A 30% win rate can beat a 60% win rate, and the arithmetic is not close. Win rate on its own settles nothing. The ten figures below each answer a different question about the same set of closed trades. Together they separate results that came from an edge from results a rising market handed you.
Why trading metrics matter
Without them you are reading an account balance and guessing at the cause. A green month looks identical whether your entries were good or whether everything you touched happened to go up.
Metrics split the balance into parts you can act on. One number says how often you are right. Another says what being right is worth. A third combines the two into what the system returns per trade. When your results change, the parts show you which one moved.
Essential trading metrics
1. Win rate
The share of your closed trades that finished profitable.
Winning trades / total closed trades x 100. Forty five winners out of 100 closed trades is a 45% win rate.
Win rate tells you how often you are right. It says nothing about the size of being right. Take two systems where every loser costs exactly 1R, the amount you risked going in (section 5 defines R). A 30% win rate that averages 4R per winner returns 0.3 x 4 - 0.7 x 1 = +0.5R per trade. A 60% win rate that averages 0.5R per winner returns 0.6 x 0.5 - 0.4 x 1 = -0.1R per trade. The higher win rate is the system that loses money.
So win rate is read beside average win and average loss, never alone.
2. Average win and average loss
The mean size of your winners and the mean size of your losers.
Average win = total profit from winners / number of winners. Average loss = total loss from losers / number of losers. If your winners total $24,000 across 30 trades and your losers total $16,000 across 40 trades, that is an $800 average win against a $400 average loss, a ratio of 2 to 1.
The pair tells you what your exits achieve, as opposed to what you planned at entry. It is also what sets your breakeven win rate. Break even means winners and losers cancel:
win rate x average win = loss rate x average loss
At a 2 to 1 ratio that solves to a 33.3% win rate. Below it you lose money, above it you make money, and the further above, the more per trade. No single ratio is correct for every system. The ratio you need depends on the win rate you achieve.
3. Profit factor
Gross profit divided by gross loss, across every closed trade in the period.
$40,000 of gross profit against $20,000 of gross loss is a profit factor of 2.0. You made two dollars for every dollar you lost.
Profit factor below 1.0 means your losses outweighed your gains over the period. At exactly 1.0 they cancel. Above 1.0 you are ahead. What counts as a good reading above 1.0 is a judgment, not a fact. It depends on how many trades produced the figure, and on how much of the gross profit came from one or two outliers.
The figure is a ratio, so it does not tell you the size of your edge in money. That is expectancy.
4. Expectancy
What one trade returns on average.
(win rate x average win) - (loss rate x average loss)
Take a 45% win rate, an $800 average win and a $400 average loss. That is (0.45 x $800) - (0.55 x $400) = $360 - $220 = $140 per trade.
Positive expectancy is what makes a system worth trading. Negative expectancy means more trades cost you more money, however good any single trade felt.
One detail worth getting right: a break-even trade is neither a win nor a loss, but it still counts in the trade total. So loss rate is not always 1 minus win rate. Swingfolio computes it that way, keeping break-evens in the denominator and out of both averages, which is why an account with several scratched trades shows a lower expectancy than the naive formula suggests.
5. R-multiple
Result of a trade expressed as a multiple of what you risked on it.
Entry at $50 with a stop at $47 puts 1R at $3 a share. Exit at $56 is a $6 gain, so $6 / $3 = 2R. The same trade stopped out is -1R.
R normalises trades of different sizes onto one scale. A $2,000 win on a $4,000 risk and a $200 win on a $400 risk are both 0.5R, and both say the same thing about how the trade was executed.
Averaging R is where it gets slippery, because one freak winner drags the mean. Swingfolio's "Average R-Multiple" card on the Analytics page handles that by trimming the most extreme 5% at each end of the distribution and printing the median beside the trimmed mean. Below 20 trades it skips the trimming and marks the card "Low sample" with a count against that threshold, because clipping a tail of two trades removes signal rather than noise.
6. Maximum drawdown
The largest peak to trough fall in account value over the period.
(peak - trough) / peak x 100. An account that peaks at $55,000 and falls to $48,000 has lost $7,000 from the peak, so $7,000 / $55,000 = 12.7%.
Two things follow. The first is arithmetic: getting back to $55,000 from $48,000 takes a 14.6% gain, because $7,000 / $48,000 = 14.6%. The percentage you need back is larger than the percentage you lost, and the gap widens the deeper the fall goes. The second is that maximum drawdown is the worst stretch you have already lived through, so it sets a floor for what you know you can sit through without abandoning the plan.
Swingfolio's Drawdown card carries both readings in its header: "Max" for the deepest fall on record and "Current" for where you sit now, which reads "At Peak" when the account is at a new high.
7. Return on account
Percentage change in trading capital over a period.
