What Is Expectancy in Trading?

Win rate on its own tells you nothing. Neither does reward to risk. Put them together and you get the one number that answers the real question: is this process worth repeating?

Expectancy is what a typical trade is worth once you average winners and losers. It's measured in R, where 1R is the fixed dollar amount you risk on each trade, so it works the same on any account size.

As Chad puts it, "You wanna focus on winning trades, not taking trades."

How do you calculate expectancy?

The formula has four inputs, all taken from your trading journal:

Expectancy (R) = (win rate × average win in R) - (loss rate × average loss in R)

Win rate and loss rate add up to 100%. Average loss should sit close to 1R if you size every trade from its stop and keep the stop where it belongs. It runs a little above 1R once fees and slippage are included.

Break-even is where expectancy equals zero. For a fixed reward to risk of 1:N, that happens at a win rate of 1 divided by (1 + N). At 1:1 you break even at 50%. At 1:3 you break even at 25%, one winning trade in four, before costs.

Expectancy also links directly to profit factor, gross winnings divided by gross losses. A profit factor above 1 and a positive expectancy are two views of the same fact.

The ratio you accept matters WAY more than most traders think.

What does expectancy look like with numbers?

Illustrative numbers only, not a result or a target. Say a journal of 100 trades shows:

• Win rate: 35%, so loss rate 65%
• Average win: 3.2R
• Average loss: 1.0R

Expectancy = (0.35 × 3.2) - (0.65 × 1.0) = 1.12 - 0.65 = 0.47R per trade, before costs.

If fees and slippage average 0.05R per trade, expectancy after costs is about 0.42R. Profit factor is 1.12 divided by 0.65, about 1.72.

Now a hypothetical journal of 1:1 trades where winners slightly outnumber stopped trades, 0.52 to 0.48: (0.52 × 1) - (0.48 × 1) = 0.04R before costs, which costs can easily wipe out. The higher hit ratio looks better on a screenshot, right? The lower one with bigger winners is the stronger process.

Where does expectancy live in our process?

The Decision Protocol ends with two steps that exist to measure it: journal and review. Every trade gets logged in R, and the review asks whether the process produces a positive average after costs over a meaningful sample.

The other steps protect the inputs. A fixed risk per trade keeps 1R the same size every time, so the numbers mean something. Stops at the invalidation level keep the average stopped trade near 1R. And the 1:3 filter sets a floor under the average winner, which is why it lowers the win rate a process needs. Chad's rule is the short version: "I don't take any trades under one to three."

Protect the inputs and the number tells the truth.

What wrecks the expectancy number?

Too few trades. Ten trades can show almost any win rate by chance. Under fifty or so is a rough early read.

Breaking the unit. If risk per trade changes with mood (1% on one trade, 5% on the next), R stops being a consistent measure, and a single oversized stop can erase a run of good trades.

Chasing win rate. Taking profits early to keep the win rate high shrinks the average win, and expectancy usually falls even as the win rate rises. Crypto gurus love advertising a hit rate. It sounds impressive and tells you nothing.

What expectancy work should you do with your journal this week?

Log your last fifty trades in R and run the formula. Positive after costs? Keep logging. Negative? The journal just told you which input to fix.

Keep going: crypto risk management, learn crypto trading, the Decision Protocol guide, the risk to reward filter and win rate.