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Equity Curve Math: What Return Does a Series of R Results Produce?

Scenario: a trader risks 1 percent per trade and records ten trades in R: plus 2, minus 1, minus 1, plus 3, minus 1, plus 1, minus 1, plus 2, minus 1, minus 1. What is the net result?

Work it out. Sum in R: 2 minus 1 minus 1 plus 3 minus 1 plus 1 minus 1 plus 2 minus 1 minus 1. Step by step: 2, 1, 0, 3, 2, 3, 2, 4, 3, 2. The total is plus 2R.

At 1 percent risk per R, the simple return is about 2 percent. With compounding on a 10,000-dollar account, the exact figure is slightly different because each risk amount is based on the equity at that moment, but for small values the sum is a good approximation.

The statistics: the wins are plus 2, plus 3, plus 1, and plus 2, which is four wins out of ten, a 40 percent win rate. Average win: (2 plus 3 plus 1 plus 2) divided by 4, which is 2R. Average loss: 1R. Expectancy: 0.4 times 2 minus 0.6 times 1, which is plus 0.2R per trade, matching 2R over ten trades.

Largest drawdown in R: the lowest point relative to the previous peak. The running total went 2, 1, 0, 3, 2, 3, 2, 4, 3, 2. From the peak of 4, it fell to 2, a drawdown of 2R. And from the first peak of 2 it fell to 0, also 2R.

Takeaway: R lets you compute everything from a single column of numbers.

Practical step: list your last ten trades in R and compute the total, win rate, expectancy, and maximum drawdown in R.

Clear example. The compounding effect is small over ten trades but grows with more trades. Tracking results in R keeps things simple, and converting to percent only at the end avoids confusion.

The step by step running total is a good habit. It shows not just the final result but the path. Here the account went to plus 2, down to zero, up to plus 4, and back to plus 2. A trader experiencing this would have felt good after trade one, worried after trade three, confident after trade eight, and frustrated at the end. The net result of plus 2R over ten trades is solid, an expectancy of 0.2R, but the path included two drawdowns of 2R each. Recording the running total makes these swings visible and helps set realistic expectations.

I listed my last ten trades in R: plus 1.5, minus 1, minus 1, plus 0.8, minus 1, minus 1, plus 2.2, minus 1, plus 1.1 and minus 0.4. The running total goes 1.5, 0.5, minus 0.5, 0.3, minus 0.7, minus 1.7, 0.5, minus 0.5, 0.6 and 0.2. So the net result is plus 0.2R, with four wins out of ten, a 40 percent win rate.

The average win is 1.4R and the average loss is 0.9R, because my one small loss at minus 0.4R was a trade I closed early on a news headline. Expectancy comes out at 0.02R per trade, basically zero. What caught my attention was the drawdown. The peak was 1.5R after the first trade and the low was minus 1.7R, so the maximum drawdown was 3.2R, sixteen times the total profit.

A ten trade stretch can look fine in profit terms and still hide a drawdown that would end a funded account with a tight limit.

The compounding note is worth a quick illustration. On a 10,000 dollar account at 1 percent risk, trade one risks 100 dollars and wins 200, taking the account to 10,200. Trade two risks 102 and loses, taking it to 10,098. Continuing through all ten trades with compounding gives a final balance of about 10,190, a return of about 1.9 percent, slightly below the simple 2 percent. The difference comes from the order of wins and losses. For small numbers of trades and low risk, the simple sum is a good approximation, as the post says.

The expectancy calculation matches the total, as it should. Four wins averaging 2R and six losses of 1R give 0.4 times 2 minus 0.6 times 1, which is plus 0.2R per trade. Over ten trades, that's 2R. Checking that the expectancy times the number of trades equals the total is a useful sanity check when building a journal spreadsheet. If the two don't match, there's an error somewhere, often a trade recorded in pips instead of R, or a loss recorded as a positive number. The consistency check catches these quickly.

Computing the total, win rate, expectancy, and maximum drawdown from the last ten trades, as the practical step suggests, takes only a few minutes with a spreadsheet. Ten trades is too few for reliable conclusions, but it's enough to set up the calculations and make them a habit. Has anyone here started tracking their results in R rather than dollars or pips? I'd be curious whether it changed how you viewed your performance, since R makes results comparable across different position sizes and account balances. It also makes it easier to share results without revealing account size.