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Web Scraping for Stock Market Data - Limeproxies

Web Scraping for Stock Market Data – Limeproxies

The COVID-19 pandemic has proven beyond doubts that the stock market is just as volatile as any other business industry. It can crash in a second, and can also skyrocket at the flick of your fingers. Stocks are cheaper at this point due to the crisis brought about by the pandemic, and a lot of people are interested in stock market data to help with informed choices.
Unlike the general web scraping, scraping for stock market data is more specific and only useful to those interested in stock market Quick LinksJump straight to the section of the post you want to read:Web Scraping ExplainedScrape Yahoo Finance and Stock Market Data Using PythonData Scraping in Real-TimeThe Benefits of Stock Market Data Scraping to Businesses Limitations of Stock Market ScrapingRequirements for Stock Market Data ScrapingAnalyzing the Stock Market Using Python Web Scraping ExplainedWeb scraping involves extracting as much data as possible from a preset index of target websites or other sources. Companies utilize scraping for decision making and planning strategies as it gives accurate and viable information on the topic.
It’s usual to hear web scraping being mostly associated with commercial and marketing companies, but they are not the only ones that benefit from the process as everyone stands to gain from scraping stock market data. Investors are the ones who stand to benefit the most as the data benefits them in the following ways:
Real-time data
Price prediction
Stock market trends
Possibilities for investment
Price changes
Just as with web scraping for other data, stock market data scraping isn’t the easiest task to perform but yields valuable results if done right. Investors would be provided with insights on various interesting parameters that would be relevant to making the best and smartest choices.
Scrape Yahoo Finance and Stock Market Data Using PythonYou’ll first need to install Python 3 for Windows, Mac, and Linux. Then install the following packages to enable download and parsing of the HTML data: pip for package installation, Python request package for sending requests and downloading the HTML content of the target page, and then Python LXMLto parse with Xpaths.
Python 3 Code For Data Extraction From Yahoo Finance
from lxml import html
import requests
import json
import argparse
from collections import OrderedDict
def get_headers():
\ return {“accept”: “text/html, application/xhtml+xml, application/xml;q=0. 9, image/webp, image/apng, */*;q=0. 8, application/signed-exchange;v=b3;q=0. 9”,
\ “accept-encoding”: “gzip, deflate, br”,
\ “accept-language”: “en-GB, en;q=0. 9, en-US;q=0. 8, ml;q=0. 7”,
\ “cache-control”: “max-age=0”,
\ “dnt”: “1”,
\ “sec-fetch-dest”: “document”,
\ “sec-fetch-mode”: “navigate”,
\ “sec-fetch-site”: “none”,
\ “sec-fetch-user”: “? 1”,
\ “upgrade-insecure-requests”: “1”,
\ “user-agent”: “Mozilla/5. 0 (Windows NT 10. 0; Win64; x64) AppleWebKit/537. 36 (KHTML, like Gecko) Chrome/81. 0. 4044. 122 Safari/537. 36”}
def parse(ticker):
\ url = “% (ticker, ticker)
\ response = (
\ url, verify=False, headers=get_headers(), timeout=30)
\ print(“Parsing%s”% (url))
\ parser = omstring()
\ summary_table = (
\ ‘//div[contains(@data-test, “summary-table”)]//tr’)
\ summary_data = OrderedDict()
\ other_details_json_link = “0}? formatted=true&lang=en-US®ion=US&modules=summaryProfile%2CfinancialData%2CrecommendationTrend%2CupgradeDowngradeHistory%2Cearnings%2CdefaultKeyStatistics%2CcalendarEvents&”(
\ ticker)
\ summary_json_response = (other_details_json_link)
\ try:
\ json_loaded_summary = ()
\ summary = json_loaded_summary[“quoteSummary”][“result”][0]
\ y_Target_Est = summary[“financialData”][“targetMeanPrice”][‘raw’]
\ earnings_list = summary[“calendarEvents”][‘earnings’]
\ eps = summary[“defaultKeyStatistics”][“trailingEps”][‘raw’]
\ datelist = []
\ for i in earnings_list[‘earningsDate’]:
\ (i[‘fmt’])
