What Is the Risk-Reward Ratio?
💡 Definition
The Risk-Reward Ratio (RRR) compares the potential profit of a trade to the amount risked if the stop is hit. For example, risking $100 to target $300 gives a 1 : 3 ratio. The higher the reward relative to risk, the fewer wins you need to stay profitable.
RRR doesn’t predict success — it ensures mathematical consistency. Combined with win rate and position sizing, it defines your trading expectancy and long-term sustainability.
Visual Representation
Example: 1 : 2 Risk-Reward Setup
A 1 : 2 setup: risking 1R (from Entry→Stop) to make 2R (Entry→Target). With 40 % win rate, expectancy remains positive.
Core Formula & Expectancy
Basic Math
Reward / Risk = (Target−Entry) / (Entry−Stop)E(R) = (WinRate×AvgWinR) − (LossRate×AvgLossR)= 1 / (1 + RRR)Common Risk-Reward Ratios
1 : 1 (Even Money)
Break-even at 50 % win rate. Works only with high accuracy and tight control of costs/slippage.
1 : 2 (Balanced)
Sweet spot for many swing systems. Allows profitability with ~35–40 % wins.
1 : 3+
Favours asymmetric setups (trends, breakouts). Even 25 % win rate can grow equity if consistency holds.
Variable (Dynamic R)
Let winners run with trailing stops: initial 1 : 2 may expand to 1 : 5+. Requires patience and tracking of average R.
Why Risk-Reward Matters
- Mathematical Edge: Positive expectancy = balance of win % × reward : risk.
- Psychological Edge: Knowing R multiple keeps emotions in check — every trade’s cost is fixed.
- Scalability: Consistent R values let you compare systems and track edge quality.
- Compounding: Favour asymmetric payoffs: small known risk, open upside.
Practical Playbook
Applying RRR to Every Trade
1 ) Define entry, stop, and realistic target before entering — no target, no trade.
2 ) Compute RRR and ensure it meets your system minimum (e.g., ≥ 1 : 2).
3 ) Size position from stop distance (Position Sizing = Risk / Stop).
4 ) Use R-multiples for journal metrics (e.g., +2.3R, −1R).
5 ) Filter setups: avoid low-R trades unless win % or context justifies it.
6 ) Review expectancy monthly — small improvements in R or win % compound massively.
Common Mistakes
⚠️ Avoid These Errors
- Entering trades without a defined stop or target (RRR undefined).
- Chasing huge R setups with unrealistic probabilities or liquidity.
- Using fixed R across all markets/timeframes without adjusting for volatility.
- Ignoring partial exits that reduce average R.
- Widening stops post-entry — doubles risk, halves R instantly.
Advanced Concepts
📊 R-Multiple Tracking
Record every trade’s outcome in R. Average R > 0 = edge. Watch distribution for skew/fat-tail performance.
🧭 Probabilistic Thinking
View outcomes as distributions: many −1R, few +3R+ outliers drive equity growth.
🔗 RRR + Win Rate Optimization
Tweak stops/targets to shift the balance — slightly tighter stops can double RRR if still hit reasonably often.
💹 Dynamic Exit Scaling
Scale out partials at +1R/+2R, trail remainder — improves realized R while smoothing equity curve.
The Bottom Line
The Risk-Reward Ratio is the backbone of trade math. Define risk first, project reward realistically, and ensure every setup’s expectancy is positive. Manage in R-multiples, never widen stops, and let asymmetric payoffs do the compounding.