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Expectancy Calculator

Essential metrics to analyze and optimize your trading performance.

Expectancy

Expectancy tells you the average amount you can expect to win (or lose) per trade. A positive expectancy means you mathematically have an edge.

Formula: (Win Rate × Average Win) − (Loss Rate × Average Loss)
Loss Rate (%)45.00%
Expected Value Per Trade₹42.50Positive edge

How to calculate this metric

Follow these four steps using real trade data to calculate a reliable expectancy figure.

Expectancy = (Win Rate × Avg Win) − (Loss Rate × Avg Loss)
  1. 1

    Determine your win rate — Calculate the percentage of winning trades from your trade history. e.g. 55 wins out of 100 trades = 55% win rate. Loss rate is simply 100% minus win rate.

  2. 2

    Calculate your average win — Sum all profits from winning trades and divide by the number of winning trades. Use net figures after all charges.

  3. 3

    Calculate your average loss — Sum all losses from losing trades and divide by the number of losing trades. Use the absolute value — treat it as a positive number in the formula.

  4. 4

    Apply the formula — e.g. (0.55 × ₹200) − (0.45 × ₹150) = ₹110 − ₹67.50 = ₹42.50 expected per trade. Multiply by your planned number of trades to project total edge.

Annual edge ≈ Expectancy per trade × Number of trades per year

What is a good about this metric?

Any positive expectancy means a mathematical edge exists. But the edge must be large enough to survive real-world trading costs and still compound meaningfully.

Below ₹0

Negative edge

You lose money on average per trade — do not trade this system live.

₹0 – ₹20

Marginal

Tiny edge that fees and slippage will likely erase in live trading.

₹20 – ₹80

Good

Meaningful edge per trade — viable for consistent compounding.

₹80 or above

Excellent

Strong edge — scales well with higher position sizes.

Expectancy is the single most important metric for evaluating a trading system. A positive expectancy of ₹30 or more per trade, after realistic costs, is a solid benchmark for taking a system live.

Common metric of this mistakes

These mistakes cause traders to overestimate their edge and size positions too aggressively.

  • Using theoretical win rate instead of actual

    Plugging in a hoped-for win rate rather than your real historical rate produces fantasy expectancy. Always use at least 50–100 trades of real data.

  • Ignoring trading costs

    Brokerage, STT, and slippage reduce both average win and increase average loss. A ₹42 expectancy can turn negative once ₹15 per trade in costs is applied.

  • Treating expectancy as fixed

    Expectancy changes as market conditions shift. Recalculate it quarterly — a strategy that had ₹60 expectancy in a trending market may drop to ₹10 in a sideways one.

  • Confusing per-trade expectancy with annual return

    High expectancy per trade combined with very low trade frequency may produce a modest annual return. Always multiply by expected number of trades per year.

  • Optimising win rate and average win independently

    Both inputs interact — raising win rate often lowers average win (you exit earlier). Optimise the expectancy output as a whole, not each input in isolation.