Direct Answer
A risk-reward ratio compares the distance from entry to your stop against the distance from entry to your target: entry $100, stop $90, target $120 is $10 of risk against $20 of reward, a 1:2 ratio. There's no universally correct ratio, because a ratio on its own says nothing about how often the target is actually reached, a demanding 5:1 target that price rarely hits can produce worse results than a modest 1.5:1 target that hits regularly.
Key Takeaways
- Risk-reward ratio = reward per unit ÷ risk per unit; for a long trade that's (target − entry) ÷ (entry − stop), and the ratio is unitless so it doesn't change with position size.
- Raising a target typically raises the ratio while lowering the win rate, since price has further to travel, the page shows two strategies with an identical 1:4 ratio producing opposite outcomes (+$250 versus −$250) because their win rates differed.
- Expectancy = (win rate × average win) − (loss rate × average loss); a positive figure describes a past sample's average result, not a guaranteed forecast, and can arise by chance in a small sample.
- A strategy can be profitable with a win rate below 50% if average wins are large enough relative to average losses, and unprofitable with a win rate above 50% if rare losses are large enough.
What Is a Risk-Reward Ratio, and What Is a Good One?
A risk-reward ratio compares the distance from entry to your stop against the distance from entry to your target: entry $100, stop $90, target $120 is $10 of risk against $20 of reward, a 1:2 ratio. There's no universally correct ratio, because a ratio on its own says nothing about how often the target is actually reached, a demanding 5:1 target that price rarely hits can produce worse results than a modest 1.5:1 target that hits regularly.
How to Calculate the Ratio
For a long trade:
Risk per unit = Entry price − Stop price
Reward per unit = Target price − Entry price
Risk-reward ratio = Reward per unit ÷ Risk per unit
For a short trade both distances flip: risk per unit is stop minus entry, reward per unit is entry minus target. The ratio itself is unitless, so it doesn't change with position size, doubling the position doubles both the potential loss and the potential gain.
Writing it as "1:2" means one unit of risk for two units of reward. Some traders instead express it as a single number ("a 2R target") or invert it into a reward-to-risk ratio. The convention matters less than being consistent, since a mislabeled 2:1 and 1:2 describe opposite trades.
Why the Ratio Alone Doesn't Tell You If a Strategy Works
Raising a target raises the ratio and usually lowers the win rate at the same time, because price has further to travel before the target is reached. The two move against each other, so improving one figure in isolation can quietly worsen the overall result.
| Approach | Ratio | Win rate | Result per 10 trades at $100 risk |
|---|---|---|---|
| Close target | 1:1 | 60% | 6 wins × $100 − 4 losses × $100 = +$200 |
| Distant target | 1:4 | 25% | 2.5 wins × $400 − 7.5 losses × $100 = +$250 |
| Distant target, worse hit rate | 1:4 | 15% | 1.5 wins × $400 − 8.5 losses × $100 = −$250 |
Hypothetical example, for education only.
Rows two and three have an identical 1:4 ratio and opposite outcomes. The ratio was never the deciding factor; the win rate paired with it was.
What Is Trade Expectancy?
Expectancy is the average amount a strategy wins or loses per trade, combining win rate and average trade size into one figure:
Expectancy = (Win rate × Average win) − (Loss rate × Average loss)
Hypothetical example, for education only.
A strategy wins 45% of the time, averaging $300 on winners, and loses 55% of the time, averaging $150 on losers:
- Winning component: 0.45 × $300 = $135
- Losing component: 0.55 × $150 = $82.50
- Expectancy: $135 − $82.50 = $52.50 per trade, before fees and slippage
A positive expectancy means the strategy gained on average across the trades measured. It's a description of a past sample, not a forecast, a small sample can show positive expectancy purely by chance, and a strategy's expectancy can change when market conditions do.
Can a Strategy Win Less Than Half the Time and Still Make Money?
Yes, and the reverse is also true. A 40% win rate is profitable when average wins are large enough relative to average losses; an 80% win rate loses money when the rare losses dwarf the frequent small wins. What matters is the product of frequency and size, not either one alone.
| Win rate | Average win | Average loss | Expectancy per trade |
|---|---|---|---|
| 40% | $500 | $200 | (0.40 × 500) − (0.60 × 200) = +$80 |
| 80% | $100 | $500 | (0.80 × 100) − (0.20 × 500) = −$20 |
| 50% | $250 | $250 | (0.50 × 250) − (0.50 × 250) = $0 |
The third row is the break-even case worth remembering: a 1:1 ratio at a 50% win rate produces nothing before costs, and a guaranteed loss after them.
Fees and Slippage Make Every Ratio Worse
The ratio measured from entry, stop, and target prices is a pre-cost figure. Round-trip exchange fees and slippage widen the realized loss and shrink the realized gain, so the after-cost ratio is always worse than the one on the chart. The effect is proportionally largest on tight targets: costs of 0.3% round-trip barely dent a 10% target but consume a meaningful share of a 1% one.
Hypothetical example, for education only.
A $2,000 position with a 0.1% fee per side and 0.05% slippage per side pays roughly $6 round-trip. Against a planned $100 loss and $200 gain, the realized figures become about $106 and $194, a 1:2 ratio on the chart, closer to 1:1.83 in practice. Run your own numbers through the crypto position-size calculator, which reports the fee burden as a percentage of planned risk alongside the reward-to-risk output.
