The core problem traders face is judging whether a trade is worth taking. Many focus only on the potential profit, but ignore how much they stand to lose if it goes wrong. This imbalance leads to repeated small wins erased by a single large loss.
This is why it is important to understand and think in terms of the ratio of risk to reward. And in this blog we will explain exactly that.
Risk-to-reward ratio (RRR) is a way of quantifying decision-making in trading.
Instead of going by gut feeling, it forces you to ask: “How much am I risking vs. how much can I make?”
The risk-reward ratio (R:R) compares the potential loss on a trade (risk) to the potential profit (reward). It is calculated as:
Risk-to-reward ratio = Potential loss/potential profit.
Example:
Trading is a volume game. How much you win matters more than how often you win.
For example, if you won only 4 out of 10 trades that you placed, but if your loss-to-profit ratio is 1:3 it means you are still profitable.
If you apply a risk-to-reward ratio in all your trades, it will act as a filter and help you avoid trades that look attractive but are mathematically stacked against you.
The philosophy behind the risk-to-reward ratio is simple: survival and growth in trading come from controlling losses while letting profits run.
In practice, this means rejecting trades with poor payoff and consistently targeting setups where the potential gain justifies the risk. Over time, this discipline compounds into profitability.
Now that you know how to apply the risk-to-reward ratio in a single trade, let’s explore advanced ways to use it for smarter spot and futures strategies.
Most retail traders risk the same amount on every trade, no matter how good or bad the setup is, which means they often over-commit on weak opportunities and under-commit on strong ones.
The risk-to-reward ratio fixes this by guiding you to size up only when the payoff is attractive, and scale down when it’s not. This leads to wasted capital on low-quality opportunities.
Instead, you can use the risk-reward ratio to guide your bet size. For example, if BTC is at $107,000 risk $100 for $100 reward (1:1), keep it small at 0.01 BTC ($1,070). But if the setup risks $100 for $300 reward (1:3), you can go bigger, say 0.03 BTC ($3,210), since the odds are stacked better.
| Parameter | Case A: Weak Setup (1:1 R:R) | Case B: Strong Setup (1:3 R:R) |
| Entry Price | $107,000 | $107,000 |
| Position Size | 0.01 BTC ($1,070) | 0.03 BTC ($3,210) |
| Stop Loss (SL) | $106,900 (–$100) | $106,900 (–$100) |
| Take Profit (TP) | $107,100 (+$100) | $107,300 (+$300) |
| Risk Amount | $100 | $100 |
| Potential Reward | $100 | $300 |
| Risk-to-Reward Ratio | 1:1 | 1:3 |
| Profit if TP Hits | +$100 | +$300 |
| Loss if SL Hits | –$100 | –$100 |
| Parameter | Case A: Weak Setup (1:1 R:R) | Case B: Strong Setup (1:3 R:R) |
| Entry Price | $107,000 | $107,000 |
| Contract Size | 0.1 BTC ($10,700 notional) | 0.3 BTC ($32,100 notional) |
| Leverage | 10x | 10x |
| Margin Required | $1,070 | $3,210 |
| Stop Loss (SL) | $106,900 (–$100 per 0.1 BTC) | $106,900 (–$100 per 0.1 BTC) |
| Take Profit (TP) | $107,100 (+$100 per 0.1 BTC) | $107,300 (+$300 per 0.1 BTC) |
| Risk Amount | $100 | $100 |
| Potential Reward | $100 | $300 |
| Risk-to-Reward Ratio | 1:1 | 1:3 |
| Profit if TP Hits | +$100 | +$300 |
| Loss if SL Hits | –$100 | –$100 |
ALSO READ: How Much Leverage Is Too Much? A Risk-Based Guide for Crypto Futures
When most traders set up a trade, they usually decide on a single exit point. Either a take-profit level or a stop-loss.
Some more disciplined traders do set both. But even then, these levels are often chosen arbitrarily: a stop-loss is placed “just below support” or a take profit set at a “round number.”
