Risk-Reward Ratio in Crypto: What Is a Good One and How Do You Backtest It?

Risk-reward ratio is the most quoted number in crypto trading and the least understood. Ask ten traders what a good one is and most will say something like "at least 1:2", then struggle to explain what it actually measures, where it comes from, and why their own trades never seem to hit it.

The definition is simple: the distance from entry to take profit divided by the distance from entry to stop loss. If you risk $100 to make $200, the trade is 1:2. The confusion starts when the ratio meets reality, because a ratio that never gets tested against historical data is just a preference dressed up as a number.

What Risk-Reward Ratio Actually Measures

Risk-reward ratio measures the asymmetry of a single trade plan: how much you stand to lose relative to how much you stand to gain. It is defined before the trade, at the moment you set the stop loss and the take profit.

The formula is straightforward:

• Risk per trade: entry price minus stop loss price

• Reward per trade: take profit price minus entry price

• Risk-reward ratio: reward divided by risk

A 1:2 setup means the take profit is twice as far from entry as the stop loss. The ratio says nothing about how often the trade wins. That is the point most traders miss: the ratio describes the payoff, the win rate describes the frequency, and the two only make sense together.

Why Ratio Alone Does Not Make a Strategy Profitable

A 1:2 ratio with a 30% win rate loses money over time. A 1:1 ratio with a 60% win rate makes money. The math of expectancy combines both numbers:

• Expectancy per trade: (win rate x average win) minus (loss rate x average loss)

The ratio sets the size of the wins and losses. The win rate sets how often each side shows up. Neither alone determines profitability, and optimizing one without the other is how strategies look great on paper and fail in the account.

This is also why "what is a good risk-reward ratio" has no single answer. The good ratio is the one that, combined with the strategy's actual win rate, produces positive expectancy. And the only way to know the actual win rate is to test the strategy on historical data.

The Relationship With Win Rate

Because the ratio and the win rate trade off against each other, the ratio a strategy should target depends on what the strategy can realistically deliver:

The table is the whole argument. A 1:4 strategy only needs to win one trade in five to break even, which sounds wonderful, until you notice that wide take profits are harder to hit and the strategy's realized win rate may drop below 20%. The ratio and the win rate are a package deal, and the package only works if the backtest confirms both halves.

How Backtests Reveal Your Real Risk-Reward

Backtesting settles the question that ratio math cannot: what win rate does this strategy actually produce? A backtest runs the exact rules across years of historical data and reports the realized outcomes, including the average win and the average loss.

Those two realized numbers are the strategy's effective risk-reward in action. A strategy with a 1:2 target ratio whose backtest shows an average win only 1.4 times the average loss is not delivering its target, and the gap is the kind of fact that only shows up on historical data.

This is the CoinQuant workflow: set your stop loss and take profit rules, run the backtest, and read the realized payoff in the results. The metrics page reports average win, average loss, win rate, and profit factor together, so you can see whether the ratio you planned is the ratio you actually get.

Common Mistakes to Avoid

• Picking a ratio before knowing the win rate. A target ratio without a tested win rate is a guess. Backtest first, then choose the ratio the strategy can actually deliver.

• Ignoring drawdown in the ratio discussion. A 1:3 ratio with a 60% drawdown is a different risk profile from a 1:2 with 20%, even if the expectancy is similar. Ratio describes trades, drawdown describes the account.

• Forgetting fees and slippage. They shrink the realized average win and grow the realized average loss. A ratio that breaks even before costs can lose money after them, which is why the backtest must model fees.

• Chasing the highest ratio. The highest ratio is not the best ratio. The best ratio is the one that, at the strategy's tested win rate, produces positive and repeatable expectancy.

The Practical Lesson

• Risk-reward ratio is the planned asymmetry of a trade: reward divided by risk, set before entry

• It only matters together with win rate: expectancy is the product of the two

• A good ratio is one the strategy can actually deliver, and the backtest is the only way to know

• Model fees and slippage, watch drawdown, and judge the realized payoff, not the planned one

The next time someone tells you a strategy is good because it uses a 1:3 risk-reward, ask for the win rate, the average win, the average loss, and the drawdown. If those numbers come from a backtest with fees modeled, you are looking at evidence. If they come from a preference, you are looking at marketing.

See the realized risk-reward of your own strategies in every backtest report. Start your first backtest on CoinQuant