Trading expectancy is what one trade is worth to you on average — your win rate, average win and average loss combined into a single number. Enter three figures below to see whether your system actually has an edge.
Use closed trades only, and net of brokerage and taxes if you have them. Averages, not totals.
Enter the average loss as a positive number. Everything updates as you type.
Trading expectancy is the average amount you can expect to make or lose on each trade, over a large number of trades. It is the single number that answers the only question that matters about a system: if I keep doing this, does it make money?
Most traders judge a system by its win rate, because a win rate is easy to feel. Expectancy is better, because it accounts for the two things a win rate ignores — how much you make when you are right, and how much you give back when you are wrong. A trader winning 70% of the time can be steadily going broke, and a trader winning 35% of the time can be compounding nicely. Only expectancy tells them apart.
The formula is short:
Expectancy = (Win% × Average Win) − (Loss% × Average Loss)
Take a trader who wins 40% of the time, makes ₹3,000 on an average winner and loses ₹1,500 on an average loser:
Every trade this trader takes is worth ₹300 on average — not because any single trade returns ₹300, but because that is what the distribution pays out over time. Twenty trades a month is ₹6,000 a month from the same behaviour repeated.
Rupee expectancy depends on your position size, which makes it hard to compare across accounts or across time. Dividing by the average loss removes size from the picture and gives expectancy in R, where 1R is the amount you typically lose on a losing trade.
Expectancy in R = Expectancy ÷ Average Loss — in the example above, ₹300 ÷ ₹1,500 = 0.20R per trade.
0.20R means each trade returns a fifth of what a typical loser costs. That is the number to track over time, because it stays honest when you scale your size up or down. If you want the underlying framework, we go deeper in the trading expectancy formula explained, and R itself is covered in R-multiple explained.
Any positive number is mathematically profitable. Whether it is worth trading is a different question, because a thin edge is easily eaten by costs and by a few undisciplined days.
| Expectancy (R) | Reading | What it usually means |
|---|---|---|
| Below 0 | Negative | The system loses money over time. More trades make it worse, not better. |
| 0 – 0.10R | Marginal | Real but fragile. Brokerage, STT and slippage can erase it entirely. |
| 0.10 – 0.30R | Workable | A genuine edge. Most consistently profitable retail systems live here. |
| 0.30 – 0.50R | Strong | A well-defined setup executed with discipline. |
| Above 0.50R | Excellent | Rare, and usually comes with low frequency or a narrow market regime. |
Be suspicious of a very high expectancy from a small sample. Thirty trades containing one exceptional winner will report a number you cannot repeat.
The most useful thing this calculator does is show how little your win rate means on its own. Every row below is profitable or unprofitable regardless of how it feels:
| Win rate | Avg win | Avg loss | Expectancy | Verdict |
|---|---|---|---|---|
| 80% | ₹500 | ₹3,000 | −₹200 | Loses money |
| 60% | ₹1,000 | ₹1,500 | ₹0 | Break-even |
| 50% | ₹2,000 | ₹1,500 | ₹250 | Works |
| 35% | ₹4,500 | ₹1,500 | ₹600 | Works well |
The trader at 80% is the one most likely to believe they are good at this. They are right about being right — they are simply paid badly for it. This is the single most common way a disciplined-looking trading account bleeds out.
For any reward-to-risk ratio, there is a win rate below which the system loses money:
Break-even win rate = 1 ÷ (1 + Reward:Risk)
The calculator shows your margin over this line. A margin of one or two points is not an edge; it is noise. If yours is thin, the fix is usually the reward-to-risk ratio rather than the win rate, because raising your win rate means being right more often, while raising your reward-to-risk mostly means holding winners longer and sizing stops properly.
Only three inputs feed expectancy, so there are only three ways to improve it.
This is the fastest lever and the one most under your control. It is not about being right; it is about the size of being wrong. A stop that is honoured is what makes the average loss a number you chose rather than a number the market chose for you. Traders whose average loss is far larger than their planned risk usually have a discipline problem rather than a strategy problem — the R-multiple calculator will show it trade by trade.
Usually a matter of exits. Taking profit early feels responsible and quietly caps the numerator of your edge. If your average win keeps landing just under your average loss, the exits are the problem, not the entries.
The slowest and least reliable lever, because it means improving selection. Most traders reach for this one first. It is worth attempting only after the first two are stable — and often, fewer and better trades fix it as a side effect. If overtrading is what is dragging your selection down, how to stop overtrading covers the mechanism.
Expectancy calculated on gross P&L flatters you. In Indian markets, brokerage, STT, exchange charges, GST, stamp duty and SEBI fees come off every trade, and for intraday traders they are frequently the difference between a positive and a negative system.
A system at 0.08R gross with round-trip costs of ₹120 on a ₹1,500 average loss is losing 0.08R to costs — precisely nothing left. Use net figures if you have them. If you do not know your true cost per trade, that is worth finding out before you conclude anything from a marginal number here.
Expectancy is an average, and averages from small samples mostly measure luck. A rough guide:
Also compute it per setup rather than across everything. A trader with three strategies often has one carrying the account and one quietly draining it, and a blended figure hides both.
The average profit or loss per trade over a large sample: (win rate × average win) − (loss rate × average loss). Positive means the system makes money over time; negative means it loses, no matter how good individual trades felt.
Expectancy = (Win% × Average Win) − (Loss% × Average Loss). At a 40% win rate with an average win of ₹3,000 and an average loss of ₹1,500: (0.40 × 3,000) − (0.60 × 1,500) = ₹300 per trade. Divide by the average loss to express it in R — here, 0.20R.
Anything above zero is mathematically profitable, but it has to survive costs. In R terms, 0.1R to 0.2R is a real but thin edge, 0.3R to 0.5R is a solid retail system, and above 0.5R is excellent and usually comes with low frequency. It matters more that the figure covers at least 50 to 100 trades and includes brokerage, STT and slippage.
At least 50 to see a signal, and closer to 100 before acting on it. Below about 30 trades a single outsized winner can flip a losing system into an apparently profitable one — an average from a small sample mostly measures luck.
Yes, comfortably — provided your winners are large enough. At 35% with a 3:1 reward-to-risk you earn 0.4R per trade, which is a strong system. The break-even win rate at 3:1 is only 25%.
Usually one of three things: the sample is too small, costs were not included, or your position size varies so much that a few large losers outweigh many small winners. Expectancy assumes consistent sizing — if yours moves around, the rupee figure is not describing your account.
Yes. The formula is indifferent to instrument. It matters more for intraday and F&O, where costs are heavier and a marginal edge disappears faster.
Entirely. It runs in your browser, needs no signup, and stores nothing unless you ask us to email you the result.
The number above is only as good as the three figures you typed. Import your tradebook from Zerodha, Upstox, Dhan or any broker and TradeDiary computes expectancy, R-multiples and win rate per setup — from your actual fills, net of charges, updated as you trade.
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