Bharath Shiksha

Free Tool

Trade Expectancy Calculator

Expectancy is the rupee value of an average trade in your system, the one number that decides whether a method compounds or bleeds. This tool computes it per trade in rupees and in R, per rupee risked, and against the breakeven win rate for your payoff, so you can see plainly why a 40 percent win rate at a 3 to 1 payoff beats a 70 percent win rate at 1 to 2.

Win rate is vanity, expectancy is sanity. A system is a distribution, not an average, and you trade the path, not the mean.

Scenario preset

Quick pick

Win rate

Share of trades that close above breakeven. Estimate over at least 30 to 50 trades; a smaller sample makes expectancy unreliable.

Average win and loss

Reward as a multiple of risk. 2 means the average winner is twice the amount you risked per trade.
Loss as a multiple of risk, entered positive. 1 means the average loser equals one unit of risk, a clean stop.

Project a rupee total (optional)

Opportunities over the horizon you care about. Typical retail: 100 to 400 a year.
Rupees risked per trade. Used with R inputs to turn expectancy into a rupee total. Ignored if you entered rupee win and loss.

Expectancy per trade

−

Positive edge

Expectancy in R

−

Per rupee risked

−

Breakeven win rate

−

What the inputs imply

Win rate, p

−

Payoff ratio, b (win / loss)

−

Breakeven win rate for this payoff

−

Margin vs breakeven

−

Expectancy against win rate, at your payoff

The line is expectancy per R as the win rate varies, holding your payoff ratio fixed. It crosses zero at the breakeven win rate. Your point is plotted: green above breakeven, coral below.

What this expectancy compounds to

HorizonTradesExpected total (₹)

Expected total is expectancy per trade times the number of trades, before costs. It assumes each trade is independent and the edge is stationary; the realised path swings widely around this average, which is the subject of the failure modes below.

Flags to review before trusting this number

    The arithmetic is a weighted subtraction. The hard part is upstream: measuring the win rate and the average win and loss honestly over a large enough sample, and then holding the discipline that keeps the expectancy positive, letting winners run and cutting losers at the stop rather than the reverse. That upstream work, building a method with a durable edge and the temperament to trade it, is what the method we teach is built around.

    Take the free diagnostic See the method

    The one principle

    The number that decides whether a system makes money is expectancy, not win rate. Expectancy weighs how often you win against how much you win and lose, and it is the only figure that combines both into one answer. A 40 percent win rate with a 3 to 1 payoff returns 0.6 rupees for every rupee risked; a 70 percent win rate with a 1 to 2 payoff returns almost nothing and turns negative after costs. Win rate on its own is meaningless without the payoff ratio beside it, because the same win rate can describe a strong system or a losing one depending entirely on the size of the wins against the losses.

    Retail traders chase win rate because it feels like being right; institutions track expectancy because it is what pays. The SEBI FY25 finding that over 91 percent of individual traders in the equity derivatives segment were net loss-making, with aggregate net losses near 1,05,603 crore rupees, is at root an expectancy failure: a great many of those accounts trade methods with negative expectancy, and no position size, no frequency, and no conviction can make a negative-expectancy system profitable. The edge has to exist in the math before anything else matters.

    The math, derived

    Expectancy is the probability-weighted average of the two outcomes a trade can have. You win with probability p and gain the average win; you lose with probability 1 minus p and give back the average loss. The average result per trade is one minus the other:

    E = p × avg win − (1 − p) × avg loss
    in R multiples, where 1R is the amount risked, avg loss is often 1R:
    ER = p × reward multiple − (1 − p) × loss multiple
    expectancy per rupee risked = E in R when the loss is exactly 1R

    Worked on the defaults above: a 55 percent win rate, an average winner of 3,000 rupees and an average loser of 1,500 rupees gives E equal to 0.55 times 3,000 minus 0.45 times 1,500, which is 1,650 minus 675, or 975 rupees per trade. In R terms, winners at 2R and losers at 1R give E equal to 0.55 times 2 minus 0.45 times 1, which is 1.10 minus 0.45, or 0.65R. The system returns about 65 paise for every rupee put at risk, before costs. That per-rupee figure is the cleanest way to compare two systems that trade different instruments at different absolute sizes.