The risk-to-reward ratio can be used to bring structure and logic to those choices. Instead of randomly placing a stop just because it “feels safe” or setting a target because it’s a neat round number, you can design exits around ratios that make sense mathematically.
Say you split your $1,000 capital into two $500 lots and buy Bitcoin at $110,200.9. With a stop at $109,700, each lot risks about $2.28 (≈0.45%).
This way, one half of your position locks in safety quickly, while the other half keeps your upside open. Instead of choosing between “booking profits” and “holding for more,” you achieve both — and the overall risk-to-reward across the two positions stays stacked in your favor.
Similarly, you can deploy the same strategy in futures trading as well. Say you split your $1,000 margin into two $500 lots and go long BTCUSDT futures at $110,200.9 with 10x leverage. That gives you control of about 0.09 BTC notional ($9,000). With a stop at $109,700, each $500 lot risks around $22.8 (≈0.45% of notional).
Just like in spot, one half of your futures position secures gains quickly, while the other half stays open for a bigger run. The only difference is that leverage magnifies both the risk and the reward, which makes structuring exits around the risk-to-reward ratio even more critical.
Instead of exiting everything at one point, you can split your trade:
Many traders treat Stop loss as something fixed: place it once and forget it.
The problem is that markets move dynamically.
If price moves in your favor and you keep your stop at the original level, you’re still carrying the same downside risk even though your trade is working. Worse, you can sometimes turn a winning trade back into a loser simply because you didn’t adjust.
The risk-to-reward ratio can be used to bring discipline to this process. Instead of leaving your stop anchored where you first placed it, you can shift it upwards (in a long trade) to lock in profit and protect your capital, while still leaving room for the trade to run.
Say you put $1,000 into Bitcoin at $110,200.9 with a stop at $109,700. Initially, you risk about $4.55 (≈0.45%) for the chance to target $113,000. But once the price reaches $111,200, instead of leaving your stop at $109,700, you move it up to your entry at $110,200.9.
Now, the worst-case outcome is breakeven, and the best-case is still a $2,800 move to $113,000, which makes your effective risk-to-reward infinite (no downside, all upside).
FUTURES TRADING EXAMPLE: Suppose you go long with $1,000 margin at $110,200.9 on 10x leverage, controlling about 0.09 BTC notional ($9,000). With a stop at $109,700, your risk per $500 lot is around $22.8 (≈0.45% of notional). As the price climbs to $111,200, instead of keeping the stop at $109,700, you slide it up to your entry.
This way, you’ve eliminated downside risk while still keeping the upside open, and because of leverage, protecting your margin like this is the difference between compounding gains and blowing up an account.
When most traders judge their performance, they focus only on their win rate, i.e., the percentage of trades that end in profit.
Considering just win rate alone is a shallow approach to assessing your trading success. It counts only the number of trades won versus lost, without considering the size of those wins or losses. A trader might win 7 out of 10 trades, but if the 3 losing trades are much larger than the 7 small winners, the account still ends in the red.
The risk-to-reward ratio gives you a way to cut through this illusion by focusing on expectancy.
| Expectancy measures the average amount you can expect to gain (or lose) per trade over many trades.It combines your win rate with your risk-to-reward ratio, showing whether your strategy is profitable in the long run, regardless of how many trades you win. Example: If you win 40% of trades with a 1:3 ratio, expectancy stays positive, meaning you can lose more trades than you win and still make money. |
Expectancy combines win rate and risk-to-reward ratio into a single formula:
Expectancy = (Win percentage × Average Reward) – (Loss percentage × Average Risk).
For example, if you win 40% of trades with a 1:3 ratio, your expectancy is still positive — meaning you can lose more trades than you win and still make money.
Say you put $1,000 into Bitcoin at $110,200.9 with a stop at $109,700 (risk = $4.55) and a target at $111,200 (reward = $9.1, R:R = 1:2). If you win 50% of such trades, your expectancy is:
That means even with coin-flip accuracy, you’re profitable over time.
