Financial Data Scraping with CAPTCHA Handling

Financial platforms like stock screeners, SEC EDGAR, and trading platforms protect data with CAPTCHAs to prevent automated extraction. CaptchaAI handles these challenges programmatically so you can collect market data at scale.

Where CAPTCHAs Appear in Finance

Stock Screener Scraping

import requests
import time
from bs4 import BeautifulSoup
import re

CAPTCHAAI_KEY = "YOUR_API_KEY"
CAPTCHAAI_URL = "https://ocr.captchaai.com"


def solve_captcha(method, sitekey, pageurl, **kwargs):
    data = {
        "key": CAPTCHAAI_KEY,
        "method": method,
        "googlekey": sitekey,
        "pageurl": pageurl,
        "json": 1,
    }
    data.update(kwargs)

    resp = requests.post(f"{CAPTCHAAI_URL}/in.php", data=data)
    task_id = resp.json()["request"]

    for _ in range(60):
        time.sleep(5)
        result = requests.get(f"{CAPTCHAAI_URL}/res.php", params={
            "key": CAPTCHAAI_KEY, "action": "get",
            "id": task_id, "json": 1,
        })
        r = result.json()
        if r["request"] != "CAPCHA_NOT_READY":
            return r["request"]

    raise TimeoutError("Solve timeout")


class FinancialScraper:
    def __init__(self, proxy=None):
        self.session = requests.Session()
        if proxy:
            self.session.proxies = {"http": proxy, "https": proxy}
        self.session.headers.update({
            "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) "
            "AppleWebKit/537.36 Chrome/126.0.0.0 Safari/537.36",
            "Accept-Language": "en-US,en;q=0.9",
        })

    def scrape_screener(self, url):
        """Scrape stock screener, handling CAPTCHA if triggered."""
        resp = self.session.get(url, timeout=30)

        # Check for CAPTCHA
        sitekey_match = re.search(r'data-sitekey="([^"]+)"', resp.text)
        if sitekey_match:
            sitekey = sitekey_match.group(1)
            token = solve_captcha("userrecaptcha", sitekey, url)

            # Resubmit with token
            resp = self.session.post(url, data={
                "g-recaptcha-response": token,
            })

        return self._parse_stocks(resp.text)

    def _parse_stocks(self, html):
        soup = BeautifulSoup(html, "html.parser")
        stocks = []
        for row in soup.select("table.screener-table tr")[1:]:
            cols = row.select("td")
            if len(cols) >= 8:
                stocks.append({
                    "ticker": cols[1].get_text(strip=True),
                    "company": cols[2].get_text(strip=True),
                    "sector": cols[3].get_text(strip=True),
                    "price": cols[6].get_text(strip=True),
                    "change": cols[7].get_text(strip=True),
                })
        return stocks


# Usage
scraper = FinancialScraper(
    proxy="http://user:pass@residential.proxy.com:5000"
)
stocks = scraper.scrape_screener("https://screener.example.com/screener.ashx?v=111")
for stock in stocks[:5]:
    print(f"{stock['ticker']}: {stock['price']} ({stock['change']})")

SEC EDGAR Filing Extraction

SEC EDGAR implements rate limiting and CAPTCHAs for high-volume access:

import json


class SECFilingScraper:
    BASE_URL = "https://efts.sec.gov/LATEST"

    def __init__(self, user_agent_email, proxy=None):
        self.session = requests.Session()
        if proxy:
            self.session.proxies = {"http": proxy, "https": proxy}
        # SEC requires identifying User-Agent
        self.session.headers.update({
            "User-Agent": f"CompanyName admin@{user_agent_email}",
            "Accept": "application/json",
        })

    def search_filings(self, company, filing_type="10-K"):
        """Search EDGAR for specific filing types."""
        url = f"{self.BASE_URL}/search-index"
        params = {
            "q": company,
            "dateRange": "custom",
            "forms": filing_type,
        }

        resp = self.session.get(url, params=params, timeout=30)

        # Handle CAPTCHA if triggered
        if "captcha" in resp.text.lower() or resp.status_code == 403:
            sitekey = self._extract_sitekey(resp.text)
            if sitekey:
                token = solve_captcha("userrecaptcha", sitekey, url)
                resp = self.session.post(url, data={
                    **params,
                    "g-recaptcha-response": token,
                })

        return resp.json() if resp.status_code == 200 else {}

    def download_filing(self, filing_url):
        """Download individual filing document."""
        resp = self.session.get(filing_url, timeout=60)
        if resp.status_code == 200:
            return resp.text
        return None

    def _extract_sitekey(self, html):
        match = re.search(r'data-sitekey="([^"]+)"', html)
        return match.group(1) if match else None


# Usage
sec = SECFilingScraper(
    user_agent_email="example.com",
    proxy="http://user:pass@proxy.example.com:5000",
)
filings = sec.search_filings("Apple Inc", "10-K")

Turnstile-Protected Market Data

def scrape_turnstile_market_data(url, sitekey):
    """Handle Cloudflare Turnstile on financial data sites."""
    token = solve_captcha("turnstile", sitekey, url)

    session = requests.Session()
    session.headers.update({
        "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) "
        "AppleWebKit/537.36 Chrome/126.0.0.0 Safari/537.36",
    })

    resp = session.post(url, data={
        "cf-turnstile-response": token,
    }, timeout=30)

    return resp.json() if resp.status_code == 200 else None

Scheduled Data Collection

import csv
from datetime import datetime


def daily_market_snapshot(tickers, output_dir="data"):
    """Collect daily stock data, handling CAPTCHAs automatically."""
    scraper = FinancialScraper(
        proxy="http://user:pass@residential.proxy.com:5000"
    )

    date_str = datetime.now().strftime("%Y-%m-%d")
    results = []

    for ticker in tickers:
        url = f"https://screener.example.com/quote.ashx?t={ticker}"
        try:
            data = scraper.scrape_screener(url)
            if data:
                results.extend(data)
            time.sleep(2)  # Rate limit
        except Exception as e:
            print(f"Error on {ticker}: {e}")

    # Save to CSV
    filepath = f"{output_dir}/market_{date_str}.csv"
    with open(filepath, "w", newline="") as f:
        writer = csv.DictWriter(f, fieldnames=["ticker", "company", "sector", "price", "change"])
        writer.writeheader()
        writer.writerows(results)

    print(f"Saved {len(results)} records to {filepath}")
    return results


# Run daily
tickers = ["AAPL", "GOOGL", "MSFT", "AMZN", "TSLA"]
daily_market_snapshot(tickers)

Rate Limiting Best Practices

Financial sites are stricter about automated access:

Troubleshooting

FAQ

Is scraping financial data legal?

Public financial data (SEC filings, stock quotes) is generally permissible. Always respect terms of service and rate limits. SEC EDGAR explicitly provides EDGAR access for research purposes.

Why do financial sites use CAPTCHAs?

To prevent high-volume automated extraction that could enable market manipulation, competitive intelligence gathering, or excessive server load.

How often should I scrape market data?

For stock prices: once per minute during market hours maximum. For filings: once daily is typical. Over-scraping triggers CAPTCHAs faster.

Related Guides

• Proxy Quality Affects Solve Rate
• Rotating Residential Proxies

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