Risk-Reward Ratio: Thinking in R Instead of Dollars

The risk-reward ratio becomes usable the day you stop counting it in dollars. R is your risk unit: the distance between your entry and your stop, normalised to 1. A 2R gain is worth twice your risk, whatever the instrument, whatever the size. That conversion changes three things: what your journal measures, how you compare two methods, and the decision to exit at TP1.

Why a journal in dollars measures nothing you can act on

Picture two days. Monday, you make $180 on gold. Tuesday, you lose $90 on EURUSD. Net: +$90. You feel competent.

Now the R figures. Monday, you were risking $150: the trade is worth +1.2R. Tuesday, you were risking $30: the trade is worth −3R, because you moved the stop. Real net: −1.8R. You're losing money, and the dollar figure hid it from you because position size masked the quality of the decision.

That's the structural flaw of a journal kept in currency: it adds up two variables (the decision and the size) and you can no longer separate them. R neutralises size. Only the decision remains.

Practical corollary: for R to be reliable, your risk has to be constant as a percentage of capital. If you risk 1% one day and 3% the next, your R values remain comparable to each other but your dollar curve will bear no relation to your R curve. The position size calculator exists precisely for that: locking risk before entry, so that 1R always means the same thing.

The six columns that are enough for a journal in R

A useful journal fits in a spreadsheet. Only add columns when you already know which question they'll answer.

1. Instrument and timeframe. You'll want to segment later: gold on M15 and EURUSD on H1 don't behave the same way.
2. Risk in currency (1R). The amount the stop costs. That's your yardstick for the day.
3. Planned ratio. Entry-to-target distance divided by entry-to-stop distance, written down before entry. Non-negotiable afterwards.
4. Result in R. Positive or negative, with decimals. −0.3R, +1.7R, +3R.
5. Deviation from plan. Zero if you respected entry, stop and exit. Otherwise, a short note: "stop widened", "exited early out of fear", "entered without a confirmed candle close".
6. Maximum favourable excursion, in R. How much the trade was worth at its best moment. This is the most underrated column: it decides your exit policy.

Thirty rows are enough to start seeing something. A hundred rows to conclude. Count on a few weeks, not a few days.

Comparing two strategies: expectancy in R per trade

The question "what risk-reward ratio should I target?" has no answer on its own. It only exists paired with the win rate. The formula fits on one line:

Expectancy = (win rate × average gain in R) − (loss rate × 1R)

Here are four profiles, all plausible, with their expectancy per trade. The figures are illustrative teaching examples, not measured results.

Two lessons. First, the "selective breakout" row has the best expectancy and the worst win rate: psychological comfort and profitability don't point in the same direction. Second, the last row shows that a 1:1 ratio at 50% leaves no margin: spread, commission and swap tip it below zero.

To compare two methods honestly, you need the same candle series, the same entry rule and a pessimistic assumption on ambiguous candles. The PIPSTER PRO backtest replays the series candle by candle and tests the stop before the target when both fall inside the same candle — which avoids overstating setups where price swept both levels.

A full worked example on gold

Assume a $10,000 account and 1% risk, that is $100 per trade. That's your 1R.

• Buy signal on XAUUSD validated at the close of an H1 candle, entry at 2,412.00.
• Structural stop below the last swing with an ATR buffer: 2,404.00. Distance: $8.00, i.e. 800 points on current quoting conventions.
• Targets: TP1 at 1R = 2,420.00 · TP2 at 2R = 2,428.00 · TP3 at 3R = 2,436.00.
• Size: $100 of risk divided by $8.00 of distance gives 12.5 units, i.e. 0.125 lot on a standard 100-ounce contract.

Three possible outcomes, and what you write in the journal. Stop hit: −1R = −$100. TP2 reached then exit: +2R = +$200. Price rose to 2,431 then came back to the moved break-even point: 0R, with 2.4R of maximum favourable excursion — the column that will tell you, a month from now, that you regularly let more than 2R slip away.

Note that the points-to-currency conversion depends on the contract. In forex, the pip value changes with the pair and the account currency; R does not.

Exiting at TP1: what the distribution actually tells you

Exiting at 1R feels good. The trade is green, the day is done, the win rate climbs. It's also the quietest way to make a method unprofitable.

The arithmetic reason: if your gain is capped at 1R and your losses are worth 1R, you need more than 50% win rate after costs — while you're cutting precisely the trades that were about to become your best. Trend-following methods live off the tail of their distribution — a few trades at 3R or 4R that fund a long run of −1R. Closing at TP1 means removing the tail and keeping the run.

The "maximum favourable excursion" column settles this debate with data instead of opinions. Three readings:

1. If 60% of your winning trades exceed 2.5R before coming back: your TP1 is too close, test a partial exit with the stop moved to entry after 1R.
2. If your winners peak around 1.2R then recede: your target is consistent with the market you trade, don't stretch it artificially.
3. If favourable excursion often exceeds 1R but your results sit at 0R: the problem isn't the target, it's a trailing stop that's too tight.

The sensible compromise for most accounts: take a third off at TP1, secure the rest, and let it run towards TP2 and TP3. You keep part of the tail without suffering every single return to break-even. That's exactly why every PIPSTER PRO signal displays three targets expressed in R multiples rather than a single price: the exit decision becomes a documented choice, not a reaction.

Legitimate objections, short answers

What if the market gaps below my stop?

You take more than 1R. That's a fact to record, not to ignore: log −1.8R if you lose 1.8 times your risk. Those events are the reason you keep unit risk at 1% rather than 5%, and the reason you check the market sessions calendar before holding a position over a weekend or a major release.

How long before I have valid statistics?

Thirty trades for a trend, a hundred for a conclusion, provided you change nothing in between. Modifying the method after ten trades resets the counter: you're then measuring a sample of ten, whose variance makes any reading useless.

My prop firm counts in dollars, not in R

Translate it once. A 5% daily loss limit with 1% risk per trade means five stops. You no longer have to watch a balance: you count to five. It's one of the rare cases where thinking in R also simplifies compliance with the account rules.

Putting R in place this week

Three actions, in this order. Set a single percentage risk for all your trades. Open a spreadsheet with the six columns above and fill it in the same day each trade closes, not on Sunday. Then, after thirty rows, calculate your expectancy and look at the favourable excursion column before touching anything else.

If you want entry, stop, the three targets and the size already calculated in R at the moment of the signal, the free Discovery plan lets you test the principle on your own instruments before going further. None of this guarantees a result: it only guarantees that you're measuring the right thing.