How to Use Python to Scrape Google Finance
There is absolutely nothing more important when it comes to making financial decisions than to have the most information and up to date data possible. Data helps with decision making, and that often means reducing risk and losses in the financial sector. Getting that data can be challenging. For many techy people, it is possible to use Python for Google Finance, capturing all of the financial data you need with ease.
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Why Google Finance?
Google Finance is one of the most trusted resources for up-to-the-minute information on everything from stocks and bonds to changing interest rates on loans. Most financial analysts and investors know what Google Finance is and may even rely on it for:
- Financial news and world news that could impact financial markets
- Real-time stock quotes
- Financial data and changes that could impact specific commodities
It is one of the best resources for gathering such information. To get that information, Google pulls it from numerous websites, making it easier for investors to have a single solid location to go to for the information they need. It helps you to have anything you need to monitor at your fingertips without having to go to a dozen websites to access it.
However, the information that Google Finance offers and provides to you is public information. It is not locked behind a secret password. With these tips and strategies to use Python for Google Finance scraping, you will be able to get the information you need, including stock titles, price changes, and current stock market conditions. There are so many ways to gather and use data that it is simply necessary to have a single resource available to help you.
How to Use Google Finance Python Scraping
To access Google Finance Data with Python, there are several steps you need to follow. The process will involve installing libraries as well as strategies to help you. You can use Scraping Robot’s API to help with you process.
Download the Required Libraries
It will be necessary to download required libraries. You have various options to do so, but we will use Beautiful Soup 4. This is a library that makes gathering information from web pages easy to do. This tool will parse and extract the data from the HTML you are scraping.
Build Out the Core Structure
What do you want the Google Finance scraper to do for you? You need to define that as the next step in this process. You will create general logic that will help to define the functionality of the tool.
To create the functionality necessary to gather data from the URLs, we need to have a custom designed solution. Then, you will need a list of the URLs use that have the critical information you are looking for to scape. We will offer some suggestions here.
Once we set up this logic, you will then take the URLS and collect information that is needed from them. The tool will then save it as a JSON file, and you can use it to fit your needs.
We recommend using the Scraping Robot API for this task.
The Scraping Robot API exposes a single API endpoint. Simply send an http-request to https://api.scrapingrobot.com with your API key, passed as a query-parameter, and get the needed data.
All task-parameters are passed in a POST-body as a JSON-object.
Once you do this, you will then need to create a function that will accept a BeautifulSoup4 object from the HTML of the page itself. This function is designed to create and return an object that contains specific stock information. Here is an example to get you started:
def extract_finance_information_from_soup(soup_of_the_whole_page):
#Put data extraction here
listing = {}
return listing
When you combine this with your Scraping Robot API code, you end up with a tool that will take the URLS as a parameter and then return an object of extracted data for you. Once you do that, the next step is to save the file. Do this to do so:
def save_results(results, filepath):
with open(filepath, ‘w’, encoding=’utf-8′) as file:
json.dump(results, file, ensure_ascii=False, indent=4)
return
Here is an example you can use. Update the URLs to match the specific objectives you have (more on that in a moment). You will find that this is a rather straightforward process you can customize to meet any needs you have:
def main():
results_file = ‘data.json’
urls = [
‘https://www.google.com/finance/quote/BNP:EPA?hl=en’,
‘https://www.google.com/finance/quote/.DJI:INDEXDJX?hl=en’,
‘https://www.google.com/finance/quote/.INX:INDEXSP?hl=en’
]
constructed_finance_results = extract_finance_data_from_urls(urls)
save_results(constructed_finance_results, results_file)
Developing the Functions for Google Finance Python Scraping
The above information is a starting point, but you also need to build out a strategy that works for your specific needs. To do that, you’ll want to take a moment to consider how you plan to use this information so that you can then collect the necessary information from the appropriate URLs. The following are some of the most important and selected Python Google Finance data you may wish to obtain.
Pricing information
Pricing data is perhaps the most important and valuable component of this entire process. It changes so often and is so important to decision-making that having very specific, real-time information can be critical to decision-makers.
To create a function to collect prices, it gets a bit complicated. You will need to dive in and really check out the dynamic pages on Google Finance. These are some of the most robust pages out there, which is what makes this process more challenging.
To offer some insight, if you look at Google Finance, most of the data is located within a container called “Main.” You will then need to find the div, which is what will include the price. In our example, that is AHmHK (from our browsing of the Google Finance site).
You will then use this data to create your function. Here is what it will look like:
def get_price(soup_element):
price = soup_element.find(‘main’).find(‘div’,’AHmHk’).get_text()
return price
Stock price changes
Another commonly sought-after bit of data is the percentage of the stock price change. This provides usable information for decision-making (instead of just looking at the price, it is often more important to consider the percentage of change present). You can use Google Finance Data Python scraping to capture that information as well.
Specifically, we are looking at historical data for price changes. You already know that the container called “main” is the hub. If you look there, you can choose the details you need. Specifically, to choose an inner div, we will select what only contains the price change data. In our example, that’s JwB6zf.
def get_change(soup_element):
change = soup_element.find(‘main’).find(‘div’,’JwB6zf’).get_text()
return change
You can see exactly how this process is working at this point and adjust the process to gather the information that you need. Note that the Google Finance function necessary could change over time. Make sure you verify this information before you run these scripts or you may miss key data.
Stock Title
A final piece of information that is commonly selected when it comes to Google Finance Python scraping is the stock title. That is, it’s the name of the stock. You need that information in order to distinguish data from one source to the other.
To gather this information, follow the same process you did before using the “main” container. Then, for the inner div, we found “zzDege” to use. Use that data to create the function that will extract the page title for you to use.
def get_name(soup_element):
name = soup_element.find(‘main’).find(‘div’,’zzDege’).get_text()
return name
With this information you can then add this to the designated place you created in the first step. Then, you can add them all to the place together to finish up writing the necessary code.
Make sure to go back and test out the code you write. Do not simply count on it to be accurate and up to date (and definitely make sure you look for the specific data you need). With this method, you are able to capture a lot of the data you need rather easily.
How Scraping Robot Can Make the Process Easier for You
When it comes to Python Google Finance web scraping, it is rather easy to see the benefit of this process. It allows you to capture the information you need that you can then use to track changes, make decisions, or even to predict what comes next. Though you may benefit from the addition of other tools and perhaps AI to help with some of the processes, also consider the value of using Scraping Robot.
Take a minute to check out Scraping Robot for your API tool. When you do, our scraping API makes this entire process a bit easier and more efficient for you. You can capture the information and data you need using Python for Google Finance web scraping. Contact us now to learn how we can help you.
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