Scraping Yahoo Finance’s most active stocks

Subject: Web and Social Media Scraping
University of Warsaw, Data Science and Business Analytics Master's Degree Program in Faculty of Economic Sciences

Introduction

We have designed mechanisms which scrap the most active stocks of yahoo finance using beautifulsoup, scrapy and selenium separately. The three methods all achieve the same information. The scraped data output is later analysed, enabling us to compare the results.
Our way of working has a perfect collaborative and proper work on GitHub. There is a general project repository and project developed by cloning the forked project in our accounts from the main repository and creating pull requests with descriptive commit messages. You can track the commit history to observe how this project developed in here.
Please find project requirements for packages inside the requirements.txt. Dependencies for creating virtual environment described below.
Furthermore, there is a Makefile created into the repository and processes mentioned below handled automatically:

• Creating virtual environment,
• Installing required packages from requirements.txt file,
• Clean virtual environment.

Scraper Mechanism

Beautiful Soup

For the BeautifulSoup part of this project, in addition to other libraries BeautifulSoup library was used. Another python library, requests , gets the html of the webpage at url https://finance.yahoo.com/most-active, which is later parsed by BeautifulSoup.\ The webpage has a table with 24 rows and numerous columns. Every row has the symbol attribute, which is later used finding the links to the company’s yahoo-finance page.

"https://finance.yahoo.com/quote/" + symbol + "?p=" + symbol

For every link defined in such a way, first the html copy of the page via the link is requested and then parsed. The variables such as Previous Close, Open, etc are distributed among two tables: table 1 and table 2. Within their respective tables, every variable is specified by data-test attribute, allowing us to specify and extract a variable of interest one at a time.\ The extracted values are appended to already created lists, finally making up a dataframe with the variable names as column headers and the values as a column vector. The dataframe is exported to the csv file stocks_bs.csv.

To run the bs.py file on the command line, use the command

# for python3 users
…. : ~<project_root>$ python3 bs.py 
# for python2 users
…. : ~<project_root>$ python bs.py 

You may as well run the file using your favorite python IDE.

Scrapy

For the purpose of scraping Yahoo Finance web page, we also used Scrapy framework. The process could be divided into two parts:
1.Scraping the links for each of the Most Active companies, 2.Scraping data for each of the companies.

Links creation happens similarity to the Beautiful Soup and Selenium part of the project. The links are created by adding to a base link the ending specified for each company. To extract them, we used selectors, specifically XPATH.\ The scrapping happens with the use of spiders, first spider is created for the purpose of extracting the links, and the second one to get the data dor each company. Spiders are basically a programs downloading the content of a given pages, they are using a parse function which is called when the URL is crawled and it includes the extraction logic. As not all the information about the companies could be extracted using the same xpath, we used two diffrent xpaths.

The data in our project is stored in a csv files. This is specified by the FEED_FORMAT = 'csv'. What is important to note is that, to run the spiders,\

USER_AGENT = 'Mozilla/5.0 (compatible; Googlebot/2.1; +http://google.com/bot.html)' has to be used in settings.py file.

To run the code provided please follow steps below:

1. In the terminal write: “ scrapy start project Scraping_Stock_Prices (the name can be different)”
2. Paste spiders provided into the spiders folder created by scrapy.
3. In the file “settings.py” change the USER AGENT.
4. In the terminal execute the command: scrapy crawl link_lists -o link_list.csv.
5. Then execute: scrapy crawl stocks -o stocks.csv
6. Take a look at the stocks.csv if the data is scrapped.

Selenium

For selenium scraper, a driver for Mozilla Firefox was used. Python script is using command line arguments to get input from the users. There are 3 command lines arguments in the python script.

1. Flag (--watchlist): This flag is a boolean variable, it is not required and its default value is False.
   Aim of this flag is to give opportunity to scrape data from user’s own watchlist in yahoo finance website.
   For this purpose, users should have a favourite list with a My Watchlist name that stores their favourite companies.
   Thanks to this list, users are able to monitor stock price information about their favourite companies easily.
   If users have a list named My Watchlist in yahoo finance page, they are able to input this flag as True.
   Thanks to this flag, selenium proceeds the scraping mechanism starting with a login process and going into a favourite list named My Watchlist.
   Furthermore,

   • Selenium bot opens every single company link in this favourite list one by one in a new tab
   • Scrape data inside the new tab
   • Save scraped data into a dictionary of lists
   • Close new tab
   • Open another company page in a new tab… This process continue till the last company data scraped and saved into the dictionary. After that, selenium bot terminates itself and scraped data in dictionary converted as pandas dataframe, finally all scraped data is saved into a csv file into the project root directory.