    The breakeven win rate identity

    Set expectancy to zero and solve for the win rate, and a clean identity falls out. Let b be the payoff ratio, the average win divided by the average loss. Then:

    0 = p × b − (1 − p) × 1
    rearranged, the breakeven win rate is:
    pbe = 1 ÷ (1 + b) = avg loss ÷ (avg win + avg loss)
    above pₖₑ the system is positive; below it, negative

    This is the single most useful line in the whole subject. It tells you, for any payoff ratio, the exact win rate you must clear to break even, so you never again judge a win rate in isolation. At a 1 to 1 payoff the breakeven is 50 percent; at 2 to 1 it is 33.3 percent; at 3 to 1 it is 25 percent; and at a 1 to 2 payoff, where losers are twice the size of winners, it climbs to 66.7 percent. A 70 percent win rate sounds untouchable until you notice that at a 1 to 2 payoff it clears its 66.7 percent breakeven by barely three points, an edge that ordinary costs erase.

    Read expectancy as a receipt, not a promise. The formula does not forecast your next trade; it summarises the average of many trades you have already characterised. Every figure this tool produces is the arithmetic of the numbers you type in. Its job is to tell you whether the edge you have measured is positive and by how much, not to predict what any account will do.

    Two systems, one expectancy

    The clearest way to break the win-rate reflex is to put two systems side by side that feel worlds apart and yet carry the identical edge. A 60 percent win rate at a 1 to 1 payoff and a 30 percent win rate at a 3 to 1 payoff both produce an expectancy of exactly 0.20R per trade. One wins twice as often as the other; both make the same money per rupee risked.

    Two systems with very different win rates and the same expectancy A high win-rate scalper at 60 percent and a 1 to 1 payoff, and a low win-rate trend follower at 30 percent and a 3 to 1 payoff, both produce an expectancy of 0.2 R per trade. The win rates differ sharply but the expectancy is identical. Same expectancy, opposite temperaments. Both systems return 0.2 R for every rupee risked, per trade. E = 0.2R Scalper 60% win, 1 : 1 payoff win +1R 60% often loss −1R 40% often Trend follower 30% win, 3 : 1 payoff win +3R 30% often loss −1R 70% often
    Neither system is better on the math; they are the same edge. The scalper wins often and small and needs the discipline not to let one loss run past 1R, since a single 3R loss wipes out three winners. The trend follower loses most of the time and needs the discipline to sit through the strings of small losses and let the rare 3R winner arrive, because cutting it short collapses the whole expectancy. The choice between them is temperament and market regime, never which one makes more money.

    Reference: the expectancy grid

    The grid below is expectancy per R, computed from E equals p times b minus (1 minus p), for a range of win rates and payoff ratios. Read down a column to see how much win rate you need at a fixed payoff; read across a row to see how a better payoff rescues a low win rate. Coral cells are negative expectancy, where the system loses on average; green cells are positive. The break between them is the breakeven diagonal that the identity above predicts.

    Expectancy per R, by win rate (rows) and payoff ratio b (columns), where b is average win divided by average loss and the loss is 1R. A figure of, say, +0.50 means the system returns 0.50 rupees for every rupee risked, per trade, before costs. This is the mathematical output of the formula, not a prediction of any account's results.
    Win rateb = 0.5 (1:2)b = 1.0 (1:1)b = 2.0 (2:1)b = 3.0 (3:1)
    30%−0.55−0.40−0.10+0.20
    40%−0.40−0.20+0.20+0.60
    50%−0.250.00+0.50+1.00
    60%−0.10+0.20+0.80+1.40
    70%+0.05+0.40+1.10+1.80

    Two things jump out. Read the 70 percent row: at a 1 to 2 payoff it is barely positive at plus 0.05R, an edge so thin that costs erase it, while the same 70 percent at 2 to 1 is a commanding plus 1.10R. And read the 40 percent row: negative until the payoff reaches 2 to 1, then strongly positive at 3 to 1. The win rate you should want depends entirely on the payoff you can realistically achieve, which is why the two are meaningless apart.

    Reference: breakeven win rate by payoff

    This is the identity p breakeven equals 1 divided by (1 plus b) tabulated, the win rate at which expectancy is exactly zero for each payoff. Any realised win rate above the breakeven line is a positive-expectancy system; anything below it loses, no matter how it feels. The margin column is the cushion a strong retail system typically wants above breakeven, because costs and estimation error eat into a thin edge first.

