How to Actually Read Your Trading Data: A Weekly Review Guide

You signed up, you've been logging trades, and now you're staring at a dashboard full of numbers. Win rate: 48%. Profit factor: 1.09. Average winner: $124. Average loser: $138. Net P&L: +$340 over 60 trades.

Is that good? Which number do you fix first? What does any of it actually mean in practice?

This is the guide the rest of the blog doesn't cover: not why to journal, not how to set one up - but what to actually do with the data once you have it. It maps directly to the numbers in your dashboard, walks through how to interpret each one, and ends with a repeatable weekly review process you can run in 20 minutes.

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Part 1: Understanding Your Core Metrics

Profit Factor

What it is: Total gross profit ÷ total gross loss. The single most useful number on your dashboard.

What it tells you: Whether your winners are outweighing your losers in dollar terms - independent of how often you win.

How to read it:

Profit factor	What it means for you
Below 0.8	Significantly losing - strategy or execution needs a real look
0.8–1.0	Slightly losing - small changes could flip this
1.0–1.3	Marginally profitable - transaction costs eat into this fast
1.3–1.8	Solid - consistent profitability, room to scale
Above 2.0	Strong edge, or sample is too small to trust yet

Example 1 - The misleading comfort zone: Daniel had a profit factor of 1.11 over 80 trades and felt fine about it. When he looked more closely, that 1.11 was an average of two very different setups: his opening-range breakouts had a profit factor of 1.74, and his afternoon momentum plays had a profit factor of 0.68. The two were averaging each other out. He wasn't marginally profitable - he had one strong strategy and one losing one running simultaneously.

Example 2 - The sample-size trap: Yuki had a profit factor of 2.4 after 22 trades and was ready to scale up. That number isn't meaningful at 22 trades - the confidence interval is enormous. She logged another 80 trades. Profit factor settled at 1.38. Still a good edge - but very different from 2.4, and very good she hadn't doubled her position size.

The rule: Don't draw conclusions from profit factor until you have at least 80–100 trades in the sample. Before that, treat it as directional information, not a verdict.

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Win Rate

What it is: Winning trades ÷ total trades.

What it tells you: How often you're right. On its own, almost nothing useful.

The math that matters:

Win rate	Minimum avg-win:avg-loss ratio to break even
30%	2.33:1
40%	1.50:1
50%	1.00:1
60%	0.67:1
70%	0.43:1

If your win rate is 45% and your average winner is $180 against an average loser of $140, you have a positive edge (1.29:1 reward-to-risk where break-even is 1.22:1). If your win rate is 55% but your average winner is $90 against an average loser of $160, you're losing money.

Example 3 - Win rate without context: Marcus tracked his win rate obsessively - it hovered around 52–56% each month. He felt like a winning trader. His profit factor was 0.87. His average loss ($204) was significantly larger than his average win ($142). He was winning more than half his trades and losing money overall because he was letting losers run past his stop level on about a third of his losing trades. Win rate looked fine. Profit factor told the real story.

Example 4 - Low win rate, strong edge: Priya's options strategy won on 31% of her trades. New traders looking at that number would assume she was failing. Her average winner was +$680 and her average loser was -$145 - a 4.7:1 reward-to-risk ratio. Her profit factor was 1.49. A "terrible" win rate paired with disciplined exits produced a better edge than most traders with 60%+ win rates.

What to look for: Win rate is most useful when you compare it across conditions. Your overall win rate is almost always an average of several different rates that each tell you something specific.

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Average Winner vs. Average Loser

What it is: Mean P&L of your profitable trades versus mean P&L of your unprofitable trades (in dollar or percentage terms).

What it tells you: Whether you're letting winners run and cutting losers fast, or doing the opposite.

Example 5 - The pattern most traders don't see: James tracked his trades for three months. Average winner: $88. Average loser: $171. He was cutting winners almost the moment they moved in his direction and holding losers well past his stop hoping they'd come back. His win rate was 58% - which felt good - but his reward-to-risk was 0.51. He needed a win rate of 66% just to break even.

When he looked at his individual trade notes, the pattern was obvious in hindsight: nearly every losing trade had a note that said something like "almost got back to entry" or "was close." He was making an active decision to hold, not a passive one.

Example 6 - What improvement looks like: After identifying the pattern, James committed to one rule: exit at the planned stop, no exceptions, for 60 trades. His average loser dropped from $171 to $112. His win rate fell slightly (from 58% to 51%) because some trades he used to hold through now closed as small losses instead of recoveries. But his profit factor moved from 0.73 to 1.31.

Same setups. Same entries. Different exits.

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Performance by Asset Class

What it is: Your key metrics - win rate, profit factor, net P&L - broken down by the type of instrument you traded.

Why it matters: Most traders who trade multiple asset classes are profitable in some and losing in others. The composite number hides this.

