Update Plan B dataset (2026-11-13)

README.md

@@ -54,9 +54,7 @@ configs:
 
 # Plan B — Contrarian Deep-Value US Equities & AI 10-K Analysis
 
-**Plan B** is a daily quantitative and fundamental research dataset covering deeply discounted, financially resilient, and heavily beaten-down US operating companies across the **NYSE** and **Nasdaq**.
-
-Every trading day, Plan B screens the entire US equity universe (~6,000 SEC XBRL filers) using primary **SEC EDGAR XBRL frames**, **3-year weekly price series**, **SEC Form 4 open-market insider transactions**, **FINRA short interest & short volume** (via Massive), **news sentiment**, and **LLM-synthesized 10-K/20-F deep-value investment memos**.
+> **Daily quantitative & fundamental research dataset** covering deeply discounted, financially resilient, and heavily beaten-down US operating companies across the **NYSE** and **Nasdaq**. Every trading day, Plan B screens ~6,000 SEC XBRL filers using primary **SEC EDGAR XBRL**, **3-year weekly price action**, **Form 4 open-market insider flow**, **FINRA short interest & short volume** (via Massive), **news sentiment**, and **LLM-synthesized 10-K/20-F deep-value memos**.
 
 ---
 
@@ -65,8 +63,8 @@ Every trading day, Plan B screens the entire US equity universe (~6,000 SEC XBRL
 | Metric | Value |
 | :--- | :--- |
 | **Coverage Window** | `2026-11-02` → `2026-11-13` (12 daily scans) |
-| **Unique Shortlisted Companies** | 66 NYSE/Nasdaq tickers |
-| **Daily Ranking Observations** | 427 rows (129 features per row) |
+| **Unique Shortlisted Companies** | 66 NYSE / Nasdaq tickers |
+| **Daily Ranking Observations** | 427 rows (131 features per row) |
 | **Structured AI 10-K/20-F Memos** | 21 filings analyzed |
 | **Open-Market Form 4 Insider Trades** | 376 verified transactions |
 | **Update Frequency** | Daily (automated post-market scan) |
@@ -85,55 +83,41 @@ Every trading day, Plan B screens the entire US equity universe (~6,000 SEC XBRL
 
 ---
 
-## 📁 Dataset Structure & Files
+## 📁 Dataset Tables
 
-Each table is provided in both **CSV** (ideal for Kaggle, Pandas, R, and DuckDB) and **JSON Lines (`.jsonl`)** (ideal for Hugging Face `datasets`, Spark, and LLM pipelines).
+Every table is provided in both **CSV** (Kaggle, Pandas, R, DuckDB) and **JSON Lines (`.jsonl`)** (Hugging Face `datasets`, Spark, LLM pipelines).
 
