An R-multiple measures a trade in units of the risk you took on it, not in rupees. Risk 2,000 and make 4,000 and the trade is plus 2R, the same quality whether the account is small or large. This tool converts a trade to R gross and net of the Indian cost stack, logs your trades, and reads the whole record on the only scale that judges a strategy: win rate, average win and loss in R, and expectancy per trade.
Rupees measure how large you bet. R-multiples measure how well you traded. Only one of them belongs on a scoreboard.
Arithmetic, not a forecast. Every figure here is exact arithmetic on the trades you enter. A positive expectancy on your logged trades describes that sample; it is not a prediction of the next trade or a return you can expect to earn. The default trades are illustrative, clear them before entering your own.
The trade
Example
The stop you set at entry. This fixes 1R and must never be moved after the fact.
Used to compute the round-trip cost on representative Indian rates, as of July 2026.
R-multiple (gross)
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R-multiple (net of costs)
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Initial risk (1R)
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Net P&L
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Gross R vs net R: what costs quietly take
Gross P&L
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Round-trip cost
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Cost, in R
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Net R
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Your logged record, in R
Each bar is one trade's R-multiple, green for a win, coral for a loss. The gold line is the running cumulative R, the equity curve in risk units. This is what a strategy actually looks like.
Winning trade (+R) Losing trade (−R) Cumulative R
Expectancy, from your log
Expectancy per trade
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Trade log (this session)
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R (gross)
R (net)
Type
Trades persist in this browser tab only. Nothing is transmitted or stored on our servers.
What to check before trusting these numbers
The arithmetic is easy. The hard part is keeping an honest R-journal: measuring against the initial stop every time, logging the losers as faithfully as the winners, and judging the strategy on expectancy rather than on how often it felt right. That discipline, and the sizing that decides how much an R is worth, is what the method we teach is built around.
A trade has two facts: how good the decision was, and how large the bet was. Rupees fuse them, so a big win from a reckless bet outscores a disciplined one, and a journal kept in rupees quietly rewards taking more risk. An R-multiple divides the outcome by the risk taken, cancelling the bet size and leaving only the quality of the trade. Once every trade is in R, they become comparable across accounts, instruments and years, and the record can be reduced to the one number that judges a strategy: expectancy, the average R per trade. Win rate feels like the scoreboard; expectancy is the scoreboard.
The absence of this discipline is visible at national scale. SEBI's FY25 study of the equity derivatives segment found about 91 percent of individual traders net loss-making, with aggregate net losses near 1,05,603 crore rupees. Much of that is not a signal problem but a measurement problem: records kept in rupees and in feelings, win rates chased while expectancy bled, costs ignored until they had eaten the edge. You cannot fix what you refuse to measure correctly, and R-multiples are how a serious trader measures.
The math, derived
Read a trade from the stop outward. The distance from entry to the initial stop is the risk you accepted per unit; multiply by size and it is one R in rupees. The R-multiple is simply the profit or loss expressed in those units.
risk per unit = |entry − stop|
1R in rupees = risk per unit × size
R (long) = (exit − entry) ÷ (entry − stop)
R (short) = (entry − exit) ÷ (stop − entry)
net R = (gross P&L − round-trip cost) ÷ 1R in rupees then, across a record:
expectancy = mean of all trade R = win% × avg win R − loss% × avg loss R
Worked on the default trade: entry 100, stop 95, so risk per unit is 5 and, at 200 units, 1R is 1,000 rupees. An exit at 107.5 is a profit of 7.5 per unit, and 7.5 divided by 5 is plus 1.5R gross. The gross rupee profit is 1,500. The round-trip cost of that intraday equity trade is a few tens of rupees, so the net R sits just below 1.5. On a much smaller position the same 1.5R gross can fall well under 1R net, which is the entire reason the tool reports both.
Why the initial stop is the only honest denominator. R is defined against the risk you accepted at entry. Widening the stop after the trade moves against you shrinks the loss on paper by enlarging the denominator, turning a real minus 2R into a flattering minus 1R. The R that belongs in the journal is always measured against the original entry and original stop, whatever you later do to manage the position.
Why rupees lie, drawn
Two traders book the same rupee profit on the same day. Judged in rupees they look identical. Judged in R, one made a far better trade than the other, and only the R view tells you which process to repeat.
