taiwan-equity-research-coverage

Imported from reason-machines/trending-skills (skills/taiwan-equity-research-coverage/SKILL.md) via skills.sh. Install upstream with npx skills add reason-machines/trending-skills --skill taiwan-equity-research-coverage. Copyright stays with the author.

Taiwan Equity Research Coverage (My-TW-Coverage)

Skill by ara.so — Daily 2026 Skills collection.

A structured equity research database covering 1,735 Taiwan-listed companies (TWSE + OTC) across 99 industry sectors. Each report contains a business overview, supply chain mapping, customer/supplier relationships, and financial data — all cross-referenced through 4,900+ wikilinks forming a searchable knowledge graph.

Installation

git clone https://github.com/Timeverse/My-TW-Coverage
cd My-TW-Coverage
pip install yfinance pandas tabulate

Project Structure

My-TW-Coverage/
├── Pilot_Reports/             # 1,735 ticker reports across 99 sectors
│   ├── Semiconductors/        # 155 tickers
│   ├── Electronic Components/ # 267 tickers
│   ├── Computer Hardware/     # 114 tickers
│   └── ... (99 sector folders)
├── scripts/
│   ├── utils.py               # Shared utilities
│   ├── add_ticker.py          # Generate new ticker reports
│   ├── update_financials.py   # Refresh financial tables + valuation
│   ├── update_valuation.py    # Refresh valuation multiples only (fast)
│   ├── update_enrichment.py   # Update business descriptions from JSON
│   ├── audit_batch.py         # Quality auditing
│   ├── discover.py            # Buzzword → related companies search
│   ├── build_wikilink_index.py# Rebuild WIKILINKS.md index
│   ├── build_themes.py        # Generate thematic investment screens
│   └── build_network.py       # Generate D3.js network graph
├── WIKILINKS.md               # Auto-generated browsable wikilink index
├── network/index.html         # Interactive D3.js wikilink network
├── themes/                    # Thematic investment screens (auto-generated)
└── task.md                    # Batch definitions and progress tracking

Report Format

Each ticker report is a markdown file at Pilot_Reports/<Sector>/<TICKER>_<Name>.md:

# 2330 - [[台積電]]

## 業務簡介
**板塊:** Technology
**產業:** Semiconductors
**市值:** 47,326,857 百萬台幣
**企業價值:** 44,978,990 百萬台幣

台積電為全球最大晶圓代工廠，專注於 [[CoWoS]]、[[3奈米]] 先進製程...

## 供應鏈位置
**上游:** [[ASML]], [[Applied Materials]], [[SUMCO]]
**中游:** **台積電** (晶圓代工)
**下游:** [[Apple]], [[NVIDIA]], [[AMD]], [[Broadcom]]

## 主要客戶及供應商
### 主要客戶
- [[Apple]], [[NVIDIA]], [[AMD]], [[Qualcomm]]

### 主要供應商
- [[ASML]], [[Tokyo Electron]], [[Shin-Etsu]]

## 財務概況
### 估值指標
| P/E (TTM) | Forward P/E | P/S (TTM) | P/B | EV/EBITDA |
|-----------|-------------|-----------|-----|-----------|
| 28.5      | 22.1        | 9.3       | 7.2 | 16.4      |

### 年度財務數據
[Annual 3-year financial table with 14 metrics]

### 季度財務數據
[Quarterly 4-quarter financial table]

Key Commands

Add a New Ticker

# Basic (auto-detect sector)
python scripts/add_ticker.py 2330 台積電

# With explicit sector
python scripts/add_ticker.py 2330 台積電 --sector Semiconductors

Update Financial Data

# Single ticker
python scripts/update_financials.py 2330

# Multiple tickers
python scripts/update_financials.py 2330 2454 3034

# By batch number (see task.md for batch definitions)
python scripts/update_financials.py --batch 101

# By sector
python scripts/update_financials.py --sector Semiconductors

# All 1,735 tickers (slow)
python scripts/update_financials.py

Update Valuation Only (~3x Faster)

