R-Multiples Explained: Measuring Trade Performance

R-multiples normalize your trade results by expressing profit or loss as a multiple of your initial risk. This makes it possible to compare trades across different stocks, position sizes, and time periods on equal footing.

What is an R-Multiple?

R stands for the initial risk on a trade. An R-multiple expresses your result as a multiple of that initial risk.

Formula: R-Multiple = Profit or Loss / Initial Risk

Simple Example

Trade Setup:

• Entry: $50
• Stop Loss: $48
• Initial Risk (1R): $2 per share

If you exit at $54:

• Profit: $4 per share
• R-Multiple: $4 / $2 = 2R

If you hit your stop at $48:

• Loss: $2 per share
• R-Multiple: -$2 / $2 = -1R

Why R-Multiples Matter

Normalizes Different Trade Sizes

Without R-multiples:

• Trade A: Made $500
• Trade B: Made $300

You cannot tell which trade performed better without knowing the risk.

With R-multiples:

• Trade A: Made 1R ($500 on $500 risk)
• Trade B: Made 3R ($300 on $100 risk)

Trade B performed three times better despite smaller dollar profit.

Enables Objective Comparison

R-multiples let you compare:

• Different stocks at different prices
• Different position sizes
• Different time periods

Reveals Strategy Performance

Your R-multiple distribution shows:

• Average winning trade in R
• Average losing trade in R
• Win rate at different R levels
• Overall expectancy

Calculating R-Multiples

Step 1: Define Initial Risk (1R)

Your initial risk is: 1R = Entry Price - Stop Loss Price (per share)

Example: Entry: $75 Stop: $72 1R = $3 per share

Step 2: Calculate Result in R

Once the trade closes: R-Multiple = (Exit Price - Entry Price) / 1R

Winners: Exit at $81: ($81 - $75) / $3 = 2R Exit at $87: ($87 - $75) / $3 = 4R

Losers: Stopped at $72: ($72 - $75) / $3 = -1R Exit early at $73.50: ($73.50 - $75) / $3 = -0.5R

Step 3: Record in Your Journal

Every trade should include:

• Entry price
• Stop loss (defines 1R)
• Exit price
• R-Multiple result

R-Multiple Distribution

Reading Your Distribution

Track R-multiples over many trades to see patterns:

Example Distribution (100 trades):

• -1R: 45 trades (stopped out)
• -0.5R: 5 trades (early exit at loss)
• 0R: 5 trades (breakeven)
• 1R: 20 trades
• 2R: 15 trades
• 3R: 7 trades
• 4R+: 3 trades

Analyzing Your Distribution

From the example above:

• Win rate: 45% (45 winning trades)
• Average winner: 1.8R
• Average loser: -0.95R

Expectancy: (0.45 x 1.8R) + (0.55 x -0.95R) = 0.81 - 0.52 = 0.29R per trade

Positive expectancy. The system is profitable.

Using R-Multiples for Targets

Setting R-Based Targets

Use R-multiples instead of arbitrary price targets:

Minimum Target: 2R (ensures positive expectancy at 40% win rate) Standard Target: 2-3R (good balance) Extended Target: 4R+ (let winners run)

Scale-Out Strategy Using R

Exit in portions as the trade progresses:

• Exit 1/3 at 1R (lock in profit)
• Exit 1/3 at 2R (book a good gain)
• Trail stop on final 1/3 (let it run)

Average exit might be 2R with this approach.

R-Multiples and Position Sizing

The Connection

If you risk 1% of your account per trade:

• 1R loss = -1% account
• 2R win = +2% account
• 3R win = +3% account

This makes account performance trackable in R.

Account Growth in R

Example Month (20 trades):

• Total R: +12R
• Risk per trade: 1%
• Account growth: +12%

Clean and trackable.

Expectancy: The Metric That Matters Most

Defining Expectancy

Expectancy is your average R per trade over time:

Formula: Expectancy = (Win Rate x Avg Win R) - (Loss Rate x Avg Loss R)

Expectancy Examples

Positive Expectancy System:

• Win Rate: 40%
• Avg Win: 2.5R
• Avg Loss: -1R
• Expectancy: (0.4 x 2.5) - (0.6 x 1) = 1.0 - 0.6 = +0.4R

Negative Expectancy System:

• Win Rate: 50%
• Avg Win: 1R
• Avg Loss: -1.2R
• Expectancy: (0.5 x 1) - (0.5 x 1.2) = 0.5 - 0.6 = -0.1R

The second system has a higher win rate and still loses money.

Expectancy Benchmarks

Tracking R-Multiples

Per-Trade Records

For each trade:

1. Entry price
2. Initial stop loss
3. 1R value (entry - stop)
4. Exit price(s)
5. Final R-multiple
6. Notes on trade

Monthly R-Multiple Summary

Track monthly:

• Total trades
• Winners/Losers
• Total R gained/lost
• Average R per trade (expectancy)
• Largest winner (in R)
• Largest loser (should be close to -1R)

R-Tracking in SwingFolio

SwingFolio calculates for you:

• R-multiple for every trade
• Running expectancy
• R-distribution charts
• Performance trends in R

Common R-Multiple Mistakes

Mistake 1: Not Defining 1R Before Entry

Problem: Calculating R after the fact Solution: Define stop loss (and thus 1R) before entering

Mistake 2: Moving Stop and Not Adjusting R

Problem: Moving the stop changes 1R, confusing your metrics Solution: 1R is the initial risk, fixed at entry

Mistake 3: Ignoring Partial Exits

Problem: Not accounting for scaled exits Solution: Calculate weighted average R for partial exits

Mistake 4: Focusing Only on Win Rate

Problem: 60% win rate means nothing without R data Solution: Consider both win rate and R-multiple together

R-Multiple Quick Reference

Putting It Together

R-multiples give you a single unit of measurement across all your trades. Define 1R before you enter. Track your R-distribution over time. Focus on expectancy, the average R per trade, because that number tells you whether your system makes money. Target at least 2R on winners to stay profitable at typical win rates.

Track Your R-Multiples

SwingFolio calculates R-multiples for every trade and surfaces your expectancy trends over time. Start tracking and measure your trading in the units that count.