Being a software engineer who works extensively with financial data, I recently hit a wall with traditional stock market APIs. After getting frustrated with rate limits and expensive subscriptions, I decided to build my own solution using web scraping. Here's how I did it, and what I learned along the way. Introduction: Why I Needed a Different Approach My breaking point came during a personal project where I was trying to analyze market trends. Yahoo Finance's API kept hitting rate limits, and Bloomberg Terminal's pricing made me laugh out loud - there was no way I could justify that cost for a side project. I needed something that would let me: Fetch data without arbitrary limits
Get real-time prices and trading volumes
Access historical data without paying premium fees
Scale up my analysis as needed The Web Scraping Solution After some research and experimentation, I settled on scraping data from two main sources: CNN Money for trending stocks and Yahoo Finance for detailed metrics. Here's how I built it: Setting Up the Basic Infrastructure First, I installed the essential tools: pip install requests bs4 Then I created a basic scraper that could handle network issues gracefully: import requests
from bs4 import BeautifulSoup
import time
import logging

def make_request(url, max_retries=3):
    headers = {
        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
    }
    
    for attempt in range(max_retries):
        try:
            return requests.get(url, headers=headers, timeout=10)
        except Exception as e:
            if attempt == max_retries - 1:
                raise
            time.sleep(attempt + 1) Grabbing Trending Stocks I started with CNN Money's hot stocks list, which gives me three categories of stocks to track: def get_trending_stocks():
    url = 'https://money.cnn.com/data/hotstocks/index.html'
    response = make_request(url)
    soup = BeautifulSoup(response.text, "html.parser")

    tables = soup.findAll("table", {"class": "wsod_dataTable wsod_dataTableBigAlt"})
    categories = ["Most Actives", "Gainers", "Losers"]
    
    stocks = []
    for i, table in enumerate(tables):
        for row in table.findAll("tr")[1:]:  # Skip headers
            cells = row.findAll("td")
            if cells:
                stocks.append({
                    'category': categories[i],
                    'symbol': cells[0].find(text=True),
                    'company': cells[0].span.text.strip()
                })
    
    return stocks Getting the Financial Details For each trending stock, I fetch additional data from Yahoo Finance: def get_stock_details(symbol):
    url = f"https://finance.yahoo.com/quote/{symbol}"
    response = make_request(url)
    soup = BeautifulSoup(response.text, "html.parser")

    data = {}
    
    # Find the main quote table
    table = soup.find("table", {"class": "W(100%)"})
    if table:
        for row in table.findAll("tr"):
            cells = row.findAll("td")
            if len(cells) > 1:
                key = cells[0].text.strip()
                value = cells[1].text.strip()
                data[key] = value
    
    return data The Gotchas I Encountered Building this wasn't all smooth sailing. Here are some real issues I hit and how I solved them: Rate Limiting: Yahoo Finance started blocking me after too many rapid requests. I added random delays between requests: time.sleep(random.uniform(1, 3))  # Random delay between 1-3 seconds Data Inconsistencies: Sometimes the scraped data would be malformed. I added validation: def validate_price(price_str):
    try:
        return float(price_str.replace('$', '').replace(',', ''))
    except:
        return None Website Changes: The sites occasionally update their HTML structure. I made my selectors more robust: # Instead of exact class matches, use partial matches
table = soup.find("table", class_=lambda x: x and 'dataTable' in x) Storing and Using the Data I keep things simple with CSV storage - it's easy to work with and perfect for my needs: import csv
from datetime import datetime

def save_stock_data(stocks):
    timestamp = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
    