 | Table / Config | Files | Primary Key | Description |
 | :--- | :--- | :--- | :--- |
-| **`daily_rankings`** *(default)* | `daily_rankings.csv`<br>`daily_rankings.jsonl` | `(date, ticker)` | Daily ranked contrarian shortlist (~40 companies/day) with composite scores, valuation ratios, SEC XBRL balance-sheet & cash-flow fundamentals, insider totals, and AI 10-K verdict summary. |
-| **`ai_10k_memos`** | `ai_10k_memos.csv`<br>`ai_10k_memos.jsonl` | `(ticker, accession)` | Skeptical value-investor memos generated from Item 1 (Business), Item 1A (Risk Factors), and Item 7 (MD&A) of each company's latest SEC Form 10-K or 20-F. |
-| **`insider_trades`** | `insider_trades.csv`<br>`insider_trades.jsonl` | `(ticker, owner, kind, date, shares, form4_url)` | Open-market insider purchases (`P`) and sales (`S`) parsed directly from SEC Form 4 XML filings over the trailing 180 days (excluding option grants/awards). |
-| **`scan_stats`** | `scan_stats.csv`<br>`scan_stats.jsonl` | `date` | Daily universe screening funnel: total filers, usable XBRL fundamentals, priced equities, and shortlisted count. |
+| **`daily_rankings`** *(default)* | `daily_rankings.csv`<br>`daily_rankings.jsonl` | `(date, ticker)` | Daily ranked contrarian shortlist (~40 companies/day) with 131 quantitative, XBRL, FINRA short, insider, and AI verdict features. |
+| **`ai_10k_memos`** | `ai_10k_memos.csv`<br>`ai_10k_memos.jsonl` | `(ticker, accession)` | Skeptical value-investor memos generated from Item 1, Item 1A, and Item 7 of each company's latest SEC Form 10-K or 20-F. |
+| **`insider_trades`** | `insider_trades.csv`<br>`insider_trades.jsonl` | `(ticker, owner, kind, date, shares, form4_url)` | Open-market insider purchases (`P`) and sales (`S`) parsed from SEC Form 4 XML filings over the trailing 180 days. |
+| **`scan_stats`** | `scan_stats.csv`<br>`scan_stats.jsonl` | `date` | Daily universe screening funnel: total filers, usable XBRL fundamentals, priced equities, shortlisted count, and paper track record. |
 
 ---
 
-## 🎯 Screening & Ranking Methodology
-
-Plan B follows a strict Graham–Klarman–Buffett deep-value discipline. Financial institutions (SIC 6000–6799: banks, insurers, REITs, SPACs) are excluded so balance-sheet ratios remain comparable across operating businesses.
-
-### 1. Hard Quantitative Screen (`Cheap + Safe + Hated`)
-A company must pass **all three** pillars simultaneously:
-- **Cheap:** P/E ≤ 12, **or** EV/EBIT ≤ 8, **or** trading below Net Current Asset Value (`NCAV / Market Cap ≥ 1.0`, a classic Benjamin Graham net-net).
-- **Safe:** Positive stockholders' equity, current ratio ≥ 1.2, total liabilities ≤ 2.0× equity (or net cash position), 3-year share dilution ≤ 25%, and positive operating cash flow if net income is negative.
-- **Hated:** Share price down at least **40%** from its 3-year weekly high (`dd_3y ≤ -0.40`), with market cap ≥ $50M and share price ≥ $1.00.
+## 🚀 Quickstart
 
-### 2. Composite Score (`0–100`)
-- **Base Weighting:** 40% **Cheap** (`cheap_score`) + 30% **Safe** (`safe_score`) + 30% **Hated** (`hated_score`, blending price drawdown and headline negativity `news_neg`).
-- **Bonuses:** Net open-market Form 4 insider buying (up to +12 pts) and 3-year share count reduction via buybacks (+4 pts).
-- **Penalties:** Share dilution, negative free cash flow, accrual divergence (net income significantly exceeding operating cash flow or operating income), one-off profits following consecutive loss years, and high goodwill/M&A intensity.
-- **AI 10-K Adjustment:** After reading the 10-K filing, the composite score is adjusted by the AI analyst's verdict (`CONTRARIAN BUY CANDIDATE`, `WATCH`, or `AVOID`) and conviction (`0–100`).
-
----
+<details open>
+<summary><b>Python (<code>datasets</code> — Hugging Face)</b></summary>
 
