What Is R in Trading? R-Multiples Explained With Examples

Published 18 July 2026 · Artha Education · ~7 min read

You've seen results written as "+1.4R" or "−1R" and wondered why serious traders don't just say how many rupees they made. The answer is that rupees hide information and R reveals it. R-multiples are the single most useful accounting convention in trading — they make results comparable across account sizes, make fake track records harder to construct, and force you to think about risk before reward. Here's the whole idea in one page.

The definition: R is the risk you chose before the trade

R stands for your initial risk per trade — the amount you stand to lose if your stop loss is hit. Every trade's outcome is then expressed as a multiple of that amount:

• Stop hit → you lose exactly what you planned → −1R
• Target at twice the stop distance hit → +2R
• Exited flat at entry → 0R
• Made half your risked amount → +0.5R

A worked example on an NSE stock. Say a breakout setup gives an entry at ₹500 with a stop at ₹490 — ₹10 of risk per share. Following the 1% rule on a ₹1,00,000 practice account, you're willing to risk ₹1,000, so you take 100 shares. That ₹1,000 is your R for this trade.

• If price falls to ₹490, you lose ₹1,000 → −1R.
• If price reaches ₹520 (twice the stop distance), you make ₹2,000 → +2R.
• If you exit at ₹514 into weakness, you made ₹1,400 → +1.4R.

Notice what R is not: it's not a percentage of your account, and it's not fixed by the market. It's fixed by you, at entry, by where you put your stop and how many shares you took. That's the point — R measures outcomes against your own plan.

Why rupee P&L hides the truth

"I made ₹15,000 today" tells you nothing by itself. On what capital? Risking how much? A trader who makes ₹15,000 risking ₹5,000 had a +3R day — excellent. A trader who makes ₹15,000 risking ₹50,000 had a +0.3R day and will hand it all back the first time a couple of stops hit. Same rupees, completely different quality of trading.

This is exactly why screenshot P&L culture is so misleading. A big green number is trivially manufactured: take enormous position sizes, win a coin-flip, post the winner. The size of the number proves nothing about the edge behind it. R strips that trick away — expressed in R, the oversized gamble and the disciplined trade are finally measured on the same scale, and the gamble stops looking impressive.

R makes any two traders — or any two strategies — comparable

Because R normalises by risk, a +1.4R day means the same thing for a student practising with a ₹50,000 paper account and a professional running ₹50 lakh: both made 1.4× what they risked. That lets you evaluate a methodology independently of the money behind it — which is the honest way to evaluate one, because the money behind it can always change, but the edge either exists or it doesn't.

It also makes your own journal readable. Thirty trades logged in rupees is a noise of position sizes and market moods. The same thirty trades logged in R answer the only questions that matter: how often do you win, how big are the wins in units of risk, and how big are the losses?

Expectancy: the number R exists to compute

Once results are in R, you can compute expectancy — the average R you earn per trade over many trades:

Expectancy = (win rate × average win in R) − (loss rate × average loss in R)

Example: a breakout strategy that wins only 45% of the time, with winners averaging +2R and losers −1R:

(0.45 × 2R) − (0.55 × 1R) = 0.90R − 0.55R = +0.35R per trade

Positive — this "loses more often than it wins" strategy makes money over time, because the wins are structurally larger than the losses. This is the deep reason disciplined breakout traders insist on a minimum 1:2 risk:reward and refuse to chase entries that shrink it: the whole profitability of the approach lives in keeping winners near +2R. Flip it around and you also see why a 90% win rate can be worthless — if the 10% of losers are −10R blowups, the expectancy is deeply negative. Win rate without R is marketing; win rate with R is mathematics.

Count your costs in R too

Brokerage, STT, exchange charges and slippage are real, and on intraday round-trips they add up. An honest R record charges them against every trade. Artha's paper-trading system, for example, deducts 0.12% of turnover per round-trip — roughly what a discount-broker intraday trade actually costs including realistic slippage — so a trade that hit its +2R target might record as +1.8R net. If a track record never mentions costs, assume the numbers are flattered.

Why public track records should be published in R

There's one more reason we publish results as R-multiples rather than price levels, and it's worth knowing as a consumer of trading content. In India, SEBI's investor-education rules restrict unregistered educators from broadcasting live or recent price data — a guardrail against "education" that functions as disguised tips. Results in R (with timestamps proving the setup was logged before the outcome) carry all the information that matters about an edge, while carrying none of the "buy this at this price" signal content. When you see an education page publishing R-numbers with receipts instead of price calls with rocket emojis, that's what compliance-respecting transparency looks like — and it's a green flag, not a limitation. Our guide to reading a trading track record covers the other green and red flags.

Using R in your own practice

Start on paper, not with money — the habit is the hard part, not the arithmetic. For every practice trade: fix the stop before entry, size the position so the stop costs a fixed 1% of the account, and log the result in R alongside the rupees. After thirty trades you'll have a real expectancy number for your own trading — which puts you ahead of the large majority of intraday traders who, per SEBI's own loss data, never measure theirs at all.