What to Log in a Trading Journal (Field List)

Most trading journals fail for the same reason: the fields the trader recorded could not answer the questions they later asked. Deciding what to log in a trading journal, field by field, is what separates a record you can query from one that stays silent. You log a symbol, an entry, an exit, and a dollar figure. Three months on you want to know whether your morning trades beat your afternoon ones, or whether your high-conviction setups actually deserve the size you give them. The data is not there. You cannot go back and reconstruct how you felt or how far the market had already run, so the journal says nothing about the exact things it was supposed to reveal.

The fix is to decide up front what belongs in every entry. Some of it you can only capture in the moment. Some of it you compute later. This post is the complete list, split along that line.

The one rule that shapes the whole list

Fields fall into two groups, and confusing them is where most journals go wrong.

The first group is captured data: facts that exist only at the moment of the trade and vanish if you do not write them down. Your entry price, the setup you saw, your confidence, the market regime, how you felt. Miss these and they are gone. No import or spreadsheet formula brings them back.

The second group is computed metrics: numbers you derive from the captured fields. Win rate, profit factor, R-multiple, expectancy, average hold time. You never type these in. A journal that stores your raw fields cleanly calculates all of them on demand.

The practical takeaway: spend your effort on capture. Do not waste a keystroke on anything a tool can compute, and never skip a captured field because you are "sure you will remember." You will not.

What to log in a trading journal, group by group

Here is the full list of what to write down at the trade. Not every field applies to every style, but this is the superset to work from. Read it as a menu, then trim it in the next section.

Identity and mechanics

These anchor the trade in time and price. They are the least glamorous fields and the ones you can least afford to get wrong, because every computed metric downstream inherits their errors.

• Symbol / instrument. The exact ticker, contract, or pair. Be precise about which futures month or which exchange if that matters to you.
• Direction. Long or short. Trivial to log, easy to leave implicit, and a nightmare to infer later from price alone.
• Entry date and time. Timestamp to the minute if you are intraday. The session and time-of-day patterns you will eventually hunt for live in this field.
• Exit date and time. Same precision. Entry and exit timestamps together give you hold time for free.
• Entry price and exit price. The actual fills, not the price you wanted. If you scaled in or out, this gets more involved, and there is a right way to handle it covered below.
• Position size. Shares, contracts, lots, or units. Size is the multiplier on everything, so a wrong quantity quietly corrupts your P&L and your R.
• Fees and commissions. Log them. The gap between gross and net P&L is exactly where costs hide, and the academic record is blunt about how much they matter (see Sources).

Risk and plan

These fields turn a trade from a bet into a tested decision. They are what let you separate a good process from a lucky outcome.

• Stop-loss price. Where you planned to be wrong. Without it you cannot compute R, and you cannot tell a disciplined loss from a runaway one.
• Target price. Where you planned to take profit. The distance between stop and target is your planned reward-to-risk.
• Planned risk (in currency or percent). How much you were willing to lose on the trade. This is the denominator that makes wildly different position sizes comparable.
• Setup or pattern. The named reason the trade existed. This field is the join key for almost every useful analysis you will run, so define each setup tightly enough that grouped stats mean something.
• Strategy. If you run more than one system, tag which one this trade belongs to. Mixing systems in one undifferentiated pile is how genuine edge gets buried under noise.

Context and market conditions

A setup does not perform in a vacuum. The same pattern behaves differently in a trending market than in a chop, and the only way to slice your edge by regime is to have written the regime down. Keep this compact: a handful of tags, not a paragraph. The mechanics of doing this well are covered in how to log market conditions.

• Trend or regime. Trending, ranging, or volatile, from your own simple rubric.
• Relative volatility. High or low versus normal for the instrument.
• Session context. Pre-market, open, midday, close, or overnight, depending on what you trade.
• Broader-market note. A one-line read on the index or sector backdrop, if it informed the trade.

The behavioral layer

This is the group traders skip, and it is the group that pays the most on review. Decades of behavioral-finance research show that predictable human tendencies drive a large share of poor outcomes. Kahneman and Tversky's prospect theory established that losses loom larger than equivalent gains (Kahneman and Tversky, 1979); their later work put the asymmetry at roughly two to one (Tversky and Kahneman, 1992). That is a big part of why traders cut winners early and let losers run. Terrance Odean documented that same disposition effect in the accounts of 10,000 real traders (Odean, 1998). You cannot manage a bias you never recorded.

• Confidence. A number, not a mood. A 1-to-5 conviction score logged before the outcome is known lets you later test whether your gut is calibrated against results.
• Emotional state. Calm, anxious, FOMO, bored, revenge-seeking. Keep it to a short fixed vocabulary so it stays countable rather than turning into free-form venting.
• Rule adherence. A plain yes or no: did you follow your plan? This single field is the cleanest way to separate skill from luck, because a winning trade that broke your rules is a process failure wearing a costume.
• Thesis note. One or two sentences on why you took the trade and what would prove you wrong. Written before entry, it is the anti-hindsight anchor that keeps your later review honest.

Evidence

• Chart screenshot. A snapshot of the setup at entry, annotated if you can. On review, a picture reconstructs your read faster than any note.
• Post-trade note. The one lesson, written after exit while it is fresh.