Example 7 - The hidden subsidy: Elena traded stocks, ETFs, and crypto. Her overall profit factor was 1.14 - okay but not impressive. When her journal broke it down:

Asset class	Trades	Win rate	Profit factor
Stocks (large cap)	89	52%	1.58
ETFs	34	44%	1.21
Crypto	61	39%	0.71

Her crypto trading was actively losing money and nearly cancelling out her stock trading edge. She hadn't noticed because the composite number was positive. She stopped trading crypto for two months. Her overall profit factor moved to 1.43.

Example 8 - Multi-asset as a discovery tool: Ryan assumed his edge was in his entry timing - he'd studied a specific breakout pattern across several instruments. When he broke down his data by asset class, a different picture emerged:

Asset class	Trades	Profit factor
EUR/USD	41	1.62
GBP/USD	38	1.18
Gold (XAU)	29	0.84
Crude oil	22	0.61

The same setup, applied consistently, produced a strong edge in currency pairs and a losing one in commodities. He hadn't discovered a universal edge - he'd discovered an edge in specific instruments that happened to extend to some others. Knowing this changed where he directed his attention.

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Part 2: Reading Patterns Across Your Trades

Win Rate by Day of Week

Your aggregate win rate is almost never uniform across days. Most traders have meaningful performance differences between trading days - often without knowing it.

Example 9 - The Friday problem: After 90 logged trades, James's day-of-week breakdown:

Day	Trades	Win rate	Net P&L
Monday	18	56%	+$620
Tuesday	21	62%	+$840
Wednesday	17	53%	+$390
Thursday	19	47%	-$180
Friday	15	33%	-$690

Friday wasn't slightly worse - it was destroying two days of gains every week. He had no conscious awareness of this. When he went back through his Friday trade notes, the pattern was clear: low-conviction entries made as the week wound down, position sizing that didn't reflect the lower conviction, and a tendency to trade more impulsively when he felt like the week's gains needed protecting.

He stopped trading after noon on Fridays. His monthly P&L improved by roughly $800 without any change to his strategy.

Example 10 - The Tuesday edge: Aisha noticed the opposite pattern. Her Tuesday trading was consistently her best by a significant margin:

Day	Profit factor
Monday	1.04
Tuesday	1.89
Wednesday	1.22
Thursday	1.31
Friday	0.94

Tuesday was the day after any weekend news had been absorbed, but before mid-week positioning shifts. For her specific strategy (mean reversion on equity indices), that environment was consistently favorable. She started treating Tuesdays as her primary trading day and other days as secondary.

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Win Rate by Session / Time of Day

Markets behave differently during different sessions. If you're trading equity markets, the open (9:30–10:30am), midday (11am–2pm), and close (2–4pm) have distinct volatility profiles. Forex traders see similar distinctions across Asian, London, and New York sessions.

Example 11 - The midday trap: Carlos traded US equity futures full-time. He logged every trade for four months. Session breakdown:

Session	Trades	Win rate	Net P&L
9:30–10:30am (open)	47	57%	+$3,240
10:30am–12pm	38	42%	-$890
12–2pm (midday)	29	34%	-$1,620
2–4pm (close)	31	52%	+$1,100

The midday session - lower volume, choppier price action - was actively losing him money at a rate that offset most of his open-session gains. He cut midday trading entirely and shifted to a two-hour break from 11am to 1pm. His monthly net P&L nearly doubled, not because his morning trading improved but because he stopped leaking money into the afternoon.

Example 12 - Finding a hidden session edge: Fatima traded GBP/USD on the 15-minute chart. She assumed London session was her best - she'd built her strategy around it. Her data disagreed:

Session	Trades	Profit factor
Asian (00:00–08:00 GMT)	12	0.88
London open (08:00–10:00 GMT)	51	1.44
London/NY overlap (13:00–17:00 GMT)	38	1.91
NY close (17:00–21:00 GMT)	19	0.73

The London/New York overlap was her strongest period - almost 1.9x what her London open performance produced. She'd been ending her sessions before the overlap most days. Once she shifted her hours, her overall profit factor moved from 1.31 to 1.57.

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Setup Performance Breakdown

If you've been tagging your trades by setup type, this is where some of the most actionable data lives.

Example 13 - One setup carrying the account: Michael traded three setups: breakout pullbacks, opening range breakouts, and VWAP reclaims. Overall profit factor: 1.23. Setup breakdown:

Setup	Trades	Win rate	Profit factor
Breakout pullback	44	59%	1.76
Opening range breakout	37	43%	1.02
VWAP reclaim	41	38%	0.79

His VWAP reclaim trades were losing money. His opening range breakouts were barely break-even after fees. His breakout pullback setup was the only one with a real edge - and it was being diluted by 78 other trades.

