5 Easy ways to access historical financial data sets with Python

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5 Easy ways to access historical financial data sets with Python

Financial analysis is crucial for every company. It helps to provide information that will determine the credibility of a company. The insights generated by a financial analyst can also help in planning and managing future finances. However, if you want to do financial analysis accurately, you need to get relevant data. You can use the data available across Python libraries, particularly for historical analysis. To learn this, you can enroll in Imarticus Learning's Financial analyst program.

What Are The Different Ways of Accessing Historical Financial Data Sets With Python? 

If you are a chartered financial analyst, you will be able to access financial data sets for various purposes. There are several Python libraries that allow easy retrieval of historical financial data. You can use any one of the following Python packages to obtain financial data from one or more stocks. 

  • Pandas DataReaders

If you are looking for a free Python library to source and manipulate data from, you can use the Pandas DataReaders. Pandas DataReaders is not a data source. It is a PyData Stack API that you can use to obtain data from various sources like Alpha Vantage, and the database of Federal Reserve Economic Data (FRED). 

  • Tiingo

If you need historical data related to stocks, you should take a look at Tiingo's library. You can use Python to access the Tiingo API which is quite easy to navigate and developer-friendly. If you have the API key, you can easily obtain all the necessary data. Tiingo is best for financial companies and institutions that require stock data for analysis. 

  • Quandl

Quandl offers economic, financial, and alternative data You can access this data by using Python. If you use Python, you will be able to connect to the API and obtain the necessary data.   

  • Yahoo! Finance

Yahoo! Finance offers various types of data, including fundamental and option data, market financials, cryptocurrency data, financial reports, commodities futures, as well as bonds and equities. With Python, you can retrieve all available data and use it for accurate analysis. 

  • Twelve Data

At times, you may need both historical and real-time data. This is especially important if you want to compare insights after analysis of both types of data. Twelve Data allows financial analysts to access relevant financial data through API. You can also use several technical indicators and even create dynamic graphs after obtaining the insights.  

There are several financial modelling courses that help students learn how to access such libraries. You can opt for one of these courses and become a financial analyst with essential Python skills. 

How Can You Learn Financial Analysis? 

To be able to access historical financial data, you will need to learn the basics of financial analysis. Imarticus Learning's Financial Analysis Prodegree course offers this and more. The course is in collaboration with KPMG and provides toolkits that help with various concepts like corporate finance, private equity, and investment banking.

You can become a financial analyst after completing this course and even transition from your previous profession to a successful career in the finance sector. You can attend live lectures and interact with your peers and course instructors. Such learning sessions will help you clear doubts and develop essential skills. Imarticus Learning also provides industry certifications to all students. 

This is one of the best financial modelling courses and includes six different projects that are based on real-world scenarios. You will be prepared for the industry and can explore various aspects of financial analysis once you receive your certificate. 

After completing the Financial Analysis Prodegree, you can become a chartered financial analyst. Since Imarticus Learning offers extensive career support, you will get many opportunities and will be able to find a rewarding job in your field of interest.

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