Expectancy Calculator
Estimate the average rupee and R outcome per trade from your win rate, average win and average loss.
Quick answer: Expectancy is the average result you can expect per trade over many trades. It weights the average win by the probability of winning and subtracts the average loss weighted by the probability of losing. A positive expectancy means the system makes money on average; the tool also expresses the figure in R, where one R is the size of the average loss.
How to use it
Enter your historical win rate and the average rupee size of a winning and a losing trade. The output is the expected rupee value per trade and the same figure in R, where one R equals the average loss. A positive number means the system gains on average; a negative number means it bleeds even if the win rate looks high.
Formula
Expectancy = (Win% ÷ 100 × Average win) − (1 − Win% ÷ 100) × Average loss
Expressed in R by dividing by the average loss, so R normalises the result to units of typical risk.
Limitations of the Expectancy Calculator
The Expectancy Calculator is a teaching aid, not a live risk system. It does not model the following:
- The sample size behind the win rate and averages — a positive expectancy from few trades has a wide error and may be luck
- Trading costs, which reduce both the average win and the realised expectancy
- The distribution and sequence of returns — one large outlier win can dominate the average
- Regime change; a historical expectancy need not persist once the strategy trades live
Frequently asked questions
What does expectancy in R mean for a backtest?
R is the average loss treated as one unit of risk, so a backtested expectancy of 0.3R means each trade returned, on average, 30 percent of what was risked. Because it is size-independent, R-multiple expectancy lets you compare a strategy's edge across instruments and across different backtests.
How many backtested trades do I need to trust an expectancy?
Many, because expectancy is an average with a wide confidence interval on small samples. A positive expectancy over 30 trades could easily be negative in truth, especially if it is driven by one or two large winners, so a short backtest's expectancy is barely informative.
Should backtested expectancy use gross or net trades?
Net of all costs — STT, brokerage, GST, stamp duty and slippage. A thin positive gross expectancy in a backtest can become zero or negative once realistic Indian transaction costs are applied, so always compute it on post-cost trade results.
Why do outliers matter when reading a backtest's expectancy?
Because trade outcomes are often fat-tailed, a sample expectancy can be dominated by one or two large trades that may not recur. Examining the distribution of R-multiples, not just the mean, guards against trusting an outlier-driven backtest figure.
Why can a backtested expectancy fail in live trading?
Because it assumes the future resembles the sampled regime, and edges can decay or vanish as conditions change or the trade becomes crowded. A backtested expectancy is a hypothesis to be confirmed by out-of-sample and forward testing, not a fact about the future.
Runs entirely in your browser — no data leaves your device. Illustrative and educational only; real-world charges and market conditions apply in practice.