Unlocking Stock Markets Data with Zenscrape and Marketstack: A Simple Prototype

Roundup: Quick Python Tutorial for Stock Markets Data

In this roundup we will show you how a few lines of Python code can create a web application that provides real-time stock markets data and news updates. No longer will you need to jump between multiple platforms or rely on scattered pieces of information. With Zenscrape and Marketstack from APILayer in your arsenal, and the power of Python to weave them together, you’ll be on your way to making more informed decisions in no time!

Additionally, with PythonAnywhere, users can effortlessly create and manage their HTML templates (these are the HTML files called by the FLASK application), making it straightforward for beginners to establish their first web interface. You will store your HTML file in the templates folder.

We crafted our prototype using PythonAnywhere, a versatile cloud platform that allows users to easily run and deploy Python applications. For those just starting out, PythonAnywhere provides a default file named flask_app.py, which serves as the main entry point for Flask web applications. This is where you will put our backend code.

PIP Installs PythonAnywhere

It is very easy to do your PIP installs of Python libraries for your Python web application in PythonAnywhere. Navigate to Open Bash console here below the Dashboard.

Once you know where your flask_app.py , HTML template files are stored, and how to PIP install Python libraries on PythonAnywhere, you can be building powerful Python web applications in minutes.

Additionally, if you are new to FLASK programming, use AI services like ChatGPT to help you turn your normal Python applications into FLASK applications with web page interfaces.

Why Use Zenscrape and Marketstack?

Zenscrape: This is a web scraping subscription service offered by APILayer that allows you to fetch web content from various sources without diving deep into the intricacies of web scraping.

Using Zenscrape in conjunction with BeautifulSoup offers a powerful combination for web scraping. Here’s why:

Introduction to BeautifulSoup: First and foremost, for those unfamiliar with BeautifulSoup, it’s a free Python library used by developers to extract data from HTML and XML documents. It provides Pythonic idioms for iterating, searching, and modifying the tree, making data extraction a breeze.
Bypassing Restrictions: Websites often have measures in place to deter or limit web scraping. Zenscrape is designed to circumvent these restrictions. As a cloud web scraping API, Zenscrape handles challenges such as CAPTCHAs, AJAX requests, and browser rendering. It fetches the raw HTML, ensuring you can access page content that might otherwise be challenging to retrieve.
Data Extraction and Parsing: Once you’ve obtained the raw HTML using Zenscrape, BeautifulSoup allows for effortless navigation and parsing of this content. With its intuitive functions, you can easily sift through the HTML, target specific elements, and extract the data you need.
Flexibility: The combination of Zenscrape and BeautifulSoup means you have both power and precision at your disposal. While Zenscrape fetches the content, BeautifulSoup gives you the tools to refine, process, and extract precisely what’s relevant.
Dynamic Content Handling: Some websites rely heavily on JavaScript to display content. Zenscrape can render these dynamic pages, ensuring that even JavaScript-loaded content is accessible. Once fetched, BeautifulSoup can parse and extract data from such content seamlessly.
Efficiency: Using Zenscrape to obtain the web pages and BeautifulSoup to parse them is often more efficient than relying solely on heavier browser automation tools. This duo can expedite the scraping process, making it faster and less resource-intensive.
Consistency and Reliability: By leveraging Zenscrape to deal with various web servers, IP bans, and user-agent restrictions, you ensure a consistent data feed. BeautifulSoup, with its robust parsing capabilities, then ensures that data extraction is reliable and accurate.

In essence, while Zenscrape acts as a reliable courier delivering you the package (web content), BeautifulSoup is the skilled craftsman that helps you unbox and make sense of its contents. Together, they form a potent duo for any web scraping endeavor.

Marketstack: Marketstack is a stock markets data API that provides a reliable source for both real-time and historical stock markets data.

. It offers a range of features, including:

Access to stock prices, volume, market capitalization, and more: Users can obtain real-time stock markets data for any ticker down to the minute, request intraday quotes, or search 30+ years of accurate historical market data.
Data from exchanges around the world: The API collects data from 70 global exchanges, including Nasdaq, NYSE, and more, and supports 170,000+ worldwide stock tickers.
An easy-to-use API for seamless integration into applications: The API is built on top of scalable, cutting-edge cloud infrastructure and offers a simple, powerful, and scalable REST API with an uptime of close to 100%.

