Expectancy
Expectancy is the average amount a strategy makes or loses per trade: (win rate × average win) − (loss rate × average loss).
Win rate tells you how often you win. Reward-to-risk tells you how much. Expectancy combines the two into the single number that decides whether a strategy makes money over time: the average result per trade. Positive expectancy means the strategy earns on average; negative means it loses, however good individual trades feel. This entry shows the formula, how to express it in R, and how to use it to compare setups and plan.
- Reading time
- 5 min
- Also called
- Expected value, Trading expectancy, Expectancy per trade
- Updated
Expectancy in plain words
If you took the same kind of trade a thousand times, expectancy is what you would make or lose on an average one.1 It is calculated as win rate × average win − loss rate × average loss.2
Expressing wins and losses in R — multiples of the amount risked — makes expectancy comparable across trades of different sizes. An expectancy of +0.3R means that, on average, each trade makes 30% of what it risks. See R-Multiple.
How to calculate and use expectancy, step by step
Gather your trades
Use a journal or a backtest with enough trades of the same setup. See Trading Journal.
Calculate win rate and loss rate
Win rate = winners ÷ all trades; loss rate = 1 − win rate (counting break-evens consistently). See Win Rate.
Calculate average win and average loss
Preferably in R, after costs.
Apply the formula
Expectancy = win rate × average win − loss rate × average loss.
Multiply by frequency
Expectancy × trades per month gives the expected result per month, in R or money.
Sanity-check it
A small sample, unusual market or a few huge outliers can distort the number. Recalculate as more trades arrive.
Worked examples
Example 1 — The basic calculation
PositiveOver 60 trades: 24 winners averaging +2.0R, 36 losers averaging −1.0R.
| Win rate / loss rate | 40% / 60% |
| Expectancy | 0.40 × 2.0 − 0.60 × 1.0 = +0.20R |
| At $100 risk per trade | +$20 per trade on average |
Lose six times out of ten and still make money, because winners are twice the size of losers.
Example 2 — Expectancy over a month
MonthlyThe same trader takes 25 trades a month and risks 1% of a $20,000 account ($200) per trade.
| Expected R per month | 25 × 0.20R = 5R |
| In money | 5 × $200 = $1,000 |
| As % of account | ≈ 5% a month, before variance |
Expectancy is an average; individual months will vary widely around it, including losing months.
Example 3 — Costs flip the sign
CostsA scalping strategy has 55% winners averaging +0.8R and losers averaging −0.9R before costs. Costs average 0.1R per trade.
| Before costs | 0.55 × 0.8 − 0.45 × 0.9 = +0.035R |
| After 0.1R costs | +0.035R − 0.1R = −0.065R |
Small edges are fragile. Short-term strategies need expectancy well above their costs. See Slippage and Trading Fees and Commissions.
Try it: calculate your expectancy
Enter your win rate, average win and average loss. The calculator shows the expectancy per trade and what it adds up to over a month. It runs in your browser.
| Break-even win rate at 1:2 | 33.3% |
| Expectancy per trade | +0.20R (+$20.00) |
| Expected result over 100 trades | +$2,000.00 |
On average this combination makes money — if the win rate and ratio are real, measured over many trades.
Before commissions, spreads and slippage, which lower the real result. Actual sequences of wins and losses vary widely.
Same expectancy, different strategies
| Aspect | Win rate | Avg win | Avg loss | Expectancy |
|---|---|---|---|---|
| Trend following | 30% | +3.0R | −1.0R | +0.20R |
| Swing trading | 45% | +1.67R | −1.0R | ≈ +0.20R |
| Mean reversion | 60% | +1.0R | −1.0R | +0.20R |
Why expectancy matters
Is there an edge?
Positive expectancy after costs, over a meaningful sample, is the basic test of a trading edge. See Trading Edge.
Comparing setups
Ranking setups by expectancy shows where to focus.
Planning
Expectancy × frequency × risk per trade gives a realistic expected result to plan around.
Choosing targets
Moving targets changes both win rate and average win; expectancy shows which combination is best. See Take Profit.
Advantages and disadvantages
Advantages
- One number that combines win rate and payoff size.
- Comparable across setups and position sizes when expressed in R.
- Directly links strategy statistics to expected profit.
Disadvantages
- It is an average; actual results vary widely around it.
- Needs a large sample to be reliable.
- Ignores the order of wins and losses, so says nothing about drawdowns.
- Historical expectancy can change when markets change.
Going deeper
Read as far as you need. Each level builds on the one before it.
Positive vs. negative
Beginner
With positive expectancy, more trades tend to mean more profit. With negative expectancy, more trades tend to mean bigger losses — no position-sizing method can fix that.
Expectancy and profit factor
Intermediate
Profit factor — gross profits divided by gross losses — tells a similar story in a different form. A profit factor above 1 corresponds to positive expectancy. Expectancy in R is easier to use for planning, because it scales directly with risk per trade.
Variance and the path
Advanced
Two strategies with the same expectancy can have very different drawdowns. Lower win rates usually mean longer losing streaks, so they need smaller risk per trade to survive the same expectancy. See Drawdown and Risk of Ruin.
Common mistakes
Judging by win rate alone
A high win rate with small wins and big losses can have negative expectancy.
Calculating before costs
Use net results; costs can turn a small positive expectancy negative.
Trusting a tiny sample
Ten good trades can show a large expectancy by chance. See Sample Size and Statistical Significance.
Mixing setups
Averaging several setups together can hide one that loses money.
Check your understanding
Test yourself before moving on. Each answer comes with an explanation.
Key points to remember
- Expectancy = win rate × average win − loss rate × average loss.
- Express it in R to compare trades and setups of different sizes.
- Positive expectancy after costs is the core of a trading edge.
- It is an average: individual results and drawdowns vary around it.
- Use a large, clean sample and recalculate as conditions change.
Frequently asked questions
What is a good expectancy?
Can position sizing turn a negative expectancy positive?
Is expectancy the same as expected value?
Further learning
- Win Rate
One half of the expectancy formula.
- Risk-to-Reward Ratio
The other half: how big wins are compared with losses.
- Backtesting
How to estimate expectancy before trading.
- Trading Journal
Where real expectancy is measured.
Sources
This article is written in our own words. The sources below support the specific facts, rules and figures marked with a number in the text. Links open the original publisher’s page.
- CME Group. Misconceptions of Taking Losses (opens in a new tab) Checked .
- CME Group. The Mathematics of Trading Success (opens in a new tab) Checked .
Educational content, not financial advice. Examples use hypothetical prices and simplified costs; actual fees, margin rules and regulations vary by broker, market and country. We are not affiliated with any publisher linked above. Last reviewed . Report an error or request a source credit.