Trading expectancy estimates the average amount a trading process would be expected to gain or lose per trade if its historical outcome distribution continued. It combines how often trades win with the average size of wins and losses.
Key takeaways
- Win rate is only one part of performance.
- Expectancy also depends on average win and average loss.
- A high win rate can still have negative expectancy if losses are much larger than wins.
- A lower win rate can have positive expectancy when winners are sufficiently larger than losses.
- Break-even trades, costs, slippage and sample size should be handled consistently.
The basic expectancy formula
A common form is:
Expectancy = (win probability × average win) - (loss probability × average loss).
If outcomes are measured in R, the result can also be expressed in R per trade. Break-even outcomes can be included with an outcome of 0R when that matches the methodology.
Example: lower win rate, positive expectancy
Suppose a system wins 40% of trades. Its average winner is +2R and its average loser is -1R. Ignoring break-even trades and costs for simplicity:
(0.40 × 2R) - (0.60 × 1R) = +0.20R per trade.
The win rate is below 50%, yet the simplified expectancy is positive because average winners are larger than average losses.
Example: high win rate, negative expectancy
Now imagine a strategy wins 80% of the time, but the average winner is +0.25R and the average loser is -2R:
(0.80 × 0.25R) - (0.20 × 2R) = -0.20R per trade.
The high win rate feels attractive, but the outcome distribution is negative in this simplified example.
Where do break-even trades fit?
Break-even outcomes should be defined transparently. If a trade closes at 0R, it contributes zero to the outcome but still affects the number of trades in the sample. Different dashboards can classify wins differently, so methodology must be understood before comparing percentages.
Why costs matter
Spread, commission and slippage reduce real results. An expectancy calculation based on idealized fills can overstate live performance, especially for high-frequency or small-target strategies.
Why sample size matters
Expectancy estimated from a small number of trades can change dramatically when a few new outcomes are added. The metric becomes more informative as the sample includes more trades and more varied market conditions, but it can never guarantee future results.
Expectancy vs total return
Total return tells you what happened over a specific sample. Expectancy describes the average outcome per trade within the assumptions of that sample. Two strategies can generate the same total R with very different trade counts, distributions and drawdowns.
How does this relate to NGF performance data?
NGF performance snapshots are presented with their stated timeframe, trade sample and methodology rather than reducing everything to one headline percentage. That context matters because metrics such as TP1 hit rate, win rate, average R and total R describe different parts of the outcome distribution.
Bottom line
Win rate answers “how often?” Expectancy asks “how much, on average?” A proper performance review needs both frequency and payoff.
Read Win Rate vs Risk-to-Reward, R-Multiple Explained and Drawdown Explained.
This article is educational only and is not financial advice. Historical expectancy does not guarantee future expectancy.