R-Multiple Tracker: Measure Trading Expectancy, SQN and Edge
An R-multiple expresses each trade's result as a multiple of the risk taken: R-multiple = profit in pips / initial stop distance in pips. This free tracker logs your trades, computes mean R, expectancy, win rate, SQN, profit factor, max drawdown in R, and runs 1,000-iteration Monte Carlo and what-if analysis to reveal your true edge.
- 1R is your initial risk: the distance from entry to stop loss, measured in pips. The tool computes R-multiple as profit pips divided by that stop distance, so a trade that makes twice its risk is +2R.
- Expectancy is the headline metric: expectancy = (win% x avg win R) minus (loss% x avg loss R). A positive number means each trade earns that many R on average over a large sample.
- The tool classifies a trade as a win when R is above +0.1, a loss when below -0.1, and breakeven between -0.1 and +0.1. Big winners are 2R or more; big losers are -0.9R or worse.
- System Quality Number (SQN) is (mean R / std dev of R) x sqrt(number of trades, capped at 100). Bands: 2.0-2.5 Average, 3.0+ Excellent, 5.1+ Superb, 7.0+ Holy Grail.
- Dollar P&L uses a simplified $10 per pip per standard lot for USD-quoted pairs, so figures are approximate position-size estimates, not broker-exact amounts.
Add Trade
Monthly R Goal
Data Management
Performance Summary
R-Equity Curve
R-Multiple Distribution
Detailed Statistics
Strategy Comparison
Advanced Analysis
Trade History
What is an R-multiple in trading?
An R-multiple normalises every trade to the risk you took on it. 1R is your initial risk: the distance from your entry price to your stop loss. If a trade gains twice that distance it is +2R; if it hits the stop it is -1R. Because R is a ratio, a +1R win on a tiny scalp and a +1R win on a swing trade count equally, letting you compare results across pairs, timeframes, and account sizes.
This tracker measures the stop distance and the result in pips, then divides one by the other:
1R (stop distance) = |entry - stop loss| / pip sizeprofit pips = (exit - entry) x direction / pip sizeR-multiple = profit pips / stop distance pips
For EUR/USD the pip size is 0.0001; for JPY pairs it is 0.01. Direction is +1 for long and -1 for short, so a short that falls in your favour produces positive pips. A trade is only valid if the stop distance is non-zero.
How do you use the R-Multiple Tracker?
- Add a trade. Enter the date, pick a pair, choose Long or Short, then type your entry price, stop loss, exit price, and lot size (defaults to 0.10). Optionally tag a strategy (Breakout, Pullback, Trend Follow, and others) and an exit reason.
- Click Add Trade. The tool instantly computes the R-multiple and updates every panel: performance summary, R-equity curve, distribution histogram, detailed statistics, and strategy comparison.
- Import in bulk. Use Import CSV to load history from your broker or journal. Headers are auto-detected from common aliases (entry/open, sl/stop_loss, exit/close, etc.); rows missing entry, stop, or exit are skipped.
- Load Demo (30 trades) to explore the analytics instantly, or Export CSV to back up your data, which is saved locally in your browser.
A strategy filter recalculates every statistic for a single setup, and a Monthly R Goal bar tracks cumulative R for the current calendar month against a target you set (default 5R).
How does the tool calculate expectancy, SQN and the statistics?
Once trades are logged, the statistics engine derives your edge from the list of R-multiples:
| Metric | How it is calculated |
|---|---|
| Mean R / Trade | Simple average of all R-multiples (rounded to 3 decimals). |
| Expectancy | (win% x avg win R) - (loss% x avg loss R), where avg loss R is a positive magnitude. |
| Win / Loss / Breakeven % | Win = R > +0.1, Loss = R < -0.1, Breakeven = -0.1 to +0.1. |
| Profit Factor | Sum of winning R divided by sum of losing R (shown as the infinity symbol if there are no losers). |
| SQN | (mean R / std dev R) x sqrt(min(n, 100)). Sample size is capped at 100 trades. |
| Max Drawdown (R) | Largest peak-to-trough drop in the cumulative R-equity curve. |
| Skewness | Third standardised moment; positive means a right tail of large winners. |
Standard deviation uses the population formula (dividing by n, not n-1). SQN bands label your system: below 1.6 Poor, 1.6-2.0 Below Average, 2.0-2.5 Average, 2.5-3.0 Good, 3.0-5.1 Excellent, 5.1-7.0 Superb, and 7.0+ Holy Grail.