Now cut the list down

A field list is a menu, not an obligation. Logging all twenty-plus fields on every trade is how journaling dies in week two: the friction becomes unbearable and you quietly stop. The research on why detail matters does not mean more fields are always better. It means the right fields, recorded consistently, beat an exhaustive template you abandon.

A practical rule: capture every identity and mechanics field always, because they are non-negotiable and cheap. Capture risk and plan always, because they unlock your most important metrics. Then pick two or three fields from the behavioral and context groups that map to a problem you actually have. If you know you chase, log emotion. If you oversize, log confidence. Add fields as questions arise, never before.

The fields you never type: computed metrics

Everything below is derived. You should never enter these by hand, and if a journal asks you to, it is making you do arithmetic a database should do. They are listed here so you know what your captured fields are feeding.

• Net P&L. Gross result minus fees. Costs are not a rounding error. In the canonical study of individual-investor performance, the households that traded most earned about 11.4% a year net of costs while the market returned about 17.9%, and the least active traders earned roughly 18.5% (Barber and Odean, 2000). The difference was largely costs. Your net-P&L field is where that story shows up in miniature.
• R-multiple. Result expressed in units of planned risk, so a win of two-times-risk reads as +2R regardless of position size. This is what makes trades of different sizes comparable on one scale.
• Win rate, profit factor, expectancy. Your hit rate, the ratio of gross wins to gross losses, and the average expected result per trade.
• Average win, average loss, and their ratio. The shape of your edge, which win rate alone hides.
• Hold time and drawdown. Computed straight from your timestamps and equity curve.

If your captured fields are clean, all of these fall out automatically. If they are messy, no metric can save you, which is why a periodic data check on the journal itself matters. That is a separate discipline covered in how to run a trading journal audit.

A tagging system keeps the captured fields usable

Two of your most valuable captured fields, setup and mistake, only pay off if they stay consistent. If you write "breakout" on one trade and "b/o" on the next and "range break" on a third, no filter can group them, and your history becomes unqueryable. The answer is a small, deliberate tag taxonomy decided in advance rather than invented per trade. Building one that stays clean as your trade count grows is enough of a data-modeling problem that it deserves its own treatment in how to build a trading journal tagging system.

Frequently Asked Questions

What is the minimum I should log to still get value?

Six fields: symbol, direction, entry, exit, size, and the reason you took the trade. That gives you net P&L and lets you group by setup, which is enough to start finding patterns. Add stop and planned risk as soon as you can, because those unlock R-multiple and turn outcomes into process feedback. Everything else is an upgrade you earn by keeping the habit alive.

Should I log every trade or only the interesting ones?

Every trade, including the boring ones and especially the losers. A journal that quietly omits the trades you would rather forget becomes a highlight reel rather than a record, and it will overstate your edge. The value of the dataset comes from its completeness, not its flattering entries.

How do I handle trades where I scaled in or out?

Do not average everything into a single made-up entry and exit price by hand. Record each execution as its own fill with its own price, size, and timestamp, and let your journal compute the blended average price and net P&L from those. Hand-averaging is where multi-execution trades start to lie about their real cost basis.

Do I really need to log emotions and confidence, or is that soft?

They are only soft if you log them softly. A confidence score is a number you can back-test against outcomes to see whether your conviction predicts anything. An emotion tag from a fixed short list is countable, so you can measure whether your FOMO entries actually lose money. Logged as structured fields rather than diary prose, the behavioral layer is some of the hardest data in the journal.

Where should all this live?

Anywhere you will actually keep it up, so long as your captured fields stay clean and consistent. A spreadsheet works until the computed side (R-multiples, per-setup breakdowns, equity curves) becomes tedious to maintain by hand. A purpose-built journal like TradeReveal stores the raw fields and computes win rate, profit factor, R-multiple, expectancy, and drawdown for you, and its free core lets you tag setups and mistakes without a per-trade math tax. The tool matters less than the discipline of logging the right fields every time.

Final Thoughts

A trading journal is only as good as the questions it can answer, and it can only answer questions you gave it data for. Sort your fields once into what you must capture in the moment and what a tool can compute later. Then log the capture side without exception and let the metrics take care of themselves. Start with mechanics and risk on every trade, add a behavioral field or two aimed at a real weakness, and resist the urge to build a template so heavy you abandon it. The traders who improve are the ones whose journal, months later, can still tell them the truth.

Sources

• Kahneman, D. and Tversky, A. (1979). "Prospect Theory: An Analysis of Decision under Risk." Econometrica, 47(2), 263-291. Econometric Society listing
• Tversky, A. and Kahneman, D. (1992). "Advances in Prospect Theory: Cumulative Representation of Uncertainty." Journal of Risk and Uncertainty, 5(4), 297-323. SpringerLink listing
• Odean, T. (1998). "Are Investors Reluctant to Realize Their Losses?" The Journal of Finance, 53(5), 1775-1798. Author PDF, UC Berkeley Haas
• Barber, B. M. and Odean, T. (2000). "Trading Is Hazardous to Your Wealth: The Common Stock Investment Performance of Individual Investors." The Journal of Finance, 55(2), 773-806. Author PDF, UC Berkeley Haas

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Happy Trading,

The TradeReveal Team