He stopped taking VWAP reclaim setups and only took opening range breakouts when they perfectly met every criterion. His overall profit factor moved to 1.64. He was trading fewer trades, doing less work, and making more money.

Example 14 - Setup quality versus setup type: Naomi tracked not just the setup type but also a simple quality rating (A, B, or C) based on how cleanly the setup formed. Result over 120 trades:

Setup quality	Trades	Win rate	Profit factor
A (perfect criteria met)	31	68%	2.14
B (most criteria met)	52	49%	1.18
C (marginal, forced)	37	29%	0.61

Her C trades - the ones she took when she was bored, impatient, or trying to "make something happen" - were her biggest source of losses. She eliminated C trades entirely. Her trade frequency dropped by about 30%. Her monthly P&L improved by 40%.

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Rule Compliance Analysis

If you tag each trade with whether it fully followed your defined rules, this becomes one of the most powerful analyses available.

Example 15 - The cost of one deviation: Over 100 trades, Thomas tracked rule compliance carefully:

Trade type	Trades	Win rate	Avg P&L	Profit factor
Full rule compliance	67	61%	+$94	1.82
Minor deviation (one rule bent)	21	38%	-$47	0.71
Major deviation (setup improvised)	12	25%	-$182	0.34

His compliant trades had a genuine edge. His deviated trades were destroying nearly a third of his compliant profits. The most expensive trades were the improvised ones - often taken out of boredom or after a losing streak when he felt he needed to "make it back."

He added a simple pre-trade check: before entering, he had to write (not just think) the answer to "does this meet all my criteria?" The act of writing forced a pause that caught most of the impulsive entries.

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Part 3: The Weekly Review Process

With the metrics above understood, the weekly review becomes concrete rather than abstract. Here's the process, step by step.

When to do it

Same time every week. Friday after the close or Saturday morning work well - the week is complete, the next session hasn't started. Block 20 minutes. This is not optional time.

Step 1: Filter to this week only (2 minutes)

Pull up only your trades from the past five trading days. You're not looking at your entire history - you're reviewing one week in isolation so current patterns aren't washed out by older data.

Step 2: Read every losing trade, categorize each one (5 minutes)

Go through each loss. For each one, assign it to one of three categories:

Category A - Valid setup, market didn't cooperate. The trade fully met your criteria. It set up cleanly. It just lost. This is the normal distribution of a probabilistic edge. No action needed.

Category B - Rule deviation. You entered without meeting all your criteria, moved a stop, held past your target hoping for more, or took a trade in a session or condition you know is weak for you. This is a process failure.

Category C - Impulsive trade. No defined setup. You just entered because you felt like you should be in the market. This is the most expensive category.

The goal isn't self-criticism - it's classification. Category A losses tell you nothing actionable. Category B and C losses tell you exactly what to address.

Example 16 - One week's categorization: Sofia's week: 11 trades, 5 winners, 6 losers. Net P&L: -$340.

Going through each loser:

• Trade 3: Valid breakout setup, stopped out cleanly. Category A.
• Trade 5: Took a setup before volume confirmed. Category B (one criterion skipped).
• Trade 7: Held past stop for 8 minutes hoping it would recover. Category B (stop management deviation).
• Trade 9: No real setup, just hadn't traded since 10am and felt like she needed to. Category C.
• Trade 11: Clean setup, adverse market reaction to unexpected news. Category A.
• Trade 12: Entered with too large a position, stopped out quickly. Category B (position sizing deviation).

Two Category A losses (acceptable), one Category C (impulsive, most expensive at -$310), three Category B (process deviations). Her week's actual problem was concentrated in those four deviated trades: -$580 combined. Her Category A losses totaled -$140 combined. She was profitable on her clean trades.

Step 3: Read your winning trades, note what they share (3 minutes)

Go through each winner. What did they have in common? Look for:

• Time of day
• Setup type
• Volume / volatility conditions
• Position relative to a key level
• How long you held them

You're looking for your edge in its clearest form. Winning trades, especially consistent winners, are your best evidence of what your strategy actually is.

Example 17 - Finding the pattern in winners: Over one week, Leo had 7 winners out of 14 trades. When he listed what the 7 winners shared:

• All 7 were taken in the first 90 minutes of the session
• 6 of the 7 occurred after a clear consolidation period of at least 20 minutes
• All 7 had volume at entry that was above the 20-bar average

His 7 losers: only 2 were in the first 90 minutes, only 1 had above-average volume at entry, and none had a clear consolidation period. His edge had specific conditions attached. He'd been taking it anywhere.

Step 4: Check your key metrics against your 30-trade rolling average (3 minutes)

Pull up:

• This week's profit factor
• This week's average position size
• This week's trades per day
• This week's rule compliance rate

Compare each one to your most recent 30-trade rolling average. Significant deviation in any direction - not just worse, also better - is worth noting.