    Breakeven win rate for each payoff ratio, computed from p breakeven equals average loss divided by (average win plus average loss). The comfortable-target column is a judgement, not a rule: the cushion above breakeven that absorbs costs and the noise in a real win-rate estimate.
    Payoff (reward : risk)Payoff ratio bBreakeven win rateComfortable target
    1 : 2 (losers twice winners)0.566.7%above 75%
    1 : 11.050.0%above 55%
    1.5 : 11.540.0%above 47%
    2 : 12.033.3%above 40%
    3 : 13.025.0%above 32%

    The table is why professional systems optimise the payoff before the win rate. Lifting a win rate is hard and quickly runs into a ceiling; widening the payoff by letting winners run and cutting losers cleanly moves the breakeven line down and turns marginal systems robust. A 3 to 1 payoff asks the market to be right only one time in four, and that is a far easier bar to clear consistently than winning three trades in four.

    The average is not the path

    Expectancy is the centre of a distribution, and the account experiences the whole distribution, not the centre. A positive-expectancy system still produces losing streaks, and the equity curve wanders far from the smooth line the average implies. Two traders running the identical positive-expectancy method can end a year in different places purely because one met a deeper drawdown early, and if that drawdown is deep enough it ends the account before the average has time to arrive.

    Expectancy is the smooth line; the account trades the jagged one A straight rising line shows the expected equity, expectancy times the number of trades. A jagged line with the same expectancy shows a realised account that swings around the expected line and suffers a deep drawdown partway through before recovering. The variance around the average, and the depth of the drawdowns, is what threatens survival even when expectancy is positive. You trade the path, not the mean. Two accounts, the same positive expectancy, different journeys. Account equity Trades over time expected: E × trades deepest drawdown the streak that can end the account realised path swings around the average
    Expectancy tells you the edge exists; it does not tell you that you survive to collect it. The gap between the smooth line and the jagged one is variance, and the deepest trough is the drawdown that can breach a stop-loss on the account itself before the average asserts. This is why expectancy is necessary but not sufficient: you also need sizing small enough that a normal losing streak stays survivable. That is the job of the companion position sizing and risk of ruin tools.

    Failure modes: where the clean number still breaks

    A positive expectancy on paper is not a licence to trade. Six conditions detach the printed number from what the account actually earns, and each has turned a system that looked profitable into one that was not.

    1. The small-sample illusion. Expectancy from 20 or 30 trades is noise wearing the costume of a measurement. An average is only as trustworthy as the sample it is drawn from, and a handful of lucky winners, or one large loss you happened to avoid, can make a break-even system look excellent or a good one look broken. Treat expectancy from fewer than 30 to 50 trades as a hypothesis, and demand more the lower your win rate, because the infrequent large winners that carry a low-win-rate system are exactly the outcomes a small sample cannot count reliably.
    2. The non-stationary edge. Expectancy assumes p and the payoff are fixed, but a real market edge decays as regimes change, liquidity shifts, and other participants adapt to the same pattern. An expectancy measured on last year's trades can be gone this year, and by the time the realised win rate has fallen far enough to notice, the losses have already accrued. Re-estimate on a rolling window and let the number fall when the edge does, rather than trusting a figure computed on a market that no longer exists.
    3. The average versus the realised path. Expectancy is the mean of a distribution; you trade the path through it. Positive expectancy still produces losing streaks, and a deep enough drawdown ends the account before the average arrives. The edge being positive does not guarantee you survive to collect it, which is why expectancy must always be paired with sizing that keeps an ordinary losing run survivable.
    4. Averaging away a fat-tail loss. The average loss in your log is only as honest as your worst loss. If your stops usually hold but one trade gapped through and lost 6R, the mean quietly absorbs it, and your expectancy looks fine while your true risk distribution has a tail that can take several years of edge in a single event. Look at the largest losses directly, not just the average, because expectancy computed on a loss distribution with a hidden fat tail understates the risk badly.
    5. Survivorship in your own trade log. A log records the trades you took and remembered to enter. Trades cut early out of fear, scratched positions never logged, and the discipline lapses you would rather forget are silently dropped, and every one of them flatters the expectancy. The number describes an idealised version of your trading, not the version your broker statement shows. Reconcile the log against the statement, because the gap between them is where a positive measured expectancy hides a negative realised one.
    6. Costs subtracted last, or not at all. Gross expectancy is not what lands in the account. Every round trip carries STT, exchange charges, an 18 percent GST on brokerage and charges, stamp duty and the SEBI turnover fee, and the 2026 Budget raised STT on options to 0.15 percent of the sell-side premium and on futures to 0.05 percent of the sell-side turnover from 1 April 2026. A gross expectancy of 0.2R against 0.15R of cost is a net 0.05R, and a small rise in cost or a small fall in the win rate tips it below zero. Always compute expectancy net of the full round-trip cost stack.