    with open('stock_data.csv', 'a', newline='') as file:
        writer = csv.writer(file)
        for stock in stocks:
            writer.writerow([timestamp, stock['symbol'], 
                           stock['price'], stock['volume']]) What I Learned After running this scraper for several weeks, here are my key takeaways: Web scraping isn't just a hack - it's a viable alternative to expensive APIs when done rightly.
Building in error handling and logging from the start saves huge headaches later.
Stock data is messy - so always validate what you scrape.
Starting simple and iterating works better than trying to build everything at once! What's Next? I'm currently working on adding: News sentiment analysis
Basic pattern recognition
A simple dashboard for visualization Also, would you like to integrate this scraper with machine learning models to predict stock trends? Let me know in the comments! Being a software engineer who works extensively with financial data, I recently hit a wall with traditional stock market APIs. After getting frustrated with rate limits and expensive subscriptions, I decided to build my own solution using web scraping. Here's how I did it, and what I learned along the way. Introduction: Why I Needed a Different Approach My breaking point came during a personal project where I was trying to analyze market trends. Yahoo Finance's API kept hitting rate limits, and Bloomberg Terminal's pricing made me laugh out loud - there was no way I could justify that cost for a side project. I needed something that would let me: Fetch data without arbitrary limits Get real-time prices and trading volumes Access historical data without paying premium fees Scale up my analysis as needed Fetch data without arbitrary limits Get real-time prices and trading volumes Access historical data without paying premium fees Scale up my analysis as needed The Web Scraping Solution After some research and experimentation, I settled on scraping data from two main sources: CNN Money for trending stocks and Yahoo Finance for detailed metrics. Here's how I built it: Setting Up the Basic Infrastructure First, I installed the essential tools: pip install requests bs4 pip install requests bs4 Then I created a basic scraper that could handle network issues gracefully: import requests
from bs4 import BeautifulSoup
import time
import logging

def make_request(url, max_retries=3):
    headers = {
        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
    }
    
    for attempt in range(max_retries):
        try:
            return requests.get(url, headers=headers, timeout=10)
        except Exception as e:
            if attempt == max_retries - 1:
                raise
            time.sleep(attempt + 1) import requests
from bs4 import BeautifulSoup
import time
import logging

def make_request(url, max_retries=3):
    headers = {
        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
    }
    
    for attempt in range(max_retries):
        try:
            return requests.get(url, headers=headers, timeout=10)
        except Exception as e:
            if attempt == max_retries - 1:
                raise
            time.sleep(attempt + 1) Grabbing Trending Stocks I started with CNN Money's hot stocks list, which gives me three categories of stocks to track: def get_trending_stocks():
    url = 'https://money.cnn.com/data/hotstocks/index.html'
    response = make_request(url)
    soup = BeautifulSoup(response.text, "html.parser")

    tables = soup.findAll("table", {"class": "wsod_dataTable wsod_dataTableBigAlt"})
    categories = ["Most Actives", "Gainers", "Losers"]
    
    stocks = []
    for i, table in enumerate(tables):
        for row in table.findAll("tr")[1:]:  # Skip headers
            cells = row.findAll("td")
            if cells:
                stocks.append({
                    'category': categories[i],
                    'symbol': cells[0].find(text=True),
                    'company': cells[0].span.text.strip()
                })
    
    return stocks def get_trending_stocks():
    url = 'https://money.cnn.com/data/hotstocks/index.html'
    response = make_request(url)
    soup = BeautifulSoup(response.text, "html.parser")

    tables = soup.findAll("table", {"class": "wsod_dataTable wsod_dataTableBigAlt"})
    categories = ["Most Actives", "Gainers", "Losers"]
    
    stocks = []
    for i, table in enumerate(tables):
        for row in table.findAll("tr")[1:]:  # Skip headers
            cells = row.findAll("td")
            if cells:
                stocks.append({
                    'category': categories[i],
                    'symbol': cells[0].find(text=True),
                    'company': cells[0].span.text.strip()
                })
    
    return stocks Getting the Financial Details For each trending stock, I fetch additional data from Yahoo Finance: def get_stock_details(symbol):
    url = f"https://finance.yahoo.com/quote/{symbol}"
    response = make_request(url)
    soup = BeautifulSoup(response.text, "html.parser")