\ earnings_date = ‘ to ‘(datelist)
\ for table_data in summary_table:
\ raw_table_key = (
\ ‘. //td[1]//text()’)
\ raw_table_value = (
\ ‘. //td[2]//text()’)
\ table_key = ”(raw_table_key)()
\ table_value = ”(raw_table_value)()
\ ({table_key: table_value})
\ ({‘1y Target Est’: y_Target_Est, ‘EPS (TTM)’: eps,
\ ‘Earnings Date’: earnings_date, ‘ticker’: ticker,
\ ‘url’: url})
\ return summary_data
\ except ValueError:
\ print(“Failed to parse json response”)
\ return {“error”: “Failed to parse json response”}
\ except:
\ return {“error”: “Unhandled Error”}
if __name__ == “__main__”:
\ argparser = gumentParser()
\ d_argument(‘ticker’, help=”)
\ args = rse_args()
\ ticker =
\ print(“Fetching data for%s”% (ticker))
\ scraped_data = parse(ticker)
\ print(“Writing data to output file”)
\ with open(”% (ticker), ‘w’) as fp:
\ (scraped_data, fp, indent=4)
Data Scraping in Real-TimeSince the stock market is in constant up and down movements, it’s best to use a scraper that extracts data in real-time. All the processes of web scraping would be carried out in real-time with a real-time scraper so that whatever data you have would be viable then, allowing for the best and most accurate decisions and to be made.
Interesting Read: Building Your Own Yellow Pages Scraper
Real-time web scrapers are more expensive than the slower ones but are the best choices for investment firms and businesses that depend on accurate data in a market as volatile as stocks.
The Benefits of Stock Market Data Scraping to Businesses All businesses can benefit from web scraping in one form or another especially for data such as economic trends, user data, and then the stock market. Before investment firms go into investing in a particular stock, they make use of web scraping tools and analyze the extracted data to guide their decisions.
Stock market investment isn’t usually considered safe to say the least as it’s very volatile and prone to change. Each of the volatile variables involved in stock investment plays a huge role in the value of stocks and stock investment is only considered safe to an extent when all these volatile variable have been analyzed over time and studied
To accumulate as much data as would be necessary, you need to practice stock market data scraping. This implies that much data would have to be gathered from the stock market using a stock market scraping bot.
The software will first collect all the information that is valuable to your cause, and then parse it so it can be studied and analyzed for smart decision making.
Sources of Stock Market Data
Professionals have different APIs they use to their advantage when collecting stock market data from the web. Google Finance was the real deal back in the day but since 2012 its use has seen a decline.
One of the most popular options you can use is Yahoo Finance. Their API has been on and off over the years as it has depreciated and been revived from time to time. There are other companies whose APIs you can also use if Yahoo Finance doesn’t suit your project perfectly.
Limitations of Stock Market ScrapingWeb scraping isn’t as straightforward as it may sound, and involves different steps and processes that need accuracy and timely executions to extract accurate and viable data. Most times these processes would be met with preventive measures** **that are put in place to prevent web scraping.
So most big companies choose to make their tools to overcome the obstacles to a seamless flow of web scraping processes. One of the most common issues with web scraping is blocked IP. Once an IP address is blocked, the web scraper won’t have access to the directory and there would be no extracted information.
Most of the limitations to web scraping can be avoided by programming the stock market data scraper uniquely and then using proxies. Even though it is impossible to avoid most of the restrictions on web scraping, a unique tool helps.
