Expectancy Calculator

What is expectancy

Expectancy is the average rupee amount a single trade makes or loses over a long run of trades. It is the one number that combines how often you win with how much you win when you do, which is why it settles arguments that win rate alone cannot. A trader winning 35% of the time can comfortably out-earn one winning 70% of the time, if the winners are large enough and the losers are cut small.

The formula

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

Average loss is used as a positive magnitude, so the subtraction applies the sign once. Win rate and loss rate are each measured against the total number of trades. That matters when you have breakeven trades: a trade that closed at exactly your entry is neither a win nor a loss, so it belongs in neither rate. Calculators that use 1 − win rateas the loss rate quietly count every breakeven trade as a full loser and report an expectancy worse than your actual results. This calculator counts wins, losses and breakevens separately, which is why expectancy here always equals your net P&L divided by your trade count.

Worked example

Say your last 10 trades were 4 winners averaging ₹6,000, 5 losers averaging ₹3,000, and 1 breakeven.

• Win rate is 4 out of 10, or 40%. Loss rate is 5 out of 10, or 50%.
• Average win × win rate = ₹6,000 × 0.4 = ₹2,400.
• Average loss × loss rate = ₹3,000 × 0.5 = ₹1,500.
• Expectancy = ₹2,400 − ₹1,500 = ₹900 per trade.

Cross-check it: the winners made ₹24,000, the losers cost ₹15,000, so net P&L is ₹9,000 across 10 trades — ₹900 each. Profit factor, the gross profit divided by gross loss, is ₹24,000 ÷ ₹15,000 = 1.6. Note that you lose more often than you win here and are still solidly profitable, because the average winner is twice the average loser.

What is a good expectancy

There is no universal rupee threshold, because expectancy scales with your position size. Doubling your size doubles your expectancy without improving your trading at all. To compare yourself against yourself over time, or against any other trader, use the average R-multiple instead — expectancy expressed in units of your risk rather than in rupees. An average R of 0.3 means you make 0.3 times your risk per trade, whether you risk ₹1,000 or ₹1,00,000.

As rough calibration: an average R above 0 means you have a real edge and the main job is to keep sizing consistent. Between 0.1 and 0.3 is a workable retail edge that compounds meaningfully over a few hundred trades. Above 0.5 sustained across a large sample is genuinely strong, and worth double-checking for survivorship bias in how you recorded the trades. A profit factor above 1.5 and expectancy above zero is a reasonable pair of targets to hold yourself to.

How to read a negative expectancy

A negative number means that, at your current win rate and average win and loss sizes, every additional trade costs you money on average. Trading more frequently will lose money faster, and position sizing cannot rescue it — sizing changes the speed, not the sign.

The useful response is to look at which of the three inputs is doing the damage. If your win rate is respectable but expectancy is negative, your losers are too big relative to your winners: that is a stop-loss discipline problem, usually cutting winners early while letting losers run. If your average win comfortably exceeds your average loss but you win rarely, your entries are the problem. And if the sample is small, be careful — twenty trades is noise, not evidence. A handful of outliers can flip the sign in either direction, which is the argument for judging expectancy over a few hundred trades rather than a few dozen.

Expectancy is calculated on the trades you enter. Charges, slippage and taxes are not deducted unless your entry and exit prices already reflect them, so a marginal positive expectancy may be negative after real-world costs.