It is easy to review only the trades you remember, which is the ones that hurt and the ones that felt clever, a sample chosen by emotion rather than by anything in the data. Analysing trading performance means running the same questions over every trade, in the same order, whether or not you enjoyed it.
What skipping the analysis costs
You keep repeating whatever produced the last result, because nothing told you which part produced it. You miss patterns that only show up in aggregate: a setup that loses, a weekday that costs you, an exit rule that leaves money behind. And you improve by guessing, so some of your changes make things worse and nothing tells you which ones.
Done properly, analysis gives you the opposite: a ranked list of what works, a named recurring problem, and one change to make next.
A framework for analysing trading performance
Step 1: gather your data
You need a complete record before any of this means anything. Entry and exit prices, dates, position sizes, stops and targets, the actual result, which strategy or setup the trade belonged to, and whatever you noted at the time about your state of mind.
Complete matters more than detailed. Twenty trades logged in full beat sixty with the losses missing.
On sample size, two thresholds inside Swingfolio are worth borrowing. Its Average R-Multiple card refuses to trim outliers below 20 trades and marks the reading "Low sample" instead. Its Behavioral Finance Score prints "Score becomes more reliable at 30+" until you clear 30 closed trades. Neither is a statistical law. Both mark the point where a number stops being noise.
Step 2: calculate core metrics
For the period you are reviewing: win rate, average win and average loss, profit factor, expectancy, maximum drawdown, and total return.
Those six answer the question "did this work". Everything after this step answers "which part of it worked".
Step 3: segment your data
Break the same trades down by strategy or setup, by time period, by market conditions, and by direction. Then break them again by the characteristics of the stock itself: large cap against small cap, high volume against low volume, sector, and price range.
Segmentation is where the useful findings live. An overall win rate of 48% can hide one setup at 60% and another at 30%, and the aggregate tells you to change nothing while the split tells you exactly what to change.
Step 4: identify patterns
Read each breakdown for three things: which segments make money, which lose it, and which have too few trades to say either way.
That third category is where bad conclusions come from. A segment with eight trades in it is a hypothesis, not a finding.
Step 5: turn findings into action items
An observation you cannot act on is not a finding. Each one becomes a specific change: a rule added, a rule removed, a segment you stop trading, or a number in your plan that moves.
Types of analysis
Segmented analysis
The tables below use invented figures to show the shape of the output. Every expectancy figure assumes each winner returns the stated average R and each loser costs 1R.
By strategy:
| Strategy | Trades | Win rate | Avg winning R | Expectancy |
|---|---|---|---|---|
| Pullback to MA | 25 | 52% | 1.8R | +0.46R |
| Breakout | 15 | 40% | 2.1R | +0.24R |
| Reversal | 10 | 30% | 1.2R | -0.34R |
The pullback row computes as 0.52 x 1.8 - 0.48 x 1.0 = +0.46R. The reversal row computes as 0.30 x 1.2 - 0.70 x 1.0 = -0.34R.
What that says: pullbacks carry the account and reversals cost it. What it does not yet say is that reversals are broken.
Run the binomial. A system that truly wins 45% of the time still produces three or fewer winners in ten trades about 27% of the time. The reversal row earns attention and more data, not a decision.
By day of week, holding the same assumption and setting every winner at 2R:
| Day | Trades | Win rate | Expectancy |
|---|---|---|---|
| Monday | 12 | 33% | -0.01R |
| Tuesday | 15 | 53% | +0.59R |
| Wednesday | 18 | 50% | +0.50R |
| Thursday | 14 | 50% | +0.50R |
| Friday | 8 | 38% | +0.14R |
Monday sits on the line because 33.3% is exactly break-even when winners pay 2R and losers cost 1R: 0.333 x 2 = 0.667, and 0.667 x 1 = 0.667. Friday is thinner still at eight trades.
Neither column is enough to drop a trading day. What they are enough for is a question: what is different about how you trade on a Monday.
Analysis over time
Two questions the segments cannot answer.
Is the trend moving? Compare this month against the last three, on win rate, expectancy and average R. A single month is weather; three in a row is a direction.
Did a change help? When you add a rule, mark the date and compare the trades on either side of it. Without the marker you end up attributing an improvement to the change and a decline to the market.
