Risk Reward Ratio Calculator

How to use this calculator

Enter your entry price, stop loss level, and target price. Add your position size in shares if you want dollar amounts for total risk and reward.

The calculator returns your risk/reward ratio, the breakeven win rate required to be profitable at this ratio, and your maximum dollar risk and potential reward.

The risk/reward ratio formula

The numerator is your planned reward per share. The denominator is your planned risk per share — the distance from entry to stop.

A ratio of 1.0 means you’re risking $1 to make $1. A ratio of 3.0 means you’re risking $1 to potentially make $3. Everything else being equal, higher is better. But “everything else” includes win rate, which is where most traders focus the wrong way.

Why R:R alone means nothing without win rate

A 10:1 risk/reward ratio sounds incredible. But if your setup only wins 8% of the time, your expected value is negative.

Expected value = (Win Rate × Reward) − (Loss Rate × Risk) At 10:1 with 8% win rate: (0.08 × 10) − (0.92 × 1) = 0.80 − 0.92 = −$0.12 per $1 risked

That’s a losing strategy despite a glittering R:R ratio.

Conversely, a 1.2:1 strategy with a 65% win rate has positive expected value.

(0.65 × 1.2) − (0.35 × 1) = 0.78 − 0.35 = $0.43 per $1 risked

The full picture requires both numbers. Traders who focus only on R:R without tracking win rate make one of the most common and costly mistakes in trading.

Breakeven win rate: what it tells you

The breakeven win rate is the minimum percentage of trades you need to win to not lose money, given your R:R ratio.

At 3:1, you can be wrong 75% of the time and still break even. That’s a very different psychological experience from trading a 1:1 strategy where every losing streak feels like disaster.

Knowing your breakeven win rate lets you judge whether your actual win rate gives you a positive edge. If your strategy has historically won 40% of trades and you’re taking 2:1 setups (breakeven = 33%), you have a 7-point buffer. That buffer is your statistical edge.

The minimum 2:1 standard

Most experienced traders set a minimum R:R requirement of 2:1 before entering any trade. Here’s the reasoning.

If you trade 2:1 and win 40% of trades, your long-run expected return is: (0.40 × 2) − (0.60 × 1) = 0.80 − 0.60 = $0.20 per dollar risked

If you drop to 1.5:1 and your win rate stays at 40%: (0.40 × 1.5) − (0.60 × 1) = 0.60 − 0.60 = $0.00 — break-even at best

The 2:1 minimum creates a buffer so that even if your win rate drops from your historical average, you still have positive expectancy.

Many professional traders aim for 3:1 or higher on swing trades. Day traders sometimes accept 1.5:1 if their win rate is reliably above 55%.

How to set your stop loss correctly

The R:R calculation is only useful if your stop loss is set based on market structure, not on how much money you’re willing to lose.

Wrong approach: “I only want to risk $150 on this trade, so I’ll put my stop $1.50 below entry on a 100-share position.”

That stop is arbitrary. It might be placed in the middle of a support zone, or in the middle of a normal intraday price range, or inside the bid-ask spread. It’ll get hit constantly by normal price noise.

Right approach: identify where the trade is technically wrong. If you’re buying a breakout of a resistance level, the trade is wrong if price falls back below that level. Put your stop there. Then calculate what that distance is, and size your position based on how many shares you can buy while keeping dollar risk within your limit.

If the technical stop gives you a 1:0.5 R:R to your target, either pass on the trade or move your target further out.

The stop drives the R:R, not the other way around.

R:R across different trading timeframes

Day traders, swing traders, and position traders face different R:R realities.

Day traders typically work with smaller price moves and tighter stops. Their R:R is often 1:1 to 2:1, compensated by high win rates (55–65% in skilled practitioners) and volume of trades. The mathematical edge is smaller per trade, accumulated over dozens of trades per day.

Swing traders hold positions for days to weeks. Price has more room to move, so stops can be wider and targets further out. Swing setups often offer 2:1 to 4:1 R:R. Win rates are typically 40–55%. The wider range means individual trades have more variance, but fewer trades means each matters more.

Position traders and investors hold for weeks to months. They might look for 5:1 or 10:1 setups on stocks showing strong fundamental and technical momentum. Their win rate might be 30–40%, but winning trades can produce 30–50% returns against 5–10% stop losses.

Each style can be profitable. The math just has to work: (Win Rate × Average Reward) > (Loss Rate × Average Risk).

Common R:R calculation mistakes

Using the wrong entry price. If you’re placing a limit order, use your limit price as entry. If you’re planning a market order at open, the actual fill might be 0.5% different from your reference price — adjust for that.

Ignoring commissions and spread. Every share has a bid-ask spread. A $0.05 spread on a $0.50 stop means commissions and slippage represent 10% of your risk budget. For small price-range trades, this erodes the R:R significantly.

Not adjusting targets as trades develop. The initial R:R is your planning number. Once a trade is live, some traders trail their stop toward breakeven once the trade moves in their favor. That changes the realized R:R on the winner compared to the planned R:R.

Changing the stop after entry. Widening a stop after it almost gets hit is one of the most destructive habits in trading. It turns a 2:1 trade into a 1:1 trade in the middle of a losing move. Set the stop before you enter and don’t touch it unless the technical reason for the stop has changed.

R:R and portfolio-level risk

Individual trade R:R is a position-level concept. Portfolio-level risk management builds on it.

If you take 10 trades per month with average 2:1 R:R and a 45% win rate, your monthly expected return per dollar risked is: (0.45 × 2) − (0.55 × 1) = 0.90 − 0.55 = $0.35

If you risk 1% of account per trade and take 10 trades, you’re deploying 10% of account risk capacity per month. Your expected monthly gain is 3.5% of account.

But variance matters too. With 10 trades at 45% win rate, the standard deviation of outcomes is meaningful. You could have months where all 10 trades lose — that’s unlikely but not impossible. Position sizing (how much you risk per trade) is what limits the damage in worst-case months.

R:R tells you the quality of each bet. Position sizing tells you how much to bet. Both are required for long-run success.

R:R in options trading

Options have asymmetric payoff profiles by nature, which changes how R:R works.

A long call option has a maximum loss of the premium paid and an unlimited upside (in theory). If you pay $200 for a call option and it can theoretically double or triple the value on a big move, the R:R profile looks attractive.

But the probability of expiration worthless (for out-of-the-money calls) can be 70%+. An apparent 5:1 R:R with a 70% loss rate has negative expected value: (0.30 × 5) − (0.70 × 1) = 1.5 − 0.7 = $0.80 per dollar risked. That’s positive EV. But a 5:1 R:R with an 80% loss rate: (0.20 × 5) − (0.80 × 1) = 1.0 − 0.8 = $0.20. Still positive, but thin.

Options traders who ignore win probability and focus only on how big the payoff could be are making a systematic mistake. The breakeven win rate formula applies just as much to options as to stocks.

Realistic expectations from R:R management

New traders often expect R:R discipline to produce immediately obvious results. It rarely works that way.

Trading edge is measured over 100+ trades, not 10–20. In any short sample, a 2:1 strategy can have a painful run of losses that looks indistinguishable from a broken system.

The discipline of taking only 2:1+ setups does two things that compound over time:

First, it keeps your average winner larger than your average loser. Even in a period of low win rates, the math protects you from catastrophic drawdowns.

Second, it forces selectivity. If you require 2:1, you’ll pass on a lot of marginal trades. Fewer trades with better edge tends to outperform more trades with worse edge, all else equal.

The long-run math works. The short-run experience requires patience and a large enough sample to let the edge manifest. That’s the hardest part — not the calculation, but the consistency.