BUILD: COURSE 2 | LESSON 1
Expectancy: win rate × risk-reward is your real edge
Learning objectives
Calculate the expectancy of a trading approach from win rate, average win and average loss, and interpret the result in R-multiples.
Explain why a high win rate can still lose money, and why a 35% win rate can be profitable.
Estimate how many trades you need before your results mean anything, and why short streaks prove nothing.
You have finished Foundations, so you know how to place a trade with a stop and a target. This lesson is about the question underneath all of that: when you take the same kind of trade many times, do you come out ahead? That single number — expectancy — is what people mean when they say "edge". Everything else in this course (position sizing, drawdowns, correlation) is about protecting an edge or surviving without one. Nothing protects you from a negative expectancy.
The formula, with real numbers
Expectancy is the average amount you make (or lose) per trade, over many trades:
Expectancy = (win% × average win) − (loss% × average loss)
Take a trader with 100 closed trades: 45 winners averaging $60, and 55 losers averaging $40.
- Expected gain per trade from winners: 0.45 × $60 = $27.00
- Expected loss per trade from losers: 0.55 × $40 = $22.00
- Expectancy = $27.00 − $22.00 = +$5.00 per trade
A 45% win rate — losing more often than winning — makes money, because the average win is 1.5× the average loss. Now flip it. A trader wins 70% of the time but takes quick $20 profits and lets losers run to $60:
- 0.70 × $20 = $14.00
- 0.30 × $60 = $18.00
- Expectancy = −$4.00 per trade
Winning 7 trades out of 10 and losing money overall. This is not a contrived example — it is the most common failure pattern in retail trading, because taking small profits and hoping losers come back both feel good in the moment. Your account balance doesn't care how often you were right. It cares about the product of how often and how much.
Think in R, not in dollars
Dollar expectancy depends on your account size and position size, so traders normalise it. Define 1R as the amount you risk on a trade (the distance from entry to stop, in money). A trade that hits a target twice as far as the stop is a +2R win; a stopped-out trade is −1R.
Expectancy in R: E = (win% × avg win in R) − (loss% × avg loss in R)
A system that wins 40% of the time at +2R and loses 60% at −1R:
E = (0.40 × 2) − (0.60 × 1) = 0.80 − 0.60 = +0.20R per trade
Meaning: over many trades, you expect to earn 0.20 × your per-trade risk, per trade. Risk $50 a trade and take 20 trades a month → expected +$200/month before costs. Two immediate lessons fall out of the R framing:
- Break-even win rate is set by your risk-reward ratio. If you take trades at reward:risk of R:1, you break even when win% = 1 / (1 + R). At 1:1 you need >50%. At 2:1 you need >33.3%. At 3:1 you need >25%. At 0.5:1 (risking 2 to make 1) you need >66.7% — a brutal hurdle.
- Costs eat expectancy directly. Spread and swap might cost 0.05–0.15R per trade on short-term setups. A raw edge of +0.20R can become +0.05R after costs — or zero. The tighter your stop and the shorter your hold time, the larger costs loom relative to R.
Why 20 trades tell you almost nothing
Here is the uncomfortable part. Even a genuinely positive-expectancy system produces long, convincing losing streaks. With a 40% win rate, the probability of losing any single trade is 60%; the probability of 5 losses in a row starting from any trade is 0.6⁵ ≈ 7.8%. Across 100 trades you should expect at least one streak of 5–7 straight losses. That is the system working normally.
The reverse also holds: a losing system can look brilliant for 20 trades. Flip the −0.067R coin from our 70%-win-rate example (−$4 expectancy on a $60 average loss, i.e. −$4/$60 per R risked) 20 times and there's a very real chance you're up money and feeling like a genius. As a rough rule of thumb, treat anything under ~100 trades as noise, and even 100 trades only gives you a coarse estimate of win rate (±10 percentage points is easily within luck). This is why the journaling habit from Foundations matters: your journal is the dataset from which your real expectancy is eventually measured. No journal, no denominator, no edge — just anecdotes.
Practical implication: judge yourself on process adherence per trade and expectancy per 50–100 trades, never on this week's P&L.
Expectancy is a property of a process, not a person
One more reframe before the rest of the course. Expectancy belongs to a specific, repeatable process: "buy pullbacks to the 20 EMA in an uptrend on EUR/USD H4, stop below the swing, target 2R" has an expectancy. "I trade whatever looks good" does not — there is no stable thing to measure. If you change stops mid-trade, skip setups after losses, or double size after wins, you are trading a different (usually worse) system each time, and the numbers you compute afterwards describe nothing.
This is why Course P3 will force every strategy into explicit written rules: not because rules are magic, but because only a rules-based process has a measurable expectancy, and only a measurable expectancy can be improved. The rest of this course assumes you have — or are building — such a process, and teaches you how to size it (P2.2), survive its drawdowns (P2.3), manage its open trades (P2.4), and avoid stacking its risk (P2.5).
Key takeaways
Expectancy = (win% × avg win) − (loss% × avg loss). It is the only number that says whether an approach makes money over time.
Win rate alone is meaningless: 70% winners can lose money, 35% winners can be very profitable. Win rate and reward:risk trade off against each other.
Break-even win rate = 1 / (1 + reward:risk). At 2:1 you only need to be right one time in three.
Spread, commission and swap subtract directly from expectancy — a thin edge can vanish into costs, especially on tight stops.
Fewer than ~100 trades is statistical noise. Losing streaks of 5+ are normal for healthy systems; judge the process, not the week.