Expectancy Calculator
Free Expectancy Calculator for trading performance. Enter your numbers to see returns, costs, and optimized scenarios instantly.
Formula
Expectancy = (Win Rate × Avg Win) − (Loss Rate × Avg Loss)
Expectancy calculates the average expected profit or loss per trade. It multiplies the probability of winning by the average win and subtracts the probability of losing multiplied by the average loss. A positive expectancy means the strategy is profitable over many trades. Monthly expectancy multiplies per-trade expectancy by the number of trades per month.
Worked Examples
Example 1: Day Trading Strategy Expectancy
Problem: Win rate: 55%, Avg win: $200, Avg loss: $120, 20 trades/month.
Solution: Expectancy = (0.55 × $200) - (0.45 × $120)\n= $110 - $54 = $56 per trade\nMonthly = $56 × 20 = $1,120\nAnnual = $1,120 × 12 = $13,440\nProfit Factor = $110 / $54 = 2.04
Result: $56 per trade | $1,120/month | $13,440/year | PF: 2.04
Example 2: Swing Trading High RR Strategy
Problem: Win rate: 40%, Avg win: $500, Avg loss: $150, 8 trades/month.
Solution: Expectancy = (0.40 × $500) - (0.60 × $150)\n= $200 - $90 = $110 per trade\nMonthly = $110 × 8 = $880\nRisk-Reward Ratio = $500 / $150 = 3.33:1
Result: $110 per trade | $880/month | 3.33:1 RR
Frequently Asked Questions
What is trading expectancy?
Trading expectancy (also called expected value or mathematical edge) is the average amount you expect to win or lose per trade over a large number of trades. It is calculated as: (Win Rate × Average Win) - (Loss Rate × Average Loss). A positive expectancy means your strategy is profitable over time, while a negative expectancy means you will lose money in the long run. Expectancy is the single most important metric for evaluating a trading strategy because it combines both win rate and risk-reward ratio into one number.
How do I calculate expectancy for my trading?
To calculate expectancy: 1) Determine your win rate from at least 30-50 trades (more is better). 2) Calculate your average winning trade amount. 3) Calculate your average losing trade amount. 4) Apply the formula: Expectancy = (Win Rate × Avg Win) - (Loss Rate × Avg Loss). For example, with 55% win rate, $200 average win, and $120 average loss: Expectancy = (0.55 × $200) - (0.45 × $120) = $110 - $54 = $56 per trade. This means you can expect to make $56 on average per trade over time.
What is a good expectancy per trade?
A 'good' expectancy depends on your trading style and account size. Any positive expectancy is technically profitable, but you want enough edge to cover commissions, slippage, and psychological errors. As a guideline, an expectancy of $0.20-0.50 per dollar risked is considered good, while $0.50+ per dollar risked is excellent. In dollar terms, if you risk $100 per trade, an expectancy of $20-50 per trade is solid. The most important thing is that the expectancy remains consistently positive across different market conditions.
Why is expectancy more important than win rate?
Win rate alone tells you nothing about profitability. A 90% win rate strategy that wins $10 per trade but loses $100 per trade has a negative expectancy: (0.90 × $10) - (0.10 × $100) = $9 - $10 = -$1 per trade. Meanwhile, a 35% win rate strategy with 4:1 reward-to-risk has positive expectancy: (0.35 × $400) - (0.65 × $100) = $140 - $65 = $75 per trade. Expectancy captures the full picture by combining win rate with average win and loss sizes. Always optimize for expectancy, not win rate.
How does expectancy relate to profit factor?
Profit factor is a related metric: Gross Profits / Gross Losses, or equivalently (Win Rate × Avg Win) / (Loss Rate × Avg Loss). A profit factor above 1.0 means positive expectancy (profitable), and below 1.0 means negative expectancy (unprofitable). A profit factor of 1.5 means you make $1.50 for every $1 lost, while 2.0 means you make $2 for every $1 lost. Most successful traders have profit factors between 1.3 and 2.5. Extremely high profit factors (above 3.0) over a large sample are rare and may indicate curve-fitting or a very small sample size.
How many trades do I need for a reliable expectancy calculation?
A minimum of 30 to 50 trades is needed for a rough estimate, but 100 or more trades provides a statistically meaningful expectancy figure. The law of large numbers states that actual results converge toward the expected value as the sample size increases. With fewer than 30 trades, random variance can make a losing strategy appear profitable or vice versa. Ideally, the trades should span different market conditions including trending and ranging markets. Many professional traders require at least 200 trades across multiple market cycles before they consider an expectancy figure reliable enough to risk real capital on.