2. Flag (--email): This flag is used for watchlist scraping mechanism. --email flag is not required and its default value is None. This is another opportunity for users who want to input email addresses before selenium bot start. Thanks to this, users can input their email address safely and get the output faster.

3. Flag (--passwd): This flag is used for watchlist scraping mechanism. --passwd flag is not required and its default value is None. This is another opportunity for users who want to input passwords before selenium starts. Thanks to this, users can input their password safely and get the output faster than input during selenium running.

Please mind, --watchlist, --email and --passed flags are not required to scrape data. If users do not prefer to scrape data from watchlist, the program automatically proceeds the same process to scrape data from Stocks:Most Active page under the Markets tab

Here is the correct way of flag usage to scrape data from “My Watchlist” list:

# Regular Scraper for Stocks: Most Active page.
…. : ~<project_root>$ python3 selenium_scraper.py
# Optional Scraper for favourite list named “My Watchlist”
…. : ~<project_root>$ python3 selenium_scraper.py --watchlist True --email “abc.def@uw.edu.pl” --passwd “abcdef123.”

(Please mind, if users prefer a different name for a favourite company list name, they can only hardcode the name of the list into a python script. Secondly, users are able to optimize the sleep times based on their computer capacity. Because, response times may vary for different computers. So, if you have no output from, the Selenium scraper, please update the sleep times inside the python script by hand.)

Technical Description of Output

For all “Beautiful Soup”, “Scrapy” and “Selenium” scrapers, scraped data from the yahoo finance page is stored in a csv file. Schema for all scraper output is exactly the same in each other. There are 16 columns in the output file.
Please observe a sample table for scraped data from every single company in the Stocks: Most Actives page (or My Watchlist, if --watchlist is True for selenium) below.

Company names, and the values inside the features in the output file may be different for all scrapers, because output depends on:

• The time when scraper scripts run and generate the output file,
• The domain (may different county domains used),
• Values inside the table that scrapers scrape data (if values have different type or tag/class name inside the html, scraper may pass this company).

As a result of the points above, it is not guaranteed that every single output file generated from scrapers will be the same, because yahoo finance is a dynamic website. On the other hand, it is guaranteed that every single output file generated from the scrapers have exactly the same output schema based on the features that mentioned above.
All scrapers scrape the same information but not the same value. So this situation is not contradicted by the project goal that All scrapers should scrape the same information from the domain of your choice.

Elementary Data Analysis

Aim of this elementary data analysis is to show collected data can be used for further analysis, but nothing more detailed.
The number of companies is limited to 5 for clearer presentation.

Please find some additional statistical analysis from the output file below.

Labour Division

Task	Contributors
Beautiful Soup	Tesfahun
Scrapy	Anna
Selenium	Alparslan
Github	Tesfahun, Anna, Alparslan
Project Report	Tesfahun, Anna, Alparslan

Group Members

Virtual Environment with Project Requirements

Python 3.9.0 was used to develop for this project. Please mind, python version before you create virtual environment
You can find the required python packages and their versions in requirements.txt file.
Thanks to Makefile, you can create a virtual environment, install required packages and delete virtual environment automatically. If you do not want to use virtual environment, then you should install required packages in your base python interpreter.

• make venv creates python virtual environment.
• make require install required packages.
• make clean delete venv file.

You can simply run the code like as follows from command line:

**LINUX**
## For linux, you need to update the makefile commands compatible for linux environment.
...@...:project_root$ make venv
...@...:project_root$ make require
...@...:project_root$ python3 some_file_1.py
...@...:project_root$ python3 some_file_2.py

**WINDOWS**
project_root> make venv
project_root> make require
project_root> python some_file_1.py
project_root> python some_file_2.py

If you face problem about creating virtual environment from Makefile, you can create virtual environment manually and install packages from requirements.txt file.