If you've spent any time around active traders, you've probably seen results reported as "+2R" or "-1R" instead of a dollar figure or a percentage. This is the R-multiple, and it's one of the more useful — and more misunderstood — tools in trading performance measurement. Understanding it matters whether you're evaluating your own strategy or trying to make sense of a track record someone else is showing you.
The basic idea
An R-multiple expresses a trade's outcome as a multiple of the amount you risked on that trade, rather than as a raw dollar amount or a percentage return on your account. The concept was popularized by trader and author Van Tharp in his book Trade Your Way to Financial Freedom, and it has since become a standard unit in trading-system design (Trade Journal AI).
The formula is straightforward:
The sign flips for short positions, since the stop sits above the entry rather than below it. In practice, before you enter a trade you define your initial risk — called "1R" — as the distance between your entry price and your stop-loss, multiplied by your position size. Once the trade closes, you divide the actual profit or loss by that 1R figure to get the result in R terms (Trade Journal AI).
Worked example. Say you buy a stock at $50 with a stop at $48.50. Your risk per share is $1.50 — that's your 1R. If your position-sizing rule caps risk at $200 per trade, you'd hold roughly 133 shares ($200 ÷ $1.50) (TradeZella position size calculator). If that trade later closes at a $3.00 gain per share, your result is 2R — twice your initial risk — regardless of how many shares you held or how much capital the position represented in dollar terms.
Why traders use R instead of dollars or percentages
The dollar profit or loss on a trade depends heavily on position size, leverage, and account size — none of which tell you anything about the quality of the decision that produced the trade. R-multiples strip that variability out. A 2R trade always means the position returned twice what was risked, whether the account is $1,000 or $1,000,000, and whether the instrument is a stock, a crypto pair, or a futures contract (Trade Journal AI). That consistency is what makes it possible to compare, aggregate, and audit a series of trades across different markets and time periods on equal footing — which is exactly why we report our own public track record in R rather than in dollars.
The part most marketing leaves out: win rate alone doesn't tell you anything
This is the most important, and most commonly misused, part of the concept. A high win rate sounds impressive, but on its own it says nothing about whether a strategy makes money. The number that actually determines profitability is expectancy:
Consider two hypothetical strategies:
- Strategy A: 90% win rate, average winner of +0.2R, average loser of −5R. Expectancy works out to roughly −0.32R per trade — a losing strategy despite winning nine times out of ten, because the rare losses are disproportionately large (Trade Journal AI).
- Strategy B: 35% win rate, average winner of +3R, average loser of −1R. Expectancy works out to roughly +0.40R per trade — a profitable strategy, even though it loses more often than it wins.
Position sizing itself matters as much as trade selection: the same entry and exit produce different real-world risk depending on how many shares or contracts are behind them, and inappropriate sizing — risking too much per idea — can turn a mathematically sound system into a portfolio-ending one (TradeZella).
The practical takeaway: any time you see a strategy or service advertised primarily on win rate ("85% accurate," "92% win rate"), treat that number as incomplete. It tells you nothing about the size of the losses on the other side of the ledger. A trading system with negative expectancy will lose money over a large enough sample even if short stretches look profitable due to normal variance (Trade Journal AI).
How to use this when evaluating any track record
Whether you're reviewing your own results or a research provider's published history, the same three questions apply:
- What is the win rate, and what are the average winning and losing R-multiples separately? A single blended average-return figure can hide a lot.
- What is the implied expectancy per trade? Multiply through the formula above rather than taking a headline win-rate figure at face value.
- What is the sample size? Ten or twenty trades can look great or terrible purely from variance. A meaningful read typically requires a larger closed sample (Trade Journal AI).
Regulators have specifically flagged the broader category of unverified social-media and group-chat trading claims as a common vector for misleading or fraudulent promotion — a further reason to look for track records that report every outcome, wins and losses, rather than curated highlights (SEC Investor.gov: social media and stock tip scams).
How Belfed publishes in R
We report our own results the same way this article describes, because it's the only honest way to make a multi-asset track record comparable. Every closed idea — equities and crypto, long and short — is logged on our public track record with entry, exit, and result in R, alongside win rate and average win/loss size, exactly as described in our own performance methodology. We don't remove losing trades from that list, and the sample sizes behind our published totals are disclosed alongside the numbers themselves — a modest sample, by design, since we'd rather show a real, ongoing record than a curated one.
The bottom line
R-multiples are a normalization tool, not a promise of performance. They let you compare trade quality across different position sizes, instruments, and account sizes on a consistent scale — but the number only becomes meaningful once you also know the win rate, the average size of wins versus losses, and the sample size behind it. None of that math changes the fact that every trade, and every trading strategy, carries a real risk of loss, and no historical statistic, however it's calculated, can guarantee what happens next.