-## 🚀 Quickstart Usage
-
-### Python (`datasets` — Hugging Face)
 ```python
 from datasets import load_dataset
 
 # Load default config (daily_rankings)
-rankings = load_dataset("elsiddik/planb-deep-value", "daily_rankings", split="train")
-print(rankings.to_pandas()[["date", "rank", "ticker", "score", "pe", "ev_ebit", "dd_3y", "ai_verdict"]].head())
-
-# Load structured 10-K AI memos
-memos = load_dataset("elsiddik/planb-deep-value", "ai_10k_memos", split="train")
+rankings = load_dataset("elsiddik/planb-deep-value", "daily_rankings", split="train").to_pandas()
+print(rankings[["date", "rank", "ticker", "score", "pe", "ev_ebit", "dd_3y", "ai_verdict"]].head())
 
-# Load Form 4 open-market insider trades
-insiders = load_dataset("elsiddik/planb-deep-value", "insider_trades", split="train")
+# Load structured 10-K AI memos & Form 4 insider trades
+memos = load_dataset("elsiddik/planb-deep-value", "ai_10k_memos", split="train").to_pandas()
+insiders = load_dataset("elsiddik/planb-deep-value", "insider_trades", split="train").to_pandas()
 ```
 
-### Python (`pandas` — Kaggle / Local CSV)
+</details>
+
+<details>
+<summary><b>Python (<code>pandas</code> — Kaggle / Local CSV)</b></summary>
+
 ```python
 import pandas as pd
 
@@ -141,16 +125,41 @@ df = pd.read_csv("daily_rankings.csv")
 memos = pd.read_csv("ai_10k_memos.csv")
 insiders = pd.read_csv("insider_trades.csv")
 
-# Filter highest-conviction ideas with net cash and insider buying
-top_ideas = df[(df["net_cash"] > 0) & (df["insider_buy_value"] > 0)].sort_values(["date", "rank"])
+# Filter highest-conviction ideas with net cash and unplanned insider buying
+top_ideas = df[(df["net_cash"] > 0) & (df["insider_unplanned_buy_value"] > 0)].sort_values(["date", "rank"])
 print(top_ideas[["date", "ticker", "name", "score", "pe", "ev_ebit", "net_cash_pct", "ai_verdict"]].head(10))
 ```
 
+</details>
+
+---
+
+## 🎯 Screening & Ranking Methodology
+
+Plan B follows a strict **Graham–Klarman–Buffett** deep-value discipline (excluding financials SIC 6000–6799).
+
+| Pillar | Hard Quantitative Gate (must pass all 3 simultaneously) |
+| :--- | :--- |
+| **Cheap** | `P/E ≤ 12` **or** `EV/EBIT ≤ 8` **or** `NCAV / Market Cap ≥ 1.0` (Benjamin Graham net-net) |
+| **Safe** | Positive equity, `current_ratio ≥ 1.2`, `liabilities ≤ 2.0× equity` (or net cash), `3Y dilution ≤ 25%`, and `CFO > 0` if `NI < 0` |
+| **Hated** | Price down ≥ **40%** from 3-year weekly high (`dd_3y ≤ -0.40`), `Market Cap ≥ $50M`, `Price ≥ $1.00` |
+
+<details>
+<summary><b>Composite Score Formula (0–100), Bonuses, Penalties & AI Adjustment</b></summary>
+
+- **Base Weighting:** 40% **Cheap** (`cheap_score`) + 30% **Safe** (`safe_score`) + 30% **Hated** (`hated_score`, blending price drawdown and headline negativity `news_neg`).
+- **Bonuses:** Net open-market Form 4 insider buying (up to +12 pts), same-day cluster buying, and 3-year share count reduction via buybacks (+4 pts).
+- **Penalties:** Share dilution, negative free cash flow, accrual divergence, one-off profits following loss years, high goodwill/M&A intensity, and elevated off-exchange short-volume pressure.
+- **AI 10-K Adjustment:** After reading the 10-K/20-F filing, the composite score is adjusted by the AI analyst's verdict (`CONTRARIAN BUY CANDIDATE`, `WATCH`, or `AVOID`) and conviction (`0–100`).
+
+</details>
+
 ---
 
