/trade-postmortem

description: Review a closed trade or a year of trades — process vs. outcome matrix, plan adherence, the biases that showed up, what the pre-trade skills said, and the one lesson for the journal; reads closed thesis-tracker files

Trade Post-Mortem

⚠️ Data Verification — Do This Before Any Analysis

Before running any analysis, always retrieve the latest market data for the ticker:

1. **Fetch current price** — use web search or ask the user for the live price, 52-week range, and market cap. Never assume a price from training data. 2. **Confirm key figures** — every fill (date, side, shares, price, fees) from the thesis file's decision log or the broker export; dividends received while held; the **total-return** closes of SPY and the chosen sector ETF on the exact entry and exit dates (adjusted for distributions); the stock's beta over the holding window; and the KPI values that were current on the day each decision was made. A post-mortem built on remembered fills or end-of-month benchmark prices grades the wrong trade. 3. **State your data source** — fill in the `Data & Sources` header (next section) so the origin, as-of date, retrieval path, and confidence of every figure are explicit at the top of the output. 4. **Flag stale data explicitly** — if live data is unavailable, display this warning before proceeding:

> ⚠️ **Live data unavailable.** The following analysis uses training-data estimates which may be significantly out of date. Verify all prices and metrics before making any decisions.

Never silently substitute training-data estimates for current prices. When in doubt, ask the user to paste the latest quote.

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📋 Data & Sources Header — Open Every Output With It

The first thing in the output is this provenance block, filled in — never left as placeholders. It is the standard documented on the [Data & Accuracy](https://yennanliu.github.io/InvestSkill/data-and-accuracy.html) page and the first thing `result-validator` looks for:

Data & Sources
  As of:      <date the figures represent, e.g. 2026-06-30>
  Source:     <primary docs — SEC EDGAR 10-K/10-Q, company IR, FRED, exchange data …>
  Retrieval:  <pasted by user | web/tool retrieval | model memory>
  Confidence: <HIGH | MEDIUM | LOW>

• `Retrieval: model memory` must be paired with `Confidence: LOW` — memory is a placeholder until confirmed against a primary source.
• Mixed sources: list each with its own as-of date rather than blending them.
• Sources disagree on the same figure (a closing price, a deal term): prefer the primary source (SEC filing, company release, exchange data) over an aggregator, then the most recent dated one. Show both values with their sources and dates, say which you used and why, and never average them.
• Data the user pasted is reported as `pasted by user`; do not upgrade its confidence beyond what the user's own source supports.

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🌐 Output Language — `--lang zh-TW`

English is the default. With `--lang zh-TW` — or when the user writes in Traditional Chinese and asks for a Chinese answer — write the whole analysis in Traditional Chinese (繁體中文, Taiwan usage), keeping it machine-checkable:

• **Translate** every heading, narrative paragraph, table header and bullet. Give each metric's English name in brackets the first time it appears — 自由現金流（Free Cash Flow, FCF） — so the reader can match it to the glossary and to English analyses.
• **Keep in English** tickers, figures and units, SEC form and item names (10-K Item 7, Form 4), the four `Data & Sources` field labels (`As of`, `Source`, `Retrieval`, `Confidence` — the Retrieval value may be written in Chinese, e.g. 使用者提供), and the Investment Signal box and JSON footer exactly as specified — field names and the BULLISH / NEUTRAL / BEARISH, HIGH / MEDIUM / LOW, BUY / HOLD / SELL, STRONG / MODERATE / WEAK values. Tools parse them.
• Title the invalidation section **論點失效條件（Thesis Invalidation）**, and close with the disclaimer in Chinese: **免責聲明：** 僅供教育用途，非投資建議。
• Use Taiwan financial terms: 殖利率、本益比、營業利益率、自由現金流、聯準會、選擇權、買權／賣權、除息日、預扣稅.

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Overview

Every other skill in the catalog looks forward. `thesis-tracker` writes the contract before the trade and re-checks it while the position is open; `position-ladder` plans the entries and trims; `tax-lens` prices the exit. Once the position is closed, nothing looks back — and looking back is the only way an investor learns whether their *process* works, as distinct from whether they were lucky this time.

**This skill grades the decision separately from the result.** It takes a closed trade (or a year of them), reconstructs what the investor knew and planned at each decision point, and answers five questions in order: *Which quadrant of the process-vs-outcome matrix is this? Did I do what my plan said? Which biases showed up, and where is the evidence? Were the pre-trade analyses right, and for the right reasons? Where did the return actually come from?* It ends with one sentence for the journal — the lesson — and a **Process Score 0–10**.

It reuses the vocabulary of Learning lesson 13, *Psychology & Process* (`site/content/LEARNING-PSYCHOLOGY.md`): the four quadrants (*deserved success*, *bad luck*, *dumb luck*, *deserved loss*), the eight biases and their antidotes, the journal template, and the sell discipline of rules vs. reasons.

**What it is not.** It is not a re-analysis of the stock with hindsight, and it is not a tax calculation (`tax-lens` does that). The signal block at the end is the only forward-looking part: the view on *re-entering the ticker today*, informed by what the post-mortem learned. Pairs with `thesis-tracker` (its closed files are this skill's primary input), `position-ladder` (the plan the sizing is graded against), `tax-lens` (after-tax result and wash-sale check on any re-entry), and `learning-coach` (to turn the lesson into a drill).

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1. Modes and Inputs

| Mode | Invocation | What it does | |------|------------|--------------| | **