How to Calculate Trading Expectancy (With a Worked Example)

Most traders obsess over win rate. It's the wrong number to fixate on. You can win 80% of your trades and still blow up, or win 40% and print money — it depends entirely on how much you make when you're right versus how much you lose when you're wrong. Expectancy is the single metric that folds both of those into one answer: does this strategy make money when I repeat it?

What expectancy actually is

Expectancy is your average profit or loss per trade, measured across every trade a strategy produces — winners, losers, and scratches. It's not "how often am I right." It's "what is one trade worth to me, on average, before I've placed it."

Expectancy = (Win% × Average Win) − (Loss% × Average Loss)

If that number is positive, every trade you take with this setup is worth money to you in the long run. If it's negative, no amount of clever position sizing fixes it — you're just compounding a losing edge faster.

A worked example

Say you've traded a single breakout setup and pulled the stats from your journal. Here's what a hypothetical 100 trades look like:

• Win rate: 45% (you win 45 of every 100 trades)
• Average win: $300
• Average loss: $150

Plug those into the formula. First convert the rates to decimals — 45% wins means 55% losses:

• Win side: 0.45 × $300 = $135
• Loss side: 0.55 × $150 = $82.50
• Expectancy = $135 − $82.50 = $52.50 per trade

So even though you lose more often than you win, this setup is worth $52.50 every time you pull the trigger. Over those 100 trades, that's roughly $5,250 of expected profit before fees. Take the setup 300 times a year and, assuming nothing degrades, you'd expect around $15,750 from it.

Notice what carried the math: the winners are twice the size of the losers (a 2:1 reward-to-risk), and that 2:1 more than compensates for the sub-50% win rate. Flip the average win down to $150 with the same 45% rate and expectancy goes to (0.45 × $150) − (0.55 × $150) = −$15 per trade — a losing system with the exact same win rate. Win rate alone tells you nothing.

Expectancy per dollar risked

Dollar expectancy is useful, but it's tied to your position size. Trade bigger and the number inflates without your edge improving. To compare setups cleanly, express expectancy per dollar risked — this is where R-multiples come in.

An R-multiple is a trade's result measured in units of the risk you took. Risk $100 and make $300, that's +3R. Risk $100 and lose it, that's −1R. When your average loss equals your planned risk (1R), the expectancy formula gives you expectancy in R directly.

In the example above, if your risk per trade was $150, then your average win of $300 is +2R and your average loss is −1R:

• (0.45 × 2R) − (0.55 × 1R) = 0.90R − 0.55R = +0.35R per trade

Now you have a number that's independent of size. A setup that returns 0.35R and one that returns 0.20R are directly comparable, whether you were trading 100 shares or 10,000. R-expectancy is the honest way to rank strategies against each other.

Why sample size matters

Here's the trap that fools nearly everyone: a great expectancy over 12 trades is luck, not edge. Small samples are dominated by variance. A couple of oversized winners can make a mediocre setup look brilliant, and one nasty losing streak can bury a genuinely good one.

There's no magic threshold, but treat anything under ~30 trades as a rumor, ~50 as a working hypothesis, and 100+ before you'd bet real size on the number. And be honest about how you got the sample — 100 trades of the same setup in similar conditions tells you far more than 100 trades scattered across five different strategies and three market regimes.

How to actually use it

Expectancy isn't a report-card stat you glance at once a quarter. It's a decision tool:

• Rank your setups by expectancy. Not overall expectancy — per setup. Your blended number hides everything. You almost always have one or two setups carrying the account and one or two quietly bleeding it.
• Size up the winners. The setup with the highest expectancy per R deserves more of your capital and more of your attention.
• Cut the negatives. A setup with a reliably negative expectancy over a real sample is not a "work in progress." It's a leak. Stop trading it or rebuild it from scratch.
• Recheck after changes. Tightened your stop? Added a filter? That's a new setup with a new expectancy. Old stats don't carry over.

This is exactly why breaking metrics down per setup matters — a point worth reading alongside the other journal metrics that actually move your P&L.

Common mistakes

• Too-small sample. Covered above, but it's the number one error. Don't size up on 15 trades.
• Ignoring fees and slippage. A setup with +$8 expectancy and $6 of round-trip costs is barely breakeven, not a goldmine. Compute expectancy on net results — after commissions, spread, and realistic slippage — or the number lies to you.
• Grading on outcome, not process. A trade that made money because you froze and got bailed out by a gap is not a win for your setup — it's noise you shouldn't reward. Tag trades by whether you followed the plan, and you'll see your true expectancy separate from your lucky one.
• Averaging across regimes. A setup can have +0.4R in a trending market and −0.3R in chop. Blended, it looks flat. Segment it.

Let the journal do the arithmetic

The formula is simple; the discipline of logging every trade cleanly — net of costs, tagged by setup — is the hard part. That's the whole point of Sutekka: it computes expectancy for you, both overall and broken out per setup, straight from your imported trades, so you can see at a glance which strategies to feed and which to kill.

Stop guessing which setups are pulling their weight. Start free, import your trades, and let the numbers tell you where your edge actually lives.