R-multiples explained: how to measure trades by the risk you took

A $20 profit tells you very little on its own. If you risked $10 to make it, that is a strong result. If you risked $200, it is a small one. R-multiples fix this by measuring every trade against the amount you planned to lose.

This guide explains what 1R means, how to work out expectancy in R, and why a high win rate can still hide a losing approach. It is general education only. It does not recommend any trade or strategy.

What 1R means

The idea is usually traced to trading coach Van K. Tharp. His definition is short: "All profits can be expressed as a multiple of the initial risk (R)."

In practice, an R-multiple is a trade's profit or loss divided by its initial risk, R: the amount the trade would lose if price reached the initial stop. Tharp defines the R-value as "the initial risk taken in a given position", set by the initial stop loss.

You can state R in two ways. R can represent either your risk per unit, such as $10 per share, or it can represent your total risk. Most traders use the total cash amount because it ties straight to the account balance.

Losses are scored the same way as gains. A trade that hits a 2R target returns twice the risk. A loss that hits the stop is -1R.

Hypothetical example. You have a $100 account and decide to risk $2 on one trade. You buy at $50 with a stop at $49, so each unit risks $1, and you buy 2 units. Your 1R is $2.

• You exit at $55: profit is $10, or +5R.
• You exit at $49.50: loss is $1, or −0.5R.
• The stop is hit at $49: loss is $2, or −1R.

How to calculate an R-multiple

For a long trade the steps are:

1. Initial risk per unit = entry price − stop price. 2. 1R in cash = risk per unit × position size. 3. R-multiple = actual profit or loss (after costs) ÷ 1R.

For a short trade, flip the first step: stop price − entry price.

Two rules keep the numbers honest. First, set R from the initial stop, not one you moved later. Second, use your real results after spread, commission and slippage.

A −1R loss is not a hard floor. Tharp notes that losses "can be bigger than -1R when the market gaps against you", and that slippage and costs have the same effect. A stop order caps your planned risk. It does not guarantee the price you get.

Why measure in R instead of dollars or pips

R puts every trade on the same scale. A stock trade, a forex trade and a futures trade can all be compared once each is stated as a multiple of its own risk.

It also lets you compare traders. Tharp's argument was that two traders running very different position sizes and account balances can still compare the quality of their decision-making directly, as long as both are speaking in R.

R works best alongside consistent position sizing. With percent-risk sizing, Tharp explains, 1R represents the same share of equity whichever market or date the trade comes from. The Van Tharp Institute puts it simply: position sizing allows you to standardize R multiple across trades.

If your risk per trade swings around, the R record becomes harder to read. One trade guide lists the mistake of an inconsistent R definition: varying risk per trade skews metrics.

Expectancy in R

Once you have a set of trades in R, you can describe the whole set. A trading system can be characterized as a distribution of the R-multiples it generates. Expectancy is simply the mean, or average, R-multiple generated.

There are two equivalent ways to calculate it:

• Simple average: add every trade's R-multiple and divide by the number of trades.
• Formula: (Win Rate x Average Win in R) - (Loss Rate x Average Loss in R).

Hypothetical worked example. A trader logs 10 trades:

Expectancy = +2R ÷ 10 trades = +0.2R per trade.

Using the formula: (0.40 × 2) − (0.60 × 1) = 0.8 − 0.6 = +0.2R. Same answer.

If this trader's 1R was $2, the sample averaged $0.40 per trade. That describes what already happened in these 10 trades. It is not a forecast. When every trade risks the same cash amount, currency expectancy equals R expectancy × 1R.

Why win rate alone says little

Win rate tells you how often you were right. It says nothing about how much you made when right or lost when wrong. Expectancy needs both.

Hypothetical comparison. Here are two made-up rule sets, each with an average loss of −1R:

Rule set A wins four times out of five and still loses money on average. Rule set B loses seven times out of ten and still has positive expectancy in this sample.

A useful check is the breakeven win rate. When the average loss is −1R, it equals 1 ÷ (1 + average win in R):

• Average win of +0.5R → breakeven win rate of about 67%
• Average win of +1R → 50%
• Average win of +2R → about 33%

These come from the arithmetic above, not from any market data.

Win rate and payoff are linked, which is why chasing either one alone can mislead you. Moving profit targets closer usually raises the win rate but shrinks the average winner. Tightening stops may cut the size of each loss but trigger more of them. Expectancy shows the net effect.

