Most traders track win rate because it's intuitive — it feels good to be right more than you're wrong. But win rate without context is nearly meaningless. To know whether a system actually makes money, you need expectancy: the average amount earned or lost per trade, expressed as a multiple of your initial risk.
Expectancy is the answer to: "If I take 1,000 more trades with this system, will I make money or lose it?" Win rate cannot answer that question. Expectancy can.
The Expectancy Formula
Where:
- Win Rate — the proportion of trades that close profitable (e.g., 0.45 for 45%)
- Avg Win R — the average R-Multiple of winning trades (e.g., 2.2)
- Loss Rate — 1 minus win rate (e.g., 0.55)
- Avg Loss R — the average R-Multiple of losing trades, expressed as a positive number (e.g., 1.0 for full -1R stops)
Worked Examples
System A: Trend Follower (40% Win Rate)
This system loses more often than it wins, but every winning trade more than compensates for the losses. Over 100 trades, expect approximately +40R of profit before sizing effects.
System B: High Win Rate (70%) — But Negative Expectancy
A 70% win rate feels great. But this system systematically loses money — small winners are overwhelmed by large occasional losses. This is the classic pattern of a "revenge trader" or someone who cuts winners too early and holds losers too long.
Expectancy Benchmarks
| Expectancy | Interpretation | Action |
|---|---|---|
| Above +0.5R | Excellent system edge | Optimize position sizing |
| +0.2R to +0.5R | Solid, tradable edge | Trade it; monitor for decay |
| 0 to +0.2R | Marginal — costs may erase it | Validate with larger sample |
| Negative | Losing system | Do not size up; diagnose first |
Expectancy vs Win Rate: The Full Picture
The table below shows how the same win rates produce opposite outcomes depending on R-ratios:
| Win Rate | Avg Win R | Avg Loss R | Expectancy |
|---|---|---|---|
| 35% | 3.0R | 1.0R | +0.40R |
| 50% | 1.5R | 1.0R | +0.25R |
| 65% | 0.8R | 1.2R | +0.10R |
| 70% | 0.4R | 1.5R | −0.17R |
The 35% win rate system outperforms the 70% win rate system by 0.57R per trade. Win rate alone would suggest the opposite conclusion. This is why win rate lies to you.
Expectancy vs SQN: What Each Measures
Expectancy measures the average outcome. SQN (System Quality Number) measures the consistency of that outcome relative to its variance. A system with +0.5R expectancy but wildly variable outcomes (SQN 1.2) requires deeper drawdowns to realize its edge than a system with +0.3R expectancy but very consistent outcomes (SQN 3.0). Both metrics are needed:
- Expectancy answers: does this system make money?
- SQN answers: how smoothly does it make money?
- Monte Carlo answers: what's the worst-case path through that edge?
Expectancy and Position Sizing
Positive expectancy is a prerequisite for position sizing decisions to matter in your favour. Kelly Criterion determines the mathematically optimal fraction of your account to risk per trade — but it assumes positive expectancy as a starting condition. Applying Kelly to a negative-expectancy system makes you lose money faster, not slower. Verify expectancy first. Then optimize sizing.
Similarly, Fixed-R position sizing ensures that your expectancy calculation is meaningful by keeping 1R constant across all trades. If your position size varies arbitrarily, your expectancy estimate is contaminated by sizing noise — a big winner might look like +4R but was actually a case of over-sizing on one trade.
How SignalDeck Tracks Expectancy
SignalDeck calculates expectancy automatically from your logged R-Multiples. As your trade count grows, the expectancy estimate stabilizes. The platform flags when your rolling expectancy diverges significantly from your long-run average — a signal that your edge may be degrading. Filter by strategy tag, instrument, or date range to isolate expectancy by segment — so you can see whether a single instrument or session is dragging your overall expectancy down. Try it free during beta.
Frequently Asked Questions
What is trading expectancy?
Trading expectancy is the average amount you earn or lose per trade, expressed as a multiple of your initial risk (R). Formula: Expectancy = (Win Rate × Average Win R) − (Loss Rate × Average Loss R). A positive expectancy means the system makes money over a large enough sample. A negative expectancy means it loses money regardless of short-term results.
What is the expectancy formula in trading?
Expectancy = (Win Rate × Average Win in R) − (Loss Rate × Average Loss in R). Example: Win Rate 45%, Average Win +2.2R, Average Loss -1.0R. Expectancy = (0.45 × 2.2) − (0.55 × 1.0) = 0.99 − 0.55 = +0.44R per trade.
What is a good expectancy for a trading system?
Any positive expectancy is theoretically profitable. In practice, above +0.3R per trade is solid for a discretionary system. Above +0.5R is excellent. Below +0.1R is borderline — transaction costs and slippage may erase the edge live. Sample size matters: low-trade-count estimates are noisy.
How is expectancy different from win rate?
Win rate only tells you how often you win. A 70% win rate with 0.4R average wins and 1.5R average losses loses −0.17R per trade on average. Expectancy combines win rate, average win size, and average loss size into a single number that tells you the full picture.
How does expectancy relate to position sizing?
Positive expectancy is a prerequisite for position sizing decisions to matter. Kelly Criterion and Fixed-R sizing only make sense for systems with positive expectancy — they determine how much of your edge to extract, not whether an edge exists. Calculate expectancy first, then optimize sizing.
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Know your expectancy before your next trade.
SignalDeck calculates expectancy automatically from your R-Multiple history, updates as you trade, and flags when it diverges from your benchmark. Free during beta.