What is R-multiple in trading?

R-multiple is the ratio of a trade's profit (or loss) to the initial risk taken on the trade. Originally formalised by Van Tharp. R = profit_per_share ÷ |entry − stop_loss|. A +1R trade made exactly as much as you risked; a -1R trade hit your stop; a +2R trade met a 1:2 reward-to-risk target.

Why measure trades in R instead of rupees?

Because rupee P&L conflates two separate skills: how good your edge is (captured by R) and how big you sized the bet (captured by position size). A ₹5,000 win on ₹10,000 risk (0.5R) is materially worse than a ₹2,000 win on ₹500 risk (4R). Indian retail traders who only track ₹ make decisions based on size, not edge — and over time this hides whether their strategy is actually positive-expectancy.

What is a good average R-multiple for retail Indian traders?

Profitable Indian retail traders cluster around +0.3R to +1.5R average across hundreds of trades, depending on style. Trend-followers tend toward higher avg R (large winners offset frequent small losses). Mean-reversion / range traders average lower R but win more often. Anything above +0.5R consistently across 200+ trades suggests a real edge.

How is R-multiple different from risk-reward ratio?

Risk-reward ratio (R:R) is a planned, pre-trade ratio: how far is your target vs. your stop, before entry. R-multiple is a post-trade measurement: what actually happened, in units of the risk you took. A 1:3 R:R plan can still produce a +0.5R outcome if you exit early or trail too tight.

Does R-multiple work for NSE F&O / options trades?

Yes. For options, the per-share risk is (entry premium − stop premium). R-multiple is calculated on premium, not the underlying. For multi-leg strategies (iron condor, strangle), the initial risk = max loss at expiry; R-multiple measures realised P&L against that maximum. Net premium received minus actual exit cost = numerator.

R-Multiple Calculator India 2026 — Measure Trades in Units of Risk | ArthaLearn

The R-multiple insight

Two trades with identical P&L can have completely different R-multiples — and the R-multiple is what tells you whether your strategy actually has an edge.

Trade A: ₹5,000 profit on ₹10,000 risk → +0.5R (took half the planned reward)
Trade B: ₹5,000 profit on ₹2,500 risk → +2.0R (textbook 1:2 winner)

Both made ₹5,000. Only Trade B was a "good" trade in process terms. Tracking ₹ alone hides this — tracking R reveals it.

Expectancy = the only metric that matters long-term

Expectancy (in R) = (Win % × Avg Winning R) − (Loss % × Avg Losing R)

Example: 45% win-rate, avg win +1.8R, avg loss -1.0R
       = (0.45 × 1.8) − (0.55 × 1.0)
       = 0.81 − 0.55
       = +0.26R per trade

Over 200 trades risking 1% each: +0.26R × 200 = +52% account return
(before slippage, brokerage, taxes — but the strategy is positive-expectancy)

Common R-distribution traps among Indian retail

"Move stop to break-even" syndrome: reduces avg losing R toward 0R but also caps avg winning R below 1R as more trades scratch out. Net expectancy often drops.
Cutting winners early at 0.5R: common in F&O premium selling. Mathematically guarantees a 1:1 winrate-required minimum just to break even.
Letting losers run past stop: a single -3R or -5R trade undoes 5-10 normal +0.5R-+1R wins. Discipline on the loss side is what compounds.

Related tools

Position Size Calculator — fix the rupee risk before each trade
Max Drawdown Calculator — see how deep your equity curve has gone underwater
Risk-Reward Ratio for Indian Trading
Trading Journal for Indian Traders — Best Practices
Kelly Criterion Calculator — sizing for known expectancy

The R-multiple insight

Two trades with identical P&L can have completely different R-multiples — and the R-multiple is what tells you whether your strategy actually has an edge.

Trade A: ₹5,000 profit on ₹10,000 risk → +0.5R (took half the planned reward)

Trade B: ₹5,000 profit on ₹2,500 risk → +2.0R (textbook 1:2 winner)

Both made ₹5,000. Only Trade B was a "good" trade in process terms. Tracking ₹ alone hides this — tracking R reveals it.

Expectancy = the only metric that matters long-term

Expectancy (in R) = (Win % × Avg Winning R) − (Loss % × Avg Losing R) Example: 45% win-rate, avg win +1.8R, avg loss -1.0R = (0.45 × 1.8) − (0.55 × 1.0) = 0.81 − 0.55 = +0.26R per trade Over 200 trades risking 1% each: +0.26R × 200 = +52% account return (before slippage, brokerage, taxes — but the strategy is positive-expectancy)

Common R-distribution traps among Indian retail

"Move stop to break-even" syndrome: reduces avg losing R toward 0R but also caps avg winning R below 1R as more trades scratch out. Net expectancy often drops.

Cutting winners early at 0.5R: common in F&O premium selling. Mathematically guarantees a 1:1 winrate-required minimum just to break even.

Letting losers run past stop: a single -3R or -5R trade undoes 5-10 normal +0.5R-+1R wins. Discipline on the loss side is what compounds.

R-Multiple Calculator (India)

The R-multiple insight

Expectancy = the only metric that matters long-term

Common R-distribution traps among Indian retail

Related tools

R-Multiple Calculator (India)

The R-multiple insight

Expectancy = the only metric that matters long-term

Common R-distribution traps among Indian retail

Related tools