    The expectancy lens on the SEBI base rate

    Why does a segment where some participants clearly have an edge still show over 91 percent net loss-making? The expectancy frame answers it in one line: most of those accounts are trading methods with negative expectancy, and the rest are often trading a positive expectancy at a size that lets the drawdown end them first. A negative-expectancy system cannot be rescued by any amount of frequency, leverage, or conviction, because each of those only multiplies a number that is already below zero. The FY25 aggregate net loss of about 1,05,603 crore rupees, up roughly 41 percent year on year, is what negative expectancy at scale looks like in a leveraged market.

    Expectancy tells you whether the edge is positive; it is the first gate, not the last. Once it is positive, the Kelly calculator turns the same win rate and payoff into a growth-optimal bet size, the risk of ruin calculator shows how large a losing run has to be before it becomes terminal at your sizing, and the position sizing calculator sets the rupee size of each trade from the stop. For the behavioural half of the FY25 statistic, why the discipline that keeps expectancy positive is so hard to hold, see why Indian traders lose money.

    Common Questions

    Frequently Asked Questions

    Expectancy is the average rupee result of a trade in your system, taken over many trades. It is the win rate times the average win, minus the loss rate times the average loss: E equals p times average win minus (1 minus p) times average loss. A positive expectancy means the system adds to the account on average with every trade taken; a negative expectancy means it subtracts on average, and no amount of trading frequency or position sizing can fix that. Expectancy, not win rate, is the number that decides whether a method makes money, because it weighs how often you win against how much you win and lose. A 40 percent win rate with winners three times the size of losers has a strongly positive expectancy, while a 70 percent win rate with losers twice the size of winners has a negative one.

    In rupees, expectancy per trade is p times the average winning trade minus (1 minus p) times the average losing trade, where p is the win rate as a decimal and the average loss is entered as a positive number. On a 55 percent win rate with an average winner of 3,000 rupees and an average loser of 1,500 rupees, expectancy is 0.55 times 3,000 minus 0.45 times 1,500, which is 1,650 minus 675, or 975 rupees per trade. The same calculation in R multiples, where one R is the amount risked, is p times the reward multiple minus (1 minus p) times the loss multiple. With winners at 2R and losers at 1R that is 0.55 times 2 minus 0.45 times 1, which is 0.65R per trade, so the system returns about 0.65 rupees for every rupee risked, before costs.

    The breakeven win rate is the win rate at which expectancy is exactly zero for a given payoff ratio b, where b is the average win divided by the average loss. It is p breakeven equals 1 divided by (1 plus b), which is the average loss divided by the sum of the average win and the average loss. At a 1 to 1 payoff you need to win 50 percent of the time just to break even; at 2 to 1 you need 33.3 percent; at 3 to 1 only 25 percent; and at a 1 to 2 payoff, where losers are twice the size of winners, you need to win 66.7 percent. Every win rate above its breakeven line is a positive-expectancy system and every win rate below it is a losing one, which is why the payoff ratio and the win rate must always be read together, never separately.

    No, and treating it as one is the most common reason a trader with a high hit rate still loses money. Win rate says how often you win; it says nothing about how much you win versus how much you lose. A 70 percent win rate feels excellent, but if the losing 30 percent of trades are each twice the size of the winners, the payoff ratio is 1 to 2, the breakeven win rate is 66.7 percent, and a 70 percent hit rate clears it only barely, leaving a thin edge that costs erase. A 40 percent win rate with a 3 to 1 payoff, by contrast, has a breakeven of 25 percent and sits far above it. Win rate is vanity, expectancy is sanity: only expectancy combines the frequency and the size of outcomes into the one number that determines whether the account grows.

    Yes, and most trend-following systems are exactly this. A method that wins only 35 to 40 percent of the time can be strongly profitable if its winners are several times the size of its losers, because expectancy weighs size as heavily as frequency. At a 3 to 1 payoff the breakeven win rate is just 25 percent, so a 40 percent win rate produces an expectancy of 0.6R per trade, a robust edge. The cost of a low win rate is psychological, not mathematical: you sit through long strings of small losses waiting for the occasional large winner that carries the whole system, and the temptation to cut winners early or widen stops to raise the hit rate is what destroys the expectancy. The math rewards letting winners run; the discipline to do it is the hard part.