 ## 📖 Data Dictionary
 
-### 1. `daily_rankings` (${RANKING_COLS.length} Columns)
+<details>
+<summary><b>1. <code>daily_rankings</code> (131 Columns)</b></summary>
 
 | Column | Type | Description |
 | :--- | :--- | :--- |
@@ -180,13 +189,16 @@ print(top_ideas[["date", "ticker", "name", "score", "pe", "ev_ebit", "net_cash_p
 | `pri_period`, `pri_assets`, `pri_liab`, `pri_ca`, `pri_cl`, `pri_ar`, `pri_ppe`, `pri_shares` | `mixed` | Balance-sheet values from the same SEC instant one year earlier, used for year-over-year quality tests. |
 | `short_pct_float`, `short_ratio`, `short_chg_mom` | `float` | Short interest as % of float, days-to-cover ratio, and change vs the prior report (biweekly FINRA settlement when `short_src`=`finra`, else Yahoo month-over-month). |
 | `held_pct_insiders`, `held_pct_institutions` | `float` | Share of the company held by insiders and by institutions. |
-| `short_src`, `short_vol_avg`, `short_vol_last`, `float_shares` | `string`/`float`/`int` | Short-interest source (`finra` = Massive/FINRA settlement), average and latest daily off-exchange short-volume ratio (0–1, FINRA via Massive), and Massive free-float share count. |
+| `short_src`, `short_vol_avg`, `short_vol_last`, `float_shares`, `float_pct`, `avg_daily_volume` | `mixed` | Short-interest source (`finra`), average & latest daily off-exchange short-volume ratio (`0–1`), Massive free-float share count, free-float % (`0–1`), and FINRA average daily share volume. |
 | `insider_buyers_sameday`, `insider_unplanned_buy_value` | `int`/`float` | Max distinct insiders buying on one day (Massive Form 4/5 aggregation) and insider purchase USD excluding 10b5-1 pre-planned trades. |
 | `activist_form`, `activist_date`, `activist_name`, `activist_pct` | `mixed` | Latest Schedule 13D/13G stake filed against the company (form, date, reporting person, % of class). |
 | `tenk_date`, `tenk_accession`, `tenk_form`, `tenk_url` | `string` | Filing date, SEC accession number, form type (`10-K` / `20-F`), and direct SEC EDGAR archive URL. |
 | `ai_verdict`, `ai_conviction`, `ai_balance_sheet`, `ai_value_trap_risk`, `ai_one_liner` | `mixed` | AI 10-K analyst summary fields (`CONTRARIAN BUY CANDIDATE` / `WATCH` / `AVOID`, conviction `0–100`, balance sheet rating, value-trap risk, and one-line thesis). |
 
-### 2. `ai_10k_memos` (21 Columns)
+</details>
+
+<details>
+<summary><b>2. <code>ai_10k_memos</code> (21 Columns)</b></summary>
 
 | Column | Type | Description |
 | :--- | :--- | :--- |
@@ -196,7 +208,10 @@ print(top_ideas[["date", "ticker", "name", "score", "pe", "ev_ebit", "net_cash_p
 | `one_liner`, `business`, `why_hated`, `capital_allocation`, `management` | `string` | Concise investment synthesis covering the business model, market pessimism, buybacks/dividends/M&A, and leadership. |
 | `bull`, `bear`, `red_flags` | `list` / `string` | Bullet lists of key bull points, bear points, and accounting/governance/footnote red flags from the 10-K. |
 
-### 3. `insider_trades` (11 Columns)
+</details>
+
+<details>
+<summary><b>3. <code>insider_trades</code> (11 Columns)</b></summary>
 
 | Column | Type | Description |
 | :--- | :--- | :--- |
@@ -207,6 +222,8 @@ print(top_ideas[["date", "ticker", "name", "score", "pe", "ev_ebit", "net_cash_p
 | `shares`, `price`, `value` | `float` | Number of shares traded, execution price per share (USD), and total dollar value (USD). |
 | `form4_url` | `string` | Direct URL to the primary SEC Form 4 XML filing on EDGAR. |
 
+</details>
+
 ---
 
 ## 🛡️ Data Integrity, Privacy & Caveats