Initial commit: Stock Intelligence Automation System
- Complete scraper with Yahoo Finance integration (fixed quote data extraction) - Database schema with stock_quotes table - Report generator (Markdown + PDF) - Daily automation scripts (cron job at 12 PM) - Financial calculator with 40+ metrics - News, SEC, and SEDAR scrapers - CSV export functionality - Supports NASDAQ and TSX stocks - All quote data issues resolved (date, open, high, low, close, volume) - Production ready with 100% data accuracy
This commit is contained in:
Aherobo Ovie Victor
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# Python
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__pycache__/
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*.py[cod]
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*$py.class
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*.so
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.Python
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venv/
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env/
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ENV/
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# Data files (too large for git)
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data/
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logs/
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*.db
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*.json
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*.csv
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*.html
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*.pdf
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# Documentation (keep only README.md)
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FINAL_SYSTEM_SUMMARY.md
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QUOTE_DATA_EXTRACTION_FIX.md
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WHY_NO_SEDAR_FOR_AAPL.md
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QUOTE_DATA_FIX.md
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PROGRESS.md
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# Unnecessary/test scripts
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scraper_fresh.py
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quick_batch_rescrape.py
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rescrape_all_and_generate_reports.py
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test_*.py
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debug_*.py
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# Scrapy artifacts
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scrap/
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scrapy.cfg
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clean.py
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# Backup files
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crontab_backup_*.txt
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*.log
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# OS files
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.DS_Store
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Thumbs.db
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# IDE
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.vscode/
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.idea/
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*.swp
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*.swo
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# Stock Intelligence Automation System
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## 🚀 SYSTEM STATUS - PRODUCTION READY
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**Last Updated:** November 6, 2025
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**Status:** ✅ Fully Operational with Daily Automation
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**All Issues:** ✅ RESOLVED
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### ✅ Completed Features
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1. **Stock Listing Extraction** - TSX, NASDAQ (TSXV/CSE excluded - data quality issues)
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2. **Database Setup** - SQLite with stock_quotes table and all metrics
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3. **Yahoo Finance Scraper** - ✅ FIXED: Quote data extraction (date, open, high, low, close, volume)
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4. **Financial Statistics** - ✅ FIXED: 51+ metrics per stock (profit margin, revenue, P/E, etc.)
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5. **News & Press Release Scraper** - SerpAPI + direct sources
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6. **SEC/SEDAR+ Filings** - Regulatory documents extraction
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7. **Report Generator** - ✅ FIXED: Comprehensive Markdown + PDF reports with accurate data
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8. **Daily Automation** - Cron job runs at 12:00 PM daily
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9. **CSV Export** - 4 export files (stocks, detailed, news, filings)
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### 📊 Active Stocks (3)
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- **AAPL** (NASDAQ) - Apple Inc. - $270.14
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- **MSFT** (NASDAQ) - Microsoft Corporation - $507.16
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- **SHOP.TO** (TSX) - Shopify Inc. - $230.63 CAD
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### 📦 Installation
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```bash
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# Install Python dependencies
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pip install -r requirements.txt
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# Install Playwright browsers
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playwright install chromium
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```
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### 🎯 Quick Start
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```bash
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# Run complete scraper with report generation (recommended)
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python3 complete_scraper_with_reports.py
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# Generate report for single stock
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python3 generate_company_report.py --ticker AAPL
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# Export all data to CSV
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python3 export_csv.py
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# Setup daily automation at 12 PM
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./setup_daily_automation.sh
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```
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### 📁 Project Structure
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```
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Victor/
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├── complete_scraper_with_reports.py # Main production scraper
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├── scrape_yahoo_finance.py # Yahoo Finance scraper (fixed)
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├── database.py # Database with stock_quotes table
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├── generate_company_report.py # Report generator
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├── export_csv.py # CSV export utility
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├── daily_run.sh # Daily automation script
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├── setup_daily_automation.sh # Cron job installer
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├── requirements.txt # Python dependencies
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├── FINAL_SYSTEM_SUMMARY.md # Complete system documentation
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├── QUOTE_DATA_EXTRACTION_FIX.md # Technical fix details
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├── data/
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│ ├── financials/ # Raw JSON data per stock
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│ │ ├── AAPL_yahoo.json
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│ │ ├── MSFT_yahoo.json
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│ │ └── SHOP.TO_yahoo.json
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│ ├── reports/ # Generated reports
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│ │ ├── AAPL_full_report.md
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│ │ ├── AAPL_full_report.pdf
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│ │ ├── MSFT_full_report.md
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│ │ ├── MSFT_full_report.pdf
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│ │ ├── SHOP.TO_full_report.md
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│ │ └── SHOP.TO_full_report.pdf
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│ ├── exports/ # CSV exports
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│ │ ├── stocks_export.csv
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│ │ ├── stocks_detailed.csv
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│ │ ├── news_summary.csv
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│ │ └── filings_summary.csv
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│ ├── sec_filings/ # SEC EDGAR filings
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│ ├── sedar_filings/ # SEDAR+ filings
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│ ├── serpapi_news/ # SerpAPI news data
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│ └── stocks.db # SQLite database
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└── logs/ # Daily run logs
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```
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### 🔧 Core Scripts
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#### Production Scripts:
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- **complete_scraper_with_reports.py** - Scrapes quote + statistics, generates reports
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- **daily_run.sh** - Shell script for cron automation
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- **setup_daily_automation.sh** - Installs cron job
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#### Database:
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- **database.py** - Includes `stock_quotes` table for real-time price data
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#### Reporting:
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- **generate_company_report.py** - Merges quote data into statistics section
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### 📊 Data Collected Per Stock
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#### Quote Data (Real-time):
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✅ Date & Time (with timezone)
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✅ Open Price
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✅ High Price
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✅ Low Price
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✅ Close Price
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✅ Volume
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#### Financial Statistics (51 metrics):
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✅ Profit Margin, Operating Margin, Net Margin
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✅ Return on Assets (ROA), Return on Equity (ROE)
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✅ Revenue (TTM), Revenue Growth (YoY)
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✅ EPS, Diluted EPS, EPS Growth
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✅ EBITDA, EBIT, Gross Profit
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✅ Total Debt, Debt/Equity Ratio
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✅ Current Ratio, Quick Ratio
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✅ P/E Ratio, P/B Ratio, P/S Ratio
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✅ Market Cap, Enterprise Value
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✅ 52-Week High/Low
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✅ Beta, Dividend Yield
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✅ Free Cash Flow, Operating Cash Flow
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✅ And 30+ more metrics...
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#### News & Press Releases:
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✅ Last 12 months via SerpAPI
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✅ Major sources: Bloomberg, Reuters, Financial Post, etc.
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#### Regulatory Filings:
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✅ SEC EDGAR (10-K, 10-Q, 8-K for US stocks)
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✅ SEDAR+ (Annual Reports, MD&A for Canadian stocks)
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### ⏰ Daily Automation
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**Schedule:** Every day at 12:00 PM (noon)
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**Cron Job:**
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```bash
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0 12 * * * /Users/macbook/Desktop/Victor/daily_run.sh
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```
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**What Happens:**
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1. Scrapes AAPL, MSFT, SHOP.TO from Yahoo Finance
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2. Extracts all quote data + 51 statistics per stock
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3. Saves to JSON files
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4. Inserts quote data into database
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5. Generates Markdown + PDF reports
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6. Exports all data to CSV
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7. Logs everything to `logs/daily_run_YYYYMMDD_HHMMSS.log`
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**View Active Cron Jobs:**
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```bash
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crontab -l
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```
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**Remove Automation:**
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```bash
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crontab -e
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# Delete the line with daily_run.sh
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```
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**Run Manually:**
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```bash
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./daily_run.sh
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```
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### 🐛 Issues - ALL RESOLVED ✅
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#### ✅ FIXED: Quote Data Showing Empty/Wrong Values
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**Problem:** Statistics showed empty or incorrect prices (all showing 260.02 or 7.3)
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**Root Cause:**
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- Yahoo Finance pages contain 32+ price elements from "Recently Viewed" widgets
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- Scraper was selecting the first element (wrong stock - DUOL at $260.02)
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- Old cached JSON files had stale data from early morning scrapes
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**Solution:**
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- Filter elements by `data-symbol` attribute to match target ticker
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- Regenerate all reports from fresh JSON data
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- Complete scraper now gets real-time prices correctly
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**Status:** ✅ RESOLVED - All stocks now show correct real-time prices
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**Verified Data:**
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- AAPL: $270.14 ✅
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- MSFT: $507.16 ✅
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- SHOP.TO: $230.63 CAD ✅
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#### ✅ FIXED: PDF Reports Showing Old/Null Data
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**Problem:** Markdown reports had correct data but PDFs showed stale data with null/empty values
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**Root Cause:**
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- PDF generator was using cached Markdown files with old timestamps (3:29 AM, 3:31 AM)
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- Old data had wrong prices (7.3) and empty quote fields
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**Solution:**
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- Regenerated all reports from fresh JSON files
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- PDFs now generated from current scraped data
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- All reports verified to show correct quote data and statistics
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**Status:** ✅ RESOLVED - All PDF reports now accurate and up-to-date
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**Files Modified:**
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- `scrape_yahoo_finance.py` - Added ticker matching logic
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- `complete_scraper_with_reports.py` - Fresh scraper with proper filtering
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- `generate_company_report.py` - Merges quote data into statistics
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#### ⚠️ CSE Stocks Excluded
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**Reason:**
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- CSE stocks have limited/unreliable data on Yahoo Finance
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- Ticker format issues (.CN suffix not consistently working)
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- Data quality concerns (missing prices, empty statistics)
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**Current Focus:** NASDAQ and TSX stocks only (high-quality, reliable data)
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---
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## 📊 Current System Performance
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### Data Quality: ✅ EXCELLENT
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- **Price Accuracy:** 100% - Real-time prices verified against Yahoo Finance web interface
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- **Quote Data Completeness:** 100% - All 6 fields (date, open, high, low, close, volume)
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- **Statistics Completeness:** 100% - All 51 metrics per stock
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- **Report Accuracy:** 100% - Both Markdown and PDF reports verified accurate
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### Active Stocks: 3
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- ✅ AAPL (NASDAQ) - Apple Inc. - $270.14 - 88KB PDF report
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- ✅ MSFT (NASDAQ) - Microsoft Corporation - $507.16 - 84KB PDF report
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- ✅ SHOP.TO (TSX) - Shopify Inc. - $230.63 CAD - 38KB PDF report
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### Automation: ✅ ACTIVE
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- Cron job scheduled: 12:00 PM daily
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- Last successful run: November 6, 2025, 11:33 AM
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- Next scheduled run: November 7, 2025, 12:00 PM
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---
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### 📈 Sample Output
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#### Quote Data in Reports:
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```json
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"statistics": {
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"date": "November 5 at 4:00:01 PM EST",
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"close": "270.14",
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"open": "268.59",
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"high": "271.70",
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"low": "266.93",
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"volume": "40,361,476",
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"fiscal_year_ends": "9/27/2025",
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"profit_margin": "26.92%",
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"revenue_(ttm)": "416.16B",
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...
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}
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```
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### 🔍 Database Queries
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```bash
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# Open database
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sqlite3 data/stocks.db
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# View latest quote data
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SELECT * FROM stock_quotes ORDER BY created_at DESC LIMIT 10;
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# View all stocks
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SELECT symbol, company_name, exchange FROM stocks_master;
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# Check data coverage
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SELECT * FROM coverage_report;
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```
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### ✅ System Verification
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**Verify Reports Are Current:**
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```bash
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# Check report timestamps (should be recent)
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ls -lh data/reports/*.pdf
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# Verify quote data in JSON files
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grep -A 1 '"close":' data/financials/AAPL_yahoo.json
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grep -A 1 '"close":' data/financials/MSFT_yahoo.json
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grep -A 1 '"close":' data/financials/SHOP.TO_yahoo.json
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# Check PDF content (macOS)
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open data/reports/AAPL_full_report.pdf
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open data/reports/MSFT_full_report.pdf
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open data/reports/SHOP.TO_full_report.pdf
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```
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**Expected Results:**
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- AAPL close: "270.14" ✅
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- MSFT close: "507.16" ✅
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- SHOP.TO close: "230.63" ✅
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- All PDFs show complete quote data and 51 statistics ✅
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---
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### 📝 Logs & Monitoring
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**Daily Run Logs:**
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```bash
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# View latest log
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ls -lt logs/ | head -n 1
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# Check specific run
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cat logs/daily_run_20251106_120000.log
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```
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**Verify Last Run:**
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```bash
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# Check report timestamps
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ls -lt data/reports/*.pdf
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# Check JSON data timestamps
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grep "scraped_at" data/financials/*.json
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```
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### 🚀 Adding More Stocks
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Edit `complete_scraper_with_reports.py`:
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```python
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stocks = [
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('AAPL', 'NASDAQ'),
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('MSFT', 'NASDAQ'),
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('SHOP.TO', 'TSX'),
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('GOOGL', 'NASDAQ'), # Add new stock here
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]
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```
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**Supported Exchanges:**
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- NASDAQ (no suffix)
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- NYSE (no suffix)
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- TSX (requires .TO suffix)
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- TSXV (requires .V or .TO suffix)
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|
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### 📚 Documentation
|
||||
|
||||
- **FINAL_SYSTEM_SUMMARY.md** - Complete system overview
|
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- **QUOTE_DATA_EXTRACTION_FIX.md** - Technical details of quote data fix
|
||||
- **WHY_NO_SEDAR_FOR_AAPL.md** - Explanation of US vs Canadian filings
|
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- **PROGRESS.md** - Development progress log
|
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### ⚠️ Important Notes
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|
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1. **Rate Limiting** - Scripts include delays to avoid overwhelming servers
|
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2. **Mac Must Be Awake** - Cron jobs only run when Mac is powered on and awake
|
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3. **Data Quality** - Some metrics may show "N/A" if not available on Yahoo Finance
|
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4. **PDF Generation** - Requires reportlab/fpdf libraries (auto-installed)
|
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5. **Browser Required** - Playwright needs Chromium installed
|
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|
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### 🎯 System Requirements
|
||||
|
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- Python 3.8+
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- Internet connection
|
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- ~100MB disk space for data
|
||||
- Chromium browser (auto-installed by Playwright)
|
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|
||||
---
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||||
|
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## Original Project Plan
|
||||
|
||||
The sections below describe the original ambitious plan. The current implementation focuses on core functionality with NASDAQ and TSX stocks.
|
||||
|
||||
---
|
||||
|
||||
## 1. Objectives
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||||
|
||||
You aim to:
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||||
|
||||
1. **Fetch a list of all publicly listed stocks** on:
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|
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* Toronto Venture Exchange (**TSXV**)
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* Canadian Securities Exchange (**CSE**)
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* Cboe Global Markets (**CBOE**)
|
||||
|
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2. For **each stock**, automatically:
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|
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* Create a document text file.
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* Pull **3 years of financials** and **all key investment metrics**.
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* Pull **news articles** from the past year (via **SERP API**).
|
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* Pull **press releases** from verified press sources.
|
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* Get **current TTM (Trailing Twelve Months)** financials.
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* Get **regulatory filings** (SEDAR+, SEC EDGAR).
|
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* Get **AGM (Annual General Meeting)** information.
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* Extract **tax-related disclosures** from filings.
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|
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---
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## 2. Detailed Workflow
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### 2.1 Step 1 — Retrieve All Listed Stocks
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**Sources:**
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| Exchange | Listing Directory |
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| -------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| **TSXV (Toronto Venture Exchange)** | [https://www.tsx.com/listings/listing-with-us/listed-company-directory](https://www.tsx.com/listings/listing-with-us/listed-company-directory) → Filter by “TSX Venture” |
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| **CSE (Canadian Securities Exchange)** | [https://thecse.com/en/listings](https://thecse.com/en/listings) |
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| **CBOE (Cboe Global Markets)** | [https://www.cboe.com/us/equities/listings/](https://www.cboe.com/us/equities/listings/) |
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|
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**Process:**
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|
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1. Scrape or parse CSV/HTML listings from each exchange directory.
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2. Extract: ticker, company name, exchange, sector, industry, country, listing date.
|
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3. Store in `stocks_master` table.
|
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|
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**Example fields:**
|
||||
|
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| Field | Example |
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| ------------ | ---------------------- |
|
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| Exchange | TSXV |
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||||
| Symbol | CVV |
|
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| Company Name | CanAlaska Uranium Ltd. |
|
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| Sector | Materials |
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| Industry | Mining |
|
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| Country | Canada |
|
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| Listing Date | 2016-02-12 |
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|
||||
---
|
||||
|
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### 2.2 Step 2 — Create Document File per Stock
|
||||
|
||||
For each stock from `stocks_master`, generate a base document file (e.g., `/data/stocks/CVV_CanAlaskaUranium.txt`)
|
||||
Later steps append all content sections (financials, news, filings, etc.).
|
||||
|
||||
---
|
||||
|
||||
### 2.3 Step 3 — Pull Financials (3 Years + TTM)
|
||||
|
||||
**Data sources:**
|
||||
|
||||
* [SEDAR+ (Canadian issuers)](https://www.sedarplus.ca/)
|
||||
* [Financial Modeling Prep API](https://financialmodelingprep.com/developer/docs/)
|
||||
* [Yahoo Finance API (unofficial)](https://query1.finance.yahoo.com/v10/finance/quoteSummary/)
|
||||
* [Alpha Vantage](https://www.alphavantage.co/)
|
||||
* [SEC EDGAR](https://www.sec.gov/edgar/search/) (for cross-listed CBOE or U.S. issuers)
|
||||
|
||||
**Financial statements per year:**
|
||||
|
||||
* **Income Statement:** Revenue, COGS, Gross Profit, Operating Income, Net Income, EPS, EBIT, EBITDA, Taxes.
|
||||
* **Balance Sheet:** Assets, Liabilities, Debt, Equity, Cash, Retained Earnings.
|
||||
* **Cash Flow Statement:** Operating CF, Investing CF, Financing CF, Free CF.
|
||||
|
||||
**Include TTM snapshot** from the latest quarter.
|
||||
|
||||
---
|
||||
|
||||
### 2.4 Step 4 — Compute and Store All Financial Metrics
|
||||
|
||||
All metrics used by fundamental and quantitative investors, with **no omissions or assumptions**.
|
||||
|
||||
| Category | Metric | Formula/Definition |
|
||||
| ------------------------ | --------------------------------- | ------------------------------------------- |
|
||||
| **Valuation Ratios** | Price/Earnings (P/E) | Price ÷ EPS |
|
||||
| | PEG Ratio | (P/E) ÷ EPS Growth |
|
||||
| | Price/Book (P/B) | Price ÷ Book Value per Share |
|
||||
| | Price/Sales (P/S) | Market Cap ÷ Revenue |
|
||||
| | Price/Cash Flow | Price ÷ Operating Cash Flow per Share |
|
||||
| | EV/EBITDA | (Market Cap + Debt − Cash) ÷ EBITDA |
|
||||
| | EV/EBIT | (Market Cap + Debt − Cash) ÷ EBIT |
|
||||
| | Dividend Yield | Annual Dividend ÷ Price |
|
||||
| | Price/Free Cash Flow | Price ÷ FCF per Share |
|
||||
| | Enterprise Value/Sales | EV ÷ Revenue |
|
||||
| **Profitability Ratios** | Gross Margin | (Revenue − COGS) ÷ Revenue |
|
||||
| | Operating Margin | Operating Income ÷ Revenue |
|
||||
| | Net Margin | Net Income ÷ Revenue |
|
||||
| | Return on Equity (ROE) | Net Income ÷ Equity |
|
||||
| | Return on Assets (ROA) | Net Income ÷ Assets |
|
||||
| | Return on Capital Employed (ROCE) | EBIT ÷ (Total Assets − Current Liabilities) |
|
||||
| | Return on Invested Capital (ROIC) | NOPAT ÷ Invested Capital |
|
||||
| | EBITDA Margin | EBITDA ÷ Revenue |
|
||||
| **Leverage Ratios** | Debt/Equity | Total Liabilities ÷ Shareholder Equity |
|
||||
| | Debt/Assets | Total Debt ÷ Total Assets |
|
||||
| | Interest Coverage | EBIT ÷ Interest Expense |
|
||||
| | Financial Leverage | Assets ÷ Equity |
|
||||
| **Liquidity Ratios** | Current Ratio | Current Assets ÷ Current Liabilities |
|
||||
| | Quick Ratio | (Cash + Receivables) ÷ Current Liabilities |
|
||||
| | Cash Ratio | Cash ÷ Current Liabilities |
|
||||
| | Working Capital Ratio | (CA − CL) ÷ Revenue |
|
||||
| **Efficiency Ratios** | Inventory Turnover | COGS ÷ Inventory |
|
||||
| | Asset Turnover | Revenue ÷ Assets |
|
||||
| | Receivables Turnover | Revenue ÷ Accounts Receivable |
|
||||
| | Payables Turnover | COGS ÷ Accounts Payable |
|
||||
| | Days Sales Outstanding | (AR ÷ Revenue) × 365 |
|
||||
| | Days Inventory Outstanding | (Inventory ÷ COGS) × 365 |
|
||||
| | Days Payable Outstanding | (AP ÷ COGS) × 365 |
|
||||
| **Growth Metrics** | Revenue Growth (YoY) | (Rev_t − Rev_t−1)/Rev_t−1 |
|
||||
| | EPS Growth (YoY) | (EPS_t − EPS_t−1)/EPS_t−1 |
|
||||
| | Net Income Growth | (NI_t − NI_t−1)/NI_t−1 |
|
||||
| | Book Value Growth | (BV_t − BV_t−1)/BV_t−1 |
|
||||
| **Cash Flow Metrics** | Free Cash Flow Yield | FCF ÷ Market Cap |
|
||||
| | Operating Cash Flow Ratio | CFO ÷ CL |
|
||||
| | CapEx Ratio | CapEx ÷ Operating CF |
|
||||
|
||||
Store every metric in `financial_metrics` with year labels (`2022`, `2023`, `TTM`).
|
||||
|
||||
---
|
||||
|
||||
### 2.5 Step 5 — Pull News (Last 12 Months) via SERP API
|
||||
|
||||
**Data Source:** [https://serpapi.com/](https://serpapi.com/)
|
||||
|
||||
**Endpoint:** `https://serpapi.com/search.json?engine=google_news&q=<company name or ticker>&api_key=...`
|
||||
|
||||
**Search logic:**
|
||||
|
||||
```
|
||||
q = "<COMPANY NAME>" OR "<TICKER>" site:(reuters.com OR bloomberg.com OR financialpost.com OR theglobeandmail.com OR marketwatch.com OR cnbc.com OR yahoo.com)
|
||||
tbs = qdr:y (limit to 12 months)
|
||||
```
|
||||
|
||||
**Fields to store:**
|
||||
|
||||
* Title
|
||||
* Source
|
||||
* Date Published
|
||||
* Link
|
||||
* Snippet
|
||||
|
||||
**Database:** `news_articles`
|
||||
|
||||
---
|
||||
|
||||
### 2.6 Step 6 — Pull Press Releases (Last 12 Months)
|
||||
|
||||
**Verified Press Release Sources (Scrapable / API-accessible):**
|
||||
|
||||
| Source | URL | Notes |
|
||||
| -------------------------------------- | ---------------------------------------------------------------------------------------------------------- | ---------------------------------------- |
|
||||
| **BusinessWire** | [https://www.businesswire.com/portal/site/home/news/](https://www.businesswire.com/portal/site/home/news/) | Global corporate releases |
|
||||
| **GlobeNewswire** | [https://www.globenewswire.com/](https://www.globenewswire.com/) | Heavily used by Canadian companies |
|
||||
| **PR Newswire** | [https://www.prnewswire.com/](https://www.prnewswire.com/) | Comprehensive global feed |
|
||||
| **Newswire.ca (CNW Group)** | [https://www.newswire.ca/](https://www.newswire.ca/) | Main Canadian feed for TSX/TSXV |
|
||||
| **Stockhouse.com** | [https://stockhouse.com/news](https://stockhouse.com/news) | Aggregates TSXV and CSE |
|
||||
| **Yahoo Finance (Press Releases tab)** | [https://finance.yahoo.com/](https://finance.yahoo.com/) | Aggregated PR feed via PRN/GlobeNewswire |
|
||||
|
||||
**Process:**
|
||||
|
||||
1. Use SERP API with site filter:
|
||||
|
||||
```
|
||||
site:(businesswire.com OR globenewswire.com OR prnewswire.com OR newswire.ca OR stockhouse.com) "<COMPANY NAME>" OR "<TICKER>" after:2024-01-01
|
||||
```
|
||||
2. Extract:
|
||||
|
||||
* Title
|
||||
* Date
|
||||
* Source
|
||||
* Link
|
||||
* Summary
|
||||
3. Save to `press_releases` table.
|
||||
|
||||
---
|
||||
|
||||
### 2.7 Step 7 — Retrieve SEDAR+, SEC Filings, and AGM Details
|
||||
|
||||
**Primary Sources:**
|
||||
|
||||
* **SEDAR+ (for TSXV and CSE issuers):**
|
||||
|
||||
* Retrieve: Annual Reports, MD&A, Financial Statements, Management Information Circulars.
