R-multiple and expectancy — does your trading system actually make money? — trading basics, chapter 8
Win rate is the most over-rated number in trading. R-multiples measure every trade in units of risk, and expectancy tells you what you make per trade on average — the one number that decides if a system is worth running.
The standard belief is that a good trader wins most of their trades — that a high win rate is the goal. The story is not just half-right, it's actively misleading. Win rate alone tells you almost nothing about whether a system makes money. A trader who wins 90% of trades can lose everything; a trader who wins 35% can compound steadily. The number that actually matters combines how often you win with how much you win versus lose — and the cleanest way to measure it is the R-multiple.
A more accurate frame: stop counting trades as wins and losses, and start measuring them in units of risk (R). Then a single formula — expectancy — tells you what you make per trade on average, which is the only verdict on whether a system is worth running. This chapter defines R, builds expectancy from it, and shows why chasing win rate is a trap.
The TL;DR. R is your risk on a trade — the dollar amount from entry to stop, which you've fixed at ~1% of your account. Every outcome is then measured in R: a trade that makes twice your risk is +2R; a trade stopped out is −1R. Expectancy = (win rate × average win in R) − (loss rate × average loss in R). Positive expectancy means the system makes money over time. That's the whole verdict.
R — measuring trades in units of risk
From chapter 7, every trade has a defined risk: the distance from entry to stop, times your shares, sized to ~1% of the account. Call that risk 1R. It's your unit.
Now express every outcome in R instead of dollars:
- Stopped out at your stop? You lost your risk: −1R.
- Made a profit equal to twice your risk? +2R.
- Closed a winner at three times your risk? +3R.
- Cut a loss early, before the full stop? Maybe −0.5R.
Why this is powerful: R normalizes every trade to the same scale, regardless of the stock's price or the position's dollar size. A $50 stock and a $500 stock, a $2,000 position and a $5,000 position — all measured in R, they're comparable. R is the universal language of trade outcomes, and it's exactly how QA reports its own strategy results (the walk-forward sweep in chapter 10 grades every strategy this way).
Risk/reward — set the R before you enter
Before any trade, you can read its risk/reward ratio straight off the chart from chapter 5:
- Risk = entry to stop (below support).
- Reward = entry to target (the next resistance).
If you buy at $50.00, stop at $48.00 ($2 risk) and target $56.00 ($6 reward), that's a 3:1 trade — a potential +3R against −1R. The ratio is the trade's quality, known before you commit a dollar.
The discipline this enforces: only take trades where the reward meaningfully exceeds the risk. A common minimum is 2:1 — risk 1R to make at least 2R. This single filter does enormous work, because (as the next section shows) it lets you be wrong most of the time and still win.
A 2:1 minimum makes a low win rate profitable. Risk 1R to make 2R, win just 40% of the time: out of 10 trades, 4 winners × +2R = +8R, 6 losers × −1R = −6R, net +2R. You lost more trades than you won and still made money. That's the entire reason professionals obsess over risk/reward and shrug at win rate. The payoff ratio does the heavy lifting.
Expectancy — the one number that decides everything
Expectancy is your average profit (or loss) per trade, measured in R. The formula:
Expectancy = (Win% × Average Win in R) − (Loss% × Average Loss in R)
Two example systems make the win-rate trap obvious:
System A — high win rate, terrible expectancy. Wins 90% of trades for +0.5R, but the 10% of losers run to −5R (no stop discipline). Expectancy = (0.90 × 0.5) − (0.10 × 5.0) = 0.45 − 0.50 = −0.05R per trade. A 90% win rate that loses money, because the rare losers are catastrophic. This is the account that grinds up for months then gives it all back in two trades.
System B — low win rate, strong expectancy. Wins 40% of trades for +2.5R, losers cut at −1R. Expectancy = (0.40 × 2.5) − (0.60 × 1.0) = 1.0 − 0.6 = +0.4R per trade. Loses 60% of the time and makes +0.4R every trade on average. Over 200 trades at 1% risk, that compounds powerfully.
System B is the professional model and System A is the beginner trap dressed up as success. The verdict is expectancy, and expectancy rewards cutting losers fast and letting winners run — never the reverse.
Why "let winners run, cut losers fast" is just expectancy
Every trading cliché you've heard is really a statement about expectancy:
- "Cut your losses." Caps average loss near −1R, protecting the negative term.
- "Let your winners run." Raises average win in R, growing the positive term.
- "The trend is your friend." Trends produce the occasional +5R outlier that lifts expectancy single-handedly.
The destructive instinct — snatching small wins quickly while letting losers run "until they come back" — does the exact opposite: tiny average wins, huge average losses, negative expectancy. It feels good (lots of wins!) and bleeds the account (System A). The math doesn't care how it feels.
QA's R-multiple calculator computes your expectancy from your own win rate and average R values — feed it a few months of your trade log and it tells you, honestly, whether your system has an edge. Plan a trade's R here:
Take-profit legs
Blended outcome
Fill entry, stop, and at least one leg.
What R-multiple means
R is the per-share risk from entry to stop. A trade that hits +2R makes twice what a full stop-out loses; a partial at +1R covers the other side's loss if the rest goes against you.
Scaling out at fractional R targets is how trend-followers convert asymmetric setups into break-even or better without a magic exit. Blended R lets you compare alternative scale-out plans before the trade rather than rationalizing them afterwards.
You need a sample — one trade proves nothing
Expectancy is a statistical property. A single trade, even ten trades, tells you almost nothing — variance dominates small samples. A positive-expectancy system can lose its first five trades; a negative one can win its first ten. You need a meaningful sample (dozens of trades, ideally on consistent rules) before the expectancy estimate means anything. This is also why QA validates strategies across out-of-sample windows rather than trusting one good run — the subject of chapter 10.
What to watch as you start
- Expectancy, not win rate. If you track one number about your trading, track average R per trade. A high win rate with negative expectancy is the most dangerous illusion in the game.
- Risk/reward before entry. If a trade isn't at least ~2:1, the math is fighting you from the start. Skip it.
- Your average loss size. It should cluster near −1R. Losers running well past −1R means your stops aren't being honored — the fastest route to System A.
- Sample size before judging. Don't conclude your system works (or fails) from a handful of trades. Variance lies in the short run; expectancy only emerges over a sample.
Run your numbers on the R-multiple calculator. The next chapter covers the hidden risk that breaks naive diversification: correlation — why your carefully separate positions can all be the same bet.
Next in this series: Correlation and diversification — why owning ten stocks isn't ten bets if they all move together.
See it live: /tools/r-multiple for the calculator. QA grades its own bot strategies in R — live telemetry is part of /pro.
QuantAbundancia is educational research. Nothing here is investment advice. See /disclosures.
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