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
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Aherobo Ovie Victor
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"""
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Scrape financial data from Yahoo Finance (no API key needed)
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Gets financials, ratios, and key metrics for each stock
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"""
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import asyncio
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import json
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import os
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from datetime import datetime
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from playwright.async_api import async_playwright
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import time
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import re
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class YahooFinanceScraper:
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def __init__(self, output_dir="data/financials"):
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self.output_dir = output_dir
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os.makedirs(output_dir, exist_ok=True)
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async def scrape_stock_data(self, ticker, exchange=""):
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"""Scrape comprehensive data for a single stock"""
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print(f"\n🔍 Scraping {ticker}...")
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# Format ticker for Yahoo Finance
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yahoo_ticker = ticker
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# Canadian stocks need exchange-specific suffixes
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if exchange in ['TSX', 'TSXV', 'TSX/TSXV']:
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if not ticker.endswith('.TO') and not ticker.endswith('.V'):
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yahoo_ticker = f"{ticker}.TO" # Try TSX first
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# CSE (Canadian Securities Exchange) stocks use .CN suffix
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# CSE tickers in database may have "T2" prefix which needs to be removed
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elif exchange == 'CSE':
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# Remove T2 prefix if present (e.g., T2AAA -> AAA)
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clean_ticker = ticker.replace('T2', '') if ticker.startswith('T2') else ticker
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# Remove any suffix after a dot (e.g., T2AAAWH.U -> AAAWH)
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if '.' in clean_ticker:
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clean_ticker = clean_ticker.split('.')[0]
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yahoo_ticker = f"{clean_ticker}.CN"
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print(f" CSE stock: {ticker} -> {yahoo_ticker}")
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stock_data = {
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'ticker': ticker,
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'exchange': exchange,
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'yahoo_ticker': yahoo_ticker,
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'scraped_at': datetime.now().isoformat(),
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'profile': {},
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'quote': {}, # Real-time quote data
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'financials': {},
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'statistics': {},
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'analysis': {},
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'error': None
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}
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async with async_playwright() as p:
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# Launch with no-cache to avoid stale data
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browser = await p.chromium.launch(
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headless=True,
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args=['--disable-blink-features=AutomationControlled']
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)
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context = await browser.new_context(
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viewport={'width': 1920, 'height': 1080},
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user_agent='Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36'
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)
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page = await context.new_page()
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try:
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# 1. Get Summary/Statistics page
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url = f"https://finance.yahoo.com/quote/{yahoo_ticker}"
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print(f" Loading {url}...")
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await page.goto(url, wait_until='domcontentloaded', timeout=60000)
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await asyncio.sleep(5) # Wait for dynamic content to load
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# Check if ticker exists
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page_content = await page.content()
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if "Symbol Lookup" in page_content or "Symbols similar to" in page_content:
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print(f"⚠️ {yahoo_ticker} not found on Yahoo Finance")
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stock_data['error'] = 'Ticker not found'
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# Try alternative suffix for TSXV
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if yahoo_ticker.endswith('.TO'):
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yahoo_ticker = f"{ticker}.V"
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print(f" Trying {yahoo_ticker}...")
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url = f"https://finance.yahoo.com/quote/{yahoo_ticker}"
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await page.goto(url, wait_until='domcontentloaded', timeout=60000)
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await asyncio.sleep(5)
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page_content = await page.content()
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if "Symbol Lookup" in page_content:
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await browser.close()
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return stock_data
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else:
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stock_data['yahoo_ticker'] = yahoo_ticker
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stock_data['error'] = None
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# Extract key stats and quote data from summary
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try:
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# Get real-time quote data from the quote header section
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# Initialize quote fields to empty to avoid caching from previous runs
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stock_data['quote'] = {
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'date': '',
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'open': '',
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'high': '',
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'low': '',
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'close': '',
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'volume': ''
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}
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# Close (current price)
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price_elem = await page.query_selector('[data-field="regularMarketPrice"]')
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if price_elem:
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price_text = await price_elem.inner_text()
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# Remove whitespace and newlines
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price_text = ' '.join(price_text.split())
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print(f" Raw price text: '{price_text}'")
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try:
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current_price = float(price_text.replace(',', ''))
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stock_data['profile']['current_price'] = current_price
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stock_data['quote']['close'] = price_text
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print(f" Parsed price: {current_price}")
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except ValueError:
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print(f" Warning: Could not parse price: {price_text}")
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# Open price
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open_elem = await page.query_selector('[data-field="regularMarketOpen"]')
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if open_elem:
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open_text = await open_elem.inner_text()
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stock_data['quote']['open'] = ' '.join(open_text.split())
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# Day range (high/low)
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range_elem = await page.query_selector('[data-field="regularMarketDayRange"]')
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if range_elem:
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range_text = await range_elem.inner_text()
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range_text = ' '.join(range_text.split())
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if ' - ' in range_text:
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low, high = range_text.split(' - ')
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stock_data['quote']['low'] = low.strip()
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stock_data['quote']['high'] = high.strip()
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# Volume
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volume_elem = await page.query_selector('[data-field="regularMarketVolume"]')
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if volume_elem:
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volume_text = await volume_elem.inner_text()
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stock_data['quote']['volume'] = ' '.join(volume_text.split())
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# Date/time - extract from page text
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page_text = await page.inner_text('body')
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# Look for "At close: November 5 at 4:00:01 PM EST" pattern
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import re
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time_match = re.search(r'At close:\s*([^\\n]+(?:EST|EDT|PST|PDT))', page_text)
