What is Average Win Loss, in one clear definition
Average Win Loss is a simple performance summary that compares the average size of winning trades to the average size of losing trades. In practice, you first separate trades into winners and losers, compute an average win and an average loss, and then compare them.
A common way to express it is a ratio:
- Average Win Loss (ratio) = (average win) / (average loss)
Important: “average loss” is usually taken as a positive magnitude (even though a loss is negative in raw P&L). That avoids sign confusion.
How the worked example works (with every assumption)
Below is a fully specified scenario with fixed assumptions. No real market data is used.
Assumptions for the example:
- You have 5 closed trades.
- “Outcome” means net profit or net loss per trade in account currency.
- Average win uses only winning trades; average loss uses only losing trades.
- Average loss is reported as a positive magnitude (the absolute value of the negative outcomes).
- Costs, spreads, and commissions are already included inside each trade’s net outcome. (If your data does not include them, your calculation changes.)
Suppose the 5 trade net outcomes are:
- Wins: +30, +10, +20
- Losses: -15, -5
Step 1: Count winners and losers
- Number of wins = 3
- Number of losses = 2
Step 2: Compute average win
- Average win = (30 + 10 + 20) / 3 = 60 / 3 = 20
Step 3: Compute average loss magnitude
- Average loss magnitude = (|−15| + |−5|) / 2 = (15 + 5) / 2 = 20 / 2 = 10
Step 4: Compute the ratio
- Average Win Loss ratio = 20 / 10 = 2
Interpretation of the computed ratio (as a descriptive statement only):
- This scenario has average wins that are twice as large as average losing magnitudes, given the stated outcome list and rules.
What does “worked example” mean here?
- It means the calculation is reproducible: another person can take the same five outcomes and confirm the same averages and ratio.
Limitations and risks (material failure modes)
Average Win Loss is useful for describing trade outcome sizes, but it can fail to reflect risk or future results.
- Small samples can mislead
- With only a few trades, one unusual win or loss can strongly change the averages and the ratio.
- Unequal “shape” of outcomes is hidden
- The ratio does not show distribution details (for example, whether most losses are small but a rare loss is very large).
- Costs and definitions can change the result
- If you exclude commissions/spreads or use gross P&L instead of net P&L, the average win and average loss can shift meaningfully.
- It does not measure survival or drawdown directly
- Two strategies can have the same average win/loss ratio but very different sequences, causing very different drawdowns.
- Survivorship and selection bias
- If you only analyze trades that were “good enough” to be recorded, or you exclude certain periods, the computed ratio may not represent the full experience.
Verification and next question to check
To verify Average Win Loss independently, you need a clear trade list and consistent rules:
- Record each closed trade’s net outcome.
- Label it winner or loser using the same sign convention.
- Compute average win over winners only.
- Compute average loss magnitude over losers only.
- Compare using your chosen representation (ratio or difference).
Next question: Do your outcomes include all relevant costs and execution effects, and are you using net results with a consistent definition of “win” and “loss”? If the answer is unclear, the calculation may be reproducible but not comparable across data sets.