What is risk/reward ratio?
Risk/reward ratio compares the potential loss on a trade (from entry to stop loss) against the potential gain (from entry to target). A 1:2 ratio means you risk $1 to make $2. Simple in theory, but most traders measure it incorrectly.
The error is using approximate numbers instead of exact price levels. “I'll risk about 2% and target 4%” produces a meaningless calculation. The only numbers that matter are your actual stop loss price and your actual target price, determined before the trade.
The risk/reward ratio formula
Risk/Reward Ratio
Risk = Entry price − Stop loss price
Reward = Target price − Entry price
R/R = Reward ÷ Risk
Example: You're entering a BTC long at $100,000. Your stop loss is at $98,000 (a $2,000 risk). Your target is $104,000 (a $4,000 reward).
Risk = $100,000 − $98,000 = $2,000
Reward = $104,000 − $100,000 = $4,000
R/R = $4,000 ÷ $2,000 = 1:2
For short positions, flip the direction: Risk = Stop loss − Entry, Reward = Entry − Target.
The R multiple framework
R multiples, popularized by Dr. Van Tharp in Trade Your Way to Financial Freedom, go one step further. Instead of expressing outcomes as a ratio, every trade result is expressed as a multiple of your initial risk: 1R.
If you risk $200 on a trade ($200 = 1R):
- →Win $600 = +3R win
- →Win $200 = +1R win (break even after risk)
- →Lose $200 = −1R loss (your max planned loss)
- →Lose $400 (stop moved) = −2R loss (never acceptable with hard stops)
This normalization is powerful: you can compare the performance of a $500 trade on a small account to a $50,000 trade on an institutional desk. Both are measured in R, making apples-to-apples comparison possible.
How to calculate your 1R (dollar risk per trade)
1R is always defined by two things: your position size and your stop loss distance.
Method A: Fixed % of account
Account = $50,000, Risk = 1%
1R = $50,000 × 1% = $500
Method B: From position and stop distance
Position = 0.5 BTC at $100,000 = $50,000 notional
Stop loss = $99,000 (1% below entry)
1R = $50,000 × 1% = $500
Both methods should agree. If they don't, your position size is misaligned with your stop distance. This is a common error in manual position sizing.
How trading fees erode your effective risk/reward
This is where most traders leave money on the table. Your risk/reward ratio is calculated on price levels, but your actual profit and loss includes commissions, spread, and (for perpetuals) funding rates. These reduce your effective reward without changing your risk.
Take the 1:2 BTC trade from earlier ($2,000 risk, $4,000 target):
Bybit USDT Perp, market orders (taker 0.055%)
Position: $50,000 notional
Entry fee: $50,000 × 0.055% = $27.50
Exit fee: $50,000 × 0.055% = $27.50
Funding (24h hold): $50,000 × 0.01% × 3 = $15.00
Total fees: $70 = 0.14R (on $500 risk)
Gross reward: $4,000 (2R)
Net reward: $4,000 − $70 = $3,930 = 1.86R
Effective ratio: 1:1.86, not 1:2
At 0.14R per trade, fees become significant when you're running 50+ trades per month. That's 7R in fees monthly, before you consider slippage. For a systematic strategy, this is the difference between a profitable backtest and a losing live system.
Calculate your exact fee cost in R →
Use our Trading Fee Calculator to see what Bybit, Binance, Hyperliquid, or any other exchange is actually costing you per trade, expressed in your R multiple.
Open fee calculatorFrom R/R to system expectancy
A single trade's R/R doesn't tell you if a strategy is profitable. You need expectancy: the average R earned per trade across many trades.
