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Expectancy

Expectancy is the single most important piece of data for telling whether a strategy actually makes money — it looks at average profit, not win rate. In short, it tells you how much you earn per trade, on average, with this strategy.

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The Coin-Flip Game

Let’s play a simple coin-flip game with these rules:
  • Heads: +$200
  • Tails: -$100
The win rate is obviously 50%. But what happens if you play this game 100 times?
Add the total gains (A) and total losses (B) and you get +$5,000. Divide by the number of games and you get \+$50 per game. That +$50 is the expectancy. It means every time you play this game, you earn $50 on average over the long run. Now let’s keep the same game but change the payouts:
  • Heads: +$100
  • Tails: -$150
The win rate is still 50%, but the expectancy is -$25. In other words, even with the same win rate, this is a game you are guaranteed to lose. Starting to make sense?

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How to Read Expectancy

Here’s how to interpret your per-trade expectancy. If your expectancy is +0.42R, read it like this:
“Every time I trade, I earn on average 42% of the risk (SL) I took on.”
If you risk $1,000 on every trade, the results look like this:
  • +$420 per trade on average
  • +$42,000** over 100 trades, **\+$420,000 expected over 1,000 trades
Of course, you don’t earn exactly $420 every time. One trade might make \+$2,000 and another might lose -$1,000 — but the average converges to \+$420.

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Expectancy Benchmarks

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3 Ways to Improve Expectancy

  1. Raise your win rate: Make your entry conditions stricter to improve accuracy.
  2. Raise your average win: Improve your R/R by holding winners longer before taking profit.
  3. Cut your average loss: On losing positions, stop out exactly as planned — no exceptions.
Most traders obsess over #1. But improving #2 and #3 is far easier and far more effective. Keep a 50% win rate and simply move your R/R from 1:1 to 1:2, and your expectancy jumps from 0 to +0.5R.

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R

R is the unit of risk you take on in a trade — put simply, the distance to your stop-loss. Case 1. Trader A
  • Enters a Bitcoin long at $90,000
  • Stop-loss (SL): $89,000
If A stops out, they lose $1,000. In other words, the risk taken in this trade is **1R = $1,000**. Now let’s express the outcomes in R:
  • Take profit at $91,000 → made $1,000, so +1R
  • Take profit at $91,500 → made $1,500, so +1.5R
  • Take profit at $92,000 → made $2,000, so +2R
  • Stop out at $89,000 → lost $1,000, so -1R

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Why Express Things in R?

You could express expectancy in plain dollars. But expressing it in R lets you compare every trade on equal footing. Suppose trader A made these three trades: In dollar terms — +2,000 / +750 / -300 — it’s hard to tell which trade was most efficient. But in R terms — +2R / +1.5R / -1R — it’s immediately clear the first was the most efficient. Trader A’s average expectancy is +0.83R, meaning A earns 83% of their chosen stop-loss risk per trade.

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The Win-Rate Trap

Expressed in R, expectancy follows this formula:
Case 1: A good trader
  • 40% win rate, average win +2.5R
  • 60% loss rate, average loss -1R
  • Expectancy = (0.4 × 2.5) - (0.6 × 1) = +0.4R
Even with just a 40% win rate, they earn 0.4R per trade on average. Over 100 trades, that’s an expected +40R. Case 2: A dangerous trader
  • 70% win rate, average win +0.8R
  • 30% loss rate, average loss -2.5R
  • Expectancy = (0.7 × 0.8) - (0.3 × 2.5) = -0.19R
A trader with a 70% win rate who loses money. Small frequent wins, occasional huge losses. This is the win-rate trap — and exactly why expectancy matters more than win rate. Stop being fooled by win rates. With positive expectancy you survive, with negative expectancy you lose — every time.

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Relationship to Risk/Reward

This R concept is the foundation of expressions traders use all the time, like a 1:2 risk/reward ratio. R/R of 1:2 = risking 1R to make 2R = an R/R ratio of 2.0. In other words, you risk losing 1 to your stop for the chance to make 2 at your target.