💼Portfolio & Analytics

Trade Journal & Analytics

Log trades with thesis & conviction; analytics reveal edge

The Trade Journal turns your trading from a sequence of disconnected bets into a dataset you can actually learn from. Every trade you log — thesis, tags, conviction, outcome — feeds an analytics engine that breaks your performance down by category, tag, conviction level, and even the hour of day you opened the position. The point is simple but uncomfortable: over a small sample, luck and skill look identical. The journal is how you tell them apart.

You'll find it at **/dashboard/journal**, or under **Tools → Trade Journal** in the sidebar.

## Why Journaling Separates Skill From Luck

A 60% win rate over 10 trades means nothing — you could flip a coin and get that. Over 200 trades, a persistent edge starts to show through the noise. But raw win rate hides the more useful truth: you are probably good at some kinds of trades and quietly terrible at others.

Most traders carry a vague self-image ("I'm a good contrarian", "I crush crypto markets") that has never been checked against data. The journal checks it. When you bucket 150 logged trades by category and see that your crypto trades are -$340 expectancy while your politics trades are +$22 per trade, the story rewrites itself. You're not a crypto trader who had a bad month. You're a politics trader who keeps donating to crypto markets.

That's the entire value proposition: **a feedback loop tight enough to change behavior**. The scanner and AI tools tell you where edge might exist. The journal tells you where *your* edge actually exists.

## What the Journal Captures

Each entry stores a structured record of one trade:

- **Market** — the question (e.g. "Will Bitcoin reach $150k by Dec 31, 2026?"). Required.

•**Category** — a free-text bucket like `crypto`, `politics`, `sports`, `economics`. This drives your most important breakdown, so keep it consistent.
•**Direction** — YES or NO.
•**Size ($)** — position size in USD.
•**Entry / Exit (¢)** — prices entered in cents (e.g. `50` for a $0.50 contract). The platform stores them as decimals internally.
•**P&L ($)** — realized profit or loss. Leave this blank while the trade is open; the analytics treat an entry without P&L as "open" and exclude it from win-rate math until you close it.
•**Conviction (1–5)** — how strongly you believed at entry, shown as ★ stars. This is the field that most traders skip and most regret skipping. More on it below.
•**Tags** — comma-separated labels like `news-driven, contrarian, high-conviction`. Up to 20 tags per entry, each up to 50 characters.
•**Thesis** — why you entered, in plain language. Markdown is supported. Up to 5,000 characters.

After a trade closes you can also add **outcome notes** (what actually happened, and whether your thesis held) and a **self-rating (1–5)** of *process* quality — distinct from whether you won. A trade can be a 5-star decision that lost, or a 1-star gamble that won. Tracking both is how you stop rewarding luck.

## Logging a Trade, Step by Step

1. Open **/dashboard/journal** and click **+ Add Entry**. 2. Fill in the **Market** title (required) and pick a **Category**. Use a category you've used before so the bucket aggregates correctly — `crypto` and `Crypto` and `BTC` are three different buckets to the analytics engine. 3. Set **Direction** (YES/NO) and **Size**. 4. Enter **Entry price in cents**. If you're logging at open and don't yet have an exit, leave Exit and P&L blank. 5. Set your **Conviction** from 1 to 5. Do this *before* you know the outcome — that's the whole point. 6. Add **Tags** that describe the *kind* of trade, not the market. Good tags: `earnings-play`, `whale-follow`, `mean-reversion`, `tilt`. Bad tags: the market name (that's already the title). 7. Write a one-paragraph **Thesis**. Even two sentences ("AI shows 12pp edge, polling supports it, liquidity is thin so sizing small") is enough to be useful in three months. 8. Click **Save Entry**.

When the trade resolves or you exit, edit the entry to add the **Exit price**, **P&L**, **outcome notes**, and your **self-rating**. Only entries with a P&L value count as "closed" in the analytics — so an honest journal requires you to come back and close trades out.

Tip: log the entry at the moment you place the trade, not at the end of the week. Thesis quality collapses with hindsight. Once you know the result, your memory quietly rewrites why you entered.

## Conviction: The Field That Earns Its Keep

Conviction (1–5) is the single most diagnostic field in the journal, because it lets the analytics answer a question nothing else can: **does your confidence predict your results?**

A skilled trader's conviction is *calibrated* — their 5-star trades win more often and earn more per trade than their 2-star trades. If the journal shows your ★5 trades have a 48% win rate and your ★2 trades have a 61% win rate, your internal confidence signal is inverted. That's gold: it means your gut is actively misleading you, and you should size *down* on your "sure things."

The **By Conviction** breakdown tab plots win rate, average P&L, and expectancy for each star level (1 through 5, plus an "unrated" bucket for entries you left blank). Over a few hundred trades, the shape of that curve tells you whether to trust yourself. Pair it with the Kelly Calculator: conviction is only a safe input to position sizing once the data shows it's calibrated.

## Reading the Analytics

At the top of the page, four cards summarize everything you've logged:

- **Total Trades** — count of all entries, with how many are closed.

•**Win Rate** — wins / closed trades. Color-coded: teal at ≥55%, amber at 50–55%, rose below 50%.
•**Total P&L** — sum across closed trades, with average per trade.
•**Expectancy** — your average expected dollars per trade, plus your **RR ratio**.