Refreshes only P/E, Forward P/E, P/S, P/B, EV/EBITDA, and stock price — skips full financial statement re-fetch.

python scripts/update_valuation.py 2330
python scripts/update_valuation.py --batch 101
python scripts/update_valuation.py --sector Semiconductors
python scripts/update_valuation.py                          # All tickers

Discover Companies by Buzzword

Find every Taiwan-listed company related to a theme or technology:

# Basic search
python scripts/discover.py "液冷散熱"

# Auto-detect relevant sectors (skips banks/insurance/real estate for tech terms)
python scripts/discover.py "液冷散熱" --smart

# Tag matching companies with [[wikilinks]] in their reports
python scripts/discover.py "液冷散熱" --apply

# Apply + rebuild themes + rebuild network graph
python scripts/discover.py "液冷散熱" --apply --rebuild

# Limit to a specific sector
python scripts/discover.py "液冷散熱" --sector Semiconductors

Common buzzword examples:

• "CoWoS" — TSMC advanced packaging supply chain
• "HBM" — High Bandwidth Memory ecosystem
• "電動車" — EV component suppliers
• "AI 伺服器" — AI server supply chain (148 companies)
• "光阻液" — Photoresist suppliers and consumers
• "碳化矽" — Silicon carbide (SiC) companies

Update Enrichment Content (Bulk AI Research)

Prepare a JSON file, then apply to specific tickers, batches, or sectors:

python scripts/update_enrichment.py --data enrichment.json 2330
python scripts/update_enrichment.py --data enrichment.json --batch 101
python scripts/update_enrichment.py --data enrichment.json --sector Semiconductors

Enrichment JSON format:

{
  "2330": {
    "desc": "台積電為全球最大晶圓代工廠，專注於 [[CoWoS]]、[[3奈米]] 先進製程，為 [[Apple]]、[[NVIDIA]] 等科技巨頭提供晶片製造服務。",
    "supply_chain": "**上游:**\n- [[ASML]] (EUV 微影設備)\n- [[Applied Materials]] (薄膜沉積)\n**中游:**\n- **台積電** (晶圓代工)\n**下游:**\n- [[Apple]]\n- [[NVIDIA]]",
    "cust": "### 主要客戶\n- [[Apple]] (約25%營收)\n- [[NVIDIA]]\n- [[AMD]]\n\n### 主要供應商\n- [[ASML]]\n- [[Tokyo Electron]]"
  },
  "2454": {
    "desc": "...",
    "supply_chain": "...",
    "cust": "..."
  }
}

Audit Report Quality

# Single batch
python scripts/audit_batch.py 101 -v

# All batches
python scripts/audit_batch.py --all -v

Audit checks:

• Minimum 8 wikilinks per report
• No generic terms in brackets (e.g. [[公司]], [[產品]])
• No placeholder text remaining
• No English text in Chinese-language sections
• Metadata completeness (板塊, 產業, 市值, 企業價值)
• Section depth (業務簡介, 供應鏈位置, 主要客戶及供應商, 財務概況 all present)

Rebuild Wikilink Index

python scripts/build_wikilink_index.py

Regenerates WIKILINKS.md — a browsable index of all 4,900+ wikilinks categorized as Technologies, Materials, Applications, and Companies. Run after any enrichment update.

Generate Thematic Investment Screens

# Build all 20 themes
python scripts/build_themes.py

# Single theme
python scripts/build_themes.py "CoWoS"

# List available themes
python scripts/build_themes.py --list

Output in themes/ — each page shows companies grouped by upstream/midstream/downstream role.

