trade-journal-shadow-review

name

trade-journal-shadow-review

description

Use when users need broker trade journal parsing, trading behavior diagnostics, FIFO roundtrip analysis, Shadow Account profile extraction, or post-trade review for 同花顺, 东方财富, 富途, generic CSV, or Excel exports. This skill uses trading_core journal adapters and Vibe-Trading-inspired Shadow Account concepts to produce win rate, profit/loss ratio, holding days, drawdown, overtrading, disposition effect, chasing momentum, anchoring, local .research artifacts, and Not investment advice without predicting returns or creating live trades.

Trade Journal Shadow Review

Use this skill when the user asks to review personal trading records, parse a broker export, diagnose behavior patterns, or create a Shadow Account profile for simulation.

Required Order

1. financial-services-skill-router
2. .agents/SKILLS_INDEX.md
3. china-market-overlay when the records include A-share, 港股, iFinD, 同花顺, or AKShare context
4. This skill
5. financial-output-qa-gate before delivery or persistence

Workflow Recipes And References

• Use .agents/workflows/trade_journal_shadow_review.json for productized broker journal review.
• Use .agents/references/output-contract.md for shared output fields.
• Use .agents/references/display-profiles.md for App/CLI/JSON presentation.
• Use .agents/references/local-research-artifacts.md for .research/ artifact paths.

Core Command

python3 -m trading_core.cli journal --file uploads/trades.xlsx

Supported inputs: .csv, .xlsx, .xls.

CLI output defaults to a Markdown review card for readability. Add --format json when the full normalized object is needed for automation.

App Conversation Display

When presenting results directly in the Codex app conversation, read references/app-conversation-display.md and use the card-style Markdown layout: one-line orientation, review card, behavior diagnostics table, Shadow Account table, next review checklist, and compact data/QA section. Keep parser assumptions, evidence gaps, and Not investment advice; do not show raw JSON unless the user asks for it.

Output Objects

• profile: trade count, closed roundtrips, average holding days, trade frequency, win rate, profit/loss ratio, total PnL, and top symbols.
• behavior_diagnostics: disposition effect, overtrading, chasing momentum, anchoring, and evidence or source gaps.
• shadow_account_profile: extracted rules from profitable closed roundtrips for simulation and review only.
• Local artifacts:
  • .research/journals/*.analysis.json
  • .research/shadow/*.shadow.json
• QA gate:
  • financial-output-qa-gate
  • python3 tools/check_research_integrity.py --input path/to/journal.json --profile journal_shadow

Guardrails

• Do not infer missing trades, fees, prices, or quantities.
• Do not promise future performance from historical behavior.
• Do not generate personal position sizing or live order instructions.
• Label parsing limitations and any missing broker columns.