Module 10 / Risk management & position sizing
Module 9 · Risk

Risk management & position sizing

R-multiples, expectancy, sizing formulas, drawdown math, and the simulator that shows why risk per trade decides survival.

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R-multiples & expectancy

Learn

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.

Expectancy = (W% × avgWin) − (L% × avgLoss) [in R] 0.40 × 2.5R − 0.60 × 1R = +0.40R per trade
Practice
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Recall0/2
Recall

Why can a 90%-win-rate system still be a guaranteed long-term loser?

Recall

What does measuring trades in R-multiples let you do?

Position sizing: the formula

Learn

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.

Risk $ = account × risk% Position = Risk $ / stop distance % $10,000 × 1% ÷ 5% stop = $2,000 position
Kelly sizing and its fractional variants are covered in Crypto Futures Mastery — fixed-fractional at 1% is the robust default that survives everything short of a bad system.
Practice
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Recall0/1
Recall

With fixed-fractional sizing, what happens to position size when you widen the stop?

Drawdown math: the cruel asymmetry

Learn

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.

DrawdownGain to recoverZone
−10%+11%Routine
−20%+25%Uncomfortable
−33%+50%Serious
−50%+100%A different career
−80%+400%Effectively terminal
Practice
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Recall0/1
Recall

After a −50% drawdown, what gain restores the account to break-even?

Risk of ruin: streaks are certain

Learn

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.

Ruin is rarely one bad trade. It's a normal losing streak hitting an oversized bet — the most preventable disaster in trading.
Practice
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Recall0/2
Recall

Two traders run the identical positive-expectancy system; one risks 1% per trade, the other 8%. Why might only one survive the year?

Recall

Why are long losing streaks 'scheduled' rather than unlucky for any real system?