| name | trade-journal |
| description | Every paper order requires a thesis recorded in the last 10 minutes. Use journal-mcp before calling any moomoo order tool — the PreToolUse hook blocks otherwise. |
Trade journal — thesis-first discipline
The self-learning loop depends on every trade having a recorded reason. The PreToolUse hook enforces this: no open thesis for the ticker in the last 10 minutes → order is blocked.
MCP server: journal-mcp. DB tables: theses, trades, notes, notes_vec, market_snapshots, post_mortems.
The required ordering (DO NOT SKIP)
1. mcp__journal-mcp__search_past_trades(query) # context from similar past setups
2. mcp__journal-mcp__record_thesis(...) # file thesis → get thesis_id
3. [sizing check — /size slash-command or sizing.py]
4. mcp__moomoo-mcp__place_paper_order(..., thesis_id=<id>) # hook checks thesis + sizing
5. [posttool_fill_capture auto-writes trades + market_snapshots]
6. Later — close_trade(trade_id, exit_price, outcome, pnl)
If you place an order without step 2 the hook returns exit 2. If you file a thesis and then size+order 11 minutes later, thesis has expired (10-min freshness window) — re-file.
Recording a thesis
Call mcp__journal-mcp__record_thesis with:
ticker: bare symbol, UPPERCASE. "AAPL", not "US.AAPL".direction: one of LONG, SHORT, LONG_CALL, LONG_PUT, SHORT_CALL, SHORT_PUT, VERTICAL_CALL_DEBIT, VERTICAL_PUT_DEBIT.thesis_text: 2-5 sentences. Why does the trade work? What's the catalyst?invalidation: concrete trigger — a price level, a date, or a data release. Vague invalidations are useless. Good: "Close below 250 OR services guide cut in 10-Q". Bad: "if the trend reverses".timeframe: "2-4 weeks", "overnight", etc.expected_return_pct / max_loss_pct: rough plan.
Recording a virtual fill (options fallback)
If place_paper_option_order returns virtual_fill_suggested=True (MoomooOpenD rejected the paper option leg), immediately call mcp__journal-mcp__record_virtual_fill with the mid-price of the contract at the time. The trade row is marked broker_order_id=VIRTUAL-<uuid> but is otherwise treated identically by post-mortem logic.
Before any research / entry
Always run mcp__journal-mcp__search_past_trades(query, k=5) with:
query = ticker + " " + candidate strategy keywords.
This pulls both semantic matches over notes (post-mortem lessons) and lexical matches over trades.strategy_label / trades.symbol. Ignoring past lessons is how you pay the same tuition twice.
Closing a trade
mcp__journal-mcp__close_trade(trade_id, exit_price, outcome, pnl):
outcome ∈ {WIN, LOSS, SCRATCH}. SCRATCH for near-breakeven exits.pnl must be passed (the tool doesn't compute it — you know the commissions + multi-leg math better).
If invalidation triggers without you closing the position (e.g., gap-down through stop), also call close_thesis(thesis_id, "triggered", note="<what happened>") so future queries don't surface a stale thesis.
Appending a lesson
mcp__journal-mcp__append_note with source="post_mortem" and tags="lesson,<ticker>,<strategy_label>,<cause>". The text is auto-embedded into notes_vec and will surface on semantic search during future research.
