What is expectancy in trading?
Win rate tells you how often you are right. Risk/reward tells you what right and wrong are worth. Expectancy is the number that combines them — and it is the only number that says whether a method deserves your money at all.
How do I calculate expectancy from my journal?
Take every closed trade, express each result as a multiple of the risk you took (a $50 win on a $50 risk is +1R; a $50 loss is −1R), then average them. That average is your expectancy in R. Multiply by your usual dollar risk to get it in money. Forty trades is the minimum before the number means anything; a hundred is better.
| Inputs | Method A | Method B |
|---|---|---|
| Win rate | 40% | 70% |
| Average win | 2.0R | 0.5R |
| Average loss | 1.0R | 1.0R |
| Expectancy | 0.4 × 2.0 − 0.6 × 1.0 = +0.20R | 0.7 × 0.5 − 0.3 × 1.0 = +0.05R |
| Per 100 trades at $50 risk | +$1,000 | +$250 |
Method B wins nearly twice as often and earns a quarter as much. Frequency of winning is not the same thing as an edge.
Why do fees erase small edges?
Expectancy is measured before costs, and costs are paid on every trade, win or lose. Suppose Method B trades a $5,000 position with a 0.1% taker fee each way: $5 in, $5 out, $10 per trade. Its expectancy is +0.05R, and at $50 risk that is +$2.50 per trade — less than the fee. After costs, the "winning" method loses $7.50 per trade, or $750 per 100 trades. Method A, at +$10 per trade before fees, keeps $0 after them: it merely breaks even. This is the arithmetic behind "trade less" — and behind why maker fees, liquid pairs and higher timeframes matter more than any indicator.
Why can a positive expectancy still lose money?
Three reasons. First, variance: a +0.2R method will produce losing months, because 100 trades is a small sample and streaks of eight losses are normal at a 40% win rate. Second, sizing: expectancy assumes a fixed risk per trade; double the size after a loss and the average no longer applies. Third, drift: the market that produced the sample changes, and the edge shrinks before the journal notices. None of these is fixed by trading more; all of them are managed by risking small enough to survive the sample.
When should I trust an expectancy number?
When it comes from at least 100 trades taken with the same rules, when it holds up in a period the rules were not designed on, and when it survives realistic fees and slippage. A backtest expectancy of +0.5R that shrinks to +0.1R after costs is telling you the truth about the method, not about the backtest. Below those conditions, treat the number as a hypothesis and size as if it might be wrong — because it might be.
FAQ
What is a good expectancy? Anything reliably positive after fees. Many durable retail methods sit between +0.1R and +0.3R per trade; numbers far above that from a short sample are usually variance, not skill.
Is expectancy the same as profit factor? No. Profit factor is gross wins divided by gross losses (a ratio); expectancy is average result per trade (an amount). A profit factor of 1.5 with tiny wins can still fail to cover fees.
How many trades do I need to measure it? Forty as an absolute minimum, a hundred before you rely on it. The journal tool recalculates it automatically as trades are logged.
Let the journal compute it for you
Log trades in R; win rate, expectancy and the equity curve update themselves — no spreadsheet.
Every key term, one roadmap
The whole slide course — ten free PDF parts, 328 pages.
Expectancy is what a journal is for. The risk/reward planner shows the expectancy a setup needs; the journal measures the one you actually have; and Lesson 1 makes the case that the job is to grow that number, not to be right on the next trade.