Two traders sit down at the end of the year. The first won 80% of his trades. The second lost more of hers than she won. The first one lost money. The second finished the year comfortably ahead.
Nothing about that is a paradox or a trick. It falls out of two multiplications anybody can do on an envelope, and the fact that most people selling systems lead with win rate, the number that means the least on its own, tells you what it is really for.
A win rate is the share of your trades that end in profit. That is all it measures. It says nothing about how big the wins are, nothing about how big the losses are, and therefore nothing about whether the account went up.
You can manufacture any win rate you like. Want 95%? Set a target one tick away and a stop fifty ticks away. You will win almost every trade and go broke on the ones you do not. Want 20%? Tiny stop, enormous target. Both fit in a headline. Only one of the two numbers you need to judge them is in the headline.
Win rate and average win-to-loss ratio are one number, not two. Neither means anything without the other. Any seller who gives you the first and not the second has made a choice about what you are allowed to work out.
Let R be the ratio of your average win to your average loss. The win rate at which you exactly break even is:
Breakeven win rate = 1 divided by (1 + R)
That is the whole of it. Three cases people actually trade:
Anything above that line makes money before costs. Anything below it loses, no matter how good it feels.
Take 100 trades, risking $100 on each, and do the arithmetic rather than trusting the formula.
At 1:1 and a 50% win rate. Fifty winners at $100 is $5,000. Fifty losers at $100 is $5,000. Net: zero. A hundred trades, a hundred commissions, and you finish behind where you started. This is the shape of most discretionary trading.
At 2:1 and a 33.3% win rate. Thirty-three winners at $200 is $6,600. Sixty-seven losers at $100 is $6,700. Net: minus $100, breakeven plus rounding. Note what happened: you were wrong two times out of three and finished level.
At 3:1 and a 25% win rate. Twenty-five winners at $300 is $7,500. Seventy-five losers at $100 is $7,500. Net: zero. Wrong three times out of four, still not losing.
Now push each a few points past breakeven, which is what a real edge looks like. At 2:1 and 40%: forty winners at $200 is $8,000, sixty losers at $100 is $6,000, net $2,000. Modest, unglamorous and entirely realistic. The same logic one notch further out is why a system that places its target at three times its risk on every entry does not need to be right most of the time: at 3:1 the line to clear is 25%.
You do not get paid for being right. You get paid for the size of being right, times how often.
Hold the win rate fixed at 40% and vary only the ratio. Same hundred trades, same $100 of risk each, so forty winners and sixty losers every time.
| Average win vs loss | Breakeven win rate | Cushion at 40% | Result over 100 trades |
|---|---|---|---|
| 0.5 : 1 | 66.7% | 26.7 points short | -$4,000 |
| 1 : 1 | 50.0% | 10.0 points short | -$2,000 |
| 1.5 : 1 | 40.0% | exactly level | $0 |
| 2 : 1 | 33.3% | 6.7 points clear | +$2,000 |
| 2.5 : 1 | 28.6% | 11.4 points clear | +$4,000 |
| 3 : 1 | 25.0% | 15.0 points clear | +$6,000 |
One column changed. The same 40% hit rate is a losing system at 1:1, exactly level at 1.5:1, and a solidly profitable one at 3:1. If you wondered why serious systems obsess over where the target sits relative to the stop, this table is the reason.
Combine the two and you get expectancy, the average dollars a system makes per trade:
Expectancy = (win rate × average win) - (loss rate × average loss)
The published Algomyth engine report does not break out an average winner and an average loser, so the two-component version cannot be run on it from the outside. The one-line equivalent can: total profit divided by trade count. The report covers January 2022 to September 2026 on a mid-size account, and shows $87,359 net across 1,164 trades:
The other half of the picture is the geometry rather than a hit rate. The target sits at three times the stop, so the breakeven line is 25%: one winner in four and the system is level. Everything above that line is the edge, and the profit factor is what tells you how far above it the run actually finished.
Do that check on any system you are considering. If the headline profit cannot be rebuilt from the component figures to within a percent or two, a number is wrong or missing, and it is worth knowing which before you pay.
Expectancy before costs is not expectancy, which is why no figure on this site is quoted that way. The $75.05 a trade above is what survives $1.90 of commission per contract round turn plus about a tick of slippage: roughly $2.40 a trade on the micro Nasdaq, $3.15 on the micro S&P. Taking that out before publishing is the harder way to report a backtest and the only one that is difficult to dress up.
Two dollars and change sounds like nothing because the expectancy is seventy-five. Put the same $2.40 against a system whose expectancy is $8 a trade and it takes nearly a third of the edge. Against a 1:1 scalping system running twenty trades a day it takes all of it. Costs scale with your trade count, not your profit, which is why high-frequency retail systems die quietly rather than dramatically.