Expectancy formula
E = (Win rate × Avg win in R) − (Loss rate × Avg loss in R)
System A: High win rate, low R/R
E = (0.65 × 1.2R) − (0.35 × 1R) = 0.78 − 0.35 = +0.43R/trade
System B: Low win rate, high R/R
E = (0.35 × 3R) − (0.65 × 1R) = 1.05 − 0.65 = +0.40R/trade
After fees (0.15R per trade)
System A net: 0.43 − 0.15 = +0.28R/trade
System B net: 0.40 − 0.15 = +0.25R/trade
Both are profitable, but fees affect them differently. High-frequency, low-R/R systems are disproportionately hurt by commissions. This is why professional algo traders model fees explicitly in backtesting: not as a percentage deducted at the end, but as a per-trade cost subtracted before computing system metrics.
Common mistakes in risk/reward calculation
Using percentage risk without defining the stop
"I risk 1%" only means 1% of account if your stop exactly places 1% of notional at risk. Move your stop tighter and your actual R drops. Define stop price first, then size the position to match your desired R.
Ignoring fees in the ratio
A 1:3 trade on paper becomes 1:2.7 after a 0.3R round-trip cost. Over 100 trades, that gap compounds significantly.
Moving stops to avoid losses
Widening your stop mid-trade doesn't change the R/R. It changes your 1R. A trade that was −1R when the stop was hit becomes −1.5R if you moved it down. Stops define your risk; moving them breaks the entire framework.
Confusing R/R with probability
A 1:5 trade isn't necessarily better than a 1:2. What matters is expectancy: a 1:5 trade that wins 10% of the time has expectancy of (0.10 × 5) − (0.90 × 1) = −0.4R. That's a losing system despite the attractive ratio.
Excluding funding rates from perp calculations
On a 3-day perpetual hold with 0.01% funding every 8h, you pay 9 funding periods = 0.09% of notional. On a $100K position that's $90, easily 0.15–0.3R depending on your account size. Always include funding in your cost model.
Frequently asked questions
What is a good risk/reward ratio for trading?
Most professional traders target a minimum of 1:2 (risk 1R to make 2R). However, the right ratio depends on your win rate. A system with a 70% win rate can be profitable at 1:1. A system with a 30% win rate needs 1:3 or better. Use the Kelly Criterion or expectancy formula to find the optimal ratio for your specific strategy.
How do you calculate risk/reward ratio?
Risk/reward ratio = (Target price − Entry price) / (Entry price − Stop loss price). If you enter BTC at $100,000, stop at $98,000, and target $104,000: risk = $2,000, reward = $4,000, ratio = 1:2. Always use your actual stop loss, not a mental one.
What is an R multiple in trading?
An R multiple standardizes trade outcomes relative to initial risk. If you risk $200 on a trade (1R) and make $600, that's a 3R win. If you lose $200, that's a −1R loss. This makes it easy to compare trades regardless of position size or account size. Popularized by Dr. Van Tharp in 'Trade Your Way to Financial Freedom'.
How do trading fees affect risk/reward ratio?
Fees reduce your effective reward and increase your effective risk. On a 1:2 trade with 0.2R in fees (round trip), your net reward drops from 2R to 1.8R while your risk stays at 1R. Your effective ratio is now 1:1.8, an 11% reduction. Use the trading fee calculator to measure this precisely for your exchange and position size.
What is the difference between risk/reward ratio and expectancy?
Risk/reward ratio measures a single trade. Expectancy measures the average outcome per trade across your system: Expectancy = (Win rate × Average win) − (Loss rate × Average loss). A system with 40% win rate and 1:2.5 R/R has expectancy = (0.40 × 2.5R) − (0.60 × 1R) = +0.4R per trade. Positive expectancy over time.
Summary
- ✓Calculate R/R from exact price levels: (Target − Entry) ÷ (Entry − Stop)
- ✓Express all outcomes in R multiples to normalize across trade sizes
- ✓1R = account size × risk percentage; define this before sizing your position
- ✓Fees always reduce your effective R/R; measure them in R, not just dollars
- ✓Use expectancy, not just R/R, to evaluate a full system
- ✓For systematic strategies: model fees, slippage, and funding explicitly in your backtester
See your fees in R, right now
Use our free Trading Fee Calculator to measure exactly what Bybit, Binance, Hyperliquid, or any other exchange costs you per round trip, expressed as an R multiple.