Marketstack is licensed and sourced from multiple high-authority market data providers around the world. The API is part of the apilayer portfolio of REST API products, sitting next to some of the most popular microservice APIs, including currencylayer, ipapi, and scrapestack. Users can sign up for free and get access to real-time stock markets data for any ticker down to the minute

Backend (Flask in Python):

This code is a Flask web application (flask_app.py) that serves two main purposes:

Fetching stock markets data using the Marketstack API.
Fetching news articles from Google News via the ZenScrape API based on country and keyword.

You can paste this code into your flask_app.py file hosted on a service like PythonAnywhere. Make sure you replace the Mediastack and Zenscrape API keys with your own.

from flask import Flask, jsonify, make_response, render_template_string, render_template

from bs4 import BeautifulSoup

import requests

app = Flask(__name__)

MARKETSTACK_API_ENDPOINT = "http://api.marketstack.com/v1/"

MARKETSTACK_API_KEY = "YOUR MEDIASTACK API KEY"

ZENSCRAPE_API_ENDPOINT = "https://app.zenscrape.com/api/v1/get"

ZENSCRAPE_API_KEY = "YOUR ZENSCRAPE API KEY"

@app.route('/stockdata')

def stockdata():

return render_template('stockdata.html')

@app.route('/stockdata/<ticker>', methods=['GET'])

def get_stock_data(ticker):

try:

response = requests.get(f"{MARKETSTACK_API_ENDPOINT}eod?access_key={MARKETSTACK_API_KEY}&symbols={ticker}")

response.raise_for_status() # Check for HTTP errors

return jsonify(response.json())

except requests.RequestException:

return jsonify({"error": "Unable to fetch stock data."})

@app.route('/webdata/<country>/<keyword>', methods=['GET'])

def get_web_data(country, keyword):

try:

headers = {

"apikey": ZENSCRAPE_API_KEY

}

# Adjust the URL for Google News (which will always be the global version)

base_url = "https://news.google.com"

params = {

"url": f"{base_url}/search?q={keyword}",

"render": "false",

"country": country.upper() # Convert the country code to uppercase for ZenScrape

}

response = requests.get(ZENSCRAPE_API_ENDPOINT, headers=headers, params=params)

response.encoding = 'utf-8' # Set the encoding explicitly

soup = BeautifulSoup(response.text, 'html.parser')

# Extract news headlines and links

articles = soup.find_all('a', href=True)

extracted_data = [{"title": article.text, "link": article['href']} for article in articles if article.text]

# Return the extracted data as JSON

return jsonify(extracted_data)

except requests.RequestException as e:

print("Error during request:", e)

return jsonify({"error": "Unable to fetch web data."})

except Exception as e:

return f"Error processing HTML: {e}"

Here’s a breakdown of what the code does:

Imports:

The foundation of any Python application lies in its imports, and this prototype is no exception. Key components include:

Flask: This micro web framework is the backbone of our application, enabling the creation of web routes and views.
jsonify: This allows us to return JSON responses seamlessly from Flask routes.
make_response: Though not utilized in the provided code, this utility can be used to create specific response objects.
render_template_string and render_template: Both are essential for rendering HTML templates, but in this instance, only render_template is put to use.
BeautifulSoup: A vital tool for web scraping, it’s adept at extracting data from HTML and XML files.
requests: Simplifying HTTP interactions, this library is crucial for fetching data from external sources.

App Initialization:

Upon diving into the application’s core, we begin with the initialization of our Flask app instance via app = Flask(__name__).

Constants:

To ensure smooth interactions with external services, several constants are defined:

MARKETSTACK_API_ENDPOINT and MARKETSTACK_API_KEY: These pertain to the Marketstack service, an API dedicated to stock markets data.
ZENSCRAPE_API_ENDPOINT and ZENSCRAPE_API_KEY: Related to ZenScrape, they provide the means to scrape web content effectively.

