Two traders sit down at the end of the year. The first won 80% of his trades. The second won 46.2% of hers. The first one lost money. The second made $274,406 gross in backtest across 4,557 trades.
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.
Win rate on its own is not a result
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.
The one thing to remember
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.
The formula that settles it
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:
- R = 1 (win as much as you lose): 1 ÷ 2 = 50%
- R = 2 (target twice the risk): 1 ÷ 3 = 33.3%
- R = 3: 1 ÷ 4 = 25%
Anything above that line makes money before costs. Anything below it loses, no matter how good it feels.
The three cases, worked out in full
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, and it is why a system that places its target at twice its risk on every entry does not need to be right most of the time.
You do not get paid for being right. You get paid for the size of being right, times how often.
Same win rate, six different outcomes
Hold the win rate fixed at Rentabilio's 46.2% and vary only the ratio. Same hundred trades, same $100 of risk each, so 46.2 winners and 53.8 losers every time.
| Average win vs loss | Breakeven win rate | Cushion at 46.2% | Result over 100 trades |
|---|---|---|---|
| 0.5 : 1 | 66.7% | 20.5 points short | -$3,070 |
| 1 : 1 | 50.0% | 3.8 points short | -$760 |
| 1.5 : 1 | 40.0% | 6.2 points clear | +$1,550 |
| 1.84 : 1 | 35.2% | 11.0 points clear | +$3,121 |
| 2 : 1 | 33.3% | 12.9 points clear | +$3,860 |
| 3 : 1 | 25.0% | 21.2 points clear | +$8,480 |
One column changed. The same 46.2% hit rate is a losing system at 1:1 and a solidly profitable one at 2:1. If you wondered why serious systems obsess over where the target sits relative to the stop, this table is the reason.
Expectancy: the number that actually pays you
Combine the two and you get expectancy, the average dollars a system makes per trade:
Expectancy = (win rate × average win) - (loss rate × average loss)
Run it on Rentabilio's published backtest figures (a 46.2% win rate, an average winner of $354 and an average loser of $193):
- 0.462 × $354 = $163.55 collected per trade, on average
- 0.538 × $193 = $103.83 paid out per trade, on average
- Difference: $59.72 per trade
Multiply by 4,557 trades and you get about $272,100. The published gross is $274,406, which is 0.8% apart, exactly the rounding error you would expect from averages quoted to the nearest dollar. Divide the published gross by the trade count directly and you get $60.22 per trade.
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.
Where the commissions go
Expectancy before costs is not expectancy. Rentabilio's published net after commissions is ≈$260,700, about 5% off gross at roughly $1 per micro contract. Per trade: $57.21 net against $60.22 gross, so costs eat about $3 per trade.
Three dollars sounds like nothing because the expectancy is sixty. Put the same three dollars against a system whose expectancy is $8 a trade and it takes 38% 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 Rentabilio figure on this page comes from a backtest over more than seven years of historical data on a $50,000 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, and what a suspiciously good one means
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 same figures. Of 4,557 trades, 46.2% win, so about 2,105 winners at $354 each is roughly $745,300 collected. The 2,452 losers at $193 each is roughly $473,200 paid out. Divide: 1.575. The published profit factor is 1.58. The numbers reconcile, which is the point of showing you the work.
A profit factor of 1.58 means the system pays out 63 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.
Check the arithmetic yourself
Win rate, average win, average loss, profit factor, trade count and drawdown are all on one page, along with the steps to reproduce the whole run in your own NinjaTrader 8.
How a 90% win rate is manufactured
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:
- 90 winners at $100 = $9,000
- 10 losers at $1,000 = $10,000
- Net: -$1,000
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.
Losing streaks are arithmetic, not malfunctions
A 46.2% win rate means the system loses 53.8% of the time. Streaks follow directly, and they are longer than intuition expects.
- Six losses in a row: 0.538 to the sixth power = 2.4%. Across 4,557 trades, that turns up roughly fifty times.
- Ten losses in a row: 0.203%. Across the same sample, about four times.
So somewhere in more than seven years of simulated history there are four separate occasions where this system lost ten trades consecutively. Not bugs: the expected behavior of a coin weighted 54/46 against you on frequency and 1.84 to 1 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.
What to ask before you pay for anything
- Win rate, average win and average loss, all three. Any two of them without the third is not an answer.
- Trade count. A 65% win rate over 40 trades is noise. Over 4,000 it is a measurement.
- Profit factor, and whether it is net of commissions. Gross-only numbers flatter high-frequency systems most.
- Maximum drawdown and longest losing streak. These tell you what you have to sit through to collect the expectancy.
- Whether the published total reconciles. Multiply expectancy by trade count. If it does not land within a couple of percent, ask why.
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.
Frequently asked questions
What is a good win rate for an automated system?
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.
Why does Rentabilio lose more trades than it wins?
Because it takes a target at twice its risk on every entry, which lowers the hit rate and raises the payoff per win. At an average win of $354 against an average loss of $193, breakeven sits at 35.2% and the backtested hit rate is 46.2%. That eleven-point cushion across 4,557 simulated trades is where the profit comes from. These are hypothetical results from historical data, not a live account.
What is expectancy and how do I calculate it?
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. For the published Rentabilio backtest that is 0.462 times $354 minus 0.538 times $193, or about $59.72 per trade before commissions. Multiplying expectancy by expected trade count is the fastest sanity check on any performance claim you are shown.
Is a high profit factor always better?
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.
How many trades do I need before the numbers mean anything?
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 88 months and 4,557 trades is better evidence than a spectacular quarter.
In short: win rate is half a number. Pair it with the average win-to-loss ratio and the breakeven line is 1 divided by (1 plus the ratio): 50% at 1:1, 33.3% at 2:1, 25% at 3:1. Rentabilio's backtested 46.2% hit rate sits 11 points above the 35.2% its 1.84 ratio requires, which produces about $59.72 of expectancy per trade and a 1.58 profit factor across 4,557 simulated trades. A 90% win rate with a hidden 10:1 loss loses money in the same arithmetic. Ask for both numbers, every time.