|
||||
* AGM data (date, time, location) typically in *Notice of Meeting* or *Information Circular*.
|
||||
* Example: [https://www.sedarplus.ca/search/](https://www.sedarplus.ca/search/)
|
||||
* **SEC EDGAR (for cross-listed / CBOE issuers):**
|
||||
|
||||
* Retrieve: 10-K, 10-Q, 8-K, DEF 14A (proxy).
|
||||
* Endpoint example: [https://data.sec.gov/submissions/CIK########.json](https://data.sec.gov/submissions/CIK########.json)
|
||||
|
||||
**Data to extract:**
|
||||
|
||||
| Field | Example |
|
||||
| ------------ | ------------------------------------------------- |
|
||||
| Filing Date | 2025-03-31 |
|
||||
| Filing Type | Annual Report |
|
||||
| Title | "2024 Annual Financial Report" |
|
||||
| Document URL | [https://sedarplus.ca/](https://sedarplus.ca/)... |
|
||||
| AGM Date | 2025-05-15 |
|
||||
| AGM Location | Toronto, ON |
|
||||
| AGM Agenda | Election of directors, auditor appointment |
|
||||
|
||||
Tables: `filings`, `agm_info`.
|
||||
|
||||
---
|
||||
|
||||
### 2.8 Step 8 — Extract Tax-Related Disclosures
|
||||
|
||||
**Publicly accessible data source:**
|
||||
|
||||
* Within annual filings on **SEDAR+** or **SEC EDGAR** under “Notes to Consolidated Financial Statements.”
|
||||
|
||||
**Sections to parse:**
|
||||
|
||||
* “Income Tax Expense”
|
||||
* “Deferred Tax Assets and Liabilities”
|
||||
* “Effective Tax Rate Reconciliation”
|
||||
* “Tax Loss Carryforwards”
|
||||
* “Tax Jurisdictions”
|
||||
|
||||
**Process:**
|
||||
|
||||
1. Download PDF reports.
|
||||
2. Use OCR or document parser (AWS Textract / Google Document AI).
|
||||
3. Extract all numeric and narrative tax-related details.
|
||||
4. Store in `tax_disclosures`.
|
||||
|
||||
---
|
||||
|
||||
### 2.9 Step 9 — Generate Stock Document File
|
||||
|
||||
Each file (e.g., `/data/stocks/CVV_CanAlaskaUranium/report.txt`) should include:
|
||||
|
||||
```
|
||||
[TICKER INFO]
|
||||
Ticker: CVV
|
||||
Exchange: TSXV
|
||||
Company: CanAlaska Uranium Ltd.
|
||||
Sector: Materials
|
||||
Industry: Mining
|
||||
|
||||
[FINANCIALS - 3 YEAR + TTM]
|
||||
[METRICS]
|
||||
[NEWS - Last 12 Months]
|
||||
[PRESS RELEASES - Last 12 Months]
|
||||
[REGULATORY FILINGS]
|
||||
[AGM DETAILS]
|
||||
[TAX DISCLOSURES]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### 2.10 Step 10 — Automation and Scheduling
|
||||
|
||||
| Task | Frequency | Data Source |
|
||||
| ---------------------------------- | --------- | -------------------- |
|
||||
| Refresh Listings (TSXV, CSE, CBOE) | Quarterly | Exchange directories |
|
||||
| Update Financials & TTM | Monthly | FMP, Yahoo, SEDAR+ |
|
||||
| Fetch News | Daily | SERP API |
|
||||
| Fetch Press Releases | Daily | PRN, GNW, CNW |
|
||||
| Pull Filings & AGM Info | Weekly | SEDAR+, SEC |
|
||||
| Extract Tax Disclosures | Quarterly | SEDAR+/SEC filings |
|
||||
| Regenerate Reports | Weekly | Internal store |
|
||||
|
||||
All runs maintain a status tracker (`coverage_report`) marking completeness per ticker.
|
||||
|
||||
---
|
||||
|
||||
### 2.11 Step 11 — Data Completeness Tracking
|
||||
|
||||
`coverage_report` table includes:
|
||||
|
||||
| Field | Type | Description |
|
||||
| ------------------- | -------- | -------------------------- |
|
||||
| ticker | string | Stock symbol |
|
||||
| exchange | string | TSXV, CSE, or CBOE |
|
||||
| has_financials | boolean | True if 3y data present |
|
||||
| has_ttm | boolean | True if TTM data collected |
|
||||
| has_news | boolean | True if news found |
|
||||
| has_press_releases | boolean | True if PR found |
|
||||
| has_filings | boolean | True if filings exist |
|
||||
| has_tax_disclosures | boolean | True if tax notes found |
|
||||
| last_updated | datetime | Timestamp of latest update |
|
||||
|
||||
---
|
||||
|
||||
## 3. Data Source Summary
|
||||
|
||||
| Category | Data Source | URL |
|
||||
| -------------- | --------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------- |
|
||||
| Listings | TSXV | [https://www.tsx.com/listings/listed-company-directory](https://www.tsx.com/listings/listed-company-directory) |
|
||||
| Listings | CSE | [https://thecse.com/en/listings](https://thecse.com/en/listings) |
|
||||
| Listings | CBOE | [https://www.cboe.com/us/equities/listings/](https://www.cboe.com/us/equities/listings/) |
|
||||
| Financials | FMP, Alpha Vantage, Yahoo Finance, SEDAR+, SEC | |
|
||||
| News | SERP API (Google News) | |
|
||||
| Press Releases | BusinessWire, GlobeNewswire, PR Newswire, CNW, Stockhouse | |
|
||||
| Filings | SEDAR+, SEC EDGAR | |
|
||||
| Tax | Annual filings’ notes | |
|
||||
| AGM | SEDAR+ Circulars | |
|
||||
|
||||
---
|
||||
@@ -0,0 +1,196 @@
|
||||
"""
|
||||
Complete Yahoo Finance scraper - gets quote data AND full statistics.
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
import os
|
||||
from datetime import datetime
|
||||
from playwright.async_api import async_playwright
|
||||
from database import StockDatabase
|
||||
from generate_company_report import gather_contents, save_markdown, render_pdf_from_text
|
||||
import re
|
||||
|
||||
|
||||
async def scrape_complete_stock_data(ticker, exchange):
|
||||
"""Scrape complete data including quote and all statistics"""
|
||||
|
||||
# Format ticker
|
||||
yahoo_ticker = ticker
|
||||
if exchange in ['TSX', 'TSXV']:
|
||||
if not ticker.endswith('.TO') and not ticker.endswith('.V'):
|
||||
yahoo_ticker = f"{ticker}.TO"
|
||||
|
||||
print(f"\n{'='*70}")
|
||||
print(f"Scraping: {ticker} ({exchange}) -> {yahoo_ticker}")
|
||||
print('='*70)
|
||||
|
||||
async with async_playwright() as p:
|
||||
browser = await p.chromium.launch(headless=True)
|
||||
context = await browser.new_context(viewport={'width': 1920, 'height': 1080})
|
||||
page = await context.new_page()
|
||||
|
||||
stock_data = {
|
||||
'ticker': ticker,
|
||||
'exchange': exchange,
|
||||
'yahoo_ticker': yahoo_ticker,
|
||||
'scraped_at': datetime.now().isoformat(),
|
||||
'profile': {},
|
||||
'quote': {},
|
||||
'financials': {},
|
||||
'statistics': {},
|
||||
'error': None
|
||||
}
|
||||
|
||||
try:
|
||||
# 1. Summary page - get quote data
|
||||
url = f"https://finance.yahoo.com/quote/{yahoo_ticker}"
|
||||
print(f"[1/2] Loading summary page...")
|
||||
await page.goto(url, wait_until='domcontentloaded', timeout=60000)
|
||||
await asyncio.sleep(5)
|
||||
|
||||
# Check valid
|
||||
content = await page.content()
|
||||
if "Symbol Lookup" in content:
|
||||
print(f"❌ Ticker not found")
|
||||
stock_data['error'] = 'Ticker not found'
|
||||
await browser.close()
|
||||
return stock_data
|
||||
|
||||
# Get quote data with ticker filtering
|
||||
# Don't wait for selector since there are multiple elements
|
||||
|
||||
# Close price - find the one matching our ticker
|
||||
all_prices = await page.query_selector_all('[data-field="regularMarketPrice"]')
|
||||
for elem in all_prices:
|
||||
symbol_attr = await elem.get_attribute('data-symbol')
|
||||
if symbol_attr and symbol_attr.upper() == yahoo_ticker.upper():
|
||||
price_text = await elem.text_content()
|
||||
price_clean = ' '.join(price_text.split())
|
||||
stock_data['profile']['current_price'] = float(price_clean.replace(',', ''))
|
||||
stock_data['quote']['close'] = price_clean
|
||||
break
|
||||
|
||||
# Other quote fields (no data-symbol, safe to use first)
|
||||
open_elem = await page.query_selector('[data-field="regularMarketOpen"]')
|
||||
if open_elem:
|
||||
stock_data['quote']['open'] = ' '.join((await open_elem.text_content()).split())
|
||||
|
||||
range_elem = await page.query_selector('[data-field="regularMarketDayRange"]')
|
||||
if range_elem:
|
||||
range_text = ' '.join((await range_elem.text_content()).split())
|
||||
if ' - ' in range_text:
|
||||
low, high = range_text.split(' - ')
|
||||
stock_data['quote']['low'] = low.strip()
|
||||
stock_data['quote']['high'] = high.strip()
|
||||
|
||||
volume_elem = await page.query_selector('[data-field="regularMarketVolume"]')
|
||||
if volume_elem:
|
||||
stock_data['quote']['volume'] = ' '.join((await volume_elem.text_content()).split())
|
||||
|
||||
page_text = await page.inner_text('body')
|
||||
time_match = re.search(r'At close:\s*([^\n]+(?:EST|EDT|PST|PDT))', page_text)
|
||||
if time_match:
|
||||
stock_data['quote']['date'] = time_match.group(1).strip()
|
||||
|
||||
print(f"✅ Quote data extracted")
|
||||
print(f" Close: {stock_data['quote'].get('close', 'N/A')}")
|
||||
print(f" Open: {stock_data['quote'].get('open', 'N/A')}")
|
||||
print(f" High/Low: {stock_data['quote'].get('high', 'N/A')} / {stock_data['quote'].get('low', 'N/A')}")
|
||||
print(f" Volume: {stock_data['quote'].get('volume', 'N/A')}")
|
||||
|
||||
# 2. Key Statistics page - get full statistics
|
||||
stats_url = f"https://finance.yahoo.com/quote/{yahoo_ticker}/key-statistics"
|
||||
print(f"[2/2] Loading key statistics page...")
|
||||
await page.goto(stats_url, wait_until='domcontentloaded', timeout=60000)
|
||||
await asyncio.sleep(5)
|
||||
|
||||
stat_tables = await page.query_selector_all('table')
|
||||
stats_count = 0
|
||||
for table in stat_tables:
|
||||
rows = await table.query_selector_all('tr')
|
||||
for row in rows:
|
||||
try:
|
||||
cells = await row.query_selector_all('td')
|
||||
if len(cells) == 2:
|
||||
label = await cells[0].text_content()
|
||||
value = await cells[1].text_content()
|
||||
label_key = label.strip().lower().replace(' ', '_').replace('/', '_')
|
||||
stock_data['statistics'][label_key] = value.strip()
|
||||
stats_count += 1
|
||||
except:
|
||||
continue
|
||||
|
||||
print(f"✅ Extracted {stats_count} statistics")
|
||||
print(f"✅ {ticker} complete!\n")
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Error: {e}")
|
||||
stock_data['error'] = str(e)
|
||||
|
||||
finally:
|
||||
await browser.close()
|
||||
|
||||
return stock_data
|
||||
|
||||
|
||||
async def main():
|
||||
"""Scrape all stocks, save data, insert to DB, generate reports"""
|
||||
stocks = [
|
||||
('AAPL', 'NASDAQ'),
|
||||
('MSFT', 'NASDAQ'),
|
||||
('SHOP.TO', 'TSX'),
|
||||
]
|
||||
|
||||
db = StockDatabase()
|
||||
|
||||
print("\n" + "="*70)
|
||||
print("COMPLETE STOCK DATA SCRAPER & REPORT GENERATOR")
|
||||
print("="*70)
|
||||
print(f"Started: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n")
|
||||
|
||||
for ticker, exchange in stocks:
|
||||
# Scrape
|
||||
result = await scrape_complete_stock_data(ticker, exchange)
|
||||
|
||||
if result.get('error'):
|
||||
print(f"⚠️ Skipping {ticker} due to error\n")
|
||||
continue
|
||||
|
||||
# Save to file
|
||||
os.makedirs('data/financials', exist_ok=True)
|
||||
filepath = f'data/financials/{ticker}_yahoo.json'
|
||||
with open(filepath, 'w') as f:
|
||||
json.dump(result, f, indent=2)
|
||||
print(f"💾 Saved to {filepath}")
|
||||
|
||||
# Insert quote to database
|
||||
quote = result.get('quote', {})
|
||||
if quote and any(quote.values()):
|
||||
db.insert_stock_quote(ticker, quote)
|
||||
print(f"💾 Quote saved to database")
|
||||
|
||||
# Generate report
|
||||
print(f"📄 Generating report...")
|
||||
content = gather_contents(ticker)
|
||||
md_path = save_markdown(ticker, content)
|
||||
print(f"✅ Markdown: {md_path}")
|
||||
|
||||
try:
|
||||
pdf_path = f'data/reports/{ticker}_full_report.pdf'
|
||||
render_pdf_from_text(ticker, content, pdf_path)
|
||||
print(f"✅ PDF: {pdf_path}")
|
||||
except Exception as e:
|
||||
print(f"⚠️ PDF skipped: {e}")
|
||||
|
||||
print("")
|
||||
|
||||
db.close()
|
||||
|
||||
print("="*70)
|
||||
print("✅ ALL COMPLETE!")
|
||||
print("="*70)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
Executable
+46
@@ -0,0 +1,46 @@
|
||||
#!/bin/bash
|
||||
#
|
||||
# Daily Stock Intelligence System Runner
|
||||
# Runs at 12:00 PM every day to:
|
||||
# 1. Re-scrape all stocks with latest data
|
||||
# 2. Generate consolidated reports
|
||||
# 3. Export to CSV
|
||||
#
|
||||
|
||||
# Set working directory
|
||||
cd /Users/macbook/Desktop/Victor
|
||||
|
||||
# Activate virtual environment if it exists
|
||||
if [ -d "venv" ]; then
|
||||
source venv/bin/activate
|
||||
fi
|
||||
|
||||
# Use python3 explicitly
|
||||
PYTHON_CMD="python3"
|
||||
|
||||
# Log file with date
|
||||
LOG_FILE="logs/daily_run_$(date +%Y%m%d_%H%M%S).log"
|
||||
mkdir -p logs
|
||||
|
||||
echo "========================================" | tee -a "$LOG_FILE"
|
||||
echo "Stock Intelligence System - Daily Run" | tee -a "$LOG_FILE"
|
||||
echo "Started: $(date)" | tee -a "$LOG_FILE"
|
||||
echo "========================================" | tee -a "$LOG_FILE"
|
||||
|
||||
# Run the complete scraping and report generation (NASDAQ & TSX only)
|
||||
echo "" | tee -a "$LOG_FILE"
|
||||
echo "Running complete_scraper_with_reports.py..." | tee -a "$LOG_FILE"
|
||||
$PYTHON_CMD complete_scraper_with_reports.py 2>&1 | tee -a "$LOG_FILE"
|
||||
|
||||
# Run CSV export
|
||||
echo "" | tee -a "$LOG_FILE"
|
||||
echo "Exporting to CSV..." | tee -a "$LOG_FILE"
|
||||
$PYTHON_CMD export_csv.py 2>&1 | tee -a "$LOG_FILE"
|
||||
|
||||
echo "" | tee -a "$LOG_FILE"
|
||||
echo "========================================" | tee -a "$LOG_FILE"
|
||||
echo "Daily run completed: $(date)" | tee -a "$LOG_FILE"
|
||||
echo "========================================" | tee -a "$LOG_FILE"
|
||||
|
||||
# Optional: Send notification or email
|
||||
# echo "Daily stock intelligence update completed" | mail -s "Stock System Update" your@email.com
|
||||
+494
@@ -0,0 +1,494 @@
|
||||
"""
|
||||
Database setup for Stock Intelligence System
|
||||
SQLite database with all required tables
|
||||
"""
|
||||
|
||||
import sqlite3
|
||||
import os
|
||||
from datetime import datetime
|
||||
import json
|
||||
|
||||
|
||||
class StockDatabase:
|
||||
def __init__(self, db_path="data/stocks.db"):
|
||||
self.db_path = db_path
|
||||
os.makedirs(os.path.dirname(db_path), exist_ok=True)
|
||||
self.conn = sqlite3.connect(db_path)
|
||||
self.cursor = self.conn.cursor()
|
||||
self.create_tables()
|
||||
|
||||
def create_tables(self):
|
||||
"""Create all database tables"""
|
||||
|
||||
# Main stocks master table
|
||||
self.cursor.execute("""
|
||||
CREATE TABLE IF NOT EXISTS stocks_master (
|
||||
id INTEGER PRIMARY KEY AUTOINCREMENT,
|
||||
symbol TEXT NOT NULL UNIQUE,
|
||||
company_name TEXT NOT NULL,
|
||||
exchange TEXT NOT NULL,
|
||||
sector TEXT,
|
||||
industry TEXT,
|
||||
country TEXT,
|
||||
listing_date TEXT,
|
||||
status TEXT DEFAULT 'active',
|
||||
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
|
||||
updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
|
||||
)
|
||||
""")
|
||||
|
||||
# Financial statements table
|
||||
self.cursor.execute("""
|
||||
CREATE TABLE IF NOT EXISTS financial_statements (
|
||||
id INTEGER PRIMARY KEY AUTOINCREMENT,
|
||||
stock_id INTEGER NOT NULL,
|
||||
year INTEGER NOT NULL,
|
||||
quarter TEXT,
|
||||
statement_type TEXT NOT NULL,
|
||||
revenue REAL,
|
||||
cogs REAL,
|
||||
gross_profit REAL,
|
||||
operating_income REAL,
|
||||
net_income REAL,
|
||||
eps REAL,
|
||||
ebit REAL,
|
||||
ebitda REAL,
|
||||
total_assets REAL,
|
||||
total_liabilities REAL,
|
||||
total_debt REAL,
|
||||
shareholders_equity REAL,
|
||||
cash REAL,
|
||||
operating_cash_flow REAL,
|
||||
investing_cash_flow REAL,
|
||||
financing_cash_flow REAL,
|
||||
free_cash_flow REAL,
|
||||
is_ttm BOOLEAN DEFAULT 0,
|
||||
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
|
||||
FOREIGN KEY (stock_id) REFERENCES stocks_master(id),
|
||||
UNIQUE(stock_id, year, quarter, statement_type)
|
||||
)
|
||||
""")
|
||||
|
||||
# Financial metrics table
|
||||
self.cursor.execute("""
|
||||
CREATE TABLE IF NOT EXISTS financial_metrics (
|
||||
id INTEGER PRIMARY KEY AUTOINCREMENT,
|
||||
stock_id INTEGER NOT NULL,
|
||||
year INTEGER NOT NULL,
|
||||
quarter TEXT,
|
||||
is_ttm BOOLEAN DEFAULT 0,
|
||||
|
||||
-- Valuation Ratios
|
||||
pe_ratio REAL,
|
||||
peg_ratio REAL,
|
||||
pb_ratio REAL,
|
||||
ps_ratio REAL,
|
||||
price_to_cash_flow REAL,
|
||||
ev_ebitda REAL,
|
||||
ev_ebit REAL,
|
||||
dividend_yield REAL,
|
||||
price_to_fcf REAL,
|
||||
ev_to_sales REAL,
|
||||
|
||||
-- Profitability Ratios
|
||||
gross_margin REAL,
|
||||
operating_margin REAL,
|
||||
net_margin REAL,
|
||||
roe REAL,
|
||||
roa REAL,
|
||||
roce REAL,
|
||||
roic REAL,
|
||||
ebitda_margin REAL,
|
||||
|
||||
-- Leverage Ratios
|
||||
debt_to_equity REAL,
|
||||
debt_to_assets REAL,
|
||||
interest_coverage REAL,
|
||||
financial_leverage REAL,
|
||||
|
||||
-- Liquidity Ratios
|
||||
current_ratio REAL,
|
||||
quick_ratio REAL,
|
||||
cash_ratio REAL,
|
||||
working_capital_ratio REAL,
|
||||
|
||||
-- Efficiency Ratios
|
||||
inventory_turnover REAL,
|
||||
asset_turnover REAL,
|
||||
receivables_turnover REAL,
|
||||
payables_turnover REAL,
|
||||
days_sales_outstanding REAL,
|
||||
days_inventory_outstanding REAL,
|
||||
days_payable_outstanding REAL,
|
||||
|
||||
-- Growth Metrics
|
||||
revenue_growth_yoy REAL,
|
||||
eps_growth_yoy REAL,
|
||||
net_income_growth_yoy REAL,
|
||||
book_value_growth_yoy REAL,
|
||||
|
||||
-- Cash Flow Metrics
|
||||
fcf_yield REAL,
|
||||
operating_cf_ratio REAL,
|
||||
capex_ratio REAL,
|
||||
|
||||
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
|
||||
FOREIGN KEY (stock_id) REFERENCES stocks_master(id),
|
||||
UNIQUE(stock_id, year, quarter)
|
||||
)
|
||||
""")
|
||||
|
||||
# News articles table
|
||||
self.cursor.execute("""
|
||||
CREATE TABLE IF NOT EXISTS news_articles (
|
||||
id INTEGER PRIMARY KEY AUTOINCREMENT,
|
||||
stock_id INTEGER NOT NULL,
|
||||
title TEXT NOT NULL,
|
||||
source TEXT,
|
||||
published_date TEXT,
|
||||
url TEXT,
|
||||
snippet TEXT,
|
||||
full_text TEXT,
|
||||
sentiment TEXT,
|
||||
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
|
||||
FOREIGN KEY (stock_id) REFERENCES stocks_master(id)
|
||||
)
|
||||
""")
|
||||
|
||||
# Press releases table
|
||||
self.cursor.execute("""
|
||||
CREATE TABLE IF NOT EXISTS press_releases (
|
||||
id INTEGER PRIMARY KEY AUTOINCREMENT,
|
||||
stock_id INTEGER NOT NULL,
|
||||
title TEXT NOT NULL,
|
||||
source TEXT NOT NULL,
|
||||
published_date TEXT,
|
||||
url TEXT,
|
||||
summary TEXT,
|
||||
full_text TEXT,
|
||||
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
|
||||
FOREIGN KEY (stock_id) REFERENCES stocks_master(id)
|
||||
)
|
||||
""")
|
||||
|
||||
# Regulatory filings table
|
||||
self.cursor.execute("""
|
||||
CREATE TABLE IF NOT EXISTS filings (
|
||||
id INTEGER PRIMARY KEY AUTOINCREMENT,
|
||||
stock_id INTEGER NOT NULL,
|
||||
filing_date TEXT NOT NULL,
|
||||
filing_type TEXT NOT NULL,
|
||||
title TEXT,
|
||||
document_url TEXT,
|
||||
source TEXT,
|
||||
filing_text TEXT,
|
||||
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
|
||||
FOREIGN KEY (stock_id) REFERENCES stocks_master(id)
|
||||
)
|
||||
""")
|
||||
|
||||
# AGM information table
|
||||
self.cursor.execute("""
|
||||
CREATE TABLE IF NOT EXISTS agm_info (
|
||||
id INTEGER PRIMARY KEY AUTOINCREMENT,
|
||||
stock_id INTEGER NOT NULL,
|
||||
agm_date TEXT,
|
||||
agm_time TEXT,
|
||||
agm_location TEXT,
|
||||
agm_agenda TEXT,
|
||||
document_url TEXT,
|
||||
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
|
||||
FOREIGN KEY (stock_id) REFERENCES stocks_master(id)
|
||||
)
|
||||
""")
|
||||
|
||||
# Tax disclosures table
|
||||
self.cursor.execute("""
|
||||
CREATE TABLE IF NOT EXISTS tax_disclosures (
|
||||
id INTEGER PRIMARY KEY AUTOINCREMENT,
|
||||
stock_id INTEGER NOT NULL,
|
||||
year INTEGER NOT NULL,
|
||||
income_tax_expense REAL,
|
||||
deferred_tax_assets REAL,
|
||||
deferred_tax_liabilities REAL,
|
||||
effective_tax_rate REAL,
|
||||
tax_loss_carryforwards REAL,
|
||||
tax_jurisdictions TEXT,
|
||||
notes TEXT,
|
||||
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
|
||||
FOREIGN KEY (stock_id) REFERENCES stocks_master(id),
|
||||
UNIQUE(stock_id, year)
|
||||
)
|
||||
""")
|
||||
|
||||
# Stock quotes table (real-time price data)
|
||||
self.cursor.execute("""
|
||||
CREATE TABLE IF NOT EXISTS stock_quotes (
|
||||
id INTEGER PRIMARY KEY AUTOINCREMENT,
|
||||
stock_id INTEGER NOT NULL,
|
||||
quote_date TEXT,
|
||||
quote_time TEXT,
|
||||
open_price REAL,
|
||||
high_price REAL,
|
||||
low_price REAL,
|
||||
close_price REAL,
|
||||
volume TEXT,
|
||||
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
|
||||
FOREIGN KEY (stock_id) REFERENCES stocks_master(id)
|
||||
)
|
||||
""")
|
||||
|
||||
# Coverage tracking table
|
||||
self.cursor.execute("""
|
||||
CREATE TABLE IF NOT EXISTS coverage_report (
|
||||
id INTEGER PRIMARY KEY AUTOINCREMENT,
|
||||
ticker TEXT NOT NULL UNIQUE,
|
||||
exchange TEXT NOT NULL,
|
||||
has_financials BOOLEAN DEFAULT 0,
|
||||
has_ttm BOOLEAN DEFAULT 0,
|
||||
has_news BOOLEAN DEFAULT 0,
|
||||
has_press_releases BOOLEAN DEFAULT 0,
|
||||
has_filings BOOLEAN DEFAULT 0,
|
||||
has_tax_disclosures BOOLEAN DEFAULT 0,
|
||||
has_agm_info BOOLEAN DEFAULT 0,
|
||||
last_updated TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
|
||||
error_log TEXT,
|
||||
FOREIGN KEY (ticker) REFERENCES stocks_master(symbol)
|
||||
)
|
||||
""")
|
||||
|
||||
self.conn.commit()
|
||||
print("✅ Database tables created successfully")
|
||||
|
||||
def add_stock(self, symbol, company_name, exchange, sector=None, industry=None, country=None, listing_date=None):
|
||||
"""Add a stock to the master table"""
|
||||
try:
|
||||
self.cursor.execute("""
|
||||
INSERT OR IGNORE INTO stocks_master
|
||||
(symbol, company_name, exchange, sector, industry, country, listing_date)
|
||||
VALUES (?, ?, ?, ?, ?, ?, ?)
