A high win rate can still lose money when your losing trades cost more, in total, than your winners earn. Win rate counts how often trades finish profitable. It does not measure their size or the costs of trading.
For example, 11 winners averaging $900 and nine losers averaging $1,100 produce a 55% win rate and zero profit before costs. Add brokerage and the same set of trades loses money.
If that sounds like your account, start with average win, average loss and the number of each. They explain the result more clearly than the win rate alone.
How a 55% win rate can finish flat
Suppose you close 20 trades. Eleven win and nine lose.
| Result | Winning trades | Losing trades |
|---|---|---|
| Number of trades | 11 | 9 |
| Average profit or loss | +$900 | −$1,100 |
| Total profit or loss | +$9,900 | −$9,900 |
Trading result before costs = $9,900 − $9,900 = $0
You finished profitable on more than half your trades, but the nine losses consumed everything the eleven winners made. Each average loss was $200 larger than the average win.
These are hypothetical results in one currency. They show the profit and loss from the 20 closed trades. Your account balance can also change because of deposits, withdrawals, open-position gains or losses, income and other charges.
For this example, the amounts exclude brokerage and other separately charged trading costs. When reviewing your own history, use a consistent net result after costs instead.
The formula that connects win rate to profit
Trading expectancy is the average profit or loss per trade. Calculated from past trades, it describes that sample; it does not promise what the next trade will return.
Use:
Expectancy = (win rate × average win) − (loss rate × average loss)
Enter rates as decimals and average loss as a positive amount. For the 20-trade example:
(0.55 × $900) − (0.45 × $1,100) = $495 − $495 = $0 per trade
You can check the result by dividing total profit or loss by the number of trades: $0 ÷ 20 = $0.
For a net calculation, classify each trade using its result after costs. Keep the same trades, currency and date range in every input. If some trades finish at exactly zero, include them in the total count and calculate win and loss rates separately. In that case, the loss rate is not simply 100% minus the win rate.
Our guide to trading expectancy and R-multiples explains how to compare outcomes with the amount originally risked.
What average win do you need at a 55% win rate?
With no breakeven trades and costs excluded, rearrange the formula:
Required average win = (loss rate ÷ win rate) × average loss
At a 55% win rate and an average loss of $1,100:
(0.45 ÷ 0.55) × $1,100 = $900
An average winner of $900 breaks even under those assumptions. A larger average winner produces a profit if the other inputs stay unchanged.
You can also calculate the required win rate:
Breakeven win rate = average loss ÷ (average win + average loss)
$1,100 ÷ ($900 + $1,100) = 55%
That is why “above 50%” is not a universal profitability threshold. Your threshold depends on what your winners earn relative to what your losers cost.
A larger average loss does not automatically mean a losing strategy
The frequency of each outcome still matters. With the same $900 average win and $1,100 average loss, a 60% win rate produces:
(0.60 × $900) − (0.40 × $1,100) = +$100 per trade before costs
The average loss remains larger, yet the sample earns money. Looking only at the gap between the two averages would miss that.
These hypothetical combinations show why you need all three inputs:
| Win rate | Average win | Average loss | Average result per trade, before costs |
|---|---|---|---|
| 55% | $900 | $1,100 | $0 |
| 55% | $1,100 | $1,100 | +$110 |
| 60% | $900 | $1,100 | +$100 |
| 70% | $300 | $1,000 | −$90 |
A 70% win rate can lose money. A 55% win rate can earn money. The percentage needs the dollar results beside it.
How trading costs turn a flat result into a loss
Assume the original 20 trades cost $10 each in round-trip brokerage: the combined cost of entering and exiting.
20 trades × $10 = $200 in costs
The $0 result before brokerage becomes −$200, or −$10 per trade. Winners now average $890 after brokerage, while losses average $1,110.
(0.55 × $890) − (0.45 × $1,110) = −$10 per trade
The SEC's guidance on investment fees recommends checking the total cost of buying and selling, including how much an investment must gain before you break even.
When reviewing recorded trades:
- Include entry and exit commissions and relevant financing or borrow charges.
- Use actual execution prices. Those already reflect the spread and execution slippage you experienced.
- Do not subtract a separate spread or slippage estimate again if it is already captured in those prices.
- Keep account-level expenses separate if you cannot reasonably assign them to individual trades.
For a fuller worked calculation, read breakeven win rate after trading costs.
Why changing the average winner is harder than changing a spreadsheet
In the original example, raising the average winner from $900 to $1,100 would increase the 20-trade result to $2,200 before costs:
11 × $1,100 − 9 × $1,100 = $2,200
That is a useful comparison, but it holds the win rate and average loss fixed. Real trading decisions can change all three.