What do the Monte Carlo, what-if and edge-decay tools show?
The Advanced Analysis panel stress-tests your results beyond the single historical sequence:
- Monte Carlo runs 1,000 simulations, randomly re-drawing your actual R-multiples with replacement (a bootstrap of your trade history) and rebuilding the equity curve each time. It reports the percentage of profitable runs, the median outcome, average and worst max drawdown, and the 10th, 25th, 50th, 75th, and 90th percentile final-R outcomes. It needs at least 5 trades.
- What If removes your worst 5% and worst 10% of trades (at least one each) and recomputes expectancy and total R, quantifying how much your large losers cost you. It needs at least 10 trades.
- Edge Decay compares your most recent window against the prior window of equal size (50 trades each, falling back to 25). It flags decay when expectancy drops by more than 0.1R and more than 20%, or improvement when it rises by the same margins.
These outputs separate genuine edge from a lucky ordering of trades, which is where most retail journals mislead traders.
Worked example: a +2R EUR/USD trade
Suppose you log a EUR/USD long: entry 1.0850, stop loss 1.0800, exit 1.0950, 0.10 lots. EUR/USD has a pip size of 0.0001.
- Stop distance (1R):
|1.0850 - 1.0800| / 0.0001 = 0.0050 / 0.0001 = 50.0 pips - Profit in pips:
(1.0950 - 1.0850) x 1 / 0.0001 = 0.0100 / 0.0001 = 100.0 pips - R-multiple:
100 / 50 = +2.00R
Because R is 2.00, the tool classifies this as a big winner (2R or more). Using the simplified $10 per pip per standard lot for USD-quoted pairs at 0.10 lots: 1R risk = 50 x 10 x 0.10 = $50.00 and profit = 100 x 10 x 0.10 = $100.00. The trade shows as +2.00R in the history table and adds 2R to your equity curve.
Why R-multiples beat tracking dollars
Dollar-based tracking blends two separate skills into one number: how you select trades and how you size positions. A trader making $500 per trade on a $100,000 account and one making $50 per trade on a $10,000 account have identical skill if their R-multiples match — both are risking the same 0.5% and earning the same R. Tracking raw dollars hides that, and it makes it impossible to compare two strategies that were run at different risk levels.
R-multiples strip out the capital question entirely. Because every result is expressed as a multiple of the risk taken, a +2R win on a micro account counts exactly the same as a +2R win on a large account. That lets you measure decision quality on its own, compare your results across periods even if you changed your risk percentage, and benchmark against other traders fairly.
Three ways to improve your expectancy
Expectancy in this tool is (win% x avg win R) - (loss% x avg loss R). There are only three levers that move it, and each maps to a number on your detailed-statistics panel:
- Raise your win rate by being more selective about which setups you take. Fewer, higher-quality entries lift the
win%term. - Increase your average winner (avg win R) by letting profits run, using trailing stops, or sizing targets at 2R or more. Trades of 2R or above are flagged as big winners by the tool.
- Shrink your average loser (avg loss R) by cutting trades quickly once the thesis is invalidated, rather than letting a planned -1R drift further.
Most traders fixate on win rate, but improving the average winner through better exit management often moves expectancy more, because it widens the gap between your average win and average loss.
A positive system does not need a high win rate
A common misconception is that profitable trading requires winning most of the time. It does not. Consider a system that wins only 35% of trades but averages +3R on winners and -1R on losers. Its expectancy is (0.35 x 3) - (0.65 x 1) = 1.05 - 0.65 = +0.40R per trade — a genuinely profitable, well-structured edge.
This is why the R-multiple distribution matters more than the win percentage alone. A handful of large winners can more than offset many small, controlled losses. Trend-following and breakout systems often look like this: a low hit rate paired with a long right tail of big winners, which shows up as positive skewness in your statistics. Negative skewness — a long tail of large losers — is the dangerous shape, because a few outsized losses can erase many small wins.
