What Is an R-Multiple
The R-multiple is a way of measuring trade outcomes relative to the risk you took. Instead of saying "I made $500 on that trade," you say "I made 2.5R" -- meaning you made 2.5 times the amount you originally risked.
This matters because a $500 profit on a trade where you risked $200 is a very different result than a $500 profit on a trade where you risked $1,000. The R-multiple captures that distinction.
The formula is straightforward:
R-Multiple = (Exit Price - Entry Price) / (Entry Price - Stop Loss)
That formula applies to long trades. For short trades, it inverts:
R-Multiple = (Entry Price - Exit Price) / (Stop Loss - Entry Price)
A trade that hits your target at +2R returned twice your initial risk. A trade that hits your stop loss should return approximately -1R (the full amount risked). Trades that you exit before the stop will return something between -1R and 0R.
How SwingFolio Captures the Data
For SwingFolio to calculate R-multiples, it needs three prices per trade: entry price, exit price, and stop loss.
The entry price and exit price are recorded when you log the trade or import it from your broker.
The stop loss is captured when you create the trade. This is a field in the trade entry form, and it represents the price at which you would have exited to cut your loss. SwingFolio stores this as the original stop loss at the time of entry -- even if you later move your stop, the initial risk calculation is based on where your stop was when you opened the position.
This is a deliberate design choice. The initial R value represents the risk you accepted when you took the trade. If your initial stop was 5% below entry, your 1R is 5% of position value. Trailing your stop later does not change the risk you agreed to upfront.
If you do not enter a stop loss, SwingFolio cannot calculate the R-multiple for that trade. The field will show as N/A. This is one reason to always set a stop loss before entering a trade -- it gives you the data you need for proper risk-adjusted analysis later.
Automatic Calculation on Trade Close
When you close a trade in SwingFolio (by entering an exit price and exit date), the R-multiple is calculated immediately.
Here is a worked example:
- Entry price: $10.00
- Stop loss: $9.00
- Exit price: $12.50
Risk per unit (1R) = $10.00 - $9.00 = $1.00
Profit per unit = $12.50 - $10.00 = $2.50
R-Multiple = $2.50 / $1.00 = 2.5R
This trade returned 2.5 times the amount risked. In R-multiple terms, this is a solid outcome regardless of the dollar amount involved.
Another example, a losing trade:
- Entry price: $25.00
- Stop loss: $23.50
- Exit price: $22.80
Risk per unit (1R) = $25.00 - $23.50 = $1.50
Loss per unit = $22.80 - $25.00 = -$2.20
R-Multiple = -$2.20 / $1.50 = -1.47R
This trade lost more than 1R, which means the trader either did not honour their stop or the price gapped through it. The R-multiple makes this immediately visible -- anything worse than -1R flags a discipline issue or an adverse gap.
How Partial Exits Affect R
SwingFolio supports partial exits, where you sell part of your position at one price and the remainder at a different price. When a trade has multiple exits, the R-multiple is calculated on the overall trade result.
For example, if you bought 1,000 shares at $5.00 with a stop at $4.70:
- Sold 500 shares at $5.60
- Sold 500 shares at $6.10
The weighted average exit price across all exits determines the trade's P&L, and the R-multiple is calculated from that aggregate result.
In practice, partial exits often improve your R-multiple because you are locking in profit on part of the position while letting the rest run. SwingFolio tracks each exit separately in the trade detail view, so you can see the contribution of each tranche.
R-Distribution Chart
Individual R-multiples tell you about specific trades. The R-distribution chart tells you about your trading as a whole.
In SwingFolio's analytics section, the R-distribution chart shows a histogram of your trade outcomes measured in R. You can see at a glance:
- How many trades clustered around -1R (stopped out at planned risk)
- How many trades fell between -1R and 0R (cut early for a smaller loss)
- How many exceeded -1R (let losses run beyond the stop -- a red flag)
- The spread of winning trades across +1R, +2R, +3R and beyond
A healthy R-distribution for a swing trader typically shows a concentration of losses around -0.5R to -1R (disciplined stops) with winning trades spread across +1R to +3R, and occasional outliers at +4R or higher.
If your distribution shows frequent results worse than -1R, you have a stop-loss discipline problem. If your wins cluster at +0.5R to +1R while rarely exceeding +2R, you might be taking profits too early.
Strategy-Level R Averages
Because every trade in SwingFolio is linked to a strategy, you can see R-multiple statistics broken down by strategy.
This answers questions like:
- Which strategy produces the highest average R?
- Which strategy has the tightest loss distribution (most consistent risk management)?
- Does my breakout strategy deliver bigger R-multiples than my mean-reversion strategy?
Navigate to any strategy's detail page to see its average R, median R, and R-distribution specific to trades executed under that strategy.
Using R-Data to Improve
Here are practical ways to use your R-multiple data.
Set realistic targets. If your historical data shows an average win of 1.8R across 200 trades, a strategy that requires consistent 4R wins to be profitable is probably unrealistic for your trading style.
Identify your edge. Calculate your expectancy in R terms: (Win Rate x Average Win in R) - (Loss Rate x Average Loss in R). A positive number means your system has a statistical edge. Track this over time.
Spot discipline problems. Trades worse than -1.5R almost always indicate a failure to execute the stop. If this happens more than 5% of the time, it is eating into your edge.
Compare strategies objectively. Two strategies might have similar win rates but very different R-profiles. A strategy with 40% wins averaging 3R and 60% losses averaging -0.8R is far better than one with 50% wins averaging 1.2R and 50% losses averaging -1R.
Track improvement over time. Filter your R-distribution by date range. If your average R is improving quarter over quarter, your trading skill is genuinely developing -- not just riding a favourable market.