If you’re interested in investing in the stock market, you know how important it is to have access to accurate and up-to-date market data. This data can help you make informed decisions about which stocks to buy or sell, when to do so, and at what price. However, retrieving and analyzing this data can be a complex and time-consuming process. That’s where Python comes in.
Python is a powerful programming language that offers a wide range of tools and libraries for retrieving, analyzing, and visualizing stock market data. In this blog, we’ll explore how to use Python to retrieve fundamental stock market data, such as earnings reports, financial statements, and other key metrics. We’ll also demonstrate how you can use this data to inform your investment strategies and make more informed decisions in the market.
So, whether you’re a seasoned investor or just starting out, read on to learn how Python can help you gain a competitive edge in the stock market.
How to retrieve fundamental stock market data using Python?
Python can be used to retrieve a company’s financial statements and earnings reports by accessing fundamental data of the stock. Here are some methods to achieve this:
1. Using the yfinance library:
One can easily get, read, and interpret financial data using Python by using the yfinance library along with the Pandas library. With this, a user can extract various financial data, including the company’s balance sheet, income statement, and cash flow statement. Additionally, yfinance can be used to collect historical stock data for a specific time period.
2. Using Alpha Vantage:
Alpha Vantage offers a free API for enterprise-grade financial market data, including company financial statements and earnings reports. A user can extract financial data using Python by accessing the Alpha Vantage API.
3. Using the get_quote_table method:
The get_quote_table method can be used to extract the data found on the summary page of a stock. This method extracts financial data from the summary page of stock and returns it in the form of a dictionary. From this dictionary, a user can extract the P/E ratio of a company, which is an important financial metric. Additionally, the get_stats_valuation method can be used to extract the P/E ratio of a company.
Python libraries for stock data retrieval: Fundamental and price data
Python has numerous libraries that enable us to access fundamental and price data for stocks. To retrieve fundamental data such as a company’s financial statements and earnings reports, we can use APIs or web scraping techniques.
On the other hand, to get price data, we can utilize APIs or packages that provide direct access to financial databases. Here are some resources that can help you get started with retrieving both types of data using Python for data science:
Retrieving fundamental data using API calls in Python is a straightforward process. An API or Application Programming Interface is a server that allows users to retrieve and send data to it using code.
When requesting data from an API, we need to make a request, which is most commonly done using the GET method. The two most common HTTP request methods for API calls are GET and POST.
After establishing a healthy connection with the API, the next step is to pull the data from the API. This can be done using the requests.get() method to pull the data from the mentioned API. Once we have the data, we can parse it into a JSON format.
Top Python libraries like pandas and alpha_vantage can be used to retrieve fundamental data. For example, with alpha_vantage, the fundamental data of almost any stock can be easily retrieved using the Financial Data API. The formatting process can be coded and applied to the dataset to be used in future data science projects.
Obtaining essential stock market information through APIs
There are various financial data APIs available that can be used to retrieve fundamental data of a stock. Some popular APIs are eodhistoricaldata.com, Nasdaq Data Link APIs, and Morningstar.
- Eodhistoricaldata.com, also known as EOD HD, is a website that provides more than just fundamental data and is free to sign up for. It can be used to retrieve fundamental data of a stock.
- Nasdaq Data Link APIs can be used to retrieve historical time-series of a stock’s price in CSV format. It offers a simple call to retrieve the data.
- Morningstar can also be used to retrieve fundamental data of a stock. One can search for a stock on the website and click on the first result to access the stock’s page and retrieve its data.
- Another source for fundamental financial company data is a free source created by a friend. All of the data is easily available from the website, and they offer API access to global stock data (quotes and fundamentals). The documentation for the API access can be found on their website.
Once you have established a connection to an API, you can pull the fundamental data of a stock using requests. The fundamental data can then be parsed into JSON format using Python libraries such as pandas and alpha_vantage.
Conclusion
In summary, retrieving fundamental data using API calls in Python is a simple process that involves establishing a healthy connection with the API, pulling the data from the API using requests.get(), and parsing it into a JSON format. Python libraries like pandas and alpha_vantage can be used to retrieve fundamental data.