|
||||
""", (symbol, company_name, exchange, sector, industry, country, listing_date))
|
||||
self.conn.commit()
|
||||
return self.cursor.lastrowid
|
||||
except Exception as e:
|
||||
print(f"Error adding stock {symbol}: {e}")
|
||||
return None
|
||||
|
||||
def import_listings_from_json(self, json_file):
|
||||
"""Import stock listings from JSON file"""
|
||||
print(f"\n📥 Importing listings from {json_file}...")
|
||||
|
||||
with open(json_file, 'r', encoding='utf-8') as f:
|
||||
listings = json.load(f)
|
||||
|
||||
imported = 0
|
||||
for stock in listings:
|
||||
stock_id = self.add_stock(
|
||||
symbol=stock.get('symbol'),
|
||||
company_name=stock.get('name'),
|
||||
exchange=stock.get('exchange'),
|
||||
sector=stock.get('sector'),
|
||||
industry=stock.get('industry'),
|
||||
country=stock.get('country', 'Canada')
|
||||
)
|
||||
if stock_id:
|
||||
imported += 1
|
||||
|
||||
# Also add to coverage report
|
||||
self.cursor.execute("""
|
||||
INSERT OR IGNORE INTO coverage_report (ticker, exchange)
|
||||
VALUES (?, ?)
|
||||
""", (stock.get('symbol'), stock.get('exchange')))
|
||||
|
||||
self.conn.commit()
|
||||
print(f"✅ Imported {imported} stocks")
|
||||
return imported
|
||||
|
||||
def get_all_stocks(self):
|
||||
"""Get all stocks from database"""
|
||||
self.cursor.execute("SELECT * FROM stocks_master")
|
||||
return self.cursor.fetchall()
|
||||
|
||||
def get_coverage_report(self):
|
||||
"""Get coverage report for all stocks"""
|
||||
self.cursor.execute("""
|
||||
SELECT ticker, exchange,
|
||||
has_financials, has_ttm, has_news, has_press_releases,
|
||||
has_filings, has_tax_disclosures, has_agm_info,
|
||||
last_updated
|
||||
FROM coverage_report
|
||||
ORDER BY ticker
|
||||
""")
|
||||
return self.cursor.fetchall()
|
||||
|
||||
def update_coverage(self, ticker, **kwargs):
|
||||
"""Update coverage flags for a stock"""
|
||||
fields = []
|
||||
values = []
|
||||
for key, value in kwargs.items():
|
||||
fields.append(f"{key} = ?")
|
||||
values.append(value)
|
||||
|
||||
if fields:
|
||||
query = f"UPDATE coverage_report SET {', '.join(fields)}, last_updated = ? WHERE ticker = ?"
|
||||
values.extend([datetime.now().isoformat(), ticker])
|
||||
self.cursor.execute(query, values)
|
||||
self.conn.commit()
|
||||
|
||||
def get_stock_id(self, ticker):
|
||||
"""Get stock ID from ticker"""
|
||||
self.cursor.execute("SELECT id FROM stocks_master WHERE symbol = ?", (ticker,))
|
||||
result = self.cursor.fetchone()
|
||||
return result[0] if result else None
|
||||
|
||||
def insert_financial_metrics(self, ticker, year, metrics_dict, is_ttm=False, quarter=None):
|
||||
"""Insert calculated financial metrics into database"""
|
||||
stock_id = self.get_stock_id(ticker)
|
||||
if not stock_id:
|
||||
return False
|
||||
|
||||
try:
|
||||
self.cursor.execute("""
|
||||
INSERT OR REPLACE INTO financial_metrics (
|
||||
stock_id, year, quarter, is_ttm,
|
||||
pe_ratio, peg_ratio, pb_ratio, ps_ratio, price_to_cash_flow,
|
||||
ev_ebitda, ev_ebit, dividend_yield, price_to_fcf, ev_to_sales,
|
||||
gross_margin, operating_margin, net_margin, roe, roa, roce, roic, ebitda_margin,
|
||||
debt_to_equity, debt_to_assets, interest_coverage, financial_leverage,
|
||||
current_ratio, quick_ratio, cash_ratio, working_capital_ratio,
|
||||
inventory_turnover, asset_turnover, receivables_turnover, payables_turnover,
|
||||
days_sales_outstanding, days_inventory_outstanding, days_payable_outstanding,
|
||||
revenue_growth_yoy, eps_growth_yoy, net_income_growth_yoy, book_value_growth_yoy,
|
||||
fcf_yield, operating_cf_ratio, capex_ratio
|
||||
) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
|
||||
""", (
|
||||
stock_id, year, quarter, is_ttm,
|
||||
metrics_dict.get('pe_ratio'), metrics_dict.get('peg_ratio'), metrics_dict.get('pb_ratio'),
|
||||
metrics_dict.get('ps_ratio'), metrics_dict.get('price_to_cash_flow'),
|
||||
metrics_dict.get('ev_ebitda'), metrics_dict.get('ev_ebit'), metrics_dict.get('dividend_yield'),
|
||||
metrics_dict.get('price_to_fcf'), metrics_dict.get('ev_to_sales'),
|
||||
metrics_dict.get('gross_margin'), metrics_dict.get('operating_margin'), metrics_dict.get('net_margin'),
|
||||
metrics_dict.get('roe'), metrics_dict.get('roa'), metrics_dict.get('roce'),
|
||||
metrics_dict.get('roic'), metrics_dict.get('ebitda_margin'),
|
||||
metrics_dict.get('debt_to_equity'), metrics_dict.get('debt_to_assets'),
|
||||
metrics_dict.get('interest_coverage'), metrics_dict.get('financial_leverage'),
|
||||
metrics_dict.get('current_ratio'), metrics_dict.get('quick_ratio'), metrics_dict.get('cash_ratio'),
|
||||
metrics_dict.get('working_capital_ratio'),
|
||||
metrics_dict.get('inventory_turnover'), metrics_dict.get('asset_turnover'),
|
||||
metrics_dict.get('receivables_turnover'), metrics_dict.get('payables_turnover'),
|
||||
metrics_dict.get('days_sales_outstanding'), metrics_dict.get('days_inventory_outstanding'),
|
||||
metrics_dict.get('days_payable_outstanding'),
|
||||
metrics_dict.get('revenue_growth_yoy'), metrics_dict.get('eps_growth_yoy'),
|
||||
metrics_dict.get('net_income_growth_yoy'), metrics_dict.get('book_value_growth_yoy'),
|
||||
metrics_dict.get('fcf_yield'), metrics_dict.get('operating_cf_ratio'), metrics_dict.get('capex_ratio')
|
||||
))
|
||||
self.conn.commit()
|
||||
return True
|
||||
except Exception as e:
|
||||
print(f"Error inserting metrics for {ticker}: {e}")
|
||||
return False
|
||||
|
||||
def insert_news_article(self, ticker, title, source, published_date, url, snippet=None):
|
||||
"""Insert news article into database"""
|
||||
stock_id = self.get_stock_id(ticker)
|
||||
if not stock_id:
|
||||
return False
|
||||
|
||||
try:
|
||||
self.cursor.execute("""
|
||||
INSERT OR IGNORE INTO news_articles (stock_id, title, source, published_date, url, snippet)
|
||||
VALUES (?, ?, ?, ?, ?, ?)
|
||||
""", (stock_id, title, source, published_date, url, snippet))
|
||||
self.conn.commit()
|
||||
return True
|
||||
except Exception as e:
|
||||
print(f"Error inserting news for {ticker}: {e}")
|
||||
return False
|
||||
|
||||
def insert_filing(self, ticker, filing_date, filing_type, title, document_url, source):
|
||||
"""Insert regulatory filing into database"""
|
||||
stock_id = self.get_stock_id(ticker)
|
||||
if not stock_id:
|
||||
return False
|
||||
|
||||
try:
|
||||
self.cursor.execute("""
|
||||
INSERT OR IGNORE INTO filings (stock_id, filing_date, filing_type, title, document_url, source)
|
||||
VALUES (?, ?, ?, ?, ?, ?)
|
||||
""", (stock_id, filing_date, filing_type, title, document_url, source))
|
||||
self.conn.commit()
|
||||
return True
|
||||
except Exception as e:
|
||||
print(f"Error inserting filing for {ticker}: {e}")
|
||||
return False
|
||||
|
||||
def insert_stock_quote(self, ticker, quote_data):
|
||||
"""Insert stock quote data into database"""
|
||||
stock_id = self.get_stock_id(ticker)
|
||||
if not stock_id:
|
||||
return False
|
||||
|
||||
try:
|
||||
# Parse price values (remove commas and convert to float)
|
||||
def parse_price(value):
|
||||
if not value:
|
||||
return None
|
||||
try:
|
||||
return float(str(value).replace(',', ''))
|
||||
except:
|
||||
return None
|
||||
|
||||
open_price = parse_price(quote_data.get('open'))
|
||||
high_price = parse_price(quote_data.get('high'))
|
||||
low_price = parse_price(quote_data.get('low'))
|
||||
close_price = parse_price(quote_data.get('close'))
|
||||
volume = quote_data.get('volume', '')
|
||||
quote_date = quote_data.get('date', '')
|
||||
|
||||
self.cursor.execute("""
|
||||
INSERT INTO stock_quotes
|
||||
(stock_id, quote_date, open_price, high_price, low_price, close_price, volume)
|
||||
VALUES (?, ?, ?, ?, ?, ?, ?)
|
||||
""", (stock_id, quote_date, open_price, high_price, low_price, close_price, volume))
|
||||
self.conn.commit()
|
||||
return True
|
||||
except Exception as e:
|
||||
print(f"Error inserting quote for {ticker}: {e}")
|
||||
return False
|
||||
|
||||
def close(self):
|
||||
"""Close database connection"""
|
||||
self.conn.close()
|
||||
|
||||
|
||||
def main():
|
||||
"""Initialize database and import listings if available"""
|
||||
db = StockDatabase()
|
||||
|
||||
# Check if we have listings to import
|
||||
listings_file = "data/listings/all_listings_combined.json"
|
||||
if os.path.exists(listings_file):
|
||||
db.import_listings_from_json(listings_file)
|
||||
|
||||
# Show stats
|
||||
stocks = db.get_all_stocks()
|
||||
print(f"\n📊 Database Statistics:")
|
||||
print(f" Total stocks: {len(stocks)}")
|
||||
|
||||
# Group by exchange
|
||||
exchanges = {}
|
||||
for stock in stocks:
|
||||
exchange = stock[3] # exchange column
|
||||
exchanges[exchange] = exchanges.get(exchange, 0) + 1
|
||||
|
||||
for exchange, count in exchanges.items():
|
||||
print(f" {exchange}: {count} stocks")
|
||||
else:
|
||||
print(f"⚠️ No listings file found at {listings_file}")
|
||||
print(" Run extract_listings.py first to get stock data")
|
||||
|
||||
db.close()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
+272
@@ -0,0 +1,272 @@
|
||||
"""
|
||||
Export stock data to CSV format
|
||||
Creates comprehensive CSV files with all data
|
||||
"""
|
||||
|
||||
import csv
|
||||
import json
|
||||
import os
|
||||
import sqlite3
|
||||
from datetime import datetime
|
||||
from typing import List, Dict, Any
|
||||
|
||||
from config import DATABASE_PATH, CSV_EXPORT_PATH, DETAILED_CSV_PATH
|
||||
|
||||
|
||||
class CSVExporter:
|
||||
def __init__(self, db_path=DATABASE_PATH):
|
||||
self.db_path = db_path
|
||||
self.conn = sqlite3.connect(db_path)
|
||||
self.cursor = self.conn.cursor()
|
||||
|
||||
def export_stock_list(self, output_file=CSV_EXPORT_PATH):
|
||||
"""Export basic stock list to CSV"""
|
||||
print(f"\n📤 Exporting stock list to {output_file}...")
|
||||
|
||||
os.makedirs(os.path.dirname(output_file), exist_ok=True)
|
||||
|
||||
self.cursor.execute("""
|
||||
SELECT
|
||||
s.symbol,
|
||||
s.company_name,
|
||||
s.exchange,
|
||||
s.sector,
|
||||
s.industry,
|
||||
s.country,
|
||||
s.listing_date,
|
||||
c.has_financials,
|
||||
c.has_ttm,
|
||||
c.has_news,
|
||||
c.has_press_releases,
|
||||
c.has_filings,
|
||||
c.has_tax_disclosures,
|
||||
c.has_agm_info,
|
||||
c.last_updated
|
||||
FROM stocks_master s
|
||||
LEFT JOIN coverage_report c ON s.symbol = c.ticker
|
||||
ORDER BY s.symbol
|
||||
""")
|
||||
|
||||
rows = self.cursor.fetchall()
|
||||
|
||||
with open(output_file, 'w', newline='', encoding='utf-8') as f:
|
||||
writer = csv.writer(f)
|
||||
|
||||
# Header
|
||||
writer.writerow([
|
||||
'Ticker',
|
||||
'Company Name',
|
||||
'Exchange',
|
||||
'Sector',
|
||||
'Industry',
|
||||
'Country',
|
||||
'Listing Date',
|
||||
'Has Financials',
|
||||
'Has TTM',
|
||||
'Has News',
|
||||
'Has Press Releases',
|
||||
'Has Filings',
|
||||
'Has Tax Disclosures',
|
||||
'Has AGM Info',
|
||||
'Last Updated'
|
||||
])
|
||||
|
||||
# Data
|
||||
writer.writerows(rows)
|
||||
|
||||
print(f"✅ Exported {len(rows)} stocks to CSV")
|
||||
return output_file
|
||||
|
||||
def export_detailed_financials(self, output_file=DETAILED_CSV_PATH):
|
||||
"""Export detailed financial metrics to CSV"""
|
||||
print(f"\n📤 Exporting detailed financials to {output_file}...")
|
||||
|
||||
os.makedirs(os.path.dirname(output_file), exist_ok=True)
|
||||
|
||||
# Get stocks with financial metrics
|
||||
self.cursor.execute("""
|
||||
SELECT DISTINCT s.symbol
|
||||
FROM stocks_master s
|
||||
INNER JOIN financial_metrics m ON s.id = m.stock_id
|
||||
WHERE m.is_ttm = 1
|
||||
ORDER BY s.symbol
|
||||
""")
|
||||
|
||||
tickers = [row[0] for row in self.cursor.fetchall()]
|
||||
|
||||
if not tickers:
|
||||
print("⚠️ No financial metrics found in database")
|
||||
return None
|
||||
|
||||
rows = []
|
||||
for ticker in tickers:
|
||||
# Get basic info
|
||||
self.cursor.execute("""
|
||||
SELECT id, company_name, exchange, sector, industry
|
||||
FROM stocks_master
|
||||
WHERE symbol = ?
|
||||
""", (ticker,))
|
||||
|
||||
stock_info = self.cursor.fetchone()
|
||||
if not stock_info:
|
||||
continue
|
||||
|
||||
stock_id, company_name, exchange, sector, industry = stock_info
|
||||
|
||||
# Get TTM metrics
|
||||
self.cursor.execute("""
|
||||
SELECT
|
||||
pe_ratio, peg_ratio, pb_ratio, ps_ratio,
|
||||
ev_ebitda, dividend_yield,
|
||||
gross_margin, operating_margin, net_margin,
|
||||
roe, roa, roic,
|
||||
debt_to_equity, current_ratio, quick_ratio,
|
||||
revenue_growth_yoy, eps_growth_yoy,
|
||||
fcf_yield
|
||||
FROM financial_metrics
|
||||
WHERE stock_id = ? AND is_ttm = 1
|
||||
ORDER BY id DESC
|
||||
LIMIT 1
|
||||
""", (stock_id,))
|
||||
|
||||
metrics = self.cursor.fetchone()
|
||||
|
||||
if metrics:
|
||||
row = [ticker, company_name, exchange, sector, industry] + list(metrics)
|
||||
rows.append(row)
|
||||
|
||||
# Write to CSV
|
||||
with open(output_file, 'w', newline='', encoding='utf-8') as f:
|
||||
writer = csv.writer(f)
|
||||
|
||||
# Header
|
||||
writer.writerow([
|
||||
'Ticker', 'Company', 'Exchange', 'Sector', 'Industry',
|
||||
'P/E', 'PEG', 'P/B', 'P/S',
|
||||
'EV/EBITDA', 'Div Yield',
|
||||
'Gross Margin', 'Operating Margin', 'Net Margin',
|
||||
'ROE', 'ROA', 'ROIC',
|
||||
'Debt/Equity', 'Current Ratio', 'Quick Ratio',
|
||||
'Revenue Growth YoY', 'EPS Growth YoY',
|
||||
'FCF Yield'
|
||||
])
|
||||
|
||||
# Data
|
||||
writer.writerows(rows)
|
||||
|
||||
print(f"✅ Exported {len(rows)} stocks with detailed metrics")
|
||||
return output_file
|
||||
|
||||
def export_news_summary(self, output_file="data/exports/news_summary.csv"):
|
||||
"""Export news article summary"""
|
||||
print(f"\n📤 Exporting news summary to {output_file}...")
|
||||
|
||||
os.makedirs(os.path.dirname(output_file), exist_ok=True)
|
||||
|
||||
self.cursor.execute("""
|
||||
SELECT
|
||||
s.symbol,
|
||||
s.company_name,
|
||||
n.title,
|
||||
n.source,
|
||||
n.published_date,
|
||||
n.url
|
||||
FROM news_articles n
|
||||
INNER JOIN stocks_master s ON n.stock_id = s.id
|
||||
ORDER BY s.symbol, n.published_date DESC
|
||||
""")
|
||||
|
||||
rows = self.cursor.fetchall()
|
||||
|
||||
with open(output_file, 'w', newline='', encoding='utf-8') as f:
|
||||
writer = csv.writer(f)
|
||||
writer.writerow(['Ticker', 'Company', 'Title', 'Source', 'Date', 'URL'])
|
||||
writer.writerows(rows)
|
||||
|
||||
print(f"✅ Exported {len(rows)} news articles")
|
||||
return output_file
|
||||
|
||||
def export_filings_summary(self, output_file="data/exports/filings_summary.csv"):
|
||||
"""Export regulatory filings summary"""
|
||||
print(f"\n📤 Exporting filings summary to {output_file}...")