Exits matter too. Letting a loser run past the stop drags the average loss below −1R. One guide warns that allowing -2R+ losses destroys expectancy.

What expectancy in R does not tell you

Positive expectancy in a trade log is a description, not a promise. Keep these limits in mind:

• Sample size. Ten or twenty trades can look very good or very bad by chance alone. A small sample says little about the future.
• Variability. Two sets of trades can share an average and still feel very different to trade. Tharp's System Quality Number (SQN) measures the relationship between the mean (expectancy) and the standard deviation of the R-multiple distribution generated by a trading system.
• Losing streaks. Low-win-rate approaches can produce long runs of −1R trades even when the average is positive. You need the money and the discipline to get through them.
• Changing markets. Results from one period may not repeat in another.
• Backtests vs. live trading. Simulated fills often leave out real slippage, gaps and missed entries.

As one prop-trading firm's guide says, tracking results this way is not, on its own, a strategy that makes anyone profitable; it's a measurement tool.

Costs, gaps and leverage: why real R drifts

Costs come out of every trade, so they push the whole R distribution down. India's market regulator gives a clear real-world view. SEBI published two related equity-derivatives studies on August 20, 2026 covering FY25 and FY26. It found that on a gross basis (before transaction costs), 82.1% of traders made losses in FY26. After costs, 87.7% of individual traders incurred net losses in FY26. SEBI pointed to transaction costs, which pushed some traders with small gross profits into net losses. Small edges are the most exposed to costs.

Leverage is where R can quickly stop matching what you planned. Leverage doesn't change the cash value of 1R if your stop and position size are set correctly. What it does is let a small deposit control a large position. That makes it easy to take a 1R that is a big share of the account, and it leaves you exposed if price gaps past your stop.

In the EU, ESMA's retail CFD rules set 30:1 for major currency pairs; 20:1 for non-major currency pairs, gold and major indices, with the CFD measures applying from 1 August 2018. A forex-broker information site reported in 2026 that ESMA's 30:1 cap on major FX pairs still applies in August 2026.

30:1 leverage means a 3.33% margin requirement. So a move of about 3.3% against a fully leveraged position wipes out the margin behind it. The rules also include a standardised percentage of margin (at 50% of minimum required margin) at which providers are required to close out one or more retail client's open CFDs. As one explainer notes, a close-out is not a stop loss you chose. If that happens, your real loss can bear little resemblance to your planned 1R.

The track record for retail leveraged trading is poor. ESMA's standard warning states that between 74-89% of retail investor accounts lose money when trading CFDs. Small accounts did especially badly in SEBI's data: in FY26, 90% of traders using less than ₹1 lakh as peak margin incurred losses, compared with 67.6% among traders using ₹10 lakh–₹1 crore.

Using R with a small account

R works the same way with $50 as with $50,000, but small accounts hit some practical limits.

Hypothetical example. A trader with a $50 account wants to risk 2% per trade, which is $1. The planned stop is 20 pips away, but the smallest position size their platform allows would lose $2 at that stop. The trader now has three choices: tighten the stop, skip the trade, or accept that 1R is really $2 (4% of the account).

The honest answer is whatever is actually at risk. If you log $2 of risk as $1, every R-multiple in your journal will be wrong.

Other small-account points:

• Spreads are a bigger share of R. A fixed spread costs more R when your stop is tight.
• Record every trade. Log entry, stop, size, exit, costs and the resulting R, including trades you'd rather forget.
• Don't move the initial stop after the fact. Redefining R after the trade flatters your record.

Where Markitel fits

Markitel is not a broker, custodian or adviser. Its signals are the output of Markitel's rule-based ten-factor technical engine. A signal does not set your R. You decide the stop, the position size and how much of your account is at risk. If you act on a signal from any source, you can log the trade in R like any other. Markitel does not claim that any signal, tool or approach is profitable.

Key takeaways

• 1R is your planned loss on a trade, set by the initial stop and position size. Every result can be stated as a multiple of it.
• Expectancy in R is your average R-multiple per trade: (win rate × average win in R) − (loss rate × average loss in R).
• Win rate alone can mislead. A high win rate with small winners can have negative expectancy, and a low win rate with large winners can be positive.
• Real R drifts from planned R because of costs, slippage, gaps and leverage-driven close-outs. Regulator data shows most retail leveraged traders lose money.
• R is a measuring tool, not an edge. A positive average in a small or simulated sample doesn't mean future trades will be profitable.