Endpoints & Routes:

The heart of our application lies in its routes:

Stock Data Page (/stockdata): A straightforward route, it renders an HTML template named stockdata.html. However, the exact content of this template remains undisclosed in the provided details.
Get Stock Data Endpoint (/stockdata/<ticker>): This dynamic route is geared towards fetching stock markets data for a given ticker, like AAPL for Apple. An HTTP request to the Marketstack API retrieves the required data, presenting it as a JSON response or an error message if any issue arises.
Get Web Data Endpoint (/webdata/<country>/<keyword>): Tailored to pull news articles from Google News, this route uses specified criteria like country and keyword. Leveraging the ZenScrape API, it scrapes the relevant content, with BeautifulSoup then parsing the HTML. The final output consists of news headlines and links, presented as a JSON response.

Error Handling:

The code checks for request errors for both Marketstack and ZenScrape using requests.RequestException. If issues are detected, the user is alerted with descriptive error messages. Additionally, the news fetching segment incorporates a broader catch mechanism to address potential HTML processing concerns.

Alternatives To Google News for Accessing Financial Data

In this roundup, we have demonstrated a prototype that scrapes Google New, but there are other alternatives to Google News for accessing financial data:

1. Reuters: A global news organization providing financial, national, and international news.

2. Bloomberg: Offers business, financial, and global news.

3. Yahoo Finance: Provides financial news, data, and commentary including stock quotes.

4. Investing.com: Comprehensive financial portal offering news, analysis, quotes, and charts.

5. Seeking Alpha: Offers stock market insights, analysis, and news.

6. MarketWatch: Provides financial news, analysis, and stock markets data.

You can easily modify the prototype to work with an alternative stock markets data of your choice. For example, if you decide to scrape Reuters, you’d modify the URL in your Zenscrape request to something like:

1	'url': f'https://www.reuters.com/search/news?sortBy=&dateRange=&blob={keyword}'

Where {keyword} would be replaced with the term or ticker you’re interested in.

Keep in mind that different websites have different structures, and the way content is presented might vary. After scraping, you’ll likely need to process the data differently based on the structure of the website you’re scraping.

Mastering Zenscrape: Advanced Techniques for Complex Web Scraping Challenges

If Zenscrape basic settings consistently struggle to scrape data from certain platforms, you might consider a variety of advanced settings that Zenscrape offers to help improve the scraping experience, especially when dealing with complex or restrictive websites.

Render JavaScript:
- Some websites rely heavily on JavaScript to load content. You can instruct Zenscrape to render JavaScript by setting the render parameter to true.
Use Premium Proxies:
- Zenscrape offers premium residential and datacenter proxies. Using these can help bypass restrictions that certain websites impose on known scraping IP addresses.
Adjust Request Delays:
- Introducing a delay between requests can help avoid triggering anti-bot measures, especially if you’re making multiple requests in quick succession.
Custom Headers:
- Some websites may restrict or block default user-agents or headers used by scraping tools. You can customize the headers sent with the request, including setting a custom User-Agent to mimic a real browser.
Post Requests:
- While many websites use GET requests to fetch data, some require POST requests, especially if there’s form data involved. Zenscrape supports POST requests if needed.
Cookie Handling:
- For websites that require session persistence or have cookie-based challenges, you can set and send cookies with your requests.
Retry Failed Requests:
- Network issues, server timeouts, or temporary blocks can lead to failed requests. Consider implementing a retry mechanism with increasing delays between retries.
Rotate User-Agents:
- Continuously using the same user-agent might make the scraper more detectable. Consider rotating between different user-agents to mimic different browsers and devices.
Capture Screenshots:
- If you’re unsure how a page is rendered or want to visually verify the content, Zenscrape allows you to capture screenshots of the scraped webpage.
Limit the Scope:

Instead of trying to scrape an entire page, limit the scope of your scraping to specific elements or sections. This reduces the load and increases the chances of successful scraping.

Remember, while these advanced options can enhance the scraping experience, it’s essential to respect the website’s terms of service and robots.txt directives. Additionally, frequent and aggressive scraping can lead to IP bans or legal issues, so always scrape responsibly and ethically.

Frontend (HTML):

Store your frontend HTML file in the templates folder for your Python Flask application.