Below that, a row of tabs switches the breakdown table between five buckets: **By Category, By Tag, By Hour of Day, By Day of Week, By Conviction**. Each row in the table shows the same columns so they're directly comparable:

- **Trades** — total entries in that bucket.

•**WR** — win rate (shows "—" if nothing in the bucket is closed yet).
•**P&L** — total realized for the bucket.
•**Avg** — average P&L per closed trade.
•**RR** — risk/reward ratio = average win ÷ average loss. Above 1.0 means your winners are bigger than your losers. Shows ∞ when you have wins but no losses in the bucket (small sample — don't over-read it).
•**Expect.** — expectancy = (avg win × win rate) − (avg loss × loss rate). **This is the number that matters most.** A bucket can have a mediocre win rate and still be your best category if the wins are large enough.

### How to actually use the breakdowns

- **By Category** — find which market types you should keep trading and which to cut. Negative expectancy in a category over 30+ closed trades is a strong signal to stop trading it, regardless of how it "feels."

•**By Tag** — this is where strategy-level insight lives. If `contrarian` trades are +$18 expectancy but `momentum` trades are −$25, you've learned something about your style that no amount of reflection would surface.
•**By Conviction** — calibration check (see above).
•**By Hour of Day / Day of Week** — buckets are computed in **UTC** from when you opened the trade. The classic finding here is "trades I open after 11pm have a 34% win rate" — i.e. tilt and tired decisions. If late-night trades bleed money, that's a behavioral fix worth more than any signal.

Gotcha: the By Tag breakdown counts one trade in *every* tag it carries, so tag totals can exceed your trade count. That's intentional — a single trade can be both `high-conviction` and `news-driven` — but it means tag buckets aren't mutually exclusive. Don't sum them expecting your total.

Gotcha: every breakdown ignores open trades. Win rate, RR, and expectancy are computed only over entries that have a P&L. If your numbers look thin, it's usually because you logged entries but never closed them out.

## Connecting the Journal to the Rest of Predite

The journal is most powerful when it's not a separate silo. Entries can be linked back to where the trade originated via an internal source reference, so trades from **Paper Trading**, your **Bots**, and **Copy Trading** can be carried into the same analytics surface instead of living in three disconnected places. That means your bot's automated fills and your manual discretionary trades get measured on the same expectancy ruler.

A practical workflow:

1. Find a signal in the **EV Scanner**, sized with the **Kelly Calculator**. 2. Validate the approach in **Paper Trading** first. 3. Once live (Bot plan), log each real trade in the journal with thesis and conviction. 4. Weekly, open the journal and read the By Category and By Conviction tabs. 5. Feed what you learn back into your scanner filters and Kelly conviction inputs.

This closes the loop: scanner → sizing → execution → journal → adjusted scanner filters.

## Sharing and Exporting

Public journal pages. Any entry you mark public is published to a shareable page at /journals/<your-handle>, viewable without login. It shows your public trades plus aggregate stats — public trade count, win rate, total P&L, and W/L record. Private entries stay private; only the ones you explicitly flag are shown. This is built for trader credibility: a public, time-stamped track record is far more convincing than a screenshot of a green day.

Data export. Your trade history is exportable as CSV from the portfolio/tax tooling, including entry and exit, realized P&L, and cost basis — the same export that produces Form 8949-style output for tax season. Pro and Bot users can also pull entries programmatically through the read-only API (filter by `tag`, `category`, or a `since` date) to build custom spreadsheets and dashboards. Up to 500 of your most recent entries load in the dashboard view; the API and CSV cover your full history.

Tip: export to a spreadsheet quarterly and run your own pivots. The built-in breakdowns cover the common cuts, but your own questions ("how do I do on markets resolving in under 7 days?") are easy to answer with the raw CSV.

## Plan Requirements

The Trade Journal and its analytics are included on the **Pro ($59/mo)** and **Bot ($99/mo)** plans. It is **not** available on Starter — the upgrade screen appears in its place. Public journal pages and CSV/API export ride along on the same Pro/Bot access.

Note that **logging** a trade doesn't require live trading — you can journal paper trades, copy trades, or trades you made anywhere, including outside Predite. **Live CLOB execution** on Polymarket is a separate capability that requires the **Bot plan** and a connected wallet, but the journal happily records trades from any source.

## Common Mistakes

- **Only logging winners.** This is the cardinal sin. It inflates every metric and teaches you nothing. Log losses *first* — they're where the lessons are.

•**Skipping conviction.** Without it, the calibration analysis is impossible and "unrated" swallows your data.
•**Inconsistent categories.** `crypto`, `Crypto`, and `BTC` fragment into three useless buckets. Pick a vocabulary and stick to it.
•**Never closing entries.** Open trades are invisible to the analytics. Come back and add P&L.
•**Reading tiny samples.** A bucket with 4 trades and a 100% win rate tells you nothing. Wait for 30+ closed trades per bucket before drawing conclusions, and treat ∞ RR as "too small to judge."
•**Logging from memory at week's end.** Your remembered thesis is fiction. Log at the moment of the trade.

## Related Docs

- [Paper Trading](/docs/paper-trading)

•[Kelly Calculator](/docs/kelly-calculator)
•[Portfolio](/docs/portfolio)
•[Reading Signals](/docs/reading-signals)

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