The rupee view rewards the larger bet; the R view rewards the better decision. Trader A risked five times as much to earn the same rupees, a plus 0.5R trade against Trader B's plus 2.5R. A journal in rupees would rate them equal and quietly push you toward A's habit of betting bigger. A journal in R shows the truth, and the truth is what you want to repeat.
Expectancy is the scoreboard, not win rate
The most common way traders lie to themselves is the win rate. It feels like the measure of skill, and it is only half of one. Expectancy is win rate crossed with the payoff, the ratio of the average win to the average loss, and the two can point in opposite directions.
The strategy that is right more often is the one that loses money. A high win rate built on small wins and rare large losses is negative expectancy in disguise; a low win rate with a strong payoff is a real edge. This is why cutting winners to book a green day, and holding losers past the stop, both feel good and both destroy the number that matters. Track expectancy in R, and the temptation to trade for the win rate loses its grip.
Costs shave every R, drawn
Gross R ignores the round trip. In India that round trip is a real bite, and it is a fixed and a proportional cost, so it hurts most on the smallest trades, exactly the ones that look like clean little winners. A gross plus 0.3R can be a net loss.
Cost is a fraction of an R, and the fraction explodes as the position shrinks. The round trip has a fixed floor and a proportional part, so on a tiny position it can exceed the entire gross gain, while on a large one it is a rounding error. Under-sizing to feel safe therefore hides a cost tax that can turn a positive-expectancy method negative. The calculator above computes the net R for your actual segment and size, so this is not a warning to remember but a number to read.
Reference: reading an R-multiple
What common R-multiples mean, and the discipline each implies. R is measured against the initial stop.
Outcome
What happened
The discipline it reflects
−1R
Stopped out exactly at plan
A clean loss, the cost of doing business. This is the good kind of loss.
Worse than −1R
Loss larger than the planned risk
A gap, slippage, or a stop that was not honoured. Investigate every one.
0R to +1R
A small win, below the risk taken
Fine occasionally; a habit of it means winners are cut too early.
+1.5R to +2R
A solid win, the workhorse outcome
The target band most methods are built around.
+3R and beyond
A runner, a trade allowed to work
Rare and disproportionately important; letting these run is where the edge lives.
Reference: win rate against payoff, in expectancy
Because expectancy is win rate crossed with payoff, the two trade off against each other. The grid shows expectancy in R for a range of win rates and payoff ratios (average win R over average loss R, with the average loss fixed at 1R). Green is positive, coral negative; the break-even line runs diagonally, and it moves.
Expectancy per trade in R, by win rate and payoff (average loss fixed at 1R). Illustrative arithmetic, not a claim about any strategy.
Win rate ↓ / Payoff →
0.5 : 1
1 : 1
2 : 1
3 : 1
30%
−0.55
−0.40
−0.10
+0.20
40%
−0.40
−0.20
+0.20
+0.60
50%
−0.25
0.00
+0.50
+1.00
60%
−0.10
+0.20
+0.80
+1.40
70%
+0.05
+0.40
+1.10
+1.80
Read the corners. A 70 percent win rate at a 0.5 payoff is barely positive; a 30 percent win rate at 3 to 1 is a real edge. The grid is why a method with a low win rate can be excellent and one with a high win rate can be a slow bleed: the payoff column moves the break-even line as much as the win-rate row does.
Failure modes: where the clean number still misleads
An R-multiple is exact, but the record it feeds can lie in six ways, each of which survives a correct calculation.
1. The stop did not hold. R assumes you exited at your stop, but a gap or an illiquid, circuit-bound counter can fill you well beyond it, making a real loss worse than minus 1R. Log the actual fill, not the planned stop, or your record will understate your true risk and flatter your worst trades.
2. Moved stops corrupt the denominator. Widening the stop after entry to soften a loss, or logging R against a trailed stop, quietly rewrites history. The only honest 1R is the distance from your original entry to your original stop; anything else is a record of rationalisation.
3. Small samples are noise. Expectancy is a mean of a skewed, heavy-tailed distribution. On ten or twenty trades a single runner or a cold streak dominates, so the figure is illustrative at best. Trust it only across many dozens of trades, and watch whether it stays stable as the count grows.
4. One outlier is carrying the result. A positive expectancy propped up by a single huge R is fragile: remove that one trade and the edge may vanish. A healthy record earns its expectancy across many trades, not from one lucky runner you may never repeat.
5. Gross R hides the cost drag. A journal kept in gross R can show a positive edge that costs have already erased, especially for small or high-frequency intraday trades. Always read the net R, because the market pays you in net rupees, not gross ones.