HTML File Code, Including Javascript

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<!DOCTYPE html>

<html>

<head>

<title>MarketSight</title>

<style>

table {

width: 100%;

border-collapse: collapse;

margin-top: 20px;

}

th, td {

border: 1px solid black;

padding: 8px;

text-align: left;

}

th {

background-color: #f2f2f2;

}

</style>

</head>

<body>

<h2>Stock Data</h2>

<button onclick="fetchStockData()">Fetch Stock Data</button>

<thead>

<tr>

<th>Close</th>

<th>Volume</th>

<th>Dividend</th>

</tr>

</thead>

<tbody>

</tbody>

</table>

<option value="us">United States</option>

<option value="gb">United Kingdom</option>

<option value="ca">Canada</option>

<option value="au">Australia</option>

<option value="fr">France</option>

</select>

<button onclick="fetchWebData()">Fetch Web Data</button>

function fetchStockData() {

const ticker = document.getElementById('stockInput').value;

fetch(`/stockdata/${ticker}`)

.then(response => response.json())

.then(data => {

const tableBody = document.getElementById('stockData').getElementsByTagName('tbody')[0];

tableBody.innerHTML = ""; // Clear previous data

data.data.forEach(stock => {

const row = tableBody.insertRow();

row.insertCell(0).textContent = new Date(stock.date).toLocaleDateString();

row.insertCell(1).textContent = stock.open.toFixed(2);

row.insertCell(2).textContent = stock.high.toFixed(2);

row.insertCell(3).textContent = stock.low.toFixed(2);

row.insertCell(4).textContent = stock.close.toFixed(2);

row.insertCell(5).textContent = stock.volume.toLocaleString();

row.insertCell(6).textContent = stock.dividend.toFixed(2);

});

}

function fetchWebData() {

const keyword = document.getElementById('webInput').value;

const country = document.getElementById('countrySelect').value; // Step 1: Get the selected country value

const excludeTitles = [

"News",

"Sign in",

"Home",

"U.S.",

"World",

"Local",

"Business",

"Technology",

"Entertainment",

"Sports",

"Science",

"Health"

];

fetch(`/webdata/${country}/${keyword}`) // Step 2: Include country in the fetch URL

.then(response => {

// Log the raw response for debugging

console.log(response);

if (!response.ok) {

throw new Error('Network response was not ok');

}

return response.json();

})

.then(data => {

const webDataDiv = document.getElementById('webData');

webDataDiv.innerHTML = ""; // Clear previous data

data

.filter(item => !excludeTitles.includes(item.title))

.forEach(item => {

const anchor = document.createElement('a');

anchor.href = "https://news.google.com/" + item.link;

anchor.textContent = item.title;

anchor.target = "_blank"; // Open links in a new tab

webDataDiv.appendChild(anchor);

webDataDiv.appendChild(document.createElement('br')); // Add a line break after each link

});

})

.catch(error => {

console.error('There was a problem with the fetch operation:', error.message);

});

}

</script>

</body>

</html>

The code provided is an HTML document that contains a table for displaying stock data and a form for fetching stock data and web data. The HTML document contains a header, body, and script section. The header section contains the title of the document and a style section that defines the style of the table. The body section contains two sections, one for stock data and another for web data.

The stock data section contains a form with an input field for entering a stock ticker and a button for fetching the stock markets data. The section also contains a table with a header row and an empty body row for displaying the fetched stock data.

The web data section contains a form with a select field for selecting a country and an input field for entering a keyword for web scraping. The section also contains a button for fetching the web data and a div for displaying the fetched web data. The script section contains two functions, one for fetching stock markets data and another for fetching web data, that use the fetch() method to make API requests to the server and update the table and div sections with the fetched data.

How Does It Work?

1. Stock Markets Data: The user inputs a stock ticker symbol, and the backend fetches real-time data for that stock using Marketstack, ensuring they have all the data points to make informed decisions.

2. Web Data: Users can input keywords related to stock market, financial institutions, or any other topic to scrape news articles from various data sources.

Benefits of Merging Zenscrape and Marketstack

Tools like Zenscrape and Marketstack provide an unparalleled advantage in today’s evolving financial data industry. They can be the game-changer for developers, investors, and market enthusiasts. As global markets continue to grow, the demand for reliable, efficient, and comprehensive stock markets data apis will rise. This prototype is just the beginning!

