Risk/Reward & R-Multiples

Introduction

A beginner judges a trade by one question: will it win? A professional knows that question is almost meaningless on its own. What matters is not how often you win, but how your wins and losses combine over many trades — the interplay of probability and payoff. A strategy can win 70% of the time and lose money; another can win just 35% of the time and compound a fortune. The framework that makes sense of this — risk/reward, R-multiples and expectancy — is what turns trading from a series of hopeful guesses into something you can actually evaluate and improve.

This lesson builds on position sizing and stop losses, because those two define the very thing we now measure: your risk on a trade. Here we learn to weigh that risk against the potential reward, to express every outcome in a single standard unit, and to calculate whether a strategy makes money at all.

Quick Definition

The risk/reward ratio compares a trade's potential reward (the move from entry to target) against its potential risk (the move from entry to stop). R is the unit of that risk — 1R is the amount you risk — so outcomes are expressed in multiples of it: a winner might be +3R, a loser −1R. Expectancy combines win rate and average payoff into the average result per trade.

Together these three ideas answer the only question that ultimately matters: over many trades, does this strategy make money? Win rate alone cannot answer it. Risk/reward and expectancy can.

The Risk/Reward Ratio

Before taking a trade, you can define two distances: how far the price would travel to your target (the reward) and how far to your stop (the risk). Their ratio is the reward-to-risk ratio. Risking $1 to make $3 is a 3:1 trade; risking $1 to make $1 is 1:1; risking $3 to make $1 is a poor 1:3.

The ratio matters because it sets the bar for how often you need to be right. A trade with a high reward relative to its risk can be wrong most of the time and still profit, because the occasional win pays for many losses. A trade with poor reward relative to risk must be right constantly just to break even — a fragile way to operate. Demanding a favourable risk/reward on every trade is one of the simplest, most powerful filters an investor can apply: it tilts the maths in your favour before probability even enters the picture.

Thinking In R

The most useful habit this framework gives you is to think in R rather than in dollars. Define 1R as the amount you risk on a trade — your entry-to-stop distance, multiplied by your position size. Then express every outcome as a multiple of R:

• A trade that hits its stop is a −1R loss (you lost exactly what you risked).
• A trade risking $200 that makes $600 is a +3R win.
• A trade you exit early for a small gain might be +0.4R.

Why bother? Because R standardises results across trades of wildly different prices and sizes. A $50 stock and a $500 stock, a 100-share trade and a 10-share trade, all become comparable when measured in R. Your trading record stops being a jumble of dollar figures and becomes a clean series — −1R, +3R, −1R, +2R, +1.5R — from which you can actually judge your strategy. R is the common language of risk that makes everything else measurable.

Expectancy: Does The Strategy Make Money?

Now we can answer the central question. Expectancy is the average result per trade you can expect over a large sample, and it combines your win rate with the size of your wins and losses:

Expectancy = (Win % × Average win) − (Loss % × Average loss)

A positive expectancy means the strategy makes money over time; a negative one means it loses, no matter how good any single trade feels. This one formula dissolves the beginner's obsession with win rate, because it shows that win rate is only half the equation — the size of wins versus losses is the other half, and the two trade off against each other.

Why A Low Win Rate Can Win

This is the framework's most liberating insight, and it overturns most beginners' intuition. Consider a strategy that wins only 40% of the time but makes +2R on winners and −1R on losers:

Expectancy = (0.40 × 2R) − (0.60 × 1R) = 0.8 − 0.6 = +0.2R per trade.

It loses more often than it wins, yet it makes a healthy 0.2R on average every single trade. Over hundreds of trades, that positive edge compounds into serious money — and crucially, it requires the emotional fortitude to endure being wrong 60% of the time while trusting the maths. Many of the most successful trading approaches in the world are exactly like this: low win rates, large winners, small losers. They work because the few big wins more than pay for the many small losses.

The mirror image is just as important. A strategy that wins 70% of the time sounds wonderful — until you learn the losses are huge: +0.5R wins and −2R losses. Expectancy = (0.70 × 0.5) − (0.30 × 2) = 0.35 − 0.6 = −0.25R — a losing strategy despite winning most of the time. This is the trap of cutting winners short and letting losers run: a comforting high win rate quietly funded by rare, catastrophic losses. Win rate without risk/reward is a vanity metric. Expectancy is the truth.

Putting It Together

These ideas connect the whole risk-management toolkit. Position sizing defines your 1R (how much you risk); the stop loss defines where 1R is lost; the risk/reward ratio and target define your potential R-multiple; and expectancy tells you whether the whole system has a positive edge. A complete trading plan, in this language, reads cleanly: risk 1% (1R) per trade, demand at least 2:1 reward-to-risk, honour the stop at −1R, and let winners reach +2R or more. If the resulting expectancy is positive, you have a strategy worth repeating; if it is negative, no amount of conviction on any single trade will save it. The framework replaces hope with arithmetic.

Common Misconceptions

• "A high win rate means a good strategy." Only if the wins aren't dwarfed by the losses. A 70% win rate with huge losers can still lose money. Expectancy, not win rate, is the verdict.
• "I need to be right most of the time." With good risk/reward you can be wrong most of the time and still profit. At 3:1, a 25% win rate breaks even.
• "A small, near-certain profit is better than a risky bigger one." Often the opposite. Cutting winners short to lock in small gains destroys your average reward and can flip a winning system to losing.
• "One trade tells me if my strategy works." Expectancy is a property of many trades. Any single result is noise; the edge only shows over a large sample.

Real-World Application

Two traders run different systems. The first wins 65% of the time and feels like a star — until they tally the results in R and find their wins average +0.7R while their losses average −1.5R, because they snatch small profits but hold losers hoping for recoveries. Expectancy = (0.65 × 0.7) − (0.35 × 1.5) = 0.455 − 0.525 = −0.07R: a losing system wearing the disguise of a high win rate. The second trader wins just 42% of the time and often feels wrong — but they cut losers at −1R and let winners run to +2.5R. Expectancy = (0.42 × 2.5) − (0.58 × 1) = 1.05 − 0.58 = +0.47R per trade: a powerful edge, compounding steadily despite the frequent losses. The lesson is stark and counter-intuitive: the trader who is "wrong" more often makes far more money, because they understood that trading is not about being right — it is about ensuring that being right pays more than being wrong costs.

Key Takeaways

• The risk/reward ratio weighs potential profit (to target) against potential loss (to stop); demanding a favourable ratio tilts the maths in your favour before probability enters.
• Think in R: 1R is the amount you risk, and expressing outcomes as +3R, −1R, etc. standardises results across all trades into one comparable language.
• Expectancy = (Win% × avg win) − (Loss% × avg loss) is the average result per trade — positive expectancy makes money, negative loses, regardless of how any single trade feels.
• A low win rate can be highly profitable if winners are large relative to losers (at 3:1, just 25% wins break even) — and a high win rate can lose if the rare losses are huge.
• Win rate alone is a vanity metric; risk/reward and expectancy are the truth. Trading is not about being right often, but about making right pay more than wrong costs.