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if time_match:
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stock_data['quote']['date'] = time_match.group(1).strip()
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except Exception as e:
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print(f" Error extracting summary: {e}")
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# Get market cap, P/E, etc from the stats table
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stat_rows = await page.query_selector_all('table tr')
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for row in stat_rows:
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try:
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cells = await row.query_selector_all('td')
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if len(cells) == 2:
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label = await cells[0].inner_text()
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value = await cells[1].inner_text()
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label = label.strip().lower().replace(' ', '_').replace('/', '_')
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stock_data['statistics'][label] = value.strip()
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except:
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continue
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except Exception as e:
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print(f" Error extracting summary: {e}")
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# 2. Get Financials page
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try:
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financials_url = f"https://finance.yahoo.com/quote/{yahoo_ticker}/financials"
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await page.goto(financials_url, wait_until='domcontentloaded', timeout=60000)
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await asyncio.sleep(5)
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# Extract financial data
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financial_tables = await page.query_selector_all('div[class*="financials"] table')
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for table in financial_tables:
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rows = await table.query_selector_all('tr')
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for row in rows:
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try:
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cells = await row.query_selector_all('td, th')
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if len(cells) >= 2:
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label = await cells[0].inner_text()
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values = []
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for i in range(1, len(cells)):
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val = await cells[i].inner_text()
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values.append(val.strip())
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label_key = label.strip().lower().replace(' ', '_')
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stock_data['financials'][label_key] = values
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except:
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continue
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except Exception as e:
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print(f" Error extracting financials: {e}")
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# 3. Get Key Statistics page
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try:
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stats_url = f"https://finance.yahoo.com/quote/{yahoo_ticker}/key-statistics"
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await page.goto(stats_url, wait_until='domcontentloaded', timeout=60000)
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await asyncio.sleep(5)
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# Extract all statistics
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stat_tables = await page.query_selector_all('table')
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for table in stat_tables:
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rows = await table.query_selector_all('tr')
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for row in rows:
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try:
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cells = await row.query_selector_all('td')
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if len(cells) == 2:
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label = await cells[0].inner_text()
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value = await cells[1].inner_text()
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label_key = label.strip().lower().replace(' ', '_').replace('/', '_')
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stock_data['statistics'][label_key] = value.strip()
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except:
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continue
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except Exception as e:
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print(f" Error extracting statistics: {e}")
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# 4. Get Analysis page (analyst ratings, growth estimates)
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try:
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analysis_url = f"https://finance.yahoo.com/quote/{yahoo_ticker}/analysis"
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await page.goto(analysis_url, wait_until='networkidle', timeout=30000)
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await asyncio.sleep(2)
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# Extract analysis data
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analysis_tables = await page.query_selector_all('table')
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for idx, table in enumerate(analysis_tables):
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table_data = []
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rows = await table.query_selector_all('tr')
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for row in rows:
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cells = await row.query_selector_all('td, th')
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row_data = []
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for cell in cells:
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text = await cell.inner_text()
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row_data.append(text.strip())
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if row_data:
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table_data.append(row_data)
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stock_data['analysis'][f'table_{idx}'] = table_data
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except Exception as e:
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print(f" Error extracting analysis: {e}")
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print(f"✅ {ticker} data scraped successfully")
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except Exception as e:
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print(f"❌ Error scraping {ticker}: {e}")
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stock_data['error'] = str(e)
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finally:
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await browser.close()
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# Save individual stock data
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output_file = f"{self.output_dir}/{ticker}_yahoo.json"
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with open(output_file, 'w', encoding='utf-8') as f:
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json.dump(stock_data, f, indent=2)
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return stock_data
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async def scrape_multiple_stocks(self, stock_list, max_stocks=None):
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"""Scrape data for multiple stocks"""
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print("=" * 60)
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print("YAHOO FINANCE SCRAPING")
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print("=" * 60)
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if max_stocks:
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stock_list = stock_list[:max_stocks]
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all_data = []
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successful = 0
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failed = 0
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for stock in stock_list:
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ticker = stock.get('symbol')
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exchange = stock.get('exchange')
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data = await self.scrape_stock_data(ticker, exchange)
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all_data.append(data)
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if data.get('error'):
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failed += 1
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else:
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successful += 1
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# Rate limiting
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await asyncio.sleep(2)
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print("\n" + "=" * 60)
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print(f"✅ Successfully scraped: {successful}")
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print(f"❌ Failed: {failed}")
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print(f"📁 Data saved to: {self.output_dir}/")
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print("=" * 60)
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return all_data
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async def main():
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"""Test the scraper with a few stocks"""
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# Load listings
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listings_file = "data/listings/all_listings_combined.json"
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if not os.path.exists(listings_file):
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print(f"❌ No listings file found at {listings_file}")
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print(" Run extract_listings.py first")
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return
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with open(listings_file, 'r', encoding='utf-8') as f:
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listings = json.load(f)
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print(f"📊 Found {len(listings)} stocks in listings")
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# Test with first 5 stocks
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scraper = YahooFinanceScraper()
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await scraper.scrape_multiple_stocks(listings, max_stocks=5)
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if __name__ == "__main__":
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asyncio.run(main())
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