Trading Expectancy Explained

A trading record can be summarised many ways, but expectancy condenses it into the most decision-relevant figure: the average result of one trade. If expectancy is positive, the sample made money per trade on average; if it is negative, the strategy lost money no matter how good individual trades felt.

With real numbers: a sample of 100 trades with 45 winners averaging $180 and 55 losers averaging $110 gives 0.45 × 180 − 0.55 × 110 = 81 − 60.50 = +$20.50 per trade before costs — about +$2,050 across the whole sample.

Expectancy is closely related to profit factor — gross profit divided by gross loss — but it keeps the per-trade scale, which is exactly what you need when reasoning about costs, trade frequency and sample sizes.

Dollar expectancy depends on position size, so it cannot compare a $1,000 account with a $100,000 one, or last year’s sizing with this year’s. The fix is the R-multiple: define 1R as the amount risked on a trade (entry to stop), then record every outcome as a multiple of it. A trade risking $100 that makes $250 is +2.5R; one that hits its stop is −1R.

An expectancy of +0.2R reads as “on average, each trade earned 20% of what it risked.” That statement is true at any account size, which is what makes R the natural unit for comparing strategies, symbols or time periods within your own results.

Expectancy makes one thing obvious: win rate on its own says nothing about profitability. What matters is the combination of win rate and the payoff ratio — average win divided by average loss. The table below uses illustrative round numbers (average loss fixed at 1R, costs ignored) to show how the two interact:

The dividing line is the breakeven win rate: 1 ÷ (1 + payoff ratio). At a 1.5 payoff you break even at 40%, so winning 45% of the time is enough; at a 0.4 payoff you need about 71.4%, which is why the 70% system in the table still loses. Strategy styles cluster along this curve: trend-following systems tend to live in the bottom rows, losing often and getting paid by occasional large winners, while mean-reversion and scalping systems live near the top, winning often and giving some of it back in larger losses. Neither side is inherently better — both can clear the line, and both can fail it. You can map the line for any payoff with the Breakeven Win Rate Calculator.

Spread, commission and swap subtract from every trade, winners and losers alike. Each charge looks small next to a single trade’s profit, but expectancy is itself a small number — so costs of a similar size can consume a large share of it, especially for strategies that trade often and aim for short moves.

Swap adds a third line for positions held overnight: a few nights of negative rollover can quietly add several dollars per trade to the cost figure in the example above. In win-rate terms, costs raise the breakeven line: a combination that clears it before costs can sit below it after them. This is why expectancy should always be computed from net results — the numbers your account actually recorded — rather than from idealised entry and exit prices.

Multiplying expectancy by the number of trades gives the expected value of a sample. At +0.13R per trade, 200 trades have an expected value of +26R — $2,600 if every trade risks $100. That is the centre of the distribution, not a promised result: actual samples land scattered around it.

Variance is not a footnote. At a 45% win rate, the chance that the next five trades all lose is 0.55⁵ ≈ 5% — and across 200 trades, a streak like that is almost certain to appear somewhere. The same positive expectancy is consistent with many different equity paths, which is exactly what Monte Carlo resampling makes visible by reshuffling one trade sample thousands of times.

Expectancy is a description of a past sample, and it inherits every limitation of that sample:

• Small samples mislead. Thirty trades can be dominated by two or three outliers; remove the single best trade and the expectancy of a short sample often changes sign.
• Regimes change. The sample came from particular market conditions — trends, volatility, spreads. A different regime can produce a different win rate and payoff from the same rules.
• Costs drift. Spreads widen around news and thin sessions, so the cost per trade baked into an old sample may understate the current one.
• It assumes the plan was followed. The −1R average loss only holds if stops are respected; one untaken stop can outweigh many disciplined trades.

The practical stance: use expectancy to describe what a strategy did, and treat any forward projection — expectancy × future trades — as a scenario whose inputs are estimates, not a forecast.

MetaTrader already reports this number: the Strategy Tester’s expected payoff is net profit divided by the number of trades. The more useful view, though, is broken down — expectancy per strategy, per symbol and per session computed from your own executed trades, so you can see which part of the account carries the edge and which part dilutes it.

To experiment with the inputs first, the free Trading Expectancy Calculator computes per-trade expectancy and sample expected value from any win rate, payoff and cost assumptions.