Access and Insights: Merging real-time and historical stock data with relevant financial news offers investors and developers a holistic market view. This integrated approach enables users to stay informed about market fluctuations, stock prices, and global markets, ensuring they have the data necessary for making informed decisions.
Optimized Workflow: With the influx of stock markets apis, financial data apis, and other data sources, it can be tedious to sift through various platforms. This solution streamlines access, reducing time spent juggling between multiple platforms and increasing time efficiency.
Expandability: Beyond providing real-time stock markets data and historical market data, this prototype lays the groundwork for advanced features. From sentiment analysis on scraped news to predictive modeling using historical data or technical indicators, the possibilities are vast. There’s also potential for integration with other apis and free stock apis, further broadening its scope.
Flexibility and Integration: In the dynamic realm of stock markets data apis and financial data industry, adaptability is crucial. Whether it’s integrating stock apis, customizing data feeds for alternative data, or tailoring the api endpoints for specific needs, this prototype offers a versatile foundation. It’s designed to adapt, grow, and cater to the evolving needs of its users.

Navigating News Websites: Best Practices for Ethical Web Scraping

Web scraping is a powerful tool for collecting data from news websites and financial news platforms. However, it’s important to follow ethical best practices to ensure compliance with legal and ethical boundaries. Here are some best practices for ethical web scraping:

Check the website’s robots.txt file to see if scraping is allowed. This file specifies which parts of the website can be scraped and which cannot.
Review the website’s Terms of Service to ensure compliance. By using a site, you might implicitly agree to its terms, which could potentially prohibit web scraping. Carefully review the site’s ToS to avoid breaching any rules.
Ensure that the website provides the kind of information you’re looking for. It’s important to only extract data that is publicly available and to avoid extracting data that is not intended for public consumption.
Use a public API when available and avoid scraping altogether if the data you’re looking for is available through the API.
Pass your data through a user agent string to identify who you are.
Start small when starting a new scraping project and work your way up. Choose a small website or subset of data to test your scraping project before scaling up.
Be respectful of websites. Avoid scraping too frequently or aggressively, as this can put a strain on the website’s servers and impact the user experience for other visitors.

By following these best practices, you can ensure that your web scraping endeavors are responsible, compliant, and ethical.

Unlocking Stock Markets Data with Zenscrape and Marketstack: A Simple Prototype

Roundup: Quick Python Tutorial for Stock Markets Data

PIP Installs PythonAnywhere

Why Use Zenscrape and Marketstack?

Backend (Flask in Python):

Imports:

App Initialization:

Constants:

Endpoints & Routes:

Error Handling:

Alternatives To Google News for Accessing Financial Data

Mastering Zenscrape: Advanced Techniques for Complex Web Scraping Challenges

Render JavaScript:

Use Premium Proxies:

Adjust Request Delays:

Custom Headers:

Post Requests:

Cookie Handling:

Retry Failed Requests:

Rotate User-Agents:

Capture Screenshots:

Limit the Scope:

Frontend (HTML):

HTML File Code, Including Javascript

How Does It Work?

Benefits of Merging Zenscrape and Marketstack

Navigating News Websites: Best Practices for Ethical Web Scraping

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Categories

Unlocking Stock Markets Data with Zenscrape and Marketstack: A Simple Prototype

Roundup: Quick Python Tutorial for Stock Markets Data

PIP Installs PythonAnywhere

Why Use Zenscrape and Marketstack?

Backend (Flask in Python):

Imports:

App Initialization:

Constants:

Endpoints & Routes:

Error Handling:

Alternatives To Google News for Accessing Financial Data

Mastering Zenscrape: Advanced Techniques for Complex Web Scraping Challenges

Render JavaScript:

Use Premium Proxies:

Adjust Request Delays:

Custom Headers:

Post Requests:

Cookie Handling:

Retry Failed Requests:

Rotate User-Agents:

Capture Screenshots:

Limit the Scope:

Frontend (HTML):

HTML File Code, Including Javascript

How Does It Work?

Benefits of Merging Zenscrape and Marketstack

Navigating News Websites: Best Practices for Ethical Web Scraping

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Leave a Reply Cancel reply

Something Fresh

What People Reading

Categories