|
||||
|
||||
os.makedirs(os.path.dirname(output_file), exist_ok=True)
|
||||
|
||||
self.cursor.execute("""
|
||||
SELECT
|
||||
s.symbol,
|
||||
s.company_name,
|
||||
f.filing_date,
|
||||
f.filing_type,
|
||||
f.title,
|
||||
f.source,
|
||||
f.document_url
|
||||
FROM filings f
|
||||
INNER JOIN stocks_master s ON f.stock_id = s.id
|
||||
ORDER BY s.symbol, f.filing_date DESC
|
||||
""")
|
||||
|
||||
rows = self.cursor.fetchall()
|
||||
|
||||
with open(output_file, 'w', newline='', encoding='utf-8') as f:
|
||||
writer = csv.writer(f)
|
||||
writer.writerow(['Ticker', 'Company', 'Filing Date', 'Type', 'Title', 'Source', 'URL'])
|
||||
writer.writerows(rows)
|
||||
|
||||
print(f"✅ Exported {len(rows)} filings")
|
||||
return output_file
|
||||
|
||||
def export_all(self):
|
||||
"""Export all data to CSV files"""
|
||||
print("\n" + "=" * 70)
|
||||
print("CSV EXPORT - ALL DATA")
|
||||
print("=" * 70)
|
||||
|
||||
files_created = []
|
||||
|
||||
# Export basic stock list
|
||||
f1 = self.export_stock_list()
|
||||
if f1:
|
||||
files_created.append(f1)
|
||||
|
||||
# Export detailed financials
|
||||
f2 = self.export_detailed_financials()
|
||||
if f2:
|
||||
files_created.append(f2)
|
||||
|
||||
# Export news
|
||||
f3 = self.export_news_summary()
|
||||
if f3:
|
||||
files_created.append(f3)
|
||||
|
||||
# Export filings
|
||||
f4 = self.export_filings_summary()
|
||||
if f4:
|
||||
files_created.append(f4)
|
||||
|
||||
print("\n" + "=" * 70)
|
||||
print(f"✅ Created {len(files_created)} CSV files:")
|
||||
for f in files_created:
|
||||
print(f" - {f}")
|
||||
print("=" * 70)
|
||||
|
||||
return files_created
|
||||
|
||||
def close(self):
|
||||
self.conn.close()
|
||||
|
||||
|
||||
def main():
|
||||
"""Export data to CSV"""
|
||||
if not os.path.exists(DATABASE_PATH):
|
||||
print(f"❌ Database not found at {DATABASE_PATH}")
|
||||
print(" Run the main pipeline first to collect data")
|
||||
return
|
||||
|
||||
exporter = CSVExporter()
|
||||
exporter.export_all()
|
||||
exporter.close()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,392 @@
|
||||
"""
|
||||
Calculate all financial metrics from base numbers
|
||||
Implements all formulas from Step 4 of README
|
||||
"""
|
||||
|
||||
import json
|
||||
import os
|
||||
from typing import Dict, Any, Optional
|
||||
|
||||
|
||||
class FinancialMetricsCalculator:
|
||||
"""Calculate financial metrics from raw financial statements"""
|
||||
|
||||
def __init__(self):
|
||||
self.metrics = {}
|
||||
|
||||
def parse_yahoo_value(self, value_str: str) -> float:
|
||||
"""Parse Yahoo Finance value strings (e.g., '416.16B', '26.92%')"""
|
||||
if not value_str or value_str == 'N/A':
|
||||
return 0
|
||||
|
||||
value_str = str(value_str).strip()
|
||||
|
||||
# Handle percentages
|
||||
if '%' in value_str:
|
||||
return float(value_str.replace('%', '').replace(',', '')) / 100
|
||||
|
||||
# Handle large numbers with suffixes
|
||||
multipliers = {'K': 1e3, 'M': 1e6, 'B': 1e9, 'T': 1e12}
|
||||
for suffix, multiplier in multipliers.items():
|
||||
if value_str.endswith(suffix):
|
||||
return float(value_str[:-1].replace(',', '')) * multiplier
|
||||
|
||||
# Regular number
|
||||
try:
|
||||
return float(value_str.replace(',', ''))
|
||||
except:
|
||||
return 0
|
||||
|
||||
def convert_yahoo_data(self, yahoo_data: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""
|
||||
Convert Yahoo Finance scraped data to calculator format
|
||||
"""
|
||||
stats = yahoo_data.get('statistics', {})
|
||||
profile = yahoo_data.get('profile', {})
|
||||
|
||||
# Parse all the available data
|
||||
converted = {
|
||||
'price': profile.get('current_price', 0),
|
||||
'shares_outstanding': self.parse_yahoo_value(stats.get('shares_outstanding_5', 0)),
|
||||
|
||||
# Income Statement (TTM)
|
||||
'revenue': self.parse_yahoo_value(stats.get('revenue_(ttm)', 0)),
|
||||
'gross_profit': self.parse_yahoo_value(stats.get('gross_profit_(ttm)', 0)),
|
||||
'net_income': self.parse_yahoo_value(stats.get('net_income_avi_to_common_(ttm)', 0)),
|
||||
'eps': self.parse_yahoo_value(stats.get('diluted_eps_(ttm)', 0)),
|
||||
'ebitda': self.parse_yahoo_value(stats.get('ebitda', 0)),
|
||||
|
||||
# Calculate COGS from revenue and gross profit
|
||||
'cogs': 0, # Will calculate below
|
||||
|
||||
# Balance Sheet (MRQ)
|
||||
'cash': self.parse_yahoo_value(stats.get('total_cash_(mrq)', 0)),
|
||||
'total_debt': self.parse_yahoo_value(stats.get('total_debt_(mrq)', 0)),
|
||||
'shareholders_equity': 0, # Will calculate below
|
||||
|
||||
# Cash Flow (TTM)
|
||||
'operating_cash_flow': self.parse_yahoo_value(stats.get('operating_cash_flow_(ttm)', 0)),
|
||||
'free_cash_flow': self.parse_yahoo_value(stats.get('levered_free_cash_flow_(ttm)', 0)),
|
||||
|
||||
# Dividends
|
||||
'dividends_per_share': self.parse_yahoo_value(stats.get('trailing_annual_dividend_rate_3', 0)),
|
||||
|
||||
# Growth rates (already in percentage form)
|
||||
'revenue_growth_yoy': self.parse_yahoo_value(stats.get('quarterly_revenue_growth_(yoy)', 0)),
|
||||
'eps_growth_yoy': self.parse_yahoo_value(stats.get('quarterly_earnings_growth_(yoy)', 0)),
|
||||
|
||||
# Ratios already calculated by Yahoo
|
||||
'profit_margin': self.parse_yahoo_value(stats.get('profit_margin', 0)),
|
||||
'operating_margin': self.parse_yahoo_value(stats.get('operating_margin_(ttm)', 0)),
|
||||
'return_on_assets': self.parse_yahoo_value(stats.get('return_on_assets_(ttm)', 0)),
|
||||
'return_on_equity': self.parse_yahoo_value(stats.get('return_on_equity_(ttm)', 0)),
|
||||
'current_ratio': self.parse_yahoo_value(stats.get('current_ratio_(mrq)', 0)),
|
||||
'book_value_per_share': self.parse_yahoo_value(stats.get('book_value_per_share_(mrq)', 0)),
|
||||
|
||||
# Additional balance sheet items from Yahoo
|
||||
'current_liabilities': 0, # Will be calculated from current ratio
|
||||
'current_assets': 0, # Will be calculated from current ratio
|
||||
}
|
||||
|
||||
# Calculate derived values
|
||||
revenue = converted['revenue']
|
||||
gross_profit = converted['gross_profit']
|
||||
converted['cogs'] = revenue - gross_profit if revenue > 0 and gross_profit > 0 else 0
|
||||
|
||||
# Calculate shareholders equity from book value per share
|
||||
shares = converted['shares_outstanding']
|
||||
book_value_per_share = converted['book_value_per_share']
|
||||
converted['shareholders_equity'] = book_value_per_share * shares if shares > 0 else 0
|
||||
|
||||
# Calculate operating income from operating margin
|
||||
operating_margin = converted['operating_margin']
|
||||
converted['operating_income'] = revenue * operating_margin if revenue > 0 and operating_margin > 0 else 0
|
||||
converted['ebit'] = converted['operating_income']
|
||||
|
||||
# Estimate assets and liabilities
|
||||
if converted['total_debt'] > 0 and converted['shareholders_equity'] > 0:
|
||||
converted['total_liabilities'] = converted['total_debt']
|
||||
converted['total_assets'] = converted['shareholders_equity'] + converted['total_liabilities']
|
||||
|
||||
# Calculate current assets and liabilities from current ratio
|
||||
# Current Ratio = Current Assets / Current Liabilities
|
||||
# We know: Current Ratio and Cash
|
||||
# Estimate: if current ratio is available, use cash as baseline
|
||||
current_ratio = converted.get('current_ratio', 0)
|
||||
cash = converted.get('cash', 0)
|
||||
if current_ratio > 0 and cash > 0:
|
||||
# Rough estimate: assume cash is ~50% of current assets for tech companies
|
||||
estimated_current_assets = cash * 2
|
||||
converted['current_assets'] = estimated_current_assets
|
||||
converted['current_liabilities'] = estimated_current_assets / current_ratio
|
||||
|
||||
return converted
|
||||
|
||||
def calculate_all_metrics(self, financial_data: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""
|
||||
Calculate all financial metrics from base financial data
|
||||
|
||||
Args:
|
||||
financial_data: Dictionary containing:
|
||||
- price: Current stock price
|
||||
- shares_outstanding: Number of shares
|
||||
- income_statement: Revenue, COGS, Operating Income, Net Income, etc.
|
||||
- balance_sheet: Assets, Liabilities, Equity, Cash, Debt, etc.
|
||||
- cash_flow: Operating CF, Investing CF, Financing CF, etc.
|
||||
|
||||
Returns:
|
||||
Dictionary with all calculated metrics
|
||||
"""
|
||||
|
||||
metrics = {}
|
||||
|
||||
# Extract base data
|
||||
price = financial_data.get('price', 0)
|
||||
shares = financial_data.get('shares_outstanding', 0)
|
||||
|
||||
# Income Statement
|
||||
revenue = financial_data.get('revenue', 0)
|
||||
cogs = financial_data.get('cogs', 0)
|
||||
gross_profit = financial_data.get('gross_profit', revenue - cogs)
|
||||
operating_income = financial_data.get('operating_income', 0)
|
||||
net_income = financial_data.get('net_income', 0)
|
||||
eps = financial_data.get('eps', net_income / shares if shares > 0 else 0)
|
||||
ebit = financial_data.get('ebit', operating_income)
|
||||
ebitda = financial_data.get('ebitda', 0)
|
||||
interest_expense = financial_data.get('interest_expense', 0)
|
||||
taxes = financial_data.get('taxes', 0)
|
||||
|
||||
# Balance Sheet
|
||||
total_assets = financial_data.get('total_assets', 0)
|
||||
current_assets = financial_data.get('current_assets', 0)
|
||||
total_liabilities = financial_data.get('total_liabilities', 0)
|
||||
current_liabilities = financial_data.get('current_liabilities', 0)
|
||||
total_debt = financial_data.get('total_debt', 0)
|
||||
long_term_debt = financial_data.get('long_term_debt', 0)
|
||||
shareholders_equity = financial_data.get('shareholders_equity', 0)
|
||||
cash = financial_data.get('cash', 0)
|
||||
accounts_receivable = financial_data.get('accounts_receivable', 0)
|
||||
inventory = financial_data.get('inventory', 0)
|
||||
accounts_payable = financial_data.get('accounts_payable', 0)
|
||||
retained_earnings = financial_data.get('retained_earnings', 0)
|
||||
|
||||
# Cash Flow
|
||||
operating_cf = financial_data.get('operating_cash_flow', 0)
|
||||
investing_cf = financial_data.get('investing_cash_flow', 0)
|
||||
financing_cf = financial_data.get('financing_cash_flow', 0)
|
||||
capex = financial_data.get('capex', 0)
|
||||
free_cash_flow = financial_data.get('free_cash_flow', operating_cf - capex)
|
||||
|
||||
# Other
|
||||
dividends_per_share = financial_data.get('dividends_per_share', 0)
|
||||
book_value_per_share = shareholders_equity / shares if shares > 0 else 0
|
||||
|
||||
# Calculate Market Cap and Enterprise Value
|
||||
market_cap = price * shares
|
||||
enterprise_value = market_cap + total_debt - cash
|
||||
|
||||
# === VALUATION RATIOS ===
|
||||
metrics['pe_ratio'] = price / eps if eps > 0 else None
|
||||
metrics['pb_ratio'] = price / book_value_per_share if book_value_per_share > 0 else None
|
||||
metrics['ps_ratio'] = market_cap / revenue if revenue > 0 else None
|
||||
metrics['price_to_cash_flow'] = price / (operating_cf / shares) if operating_cf > 0 and shares > 0 else None
|
||||
metrics['ev_ebitda'] = enterprise_value / ebitda if ebitda > 0 else None
|
||||
metrics['ev_ebit'] = enterprise_value / ebit if ebit > 0 else None
|
||||
metrics['dividend_yield'] = dividends_per_share / price if price > 0 else None
|
||||
metrics['price_to_fcf'] = price / (free_cash_flow / shares) if free_cash_flow > 0 and shares > 0 else None
|
||||
metrics['ev_to_sales'] = enterprise_value / revenue if revenue > 0 else None
|
||||
|
||||
# PEG Ratio (requires growth rate from historical data)
|
||||
eps_growth = financial_data.get('eps_growth_yoy', 0)
|
||||
pe_ratio = metrics['pe_ratio']
|
||||
metrics['peg_ratio'] = pe_ratio / (eps_growth * 100) if pe_ratio and eps_growth > 0 else None
|
||||
|
||||
# === PROFITABILITY RATIOS ===
|
||||
metrics['gross_margin'] = (revenue - cogs) / revenue if revenue > 0 else None
|
||||
metrics['operating_margin'] = operating_income / revenue if revenue > 0 else None
|
||||
metrics['net_margin'] = net_income / revenue if revenue > 0 else None
|
||||
metrics['roe'] = net_income / shareholders_equity if shareholders_equity > 0 else None
|
||||
metrics['roa'] = net_income / total_assets if total_assets > 0 else None
|
||||
metrics['roce'] = ebit / (total_assets - current_liabilities) if (total_assets - current_liabilities) > 0 else None
|
||||
|
||||
# ROIC = NOPAT / Invested Capital
|
||||
tax_rate = taxes / (net_income + taxes) if (net_income + taxes) > 0 else 0.25
|
||||
nopat = ebit * (1 - tax_rate)
|
||||
invested_capital = shareholders_equity + total_debt
|
||||
metrics['roic'] = nopat / invested_capital if invested_capital > 0 else None
|
||||
|
||||
metrics['ebitda_margin'] = ebitda / revenue if revenue > 0 else None
|
||||
|
||||
# === LEVERAGE RATIOS ===
|
||||
metrics['debt_to_equity'] = total_liabilities / shareholders_equity if shareholders_equity > 0 else None
|
||||
metrics['debt_to_assets'] = total_debt / total_assets if total_assets > 0 else None
|
||||
metrics['interest_coverage'] = ebit / interest_expense if interest_expense > 0 else None
|
||||
metrics['financial_leverage'] = total_assets / shareholders_equity if shareholders_equity > 0 else None
|
||||
|
||||
# === LIQUIDITY RATIOS ===
|
||||
metrics['current_ratio'] = current_assets / current_liabilities if current_liabilities > 0 else None
|
||||
quick_assets = cash + accounts_receivable
|
||||
metrics['quick_ratio'] = quick_assets / current_liabilities if current_liabilities > 0 else None
|
||||
metrics['cash_ratio'] = cash / current_liabilities if current_liabilities > 0 else None
|
||||
working_capital = current_assets - current_liabilities
|
||||
metrics['working_capital_ratio'] = working_capital / revenue if revenue > 0 else None
|
||||
|
||||
# === EFFICIENCY RATIOS ===
|
||||
metrics['inventory_turnover'] = cogs / inventory if inventory > 0 else None
|
||||
metrics['asset_turnover'] = revenue / total_assets if total_assets > 0 else None
|
||||
metrics['receivables_turnover'] = revenue / accounts_receivable if accounts_receivable > 0 else None
|
||||
metrics['payables_turnover'] = cogs / accounts_payable if accounts_payable > 0 else None
|
||||
metrics['days_sales_outstanding'] = (accounts_receivable / revenue) * 365 if revenue > 0 else None
|
||||
metrics['days_inventory_outstanding'] = (inventory / cogs) * 365 if cogs > 0 else None
|
||||
metrics['days_payable_outstanding'] = (accounts_payable / cogs) * 365 if cogs > 0 else None
|
||||
|
||||
# === GROWTH METRICS === (require historical data)
|
||||
metrics['revenue_growth_yoy'] = financial_data.get('revenue_growth_yoy')
|
||||
metrics['eps_growth_yoy'] = financial_data.get('eps_growth_yoy')
|
||||
metrics['net_income_growth_yoy'] = financial_data.get('net_income_growth_yoy')
|
||||
metrics['book_value_growth_yoy'] = financial_data.get('book_value_growth_yoy')
|
||||
|
||||
# === CASH FLOW METRICS ===
|
||||
metrics['fcf_yield'] = free_cash_flow / market_cap if market_cap > 0 else None
|
||||
metrics['operating_cf_ratio'] = operating_cf / current_liabilities if current_liabilities > 0 else None
|
||||
metrics['capex_ratio'] = capex / operating_cf if operating_cf > 0 else None
|
||||
|
||||
# Add base values for reference
|
||||
metrics['market_cap'] = market_cap
|
||||
metrics['enterprise_value'] = enterprise_value
|
||||
metrics['shares_outstanding'] = shares
|
||||
metrics['book_value_per_share'] = book_value_per_share
|
||||
|
||||
return metrics
|
||||
|
||||
def calculate_growth_rates(self, current_data: Dict, historical_data: Dict) -> Dict[str, float]:
|
||||
"""Calculate year-over-year growth rates"""
|
||||
|
||||
growth_rates = {}
|
||||
|
||||
# Revenue growth
|
||||
current_rev = current_data.get('revenue', 0)
|
||||
prev_rev = historical_data.get('revenue', 0)
|
||||
if prev_rev > 0:
|
||||
growth_rates['revenue_growth_yoy'] = (current_rev - prev_rev) / prev_rev
|
||||
|
||||
# EPS growth
|
||||
current_eps = current_data.get('eps', 0)
|
||||
prev_eps = historical_data.get('eps', 0)
|
||||
if prev_eps != 0:
|
||||
growth_rates['eps_growth_yoy'] = (current_eps - prev_eps) / abs(prev_eps)
|
||||
|
||||
# Net income growth
|
||||
current_ni = current_data.get('net_income', 0)
|
||||
prev_ni = historical_data.get('net_income', 0)
|
||||
if prev_ni != 0:
|
||||
growth_rates['net_income_growth_yoy'] = (current_ni - prev_ni) / abs(prev_ni)
|
||||
|
||||
# Book value growth
|
||||
current_bv = current_data.get('shareholders_equity', 0)
|
||||
prev_bv = historical_data.get('shareholders_equity', 0)
|
||||
if prev_bv > 0:
|
||||
growth_rates['book_value_growth_yoy'] = (current_bv - prev_bv) / prev_bv
|
||||
|
||||
return growth_rates
|
||||
|
||||
def format_metrics_for_display(self, metrics: Dict[str, Any]) -> str:
|
||||
"""Format metrics for human-readable display"""
|
||||
|
||||
output = []
|
||||
output.append("=" * 70)
|
||||
output.append("FINANCIAL METRICS")
|
||||
output.append("=" * 70)
|
||||
|
||||
# Valuation Ratios
|
||||
output.append("\n[VALUATION RATIOS]")
|
||||
output.append(f" P/E Ratio: {self._format_number(metrics.get('pe_ratio'))}")
|
||||
output.append(f" PEG Ratio: {self._format_number(metrics.get('peg_ratio'))}")
|
||||
output.append(f" P/B Ratio: {self._format_number(metrics.get('pb_ratio'))}")
|
||||
output.append(f" P/S Ratio: {self._format_number(metrics.get('ps_ratio'))}")
|
||||
output.append(f" EV/EBITDA: {self._format_number(metrics.get('ev_ebitda'))}")
|
||||
output.append(f" Dividend Yield: {self._format_percent(metrics.get('dividend_yield'))}")
|
||||
|
||||
# Profitability Ratios
|
||||
output.append("\n[PROFITABILITY RATIOS]")
|
||||
output.append(f" Gross Margin: {self._format_percent(metrics.get('gross_margin'))}")
|
||||
output.append(f" Operating Margin: {self._format_percent(metrics.get('operating_margin'))}")
|
||||
output.append(f" Net Margin: {self._format_percent(metrics.get('net_margin'))}")
|
||||
output.append(f" ROE: {self._format_percent(metrics.get('roe'))}")
|
||||
output.append(f" ROA: {self._format_percent(metrics.get('roa'))}")
|
||||
output.append(f" ROIC: {self._format_percent(metrics.get('roic'))}")
|
||||
|
||||
# Leverage Ratios
|
||||
output.append("\n[LEVERAGE RATIOS]")
|
||||
output.append(f" Debt/Equity: {self._format_number(metrics.get('debt_to_equity'))}")
|
||||
output.append(f" Debt/Assets: {self._format_number(metrics.get('debt_to_assets'))}")
|
||||
output.append(f" Interest Coverage: {self._format_number(metrics.get('interest_coverage'))}")
|
||||
|
||||
# Liquidity Ratios
|
||||
output.append("\n[LIQUIDITY RATIOS]")
|
||||
output.append(f" Current Ratio: {self._format_number(metrics.get('current_ratio'))}")
|
||||
output.append(f" Quick Ratio: {self._format_number(metrics.get('quick_ratio'))}")
|
||||
output.append(f" Cash Ratio: {self._format_number(metrics.get('cash_ratio'))}")
|
||||
|
||||
# Growth Metrics
|
||||
output.append("\n[GROWTH METRICS (YoY)]")
|
||||
output.append(f" Revenue Growth: {self._format_percent(metrics.get('revenue_growth_yoy'))}")
|
||||
output.append(f" EPS Growth: {self._format_percent(metrics.get('eps_growth_yoy'))}")
|
||||
output.append(f" Net Income Growth: {self._format_percent(metrics.get('net_income_growth_yoy'))}")
|
||||
|
||||
return "\n".join(output)
|
||||
|
||||
def _format_number(self, value: Optional[float], decimals: int = 2) -> str:
|
||||
"""Format number for display"""
|
||||
if value is None:
|
||||
return "N/A"
|
||||
return f"{value:.{decimals}f}"
|
||||
|
||||
def _format_percent(self, value: Optional[float], decimals: int = 2) -> str:
|
||||
"""Format percentage for display"""
|
||||
if value is None:
|
||||
return "N/A"
|
||||
return f"{value * 100:.{decimals}f}%"
|
||||
|
||||
|
||||
def example_usage():
|
||||
"""Example of how to use the calculator"""
|
||||
|
||||
# Example financial data
|
||||
financial_data = {
|
||||
'price': 50.00,
|
||||
'shares_outstanding': 10_000_000,
|
||||
'revenue': 100_000_000,
|
||||
'cogs': 60_000_000,
|
||||
'operating_income': 15_000_000,
|
||||
'net_income': 10_000_000,
|
||||
'eps': 1.00,
|
||||
'ebit': 15_000_000,
|
||||
'ebitda': 20_000_000,
|
||||
'total_assets': 200_000_000,
|
||||
'current_assets': 50_000_000,
|
||||
'total_liabilities': 80_000_000,
|
||||
'current_liabilities': 30_000_000,
|
||||
'total_debt': 40_000_000,
|
||||
'shareholders_equity': 120_000_000,
|
||||
'cash': 20_000_000,
|
||||
'operating_cash_flow': 18_000_000,
|
||||
'capex': 5_000_000,
|
||||
'free_cash_flow': 13_000_000,
|
||||
'dividends_per_share': 0.50,
|
||||
'eps_growth_yoy': 0.15,
|
||||
'revenue_growth_yoy': 0.10
|
||||
}
|
||||
|
||||
calculator = FinancialMetricsCalculator()
|
||||
metrics = calculator.calculate_all_metrics(financial_data)
|
||||
|
||||
print(calculator.format_metrics_for_display(metrics))
|
||||
|
||||
# Save to JSON
|
||||
with open('example_metrics.json', 'w') as f:
|
||||
json.dump(metrics, f, indent=2)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
example_usage()
|
||||
@@ -0,0 +1,257 @@
|
||||
"""
|
||||
Generate a consolidated company PDF report from all collected data files.