Requirements for Stock Market Data ScrapingBusinesses and investment firms that are interested in stock market investment will need to use associated tools to obtain the necessary data for informed decision making.
Data scraping isn’t a straightforward process as you may have thought, and it needs different tools in data collection, removal of variables and redundancies, and also to provide useful and viable data.
The first tool companies would need to consider is a web crawler. It enables the scraping of stock data from the stock market for analysis. You can get specialized tools to scrape the stock market but it will require added investments which can be quite expensive, depending on the size of the project.
Another requirement for data harvesting is the prerequisite data source. This includes indexes of data and are made up of stock market websites that would be scraped by the web scraper for all types of necessary data. Once the data is collected through an index, it will be analyzed and processed to take out redundancies.
Interesting Read: What You Need To Know Now About Encryption
Most high-end data scraping tools include this process, but it’s not difficult to build a data parser to serve the function. By analyzing and refining the redundancies from the data, what will be left is the useful data. Such data would then be further analyzed using industry-specific software for precise results.
The precise results are then used to make decisions on the particular investment they are specific to. All these processes can be carried out on a single high-end web scraper, stock market-specific software, and a few data analysts.
Analyzing the Stock Market Using Python Jupyter notebook would be used in the course of this tutorial and you can get it on GitHub.
The Setup Process
You will begin by installing jupyter notebooks as you install Anaconda
In addition to anaconda, also install other Python packages like beautifulsoup4, dill, and fastnumbers
Add the following imports to Python 3 jupyter notebooks
import numpy as np # linear algebra
import pandas as pd # pandas for dataframe based data processing and CSV file I/O
import requests # for requests
from bs4 import BeautifulSoup # for html parsing and scraping
import bs4
from fastnumbers import isfloat
from fastnumbers import fast_float
from import Pool as ThreadPool
import as plt
import seaborn as sns
from tidylib import tidy_document # for tidying incorrect html
t_style(‘whitegrid’)%matplotlib inline
from import InteractiveShell
t_node_interactivity = “all”
What You Will Need to Scrape the Required Data
Remove excess spaces between string
Some strings from web pages come with multiple spaces between the words. You can remove this with the following:
def remove_multiple_spaces(string):
\ if type(string)==str:
\ return ‘ ‘(())
\ return string
Conversion of Strings to Float
In web pages, you can find symbols mixed with numbers. You can either remove the symbols before converting, or you use the following function:
def ffloat_list(string_list):
\ return list(map(ffloat, string_list))
Send HTTP Requests in Python
Before you make a HTTP request, you need to have the URL of the target website. Make the request using, use atus_code to get HTTP status, and use ntent to get the page content.
Extract And Parse JSON Content From a Page
Extract json content from a page using (). Double check with atus_code.
Scrape and Parse HTML Data
For this we would utilize beautifulsoup4 parsing library.
Use Jupyter Notebook to Render HTML Strings
Use the following function:
from import HTML
HTML(“Rendered HTML”)
Get the Content Position Using Chrome Inspector
You’ll first need to know the HTML location of the content you want to extract before you proceed. Inspect the page using chrome inspector for Mac using the function cmd+option+I and inspect for Linux with the function Control+Shift+I.
Parse the Content and Display it With BeautifulSoup4
Parse the content using the function BeautifulSoup and then get the content from header 1 tag and render it.
A Beginner's Guide to learn web scraping with python! - Edureka