Why better matters too: After a strong week, position sizes often drift up, trade frequency rises, and rule compliance drops. Catching this pattern before it produces a bad following week is one of the most valuable things the weekly review does.

Example 18 - Catching the post-win drift: After his best week in four months (+$2,800), Ryan's weekly review numbers:

Metric	30-trade avg	This week
Trades per day	3.8	6.2
Avg position size	2 contracts	3.5 contracts
Rule compliance	79%	64%

His trade frequency had nearly doubled. His position size was 75% higher than normal. His rule compliance had dropped 15 percentage points. None of these felt dramatic in the moment - each individual trade felt justified. The weekly review made the aggregate pattern visible before it had time to damage the following week.

He reduced to minimum position size and maximum 4 trades per day for the following week as a deliberate reset.

Step 5: Write one observation and one rule for next week (3 minutes)

Not three observations. Not a list of improvements. One specific observation from the data, and one specific, testable rule it suggests for next week.

Bad: "I need to be more disciplined and wait for better setups."

Good: "My losing trades are 70% concentrated in the first and last 30 minutes of my midday session. Next week: no new entries between 11:30am and 12:30pm."

Bad: "I should stop taking B setups."

Good: "Three of my four losses this week came from trades where volume at entry was below the 20-bar average. Next week: volume confirmation is a hard requirement, not a preference."

The rule has to be specific enough that you can check compliance on each trade next week. Vague intentions don't change behavior.

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Part 4: The Monthly Review

Once a month - same process, wider lens.

Pull up your full month of data. Calculate:

Month-over-month metrics:

Metric	What to track month-over-month
Profit factor	Is it stable, improving, or declining?
Win rate by setup	Which setups are getting stronger or weaker?
Avg winner vs avg loser	Is the ratio improving?
Rule compliance rate	Trending up or down?
Trades per session	Increasing (overtrading risk) or decreasing (focus)?

Example 19 - A three-month picture:

Month	Profit factor	Rule compliance	Trades/week	Outcome
Month 1	0.88	54%	28	-$1,240
Month 2	1.14	67%	21	+$620
Month 3	1.41	79%	16	+$1,880

The improvement wasn't from a strategy change. It was from logging, reviewing, and making one specific behavioral change per week. By month three, rule compliance was up 25 percentage points, trade frequency was down 43%, and profit factor had moved from losing to genuinely solid.

The monthly review at the end of month one showed that compliance and overtrading were the primary issues. That diagnosis drove month two's focus. Month two's review showed the pattern was improving and extended it through month three.

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Putting It Together: The Full Review Checklist

Weekly (20 minutes, same time each week):

• Filter to this week's trades only
• Categorize each losing trade (A / B / C)
• Identify what winning trades shared
• Compare this week's metrics to 30-trade rolling average
• Write one observation (specific, data-driven)
• Write one rule for next week (specific, testable)

Monthly (45 minutes, first weekend of the month):

• Calculate month-over-month metrics
• Break profit factor down by setup type, session, day of week
• Identify the one condition or setup that accounted for most losses
• Identify the one condition or setup that accounted for most wins
• Decide what to eliminate, reduce, or expand in the coming month
• Write a one-paragraph summary of the month: what you learned, not just what happened

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Real Example: Four Months of Weekly Reviews

Kieran started logging trades in January. By the end of April - 16 weekly reviews later - his dashboard told a clear story.

January: Profit factor 0.91. Biggest problem identified: taking VWAP reclaim setups in low-volume midday conditions. Rule set: no VWAP reclaims after 11am.

February: Profit factor 1.18. VWAP problem reduced significantly. New problem identified: holding losers an average of 40% past the planned stop level. Rule set: hard stop on every trade placed immediately at entry, no exceptions.

March: Profit factor 1.44. Stop discipline much improved. New observation: 80% of his best trades were on Tuesday and Wednesday. Rule set: reduce position size by 50% on Monday, Thursday, Friday until data says otherwise.

April: Profit factor 1.67. Cleaner trading, concentrated on best days, stops respected.

Month	Trades	Profit factor	Net P&L
January	74	0.91	-$890
February	61	1.18	+$540
March	48	1.44	+$1,620
April	39	1.67	+$2,180

He was trading fewer and fewer trades each month as he got clearer about which ones belonged. His P&L was climbing. The weekly review was the mechanism - not the strategy, not the setups, not more screen time. One specific change per week, tracked and evaluated.

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The Bottom Line

The data in your journal dashboard is not decoration. It's a diagnostic tool - but only if you look at it with specific questions, in a consistent structure, and with the intention to change one thing at a time.

The weekly review process above is the difference between a journal that collects data and a journal that produces improvement. Twenty minutes a week, same time, same structure. The traders who do it consistently look like different traders six months later.

Run your weekly review at trade-keeper.com