Hypothetical performance. Every Algomyth figure on this page comes from a backtest over historical data, January 2022 to September 2026 on a mid-size account, not from a live account. Simulated results are prepared with hindsight, carry no financial risk, and cannot fully reflect real execution, slippage or liquidity. Past performance is not indicative of future results.
Profit factor is gross profit divided by gross loss: dollars collected for every dollar paid out. Below 1.0 it loses money. Above 1.0 it makes money.
Rebuild it from the other direction. The report publishes a profit factor of 1.46 and a total of $87,359, and those two pin down the rest. If dollars collected divided by dollars paid out is 1.46, and the difference between them is $87,359, then the losses come to roughly $189,910 and the wins to roughly $277,270. Divide those and you land back on 1.46, which is the point of showing you the work.
A profit factor of 1.46 means the system pays out about 68 cents for every dollar it takes in. That is a real, ordinary, believable edge. Which brings up the counterintuitive part: a very high profit factor in a retail backtest is usually a warning, not a boast. Profit factors of 4 or 8 over a long sample are the signature of a system fitted to its own history, or one whose losses have not happened yet. See why beautiful backtests die in live trading.
Profit factor, trade count, drawdown and the equity curve are all on one page, along with the live signal log that archives every new signal with its outcome.
Now the trick, worked out in numbers so you never have to wonder again.
Set your target at $100 and your stop at $1,000, a ratio of 0.1 to 1. Price wanders a hundred dollars in your favor far more often than a thousand against you, so you win the large majority of trades. Your screenshots are a wall of green.
The breakeven win rate at that ratio is 1 ÷ 1.1 = 90.9%. So run a hundred trades at a 90% win rate, which any marketer would call outstanding:
Ninety percent right and losing money. The wins are the marketing; the tenth trade is the product. Every variation exists in the wild: grid bots with no stop, martingale sizing that doubles after a loss, "we never take a loss, we just hold." One shape: many small wins financed by a rare enormous loss that has not shown up in the sample you were shown.
The tell is easy to check. When a seller leads with win rate, ask for the average win and average loss in dollars and the maximum drawdown. If those are unavailable, proprietary, or answered with a different number, you have learned what you needed. More of these patterns are in how to spot a trading bot scam in five minutes.
A system whose target sits three times beyond its stop is built to be wrong more often than it is right and to be paid for the difference when it is right. Streaks follow directly, and they are longer than intuition expects. Take a system that loses 60% of the time:
So somewhere in a long simulated history there are separate occasions where a system like this loses ten trades consecutively. Not bugs: the expected behavior of a coin weighted against you on frequency and three to one in your favor on size, and the reason the edge only shows up over hundreds of trades. What those streaks do to an account, and to the person watching it, is the subject of drawdown.
All five are on the performance page, and the reasoning behind the risk side is on the risk page. For the plain-language version of what a system like this is, start at what a trading bot actually is; for the honest version of the money question, how much a trading bot actually makes.
There is no good win rate in isolation, because the only thing that matters is where it sits relative to the breakeven rate implied by the average win-to-loss ratio. A system winning 30% with a 4:1 ratio is far healthier than one winning 70% with a 0.4:1 ratio. Work out 1 divided by (1 plus the ratio) and compare it against the actual hit rate; the gap between those two numbers is the edge.
Because a target at three times the stop lowers the hit rate and raises the payoff per win, and the arithmetic that follows is forgiving: at 3:1 breakeven sits at 25%, so one winner in four is enough to be level and everything past that is profit. Both orders go in with the entry, so the ratio is fixed before the trade starts rather than negotiated while it runs. The published backtest covers January 2022 to September 2026 and 1,164 trades on a mid-size account. These are hypothetical results from historical data, not a live account.
Expectancy is the average amount a system makes per trade, calculated as the win rate times the average win, minus the loss rate times the average loss. When a report does not publish an average win and loss, the shortcut is total profit divided by trade count: for the published Algomyth engine backtest, $87,359 across 1,164 trades, or about $75 a trade with costs already taken out. Multiplying expectancy by expected trade count is the fastest sanity check on any performance claim you are shown.
Up to a point. A profit factor comfortably above 1.0 across a large sample indicates a real edge, but figures of 4 or more in a retail backtest usually mean the system was fitted to its own history or its worst losses have not occurred yet. Treat an unusually high profit factor as a prompt to check the trade count, the out-of-sample period and whether costs were included.
Enough that a normal losing streak cannot dominate the result. If a system can plausibly lose ten in a row, a forty-trade sample tells you nothing about its edge. Hundreds of trades start to be informative and thousands are better, which is why a backtest spanning four and three-quarter years and 1,164 trades is better evidence than a spectacular quarter.
Everything we write comes back to one idea: verify, then decide. The live terminal exists for exactly that.
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