    More than most traders think, and far more than the 20 or 30 trades people typically judge a system on. Expectancy is an average, and an average estimated from a small sample is dominated by noise: a handful of lucky winners or one avoided disaster can make a break-even system look excellent, or a good system look broken. As a working rule, treat expectancy from fewer than 30 to 50 trades as a hypothesis, not a measurement, and even 100 trades gives only a rough estimate if the win rate is low, because the profitable outliers that carry a low-win-rate system are themselves rare. The lower your win rate and the higher your payoff ratio, the more trades you need, because more of the result depends on infrequent large winners whose true frequency a small sample cannot pin down.

    Because expectancy is the average over many trades, and you do not trade the average, you trade the path. A positive-expectancy system still produces losing streaks, and a long enough streak creates a drawdown deep enough to end the account before the average has time to assert itself. A system is a distribution, not a single number: the expectancy is its centre, but the variance around that centre, the depth and length of the drawdowns, is what actually threatens survival. This is why expectancy alone is not enough. It tells you the edge exists; position sizing and risk of ruin tell you whether you will still be trading when the edge pays off. A trader can hold a genuinely positive expectancy and still be wiped out by sizing that lets an ordinary losing run become a terminal one.

    Costs are subtracted from gross expectancy, and on small or frequent trades they can turn a positive edge negative. Every round trip in the Indian market carries securities transaction tax, exchange transaction charges, an 18 percent GST on brokerage and charges, stamp duty and the SEBI turnover fee, and the 2026 Budget raised STT on options to 0.15 percent of the sell-side premium and on futures to 0.05 percent of the sell-side turnover from 1 April 2026. If your gross expectancy is 0.2R per trade and costs amount to 0.15R, your net expectancy is only 0.05R, and a small rise in costs or a small fall in the win rate tips it below zero. Always compute expectancy net of costs, because a method that looks profitable gross can be a slow leak once the full round-trip cost stack is subtracted from every trade.

    There is no universal threshold, because expectancy per trade must be read together with how many trades the system generates and how large its drawdowns are. An expectancy of 0.1R per trade on a system that takes 500 trades a year does more work than 0.5R on a system that trades ten times a year, since total edge is roughly expectancy multiplied by the number of opportunities. What matters more than the level is the sign and the robustness: the expectancy must be clearly positive after costs, estimated over a large enough sample to trust, and stable across market conditions rather than dependent on one lucky period. A modest, durable, positive expectancy that survives costs and does not collapse when the market regime changes is worth more than a large one measured over 30 trades that vanishes the moment conditions shift.

    Where the facts come from

    Sources

    • The expectancy concept and R multiples. Van K. Tharp, Trade Your Way to Financial Freedom, which formalises expectancy as probability of win times average win minus probability of loss times average loss, and popularises measuring outcomes per unit of risk in R multiples, so that systems trading different instruments at different sizes can be compared on a single per-rupee-risked basis. vantharpinstitute.com
    • The FY25 loss base rate. SEBI study on the profit and loss of individual traders in the equity derivatives segment: over 91 percent net loss-making in FY25, with aggregate net losses of about 1,05,603 crore rupees, up roughly 41 percent from about 74,812 crore in FY24, across the top 13 brokers and around 96 lakh unique traders. business-standard.com
    • The 2026 cost schedule. The Budget 2026 STT rates referenced in the costs discussion, options at 0.15 percent of sell-side premium and futures at 0.05 percent of sell-side turnover effective 1 April 2026, plus exchange charges, GST and stamp duty, follow the NSE first-time-investor reference on levies. Verify current rates with your broker. nseindia.com
    Educational note. This tool computes figures from your own inputs; every output is illustrative and depends entirely on the win rate and the average win and loss you enter. The expectancy grid and the breakeven table are mathematical models, not predictions of any account's results, and nothing here is a claim about the returns, win rate, or profit any strategy will achieve. Nothing on this page is a recommendation to trade, to use leverage, or to buy or sell any security, and it is not investment advice. Bharath Shiksha is an educational publisher, not a SEBI-registered investment adviser or research analyst.

    Related tools and reading