Guide · Risk
Two numbers decide whether a trading strategy survives contact with reality: how much you risk per trade, and what you earn per unit of that risk. Everything below is arithmetic — no indicators, no opinions.
An R-multiple expresses a trade's result in units of the risk you took on that trade. 1R is the distance from your entry to your stop loss, in dollars. Risk $250 and make $500, and the trade is +2R. Get stopped out, and it is −1R.
The point of R is comparability. A $180 gain in a small account and a $1,800 gain in a large one are the same trade if both were +2R. Once every result is in R, you can average them, rank setups against each other, and spot the one that quietly bleeds the account.
On a $25,000 account risking 1%, you are allowed to lose $250. Buy at $50 with a stop at $47.50 and each share carries $2.50 of risk, so the position is 100 shares. Notice what decides the size: the stop does. A wider stop means a smaller position, not a bigger loss.
This is the single mechanical habit that separates traders who survive from traders who do not. It also means "I bought 100 shares because that felt right" is not a sizing method.
Run it in the free position sizer →
Take a system that wins 40% of the time for +2R and loses 60% of the time for −1R:
(0.40 × 2) − (0.60 × 1) = 0.80 − 0.60 = +0.20R per tradeOver 100 trades risking 1% of a $25,000 account, +0.20R per trade is roughly $500 of expected profit before costs. That is what a real edge looks like: small, boring, and only visible across a large sample.
| Reward : risk | Break-even win rate | What that means |
|---|---|---|
| 0.5R | 66.7% | You must be right two times out of three just to tread water |
| 1R | 50.0% | A coin flip with no edge left over |
| 2R | 33.3% | Wrong two times out of three and still profitable |
| 3R | 25.0% | Wrong three times out of four and still profitable |
Easily. Win 70% of trades at +0.3R and lose 30% at −1R:
(0.70 × 0.3) − (0.30 × 1) = 0.21 − 0.30 = −0.09R per tradeThat is a losing system with a win rate most traders would brag about. The mirror image is also true — a 35% win rate at +3R is strongly profitable and feels awful to trade. Win rate is a feeling; expectancy is the result.
Risk of ruin is the probability that an ordinary losing streak takes the account somewhere it cannot come back from. It depends on win rate and reward-to-risk, but it is dominated by risk per trade. The same edge risking 3% instead of 1% per trade converts a survivable drawdown into a terminal one, because the recovery math is brutal: a 50% drawdown needs a 100% gain to get back to flat.
Because streaks are random, the honest way to estimate this is a Monte Carlo simulation over thousands of shuffled sequences rather than a formula applied to your last twenty trades. The free risk-of-ruin simulator does that in the browser →
At least 30 for a rough read, and 100 or more before you trust it. Expectancy measured over ten trades is noise wearing a decimal point.
This matters more than it sounds. When I ran my own swing setups through a no-lookahead walk-forward test, most of them died: only the anchored-VWAP reclaim survived — and even that shrank to +0.117R over 4,933 trades (95% CI +0.057 to +0.174) once I re-tested it on a 129-symbol, 10-year universe. The earlier small-sample result I first reported did not survive that re-test — and even +0.117R isn't a clean edge: a risk-matched random entry on the same data already captures +0.086R, so the signal's own contribution is only about +0.030R, with a confidence interval that crosses zero. A VCP variant came out breakeven, and a gap and opening-range-breakout proxy came out at −0.28R and was dropped entirely. Nothing about that was visible in a small recent sample — which is exactly the point. The full write-up is here →
Educational content, not financial advice. No live profit-and-loss figures are claimed anywhere on this site; backtest and walk-forward results are always labelled as such. Full terms: /terms.html
FAQ
An R-multiple expresses a trade's result in units of the risk taken on that trade. 1R is the distance from your entry to your stop loss, in dollars. If you risk $250 and make $500, that trade is +2R. If the stop is hit, it is -1R. Because R normalises for position size and account size, a +2R trade in a $5,000 account is directly comparable to a +2R trade in a $500,000 account.
Position size = (account size x risk percent) / (entry price - stop price). On a $25,000 account risking 1%, you can lose $250. Buying at $50 with a stop at $47.50 puts $2.50 of risk on each share, so you buy 100 shares. The stop, not a round-number share count, determines the size.
Expectancy in R = (win rate x average win in R) - (loss rate x average loss in R). A system that wins 40% of the time for +2R and loses 60% of the time for -1R has expectancy of (0.40 x 2) - (0.60 x 1) = +0.20R per trade. Positive expectancy means the system makes money across a large sample; zero or negative means it does not, regardless of how the equity curve looks over ten trades.
Break-even win rate = 1 / (1 + reward-to-risk ratio). At 1R reward you need 50%. At 2R you need 33.3%. At 3R you need 25%. At 0.5R you need 66.7%. Any win rate above the break-even line for your reward-to-risk produces positive expectancy before costs.
Yes. Win rate alone says nothing about profitability. Winning 70% of trades at +0.3R and losing 30% at -1R gives (0.70 x 0.3) - (0.30 x 1) = -0.09R per trade — a losing system with a win rate most traders would envy. The reverse is also true: a 35% win rate at +3R is strongly profitable.
Risk of ruin is the probability that a losing streak takes your account below a level you cannot recover from, given your win rate, reward-to-risk, and risk per trade. It is driven far more by risk per trade than by edge: the same system risking 3% per trade instead of 1% can turn a survivable drawdown into a terminal one. A Monte Carlo simulation is the practical way to estimate it.
Enough that a normal losing streak cannot dominate the average — in practice at least 30 trades for a rough read and 100 or more before trusting the number. Expectancy measured over 10 trades is mostly noise, which is why serious validation uses walk-forward testing over a large sample rather than a recent run of results.