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A Beginner’s Guide to learn web scraping with python! – Edureka

Last updated on Sep 24, 2021 641. 9K Views Tech Enthusiast in Blockchain, Hadoop, Python, Cyber-Security, Ethical Hacking. Interested in anything… Tech Enthusiast in Blockchain, Hadoop, Python, Cyber-Security, Ethical Hacking. Interested in anything and everything about Computers. 1 / 2 Blog from Web Scraping Web Scraping with PythonImagine you have to pull a large amount of data from websites and you want to do it as quickly as possible. How would you do it without manually going to each website and getting the data? Well, “Web Scraping” is the answer. Web Scraping just makes this job easier and faster. In this article on Web Scraping with Python, you will learn about web scraping in brief and see how to extract data from a website with a demonstration. I will be covering the following topics: Why is Web Scraping Used? What Is Web Scraping? Is Web Scraping Legal? Why is Python Good For Web Scraping? How Do You Scrape Data From A Website? Libraries used for Web Scraping Web Scraping Example: Scraping Flipkart Website Why is Web Scraping Used? Web scraping is used to collect large information from websites. But why does someone have to collect such large data from websites? To know about this, let’s look at the applications of web scraping: Price Comparison: Services such as ParseHub use web scraping to collect data from online shopping websites and use it to compare the prices of products. Email address gathering: Many companies that use email as a medium for marketing, use web scraping to collect email ID and then send bulk emails. Social Media Scraping: Web scraping is used to collect data from Social Media websites such as Twitter to find out what’s trending. Research and Development: Web scraping is used to collect a large set of data (Statistics, General Information, Temperature, etc. ) from websites, which are analyzed and used to carry out Surveys or for R&D. Job listings: Details regarding job openings, interviews are collected from different websites and then listed in one place so that it is easily accessible to the is Web Scraping? Web scraping is an automated method used to extract large amounts of data from websites. The data on the websites are unstructured. Web scraping helps collect these unstructured data and store it in a structured form. There are different ways to scrape websites such as online Services, APIs or writing your own code. In this article, we’ll see how to implement web scraping with python. Is Web Scraping Legal? Talking about whether web scraping is legal or not, some websites allow web scraping and some don’t. To know whether a website allows web scraping or not, you can look at the website’s “” file. You can find this file by appending “/” to the URL that you want to scrape. For this example, I am scraping Flipkart website. So, to see the “” file, the URL is in-depth Knowledge of Python along with its Diverse Applications Why is Python Good for Web Scraping? Here is the list of features of Python which makes it more suitable for web scraping. Ease of Use: Python is simple to code. You do not have to add semi-colons “;” or curly-braces “{}” anywhere. This makes it less messy and easy to use. Large Collection of Libraries: Python has a huge collection of libraries such as Numpy, Matlplotlib, Pandas etc., which provides methods and services for various purposes. Hence, it is suitable for web scraping and for further manipulation of extracted data. Dynamically typed: In Python, you don’t have to define datatypes for variables, you can directly use the variables wherever required. This saves time and makes your job faster. Easily Understandable Syntax: Python syntax is easily understandable mainly because reading a Python code is very similar to reading a statement in English. It is expressive and easily readable, and the indentation used in Python also helps the user to differentiate between different scope/blocks in the code. Small code, large task: Web scraping is used to save time. But what’s the use if you spend more time writing the code? Well, you don’t have to. In Python, you can write small codes to do large tasks. Hence, you save time even while writing the code. Community: What if you get stuck while writing the code? You don’t have to worry. Python community has one of the biggest and most active communities, where you can seek help Do You Scrape Data From A Website? When you run the code for web scraping, a request is sent to the URL that you have mentioned. As a response to the request, the server sends the data and allows you to read the HTML or XML page. The code then, parses the HTML or XML page, finds the data and extracts it. To extract data using web scraping with python, you need to follow these basic steps: Find the URL that you want to scrape Inspecting the Page Find the data you want to extract Write the code Run the code and extract the data Store the data in the required format Now let us see how to extract data from the Flipkart website using Python, Deep Learning, NLP, Artificial Intelligence, Machine Learning with these AI and ML courses a PG Diploma certification program by NIT braries used for Web Scraping As we know, Python is has various applications and there are different libraries for different purposes. In our further demonstration, we will be using the following libraries: Selenium: Selenium is a web testing library. It is used to automate browser activities. BeautifulSoup: Beautiful Soup is a Python package for parsing HTML and XML documents. It creates parse trees that is helpful to extract the data easily. Pandas: Pandas is a library used for data manipulation and analysis. It is used to extract the data and store it in the desired format. Subscribe to our YouTube channel to get new updates..! Web Scraping Example: Scraping Flipkart WebsitePre-requisites: Python 2. x or Python 3. x with Selenium, BeautifulSoup, pandas libraries installed Google-chrome browser Ubuntu Operating SystemLet’s get started! Step 1: Find the URL that you want to scrapeFor this example, we are going scrape Flipkart website to extract the Price, Name, and Rating of Laptops. The URL for this page is 2: Inspecting the PageThe data is usually nested in tags. So, we inspect the page to see, under which tag the data we want to scrape is nested. To inspect the page, just right click on the element and click on “Inspect” you click on the “Inspect” tab, you will see a “Browser Inspector Box” 3: Find the data you want to extractLet’s extract the Price, Name, and Rating which is in the “div” tag respectively. Learn Python in 42 hours! Step 4: Write the codeFirst, let’s create a Python file. To do this, open the terminal in Ubuntu and type gedit with extension. I am going to name my file “web-s”. Here’s the command:gedit, let’s write our code in this file. First, let us import all the necessary libraries:from selenium import webdriver
from BeautifulSoup import BeautifulSoup
import pandas as pdTo configure webdriver to use Chrome browser, we have to set the path to chromedriverdriver = (“/usr/lib/chromium-browser/chromedriver”)Refer the below code to open the URL: products=[] #List to store name of the product
prices=[] #List to store price of the product
ratings=[] #List to store rating of the product
Now that we have written the code to open the URL, it’s time to extract the data from the website. As mentioned earlier, the data we want to extract is nested in

