Risk-reward ratio: the win rate you actually need

"Only take trades with at least 1:2 risk-reward" is the most repeated rule in trading education, and it's half right. Risk-reward on its own tells you nothing. It only means something next to your win rate, and the risk-reward you plan is often a long way from the one you get.

What risk-reward ratio means

Risk-reward compares what you stand to lose on a trade with what you stand to gain:

• Risk = distance from entry to stop loss.
• Reward = distance from entry to take profit.

Long EURUSD at 1.0850, stop at 1.0820 (30 pips), target at 1.0910 (60 pips): you risk 30 to make 60, a 1:2 risk-reward. Traders also write this as "2R": the reward is twice the risk.

Using R (one unit of risk) instead of pips or dollars is the useful habit here. A trade that made 90 pips with a 30-pip stop made +3R; a trade that hit its stop made −1R, regardless of lot size. R lets you compare trades of different sizes and symbols directly.

The break-even win rate

For any risk-reward, there's a win rate below which you lose money over time. If every win is R and every loss is 1:

Break-even win rate = 1 ÷ (1 + R)

The right-hand column is the one that matters. Spread and commission are a real cost on every trade: they come off your wins and add to your losses, so with a cost of c R per trade the break-even becomes (1 + c) ÷ (1 + R). On a tight 10-pip stop, a 1-pip spread is already 0.1R before the trade has done anything.

So "1:2 is good" really means "1:2 is good if you win more than about 37% of the time". A strategy that wins 30% of its 1:2 trades loses money, slowly and reliably.

Why high risk-reward isn't automatically better

Widening the target from 2R to 5R doesn't make a strategy five times better; it makes the target harder to reach. Price has to travel further before turning, so fewer trades get there. In practice, win rate falls as the target moves out, and the two effects partly cancel.

What you're actually maximising is expectancy, the average R you make per trade:

Expectancy = (win rate × average win in R) − (loss rate × average loss in R)

A 60% win rate at 1:1 has an expectancy of 0.6 × 1 − 0.4 × 1 = +0.2R. A 30% win rate at 1:3 has 0.3 × 3 − 0.7 × 1 = +0.2R. Same edge, very different experience: the first wins most days, the second can lose eight trades in a row without anything being wrong. Pick the one you can actually sit through. The expectancy post goes deeper on these numbers.

Planned R vs realised R

This is where most traders lose the plot. You plan 1:2, and then:

• You move the stop to breakeven early, and a trade that would have hit its target stops out at 0R.
• You close at +1R because it "looked like it was turning".
• You widen the stop when price approaches it, turning a −1R loss into −1.8R.
• Your stop slips on news and fills at −1.2R.

Each one feels reasonable at the time. Together they turn a planned 1:2 into a realised 1:0.9, and the break-even win rate jumps from 33% to over 50%, which is why a strategy that "should" work doesn't.

The fix starts with measuring it. For each closed trade, compute realised R as net profit ÷ the money you were risking at entry. Then compare the average with what you planned. If you keep a trading journal, add a planned_R column and the comparison takes one formula.

How to calculate your realised risk-reward

You don't need the stop distance of every past trade to get a first approximation. The ratio of your average win to average loss, the win/loss ratio, is your realised reward-to-risk in practice:

realised R ≈ average win ÷ |average loss|

Your MT5 account statement has both numbers. Over the MetaKit API, the performance endpoint returns them along with the win rate, so the whole check is one call:

curl -s "https://api.metakit.cloud/v1/accounts/2/performance?range=6m" \
  -H "Authorization: Bearer $METAKIT_KEY" \
  | jq '{win_rate, average_win, average_loss, win_loss_ratio, expectancy}'

{ "win_rate": 53.21, "average_win": 322.71, "average_loss": -228.66, "win_loss_ratio": 1.41, "expectancy": 64.74 }

Read it like this: a realised 1:1.41 needs a win rate above 1 ÷ 2.41 = 41.5% to break even, and this account wins 53%, so it has an edge. expectancy is the same calculation in money: an average of $64.74 per trade.

Run it for the last month and the last year. If the recent win_loss_ratio is well below your plan while the win rate held up, you're cutting winners short. If the win rate dropped but the ratio held, the market changed, or your entries did.

Using risk-reward properly

1. Set the stop where the trade is wrong, not where the R looks nice. A stop placed to "make 1:3 work" is a stop in a random place.
2. Then size the position from the stop. Risk a fixed fraction of the account per trade, and let the lot size fall out of the stop distance; the lot size post has the formula, and the Kelly criterion post covers how large that fraction should be.
3. Set the target from the market, the next level, a measured move, an ATR multiple, and only then check that the R is acceptable for your win rate.
4. Measure realised R monthly and compare it with planned R. That gap is usually the single biggest improvement available to a discretionary trader.

A good risk-reward ratio is simply one where your realised win rate clears the break-even line by a margin that survives costs. Everything else is decoration.