R-multiples vs win rate vs profit factor
Several metrics try to summarise a trading edge, but they are not equally useful for comparison:
| Metric | What it tells you | Limitation |
|---|---|---|
| Win rate | How often you win | Meaningless alone — a 90% win rate still loses money if losers are far larger than winners. |
| Profit factor | Gross winning R divided by gross losing R | Better, but a single ratio hides whether the result came from many small edges or one lucky outlier. |
| Expectancy (R) | Average R earned per trade | The most comparable, because it is independent of position size and combines win rate with the size of wins and losses. |
In this tracker, the profit factor is the sum of winning R over the sum of losing R (shown as the infinity symbol when there are no losers). Expectancy is the headline figure precisely because it stays valid across pairs, timeframes, and account sizes, letting you line up any strategy against any other.
Position sizing once you know your expectancy
R-multiples deliberately separate the "what to trade" decision from the "how much to risk" decision. Once you have a stable, positive expectancy from a meaningful sample, position sizing becomes a straightforward second step.
If you fix your risk so that 1R equals 1% of your account and your measured expectancy is +0.35R, then on average you expect to grow the account by about 0.35% per trade. As a simplified illustration, compounding roughly 0.35% over 100 trades (1.0035 to the power of 100) is about a 42% gain — before accounting for drawdowns, variance, and the fact that real results arrive in clusters rather than evenly. Treat it as a rough order of magnitude, not a forecast: the Monte Carlo panel exists precisely to show how widely the actual path can swing around that average.
Common R-multiple mistakes to avoid
- Moving the stop after entry. Your 1R is the initial stop distance. If you widen the stop after entering, your real risk is larger than recorded and every R-multiple downstream is distorted.
- Not recording the initial stop. The planned stop at entry defines 1R, even if you later trail it. Logging only the final exit stop breaks the calculation.
- Treating breakeven trades as wins or losses. This tool classifies any result between -0.1R and +0.1R as breakeven noise, not a win or a loss — count it that way in your own thinking too.
- Drawing conclusions from too few trades. A strategy is labelled Unproven under 15 trades, Moderate at 15-29, and Reliable only at 30 or more. Wait for the sample before trusting an expectancy figure.
- Cherry-picking which trades to log. Record every trade, including impulsive ones. The what-if panel already isolates your worst trades so you can see their cost — omitting them just hides the damage.
Frequently Asked Questions
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Any positive expectancy means a profitable edge over a large sample. In this tool, expectancy is (win% x avg win R) minus (loss% x avg loss R). Values around +0.2R to +0.5R per trade are solid for retail forex; +1R or higher is exceptional. Because expectancy is per unit of risk, it stays valid regardless of account or position size.
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1R is your initial risk: the distance in pips from your entry price to your stop loss, computed as the absolute difference divided by the pair's pip size (0.0001 for most pairs, 0.01 for JPY pairs). Every result is then divided by this distance, so hitting your stop is exactly -1R and a target at twice your risk is +2R.
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System Quality Number measures edge relative to consistency. The tool computes SQN as (mean R / standard deviation of R) multiplied by the square root of your trade count, capped at 100 trades. Bands are: under 1.6 Poor, 2.0-2.5 Average, 3.0 and above Excellent, 5.1 Superb, and 7.0 Holy Grail. Higher SQN means a steadier, more scalable edge.
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It runs 1,000 simulations, each re-drawing your actual logged R-multiples at random with replacement (a bootstrap resample of the same length as your history), then rebuilding the equity curve. It reports the share of profitable runs, the median final R, average and worst drawdown, and the 10th through 90th percentile outcomes. At least 5 trades are required to run it.
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More is better. The tool labels a strategy Unproven under 15 trades, Moderate at 15-29, and Reliable at 30 or more. What-if analysis needs 10 trades, Monte Carlo needs 5, and edge-decay detection compares two windows of 50 trades (falling back to 25). Aim for at least 30-50 trades before trusting expectancy and SQN.
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No. The tool uses a simplified $10 per pip per standard lot for USD-quoted pairs (and an equivalent approximation for JPY pairs) to estimate the dollar value of 1R and profit. These figures are approximate and do not reflect your broker's exact pip value, spread, swap, or commissions. The R-multiples themselves are precise; treat the dollar columns as estimates.