Moving a profit target farther away may increase some winners while turning others into losses. Tightening a stop may reduce some losses while stopping out more trades. Taking profits earlier may lift the win rate while reducing the average winner.
Increasing position size is also a different decision from improving trade performance. Larger positions scale dollar gains and losses, and the order in which you change size matters.
Use the risk-reward and expectancy calculator to explore combinations. Its hypothetical outputs let you test the arithmetic; they cannot establish that a new exit rule will produce those outcomes.
A trade-review checklist for a high win rate and weak returns
1. Make the comparison consistent
Choose a defined period and include every completed trade in that review. Check fees, missing exits, duplicates and currency conversion before interpreting the statistics.
Count partial exits consistently. One winning position sold in three pieces should not become three winning trades while a losing position counts only once. If your method counts exits instead of completed positions, label that method and use it throughout.
Separate closed-trade results from changes in total account equity. Open losses can offset realised gains, while a deposit can hide a decline in trading performance.
2. Find out whether a few losses explain the result
Sort losing trades by size. Check whether the largest losses followed the original plan, exceeded the planned risk, or came from unusually large positions.
A price gap can also make a planned stop loss larger than expected. FINRA explains that a stop price is not a guaranteed execution price.
Keep genuine large losses in the main calculation. You can compare results with and without an unusual trade to understand its influence, but removing it does not change what the account earned.
3. Review why winners were small
Compare actual exits with the plan recorded before entry. Were you taking partial profits earlier than intended, closing on emotion, or trading conditions in which the planned target rarely became available?
Do the same check on losing trades. A small winner is not automatically a mistake, and a large loss does not by itself prove poor discipline. The trade plan, size and execution explain whether the result came from following the method or departing from it.
4. Separate position sizing from strategy outcomes
Dollar averages show the money earned and lost, including the effect of trade size. If you risked more on losing trades than on winners, the dollar result can be poor even when the underlying setups looked reasonable.
Review each trade in R, where 1R is its original planned risk, alongside its dollar result. A $200 gain is +2R when initial risk was $100, but only +0.5R when initial risk was $400.
Keep both views. R helps compare trades taken at different sizes; dollars show their effect on the account.
5. Test one specific change
Write a change you can evaluate, such as following an existing exit rule you repeatedly overrode. Compare win rate, average win, average loss and net result together as you collect further trades.
Twenty trades can explain how you arrived at a result, but that sample alone cannot establish a durable edge. A single large result can move an average substantially. The NIST statistics handbook explains why extreme observations can pull the mean away from a typical result.
Keep the trade count visible. Review different market conditions and avoid treating a fixed number of trades as automatic proof that a strategy works.
Review average wins and losses in Swingfolio
In Swingfolio's Performance card, use the closed-trade figures for the same selected period. Compare average win, average loss and win rate. Use the dollar view when reconciling the results with money earned or lost.
The win/loss chart can show averages across time periods. Use it to identify periods worth reviewing, then inspect the underlying trades and sample counts.
An average delta compares the sizes of the average winner and loser. It is not expectancy because it does not weight those averages by how often each occurs. The profitable 60% example above still has a $200 gap in favour of the average loss.
A useful review note records the period, trade count, average win, average loss, win rate and the behaviour you plan to investigate. For a separate comparison of aggregate gains and losses, see win rate versus profit factor.
Frequently asked questions
Is a 55% win rate good for swing trading?
It depends on the realised payoff and trading costs. With an average winner of $900 and average loser of $1,100, a 55% win rate breaks even before costs. The same win rate with $1,100 average winners earns $110 per trade before costs.
Can you be profitable with a win rate below 50%?
Yes. At a 40% win rate, $1,800 average winners and $1,000 average losers produce $120 per trade before costs: 0.40 × $1,800 − 0.60 × $1,000. That is hypothetical arithmetic, not evidence that a particular strategy can achieve those figures.
Is average win divided by average loss the same as profit factor?
No. Average win divided by average loss measures the realised payoff ratio. Profit factor divides total winning profit by total losing loss, so the number of winners and losers also affects it. In the original example, the payoff ratio is about 0.82, while profit factor is 1.00 before costs.
Why is my account down if my closed trades are profitable?
Check open-position losses, withdrawals, account charges and currency movements. Closed-trade profit and total account equity measure different things. Reconcile them over the same dates before deciding that a performance statistic is wrong.
General information only. Not financial advice. All trading examples are hypothetical, and past results do not guarantee future performance.