Risk and reward describe a trade's planned downside and objective, but a ratio is only part of the picture. This lesson shows how to calculate the relationship, express results in R, and connect the numbers with win rate and costs. You will compare different outcome sequences and learn why an attractive target does not prove that a method has an edge. Keep planned numbers separate from realised results throughout the exercises.
The idea
Risk and reward compare what a trade is planned to lose with what it aims to gain. The comparison helps describe a setup, but it does not tell you whether the trade will win or whether a strategy is profitable. For a long position, planned risk per unit is entry minus stop, while planned reward per unit is target minus entry. For a short position, the directions reverse. A trade risking $2 to pursue $4 has a risk-to-reward ratio of 1:2. Some platforms display reward divided by risk instead; always check which convention is being used. Traders often express outcomes in R, where 1R is the initial planned risk. A result of +2R means a gain twice that initial amount; −1R means a loss equal to it. Actual losses can exceed −1R because of gaps, slippage, or changes to the position. A large planned reward does not automatically create an attractive opportunity. Moving a target further away improves the displayed ratio, but may reduce the chance of reaching it. Both the stop and target need a reason grounded in the method, rather than being adjusted solely to produce a nice number. Over a series of trades, outcomes depend on win rate, average win, average loss, and costs. A simplified expectancy is (win probability × average win) − (loss probability × average loss). Historical estimates are uncertain and may not persist. The realised average reward can differ from the planned target. Partial exits, early closes, missed fills, and trailing stops all change results. Review what the method actually produces after costs, not just the ratios shown before entry. Break-even arithmetic Under a simplified model with fixed wins, fixed losses, and no costs, break-even win rate equals average loss divided by average win plus average loss. With a $20 loss and $40 win, that is $20 ÷ $60, or about 33.3%. This calculation describes the assumptions; it does not tell you the probability that a real setup will win. Costs raise the required rate. If net wins are $38 and net losses are $22, the same calculation gives $22 ÷ $60, or about 36.7%. Variable exits and changing charges require actual average outcomes rather than the original target alone. Sequence and uncertainty Two sets of trades can have the same final profit but very different paths. A cluster of losses can create a large drawdown or cause a trader to abandon the method before later wins. Position sizes that change through the sequence also change the account result. Do not treat a spreadsheet expectation as money the market owes you. Estimated win rates and averages are uncertain, can come from biased samples, and can change when market conditions change.
Apply the idea
Imagine buying a simplified instrument at $100, with a planned stop at $98 and a target at $104. Risk per unit = $100 − $98 = $2. Reward per unit = $104 − $100 = $4. Risk-to-reward = $2:$4 = 1:2. For 10 units, the planned gross loss is $20 and planned gross gain is $40. If total costs were $2 per completed trade, the simplified net outcomes would be a $22 loss or a $38 gain, assuming exact execution. Real costs and fills may differ. Now consider a hypothetical set of 10 trades with exactly four $40 gross winners and six $20 gross losers. Gross result = (4 × $40) − (6 × $20) = $40. If each completed trade costs $2, the net result is $20. With only three winners and seven losers under the same assumptions, gross result = $120 − $140 = −$20; after $20 of costs, the net result is −$40. The same 1:2 planned ratio produces a different outcome because the win rate changed. These are arithmetic scenarios, not forecasts or evidence that a system has an edge. Real trades have varied sizes and outcomes, and a ten-trade sample is too small to establish reliable probabilities. Practise calculating a ratio for a long and a short setup. Then review a demo journal: compare planned R with realised R after costs. Ask whether targets were reached, exits were changed, or losses exceeded the initial allowance. Avoid judging the whole method from one impressive winning trade. Compare the plan with the journal Take ten completed demo trades using one clearly defined method. For each, record the initial planned loss, net final result, and realised R. If one trade risks $10 and another risks $20, equal dollar results do not represent equal performance in R. Calculate the average winning R, average losing R, and proportion of winners. Keep flat outcomes visible rather than silently classifying them as wins or removing them. Write down the sample size and period so that the figures retain their context. Now compare the realised average win with the target originally planned. If targets were often shortened, the displayed 1:2 setup ratio may not describe the realised method. Investigate the management decisions rather than assuming the initial ratio was achieved. Treat this as a review exercise, not statistical proof. Ten observations can highlight recordkeeping issues, but cannot establish a dependable future success rate. Continue collecting comparable records without selecting only the best examples.
Key takeaway
A risk-to-reward ratio describes the planned trade-off between a stop and a target. It is not a probability of success, and a distant target does not create an edge by itself. Evaluate realistic target placement together with win rate, average realised outcomes, costs, and execution. Use consistent R measurements to compare trades, while recognising that actual losses may exceed planned risk. The useful question is whether a repeatable method produces acceptable results across many observations after costs—not whether one setup displays a high ratio.