|
||||
|
||||
Usage:
|
||||
python generate_company_report.py --ticker AAPL
|
||||
|
||||
The script will:
|
||||
- Collect files from data/financials, data/metrics, data/reports, data/sec_filings,
|
||||
data/sedar_filings, data/serpapi_news, data/news, data/exports
|
||||
- Create a consolidated Markdown file at data/reports/{ticker}_full_report.md
|
||||
- Attempt to render a PDF at data/reports/{ticker}_full_report.pdf using reportlab or fpdf
|
||||
- If PDF libs are missing, only the Markdown will be created and instructions printed
|
||||
|
||||
"""
|
||||
import os
|
||||
import json
|
||||
import argparse
|
||||
import textwrap
|
||||
from datetime import datetime
|
||||
|
||||
DATA_DIR = 'data'
|
||||
REPORTS_DIR = os.path.join(DATA_DIR, 'reports')
|
||||
EXPORTS_DIR = os.path.join(DATA_DIR, 'exports')
|
||||
|
||||
os.makedirs(REPORTS_DIR, exist_ok=True)
|
||||
|
||||
|
||||
def read_file_if_exists(path):
|
||||
if os.path.exists(path):
|
||||
try:
|
||||
with open(path, 'r', encoding='utf-8') as f:
|
||||
return f.read()
|
||||
except Exception:
|
||||
return None
|
||||
return None
|
||||
|
||||
|
||||
def read_json_if_exists(path):
|
||||
if os.path.exists(path):
|
||||
try:
|
||||
with open(path, 'r', encoding='utf-8') as f:
|
||||
return json.load(f)
|
||||
except Exception:
|
||||
return None
|
||||
return None
|
||||
|
||||
|
||||
def gather_contents(ticker):
|
||||
t = ticker.upper()
|
||||
parts = []
|
||||
header = f"Company Consolidated Report - {t}\nGenerated: {datetime.now().isoformat()}\n"
|
||||
parts.append(header)
|
||||
parts.append('---\n')
|
||||
|
||||
# Stocks master entry
|
||||
parts.append('STOCK LISTING ENTRY:\n')
|
||||
# Query database file
|
||||
try:
|
||||
import sqlite3
|
||||
conn = sqlite3.connect('data/stocks.db')
|
||||
cur = conn.cursor()
|
||||
cur.execute('SELECT * FROM stocks_master WHERE symbol = ?', (t,))
|
||||
row = cur.fetchone()
|
||||
if row:
|
||||
cols = [c[0] for c in cur.execute('PRAGMA table_info(stocks_master)').fetchall()]
|
||||
parts.append(json.dumps(dict(zip(cols, row)), indent=2))
|
||||
else:
|
||||
parts.append('No stocks_master entry found for ' + t)
|
||||
conn.close()
|
||||
except Exception as e:
|
||||
parts.append('Could not read stocks.db: ' + str(e))
|
||||
|
||||
parts.append('\n')
|
||||
|
||||
# Exports - list export files & include small previews
|
||||
parts.append('EXPORTS:\n')
|
||||
exports = []
|
||||
for fname in os.listdir(EXPORTS_DIR) if os.path.exists(EXPORTS_DIR) else []:
|
||||
exports.append(fname)
|
||||
parts.append('\n'.join(exports) or 'No export files found')
|
||||
parts.append('\n')
|
||||
|
||||
# Financials
|
||||
parts.append('FINANCIALS (Yahoo scraped):\n')
|
||||
fin_path = os.path.join(DATA_DIR, 'financials', f'{t}_yahoo.json')
|
||||
fin = read_json_if_exists(fin_path)
|
||||
if fin is None:
|
||||
parts.append('No Yahoo Finance file: ' + fin_path)
|
||||
else:
|
||||
# Merge quote data into statistics for display
|
||||
if 'quote' in fin and 'statistics' in fin:
|
||||
quote = fin.get('quote', {})
|
||||
stats = fin.get('statistics', {})
|
||||
|
||||
# Remove empty quote fields from statistics (they're placeholders)
|
||||
quote_keys = ['date', 'close', 'open', 'high', 'low', 'volume']
|
||||
for key in quote_keys:
|
||||
if key in stats and not stats[key]:
|
||||
del stats[key]
|
||||
|
||||
# Add quote data at the top of statistics
|
||||
merged_stats = {
|
||||
'date': quote.get('date', ''),
|
||||
'close': quote.get('close', ''),
|
||||
'open': quote.get('open', ''),
|
||||
'high': quote.get('high', ''),
|
||||
'low': quote.get('low', ''),
|
||||
'volume': quote.get('volume', ''),
|
||||
}
|
||||
# Merge remaining statistics
|
||||
merged_stats.update(stats)
|
||||
fin['statistics'] = merged_stats
|
||||
|
||||
parts.append(json.dumps(fin, indent=2))
|
||||
parts.append('\n')
|
||||
|
||||
# Metrics
|
||||
parts.append('CALCULATED METRICS:\n')
|
||||
metrics_path = os.path.join(DATA_DIR, 'metrics', f'{t}_calculated_metrics.json')
|
||||
metrics = read_json_if_exists(metrics_path)
|
||||
if metrics is None:
|
||||
parts.append('No calculated metrics file: ' + metrics_path)
|
||||
else:
|
||||
parts.append(json.dumps(metrics, indent=2))
|
||||
parts.append('\n')
|
||||
|
||||
# Reports (comprehensive)
|
||||
parts.append('GENERATED REPORT (text):\n')
|
||||
rpt_path = os.path.join(DATA_DIR, 'reports', f'{t}_comprehensive_report.txt')
|
||||
rpt = read_file_if_exists(rpt_path)
|
||||
if rpt is None:
|
||||
parts.append('No comprehensive report found: ' + rpt_path)
|
||||
else:
|
||||
parts.append(rpt)
|
||||
parts.append('\n')
|
||||
|
||||
# SEC filings
|
||||
parts.append('SEC FILINGS (EDGAR):\n')
|
||||
sec_path = os.path.join(DATA_DIR, 'sec_filings', f'{t}_sec_filings.json')
|
||||
sec = read_json_if_exists(sec_path)
|
||||
if sec is None:
|
||||
parts.append('No SEC filings file: ' + sec_path)
|
||||
else:
|
||||
parts.append(json.dumps(sec, indent=2))
|
||||
parts.append('\n')
|
||||
|
||||
# SEDAR filings
|
||||
parts.append('SEDAR+ FILINGS (if any):\n')
|
||||
sedar_path = os.path.join(DATA_DIR, 'sedar_filings', f'{t}_sedar_data.json')
|
||||
sedar = read_json_if_exists(sedar_path)
|
||||
if sedar is None:
|
||||
parts.append('No SEDAR+ file: ' + sedar_path)
|
||||
else:
|
||||
parts.append(json.dumps(sedar, indent=2))
|
||||
parts.append('\n')
|
||||
|
||||
# SerpAPI news
|
||||
parts.append('SERPAPI NEWS (collected):\n')
|
||||
serp_path = os.path.join(DATA_DIR, 'serpapi_news', f'{t}_serpapi.json')
|
||||
serp = read_json_if_exists(serp_path)
|
||||
if serp is None:
|
||||
parts.append('No SerpAPI news file: ' + serp_path)
|
||||
else:
|
||||
parts.append(json.dumps(serp, indent=2))
|
||||
parts.append('\n')
|
||||
|
||||
# Regular news PR
|
||||
parts.append('DIRECT NEWS/PR SCRAPES (if any):\n')
|
||||
news_path = os.path.join(DATA_DIR, 'news', f'{t}_news_pr.json')
|
||||
news = read_json_if_exists(news_path)
|
||||
if news is None:
|
||||
parts.append('No direct news/pr file: ' + news_path)
|
||||
else:
|
||||
parts.append(json.dumps(news, indent=2))
|
||||
parts.append('\n')
|
||||
|
||||
return '\n'.join(parts)
|
||||
|
||||
|
||||
def save_markdown(ticker, content):
|
||||
md_path = os.path.join(REPORTS_DIR, f'{ticker}_full_report.md')
|
||||
with open(md_path, 'w', encoding='utf-8') as f:
|
||||
f.write(content)
|
||||
return md_path
|
||||
|
||||
|
||||
def render_pdf_from_text(ticker, text, pdf_path):
|
||||
# Try reportlab first
|
||||
try:
|
||||
from reportlab.lib.pagesizes import letter
|
||||
from reportlab.pdfgen import canvas
|
||||
import textwrap
|
||||
|
||||
c = canvas.Canvas(pdf_path, pagesize=letter)
|
||||
width, height = letter
|
||||
left_margin = 40
|
||||
right_margin = 40
|
||||
top_margin = 40
|
||||
bottom_margin = 40
|
||||
usable_width = width - left_margin - right_margin
|
||||
y = height - top_margin
|
||||
wrapper = textwrap.TextWrapper(width=95)
|
||||
|
||||
for paragraph in text.split('\n'):
|
||||
lines = wrapper.wrap(paragraph)
|
||||
if not lines:
|
||||
y -= 12
|
||||
for line in lines:
|
||||
if y < bottom_margin + 12:
|
||||
c.showPage()
|
||||
y = height - top_margin
|
||||
c.setFont('Helvetica', 9)
|
||||
c.drawString(left_margin, y, line)
|
||||
y -= 12
|
||||
c.save()
|
||||
return True, None
|
||||
except Exception as e:
|
||||
# Try fpdf
|
||||
try:
|
||||
from fpdf import FPDF
|
||||
pdf = FPDF()
|
||||
pdf.set_auto_page_break(auto=True, margin=15)
|
||||
pdf.add_page()
|
||||
pdf.set_font('Arial', size=10)
|
||||
for paragraph in text.split('\n'):
|
||||
for line in textwrap.wrap(paragraph, 90):
|
||||
pdf.cell(0, 6, line.encode('latin-1', 'replace').decode('latin-1'), ln=1)
|
||||
pdf.output(pdf_path)
|
||||
return True, None
|
||||
except Exception as e2:
|
||||
return False, f'ReportLab and FPDF not available or failed: {e} / {e2}'
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--ticker', '-t', default='AAPL', help='Ticker to generate report for')
|
||||
args = parser.parse_args()
|
||||
|
||||
ticker = args.ticker.upper()
|
||||
print(f'Gathering data for {ticker}...')
|
||||
content = gather_contents(ticker)
|
||||
|
||||
md_path = save_markdown(ticker, content)
|
||||
print('Markdown saved to', md_path)
|
||||
|
||||
pdf_path = os.path.join(REPORTS_DIR, f'{ticker}_full_report.pdf')
|
||||
ok, err = render_pdf_from_text(ticker, content, pdf_path)
|
||||
if ok:
|
||||
print('PDF generated at', pdf_path)
|
||||
else:
|
||||
print('PDF generation failed:', err)
|
||||
print('Markdown is available. Convert to PDF with pandoc or wkhtmltopdf:')
|
||||
print(f' pandoc {md_path} -o {pdf_path} # or use your preferred tool')
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
+617
@@ -0,0 +1,617 @@
|
||||
"""
|
||||
PRODUCTION-READY Stock Intelligence System
|
||||
Includes: SEC filings, SEDAR+, ownership data, tax info, AGM details, calculated metrics
|
||||
Can run daily on any stock or full universe
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import os
|
||||
import json
|
||||
import sys
|
||||
from datetime import datetime
|
||||
from typing import List, Dict, Any
|
||||
|
||||
# Import all modules
|
||||
from extract_listings import StockListingExtractor
|
||||
from database import StockDatabase
|
||||
from scrape_yahoo_finance import YahooFinanceScraper
|
||||
from scrape_news_pr import NewsPressScraper
|
||||
from scrape_serpapi import SerpAPINewsScraper
|
||||
from scrape_sec_filings import SECFilingScraper
|
||||
from scrape_sedar import SEDARPlusScraper
|
||||
from financial_calculator import FinancialMetricsCalculator
|
||||
from export_csv import CSVExporter
|
||||
from config import *
|
||||
|
||||
|
||||
class RobustStockIntelligence:
|
||||
def __init__(self):
|
||||
self.db = StockDatabase()
|
||||
self.stats = {
|
||||
'start_time': datetime.now(),
|
||||
'stocks_processed': 0,
|
||||
'financials_scraped': 0,
|
||||
'news_scraped': 0,
|
||||
'filings_scraped': 0,
|
||||
'metrics_calculated': 0,
|
||||
'errors': []
|
||||
}
|
||||
|
||||
async def step1_extract_listings(self, force_refresh=False):
|
||||
"""Extract stock listings from exchanges"""
|
||||
print("\n" + "=" * 70)
|
||||
print("STEP 1: EXTRACTING STOCK LISTINGS")
|
||||
print("=" * 70)
|
||||
|
||||
listings_file = "data/listings/all_listings_combined.json"
|
||||
|
||||
if os.path.exists(listings_file) and not force_refresh:
|
||||
print(f"📂 Loading existing listings from {listings_file}")
|
||||
with open(listings_file, 'r') as f:
|
||||
listings = json.load(f)
|
||||
print(f"✅ Loaded {len(listings)} stocks from file")
|
||||
else:
|
||||
print("🔄 Extracting fresh listings from exchanges...")
|
||||
extractor = StockListingExtractor()
|
||||
listings = await extractor.extract_all()
|
||||
|
||||
self.stats['stocks_processed'] = len(listings)
|
||||
return listings
|
||||
|
||||
def step2_import_to_database(self):
|
||||
"""Import listings to database"""
|
||||
print("\n" + "=" * 70)
|
||||
print("STEP 2: IMPORTING TO DATABASE")
|
||||
print("=" * 70)
|
||||
|
||||
listings_file = "data/listings/all_listings_combined.json"
|
||||
|
||||
if os.path.exists(listings_file):
|
||||
imported = self.db.import_listings_from_json(listings_file)
|
||||
return imported
|
||||
else:
|
||||
print(f"❌ No listings file found")
|
||||
return 0
|
||||
|
||||
async def step3_scrape_financials(self, stocks: List[Dict], use_serpapi_fallback=True):
|
||||
"""Scrape financial data with Yahoo Finance"""
|
||||
print("\n" + "=" * 70)
|
||||
print("STEP 3: SCRAPING FINANCIAL DATA")
|
||||
print("=" * 70)
|
||||
|
||||
scraper = YahooFinanceScraper()
|
||||
results = await scraper.scrape_multiple_stocks(stocks)
|
||||
|
||||
self.stats['financials_scraped'] = len([r for r in results if not r.get('error')])
|
||||
|
||||
# Update database
|
||||
for result in results:
|
||||
if not result.get('error'):
|
||||
self.db.update_coverage(
|
||||
result['ticker'],
|
||||
has_financials=True,
|
||||
has_ttm=True
|
||||
)
|
||||
|
||||
# Insert quote data if available
|
||||
quote_data = result.get('quote', {})
|
||||
if quote_data and any(quote_data.values()):
|
||||
self.db.insert_stock_quote(result['ticker'], quote_data)
|
||||
|
||||
return results
|
||||
|
||||
async def step4_calculate_metrics(self, financial_data: List[Dict]):
|
||||
"""Calculate all financial metrics from base numbers"""
|
||||
print("\n" + "=" * 70)
|
||||
print("STEP 4: CALCULATING FINANCIAL METRICS")
|
||||
print("=" * 70)
|
||||
|
||||
calculator = FinancialMetricsCalculator()
|
||||
metrics_calculated = 0
|
||||
|
||||
for data in financial_data:
|
||||
if data.get('error'):
|
||||
continue
|
||||
|
||||
ticker = data['ticker']
|
||||
print(f" Calculating metrics for {ticker}...")
|
||||
|
||||
try:
|
||||
# Convert Yahoo Finance data to calculator format
|
||||
base_data = calculator.convert_yahoo_data(data)
|
||||
|
||||
# Calculate all metrics
|
||||
metrics = calculator.calculate_all_metrics(base_data)
|
||||
|
||||
# Save metrics to file
|
||||
metrics_file = f"data/metrics/{ticker}_calculated_metrics.json"
|
||||
os.makedirs(os.path.dirname(metrics_file), exist_ok=True)
|
||||
with open(metrics_file, 'w') as f:
|
||||
json.dump(metrics, f, indent=2)
|
||||
|
||||
# Insert metrics into database
|
||||
current_year = datetime.now().year
|
||||
self.db.insert_financial_metrics(ticker, current_year, metrics, is_ttm=True)
|
||||
|
||||
metrics_calculated += 1
|
||||
|
||||
except Exception as e:
|
||||
print(f" Error calculating metrics: {e}")
|
||||
self.stats['errors'].append(f"{ticker} metrics: {e}")
|
||||
|
||||
self.stats['metrics_calculated'] = metrics_calculated
|
||||
print(f"✅ Calculated metrics for {metrics_calculated} stocks")
|
||||
|
||||
return metrics_calculated
|
||||
|
||||
async def step5_scrape_news_pr(self, stocks: List[Dict], use_serpapi=True):
|
||||
"""Scrape news and press releases"""
|
||||
print("\n" + "=" * 70)
|
||||
print("STEP 5: SCRAPING NEWS & PRESS RELEASES")
|
||||
print("=" * 70)
|
||||
|
||||
if use_serpapi:
|
||||
print("📡 Using SerpAPI for robust news collection...")
|
||||
scraper = SerpAPINewsScraper()
|
||||
results = scraper.scrape_multiple_stocks(stocks)
|
||||
else:
|
||||
print("🌐 Using direct web scraping...")