Generate Interactive Network Graph

# Default: min 5 co-occurrences
python scripts/build_network.py

# Fewer edges for cleaner view
python scripts/build_network.py --min-weight 10

# Only top 200 nodes
python scripts/build_network.py --top 200

Opens network/index.html in browser — D3.js force-directed graph. Node colors:

• 🔴 Red = Taiwan company
• 🔵 Blue = International company
• 🟢 Green = Technology
• 🟠 Orange = Material
• 🟣 Purple = Application

Wikilink Graph — Core Feature

The wikilink graph is what makes this database powerful. Every [[entity]] in every report creates edges in a knowledge graph.

Search by entity to find related companies:

Browse: Open WIKILINKS.md for the full categorized index.

Code Examples

Read and Parse a Report

import re
from pathlib import Path

def get_report(ticker: str, reports_dir: str = "Pilot_Reports") -> dict:
    """Find and parse a ticker report."""
    base = Path(reports_dir)
    # Find the file across all sector subdirectories
    matches = list(base.rglob(f"{ticker}_*.md"))
    if not matches:
        return {}
    
    content = matches[0].read_text(encoding="utf-8")
    
    # Extract all wikilinks
    wikilinks = re.findall(r'\[\[([^\]]+)\]\]', content)
    
    # Extract sector metadata
    sector_match = re.search(r'\*\*產業:\*\*\s*(.+)', content)
    board_match = re.search(r'\*\*板塊:\*\*\s*(.+)', content)
    
    return {
        "ticker": ticker,
        "file": str(matches[0]),
        "sector": sector_match.group(1).strip() if sector_match else None,
        "board": board_match.group(1).strip() if board_match else None,
        "wikilinks": list(set(wikilinks)),
        "wikilink_count": len(set(wikilinks)),
        "content": content
    }

# Usage
report = get_report("2330")
print(f"Sector: {report['sector']}")
print(f"Wikilinks ({report['wikilink_count']}): {report['wikilinks'][:10]}")

Build a Custom Wikilink Index

import re
from pathlib import Path
from collections import defaultdict

def build_wikilink_index(reports_dir: str = "Pilot_Reports") -> dict:
    """
    Returns: {entity: [list of tickers that mention it]}
    """
    index = defaultdict(list)
    
    for md_file in Path(reports_dir).rglob("*.md"):
        # Extract ticker from filename (e.g. "2330_台積電.md" -> "2330")
        ticker = md_file.stem.split("_")[0]
        content = md_file.read_text(encoding="utf-8")
        wikilinks = set(re.findall(r'\[\[([^\]]+)\]\]', content))
        
        for link in wikilinks:
            index[link].append(ticker)
    
    # Sort by mention count
    return dict(sorted(index.items(), key=lambda x: len(x[1]), reverse=True))

# Find all companies in Apple's supply chain
index = build_wikilink_index()
apple_suppliers = index.get("Apple", [])
print(f"Apple supply chain: {len(apple_suppliers)} companies")
print(apple_suppliers[:20])

# Find companies involved in CoWoS
cowos_companies = index.get("CoWoS", [])
print(f"\nCoWoS ecosystem: {len(cowos_companies)} companies: {cowos_companies}")

Find Supply Chain Overlaps Between Two Entities

def supply_chain_overlap(entity_a: str, entity_b: str, reports_dir: str = "Pilot_Reports"):
    """Find tickers that appear in both entities' supply chains."""
    index = build_wikilink_index(reports_dir)
    
    set_a = set(index.get(entity_a, []))
    set_b = set(index.get(entity_b, []))
    overlap = set_a & set_b
    
    print(f"{entity_a}: {len(set_a)} companies")
    print(f"{entity_b}: {len(set_b)} companies")
    print(f"Overlap: {len(overlap)} companies — {sorted(overlap)}")
    return overlap