(ending balance - starting balance) / starting balance x 100, tracked over whatever periods you review: monthly, quarterly, yearly.
Return is the figure that pays your bills. It is also the figure risk flatters. A 40% year taken with 8% of the account at risk on every position is a different result from the same 40% taken with 1% at risk. Only the drawdown column tells the two apart, so read return next to maximum drawdown.
Swingfolio's dashboard shows total value, total return as a percentage, and the split between realized and unrealized P&L. Each portfolio's detail screen adds cash, holdings value, win rate, profit factor and expectancy for that account on its own.
8. Number of trades
How many trades the period contains.
This is the sample size every other metric rests on. Ten trades cannot separate an edge from a run of luck, and no amount of decimal places in the win rate changes that.
The app uses two thresholds you can borrow. The Average R-Multiple card starts trimming outliers at 20 trades and flags anything below that as a low sample. The Behavioral Finance Score prints "Score becomes more reliable at 30+" underneath itself until you clear 30 closed trades. Neither is a law of statistics; both are places where a number stops being noise.
The count also catches drift in the other direction. If your plan describes four or five setups a week and you closed thirty trades in a month, the count found something before your P&L did.
9. Largest win and largest loss
Your single best and single worst result.
Both are outlier checks. A largest loss several times your average loss says a stop was moved, skipped, or gapped through. A largest win no bigger than your average win says your exits are uniform, which is the shape of a system that closes winners on a fixed rule rather than letting the good ones run.
They also tell you how much of your profit factor rests on one trade. Remove the largest win, recompute, and see what is left.
Swingfolio marks both extremes at the ends of the range line on its Average R-Multiple card, labelled "worst" and "best", so the outliers sit beside the average rather than hidden inside it.
10. Trade duration
How long you hold, measured from entry to exit.
The number matters because it either matches the style you intended or it does not. A plan written for multi-day swings that produces an average hold of half a day is not the plan you are trading.
Compare winners against losers separately. The "Holding Edge: Winners vs Losers" card on the Analytics page puts the two average hold times side by side, and the "Avg Hold" tile in the header carries the overall figure in days.
How to track these metrics
The spreadsheet method
One row per trade, with columns for entry date, exit date, entry price, exit price, stop, target, position size, fees, profit or loss, and R-multiple. Everything above is derivable from those columns.
The cost is the typing, and the typing is what decides whether the habit lasts. A log that takes fifteen minutes per trade competes with your next trade for attention.
Trading journal software
Software calculates the metrics from the trade record, which removes the arithmetic errors and the recalculation. Check what it does with foreign-currency trades and fees before you trust the totals, because those are where P&L figures quietly diverge from your broker statement.
What Swingfolio calculates
The Analytics page opens on a metrics strip. It carries Tracked-Trade P&L in your display currency, Win %, PF, Expect, Trades, Avg Hold and FX Impact, with the Average R-Multiple card directly below.
Underneath sit the breakdowns: Closed-Trade P&L over time, Rolling Profit Factor, R-Multiple Distribution, Drawdown, Win/Loss Streaks, Win/Loss by Holding Period, P&L by Setup Type, Win Rate by Day of Week, Sector P&L and Symbol Breakdown. Nothing streams live; the figures recalculate from your trade record as trades close.
Separately, the Reviews tab writes a period review on request, over a weekly, monthly, quarterly, six-month or yearly window. Each one carries the trade count, win rate and average R for the period, plus a summary and the period's largest winner and largest loser. Below those sit Strengths, Areas to Improve, a focus for the next period, and Action Items.
A metric review schedule
The cadence below is an example. Adapt every interval to how often you trade.
Daily, the check is short: today's P&L, trades taken, and whether you followed your rules on each one.
Weekly, calculate the week's return, win rate and total R. Compare them to the previous week rather than to a target.
Monthly, run the full set: every metric above, the month against the one before it, and the breakdowns by setup, day and sector.
Quarterly, the question is different. Not "how did I do" but "is this strategy still the one I want to be trading", with enough trades behind you by then to answer it.
Common metric mistakes
Tracking win rate alone. It is the easiest metric to compute and the least informative on its own. Pair it with your average win and average loss or it tells you nothing about money.
Concluding from ten trades. Small samples produce confident-looking percentages that reverse on the next five trades. The metric is not wrong; the sample is too small to read.
Ignoring drawdown. Return without drawdown describes the outcome and hides the risk that produced it.
Reviewing only when curious. Metrics checked after a good week and skipped after a bad one produce a record of your mood, not your trading.
Start tracking
Every figure above comes out of the same trade record: entry, exit, size, stop, fees. Log those consistently and the metrics compute themselves.
Start the 30-day trial and see the ten figures calculated from your own closed trades.