Risk management & position sizing
R-multiples, expectancy, sizing formulas, drawdown math, and the simulator that shows why risk per trade decides survival.
R-multiples & expectancy
Define 1R as the amount you risk on a trade — entry to stop, in money. Every outcome then becomes a multiple: a win that makes twice your risk is +2R, a stopped-out loss is −1R. Suddenly trades of any size, in any market, are comparable — and your system becomes a distribution of R.
Expectancy is that distribution's average: `winRate × avgWin − lossRate × avgLoss` (all in R). Positive expectancy is the *only* thing that makes a system worth sizing. A 40% win rate with 2.5R winners is a good business (+0.4R/trade); a 90% win rate that risks 10R to make 1R is a blowup on a timer (−0.1R/trade). Win rate alone tells you nothing.
Why can a 90%-win-rate system still be a guaranteed long-term loser?
What does measuring trades in R-multiples let you do?
Position sizing: the formula
The professional default is fixed-fractional sizing: risk the same small percentage of the account on every trade — commonly 0.5–2%. The size follows mechanically from where your stop is: risk amount divided by stop distance. Wide stop → small position; tight stop → larger position. Same R either way.
This solves the problem beginners have backwards. They pick a position size that feels exciting, then place a stop they can 'afford' — usually too tight, guaranteeing noise stops them out. The formula runs the other way: thesis sets the stop, stop sets the size.
With fixed-fractional sizing, what happens to position size when you widen the stop?
Drawdown math: the cruel asymmetry
Percentages lie to your intuition: a −50% loss needs +100% to recover. The recovery requirement grows non-linearly — small drawdowns are a toll, deep drawdowns are a trap. This asymmetry is the single strongest argument for risking small.
It also reframes what risk management is *for*: not avoiding losses (impossible) but keeping every loss in the shallow zone of the curve, where recovery is routine arithmetic instead of a heroic comeback.
| Drawdown | Gain to recover | Zone |
|---|---|---|
| −10% | +11% | Routine |
| −20% | +25% | Uncomfortable |
| −33% | +50% | Serious |
| −50% | +100% | A different career |
| −80% | +400% | Effectively terminal |
After a −50% drawdown, what gain restores the account to break-even?
Risk of ruin: streaks are certain
A 45%-win-rate system will hit 8+ consecutive losses somewhere in its first few hundred trades with near-certainty. That's not a malfunction — it's what the probability distribution *is*. The question is only whether your sizing survives the streak that is definitely coming.
Risk of ruin is the probability that drawdowns force you to stop — blown account, broken confidence, or pulled capital — before the edge can play out. It compounds from three inputs: expectancy, variance, and risk per trade. You control the third completely. At 1% risk, an 8-loss streak is −7.7%; at 5% risk, it's −33.7% and your decision-making is no longer the same person's.
Two traders run the identical positive-expectancy system; one risks 1% per trade, the other 8%. Why might only one survive the year?
Why are long losing streaks 'scheduled' rather than unlucky for any real system?