Expectancy is the single metric that tells you whether an algorithmic trading strategy makes money over time — not whether any individual trade wins, but whether the system as a whole generates a positive return per trade on average.

What Is Expectancy?

Expectancy is a mathematical measure of the average profit or loss you can expect from a single trade, based on a strategy’s historical performance. It combines win rate, average winning trade, and average losing trade into one number. A positive expectancy means the strategy makes money over a large sample of trades. A negative expectancy means it loses money, regardless of how high the win rate appears. Many traders focus on win rate alone — a mistake that expectancy directly corrects.

Why Win Rate Alone Misses the Point

A strategy can win 70% of trades and still lose money. This happens when losing trades are significantly larger than winning trades. Equally, a strategy can win only 35% of trades and remain highly profitable — if winners are three or four times the size of losers.

This metric captures both dimensions at once. It answers the question every systematic trader needs to ask: for every trade I take, what is my average financial outcome? If that number is positive, the strategy has an edge. If it is zero or negative, no improvement in execution will save it.

How Do You Calculate Expectancy?

The formula combines four inputs: win rate, average win size, loss rate, and average loss size. Investopedia’s overview of expected value covers the mathematical foundation if you want to go deeper.

Multiply your win rate by your average winning trade amount. Then multiply your loss rate (which is 1 minus your win rate) by your average losing trade amount. Subtract the second figure from the first. The result is your expectancy per trade.

For example: a strategy wins 45% of trades at an average profit of $200 per trade, and loses 55% of trades at an average loss of $100 per trade. The calculation gives (0.45 × $200) minus (0.55 × $100), which equals $90 minus $55 — a positive expectancy of $35 per trade. Over 1,000 trades, that system generates $35,000 in expected profit before transaction costs.

What Does a Good Expectancy Look Like?

Any positive figure is a viable edge. The higher the result, the stronger the edge — but context matters. A $5 return per trade on $10,000 positions generates a meaningful outcome. The same $5 on a $100 position does not.

Normalise expectancy against your average trade size to get a percentage. This makes comparison across strategies consistent. Many professional systematic traders target a normalised figure of 0.2% to 1% per trade as a benchmark for a healthy edge. For context on how this sits alongside other performance metrics, see Investopedia’s guide to the Sharpe Ratio.

Also account for transaction costs. Slippage (the difference between the expected price and the actual execution price) and trading fees reduce your effective expectancy. A strategy earning $8 per trade but paying $6 in transaction costs has a true edge of just $2.

How to Diagnose and Strengthen Your Edge

Expectancy gives you a diagnostic tool rather than just a result. If the figure is negative, identify which component fails. A low win rate with small average wins and large average losses needs either better entry timing, a tighter stop-loss, or a more favourable take-profit target.

If the result is positive but lower than expected, transaction costs are often the culprit. Running the same strategy on a less liquid market or during high-volatility periods can increase slippage and erode edge. Test the strategy across different market conditions — trending, ranging, high-volatility — to understand when it performs and when it does not.

Use this metric alongside maximum drawdown. A strong positive figure means little if extreme drawdown periods cause you to exit the strategy before it realises its statistical edge.

How to Apply Expectancy in Arrow Algo

Arrow Algo calculates expectancy automatically in every backtest report. After running a strategy across historical data on any supported exchange, the results panel shows your win rate, average win, average loss, and overall expectancy per trade.

You can use this data to iterate directly inside the visual builder. Adjust your take-profit block to extend average winners. Tighten a stop-loss block to reduce average losers. Re-run the backtest and compare results side by side. No spreadsheet work needed — the platform handles the calculation and surfaces the number you need to make an informed decision.

For a deeper look at how expectancy interacts with win rate, see our post on Win Rate: Why It’s Not Enough on Its Own.

What Are the Key Takeaways?

• Expectancy measures the average profit or loss per trade across a strategy’s full history.
• A positive expectancy confirms a statistical edge — a negative one means the strategy loses money long-term regardless of win rate.
• Expectancy combines win rate, average win, and average loss into a single actionable number.
• Always subtract transaction costs from raw expectancy to find your true edge.
• Arrow Algo surfaces expectancy automatically in every backtest report — no manual calculation required.

Disclaimer: The information provided in this article is for educational purposes only and does not constitute financial advice. Trading involves significant risk and you should only trade with capital you can afford to lose. Past performance is not indicative of future results. Always conduct your own research before making any trading decisions.

Ready to build your own automated trading strategies without writing a single line of code? Start for free at Arrow Algo and join thousands of traders who’ve made the switch to systematic trading.