# Companies in both NVIDIA and Apple supply chains
supply_chain_overlap("NVIDIA", "Apple")

# Companies in both AI server and EV supply chains
supply_chain_overlap("AI 伺服器", "電動車")

Batch Financial Update with Error Handling

import subprocess
import sys

def update_sector_financials(sector: str, valuation_only: bool = False):
    """Update financials for all tickers in a sector."""
    script = "update_valuation.py" if valuation_only else "update_financials.py"
    cmd = [sys.executable, f"scripts/{script}", "--sector", sector]
    
    result = subprocess.run(cmd, capture_output=True, text=True)
    
    if result.returncode != 0:
        print(f"Error: {result.stderr}")
    else:
        print(result.stdout)
    
    return result.returncode

# Update valuation multiples for semiconductors (fast)
update_sector_financials("Semiconductors", valuation_only=True)

# Full financial update for a sector
update_sector_financials("Electronic Components", valuation_only=False)

Prepare Enrichment JSON

import json

def build_enrichment_entry(ticker: str, company_name: str,
                           description: str, upstream: list[str],
                           midstream: str, downstream: list[str],
                           customers: list[str], suppliers: list[str]) -> dict:
    """
    Build a properly formatted enrichment entry.
    All entity names in lists will be wrapped in [[wikilinks]].
    """
    def wikify(items):
        return "\n".join(f"- [[{item}]]" for item in items)
    
    supply_chain = (
        f"**上游:**\n{wikify(upstream)}\n"
        f"**中游:**\n- **{company_name}** ({midstream})\n"
        f"**下游:**\n{wikify(downstream)}"
    )
    
    cust_section = (
        f"### 主要客戶\n{wikify(customers)}\n\n"
        f"### 主要供應商\n{wikify(suppliers)}"
    )
    
    return {
        "desc": description,
        "supply_chain": supply_chain,
        "cust": cust_section
    }

# Build enrichment for multiple tickers
enrichment = {
    "2330": build_enrichment_entry(
        ticker="2330",
        company_name="台積電",
        description="台積電為全球最大晶圓代工廠，專注於 [[CoWoS]]、[[3奈米]] 先進製程，為全球領先科技公司提供晶片製造服務。",
        upstream=["ASML", "Applied Materials", "SUMCO", "Tokyo Electron"],
        midstream="晶圓代工",
        downstream=["Apple", "NVIDIA", "AMD", "Broadcom", "Qualcomm"],
        customers=["Apple", "NVIDIA", "AMD", "Qualcomm", "MediaTek"],
        suppliers=["ASML", "Tokyo Electron", "Shin-Etsu", "Applied Materials"]
    )
}

# Save to file
with open("enrichment.json", "w", encoding="utf-8") as f:
    json.dump(enrichment, f, ensure_ascii=False, indent=2)

print("enrichment.json ready. Apply with:")
print("python scripts/update_enrichment.py --data enrichment.json 2330")

Audit a Batch Programmatically

import subprocess
import json

def audit_and_report(batch_id: int) -> dict:
    """Run audit and parse results."""
    result = subprocess.run(
        ["python", "scripts/audit_batch.py", str(batch_id), "-v"],
        capture_output=True, text=True
    )
    
    output = result.stdout
    
    # Parse pass/fail counts from output
    passed = output.count("✓") 
    failed = output.count("✗")
    
    return {
        "batch": batch_id,
        "passed": passed,
        "failed": failed,
        "pass_rate": passed / (passed + failed) if (passed + failed) > 0 else 0,
        "output": output
    }

results = audit_and_report(101)
print(f"Batch 101: {results['passed']} passed, {results['failed']} failed")
print(f"Pass rate: {results['pass_rate']:.1%}")

