Trading Statistics & Performance

How to Calculate Your Trading Edge (With a Real Example)

July 2026
In this article
  1. Why "I think this works" isn't an edge
  2. The expectancy formula
  3. A real example calculation
  4. How much data you actually need
  5. FAQ

Ask a trader if their strategy has an edge and almost all will say yes. Ask them for the number, and most can't produce one. An edge that hasn't been calculated is a belief, not a fact — and beliefs about your own trading are exactly the kind of thing that's easiest to get wrong, because every winning trade confirms it and every losing trade gets explained away.

Why "I Think This Works" Isn't an Edge

Memory is a poor substitute for a calculation. Traders tend to remember winning trades more vividly than losing ones, especially losses that get attributed to bad luck rather than a flawed setup. The result is a strategy that feels profitable in recollection but has never actually been tested against the full, unfiltered record of every trade taken.

The selective-memory problem
A trader's sense of "this setup works" is built from a biased sample — the trades that stood out, not all of them. Only a full calculation across every logged trade removes that bias and tells you what's actually true.

The Expectancy Formula

Trading edge is measured by expectancy: the average amount you can expect to win or lose per trade, expressed in the same unit across your whole strategy. It combines how often you win with how much you win and lose when you do, into a single number that either supports the belief in your edge or exposes it as false.

Formula (Win Rate × Avg Win) − (Loss Rate × Avg Loss)
Result > 0 Positive edge — statistically profitable strategy
Result ≤ 0 No edge — strategy loses money over time

A Real Example Calculation

Example — 120-Trade Sample
Win rate 42%
Average win 1.8R
Loss rate 58%
Average loss 1.0R
Expectancy = (0.42 × 1.8) − (0.58 × 1.0) +0.18R per trade

This trader wins less than half the time — 42% — but still has a real, positive edge, because the average win is nearly double the average loss. A win rate that looks unimpressive on its own is actually supporting a profitable strategy once expectancy accounts for the size of wins versus losses, which is exactly why win rate alone is a misleading number to optimize for.

How Much Data You Actually Need

Know Your Real Edge, Not Your Remembered One

Logify calculates your expectancy automatically from every logged trade, so your edge is a number, not a feeling.

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Frequently Asked Questions

What is the formula for trading edge?
Trading edge is measured as expectancy: (Win Rate × Average Win) − (Loss Rate × Average Loss). A positive result means the strategy is statistically profitable over a large enough sample size; a negative or near-zero result means it isn't, regardless of how it feels while trading it.
How many trades do I need to calculate a reliable edge?
Most statisticians consider 100+ trades a reasonable minimum for a moderately reliable expectancy estimate, since smaller samples are too easily skewed by a handful of outlier trades. 30-50 trades can give a rough early read but should be treated as provisional, not conclusive.
Can a strategy have a positive edge and still lose money?
Yes — a positive expectancy only guarantees profitability over a large sample size, not on any individual trade or short stretch of trades. A strategy with real positive expectancy can still produce a losing month purely from normal statistical variance.
Is a higher win rate always a bigger edge?
No — a high win rate with a poor risk-reward ratio can still produce negative expectancy, while a lower win rate with a strong risk-reward ratio can be solidly profitable. Expectancy is what determines edge, not win rate in isolation.