tags. So, I will find the div tags with those respective class-names, extract the data and store the data in a variable. Refer the code below:content = ge_source
soup = BeautifulSoup(content)
for a in ndAll(‘a’, href=True, attrs={‘class’:’_31qSD5′}):
(‘div’, attrs={‘class’:’_3wU53n’})
(‘div’, attrs={‘class’:’_1vC4OE _2rQ-NK’})
(‘div’, attrs={‘class’:’hGSR34 _2beYZw’})
Step 5: Run the code and extract the dataTo run the code, use the below command: python 6: Store the data in a required formatAfter extracting the data, you might want to store it in a format. This format varies depending on your requirement. For this example, we will store the extracted data in a CSV (Comma Separated Value) format. To do this, I will add the following lines to my code:df = Frame({‘Product Name’:products, ‘Price’:prices, ‘Rating’:ratings})
_csv(”, index=False, encoding=’utf-8′)Now, I’ll run the whole code again. A file name “” is created and this file contains the extracted data. I hope you guys enjoyed this article on “Web Scraping with Python”. I hope this blog was informative and has added value to your knowledge. Now go ahead and try Web Scraping. Experiment with different modules and applications of Python. If you wish to know about Web Scraping With Python on Windows platform, then the below video will help you understand how to do Scraping With Python | Python Tutorial | Web Scraping Tutorial | EdurekaThis Edureka live session on “WebScraping using Python” will help you understand the fundamentals of scraping along with a demo to scrape some details from a question regarding “web scraping with Python”? You can ask it on edureka! Forum and we will get back to you at the earliest or you can join our Python Training in Hobart get in-depth knowledge on Python Programming language along with its various applications, you can enroll here for live online Python training with 24/7 support and lifetime access.
Best Free Real-Time Stock Charts for Day Traders - The Balance

