{"id":63,"date":"2021-10-29T12:50:00","date_gmt":"2021-10-29T12:50:00","guid":{"rendered":"https:\/\/eodhd.com\/financial-academy\/?p=63"},"modified":"2025-06-19T19:38:25","modified_gmt":"2025-06-19T19:38:25","slug":"a-simple-way-to-get-a-stocks-fundamental-data","status":"publish","type":"post","link":"https:\/\/eodhd.com\/financial-academy\/fundamental-analysis-examples\/a-simple-way-to-get-a-stocks-fundamental-data","title":{"rendered":"A Simple Way to Get a Stock\u2019s Fundamental Data"},"content":{"rendered":"\n

Retrieving or scraping stock fundamental data is sometimes easier said than done. It can be surprisingly difficult to find reliable resources or websites that provide clean, structured financial data. Over the past few years, I’ve worked on various data science projects that required in-depth stock data and analysis.<\/p>\n\n\n\n

In my experience, a stock\u2019s\u00a0price history\u00a0is one of the easiest types of data to retrieve – typically via platforms like Yahoo Finance using Python libraries such as Yfinance. However, when it comes to\u00a0fundamental stock analysis using Python APIs, the process becomes significantly more complex.<\/p>\n\n\n\n

Accessing comprehensive financial statements, earnings reports, and key valuation metrics often requires deep Google searches or labor-intensive web scraping. In one of my earlier projects, I was lucky to come across a now-defunct site called stockpup.com, which offered thousands of rows of clean, downloadable fundamental data in CSV format – ideal for machine learning workflows.<\/p>\n\n\n\n

But with that resource gone, I had to search for a new way to reliably gather\u00a0fundamental stock data for analysis – and preferably one that supports automation and scalability through APIs.<\/p>\n\n\n\n

Register & Get Data<\/span><\/a><\/p>\n\n\n\n

Financial Data APIs<\/h1>\n\n\n\n

There are numerous financial data APIs out there that can give you access to a stock\u2019s fundamental data. Personally, I have found and use one website which is able to provide more than just fundamental data and it is free to sign up called \u2014 EODHD APIs<\/a><\/strong>. Disclosure: I earn a small commission from any purchases made through the link above<\/em>.<\/p>\n\n\n\n

Using this financial data API, I was able to retrieve the fundamental data from thousands of companies. The data was ready and available but now I needed to organize and format it into a Pandas DataFrame that I\u2019ll be able to use. To do so, I utilized Python to get everything set up:<\/p>\n\n\n\n

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# Libraries\nimport pandas as pd\nfrom eod import EodHistoricalData\nfrom functools import reduce\nfrom datetime import datetime, timedelta\n\n# Importing and assigning the api key\nwith open(\"..\/eodHistoricalData-API.txt\", \"r\") as f:\n    api_key = f.read()\n    \n# EOD Historical Data client\nclient = EodHistoricalData(api_key)<\/code><\/pre>\n\n                <\/div>\n                
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