Risk-reward ratio explained: the win rate you actually need
The risk-reward ratio is the most misunderstood number in trading. On its own it tells you nothing — a 5:1 trade is worthless if it only wins 5% of the time, and a 1:2 trade can be a goldmine if it wins 80%. What matters is how reward-to-risk and win rate combine into expectancy: the average profit per trade. This pairing is why a bot with a 70% win rate can still bleed money and one that loses 65% of its trades can be wildly profitable. This guide explains the ratio, the break-even win-rate math, and how to read the two numbers together.
What the risk-reward ratio is
The risk-reward ratio compares what you stand to make to what you stand to lose on a trade. If your stop is $1 below entry and your target is $2 above, you are risking 1 to make 2 — a 1:2 risk-reward, or a reward-to-risk of 2. It is set the instant you place your stop and target, before the market does anything, which is why disciplined bots define both in advance.
The break-even win rate
Every reward-to-risk implies a minimum win rate just to break even. The formula is simple: break-even win rate = 1 / (1 + reward-to-risk). The higher your reward relative to risk, the fewer trades you need to win.
| Reward : Risk | Break-even win rate | What it means |
|---|---|---|
| 1 : 1 | 50% | Must win half just to tread water |
| 2 : 1 | 33.3% | Can lose two of three and still profit |
| 3 : 1 | 25% | One winner pays for three losers |
| 1 : 2 | 66.7% | Needs a very high win rate to survive |
Expectancy ties it together
Neither number means anything alone — expectancy is the bridge. Expectancy is the average dollars (or R) you make per trade: (win% × avg win) − (loss% × avg loss). A positive expectancy means the strategy makes money over many trades; negative means it bleeds, no matter how good a single trade looks. This is the real output of the win-rate profit calculator.
The high-win-rate trap
Scam bots and grid systems often advertise a sky-high win rate by taking tiny profits and letting losers run — a 1:10 reward-to-risk. They win 90% of trades and then a single loss erases fifty wins. A high win rate with a terrible reward-to-risk is the classic signature of negative expectancy dressed up to look like genius. Always ask for the average win and average loss, never just the win rate.
Expectancy in code
python · expectancy.pydef expectancy(win_rate, avg_win_R, avg_loss_R):
return win_rate * avg_win_R - (1 - win_rate) * avg_loss_R
print(expectancy(0.35, 4, 1)) # +0.75 R per trade → profitable
print(expectancy(0.70, 1, 3)) # -0.20 R per trade → loses money
Using the ratio in a bot
Set a minimum reward-to-risk per trade (many trend bots demand at least 2:1) and let the win rate fall where it may — you only need the implied break-even rate, not a high one. Measure realised expectancy over many trades on the backtester, and remember fees and slippage quietly worsen both your average win and your effective ratio.
Frequently asked questions
What is a good risk-reward ratio?
There is no universally good ratio — it only matters relative to win rate. A 2:1 reward-to-risk is a common target because it only needs about a 33% win rate to break even, but a 1:2 ratio can be excellent if the strategy wins 80% of the time. The ratio is meaningful only when paired with the win rate through expectancy.
How do I calculate the break-even win rate?
Break-even win rate equals 1 divided by (1 plus the reward-to-risk ratio). A 1:1 trade needs 50%, a 2:1 trade needs about 33%, and a 3:1 trade needs just 25%. The higher your reward relative to risk, the fewer trades you need to win to stay profitable.
Can a high win rate still lose money?
Yes, easily. A strategy that wins 90% of trades by taking tiny profits and letting losers run can have a 1:10 reward-to-risk, where a single loss erases fifty wins, giving it negative expectancy. A high win rate with a poor reward-to-risk is a classic disguise for a losing system, which is why average win and average loss matter more than win rate alone.
What is expectancy in trading?
Expectancy is the average profit per trade, calculated as (win rate times average win) minus (loss rate times average loss), usually expressed in R or dollars. Positive expectancy means the strategy makes money over many trades; negative means it loses, regardless of how good any single trade appears. It is the number that ties win rate and risk-reward together.