Related Metrics Worth Tracking
- Profit factor, gross profit ÷ gross loss. Above 1.0 means the wins outweighed the losses over the sample.
- Payoff ratio, average win ÷ average loss. This is the realized version of your intended risk-reward ratio, and it's usually worse.
- Maximum drawdown, the largest peak-to-trough decline. A positive-expectancy strategy can still be unusable if its drawdown exceeds what you'll tolerate.
- Consecutive losses, a 40%-win-rate strategy will produce losing streaks; knowing the longest one in your sample sets expectations.
- Sample size, expectancy from 15 trades tells you very little. Larger samples across different market conditions are more informative.
The distinction between intended and realized ratio matters most here. A trading plan states the intended ratio; the payoff ratio measured from closed trades shows what actually happened, including targets missed, partial exits, and stops that filled worse than planned.
Common Mistakes
- Chasing a high ratio by moving the target further away without checking whether price realistically reaches it.
- Tightening the stop to improve the ratio, this raises the ratio and the chance of being stopped out by ordinary noise.
- Judging expectancy from a handful of trades, where randomness dominates.
- Comparing a pre-cost ratio to after-cost results and concluding the strategy degraded.
- Ignoring drawdown because expectancy is positive, the path matters as much as the average.
- Treating a positive-expectancy sample as a guarantee that future trades will behave the same way.
Limitations
Expectancy is backward-looking: it summarizes a set of trades that already happened under conditions that may not repeat. It also assumes each trade is independent, which breaks down when several correlated crypto positions are open at once and fail together. Neither the ratio nor expectancy accounts for the risk of an exchange outage, a liquidation before the stop, or a gap through both stop and target, see the full risk framework for those.
Why a Good Ratio and a Losing Strategy Coexist Comfortably
A risk-reward ratio is only half of an expectancy calculation, and quoting it alone is how strategies that lose money get described as disciplined. A setup risking one to make three needs to work often enough to cover the times it does not, and that hit rate is the harder number to establish.
Get the second number from your own records rather than from the strategy's description. Log the planned ratio and the actual outcome for every trade, including the ones closed early out of discomfort, and after a meaningful sample compare the realised figures against the intended ones. The gap between planned and realised is usually where the strategy actually stands.
The distortion this measurement invites is setting targets that make the ratio look good rather than reflecting where price plausibly goes. A profit target placed far away improves the ratio on paper and is reached less often, and the arithmetic that improved has no bearing on the arithmetic that pays.
Expectancy also assumes the sample keeps behaving like the sample. A hit rate measured across a trending stretch will not survive a ranging one, and crypto markets change character faster than most sample sizes accumulate. Treat any expectancy figure as provisional and recompute it as conditions shift.
Risk-Reward and Expectancy FAQs
What is a good risk-reward ratio for crypto?
There's no single correct ratio. A higher ratio means each win covers more losses, but ratios are only achievable if the target is realistic, a 5:1 target that price rarely reaches produces a lower win rate that can cancel out the better ratio.
How do you calculate a risk-reward ratio?
Divide the distance from entry to target by the distance from entry to stop. Entry $100, stop $90, target $120 gives $20 of reward against $10 of risk, a 1:2 risk-reward ratio.
What is trade expectancy?
Expectancy is the average amount a strategy wins or loses per trade: (win rate × average win) − (loss rate × average loss). A positive expectancy means the strategy gained on average across the sample measured; it doesn't guarantee future results.
Can a strategy be profitable with a win rate below 50%?
Yes. A 40% win rate can be profitable if the average win is large enough relative to the average loss. Conversely, an 80% win rate can lose money if the occasional losses are far larger than the frequent small wins.
Do fees and slippage change the risk-reward ratio?
Yes. Round-trip exchange fees and slippage widen the realized loss and shrink the realized gain, so the after-cost ratio is always worse than the ratio measured from entry, stop, and target prices alone.
How many trades do I need before expectancy means anything?
There's no fixed threshold, but a handful of trades is dominated by chance. More trades, spanning different market conditions rather than one favorable stretch, make the figure more informative.
How does a partial exit change the risk-reward calculation?
Taking part of a position off at a first target and letting the rest run produces a blended outcome rather than the single ratio the plan assumed. The realised reward is a weighted average across the exits, which is usually lower than the ratio quoted for the full target. Calculating the blended figure in advance is what keeps the plan honest, since a scaling approach can quietly turn an attractive ratio into an ordinary one.
Is a higher risk-reward ratio always preferable?
A more distant target improves the ratio while lowering the probability of reaching it, so the two move against each other. A strategy targeting very large ratios wins infrequently by construction and needs the tolerance and the sample size to survive long losing runs. The ratio is only meaningful alongside the hit rate it produces, which is why expectancy rather than the ratio is the figure that determines whether an approach works.
How should a risk-reward ratio be adjusted for a position held through funding payments?
Funding accrues while the position is open and reduces the realised reward without changing the stop, so a long hold at a persistently unfavourable rate erodes the ratio the trade was entered on. Estimating the expected funding cost over the intended holding period and subtracting it from the target gives a more realistic figure. Trades with modest ratios and long expected durations are the ones this affects most.