Journal fields and monthly stats
Lesson 27 · about 10 min
A swing trader makes 50 to 80 trades a year. That is a small sample, which means the journal has to capture the right fields to be useful at all. Record too little and you cannot tell which setups or regimes are working; record too much and you stop filling it in. This lesson gives the field list and the five numbers to compute from it each month.
The fields
One row per trade. Fill in the first group at entry, the second at exit, the third at the monthly review.
At entry
| Field | Example | Why it matters |
|---|---|---|
| Date, ticker | 2026-03-09, XXXX | |
| Setup | Pullback 20 EMA | Stats by setup |
| Regime score | 5 (green) | Stats by regime; the most revealing cut |
| Sector rank | 2 of 11 | Confirms the rotation filter is being used |
| Entry, stop | $60.00, $57.06 | |
| Risk in $ and % | $250, 1.0% | Confirms the multipliers were applied |
| Shares | 85 | |
| Target 1 in R | 2.0R | R:R at entry, before the trade tells you what it thinks |
| Earnings date | Apr 22 | Days to earnings at entry |
| Time-stop date | Mar 16 | |
| Plan sentence | "Partial at 66, trail 20 EMA" | The contract |
At exit
| Field | Example | Why it matters |
|---|---|---|
| Exit date(s) | Mar 13 (partial), Mar 24 | Hold time |
| Exit reason | Trail close < 20 EMA | Stop / trail / partial+trail / time / earnings / discretionary |
| Result in R | +2.6R | The only P&L number that matters |
| Max favourable excursion | +3.4R | How far it went in your favour at best |
| Max adverse excursion | −0.4R | How close it came to the stop |
| Rule breaks | None / "moved stop" | Count these |
At review
| Field | Example | Why it matters |
|---|---|---|
| Grade | A (plan followed) / B (minor deviation) / C (rule break) | Separates process from outcome |
| One-line lesson | "Entered on a gap-up trigger; R:R was 1.4 not 2" | The thing to fix |
Fifteen fields at entry and exit, two at review. In a spreadsheet, that is one row and a minute of typing per trade.
The five monthly numbers
On the first weekend of each month, compute these from every closed trade in the previous month, and cumulatively from the start of the year.
| Number | Formula | Healthy range for this playbook |
|---|---|---|
| Win rate | winners ÷ trades | 40% to 55% |
| Average winner (R) | sum of winning R ÷ winners | 1.8R or more |
| Average loser (R) | sum of losing R ÷ losers (as a positive number) | 1.0R or less |
| Expectancy (R per trade) | (win rate × avg winner) − (loss rate × avg loser) | Above 0.3R |
| Rule-break rate | trades with a rule break ÷ trades | Under 10% |
Worked: 12 trades, 6 winners averaging 2.2R, 6 losers averaging 1.1R.
expectancy = (0.5 × 2.2) − (0.5 × 1.1) = 1.10 − 0.55 = 0.55R per trade
Twelve trades at 0.55R is 6.6R for the month, which at 1% base risk is roughly 6.6% before compounding effects. The average loser of 1.1R rather than 1.0R is the number to look at: it means stops were, on average, filled 10% worse than placed, which is either slippage and gaps (acceptable, if small) or a moved stop (not acceptable).
The cuts that matter
With 12 trades a month the monthly numbers are noisy. The cuts below become meaningful at 30 or more trades, so run them quarterly and cumulatively:
- By regime score. Expectancy in weeks scored 5 to 6 versus 3 to 4.5 versus under 3. Most traders find the third bucket is negative. That table is the argument for the checklist.
- By setup. Expectancy for each of the five setups. A setup with 20+ trades and negative expectancy is either being executed wrong or does not suit you; either way, retire it for a quarter.
- By exit reason. Trades exited at the trail versus at the initial stop versus at a discretionary exit. Discretionary exits are almost always the worst bucket.
- By max favourable excursion. If many losers reached +1.5R before reversing to −1R, the partial rule is not being followed.
Expectancy by regime score (cumulative, 47 trades)
score 5-6 | +0.71R ████████████████████ n=24
score 3-4.5| +0.22R ██████ n=16
score <3 | -0.48R (negative) n=7 <- these seven trades cost 3.4R
Key idea: Fifteen fields per trade, five numbers per month, four cuts per quarter. The expectancy-by-regime cut is the one that changes behaviour.
Grading process, not outcome
The grade field exists because a trade can follow every rule and lose, or break three rules and win. Over 60 trades a year, the A-grade trades will show a better expectancy than the C-grade ones, and the size of that gap is the dollar cost of your rule breaks. Most traders who compute it find it is larger than their net result, which means the process, followed cleanly, was profitable and the deviations ate the profit.
Keeping it going
The journal is only useful if it is complete. Two tricks: fill the entry fields as part of preparing the order, so the row exists before the trade does; and do the exit fields during the nightly check on the day of exit, when the reason is fresh. Anything left for "later" gets reconstructed from memory, and memory is kind to the trader.
Try it: Set up the fields in a spreadsheet and back-fill your last ten trades from your broker's history. Compute the five monthly numbers. Then split by regime score, even with the small sample, and look at the sign of each bucket.
Recap
- Record 15 fields per trade: setup, regime score, sector rank, risk, R:R at entry, earnings and time-stop dates, exit reason, result in R, excursions, rule breaks, grade, lesson.
- Compute five monthly numbers: win rate, average winner, average loser, expectancy in R, rule-break rate.
- Cut by regime score, setup, exit reason and favourable excursion at 30+ trades.
- Grade the process separately from the outcome; the A-versus-C gap is the cost of rule breaks.
- Fill fields at order preparation and at the nightly check on exit day, never later.
See it drawn
Original diagrams for the ideas on this page. Illustrative, not real market data.