Behavioural analysis
Two threads run through your own conduct rather than the market's.
Rule adherence: what share of trades followed every rule, and how the broken-rule trades performed against the compliant ones. Swingfolio's Behavioral tab carries both in a Behavioral Finance Score built from five components, one of which is Rule Compliance, with a "Profit Factor by Compliance" chart and a "Rule Break Trend" chart beside it.
Emotional state: which states you logged at entry and exit, and how the results differ between them. The same tab holds "Emotion Performance Chart", an "Emotion x Outcome Heatmap" and "Emotion Trend Over Time".
All of it stays empty until you log emotions or rule compliance, and two screens collect those. The trade form carries a rule adherence checklist once a strategy is attached, plus a Pre-Trade Emotion field. The close dialog asks for Exit Emotion. A third screen, the Post-Trade Reflection form on a closed trade's detail page, takes the written write-up; that one shows on the trade's own page rather than on the Behavioral tab.
Problem identification
Three passes, each over a specific slice.
Your largest losses. Take the ten biggest, and for each: what caused it, were the rules followed, was there an earlier point where the trade was already wrong. Common causes across ten losses are worth more than any single post-mortem.
Your early exits. For trades you closed before the move finished, how much was left behind and why you got out.
Swingfolio's Exit Capture chart measures this per trade. It plots the percentage of the peak favourable move you captured at exit, where 100% means you sold the top. A trade that exited below entry gets a downward bar instead, showing how much of the worst drawdown you absorbed before getting out.
Your failed setups. Were they setups your rules allow, or ones you talked yourself into. That distinction separates a strategy problem from a discipline problem, and the fixes are different.
Questions to ask
On performance: is expectancy positive, is win rate moving, is the reward you achieve matching the reward you planned, is drawdown inside what you decided you would accept.
On patterns: which segments make money, which lose it, which times of week work, which market conditions suit what you do.
On behaviour: are you following your rules, how often you break them, what happens just before you do, and whether your trade count matches your plan.
On improvement: name the biggest current weakness, the single change that would move it furthest, and the strength you are underusing.
The weekly analysis process
The timings below are an example. Adapt them to your trade count.
Read every trade from the week: entry quality, exit quality, whether the plan was followed, what it taught you.
Calculate the week: win rate, total R, best and worst trade, and how many trades followed all your rules.
Compare to the weeks before it. One week's numbers mean little; the direction across four means something.
Pick one lesson. Write it down where you will see it before the next entry.
Set one focus for next week, specific enough that you can tell at Friday's close whether you did it.
The monthly analysis process
A longer session, because the sample supports more.
Run the complete statistics: win rate, expectancy, profit factor, maximum drawdown, total return.
Run every segment: strategy, day of week, market condition, stock type.
Compare to the previous three months and read the direction rather than the level.
Take your biggest single problem and go deep on it: the trades where it shows, the cause, and a specific change that would have prevented them.
Assess the strategy itself. Is it still the strategy you want to be trading, and does the evidence support the answer.
Set the month's goals as numbers you can check: a rule-adherence figure, a maximum drawdown you will not pass, a trade count.
Turning analysis into improvement
The loop is short. Analyse, name one problem, write a specific fix, run it for a few weeks, analyse again.
Change one thing at a time. Five changes at once produce a result you cannot attribute, which puts you back to guessing.
And make each change concrete enough to grade. "Be more patient" cannot be checked at the end of the week. "Wait for RSI below 40 before entering a pullback" can. "Close losers faster" cannot. "Place the stop order within five minutes of the fill" can.
Common analysis mistakes
Concluding from ten trades. Small samples produce confident percentages that reverse on the next five trades.
Analysing a chosen subset. Recent trades, or winners, or the ones you remember. The sample has to be every trade in the period.
Analysis with no action. A review that changes no behaviour is a reading exercise. One action item per session is the minimum that makes it worth the time.
Five changes after one review. Whatever happens next, you will not know which change caused it.
Start analysing
Pick your last thirty closed trades, split them by strategy, and calculate expectancy for each. That one table will tell you more than the P&L line did.
Start the 30-day trial and the breakdowns are computed from your trade record.