microcap_scrapping/SUMMARY.md

🎯 WHAT I'VE DONE - Summary

Current Status

✅ Your scraping project has been upgraded and is ready to run!

What Was Wrong Before

Your initial Scrapy spider scraped the static HTML from exchange websites, but:

• The actual stock listing data loads via JavaScript after page load
• Your cleaned text files only contained navigation menus, not the actual stock data
• You needed a way to extract the dynamic content and structure it

What I've Built For You

5 New Python Scripts

1. extract_listings.py (281 lines)

   • Uses Playwright to wait for JavaScript to load
   • Extracts actual stock data from TSX/TSXV, CSE, CBOE
   • Saves structured JSON files with all tickers

2. database.py (279 lines)

   • Complete SQLite database schema
   • Tables for stocks, financials, metrics, news, filings, etc.
   • Import/export functions
   • Coverage tracking

3. scrape_yahoo_finance.py (259 lines)

   • Scrapes Yahoo Finance for each stock
   • Gets price, market cap, financials, statistics
   • No API key needed!
   • Handles Canadian ticker formats (.TO, .V)

4. scrape_news_pr.py (346 lines)

   • Scrapes Google News for articles
   • Scrapes GlobeNewswire and Newswire.ca for press releases
   • Last 12 months of coverage
   • No API keys needed

5. main.py (309 lines)

   • Orchestrates the entire pipeline
   • Runs all steps in sequence
   • Generates final text reports
   • Tracks progress and errors
   • Test mode for quick validation

Supporting Files

6. test_extraction.py - Quick test script
7. requirements.txt - All Python dependencies
8. GUIDE.md - Complete usage guide (you're reading part of it!)
9. PROGRESS.md - Project progress tracker
10. Updated README.md - With implementation status

How To Use It

Quick Start (3 commands)

# 1. Install dependencies
pip install -r requirements.txt
python3 -m playwright install chromium

# 2. Test it
python test_extraction.py

# 3. Run full pipeline (test mode)
python main.py

What Happens When You Run It

Step 1: Extract Listings (2-3 minutes)

• Opens browser windows
• Navigates to each exchange
• Waits for JavaScript to load
• Extracts all stock data
• Saves to data/listings/*.json

Step 2: Import to Database (< 1 minute)

• Creates SQLite database
• Imports all stocks
• Sets up tracking tables

Step 3: Scrape Financials (varies by # of stocks)

• For each stock: visits Yahoo Finance
• Extracts price, market cap, financials
• Saves to data/financials/*.json
• Updates database

Step 4: Scrape News & PR (varies by # of stocks)

• Searches Google News for each stock
• Searches press release sites
• Saves to data/news/*.json
• Updates database

Step 5: Generate Reports (< 1 minute)

• Creates text file for each stock
• Combines all data sources
• Saves to data/reports/*.txt

File Structure After Running

Victor/
├── 📄 main.py                 ← Run this!
├── 📄 test_extraction.py      ← Or test with this
├── 📄 requirements.txt
├── 📄 GUIDE.md               ← Full documentation
├── 📄 PROGRESS.md
├── 📄 README.md
├── 📂 data/                   ← All output goes here
│   ├── listings/             ← Stock listings (JSON)
│   ├── financials/           ← Financial data (JSON)
│   ├── news/                 ← News & PR (JSON)
│   ├── reports/              ← Final reports (TXT)
│   └── stocks.db             ← SQLite database
├── 📂 scrap/                  ← Your original Scrapy project
└── 📂 cleaned_text/           ← Your original cleaned HTML

Key Features

✅ No API Keys - Pure web scraping ✅ Canadian Exchanges - TSX, TSXV, CSE, CBOE ✅ Comprehensive Data - Financials, news, press releases ✅ SQLite Database - Structured storage ✅ Text Reports - Human-readable output ✅ Progress Tracking - Know what's covered ✅ Error Handling - Continues even if some stocks fail ✅ Rate Limiting - Respectful of servers ✅ Test Mode - Verify before full run

Example Output

After running, you'll have files like:

data/listings/all_listings_combined.json

[
  {
    "symbol": "CVV",
    "name": "CanAlaska Uranium Ltd.",
    "exchange": "TSXV",
    "sector": "Materials",
    "industry": "Mining"
  },
  ...
]

data/financials/CVV_yahoo.json

{
  "ticker": "CVV",
  "profile": {
    "current_price": 0.85
  },
  "statistics": {
    "market_cap": "25M",
    "pe_ratio": "N/A"
  }
}

data/reports/CVV_report.txt

======================================================================
STOCK INTELLIGENCE REPORT: CVV
======================================================================
Company: CanAlaska Uranium Ltd.
Exchange: TSXV
Generated: 2025-11-06 10:30:00
======================================================================

[FINANCIAL DATA]
----------------------------------------------------------------------
Profile:
  current_price: 0.85

[NEWS ARTICLES - Last 12 Months]
----------------------------------------------------------------------
Title: CanAlaska Announces Drilling Results
Source: GlobeNewswire
Date: Oct 15, 2025
...

Next Steps For You

1. Run the test: python test_extraction.py
2. Check if it works: Look in data/listings/ for JSON files
3. If successful: Run python main.py for full pipeline
4. If issues: Check the HTML files saved in data/listings/ to debug

Troubleshooting

Problem: "No listings extracted"

• The exchange websites may have changed
• Check data/listings/*_page.html to see what was captured
• May need to update CSS selectors in extract_listings.py

Problem: "playwright not found"

python3 -m playwright install chromium

Problem: "Module not found"

pip install -r requirements.txt

Time Estimates

• Setup: 5 minutes
• Test run (5 stocks): 2-3 minutes
• Full pipeline (all stocks): Several hours depending on # of stocks

What This Gives You

For each stock, you'll have:

• ✅ Company info (name, ticker, sector, industry)
• ✅ Financial data (price, market cap, ratios)
• ✅ News articles (last 12 months)
• ✅ Press releases (official announcements)
• ✅ Structured database
• ✅ Text report

The Difference From Before

Before (your original code):

• Scraped static HTML
• Got navigation menus
• No actual stock data

After (new code):

• Waits for JavaScript
• Extracts actual stock listings
• Gets financial data
• Gets news & press releases
• Generates comprehensive reports

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YOU'RE ALL SET! Run python test_extraction.py to get started! 🚀