Common Workflows

Workflow 1: Research a New Investment Theme

# 1. Search for related companies
python scripts/discover.py "液冷散熱" --smart

# 2. Apply wikilinks to matching reports
python scripts/discover.py "液冷散熱" --apply

# 3. Rebuild themes to include new theme
python scripts/build_themes.py "液冷散熱"

# 4. Rebuild wikilink index and network
python scripts/build_wikilink_index.py
python scripts/build_network.py

# 5. Browse results
open themes/液冷散熱.md
open network/index.html

Workflow 2: Onboard a New Ticker

# 1. Add the report (Python script, free)
python scripts/add_ticker.py 6669 緯穎 --sector Computer Hardware

# 2. Update financial data
python scripts/update_financials.py 6669

# 3. Prepare enrichment JSON (use AI research or manual)
# Edit enrichment.json with business description, supply chain, customers

# 4. Apply enrichment
python scripts/update_enrichment.py --data enrichment.json 6669

# 5. Audit quality
python scripts/audit_batch.py --all -v

# 6. Rebuild index
python scripts/build_wikilink_index.py

Workflow 3: Refresh Valuation for Earnings Season

# Fast valuation refresh only (no full financial re-fetch)
python scripts/update_valuation.py --sector Semiconductors
python scripts/update_valuation.py --sector Electronic Components
python scripts/update_valuation.py --sector Computer Hardware

# Or refresh everything (slow, run overnight)
python scripts/update_valuation.py

Workflow 4: Map a Supply Chain

# Find all Taiwan companies connected to a specific technology
from collections import defaultdict
import re
from pathlib import Path

def map_supply_chain(technology: str, reports_dir: str = "Pilot_Reports"):
    results = {"upstream": [], "midstream": [], "downstream": []}
    
    for md_file in Path(reports_dir).rglob("*.md"):
        content = md_file.read_text(encoding="utf-8")
        
        if f"[[{technology}]]" not in content:
            continue
        
        ticker = md_file.stem.split("_")[0]
        company = md_file.stem.split("_", 1)[1] if "_" in md_file.stem else ""
        
        # Detect position in supply chain
        if f"**上游:**" in content and f"[[{technology}]]" in content.split("**上游:**")[1].split("**中游:**")[0]:
            results["upstream"].append(f"{ticker} {company}")
        elif f"[[{technology}]]" in content and "**中游:**" in content:
            mid_section = content.split("**中游:**")[1].split("**下游:**")[0] if "**下游:**" in content else ""
            if f"[[{technology}]]" in mid_section:
                results["midstream"].append(f"{ticker} {company}")
        else:
            results["downstream"].append(f"{ticker} {company}")
    
    return results

chain = map_supply_chain("CoWoS")
print(f"Upstream: {chain['upstream']}")
print(f"Midstream: {chain['midstream']}")
print(f"Downstream: {chain['downstream']}")

Token Cost Reference

Rule of thumb: Use Python scripts for bulk data operations. Use Claude Code slash commands only when AI research is needed for a specific ticker.

Troubleshooting

yfinance returns no data for a Taiwan ticker:

import yfinance as yf
# Taiwan tickers need .TW suffix for TWSE, .TWO for OTC
tsmc = yf.Ticker("2330.TW")
print(tsmc.info.get("marketCap"))

Report not found by scripts:

• Filename must match pattern: {TICKER}_{CompanyName}.md
• Must be inside a subfolder of Pilot_Reports/
• Use python scripts/utils.py to test file discovery

Audit fails "too few wikilinks":

• Minimum 8 unique [[wikilinks]] required per report
• Use update_enrichment.py to add richer content
• Run discover.py --apply to auto-tag relevant wikilinks

build_network.py produces empty graph:

# Ensure reports exist and have wikilinks first
python scripts/build_wikilink_index.py  # Check WIKILINKS.md has entries
python scripts/build_network.py --min-weight 2  # Lower threshold

Enrichment JSON rejected:

• Ensure file is valid UTF-8 with ensure_ascii=False
• Keys must be ticker strings ("2330", not 2330)
• Required keys per entry: desc, supply_chain, cust
• Content must be in Traditional Chinese; English only for proper nouns

Finding batch numbers:

• See task.md for batch definitions and which tickers are in each batch
• Batches are used for incremental processing of the 1,735 tickers