6. R says nothing about path or correlation. Two records with the same expectancy can feel completely different if one arrives in a smooth sequence and the other in deep clusters of losses, and R treats ten correlated trades that stopped together as ten independent minus 1R events. Expectancy is necessary, not sufficient; drawdown and correlation decide whether you survive to collect it.
The India reality: measured in rupees, lost in rupees
The case for R-multiples is sharpest in the Indian retail context. SEBI's FY25 study found about 91 percent of individual traders in the equity derivatives segment net loss-making, with aggregate net losses near 1,05,603 crore rupees, up materially on the prior year. A record kept honestly in R would have shown many of those accounts a negative expectancy long before the losses compounded, because the number does not flatter and does not forget.
Two India-specific forces make the net-R discipline non-negotiable. First, costs: the 2026 Budget lifted securities transaction tax on options to 0.15 percent of sell-side premium and on futures to 0.05 percent of sell turnover, effective 1 April 2026, on top of brokerage, exchange charges, an 18 percent GST, the SEBI fee and stamp duty. On the small, frequent trades that dominate Indian retail intraday and options activity, this cost stack eats a real slice of every R, so a gross-positive method can be net negative. Second, behaviour: a market that pays out in the rupee excitement of a big win trains traders to measure in rupees, which is exactly the scale that hides the bleed. Switching the journal to net R is the cheapest edge available, because it makes the truth legible.
Verify before you rely on rates. The cost figures the tool applies are representative published rates as of July 2026; brokerage and levies change, so confirm current rates with your broker. The SEBI FY25 figures are a dated finding, not a live statistic. Nothing here is a forecast or a recommendation.
Common Questions
Frequently Asked Questions
What is an R-multiple in trading?
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An R-multiple expresses a trade's outcome as a multiple of the risk you took on it, where one R is the rupee distance between your entry and your initial stop, times the position size. A trade that made twice what it risked is a plus 2R trade; one stopped out exactly at plan is a minus 1R trade, whatever the rupee figures behind it. The point is normalisation: a 200 rupee win on a 100 rupee risk and a 2,000 rupee win on a 1,000 rupee risk are both plus 2R, the same quality of outcome, even though the rupee amounts differ tenfold. Measuring in R lets you compare every trade you have ever taken, across account sizes, instruments and years, on one scale, which rupee profit and loss can never do.
How do you calculate an R-multiple?
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The initial risk per unit is the absolute distance between entry and stop; one R in rupees is that distance times the position size. The R-multiple is then the profit or loss per unit divided by the risk per unit. For a long, R equals exit minus entry, divided by entry minus stop. For a short the profit direction flips, so R equals entry minus exit, divided by stop minus entry. Worked: long entry 100, stop 95, so risk per unit is 5; exit 108 gives a profit per unit of 8, and 8 divided by 5 is plus 1.6R. A clean stop at 95 gives minus 5 over 5, exactly minus 1R. The formula only needs entry, stop and exit; the position size scales the rupees but never changes the R.
Why are R-multiples better than measuring profit in rupees?
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Because rupees mix together things that should be kept separate: the quality of the decision and the size of the bet. A 10,000 rupee win looks better than a 5,000 rupee win, but if the first risked 20,000 and the second risked 2,000, the second was the far better trade, plus 2.5R against plus 0.5R. Rupee profit and loss also cannot be compared across a growing account or across instruments with different prices, so a rupee-based journal quietly rewards taking more risk rather than making better decisions. R-multiples strip out the bet size and leave only the outcome relative to the risk accepted, which is the only thing a strategy can be judged on. It is the difference between measuring the process and measuring the luck of how large you happened to bet.
What is expectancy, and how does it relate to R-multiples?
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Expectancy is the average R-multiple across a set of trades, the rupees you can expect to make per rupee risked, over many trades. It equals your win rate times your average win in R, minus your loss rate times your average loss in R. A strategy that wins 40 percent of the time but makes plus 3R on winners and loses 1R on losers has an expectancy of 0.40 times 3 minus 0.60 times 1, which is plus 0.6R per trade, a strong edge despite losing more often than it wins. Expectancy, not win rate, is the actual scoreboard, because a high win rate with tiny winners and occasional large losers can still be negative. R-multiples exist precisely so that expectancy can be computed and compared, and this calculator reports it directly from your logged trades.