Similarly, you can test this in futures trading. Suppose you go long with $1,000 margin at $110,200.9 on 10x leverage, controlling 0.09 BTC notional ($9,000). With a stop at $109,700, each $500 lot risks $22.8, and a target at $111,200 yields $45.6 (R:R = 1:2). If you win only 40% of trades, expectancy is:
Even with more losses than wins, the math stays in your favor because the ratio is stacked correctly. That’s the power of using expectancy testing: it tells you if your system will make money over dozens or hundreds of trades, not just one.
Traders often pick arbitrary levels: a $500 stop because it “feels safe” or a $2,000 target because “that’s where resistance is.” The problem with this is that markets don’t move in neat, predictable steps.
Bitcoin, for example, can easily swing $1,000 in an hour on normal volatility, so a tight stop that ignores this reality will get hit repeatedly, even if your trade idea was correct. On the other hand, if you set a target that’s too far away compared to Bitcoin’s usual movement, you may end up with unrealistic trades that never get triggered.
The risk-to-reward ratio can be adapted to solve this by anchoring it to volatility.
To demonstrate how to use volatility to set a risk-to-reward (R:R) ratio, we’ll anchor our trade setup to
Bitcoin’s price movements using the Average True Range (ATR), a reliable measure of volatility. This ensures stop-loss and take-profit levels respect the market’s natural fluctuations, avoiding the pitfalls of arbitrary levels that are either too tight or unrealistically far.
Suppose you decide to invest $2,000 in Bitcoin at $60,000. You analyze Bitcoin’s 14-day ATR, which is $2,000, meaning Bitcoin typically moves $2,000 up or down daily. To create a volatility-based trade setup:
Step 1: Setting the Stop-Loss
To account for Bitcoin’s volatility, set your stop-loss at 1.5x the ATR below your entry price to avoid being stopped out by normal price swings:
This stop-loss is wide enough to handle Bitcoin’s typical daily volatility, reducing the chance of premature exits.
Step 2: Setting the Take-Profit
For a favorable R:R ratio, aim for at least 1:2, meaning your potential reward is twice your risk. Since your risk is $3,000:
This target is achievable within Bitcoin’s volatility, as a $6,000 move is 3x the daily ATR, realistic for a strong trend day.
Outcome
By using the ATR, your trade setup respects Bitcoin’s natural price swings, ensuring your stop isn’t too tight (avoiding unnecessary losses) and your target isn’t too far (keeping it attainable).
Now, let’s apply the same volatility-based approach to futures trading with leverage. Suppose you go long with $2,000 margin at $60,000 on 10x leverage, controlling a position of ~0.33 BTC ($20,000). Using the same 14-day ATR of $2,000:
Step 1: Setting the Stop-Loss
Set the stop-loss at 1.5x ATR below entry:
With 10x leverage, your actual risk is $1,000 of your $2,000 margin, or 50% of your margin.
Step 2: Setting the Take-Profit
For a 2:1 R:R ratio, your reward should be twice your risk:
Outcome
The risk-to-reward ratio is a powerful tool for crypto trading, but it’s not a standalone solution. To maximize its effectiveness, combine it with disciplined position sizing to ensure you’re allocating capital wisely based on the quality of the trade setup. Equally important is maintaining strict discipline, sticking to your predefined stop-loss and take-profit levels without letting emotions derail your plan.
Integrating risk-to-reward with robust portfolio and risk management strategies further enhances your ability to protect capital and compound gains over time.
For additional support, consider joining the Mudrex Telegram community, where you can access expert trade ideas rooted in sound risk management principles, helping you refine your approach and navigate the volatile crypto markets with confidence.
Krishnan is a Bangalore-based crypto writer dedicated to simplifying complex crypto concepts. He covers blockchain, DeFi, and NFTs, with a focus on real-world asset tokenization and digital trust. Previously he has written on Real Estate related assets for NoBroker. Krishnan holds a B.Tech degree from the College of Engineering Trivandrum.
