Roadmap

Trading Journal AI is in active development. The core review workflow is already usable, and a larger vision is taking shape around it. This page is honest about which is which — expect rough edges while things roll out.

The problem we're solving

Most trading journals start with a table of numbers, so review becomes data entry and the why behind each trade gets lost. Lessons end up scattered across spreadsheets, broker platforms, and screenshots you never look at again.

The bet here is different: repeated, low-friction review should compound into a personal playbook — what you trade, how you trade it, what invalidates it, and whether the data says your edge is actually improving. Notes become context, context becomes rules, rules become coaching, and coaching feeds a better next review.

What works today

You can use these right now:

Import broker trades from a CSV and have fills grouped into round-trip trades.
Review sessions with less friction: read the day first, then open a ticker's full-session chart with its trades in a panel right beside it — no digging through page after page to see what happened.
Write daily recaps and tag trades in your own vocabulary.
Dictate your notes instead of typing them — transcribed by a local, on-your-machine speech model, so audio never leaves your computer.
Attach image & video evidence — drop chart screenshots or screen recordings onto a trade in ticker review, so the record keeps what you actually saw.
Charts with candles and trade markers, plus calendar and reports for context.
Explore demo data to see the whole flow before importing your own.

What's coming

These are in progress or planned — treat them as direction, not shipped features:

The playbook — the heart of where this is going. Define your setups, patterns, process rules, and review criteria once, and the journal, analytics, and coach can all draw on them as context. It turns scattered reviews into a personal reference for what you trade, how you trade it, and what invalidates it.
AI coach — a review assistant that reads your trades, notes, tags, charts, and rules to give feedback that's specific to you rather than generic advice. An early version already runs with your own OpenAI key; deeper rule-checking and more providers are in progress. See The AI Coach.
Setup workspace (Settings) — one place to connect an import source, add your own language-model API key, complete a trader self-assessment, define your rules, and tune coach preferences. The first pieces — connecting a model provider and editing your playbook — are in Settings today.
Dashboard — the active-day surface that completes the loop: your morning plan and risk guardrail kept visible while you trade, timed check-ins ("should I keep trading?") that route context into the day's recap, and the coach's latest lesson carried forward as a cue for the next session. An early concept lives in the app today; the full planning–accountability–check-in flow is being built.
More broker adapters — expanding the set of broker exports that map cleanly into the app's shared trade format.

Why the playbook matters

The playbook is the connective tissue in the compounding loop. Your own examples, rules, mistakes, and setup definitions become the context the journal, analytics, and coach all draw on — so each review makes the next one sharper. It's the difference between a static journal template and a review system that grows with you: notes become context, context becomes rules, rules become coaching, and coaching feeds a better next review.

And it stays yours: the direction is that the coach can propose playbook candidates when a finding keeps repeating, but you accept, edit, or dismiss every one — nothing becomes a rule automatically.