|
||||
scraper = NewsPressScraper()
|
||||
results = await scraper.scrape_multiple_stocks(stocks)
|
||||
|
||||
self.stats['news_scraped'] = len(results)
|
||||
|
||||
# Insert articles into database and update coverage
|
||||
for result in results:
|
||||
ticker = result['ticker']
|
||||
news_articles = result.get('news_articles', [])
|
||||
press_releases = result.get('press_releases', [])
|
||||
|
||||
# Insert news articles
|
||||
for article in news_articles:
|
||||
self.db.insert_news_article(
|
||||
ticker=ticker,
|
||||
title=article.get('title', ''),
|
||||
source=article.get('source', ''),
|
||||
published_date=article.get('date', ''),
|
||||
url=article.get('link') or article.get('url', ''),
|
||||
snippet=article.get('snippet', '')
|
||||
)
|
||||
|
||||
# Insert press releases as news articles (same table)
|
||||
for pr in press_releases:
|
||||
self.db.insert_news_article(
|
||||
ticker=ticker,
|
||||
title=pr.get('title', ''),
|
||||
source=pr.get('source', 'Press Release'),
|
||||
published_date=pr.get('date', ''),
|
||||
url=pr.get('link') or pr.get('url', ''),
|
||||
snippet=pr.get('snippet', '')
|
||||
)
|
||||
|
||||
# Update coverage flags
|
||||
has_news = len(news_articles) > 0
|
||||
has_pr = len(press_releases) > 0
|
||||
|
||||
self.db.update_coverage(
|
||||
ticker,
|
||||
has_news=has_news,
|
||||
has_press_releases=has_pr
|
||||
)
|
||||
|
||||
return results
|
||||
|
||||
async def step6_scrape_sec_filings(self, stocks: List[Dict]):
|
||||
"""Scrape SEC EDGAR filings (for US-listed stocks)"""
|
||||
print("\n" + "=" * 70)
|
||||
print("STEP 6: SCRAPING SEC EDGAR FILINGS")
|
||||
print("=" * 70)
|
||||
|
||||
# Filter for US-listed or cross-listed stocks
|
||||
us_stocks = [s for s in stocks if s.get('exchange') in ['CBOE', 'NYSE', 'NASDAQ']]
|
||||
|
||||
if not us_stocks:
|
||||
print("⚠️ No US-listed stocks to process")
|
||||
return []
|
||||
|
||||
scraper = SECFilingScraper()
|
||||
results = []
|
||||
|
||||
for stock in us_stocks:
|
||||
ticker = stock['symbol']
|
||||
data = await scraper.get_complete_company_data(ticker)
|
||||
results.append(data)
|
||||
|
||||
if not data.get('error'):
|
||||
# Insert filings into database
|
||||
filings = data.get('filings', [])
|
||||
for filing in filings:
|
||||
self.db.insert_filing(
|
||||
ticker=ticker,
|
||||
filing_date=filing.get('filing_date', ''),
|
||||
filing_type=filing.get('form_type', ''),
|
||||
title=filing.get('description', ''),
|
||||
document_url=filing.get('url', ''),
|
||||
source='SEC EDGAR'
|
||||
)
|
||||
|
||||
# Insert ownership forms
|
||||
ownership = data.get('insider_ownership', [])
|
||||
for form in ownership:
|
||||
self.db.insert_filing(
|
||||
ticker=ticker,
|
||||
filing_date=form.get('filing_date', ''),
|
||||
filing_type=form.get('form_type', ''),
|
||||
title=f"Insider Transaction - {form.get('owner', '')}",
|
||||
document_url=form.get('url', ''),
|
||||
source='SEC EDGAR - Ownership'
|
||||
)
|
||||
|
||||
self.db.update_coverage(
|
||||
ticker,
|
||||
has_filings=True
|
||||
)
|
||||
|
||||
self.stats['filings_scraped'] += len([r for r in results if not r.get('error')])
|
||||
|
||||
return results
|
||||
|
||||
async def step7_scrape_sedar_filings(self, stocks: List[Dict]):
|
||||
"""Scrape SEDAR+ filings (for Canadian stocks)"""
|
||||
print("\n" + "=" * 70)
|
||||
print("STEP 7: SCRAPING SEDAR+ FILINGS")
|
||||
print("=" * 70)
|
||||
|
||||
# Filter for Canadian stocks
|
||||
canadian_stocks = [s for s in stocks if s.get('exchange') in ['TSX', 'TSXV', 'CSE']]
|
||||
|
||||
if not canadian_stocks:
|
||||
print("⚠️ No Canadian stocks to process")
|
||||
return []
|
||||
|
||||
scraper = SEDARPlusScraper()
|
||||
results = await scraper.scrape_multiple_companies(canadian_stocks)
|
||||
|
||||
# Insert filings and update database
|
||||
for result in results:
|
||||
if not result.get('error'):
|
||||
ticker = result['ticker']
|
||||
|
||||
# Insert filings
|
||||
filings = result.get('filings', [])
|
||||
for filing in filings:
|
||||
self.db.insert_filing(
|
||||
ticker=ticker,
|
||||
filing_date=filing.get('date', ''),
|
||||
filing_type=filing.get('type', ''),
|
||||
title=filing.get('title', ''),
|
||||
document_url=filing.get('url', ''),
|
||||
source='SEDAR+'
|
||||
)
|
||||
|
||||
has_agm = bool(result.get('agm_info'))
|
||||
has_tax = bool(result.get('tax_disclosures'))
|
||||
|
||||
self.db.update_coverage(
|
||||
ticker,
|
||||
has_filings=True,
|
||||
has_agm_info=has_agm,
|
||||
has_tax_disclosures=has_tax
|
||||
)
|
||||
|
||||
self.stats['filings_scraped'] += len([r for r in results if not r.get('error')])
|
||||
|
||||
return results
|
||||
|
||||
def step8_generate_reports(self):
|
||||
"""Generate comprehensive reports"""
|
||||
print("\n" + "=" * 70)
|
||||
print("STEP 8: GENERATING REPORTS")
|
||||
print("=" * 70)
|
||||
|
||||
reports_dir = "data/reports"
|
||||
os.makedirs(reports_dir, exist_ok=True)
|
||||
|
||||
stocks = self.db.get_all_stocks()
|
||||
reports_generated = 0
|
||||
|
||||
for stock in stocks:
|
||||
ticker = stock[1]
|
||||
company_name = stock[2]
|
||||
exchange = stock[3]
|
||||
|
||||
try:
|
||||
report = self._generate_comprehensive_report(ticker, company_name, exchange)
|
||||
|
||||
report_file = f"{reports_dir}/{ticker}_comprehensive_report.txt"
|
||||
with open(report_file, 'w', encoding='utf-8') as f:
|
||||
f.write(report)
|
||||
|
||||
reports_generated += 1
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Error generating report for {ticker}: {e}")
|
||||
self.stats['errors'].append(f"{ticker} report: {e}")
|
||||
|
||||
print(f"✅ Generated {reports_generated} comprehensive reports")
|
||||
return reports_generated
|
||||
|
||||
def step9_export_csv(self):
|
||||
"""Export all data to CSV files"""
|
||||
print("\n" + "=" * 70)
|
||||
print("STEP 9: EXPORTING TO CSV")
|
||||
print("=" * 70)
|
||||
|
||||
exporter = CSVExporter()
|
||||
files = exporter.export_all()
|
||||
exporter.close()
|
||||
|
||||
return files
|
||||
|
||||
def _extract_base_financials(self, yahoo_data: Dict) -> Dict:
|
||||
"""Extract base financial numbers from Yahoo Finance data"""
|
||||
base = {}
|
||||
|
||||
stats = yahoo_data.get('statistics', {})
|
||||
profile = yahoo_data.get('profile', {})
|
||||
|
||||
# Try to extract numeric values from Yahoo Finance statistics
|
||||
# This is a simplified version - actual implementation would need more parsing
|
||||
base['price'] = profile.get('current_price', 0)
|
||||
|
||||
# Parse statistics (values come as strings with formatting)
|
||||
# Example: "1.2B" -> 1200000000
|
||||
for key, value in stats.items():
|
||||
if isinstance(value, str):
|
||||
# Try to convert formatted numbers
|
||||
try:
|
||||
if 'B' in value:
|
||||
base[key] = float(value.replace('B', '').replace(',', '')) * 1_000_000_000
|
||||
elif 'M' in value:
|
||||
base[key] = float(value.replace('M', '').replace(',', '')) * 1_000_000
|
||||
elif 'K' in value:
|
||||
base[key] = float(value.replace('K', '').replace(',', '')) * 1_000
|
||||
else:
|
||||
base[key] = float(value.replace(',', '').replace('%', ''))
|
||||
except:
|
||||
pass
|
||||
|
||||
return base
|
||||
|
||||
def _generate_comprehensive_report(self, ticker: str, company_name: str, exchange: str) -> str:
|
||||
"""Generate comprehensive report with all data"""
|
||||
report = []
|
||||
report.append("=" * 80)
|
||||
report.append(f"COMPREHENSIVE STOCK INTELLIGENCE REPORT")
|
||||
report.append(f"Ticker: {ticker} | Company: {company_name} | Exchange: {exchange}")
|
||||
report.append("=" * 80)
|
||||
report.append(f"Generated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
|
||||
report.append("")
|
||||
|
||||
# Load all available data files
|
||||
data_sources = {
|
||||
'yahoo': f"data/financials/{ticker}_yahoo.json",
|
||||
'metrics': f"data/metrics/{ticker}_calculated_metrics.json",
|
||||
'news': f"data/news/{ticker}_news_pr.json",
|
||||
'serpapi': f"data/serpapi_news/{ticker}_serpapi.json",
|
||||
'sec': f"data/sec_filings/{ticker}_sec_filings.json",
|
||||
'sedar': f"data/sedar_filings/{ticker}_sedar_data.json"
|
||||
}
|
||||
|
||||
# Financial Data Section
|
||||
if os.path.exists(data_sources['metrics']):
|
||||
report.append("[CALCULATED FINANCIAL METRICS]")
|
||||
report.append("-" * 80)
|
||||
with open(data_sources['metrics'], 'r') as f:
|
||||
metrics = json.load(f)
|
||||
calculator = FinancialMetricsCalculator()
|
||||
report.append(calculator.format_metrics_for_display(metrics))
|
||||
report.append("")
|
||||
|
||||
# News Section
|
||||
news_files = [data_sources['news'], data_sources['serpapi']]
|
||||
all_news = []
|
||||
for nf in news_files:
|
||||
if os.path.exists(nf):
|
||||
with open(nf, 'r') as f:
|
||||
data = json.load(f)
|
||||
all_news.extend(data.get('news_articles', []))
|
||||
|
||||
if all_news:
|
||||
report.append("[NEWS ARTICLES - Last 12 Months]")
|
||||
report.append("-" * 80)
|
||||
for article in all_news[:15]:
|
||||
report.append(f"\nTitle: {article.get('title', 'N/A')}")
|
||||
report.append(f"Source: {article.get('source', 'N/A')}")
|
||||
report.append(f"Date: {article.get('date', 'N/A')}")
|
||||
report.append(f"URL: {article.get('link') or article.get('url', 'N/A')}")
|
||||
report.append("")
|
||||
|
||||
# SEC Filings Section
|
||||
if os.path.exists(data_sources['sec']):
|
||||
report.append("[SEC EDGAR FILINGS]")
|
||||
report.append("-" * 80)
|
||||
with open(data_sources['sec'], 'r') as f:
|
||||
sec_data = json.load(f)
|
||||
filings = sec_data.get('filings', [])[:10]
|
||||
for filing in filings:
|
||||
report.append(f"\n{filing['form_type']} - {filing['filing_date']}")
|
||||
report.append(f" {filing.get('description', 'N/A')}")
|
||||
report.append(f" URL: {filing['url']}")
|
||||
report.append("")
|
||||
|
||||
# SEDAR+ Filings Section
|
||||
if os.path.exists(data_sources['sedar']):
|
||||
report.append("[SEDAR+ FILINGS & AGM INFORMATION]")
|
||||
report.append("-" * 80)
|
||||
with open(data_sources['sedar'], 'r') as f:
|
||||
sedar_data = json.load(f)
|
||||
|
||||
# AGM Info
|
||||
agm = sedar_data.get('agm_info', {})
|
||||
if agm:
|
||||
report.append("\nAnnual General Meeting:")
|
||||
report.append(f" Date: {agm.get('date', 'N/A')}")
|
||||
report.append(f" Location: {agm.get('location', 'N/A')}")
|
||||
|
||||
# Recent filings
|
||||
filings = sedar_data.get('filings', [])[:10]
|
||||
if filings:
|
||||
report.append("\nRecent Filings:")
|
||||
for filing in filings:
|
||||
report.append(f" - {filing.get('title', 'N/A')[:70]}")
|
||||
report.append("")
|
||||
|
||||
report.append("=" * 80)
|
||||
report.append("END OF REPORT")
|
||||
report.append("=" * 80)
|
||||
|
||||
return "\n".join(report)
|
||||
|
||||
async def run_for_single_stock(self, ticker: str):
|
||||
"""Run complete analysis for a single stock (daily update mode)"""
|
||||
print("\n" + "=" * 70)
|
||||
print(f"DAILY UPDATE FOR STOCK: {ticker}")
|
||||
print("=" * 70)
|
||||
|
||||
# Get stock info from database
|
||||
self.db.cursor.execute("SELECT * FROM stocks_master WHERE symbol = ?", (ticker,))
|
||||
stock_data = self.db.cursor.fetchone()
|
||||
|
||||
if not stock_data:
|
||||
print(f"❌ Stock {ticker} not found in database")
|
||||
return
|
||||
|
||||
stock = {
|
||||
'symbol': stock_data[1],
|
||||
'name': stock_data[2],
|
||||
'exchange': stock_data[3]
|
||||
}
|
||||
|
||||
# Run all steps for this one stock
|
||||
await self.step3_scrape_financials([stock])
|
||||
await self.step5_scrape_news_pr([stock], use_serpapi=True)
|
||||
|
||||
if stock['exchange'] in ['CBOE', 'NYSE', 'NASDAQ']:
|
||||
await self.step6_scrape_sec_filings([stock])
|
||||
elif stock['exchange'] in ['TSX', 'TSXV', 'CSE']:
|
||||
await self.step7_scrape_sedar_filings([stock])
|
||||
|
||||
self.step8_generate_reports()
|
||||
self.step9_export_csv()
|
||||
|
||||
print(f"\n✅ Daily update completed for {ticker}")
|
||||
|
||||
async def run_full_pipeline(self, test_mode=False, stocks_limit=None):
|
||||
"""Run complete pipeline"""
|
||||
print("\n" + "=" * 70)
|
||||
print("PRODUCTION-READY STOCK INTELLIGENCE SYSTEM")
|
||||
print("=" * 70)
|
||||
print(f"Started: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
|
||||
if test_mode:
|
||||
print("⚠️ RUNNING IN TEST MODE")
|
||||
print("=" * 70)
|
||||
|
||||
try:
|
||||
# Step 1: Get listings
|
||||
listings = await self.step1_extract_listings()
|
||||
|
||||
if not listings:
|
||||
print("\n❌ No listings found")
|
||||
return
|
||||
|
||||
# Step 2: Import to database
|
||||
self.step2_import_to_database()
|
||||
|
||||
# Limit stocks if requested
|
||||
if stocks_limit:
|
||||
listings = listings[:stocks_limit]
|
||||
print(f"\n⚠️ Limited to {stocks_limit} stocks for testing")
|
||||
|
||||
# Step 3: Scrape financials
|
||||
financial_data = await self.step3_scrape_financials(listings)
|
||||
|
||||
# Step 4: Calculate metrics
|
||||
await self.step4_calculate_metrics(financial_data)
|
||||
|
||||
# Step 5: Scrape news (using SerpAPI for robustness)
|
||||
await self.step5_scrape_news_pr(listings, use_serpapi=True)
|
||||
|
||||
# Step 6 & 7: Scrape filings
|
||||
await self.step6_scrape_sec_filings(listings)
|
||||
await self.step7_scrape_sedar_filings(listings)
|
||||
|
||||
# Step 8: Generate reports
|
||||
self.step8_generate_reports()
|
||||
|
||||
# Step 9: Export to CSV
|
||||
self.step9_export_csv()
|
||||
|
||||
# Print final stats
|
||||
self._print_final_stats()
|
||||
|
||||
print("\n✅ PIPELINE COMPLETED SUCCESSFULLY!")
|
||||
|
||||
except Exception as e:
|
||||
print(f"\n❌ Pipeline failed: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
|
||||
finally:
|
||||
self.db.close()
|
||||
|
||||
def _print_final_stats(self):
|
||||
"""Print final statistics"""
|
||||
end_time = datetime.now()
|
||||
duration = end_time - self.stats['start_time']
|
||||
|
||||
print("\n" + "=" * 70)
|
||||
print("FINAL STATISTICS")
|
||||
print("=" * 70)
|
||||
print(f"Duration: {duration}")
|
||||
print(f"Stocks processed: {self.stats['stocks_processed']}")
|
||||
print(f"Financials scraped: {self.stats['financials_scraped']}")
|
||||
print(f"Metrics calculated: {self.stats['metrics_calculated']}")
|
||||
print(f"News articles collected: {self.stats['news_scraped']}")
|
||||
print(f"Filings scraped: {self.stats['filings_scraped']}")
|
||||
print(f"Errors: {len(self.stats['errors'])}")
|
||||
print("=" * 70)
|
||||
|
||||
|
||||
async def main():
|
||||
"""Main entry point"""
|
||||
orchestrator = RobustStockIntelligence()
|
||||
|
||||
# Check command line arguments
|
||||
if len(sys.argv) > 1:
|
||||
command = sys.argv[1]
|
||||
|
||||
if command == "--ticker" and len(sys.argv) > 2:
|
||||
# Daily update for single stock
|
||||
ticker = sys.argv[2].upper()
|
||||
await orchestrator.run_for_single_stock(ticker)
|
||||
|
||||
elif command == "--full":
|
||||
# Full pipeline, all stocks
|
||||
await orchestrator.run_full_pipeline(test_mode=False)
|
||||
|
||||
elif command == "--test":
|
||||
# Test mode with limited stocks
|
||||
limit = int(sys.argv[2]) if len(sys.argv) > 2 else 5
|
||||
await orchestrator.run_full_pipeline(test_mode=True, stocks_limit=limit)
|
||||
|
||||
else:
|
||||
print("Usage:")
|
||||
print(" python main_robust.py --test [num] # Test mode with N stocks")
|
||||
print(" python main_robust.py --full # Full pipeline, all stocks")
|
||||
print(" python main_robust.py --ticker SYMBOL # Daily update for one stock")
|
||||
|
||||
else:
|
||||
# Default: test mode with 5 stocks
|
||||
print("\n⚠️ No arguments provided. Running in test mode (5 stocks)")
|
||||
print(" Use --help to see options")
|
||||
await orchestrator.run_full_pipeline(test_mode=True, stocks_limit=5)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
@@ -0,0 +1,15 @@
|
||||
scrapy
|
||||
scrapy-playwright
|
||||
playwright
|
||||
beautifulsoup4
|
||||
html2text
|
||||
requests
|
||||
pandas
|
||||
lxml
|
||||
selenium
|
||||
fake-useragent
|
||||
google-search-results # For SerpAPI
|
||||
python-dotenv
|
||||
PyPDF2 # For PDF parsing
|
||||
pdfplumber # Alternative PDF parser
|
||||
openpyxl # For Excel export
|
||||
@@ -0,0 +1,323 @@
|
||||
"""
|
||||
Scrape news and press releases without API keys
|
||||
Uses Google search results and direct source scraping
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
import os
|
||||
from datetime import datetime, timedelta
|
||||
from playwright.async_api import async_playwright
|
||||
import time
|
||||
import re
|
||||
from urllib.parse import quote
|
||||
|
||||
|
||||
class NewsPressScraper:
|
||||
def __init__(self, output_dir="data/news"):
|
||||
self.output_dir = output_dir
|
||||
os.makedirs(output_dir, exist_ok=True)
|
||||
|
||||
async def scrape_google_news(self, company_name, ticker, max_results=20):
|
||||
"""Scrape Google News results for a stock"""
|
||||
print(f"\n🔍 Searching news for {company_name} ({ticker})...")
|
||||
|
||||
# Build search query
|
||||
query = f'"{company_name}" OR "{ticker}" (stock OR shares OR earnings)'
|
||||
encoded_query = quote(query)
|
||||
|
||||
# Limit to last 12 months
|
||||
url = f"https://www.google.com/search?q={encoded_query}&tbm=nws&tbs=qdr:y"
|
||||
|
||||
news_articles = []
|
||||
|
||||
async with async_playwright() as p:
|
||||
browser = await p.chromium.launch(headless=True)
|
||||
page = await browser.new_page()
|
||||
|
||||
try:
|
||||
await page.goto(url, wait_until='networkidle', timeout=30000)
|
||||
await asyncio.sleep(2)
|
||||
|
||||
# Extract news results
|
||||
news_items = await page.query_selector_all('div[data-sokoban-container]')
|
||||
|
||||
if not news_items:
|
||||
# Try alternative selectors
|
||||
news_items = await page.query_selector_all('div.SoaBEf, div.Gx5Zad')
|
||||
|
||||
print(f" Found {len(news_items)} potential news items")
|
||||
|
||||
for item in news_items[:max_results]:
|
||||
try:
|
||||
article = {}
|
||||
|
||||
# Get title
|
||||
title_elem = await item.query_selector('div[role="heading"], h3, .mCBkyc')
|
||||
if title_elem:
|
||||
article['title'] = await title_elem.inner_text()
|
||||
|
||||
# Get source
|
||||
source_elem = await item.query_selector('.CEMjEf, .NUnG9d span')
|
||||
if source_elem:
|
||||
article['source'] = await source_elem.inner_text()
|
||||
|
||||
# Get date
|
||||
date_elem = await item.query_selector('.OSrXXb, time')
|
||||
if date_elem:
|
||||
article['date'] = await date_elem.inner_text()
|
||||
|
||||
# Get link
|
||||
link_elem = await item.query_selector('a')
|
||||
if link_elem:
|
||||
article['url'] = await link_elem.get_attribute('href')
|
||||
|
||||
# Get snippet
|
||||
snippet_elem = await item.query_selector('.GI74Re, .Y3v8qd')
|
||||
if snippet_elem:
|
||||
article['snippet'] = await snippet_elem.inner_text()
|
||||
|
||||
if article.get('title'):
|
||||
news_articles.append(article)
|
||||
|
||||
except Exception as e:
|
||||
continue
|
||||
|
||||
print(f"✅ Extracted {len(news_articles)} news articles")
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Error scraping Google News: {e}")
|
||||
|
||||
finally:
|
||||
await browser.close()
|
||||
|
||||
return news_articles
|
||||
|
||||
async def scrape_press_releases_globenewswire(self, company_name, ticker):
|
||||
"""Scrape GlobeNewswire for press releases"""
|
||||
print(f"\n🔍 Searching GlobeNewswire for {ticker}...")
|
||||
|
||||
search_url = f"https://www.globenewswire.com/search/keyword/{quote(ticker)}"
|
||||
|
||||
press_releases = []
|
||||
|
||||
async with async_playwright() as p:
|
||||
browser = await p.chromium.launch(headless=True)
|
||||
page = await browser.new_page()
|
||||
|
||||
try:
|
||||
await page.goto(search_url, wait_until='networkidle', timeout=30000)
|
||||
await asyncio.sleep(2)
|
||||
|
||||
# Find press release items
|
||||
pr_items = await page.query_selector_all('.article-item, .result-item, article')
|
||||
|
||||
print(f" Found {len(pr_items)} press releases")
|
||||
|
||||
for item in pr_items:
|
||||
try:
|
||||
pr = {
|
||||
'source': 'GlobeNewswire'
|
||||
}
|
||||
|
||||
# Get title
|
||||
title_elem = await item.query_selector('h3, h2, .title a')
|
||||
if title_elem:
|
||||
pr['title'] = await title_elem.inner_text()
|
||||
|
||||
# Get date
|
||||
date_elem = await item.query_selector('time, .date')
|
||||
if date_elem:
|
||||
pr['date'] = await date_elem.inner_text()
|
||||
|
||||
# Get link
|
||||
link_elem = await item.query_selector('a')
|
||||
if link_elem:
|
||||
href = await link_elem.get_attribute('href')
|
||||
if href.startswith('/'):
|
||||
href = f"https://www.globenewswire.com{href}"
|
||||
pr['url'] = href
|
||||
|
||||
# Get summary
|
||||
summary_elem = await item.query_selector('p, .summary')
|
||||
if summary_elem:
|
||||
pr['summary'] = await summary_elem.inner_text()
|
||||
|
||||
if pr.get('title'):
|
||||
press_releases.append(pr)
|
||||
|
||||
except Exception as e:
|
||||
continue
|
||||
|
||||
print(f"✅ Extracted {len(press_releases)} press releases")
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Error scraping GlobeNewswire: {e}")
|
||||
|
||||
finally:
|
||||
await browser.close()
|
||||
|
||||
return press_releases
|
||||
|
||||
async def scrape_press_releases_newswire(self, company_name, ticker):
|
||||
"""Scrape Newswire.ca for press releases"""
|
||||
print(f"\n🔍 Searching Newswire.ca for {ticker}...")
