Set Risk Per Trade From Your Drawdown Limit

Decide the drawdown that would make you stop trading a method, work out the losing streak your win rate actually produces, and divide one by the other. The result is a risk per trade derived from your own tolerance and your own numbers, rather than a convention inherited from everyone repeating it.

Most people arrive somewhere between 0.5% and 1.5%, which is why the 1% rule persists. The value of doing it yourself is that the number then holds under pressure, because you know what it is protecting.

What the 1% rule rests on

It is worth being straight about this: the 1% rule is a convention, not a measured finding. There is no study that establishes it, and anyone presenting one should be read carefully.

What it does have is sound arithmetic behind it. At 1% per trade, ten consecutive losses cost roughly 10% of the account, which is unpleasant and entirely recoverable. No single trade can do structural damage. The figure is round, memorable, and sits in a sensible region.

That is a reasonable basis for a default. It is not a reason to treat the number as fixed, because nothing in it refers to your method, your win rate or what you would actually tolerate.

The three inputs

Your drawdown limit. Not the drawdown you think you should tolerate — the one at which you would genuinely stop, change the method, or walk away. If you have traded through a drawdown before, use what actually happened rather than what you intended.

Be honest here, because an aspirational figure produces a position size that fails at exactly the moment it matters. Most people’s real answer is lower than their stated one.

Your expected losing streak. The run of consecutive losses your win rate produces as a matter of routine. At a 45% win rate, runs of six and seven appear regularly across a few hundred trades. The arithmetic is in how many losing trades in a row is normal, and it needs only your loss rate.

Your correlation exposure. How many positions can fail together. Six holdings sharing a driver produce one loss six times the size, which the streak calculation counts as six separate events. Correlation and portfolio heat covers why this is usually the input people get wrong.

The calculation

Divide the drawdown limit by the expected streak length.

A trader who would stop at 15% down, with a win rate producing eight-loss runs:

15 ÷ 8 ≈ 1.9% per trade

At that size, an ordinary eight-loss run costs about 15%, which is exactly the threshold. So 1.9% is the ceiling, not the recommendation — it is the size at which a routine streak takes you to the point of quitting.

Now apply the two adjustments that always push it down.

Streaks run longer than the independence assumption predicts, because losses cluster in conditions that do not suit the method. Using a streak length half again as long is not pessimistic, it is realistic. Twelve rather than eight gives 15 ÷ 12 = 1.25%.

Correlated positions fail together. If two or three holdings can share a driver, the effective loss per “event” is a multiple of the per-trade risk. Dividing by two for a book that routinely carries pairs in the same cluster gives roughly 0.6%.

That is how a 15% tolerance and an ordinary win rate produce a number well under 1%, and why the convention sits where it does.

Reading the result

If your number lands near 1%, the convention was serving you and now you know why.

If it lands well below, that is information rather than a disappointment. It usually means one of three things: the drawdown you would tolerate is smaller than you assumed, the method produces longer streaks than you expected, or the book is more correlated than the position count suggests. All three are worth knowing, and none of them is fixed by sizing larger.

If it lands well above 1%, check the inputs before acting on it. A high stated tolerance and a short assumed streak together produce a number that looks generous and has not been tested. The asymmetry matters: sizing too small costs some return, sizing too large costs the account, and risk of ruin covers how sharply that second cost rises.

A second worked case

Run it again with different inputs, to show how much the answer moves.

A trader with a higher win rate, say 58%, would stop at a 25% drawdown. Loss rate is 0.42, and runs of five are common at that win rate while six is occasional.

Ceiling: 25 ÷ 6 ≈ 4.2% per trade.

That number should immediately look wrong, and it is instructive to see why rather than to dismiss it. It is the ceiling for a single idealised streak, and both adjustments apply with full force.

Stretch the streak for clustering, to nine: 25 ÷ 9 ≈ 2.8%.

Then halve for correlation, on a book that carries related positions: 1.4%.

So a trader with a materially better win rate and a far higher stated tolerance still lands near the conventional range. That is the useful finding. The calculation is not a licence to size larger when the inputs look favourable, because the two adjustments do most of the work and they apply regardless of how good the win rate is.

It also shows where the sensitivity actually sits. Changing the win rate moved the answer less than changing the correlation assumption did, which is the opposite of where most attention goes.

What this does not settle

The calculation gives a ceiling for ordinary conditions. Three things sit outside it.

Gap risk. It assumes each loss is the size you planned. A position gapping through its stop loses more, which is why size rather than stop placement is the real control, as stop orders are not guaranteed sets out.

Estimate error. A win rate measured over thirty trades carries enough error that the streak figure moves substantially. Expectancy and sample size covers how much. Until the sample is large, size toward the lower end of what the calculation allows.

Behaviour. The arithmetic assumes you keep following the rule through the drawdown. Most ruin in practice involves abandoning position sizing after several losses in an attempt to recover quickly, and no formula reaches that.

Using it

Run the calculation once, write the number down with the three inputs that produced it, and leave it alone. Revisit when the win rate has been measured over a materially larger sample, or when the composition of the book changes enough to alter the correlation assumption.

Not after a losing streak, and not after a winning one. The point of deriving the figure is that it survives both, and a number recalculated under pressure is just the old habit of sizing by feel wearing a spreadsheet.