Risk/reward

The risk/reward ratio compares the potential loss on a trade (the distance from entry to stop-loss) to the potential gain (the distance from entry to profit target), typically expressed as reward divided by risk, giving a ratio like 2:1 or 3:1. If a trader enters at $100, places a stop at $95 (risking $5) and a target at $115 (potential gain of $15), the risk/reward ratio is $15 ÷ $5 = 3, or 3:1. This ratio should always be calculated before the trade is placed, using the actual planned stop and target, not calculated retroactively based on how the trade happened to play out.

A subtlety that trips up many traders is that risk/reward is a planned, theoretical figure at the time of entry — it describes the trade as designed, not as it turned out. If a trader plans a 3:1 trade but exits early out of nervousness at 1:1, the realised outcome does not change the fact that the trade was designed with 3:1 risk/reward; it means the trader failed to execute the plan. Separating the planned ratio from the realised outcome is essential for honest performance review, because it isolates execution discipline as a distinct issue from strategy design.

It's also worth noting that risk/reward can be assessed at multiple points during a trade's life, not just at entry. If a trade moves favourably and the trader trails the stop upward, the risk/reward ratio from the current price to the new stop and original target changes continuously. Many disciplined traders re-evaluate this 'current' risk/reward as a trade develops to inform decisions about scaling out or tightening stops, distinct from the original planned ratio used to decide whether to take the trade at all.

A risk/reward ratio by itself says nothing about whether a strategy is profitable. Profitability depends on the combination of risk/reward and win rate, captured in the expectancy formula: expectancy = (win rate × average reward) − (loss rate × average risk). A strategy with an excellent 4:1 risk/reward ratio but a 15% win rate is likely to be a losing strategy, because the frequent small losses accumulate faster than the occasional large win compensates for. Conversely, a strategy with a modest 1:1 risk/reward ratio but a 65% win rate can be robustly profitable.

The breakeven win rate for a given risk/reward ratio can be calculated directly: breakeven win rate = 1 ÷ (1 + reward/risk ratio). For a 2:1 risk/reward ratio, breakeven win rate = 1 ÷ (1 + 2) = 33.3%; the strategy needs to win more than one in three trades to be profitable before costs. For a 1:1 ratio, breakeven win rate is 50%. For a 3:1 ratio, breakeven win rate drops to 25%. This calculation is one of the most useful tools in trading because it converts an abstract preference ('I like high risk/reward trades') into a concrete, testable question: does my actual, measured win rate at this risk/reward level exceed the breakeven threshold, with enough margin to cover commissions, spreads, and slippage?

This is why chasing very high risk/reward ratios (5:1, 10:1) as an end in itself is not automatically superior. Setups offering extremely favourable ratios often come with correspondingly lower win rates, because the market environment that produces a large potential reward for a small risk (very tight, precise entries near major structural levels) is also an environment where the entry is frequently wrong. There is no free lunch in the risk/reward-versus-win-rate trade-off; a trader's job is to find the honest, tested combination that produces positive expectancy after costs, not to assume any particular ratio is inherently good or bad.

A frequent and serious error is choosing a stop and target purely to manufacture a desired risk/reward ratio — for example, deciding 'I want 3:1' and then placing the stop and target at whatever distances produce that number, regardless of the actual market structure. This inverts the correct process. Stops should be placed at levels where the trade thesis is genuinely invalidated (below a swing low, beyond a support zone, outside a volatility band), and targets should be placed at levels with a real basis for the price to react (prior resistance, a measured move, a Fibonacci confluence, or a statistically typical move size for the instrument and timeframe).

Once stop and target are placed based on structure, the resulting risk/reward ratio is simply observed, not engineered. If the honestly-calculated ratio for a structurally sound setup comes out to 1.2:1, that is useful information — it tells the trader this particular setup requires a high win rate to be worthwhile, and they can decide whether their historical win rate for this pattern supports taking it. Manufacturing an artificial 3:1 by moving the stop closer than structure justifies, or moving the target further than the market has ever historically travelled on this setup, just produces false confidence and a trade with a lower actual win probability than the neat ratio implies.

A related discipline is measuring realistic target distances empirically rather than aspirationally, by studying how far price has actually moved on similar historical setups. If a breakout pattern on a particular stock has historically travelled an average of 4% before reversing, setting a target requiring an 8% move 'because I want better risk/reward' is not a risk/reward improvement — it is simply a lower-probability trade wearing the appearance of a better one.

One of the most damaging behavioural patterns in trading is cutting winning trades early relative to plan while letting losing trades run past the stop, which is the exact inverse of good risk/reward management and often stems from loss aversion — the psychological tendency to want to lock in a gain immediately (fear of giving it back) while hoping a loss will 'come back' before accepting it. This pattern can turn a strategy with a good planned risk/reward ratio into a poor realised one, even though nothing about the market analysis was wrong; the damage occurs entirely in execution.

A second common trap is moving a profit target further away mid-trade because the position is 'clearly running,' abandoning the original plan in pursuit of a bigger win — sometimes called 'moving the goalposts.' While trailing a stop to protect gains as a trade develops is a legitimate technique, moving the target itself without a new structural justification is usually just greed overriding a plan that was working. The reverse trap, moving a stop further away because 'it'll probably come back,' is even more damaging, since it directly increases risk on a trade already moving against the trader.

The discipline that protects against both traps is deciding, and writing down, the stop and target before entry, and requiring a specific, structural (not emotional) reason to change either one after the trade is live. If a trader finds themselves wanting to adjust a stop or target mid-trade, a useful gut check is to ask whether they would make the identical adjustment if they were looking at the chart fresh, with no position on, purely as an analyst — if the answer is no, the urge to adjust is coming from the position, not from the market.

The breakeven win rate calculated from a risk/reward ratio is a pre-cost figure; real trading involves commissions, spreads, and slippage that raise the actual win rate required to be profitable. A strategy with a theoretical 2:1 ratio and a 33.3% breakeven win rate might, after accounting for a typical spread cost equivalent to 10% of the average risk amount, actually require closer to 36-37% to break even in practice. This gap matters more for high-frequency, tight-stop strategies, where costs are a larger proportion of each trade's risk, than for longer-timeframe swing strategies with wider stops.

Traders should build a small margin of safety into their expectancy calculations to account for this — treating a strategy that backtests to exactly the breakeven win rate as a losing strategy in live trading, once realistic costs and slippage are included, and requiring a comfortable buffer (several percentage points above the theoretical breakeven win rate) before considering a strategy validated. This buffer also absorbs the natural degradation that often occurs when a backtested edge meets live execution.