|
||||
|
||||
search_url = f"https://www.newswire.ca/search/?query={quote(ticker)}"
|
||||
|
||||
press_releases = []
|
||||
|
||||
async with async_playwright() as p:
|
||||
browser = await p.chromium.launch(headless=True)
|
||||
page = await browser.new_page()
|
||||
|
||||
try:
|
||||
await page.goto(search_url, wait_until='networkidle', timeout=30000)
|
||||
await asyncio.sleep(2)
|
||||
|
||||
# Find press release items
|
||||
pr_items = await page.query_selector_all('.release-card, .news-item, article')
|
||||
|
||||
print(f" Found {len(pr_items)} press releases")
|
||||
|
||||
for item in pr_items:
|
||||
try:
|
||||
pr = {
|
||||
'source': 'Newswire.ca'
|
||||
}
|
||||
|
||||
# Get title
|
||||
title_elem = await item.query_selector('h3, h2, a.title')
|
||||
if title_elem:
|
||||
pr['title'] = await title_elem.inner_text()
|
||||
|
||||
# Get date
|
||||
date_elem = await item.query_selector('time, .date, .timestamp')
|
||||
if date_elem:
|
||||
pr['date'] = await date_elem.inner_text()
|
||||
|
||||
# Get link
|
||||
link_elem = await item.query_selector('a')
|
||||
if link_elem:
|
||||
href = await link_elem.get_attribute('href')
|
||||
if href.startswith('/'):
|
||||
href = f"https://www.newswire.ca{href}"
|
||||
pr['url'] = href
|
||||
|
||||
# Get summary
|
||||
summary_elem = await item.query_selector('p, .summary, .description')
|
||||
if summary_elem:
|
||||
pr['summary'] = await summary_elem.inner_text()
|
||||
|
||||
if pr.get('title'):
|
||||
press_releases.append(pr)
|
||||
|
||||
except Exception as e:
|
||||
continue
|
||||
|
||||
print(f"✅ Extracted {len(press_releases)} press releases")
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Error scraping Newswire.ca: {e}")
|
||||
|
||||
finally:
|
||||
await browser.close()
|
||||
|
||||
return press_releases
|
||||
|
||||
async def scrape_stock_news_and_pr(self, ticker, company_name):
|
||||
"""Scrape both news and press releases for a stock"""
|
||||
print(f"\n{'='*60}")
|
||||
print(f"SCRAPING NEWS & PR FOR: {ticker} - {company_name}")
|
||||
print(f"{'='*60}")
|
||||
|
||||
all_data = {
|
||||
'ticker': ticker,
|
||||
'company_name': company_name,
|
||||
'scraped_at': datetime.now().isoformat(),
|
||||
'news_articles': [],
|
||||
'press_releases': []
|
||||
}
|
||||
|
||||
# Scrape Google News
|
||||
news = await self.scrape_google_news(company_name, ticker)
|
||||
all_data['news_articles'] = news
|
||||
|
||||
# Small delay between requests
|
||||
await asyncio.sleep(3)
|
||||
|
||||
# Scrape GlobeNewswire
|
||||
pr_gnw = await self.scrape_press_releases_globenewswire(company_name, ticker)
|
||||
all_data['press_releases'].extend(pr_gnw)
|
||||
|
||||
# Small delay
|
||||
await asyncio.sleep(3)
|
||||
|
||||
# Scrape Newswire.ca
|
||||
pr_nw = await self.scrape_press_releases_newswire(company_name, ticker)
|
||||
all_data['press_releases'].extend(pr_nw)
|
||||
|
||||
# Save to file
|
||||
output_file = f"{self.output_dir}/{ticker}_news_pr.json"
|
||||
with open(output_file, 'w', encoding='utf-8') as f:
|
||||
json.dump(all_data, f, indent=2)
|
||||
|
||||
print(f"\n📊 Summary for {ticker}:")
|
||||
print(f" News articles: {len(all_data['news_articles'])}")
|
||||
print(f" Press releases: {len(all_data['press_releases'])}")
|
||||
print(f" Saved to: {output_file}")
|
||||
|
||||
return all_data
|
||||
|
||||
async def scrape_multiple_stocks(self, stock_list, max_stocks=None):
|
||||
"""Scrape news and PR for multiple stocks"""
|
||||
print("=" * 60)
|
||||
print("NEWS & PRESS RELEASE SCRAPING")
|
||||
print("=" * 60)
|
||||
|
||||
if max_stocks:
|
||||
stock_list = stock_list[:max_stocks]
|
||||
|
||||
all_data = []
|
||||
|
||||
for stock in stock_list:
|
||||
ticker = stock.get('symbol')
|
||||
company_name = stock.get('name')
|
||||
|
||||
data = await self.scrape_stock_news_and_pr(ticker, company_name)
|
||||
all_data.append(data)
|
||||
|
||||
# Rate limiting - be respectful
|
||||
await asyncio.sleep(5)
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
print(f"✅ Completed scraping for {len(all_data)} stocks")
|
||||
print(f"📁 Data saved to: {self.output_dir}/")
|
||||
print("=" * 60)
|
||||
|
||||
return all_data
|
||||
|
||||
|
||||
async def main():
|
||||
"""Test the scraper"""
|
||||
|
||||
# Load listings
|
||||
listings_file = "data/listings/all_listings_combined.json"
|
||||
|
||||
if not os.path.exists(listings_file):
|
||||
print(f"❌ No listings file found at {listings_file}")
|
||||
print(" Run extract_listings.py first")
|
||||
return
|
||||
|
||||
with open(listings_file, 'r', encoding='utf-8') as f:
|
||||
listings = json.load(f)
|
||||
|
||||
print(f"📊 Found {len(listings)} stocks in listings")
|
||||
|
||||
# Test with first 3 stocks
|
||||
scraper = NewsPressScraper()
|
||||
await scraper.scrape_multiple_stocks(listings, max_stocks=3)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
@@ -0,0 +1,294 @@
|
||||
"""
|
||||
Scrape SEC EDGAR filings and extract ownership data
|
||||
Gets 10-K, 10-Q, 8-K, DEF 14A, and insider ownership (Forms 3, 4, 5, 13D, 13G)
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
import os
|
||||
import re
|
||||
from datetime import datetime, timedelta
|
||||
from playwright.async_api import async_playwright
|
||||
import requests
|
||||
import time
|
||||
from typing import Dict, List, Any, Optional
|
||||
|
||||
from config import SEC_BASE_URL, SEC_API_URL, SEC_USER_AGENT, FILING_TYPES_SEC
|
||||
|
||||
|
||||
class SECFilingScraper:
|
||||
def __init__(self, output_dir="data/sec_filings"):
|
||||
self.output_dir = output_dir
|
||||
os.makedirs(output_dir, exist_ok=True)
|
||||
self.headers = {'User-Agent': SEC_USER_AGENT}
|
||||
|
||||
def get_cik_from_ticker(self, ticker: str) -> Optional[str]:
|
||||
"""Get CIK number from ticker symbol using multiple methods"""
|
||||
try:
|
||||
# Method 1: Try the company_tickers.json endpoint
|
||||
try:
|
||||
url = f"{SEC_API_URL}/files/company_tickers.json"
|
||||
response = requests.get(url, headers=self.headers, timeout=10)
|
||||
response.raise_for_status()
|
||||
|
||||
companies = response.json()
|
||||
|
||||
for company_data in companies.values():
|
||||
if company_data['ticker'].upper() == ticker.upper():
|
||||
cik = str(company_data['cik_str']).zfill(10)
|
||||
return cik
|
||||
except:
|
||||
pass # Try alternative method
|
||||
|
||||
# Method 2: Use SEC's search page (fallback)
|
||||
# Known CIKs for major companies (as fallback)
|
||||
known_ciks = {
|
||||
'AAPL': '0000320193',
|
||||
'MSFT': '0000789019',
|
||||
'GOOGL': '0001652044',
|
||||
'GOOG': '0001652044',
|
||||
'AMZN': '0001018724',
|
||||
'TSLA': '0001318605',
|
||||
'META': '0001326801',
|
||||
'NVDA': '0001045810',
|
||||
'JPM': '0000019617',
|
||||
'V': '0001403161',
|
||||
'WMT': '0000104169',
|
||||
'DIS': '0001744489',
|
||||
'NFLX': '0001065280',
|
||||
'CRM': '0001108524',
|
||||
'PYPL': '0001633917'
|
||||
}
|
||||
|
||||
if ticker.upper() in known_ciks:
|
||||
return known_ciks[ticker.upper()]
|
||||
|
||||
# Method 3: Try searching SEC's website
|
||||
search_url = f"https://www.sec.gov/cgi-bin/browse-edgar?action=getcompany&ticker={ticker}&count=1&output=atom"
|
||||
response = requests.get(search_url, headers=self.headers, timeout=10)
|
||||
if response.status_code == 200:
|
||||
# Parse CIK from response
|
||||
match = re.search(r'CIK=(\d+)', response.text)
|
||||
if match:
|
||||
return match.group(1).zfill(10)
|
||||
|
||||
return None
|
||||
except Exception as e:
|
||||
print(f"Error getting CIK for {ticker}: {e}")
|
||||
return None
|
||||
|
||||
def get_company_filings(self, cik: str, limit: int = 100) -> List[Dict]:
|
||||
"""Get recent filings for a company"""
|
||||
try:
|
||||
url = f"{SEC_API_URL}/submissions/CIK{cik}.json"
|
||||
response = requests.get(url, headers=self.headers)
|
||||
response.raise_for_status()
|
||||
|
||||
data = response.json()
|
||||
filings = []
|
||||
|
||||
recent_filings = data.get('filings', {}).get('recent', {})
|
||||
|
||||
for i in range(min(limit, len(recent_filings.get('form', [])))):
|
||||
filing = {
|
||||
'form_type': recent_filings['form'][i],
|
||||
'filing_date': recent_filings['filingDate'][i],
|
||||
'accession_number': recent_filings['accessionNumber'][i],
|
||||
'primary_document': recent_filings.get('primaryDocument', [''])[i],
|
||||
'description': recent_filings.get('primaryDocDescription', [''])[i]
|
||||
}
|
||||
|
||||
# Build document URL
|
||||
acc_no_clean = filing['accession_number'].replace('-', '')
|
||||
filing['url'] = f"{SEC_BASE_URL}/Archives/edgar/data/{cik}/{acc_no_clean}/{filing['primary_document']}"
|
||||
|
||||
filings.append(filing)
|
||||
|
||||
return filings
|
||||
except Exception as e:
|
||||
print(f"Error getting filings for CIK {cik}: {e}")
|
||||
return []
|
||||
|
||||
def get_insider_ownership(self, cik: str) -> Dict[str, Any]:
|
||||
"""Get insider ownership data from Forms 3, 4, 5"""
|
||||
try:
|
||||
filings = self.get_company_filings(cik, limit=200)
|
||||
|
||||
# Filter for ownership forms
|
||||
ownership_forms = ['3', '4', '5', 'SC 13D', 'SC 13G']
|
||||
insider_filings = [f for f in filings if f['form_type'] in ownership_forms]
|
||||
|
||||
# Parse the most recent ownership data
|
||||
ownership_data = {
|
||||
'insiders': [],
|
||||
'major_shareholders': [],
|
||||
'total_insider_shares': 0,
|
||||
'last_updated': datetime.now().isoformat()
|
||||
}
|
||||
|
||||
# Group by filer
|
||||
filers = {}
|
||||
for filing in insider_filings[:50]: # Check last 50 ownership filings
|
||||
# Would need to parse the actual XML/HTML document to get share counts
|
||||
# This is a placeholder structure
|
||||
ownership_data['insiders'].append({
|
||||
'filing_type': filing['form_type'],
|
||||
'filing_date': filing['filing_date'],
|
||||
'document_url': filing['url']
|
||||
})
|
||||
|
||||
return ownership_data
|
||||
except Exception as e:
|
||||
print(f"Error getting insider ownership for CIK {cik}: {e}")
|
||||
return {}
|
||||
|
||||
async def scrape_filing_document(self, url: str) -> Dict[str, Any]:
|
||||
"""Scrape the actual filing document for detailed information"""
|
||||
|
||||
async with async_playwright() as p:
|
||||
browser = await p.chromium.launch(headless=True)
|
||||
page = await browser.new_page()
|
||||
|
||||
try:
|
||||
await page.goto(url, wait_until='networkidle', timeout=30000)
|
||||
await asyncio.sleep(2)
|
||||
|
||||
# Extract text content
|
||||
content = await page.content()
|
||||
text = await page.inner_text('body')
|
||||
|
||||
# Extract key information
|
||||
filing_data = {
|
||||
'url': url,
|
||||
'scraped_at': datetime.now().isoformat(),
|
||||
'full_text': text[:50000], # Limit size
|
||||
'content_html': content[:50000]
|
||||
}
|
||||
|
||||
# Try to extract specific sections
|
||||
# AGM information
|
||||
agm_patterns = [
|
||||
r'annual general meeting.*?(\d{1,2}[/-]\d{1,2}[/-]\d{4})',
|
||||
r'agm.*?(\d{1,2}[/-]\d{1,2}[/-]\d{4})',
|
||||
r'shareholder meeting.*?(\d{1,2}[/-]\d{1,2}[/-]\d{4})'
|
||||
]
|
||||
|
||||
for pattern in agm_patterns:
|
||||
match = re.search(pattern, text.lower())
|
||||
if match:
|
||||
filing_data['agm_date'] = match.group(1)
|
||||
break
|
||||
|
||||
# Ownership information
|
||||
ownership_patterns = [
|
||||
r'beneficially own.*?(\d{1,3}(?:,\d{3})*)\s*shares',
|
||||
r'total shares.*?(\d{1,3}(?:,\d{3})*)',
|
||||
r'common stock.*?(\d{1,3}(?:,\d{3})*)'
|
||||
]
|
||||
|
||||
shares_owned = []
|
||||
for pattern in ownership_patterns:
|
||||
matches = re.finditer(pattern, text.lower())
|
||||
for match in matches:
|
||||
shares = match.group(1).replace(',', '')
|
||||
shares_owned.append(int(shares))
|
||||
|
||||
if shares_owned:
|
||||
filing_data['shares_mentioned'] = shares_owned
|
||||
|
||||
return filing_data
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error scraping {url}: {e}")
|
||||
return {'url': url, 'error': str(e)}
|
||||
finally:
|
||||
await browser.close()
|
||||
|
||||
async def get_complete_company_data(self, ticker: str) -> Dict[str, Any]:
|
||||
"""Get complete SEC data for a company"""
|
||||
print(f"\n🔍 Scraping SEC filings for {ticker}...")
|
||||
|
||||
# Get CIK
|
||||
cik = self.get_cik_from_ticker(ticker)
|
||||
if not cik:
|
||||
print(f"⚠️ CIK not found for {ticker}")
|
||||
return {'ticker': ticker, 'error': 'CIK not found'}
|
||||
|
||||
print(f" Found CIK: {cik}")
|
||||
|
||||
data = {
|
||||
'ticker': ticker,
|
||||
'cik': cik,
|
||||
'scraped_at': datetime.now().isoformat(),
|
||||
'filings': [],
|
||||
'ownership': {},
|
||||
'agm_info': {},
|
||||
'key_documents': {}
|
||||
}
|
||||
|
||||
# Get all filings
|
||||
all_filings = self.get_company_filings(cik, limit=100)
|
||||
data['filings'] = all_filings
|
||||
|
||||
print(f" Found {len(all_filings)} recent filings")
|
||||
|
||||
# Get most recent important filings
|
||||
important_forms = ['10-K', '10-Q', 'DEF 14A', '8-K']
|
||||
recent_important = {}
|
||||
|
||||
for filing in all_filings:
|
||||
form_type = filing['form_type']
|
||||
if form_type in important_forms and form_type not in recent_important:
|
||||
recent_important[form_type] = filing
|
||||
|
||||
# Scrape key documents
|
||||
for form_type, filing in recent_important.items():
|
||||
print(f" Scraping {form_type} from {filing['filing_date']}...")
|
||||
doc_data = await self.scrape_filing_document(filing['url'])
|
||||
data['key_documents'][form_type] = doc_data
|
||||
await asyncio.sleep(2) # Rate limiting
|
||||
|
||||
# Get ownership data
|
||||
print(f" Getting ownership data...")
|
||||
ownership = self.get_insider_ownership(cik)
|
||||
data['ownership'] = ownership
|
||||
|
||||
# Save to file
|
||||
output_file = f"{self.output_dir}/{ticker}_sec_filings.json"
|
||||
with open(output_file, 'w', encoding='utf-8') as f:
|
||||
json.dump(data, f, indent=2)
|
||||
|
||||
print(f"✅ Saved SEC data to {output_file}")
|
||||
|
||||
return data
|
||||
|
||||
async def scrape_multiple_companies(self, tickers: List[str]):
|
||||
"""Scrape SEC data for multiple companies"""
|
||||
print("=" * 70)
|
||||
print("SEC EDGAR FILING SCRAPER")
|
||||
print("=" * 70)
|
||||
|
||||
all_data = []
|
||||
|
||||
for ticker in tickers:
|
||||
data = await self.get_complete_company_data(ticker)
|
||||
all_data.append(data)
|
||||
await asyncio.sleep(3) # Respect SEC rate limits
|
||||
|
||||
print(f"\n✅ Completed scraping {len(all_data)} companies")
|
||||
return all_data
|
||||
|
||||
|
||||
async def main():
|
||||
"""Test the SEC scraper"""
|
||||
scraper = SECFilingScraper()
|
||||
|
||||
# Test with a few well-known tickers
|
||||
test_tickers = ['AAPL', 'MSFT', 'TSLA']
|
||||
|
||||
print("Testing SEC scraper with sample tickers...")
|
||||
await scraper.scrape_multiple_companies(test_tickers[:1]) # Just test one
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
+268
@@ -0,0 +1,268 @@
|
||||
"""
|
||||
Scrape SEDAR+ filings for Canadian companies
|
||||
Gets annual reports, AGM circulars, financial statements, tax disclosures
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
import os
|
||||
import re
|
||||
from datetime import datetime
|
||||
from playwright.async_api import async_playwright
|
||||
from typing import Dict, List, Any
|
||||
import time
|
||||
|
||||
from config import SEDAR_BASE_URL, SEDAR_SEARCH_URL, FILING_TYPES_SEDAR
|
||||
|
||||
|
||||
class SEDARPlusScraper:
|
||||
def __init__(self, output_dir="data/sedar_filings"):
|
||||
self.output_dir = output_dir
|
||||
os.makedirs(output_dir, exist_ok=True)
|
||||
|
||||
async def search_company(self, company_name: str, ticker: str) -> List[Dict]:
|
||||
"""Search for a company on SEDAR+"""
|
||||
print(f"\n🔍 Searching SEDAR+ for {company_name} ({ticker})...")
|
||||
|
||||
async with async_playwright() as p:
|
||||
browser = await p.chromium.launch(headless=False) # Non-headless for debugging
|
||||
page = await browser.new_page()
|
||||
|
||||
try:
|
||||
# Navigate to SEDAR+ search
|
||||
await page.goto(SEDAR_BASE_URL, wait_until='networkidle', timeout=60000)
|
||||
await asyncio.sleep(3)
|
||||
|
||||
# Try to find and use the search functionality
|
||||
# Note: SEDAR+ structure may vary, adjust selectors as needed
|
||||
search_input = await page.query_selector('input[type="search"], input[placeholder*="search"], input[name*="search"]')
|
||||
|
||||
if search_input:
|
||||
await search_input.fill(ticker)
|
||||
await search_input.press('Enter')
|
||||
await asyncio.sleep(5)
|
||||
|
||||
# Get page content to parse results
|
||||
content = await page.content()
|
||||
|
||||
# Save HTML for debugging
|
||||
debug_file = f"{self.output_dir}/{ticker}_sedar_search.html"
|
||||
with open(debug_file, 'w', encoding='utf-8') as f:
|
||||
f.write(content)
|
||||
|
||||
print(f" Saved search results to {debug_file}")
|
||||
|
||||
# Try to extract filing links
|
||||
filings = []
|
||||
links = await page.query_selector_all('a[href*="document"], a[href*="filing"]')
|
||||
|
||||
for link in links[:50]: # Get first 50 results
|
||||
try:
|
||||
href = await link.get_attribute('href')
|
||||
text = await link.inner_text()
|
||||
|
||||
filings.append({
|
||||
'title': text.strip(),
|
||||
'url': href if href.startswith('http') else f"{SEDAR_BASE_URL}{href}",
|
||||
'found_at': datetime.now().isoformat()
|
||||
})
|
||||
except:
|
||||
continue
|
||||
|
||||
print(f"✅ Found {len(filings)} potential filings")
|
||||
|
||||
return filings
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Error searching SEDAR+: {e}")
|
||||
return []
|
||||
finally:
|
||||
await browser.close()
|
||||
|
||||
async def get_filing_document(self, url: str) -> Dict[str, Any]:
|
||||
"""Download and parse a SEDAR+ document"""
|
||||
|
||||
async with async_playwright() as p:
|
||||
browser = await p.chromium.launch(headless=True)
|
||||
page = await browser.new_page()
|
||||
|
||||
try:
|
||||
await page.goto(url, wait_until='networkidle', timeout=30000)
|
||||
await asyncio.sleep(2)
|
||||
|
||||
content = await page.content()
|
||||
text = await page.inner_text('body')
|
||||
|
||||
filing_data = {
|
||||
'url': url,
|
||||
'scraped_at': datetime.now().isoformat(),
|
||||
'text_content': text[:100000], # Limit size
|
||||
'html_content': content[:100000]
|
||||
}
|
||||
|
||||
# Extract AGM information
|
||||
agm_patterns = [
|
||||
r'annual\s+general\s+meeting.*?(\d{1,2}\s+\w+\s+\d{4})',
|
||||
r'agm.*?(\d{1,2}\s+\w+\s+\d{4})',
|
||||
r'meeting\s+date.*?(\d{1,2}\s+\w+\s+\d{4})'
|
||||
]
|
||||
|
||||
for pattern in agm_patterns:
|
||||
match = re.search(pattern, text.lower())
|
||||
if match:
|
||||
filing_data['agm_date'] = match.group(1)
|
||||
break
|
||||
|
||||
# Extract location
|
||||
location_patterns = [
|
||||
r'meeting\s+location:?\s*([^\n]{10,100})',
|
||||
r'to\s+be\s+held\s+at\s+([^\n]{10,100})',
|
||||
r'location:?\s*([^\n]{10,100})'
|
||||
]
|
||||
|
||||
for pattern in location_patterns:
|
||||
match = re.search(pattern, text.lower())
|
||||
if match:
|
||||
filing_data['agm_location'] = match.group(1).strip()
|
||||
break
|
||||
|
||||
# Extract tax information
|
||||
tax_keywords = ['income tax', 'tax expense', 'effective tax rate', 'deferred tax',
|
||||
'tax loss carryforward', 'tax jurisdiction']
|
||||
|
||||
tax_sections = []
|
||||
for keyword in tax_keywords:
|
||||
pattern = rf'{keyword}.*?(\d+(?:,\d{{3}})*(?:\.\d+)?)'
|
||||
matches = re.finditer(pattern, text.lower())
|
||||
for match in matches:
|
||||
tax_sections.append({
|
||||
'keyword': keyword,
|
||||
'context': match.group(0),
|
||||
'amount': match.group(1)
|
||||
})
|
||||
|
||||
if tax_sections:
|
||||
filing_data['tax_information'] = tax_sections[:20] # Limit results
|
||||
|
||||
# Extract share ownership information
|
||||
ownership_patterns = [
|
||||
r'(insider|director|officer|founder).*?(\d{1,3}(?:,\d{3})*)\s*shares',
|
||||
r'beneficially\s+own.*?(\d{1,3}(?:,\d{3})*)\s*shares',
|
||||
r'voting\s+shares.*?(\d{1,3}(?:,\d{3})*)'
|
||||
]
|
||||
|
||||
ownership_data = []
|
||||
for pattern in ownership_patterns:
|
||||
matches = re.finditer(pattern, text.lower())
|
||||
for match in matches:
|
||||
ownership_data.append({
|
||||
'context': match.group(0)[:200],
|
||||
'shares': match.group(2) if len(match.groups()) > 1 else match.group(1)
|
||||
})
|
||||
|
||||
if ownership_data:
|
||||
filing_data['ownership_mentions'] = ownership_data[:30]
|
||||
|
||||
return filing_data
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error scraping document {url}: {e}")
|
||||
return {'url': url, 'error': str(e)}
|
||||
finally:
|
||||
await browser.close()
|
||||
|
||||
async def get_complete_company_data(self, ticker: str, company_name: str) -> Dict[str, Any]:
|
||||
"""Get complete SEDAR+ data for a company"""
|
||||
print(f"\n{'='*70}")
|
||||
print(f"SCRAPING SEDAR+ FOR: {ticker} - {company_name}")
|
||||
print(f"{'='*70}")
|
||||
|
||||
data = {
|
||||
'ticker': ticker,
|
||||
'company_name': company_name,
|
||||
'scraped_at': datetime.now().isoformat(),
|
||||
'filings': [],
|
||||
'agm_info': {},
|
||||
'tax_disclosures': {},
|
||||
'ownership_data': []
|
||||
}
|
||||
|
||||
# Search for company
|
||||
filings = await self.search_company(company_name, ticker)
|
||||
data['filings'] = filings
|
||||
|
||||
# Get details from key documents
|
||||
priority_keywords = ['annual', 'circular', 'information', 'financial statement', 'md&a']
|
||||
|
||||
priority_filings = []
|
||||
for filing in filings:
|
||||
title_lower = filing['title'].lower()
|
||||
if any(keyword in title_lower for keyword in priority_keywords):
|
||||
priority_filings.append(filing)
|
||||
|
||||
# Scrape top priority documents
|
||||
for filing in priority_filings[:5]: # Limit to top 5
|
||||
print(f" Scraping: {filing['title'][:60]}...")
|
||||
doc_data = await self.get_filing_document(filing['url'])
|
||||
filing['detailed_data'] = doc_data
|
||||
await asyncio.sleep(3) # Rate limiting
|
||||
|
||||
# Aggregate AGM information
|
||||
agm_dates = []
|
||||
agm_locations = []
|
||||
for filing in data['filings']:
|
||||
if 'detailed_data' in filing:
|
||||
if 'agm_date' in filing['detailed_data']:
|
||||
agm_dates.append(filing['detailed_data']['agm_date'])
|
||||
if 'agm_location' in filing['detailed_data']:
|
||||
agm_locations.append(filing['detailed_data']['agm_location'])
|
||||
|
||||
if agm_dates:
|
||||
data['agm_info']['date'] = agm_dates[0] # Most recent
|
||||
if agm_locations:
|
||||
data['agm_info']['location'] = agm_locations[0]
|
||||
|
||||
# Save to file
|
||||
output_file = f"{self.output_dir}/{ticker}_sedar_data.json"
|
||||
with open(output_file, 'w', encoding='utf-8') as f:
|
||||
json.dump(data, f, indent=2)
|
||||
|
||||
print(f"✅ Saved SEDAR+ data to {output_file}")
|
||||
|
||||
return data
|
||||
|
||||
async def scrape_multiple_companies(self, stock_list: List[Dict]):
|
||||
"""Scrape SEDAR+ data for multiple companies"""
|
||||
print("=" * 70)
|
||||
print("SEDAR+ SCRAPER")
|
||||
print("=" * 70)
|
||||
|
||||
all_data = []
|
||||
|
||||
for stock in stock_list:
|
||||
ticker = stock.get('symbol')
|
||||
company_name = stock.get('name')
|
||||
|
||||
data = await self.get_complete_company_data(ticker, company_name)
|
||||
all_data.append(data)
|
||||
|
||||
await asyncio.sleep(5) # Respectful rate limiting
|
||||
|
||||
print(f"\n✅ Completed scraping {len(all_data)} companies")
|
||||
return all_data
|
||||
|
||||
|
||||
async def main():
|
||||
"""Test the SEDAR+ scraper"""
|
||||
scraper = SEDARPlusScraper()
|
||||
|
||||
# Test with a sample Canadian company
|
||||
test_stocks = [
|
||||
{'symbol': 'SHOP', 'name': 'Shopify Inc.'},
|
||||
]
|
||||
|
||||
await scraper.scrape_multiple_companies(test_stocks)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
@@ -0,0 +1,215 @@
|
||||
"""
|
||||
Use SerpAPI for robust news and press release scraping
|
||||
Fallback option when direct scraping fails
|
||||
"""
|
||||
|
||||
import requests
|
||||
import json
|
||||
import os
|
||||
from datetime import datetime, timedelta
|
||||
from typing import Dict, List, Any
|
||||
import time
|
||||
|
||||
from config import SERPAPI_KEY
|
||||
|
||||
|
||||
class SerpAPINewsScraper:
|
||||
def __init__(self, output_dir="data/serpapi_news"):
|
||||
self.api_key = SERPAPI_KEY
|
||||
self.output_dir = output_dir
|
||||
os.makedirs(output_dir, exist_ok=True)
|
||||
self.base_url = "https://serpapi.com/search.json"
|
||||
|
||||
def search_google_news(self, query: str, days_back: int = 365) -> List[Dict]:
|
||||
"""Search Google News using SerpAPI"""
|
||||
print(f" Searching Google News via SerpAPI: {query}...")
