samples move wildly; a couple of lucky outliers can manufacture an edge that does not exist.
3. Outlier dependence. If removing your single best trade flips expectancy negative, you do not have a strategy, you have one lucky trade surrounded by noise. Always recalculate without the top winner and see what survives.
4. Rules that changed mid-sample. Tightening a stop or adding a filter partway through creates a history that no single strategy ever traded. The blended expectancy describes nothing you can repeat.

Costs deserve particular attention because they are the one input traders consistently underestimate, and for anyone trading actively enough for it to matter, the data feeds, platform fees, and commissions add up to a real annual line item worth tracking properly rather than guessing at. A strategy with a 0.2R expectancy that pays $3,000 a year in tooling needs to know that before it calls itself profitable.

How to calculate your own expectancy

1. Collect at least 100 closed trades from the same set of rules, with entry, exit, and the amount risked recorded for each.
2. Split them into winners and losers and compute the average result of each group, net of commissions and estimated slippage.
3. Compute the two probabilities: winners divided by total trades, and losers divided by total trades.
4. Apply the formula and divide by your average risk per trade to express the answer in R.
5. Recalculate without your best trade and without your worst. If the conclusion changes, the sample is too small or too outlier-driven to trust.

Expectancy is not a prediction

An expectancy of 0.3R does not mean the next trade returns 0.3R. It means that over a large number of trades, if conditions resemble the period you measured, the average settles near that figure. Any individual trade is a full loss or a full win, and long strings of losers are normal even in strategies with a genuine edge. The number is a planning input for position sizing, not a promise, and the market regime it was measured in can end without warning.

The practical way to get a trustworthy expectancy before risking money is to test the rule against history rather than estimating from memory. Running it through the trading strategy tester applies the rule to 20+ years of split- and dividend-adjusted daily data with costs charged per trade, then reports the results with every assumption printed and an honest verdict, including when the rule underperformed simply holding the asset. For the sizing decision that follows, position sizing covers how expectancy and risk per trade fit together, and walk-forward analysis explains how to check that the edge holds outside the window you fitted it to.