Expectancy is the single most important number in your trading. It tells you, on average, how much you make (or lose) per trade. If your expectancy is negative, no amount of discipline will save your account. If it’s positive, you have a real edge — and your only job is to protect it.

The Expectancy Formula

Expectancy = (Win Rate × Average Win) - (Loss Rate × Average Loss)

Or equivalently:

Expectancy = (Total Net P&L) ÷ (Total Number of Trades)

Let’s break it down with a real example:

Metric Value
Total trades 200
Wins 110 (55%)
Losses 90 (45%)
Average win $85
Average loss $70

Expectancy = (0.55 × $85) - (0.45 × $70) = $46.75 - $31.50 = +$15.25 per trade

This trader makes $15.25 on average per trade. Over 200 trades, that’s $3,050 in net profit.

Why Expectancy Matters More Than Win Rate

Many traders obsess over win rate. “I win 70% of my trades” sounds impressive — until you learn that their average win is $30 and their average loss is $120.

High win rate, negative expectancy:
(0.70 × $30) - (0.30 × $120) = $21 - $36 = -$15 per trade

This trader wins most of their trades but bleeds money overall.

Low win rate, positive expectancy:
(0.35 × $250) - (0.65 × $80) = $87.50 - $52 = +$35.50 per trade

This trader loses more often than they win, but each win is large enough to compensate — and then some.

The lesson: win rate alone is meaningless without considering the average win and loss sizes. Expectancy captures both.

What’s a Good Expectancy?

There’s no universal “good” number because it depends on your trade frequency and capital:

Expectancy Per Trade With 20 Trades/Day With 5 Trades/Day
+$5 $100/day ($2,200/mo) $25/day ($550/mo)
+$15 $300/day ($6,600/mo) $75/day ($1,650/mo)
+$50 $1,000/day ($22,000/mo) $250/day ($5,500/mo)

A scalper with $5 expectancy who takes 30 trades/day earns $150/day. A swing trader with $50 expectancy who takes 2 trades/day earns $100/day. Both are profitable — through completely different approaches.

How to Calculate Your Expectancy

Method 1: Simple Average

Take your total net P&L over a period and divide by the number of trades:

Your Expectancy = Net P&L ÷ Number of Trades

If you made $4,200 over 300 trades: $4,200 ÷ 300 = +$14 per trade

Method 2: Component Calculation

Calculate win rate, average win, and average loss separately:

  1. Win Rate = Winning Trades ÷ Total Trades
  2. Average Win = Sum of All Wins ÷ Number of Wins
  3. Average Loss = |Sum of All Losses| ÷ Number of Losses
  4. Expectancy = (Win Rate × Avg Win) - (Loss Rate × Avg Loss)

Method 3: Use TraderDynamiq

Import your trade history and the platform calculates expectancy automatically — overall, by symbol, by hour, by session, and by time period. This lets you see not just your overall expectancy, but where it’s highest and lowest.

Common Expectancy Problems

Problem 1: Negative Expectancy from Fees

Your gross expectancy might be positive, but after fees, it turns negative:

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  • Gross expectancy: +$8 per trade
  • Average fees per trade: -$3.50
  • Average funding per trade: -$1.20
  • Net expectancy: +$3.30 per trade

In this case, you’re fine. But if fees were $10 per trade, your net expectancy would be -$2 — losing money despite a winning strategy.

TraderDynamiq’s Fee Leak detector specifically measures this — showing you what percentage of your gross profits go to fees.

Problem 2: Expectancy Varies by Time

Your overall expectancy might be positive, but during certain hours it’s deeply negative. This means you have a time-of-day problem, not a strategy problem.

Check your hourly expectancy — if mornings are +$25/trade but late nights are -$15/trade, you know exactly when to stop trading.

Problem 3: Expectancy Varies by Symbol

Some instruments might have strong positive expectancy while others are negative. If you’re trading 10 symbols, 3 might account for all your profits while 4 are quietly draining your account.

TraderDynamiq’s Symbol Stats and Symbol Traps detector identify these patterns automatically.

How to Improve Your Expectancy

1. Increase Average Win Size

Let winners run longer. Move stop-losses to breakeven once in profit. Trail stops instead of using fixed take-profits.

2. Decrease Average Loss Size

Cut losers faster. Use tighter initial stop-losses. Implement time-based exits (if a trade isn’t working after X minutes, close it).

3. Improve Win Rate

Be more selective with entries. Only trade A-grade setups. Wait for confluence (multiple factors aligning).

4. Remove Negative-Expectancy Activities

This is where behavioral analytics shines. Instead of trying to trade better, remove the activities that have negative expectancy:
- Stop trading during your worst hours
- Stop trading negative-expectancy symbols
- Stop revenge trading clusters (which have terrible expectancy)
- Cap daily trade count to avoid low-expectancy marginal trades

TraderDynamiq’s What-If Simulator shows you exactly how much your expectancy would improve if you removed specific behaviors.

Expectancy and Position Sizing

Once you know your expectancy, you can use it to determine proper position sizing:

Kelly Criterion (simplified):
Optimal % of capital per trade = (Win Rate - Loss Rate / (Avg Win / Avg Loss)) / (Avg Win / Avg Loss)

Most traders use a fraction of Kelly (e.g., quarter-Kelly or half-Kelly) to reduce volatility.

The key insight: you can only size positions properly if you know your expectancy. Without it, you’re guessing.

The Bottom Line

Expectancy is the fundamental measure of whether your trading has a real edge. Calculate it, track it over time, and understand where it comes from (which symbols, which hours, which setups). If your expectancy is negative, fix the root cause before anything else. If it’s positive, protect it by identifying and removing the behaviors that drag it down.


Want to see the same analysis run on your own trade history? Analyse your trades free — drop your Binance, Bybit or TradingView export and get your own repeating patterns ranked by measured P&L. No account, no email, no card, and your file is never stored. Not ready to upload? Read a real report first.

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Related Reading

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