|
||||
|
||||
params = {
|
||||
'api_key': self.api_key,
|
||||
'engine': 'google_news',
|
||||
'q': query,
|
||||
'gl': 'us', # Country
|
||||
'hl': 'en', # Language
|
||||
'tbs': f'qdr:y' # Last year
|
||||
}
|
||||
|
||||
try:
|
||||
response = requests.get(self.base_url, params=params)
|
||||
response.raise_for_status()
|
||||
|
||||
data = response.json()
|
||||
|
||||
news_results = data.get('news_results', [])
|
||||
|
||||
articles = []
|
||||
for result in news_results:
|
||||
articles.append({
|
||||
'title': result.get('title'),
|
||||
'link': result.get('link'),
|
||||
'source': result.get('source', {}).get('name'),
|
||||
'date': result.get('date'),
|
||||
'snippet': result.get('snippet'),
|
||||
'thumbnail': result.get('thumbnail'),
|
||||
'scraped_via': 'SerpAPI',
|
||||
'scraped_at': datetime.now().isoformat()
|
||||
})
|
||||
|
||||
print(f" Found {len(articles)} articles")
|
||||
return articles
|
||||
|
||||
except Exception as e:
|
||||
print(f" Error searching Google News: {e}")
|
||||
return []
|
||||
|
||||
def search_google_with_site_filter(self, query: str, sites: List[str]) -> List[Dict]:
|
||||
"""Search specific sites for press releases"""
|
||||
print(f" Searching press release sites via SerpAPI...")
|
||||
|
||||
# Build site filter query
|
||||
site_filter = " OR ".join([f"site:{site}" for site in sites])
|
||||
full_query = f"{query} ({site_filter})"
|
||||
|
||||
params = {
|
||||
'api_key': self.api_key,
|
||||
'engine': 'google',
|
||||
'q': full_query,
|
||||
'tbs': 'qdr:y', # Last year
|
||||
'num': 50 # Number of results
|
||||
}
|
||||
|
||||
try:
|
||||
response = requests.get(self.base_url, params=params)
|
||||
response.raise_for_status()
|
||||
|
||||
data = response.json()
|
||||
|
||||
organic_results = data.get('organic_results', [])
|
||||
|
||||
press_releases = []
|
||||
for result in organic_results:
|
||||
press_releases.append({
|
||||
'title': result.get('title'),
|
||||
'link': result.get('link'),
|
||||
'snippet': result.get('snippet'),
|
||||
'displayed_link': result.get('displayed_link'),
|
||||
'date': result.get('date'),
|
||||
'scraped_via': 'SerpAPI',
|
||||
'scraped_at': datetime.now().isoformat()
|
||||
})
|
||||
|
||||
print(f" Found {len(press_releases)} press releases")
|
||||
return press_releases
|
||||
|
||||
except Exception as e:
|
||||
print(f" Error searching press releases: {e}")
|
||||
return []
|
||||
|
||||
def get_company_news_and_pr(self, ticker: str, company_name: str) -> Dict[str, Any]:
|
||||
"""Get comprehensive news and PR for a company"""
|
||||
print(f"\n🔍 Fetching news & PR via SerpAPI for {ticker} - {company_name}")
|
||||
|
||||
data = {
|
||||
'ticker': ticker,
|
||||
'company_name': company_name,
|
||||
'scraped_at': datetime.now().isoformat(),
|
||||
'news_articles': [],
|
||||
'press_releases': []
|
||||
}
|
||||
|
||||
# Search Google News
|
||||
news_query = f'"{company_name}" OR "{ticker}" stock earnings financial'
|
||||
news_articles = self.search_google_news(news_query)
|
||||
data['news_articles'] = news_articles
|
||||
|
||||
time.sleep(2) # Rate limiting
|
||||
|
||||
# Search press release sites
|
||||
pr_query = f'"{company_name}" OR "{ticker}"'
|
||||
pr_sites = [
|
||||
'globenewswire.com',
|
||||
'prnewswire.com',
|
||||
'newswire.ca',
|
||||
'businesswire.com',
|
||||
'stockhouse.com'
|
||||
]
|
||||
|
||||
press_releases = self.search_google_with_site_filter(pr_query, pr_sites)
|
||||
data['press_releases'] = press_releases
|
||||
|
||||
# Save to file
|
||||
output_file = f"{self.output_dir}/{ticker}_serpapi.json"
|
||||
with open(output_file, 'w', encoding='utf-8') as f:
|
||||
json.dump(data, f, indent=2)
|
||||
|
||||
print(f"✅ Saved SerpAPI data: {len(news_articles)} news, {len(press_releases)} PR")
|
||||
|
||||
return data
|
||||
|
||||
def scrape_multiple_stocks(self, stock_list: List[Dict], max_stocks: int = None):
|
||||
"""Scrape news and PR for multiple stocks"""
|
||||
print("=" * 70)
|
||||
print("SERPAPI NEWS & PRESS RELEASE SCRAPER")
|
||||
print("=" * 70)
|
||||
|
||||
if max_stocks:
|
||||
stock_list = stock_list[:max_stocks]
|
||||
|
||||
all_data = []
|
||||
|
||||
for stock in stock_list:
|
||||
ticker = stock.get('symbol')
|
||||
company_name = stock.get('name')
|
||||
|
||||
data = self.get_company_news_and_pr(ticker, company_name)
|
||||
all_data.append(data)
|
||||
|
||||
time.sleep(3) # Rate limiting for API
|
||||
|
||||
print(f"\n✅ Completed scraping {len(all_data)} stocks via SerpAPI")
|
||||
return all_data
|
||||
|
||||
def check_api_credits(self):
|
||||
"""Check remaining SerpAPI credits"""
|
||||
params = {
|
||||
'api_key': self.api_key,
|
||||
'engine': 'google',
|
||||
'q': 'test'
|
||||
}
|
||||
|
||||
try:
|
||||
response = requests.get(self.base_url, params=params)
|
||||
response.raise_for_status()
|
||||
|
||||
data = response.json()
|
||||
search_metadata = data.get('search_metadata', {})
|
||||
|
||||
print("\nSerpAPI Status:")
|
||||
print(f" Status: {search_metadata.get('status')}")
|
||||
print(f" Total time: {search_metadata.get('total_time')}s")
|
||||
|
||||
# Note: Credit info might not be directly available in response
|
||||
# Check SerpAPI dashboard for actual credit count
|
||||
|
||||
return True
|
||||
except Exception as e:
|
||||
print(f"Error checking API status: {e}")
|
||||
return False
|
||||
|
||||
|
||||
def main():
|
||||
"""Test SerpAPI scraper"""
|
||||
scraper = SerpAPINewsScraper()
|
||||
|
||||
# Check API status
|
||||
scraper.check_api_credits()
|
||||
|
||||
# Test with a sample stock
|
||||
test_stocks = [
|
||||
{'symbol': 'AAPL', 'name': 'Apple Inc.'},
|
||||
]
|
||||
|
||||
scraper.scrape_multiple_stocks(test_stocks, max_stocks=1)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,328 @@
|
||||
"""
|
||||
Scrape financial data from Yahoo Finance (no API key needed)
|
||||
Gets financials, ratios, and key metrics for each stock
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
import os
|
||||
from datetime import datetime
|
||||
from playwright.async_api import async_playwright
|
||||
import time
|
||||
import re
|
||||
|
||||
|
||||
class YahooFinanceScraper:
|
||||
def __init__(self, output_dir="data/financials"):
|
||||
self.output_dir = output_dir
|
||||
os.makedirs(output_dir, exist_ok=True)
|
||||
|
||||
async def scrape_stock_data(self, ticker, exchange=""):
|
||||
"""Scrape comprehensive data for a single stock"""
|
||||
print(f"\n🔍 Scraping {ticker}...")
|
||||
|
||||
# Format ticker for Yahoo Finance
|
||||
yahoo_ticker = ticker
|
||||
|
||||
# Canadian stocks need exchange-specific suffixes
|
||||
if exchange in ['TSX', 'TSXV', 'TSX/TSXV']:
|
||||
if not ticker.endswith('.TO') and not ticker.endswith('.V'):
|
||||
yahoo_ticker = f"{ticker}.TO" # Try TSX first
|
||||
|
||||
# CSE (Canadian Securities Exchange) stocks use .CN suffix
|
||||
# CSE tickers in database may have "T2" prefix which needs to be removed
|
||||
elif exchange == 'CSE':
|
||||
# Remove T2 prefix if present (e.g., T2AAA -> AAA)
|
||||
clean_ticker = ticker.replace('T2', '') if ticker.startswith('T2') else ticker
|
||||
# Remove any suffix after a dot (e.g., T2AAAWH.U -> AAAWH)
|
||||
if '.' in clean_ticker:
|
||||
clean_ticker = clean_ticker.split('.')[0]
|
||||
yahoo_ticker = f"{clean_ticker}.CN"
|
||||
print(f" CSE stock: {ticker} -> {yahoo_ticker}")
|
||||
|
||||
stock_data = {
|
||||
'ticker': ticker,
|
||||
'exchange': exchange,
|
||||
'yahoo_ticker': yahoo_ticker,
|
||||
'scraped_at': datetime.now().isoformat(),
|
||||
'profile': {},
|
||||
'quote': {}, # Real-time quote data
|
||||
'financials': {},
|
||||
'statistics': {},
|
||||
'analysis': {},
|
||||
'error': None
|
||||
}
|
||||
|
||||
async with async_playwright() as p:
|
||||
# Launch with no-cache to avoid stale data
|
||||
browser = await p.chromium.launch(
|
||||
headless=True,
|
||||
args=['--disable-blink-features=AutomationControlled']
|
||||
)
|
||||
context = await browser.new_context(
|
||||
viewport={'width': 1920, 'height': 1080},
|
||||
user_agent='Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36'
|
||||
)
|
||||
page = await context.new_page()
|
||||
|
||||
try:
|
||||
# 1. Get Summary/Statistics page
|
||||
url = f"https://finance.yahoo.com/quote/{yahoo_ticker}"
|
||||
print(f" Loading {url}...")
|
||||
await page.goto(url, wait_until='domcontentloaded', timeout=60000)
|
||||
await asyncio.sleep(5) # Wait for dynamic content to load
|
||||
|
||||
# Check if ticker exists
|
||||
page_content = await page.content()
|
||||
if "Symbol Lookup" in page_content or "Symbols similar to" in page_content:
|
||||
print(f"⚠️ {yahoo_ticker} not found on Yahoo Finance")
|
||||
stock_data['error'] = 'Ticker not found'
|
||||
|
||||
# Try alternative suffix for TSXV
|
||||
if yahoo_ticker.endswith('.TO'):
|
||||
yahoo_ticker = f"{ticker}.V"
|
||||
print(f" Trying {yahoo_ticker}...")
|
||||
url = f"https://finance.yahoo.com/quote/{yahoo_ticker}"
|
||||
await page.goto(url, wait_until='domcontentloaded', timeout=60000)
|
||||
await asyncio.sleep(5)
|
||||
|
||||
page_content = await page.content()
|
||||
if "Symbol Lookup" in page_content:
|
||||
await browser.close()
|
||||
return stock_data
|
||||
else:
|
||||
stock_data['yahoo_ticker'] = yahoo_ticker
|
||||
stock_data['error'] = None
|
||||
|
||||
# Extract key stats and quote data from summary
|
||||
try:
|
||||
# Get real-time quote data from the quote header section
|
||||
# Initialize quote fields to empty to avoid caching from previous runs
|
||||
stock_data['quote'] = {
|
||||
'date': '',
|
||||
'open': '',
|
||||
'high': '',
|
||||
'low': '',
|
||||
'close': '',
|
||||
'volume': ''
|
||||
}
|
||||
|
||||
# Close (current price)
|
||||
price_elem = await page.query_selector('[data-field="regularMarketPrice"]')
|
||||
if price_elem:
|
||||
price_text = await price_elem.inner_text()
|
||||
# Remove whitespace and newlines
|
||||
price_text = ' '.join(price_text.split())
|
||||
print(f" Raw price text: '{price_text}'")
|
||||
try:
|
||||
current_price = float(price_text.replace(',', ''))
|
||||
stock_data['profile']['current_price'] = current_price
|
||||
stock_data['quote']['close'] = price_text
|
||||
print(f" Parsed price: {current_price}")
|
||||
except ValueError:
|
||||
print(f" Warning: Could not parse price: {price_text}")
|
||||
|
||||
# Open price
|
||||
open_elem = await page.query_selector('[data-field="regularMarketOpen"]')
|
||||
if open_elem:
|
||||
open_text = await open_elem.inner_text()
|
||||
stock_data['quote']['open'] = ' '.join(open_text.split())
|
||||
|
||||
# Day range (high/low)
|
||||
range_elem = await page.query_selector('[data-field="regularMarketDayRange"]')
|
||||
if range_elem:
|
||||
range_text = await range_elem.inner_text()
|
||||
range_text = ' '.join(range_text.split())
|
||||
if ' - ' in range_text:
|
||||
low, high = range_text.split(' - ')
|
||||
stock_data['quote']['low'] = low.strip()
|
||||
stock_data['quote']['high'] = high.strip()
|
||||
|
||||
# Volume
|
||||
volume_elem = await page.query_selector('[data-field="regularMarketVolume"]')
|
||||
if volume_elem:
|
||||
volume_text = await volume_elem.inner_text()
|
||||
stock_data['quote']['volume'] = ' '.join(volume_text.split())
|
||||
|
||||
# Date/time - extract from page text
|
||||
page_text = await page.inner_text('body')
|
||||
# Look for "At close: November 5 at 4:00:01 PM EST" pattern
|
||||
import re
|
||||
time_match = re.search(r'At close:\s*([^\\n]+(?:EST|EDT|PST|PDT))', page_text)
|
||||
if time_match:
|
||||
stock_data['quote']['date'] = time_match.group(1).strip()
|
||||
|
||||
except Exception as e:
|
||||
print(f" Error extracting summary: {e}")
|
||||
|
||||
# Get market cap, P/E, etc from the stats table
|
||||
stat_rows = await page.query_selector_all('table tr')
|
||||
for row in stat_rows:
|
||||
try:
|
||||
cells = await row.query_selector_all('td')
|
||||
if len(cells) == 2:
|
||||
label = await cells[0].inner_text()
|
||||
value = await cells[1].inner_text()
|
||||
|
||||
label = label.strip().lower().replace(' ', '_').replace('/', '_')
|
||||
stock_data['statistics'][label] = value.strip()
|
||||
except:
|
||||
continue
|
||||
|
||||
except Exception as e:
|
||||
print(f" Error extracting summary: {e}")
|
||||
|
||||
# 2. Get Financials page
|
||||
try:
|
||||
financials_url = f"https://finance.yahoo.com/quote/{yahoo_ticker}/financials"
|
||||
await page.goto(financials_url, wait_until='domcontentloaded', timeout=60000)
|
||||
await asyncio.sleep(5)
|
||||
|
||||
# Extract financial data
|
||||
financial_tables = await page.query_selector_all('div[class*="financials"] table')
|
||||
for table in financial_tables:
|
||||
rows = await table.query_selector_all('tr')
|
||||
for row in rows:
|
||||
try:
|
||||
cells = await row.query_selector_all('td, th')
|
||||
if len(cells) >= 2:
|
||||
label = await cells[0].inner_text()
|
||||
values = []
|
||||
for i in range(1, len(cells)):
|
||||
val = await cells[i].inner_text()
|
||||
values.append(val.strip())
|
||||
|
||||
label_key = label.strip().lower().replace(' ', '_')
|
||||
stock_data['financials'][label_key] = values
|
||||
except:
|
||||
continue
|
||||
|
||||
except Exception as e:
|
||||
print(f" Error extracting financials: {e}")
|
||||
|
||||
# 3. Get Key Statistics page
|
||||
try:
|
||||
stats_url = f"https://finance.yahoo.com/quote/{yahoo_ticker}/key-statistics"
|
||||
await page.goto(stats_url, wait_until='domcontentloaded', timeout=60000)
|
||||
await asyncio.sleep(5)
|
||||
|
||||
# Extract all statistics
|
||||
stat_tables = await page.query_selector_all('table')
|
||||
for table in stat_tables:
|
||||
rows = await table.query_selector_all('tr')
|
||||
for row in rows:
|
||||
try:
|
||||
cells = await row.query_selector_all('td')
|
||||
if len(cells) == 2:
|
||||
label = await cells[0].inner_text()
|
||||
value = await cells[1].inner_text()
|
||||
|
||||
label_key = label.strip().lower().replace(' ', '_').replace('/', '_')
|
||||
stock_data['statistics'][label_key] = value.strip()
|
||||
except:
|
||||
continue
|
||||
|
||||
except Exception as e:
|
||||
print(f" Error extracting statistics: {e}")
|
||||
|
||||
# 4. Get Analysis page (analyst ratings, growth estimates)
|
||||
try:
|
||||
analysis_url = f"https://finance.yahoo.com/quote/{yahoo_ticker}/analysis"
|
||||
await page.goto(analysis_url, wait_until='networkidle', timeout=30000)
|
||||
await asyncio.sleep(2)
|
||||
|
||||
# Extract analysis data
|
||||
analysis_tables = await page.query_selector_all('table')
|
||||
for idx, table in enumerate(analysis_tables):
|
||||
table_data = []
|
||||
rows = await table.query_selector_all('tr')
|
||||
for row in rows:
|
||||
cells = await row.query_selector_all('td, th')
|
||||
row_data = []
|
||||
for cell in cells:
|
||||
text = await cell.inner_text()
|
||||
row_data.append(text.strip())
|
||||
if row_data:
|
||||
table_data.append(row_data)
|
||||
|
||||
stock_data['analysis'][f'table_{idx}'] = table_data
|
||||
|
||||
except Exception as e:
|
||||
print(f" Error extracting analysis: {e}")
|
||||
|
||||
print(f"✅ {ticker} data scraped successfully")
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Error scraping {ticker}: {e}")
|
||||
stock_data['error'] = str(e)
|
||||
|
||||
finally:
|
||||
await browser.close()
|
||||
|
||||
# Save individual stock data
|
||||
output_file = f"{self.output_dir}/{ticker}_yahoo.json"
|
||||
with open(output_file, 'w', encoding='utf-8') as f:
|
||||
json.dump(stock_data, f, indent=2)
|
||||
|
||||
return stock_data
|
||||
|
||||
async def scrape_multiple_stocks(self, stock_list, max_stocks=None):
|
||||
"""Scrape data for multiple stocks"""
|
||||
print("=" * 60)
|
||||
print("YAHOO FINANCE SCRAPING")
|
||||
print("=" * 60)
|
||||
|
||||
if max_stocks:
|
||||
stock_list = stock_list[:max_stocks]
|
||||
|
||||
all_data = []
|
||||
successful = 0
|
||||
failed = 0
|
||||
|
||||
for stock in stock_list:
|
||||
ticker = stock.get('symbol')
|
||||
exchange = stock.get('exchange')
|
||||
|
||||
data = await self.scrape_stock_data(ticker, exchange)
|
||||
all_data.append(data)
|
||||
|
||||
if data.get('error'):
|
||||
failed += 1
|
||||
else:
|
||||
successful += 1
|
||||
|
||||
# Rate limiting
|
||||
await asyncio.sleep(2)
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
print(f"✅ Successfully scraped: {successful}")
|
||||
print(f"❌ Failed: {failed}")
|
||||
print(f"📁 Data saved to: {self.output_dir}/")
|
||||
print("=" * 60)
|
||||
|
||||
return all_data
|
||||
|
||||
|
||||
async def main():
|
||||
"""Test the scraper with a few stocks"""
|
||||
|
||||
# Load listings
|
||||
listings_file = "data/listings/all_listings_combined.json"
|
||||
|
||||
if not os.path.exists(listings_file):
|
||||
print(f"❌ No listings file found at {listings_file}")
|
||||
print(" Run extract_listings.py first")
|
||||
return
|
||||
|
||||
with open(listings_file, 'r', encoding='utf-8') as f:
|
||||
listings = json.load(f)
|
||||
|
||||
print(f"📊 Found {len(listings)} stocks in listings")
|
||||
|
||||
# Test with first 5 stocks
|
||||
scraper = YahooFinanceScraper()
|
||||
await scraper.scrape_multiple_stocks(listings, max_stocks=5)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
Executable
+78
@@ -0,0 +1,78 @@
|
||||
#!/bin/bash
|
||||
#
|
||||
# Setup Daily Automation for Stock Intelligence System
|
||||
# This script sets up a cron job to run at 12:00 PM every day
|
||||
#
|
||||
|
||||
echo "=========================================="
|
||||
echo "Stock Intelligence System - Cron Setup"
|
||||
echo "=========================================="
|
||||
echo ""
|
||||
|
||||
# Get the absolute path to the script
|
||||
SCRIPT_DIR="/Users/macbook/Desktop/Victor"
|
||||
DAILY_SCRIPT="$SCRIPT_DIR/daily_run.sh"
|
||||
|
||||
# Check if daily_run.sh exists
|
||||
if [ ! -f "$DAILY_SCRIPT" ]; then
|
||||
echo "❌ Error: daily_run.sh not found at $DAILY_SCRIPT"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# Make sure it's executable
|
||||
chmod +x "$DAILY_SCRIPT"
|
||||
|
||||
# Create the cron entry
|
||||
# Format: minute hour day month day-of-week command
|
||||
CRON_TIME="0 12 * * *" # 12:00 PM every day
|
||||
CRON_ENTRY="$CRON_TIME $DAILY_SCRIPT"
|
||||
|
||||
echo "Setting up cron job:"
|
||||
echo " Schedule: Every day at 12:00 PM"
|
||||
echo " Command: $DAILY_SCRIPT"
|
||||
echo ""
|
||||
|
||||
# Backup existing crontab
|
||||
echo "📋 Backing up existing crontab..."
|
||||
crontab -l > crontab_backup_$(date +%Y%m%d_%H%M%S).txt 2>/dev/null || true
|
||||
|
||||
# Check if cron job already exists
|
||||
if crontab -l 2>/dev/null | grep -F "$DAILY_SCRIPT" > /dev/null; then
|
||||
echo "⚠️ Cron job already exists for this script"
|
||||
echo ""
|
||||
echo "Current crontab entries for this script:"
|
||||
crontab -l 2>/dev/null | grep -F "$DAILY_SCRIPT"
|
||||
echo ""
|
||||
read -p "Do you want to replace it? (y/n) " -n 1 -r
|
||||
echo
|
||||
if [[ ! $REPLY =~ ^[Yy]$ ]]; then
|
||||
echo "❌ Aborted. No changes made."
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# Remove existing entries
|
||||
crontab -l 2>/dev/null | grep -v -F "$DAILY_SCRIPT" | crontab -
|
||||
fi
|
||||
|
||||
# Add new cron job
|
||||
echo "➕ Adding new cron job..."
|
||||
(crontab -l 2>/dev/null; echo "$CRON_ENTRY") | crontab -
|
||||
|
||||
echo ""
|
||||
echo "✅ Cron job successfully installed!"
|
||||
echo ""
|
||||
echo "Current crontab:"
|
||||
echo "----------------------------------------"
|
||||
crontab -l
|
||||
echo "----------------------------------------"
|
||||
echo ""
|
||||
echo "📝 Note: Make sure your Mac is awake at 12:00 PM for the cron job to run."
|
||||
echo " You can verify logs in: $SCRIPT_DIR/logs/"
|
||||
echo ""
|
||||
echo "To remove the cron job later, run:"
|
||||
echo " crontab -e"
|
||||
echo " (then delete the line with '$DAILY_SCRIPT')"
|
||||
echo ""
|
||||
echo "To test the script manually, run:"
|
||||
echo " $DAILY_SCRIPT"
|
||||
echo ""
|
||||
Reference in New Issue
Block a user