Is a high win rate the same as a profitable strategy?
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No, and believing it is one of the most expensive mistakes in trading. Win rate is only half of expectancy; the other half is the ratio of average win to average loss, the payoff. A strategy can win 70 percent of the time and still lose money if its winners average plus 0.5R and its losers average minus 2R, because 0.70 times 0.5 minus 0.30 times 2 is minus 0.25R per trade. Conversely a 35 percent win rate with plus 3R winners and minus 1R losers is strongly positive. This is why cutting winners early to keep the win rate high, and holding losers past the stop to avoid booking a loss, are both ways of feeling good while destroying expectancy. The scoreboard is expectancy in R, and it does not care how often you are right.
How many trades do I need before my expectancy figure means anything?
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More than most people think, because expectancy is a mean and the R-distribution of trading is skewed and heavy-tailed. A handful of trades tells you almost nothing: one large winner or a run of stops can swing a small sample from strongly positive to negative, so an average R computed on ten or twenty trades is mostly noise. As a rough guide, treat well under fifty trades as illustrative only, look for stability across a few dozen more before trusting the number, and remember that a single outsized R can dominate a small log entirely. This calculator flags when the sample is small and when one trade is carrying the result, because an expectancy figure is only as trustworthy as the count and the spread of trades behind it.
Do R-multiples account for trading costs?
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Not unless you make them, and the difference matters most on exactly the small trades that look like clean winners. The gross R-multiple uses only entry, stop and exit; the net R subtracts the round-trip cost of the trade before dividing by the risk. In India that round trip is not trivial: brokerage, securities transaction tax, exchange transaction charges, an 18 percent GST on those, the SEBI turnover fee and stamp duty. On a tiny position, or a high-frequency intraday style, the cost can be a large fraction of an R, so a gross plus 0.3R trade can be a net loss. This tool computes both, using a representative Indian cost stack as of July 2026, so you can see how much of each R the costs quietly take. A journal kept in gross R flatters a strategy that costs are actually killing.
Does moving my stop change the R-multiple?
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It corrupts it, which is why the initial stop is the only honest denominator. R is defined against the risk you accepted when you entered, so the one R that means anything is fixed by your original entry and original stop. If you widen the stop after the trade goes against you, you are enlarging the denominator to make a bad trade look less bad, and a minus 2R loss gets relabelled as a minus 1R because you moved the goalposts. If you trail the stop to lock in profit, that is fine for managing the trade, but the R that goes in the journal should still be measured against the initial risk, not the trailed one. A record where R is quietly recomputed against moved stops is not a record of your strategy, it is a record of your rationalisations.
Is this R-multiple calculator a prediction of results?
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No. Every figure it shows is exact arithmetic on the trades you enter: the R-multiple of a single trade, and the win rate, average win and loss, payoff and expectancy of the trades you log. It forecasts nothing, and a positive expectancy computed on your past trades is a description of that sample, not a promise about the next trade or a return you can expect to earn. The illustrative trades loaded by default are there to show the tool working and should be cleared before you enter your own. Nothing here is a recommendation to trade or investment advice, and Bharath Shiksha is an educational publisher, not a SEBI-registered investment adviser or research analyst.
Where the facts come from
Sources
R-multiple and expectancy. The framework of measuring a trade as a multiple of its initial risk, and expectancy as the average R per trade, was popularised by Van K. Tharp, Trade Your Way to Financial Freedom. Expectancy equals win rate times average win in R minus loss rate times average loss in R, equivalently the mean of all trade R-multiples.
The retail loss base rate. SEBI study on the profit and loss of individual traders in the equity derivatives segment, FY25: about 91 percent net loss-making, aggregate net losses near 1,05,603 crore rupees. sebi.gov.in
The Indian cost stack. Securities transaction tax on options 0.15 percent of sell-side premium and on futures 0.05 percent of sell turnover, effective 1 April 2026, plus brokerage, exchange transaction charges, an 18 percent GST, the SEBI turnover fee and stamp duty. Modelled on representative published rates as of July 2026; verify current rates with your broker. nseindia.com
Educational note. This tool computes exact arithmetic on the trades and inputs you provide; every figure is illustrative and depends entirely on those inputs, and the default trades are anonymised examples, not real trades. Nothing here is a recommendation to trade or investment advice, no return is implied, and Bharath Shiksha is an educational publisher, not a SEBI-registered investment adviser or research analyst. Trading involves substantial risk of capital loss.