    data = {}
    
    # Find the main quote table
    table = soup.find("table", {"class": "W(100%)"})
    if table:
        for row in table.findAll("tr"):
            cells = row.findAll("td")
            if len(cells) > 1:
                key = cells[0].text.strip()
                value = cells[1].text.strip()
                data[key] = value
    
    return data def get_stock_details(symbol):
    url = f"https://finance.yahoo.com/quote/{symbol}"
    response = make_request(url)
    soup = BeautifulSoup(response.text, "html.parser")

    data = {}
    
    # Find the main quote table
    table = soup.find("table", {"class": "W(100%)"})
    if table:
        for row in table.findAll("tr"):
            cells = row.findAll("td")
            if len(cells) > 1:
                key = cells[0].text.strip()
                value = cells[1].text.strip()
                data[key] = value
    
    return data The Gotchas I Encountered Building this wasn't all smooth sailing. Here are some real issues I hit and how I solved them: Rate Limiting: Yahoo Finance started blocking me after too many rapid requests. I added random delays between requests: Rate Limiting : Yahoo Finance started blocking me after too many rapid requests. I added random delays between requests: Rate Limiting time.sleep(random.uniform(1, 3))  # Random delay between 1-3 seconds time.sleep(random.uniform(1, 3))  # Random delay between 1-3 seconds Data Inconsistencies: Sometimes the scraped data would be malformed. I added validation: Data Inconsistencies : Sometimes the scraped data would be malformed. I added validation: Data Inconsistencies def validate_price(price_str):
    try:
        return float(price_str.replace('$', '').replace(',', ''))
    except:
        return None def validate_price(price_str):
    try:
        return float(price_str.replace('$', '').replace(',', ''))
    except:
        return None Website Changes: The sites occasionally update their HTML structure. I made my selectors more robust: Website Changes : The sites occasionally update their HTML structure. I made my selectors more robust: Website Changes # Instead of exact class matches, use partial matches
table = soup.find("table", class_=lambda x: x and 'dataTable' in x) # Instead of exact class matches, use partial matches
table = soup.find("table", class_=lambda x: x and 'dataTable' in x) Storing and Using the Data I keep things simple with CSV storage - it's easy to work with and perfect for my needs: import csv
from datetime import datetime

def save_stock_data(stocks):
    timestamp = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
    
    with open('stock_data.csv', 'a', newline='') as file:
        writer = csv.writer(file)
        for stock in stocks:
            writer.writerow([timestamp, stock['symbol'], 
                           stock['price'], stock['volume']]) import csv
from datetime import datetime

def save_stock_data(stocks):
    timestamp = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
    
    with open('stock_data.csv', 'a', newline='') as file:
        writer = csv.writer(file)
        for stock in stocks:
            writer.writerow([timestamp, stock['symbol'], 
                           stock['price'], stock['volume']]) What I Learned After running this scraper for several weeks, here are my key takeaways: After running this scraper for several weeks, here are my key takeaways: Web scraping isn't just a hack - it's a viable alternative to expensive APIs when done rightly. Building in error handling and logging from the start saves huge headaches later. Stock data is messy - so always validate what you scrape. Starting simple and iterating works better than trying to build everything at once! Web scraping isn't just a hack - it's a viable alternative to expensive APIs when done rightly. Building in error handling and logging from the start saves huge headaches later. Stock data is messy - so always validate what you scrape. Starting simple and iterating works better than trying to build everything at once! What's Next? I'm currently working on adding: I'm currently working on adding: News sentiment analysis Basic pattern recognition A simple dashboard for visualization News sentiment analysis Basic pattern recognition A simple dashboard for visualization Also, would you like to integrate this scraper with machine learning models to predict stock trends? Let me know in the comments! machine learning models

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How to Build a No-Limits Stock Market Scraper with Python

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