Expectancy
The average rupee profit or loss generated per trade over many repetitions — the single most important number for confirming whether a trading strategy has a genuine edge or is just running on luck.
Definition
Expectancy (also called expected value per trade) is the weighted average outcome of a trading strategy, combining the probability and magnitude of wins and losses into a single number that represents how much money the strategy is expected to make — or lose — on each trade when applied repeatedly over many occurrences. It is the foundational metric for evaluating whether a strategy has a sustainable edge, because it integrates both win rate and risk-reward ratio into one figure. A positive expectancy means the strategy generates money over time; a negative expectancy means it destroys capital regardless of how good any individual run of results looks. Expectancy is the bedrock number that feeds position-sizing frameworks like the Kelly Criterion.
Why it matters
In Indian F&O markets, traders often evaluate strategies using only win rate or only profit factor, missing the complete picture. A short-seller of weekly Bank Nifty options might achieve an impressive 75% win rate and feel confident in their system, without realising that the three losing trades in every ten are each four times larger than the seven winners — producing a negative expectancy and guaranteed long-run losses. Conversely, an intraday Nifty futures trader with a 40% win rate might feel dispirited, not recognising that their disciplined stop-loss and trail rules generate an average winner three times the average loser, producing a comfortably positive expectancy.
Calculating expectancy also disciplines the process of backtesting. A strategy that shows a positive expectancy across a large, representative sample of historical trades — including volatile event days such as Union Budget, RBI policy meetings, and earnings seasons on NSE — has demonstrated a robust edge. One that shows positive expectancy only in cherry-picked calm market periods is likely to disappoint in live trading.
Formula
Expectancy = (Win Rate × Average Win) − (Loss Rate × Average Loss). All values should be expressed in rupees (or in R-multiples — units of risk — for strategy comparison across different position sizes). Loss Rate = 1 − Win Rate. A positive result confirms a positive edge. For example: Win Rate = 55%, Average Win = Rs 4,000, Loss Rate = 45%, Average Loss = Rs 3,000. Expectancy = (0.55 × 4,000) − (0.45 × 3,000) = Rs 2,200 − Rs 1,350 = Rs 850 per trade. Multiplied across 200 trades in a year, this strategy is expected to generate Rs 1,70,000 in gross profit, from which brokerage, STT, and other charges are then subtracted to arrive at net expectancy.
Example
Suppose a trader backtests a strategy of buying ATM call options on Nifty every Monday morning and closing on Wednesday close over one year of hypothetical historical data. Out of 48 trades, 21 are winners (44% win rate) with an average profit of Rs 5,500 per lot, and 27 are losers (56% loss rate) with an average loss of Rs 2,800 per lot. Expectancy = (0.44 × 5,500) − (0.56 × 2,800) = Rs 2,420 − Rs 1,568 = Rs 852 per trade. Although the strategy loses more often than it wins, the large average winners produce a positive expectancy. The trader can now use this figure to size positions appropriately using Kelly and to set realistic monthly return expectations before risking live capital.
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