Best Free Real-Time Stock Charts for Day Traders – The Balance

For Beginners and Experts
Image by Julie Bang © The Balance 2020
A number of websites and platforms provide real-time stock charting capabilities for one-minute, five-minute, and other intraday charting time frames. Some of them even do so for free. That could be a great deal, depending on your goals. Before jumping in, keep a few considerations in mind.
Paid vs. Free Real-Time Stock Charts
Free is nice, and the data might be real-time, it’s not “official. ” Free real-time stock chart data usually comes from just one data provider, which means you might not see all the price movements occurring in the stock or exchange-traded fund (ETF) you’re day trading. You may have to pay if you want to receive official price data from a market.
Free real-time data also isn’t guaranteed to be accurate or timely. When you pay for real-time, official quotes, you have some recourse if the data feed is unreliable or inaccurate. With a free site, you have to take the data as is.
That said, free real-time day trading charts are an excellent backup data source on the off chance you lose quotes from your broker. They’re also a great training tool for new traders who are looking to study day trading and craft strategies around price movements in real-time.
Here are a few of the best free real-time stock charting platforms to check out.
TradingView provides real-time stock charts that are visually appealing and can be customized with hundreds of technical indicators.
TradingView is also a social media site. Traders can easily share their charts and ideas with each other. You can even follow other traders and discuss stocks and other markets.
Just beware of who you’re watching because not everyone sharing charts and ideas will be a profitable trader.
Traders can create watchlists and alerts, see which stocks are hot, and even trade directly from TradingView charts by connecting with a broker. You can use the Strategy Tester to test a built-in strategy for a particular stock and time period. The Pine Editor feature also lets you create your own strategy to test.
TradingView also lets you chart indexes, stocks, bonds, futures, forex, cryptocurrencies, contracts for differences (CFDs), economic data, and global data, although futures data are delayed.  You can pay for upgraded options that provide additional features and official real-time data for stocks and futures markets around the globe.
TradingView offers a very extensive list of markets, indexes, and economic data. You won’t have to switch charting platforms to view charts from other markets. It’s also the most socially integrated real-time stock charts of the free providers on our list.
StockCharts is one of those charting platforms that offer both paid and free options. StockCharts’ free capabilities are pretty robust. You can do bar, line, or candlestick charting with more than 40 line studies and modifiable technical indicators.
The free version only lets you plot three indicators at one time.
Data is displayed on either a weekly or daily basis, but you can only go back three years for data unless you have a paid subscription. Other downsides to the free StockCharts option are that you can’t save your screens and the graphics are rather bland.
You can pay as little as $14. 95 a month for a Basic subscription to as much as $39. 95 a month for a Pro subscription if you want more bells and whistles. The Extra subscription, with a somewhat more advanced user interface, is available for $24. 95 a month. A free one-month trial subscription at the Extra level is available for new customers.
You can upgrade your subscription plans even more by adding on a real-time data plan. The free, Basic, Extra, and Pro accounts come with a free data plan with BATS real-time data in the U. S., but all other markets are delayed. For more real-time data, you can choose from a variety of plans that focus on specific stock exchanges. Each costs an additional $9. 95 per month. 
Yahoo! Finance
Yahoo! Finance offers free real-time quotes for stocks listed on the New York Stock Exchange (NYSE) and Nasdaq indexes that are provided by Nasdaq Last Sale.  It also offers real-time news. And its free interactive charts are quite good, with a selection of more than 100 technical indicators to choose from. 
Yahoo! Finance lets traders create an unlimited list of stocks to follow and offers daily trading ideas. It also enables you to link to your brokerage account to implement trades based on your charted strategies.
For $34. 99 a month or $349. 99 annually, you can upgrade to a Premium service that offers enhanced charting capabilities, third-party investment research, live chat support, and fewer ads, among other features. A 14-day free trial is available to see whether you think the Premium level is worth the money. 
Google Finance
It’s easy to quickly search a stock on Google, but you can also see real-time charts for different markets via Google’s Finance section. While it’s not as advanced as the others on our list, Google Finance offers simple—and free—stock charts.
It may not be the best for advanced investors, but it could be just what beginners are looking for.
You can track specific stocks, local markets, and even world markets, adding whatever you want to your watchlist. While the technical indicators are lacking, you can at least see performance over time by changing the date range. 

Frequently Asked Questions about scraping real time stock data

Can you scrape stock market data?

All businesses can benefit from web scraping in one form or another especially for data such as economic trends, user data, and then the stock market. Before investment firms go into investing in a particular stock, they make use of web scraping tools and analyze the extracted data to guide their decisions.Dec 3, 2020

How do you scrape live data?

How Do You Scrape Data From A Website?Find the URL that you want to scrape.Inspecting the Page.Find the data you want to extract.Write the code.Run the code and extract the data.Store the data in the required format.Sep 24, 2021

How do you find real time stock data?

Here are a few of the best free real-time stock charting platforms to check out.TradingView. TradingView provides real-time stock charts that are visually appealing and can be customized with hundreds of technical indicators. … StockCharts. … Google Finance.

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