Expectancy in Trading

Definition

Expectancy is the average result of a single trade, worked out across many trades. It is the trading world's term for expected value (EV) — the standard statistical idea of the average outcome of a repeated, uncertain event. In statistics, expected value is calculated by weighting each possible outcome by its probability; in trading, those probabilities are represented by the historical win rate and loss rate. It answers a simple question: if I take this trade over and over, what do I win or lose on average each time?

It combines the two pieces that matter — how often trades win, and how large the wins and losses are — into one number:

"Expectancy = (Win Rate × Average Win) − (Loss Rate × Average Loss)"

In words: take how often you win multiplied by the size of a typical win, then subtract how often you lose multiplied by the size of a typical loss. What's left is the average outcome per trade.

• If expectancy is positive, the strategy makes money on average — each trade is, on average, worth something.
• If expectancy is negative, the strategy loses money on average, however often it wins.
• If expectancy is zero, it breaks even before costs (and loses after them).

This is why expectancy — not win rate — is the number that tells you whether a strategy has an edge.

Why It Matters

Win rate alone can't tell you whether a strategy makes money, because it ignores the size of wins and losses. Expectancy fixes exactly that blind spot: it folds win rate and win/loss size together, so a single number reflects the real bottom line.

This resolves the paradox from the win rate discussion — how an 80%-winning strategy can lose money while a 30%-winning one profits. Run both through expectancy and the answer is immediate: the 80% strategy has negative expectancy (its rare losses are too big), the 30% strategy has positive expectancy (its wins are large enough to more than cover frequent small losses). Expectancy sees what win rate is blind to.

To ground it with no math beyond arithmetic: suppose a strategy wins 40% of the time, the average win is 3 units, and the average loss is 1 unit.

"Expectancy = (0.40 × 3) − (0.60 × 1) = 1.2 − 0.6 = +0.6 units per trade"

Despite losing 60% of its trades, this strategy earns an average of 0.6 units every time it's taken. Over hundreds of trades, that positive expectancy is what compounds into a profit. A strategy with negative expectancy, run the same number of times, compounds into a loss — which is why chasing win rate without checking expectancy is how losing methods get mistaken for winning ones.

What Expectancy Depends On

Expectancy is only as trustworthy as the inputs behind it. Three things matter:

• Win rate and win/loss size — the two ingredients of the formula. Neither alone is enough; expectancy needs both.
• Sample size — expectancy measured over a handful of trades is unreliable, because a few unusual outcomes can swing it dramatically. A large, consistent sample is what turns an expectancy figure into evidence rather than an accident. This is the same principle that underlies all honest performance measurement: a number is only as good as the sample behind it.
• Costs — a strategy that looks positive before commissions, spread, and slippage can be negative after them. Honest expectancy is measured net of realistic trading costs, not on paper.
• Execution consistency — expectancy assumes trades are taken according to the tested rules. Skipping trades, hesitating, or changing position size can produce real-world results very different from the measured figure.

A positive expectancy from a small, cost-free, idealised backtest is not the same as a positive expectancy that survives a large sample and real-world costs — and only the latter is meaningful.

MPM Perspective

Expectancy sits at the heart of how MPM thinks about whether any strategy has an edge — precisely because it refuses to let a flattering win rate stand in for real profitability.

Where trade-level results appear in MPM's work — for example, in a documented backtest — expectancy is part of the full picture MPM shows, alongside win rate, win/loss size, sample size, and drawdown. The point is never to present a single attractive number, but to give the complete set that lets a reader judge whether the historical evidence suggests a durable edge. A strategy is only worth taking seriously if its expectancy is positive across an adequate sample and survives realistic costs.

This connects directly to MPM's research discipline. The same scepticism that makes MPM report sample sizes and limitations on its historical measurements applies to expectancy: a good-looking expectancy from a small or idealised test is treated as a red flag to investigate, not a result to celebrate. Expectancy is a tool for honest evaluation — and honest evaluation means being hardest on the numbers that look best.

Common Misconceptions

"A high win rate means positive expectancy."

No. Win rate is only half of expectancy. A high win rate with large losses and small wins can produce negative expectancy — the strategy loses money despite winning often. Only the full calculation tells you.

"Positive expectancy guarantees profit."

Not in any single stretch. Positive expectancy means a strategy profits on average, over a large sample. Over a small number of trades, even a positive-expectancy strategy can lose — variance is real. Expectancy is an average, not a promise for the next trade.

"Expectancy from a backtest is the expectancy you'll get."

Rarely exactly. Backtested expectancy is often optimistic — it can ignore costs, assume perfect fills, or rest on too few trades. Real-world expectancy tends to be lower, which is why costs and sample size matter so much.

"A bigger expectancy is always a better strategy."

Not necessarily. A strategy with slightly lower expectancy but far more trades, or far smaller drawdowns, can be more useful in practice than one with high expectancy but rare trades or brutal losing streaks. Expectancy is essential, but it isn't the only thing that matters.

Limitations

Expectancy is one of the most useful single numbers in strategy evaluation, but it's still an average, and averages hide things. A positive expectancy says nothing about the path — a strategy can have positive expectancy and still suffer long, deep losing streaks that are hard to sit through. Expectancy needs to be read alongside drawdown and the distribution of results, not on its own.

It's also only as reliable as its sample and its cost assumptions. Expectancy from too few trades is noise; expectancy that ignores real trading costs is fiction. And like all historical measures, past expectancy does not guarantee future expectancy — market conditions change, and an edge that existed in the tested period can fade. Expectancy describes what happened; it does not promise what will.

How This Fits Into MPM

Expectancy is one of the core trading-evaluation metrics the MPM Learning Center covers, so readers can judge any strategy — MPM's or anyone else's — on whether it has a real, durable edge rather than a flattering headline number.

You'll encounter this thinking in:

• Research Papers, where any trade-level result is presented with its full context — expectancy alongside win rate, win/loss size, sample size, and drawdown.
• Reaction Library, where MPM's published measurements describe historical price behaviour (how often zones held versus broke, with sample sizes) rather than trade-level expectancy — a deliberate distinction between measuring price behaviour and evaluating a trading strategy.
• Intelligence Circle — a member-only research environment containing advanced market research, historical investigations, and trading strategies developed using the MPM research framework.

MPM's broader stance is that expectancy, honestly measured across an adequate sample and net of costs, is far more informative than any win rate — and that even a strong expectancy deserves scrutiny before it's trusted.