Risk of Ruin and Why It Matters

Notice also that the gap widens non-linearly. Ten times the risk does not produce ten times the damage, it produces a different category of outcome. That is why the phrase "risk of ruin rises sharply, not proportionally" is worth taking literally.

Account currency and leverage do not appear in that table for a reason. Neither changes the arithmetic. Leverage determines the margin a broker requires, not what a stopped-out trade costs you, so two traders at 1:30 and 1:500 with identical risk percentages face identical ruin probabilities. What higher leverage changes is how easy it becomes to place a position far larger than the rule allows, which is a behavioural risk rather than a mathematical one.

What the number does not tell you

• It assumes your inputs are stable. The calculation takes your historical proportion of winners and average R as if they were fixed properties. They are estimates from a sample, they carry error, and market conditions change underneath them.
• It assumes trades are independent. They rarely are. Correlated open positions can produce several simultaneous losses that the model counts as one, and a trader whose behaviour degrades during a losing run breaks the assumption entirely.
• It ignores the size of the worst loss. Slippage, gaps and stops that fill far from their level make the occasional loss bigger than 1R, and the tail is what ruins accounts.
• No sizing approach drives it to zero. Smaller size lowers the probability and lengthens the timeline. It never eliminates the possibility, and any framing that suggests otherwise is selling something.

Using it in practice

Treat risk of ruin as a sizing constraint rather than a forecast. The useful question is not "what is my exact probability of ruin" but "at what risk per trade does this number become unacceptable to me", and then trade below that figure with room to spare, because your inputs are estimates and the estimates are optimistic more often than not.

A workable routine: pull thirty or more of your own closed trades from the journal, compute the proportion that reached target and the average R of wins and losses, and put those into the calculator at 0.5%, 1%, 2% and 5% risk. Then set your working figure one step below the level where you start to feel uncomfortable. Pair it with a daily loss limit so that behaviour cannot quietly raise the effective risk on a bad day, and re-run it every few months as the sample grows.

The shift in thinking is the real value. It moves attention off any single trade's outcome and onto whether the whole approach survives long enough to be worth having. Trading is risky and most new traders lose money, and the arithmetic here explains a large part of why: not because their analysis was uniquely poor, but because they were sized for a market that never delivers exactly the sequence you planned for.