These data-often referred to as OHLC Chart Data-can be interpreted as Time Series data and are perfect for performing technical analysis. These financial data are generally provided in a format that includes the following information: We’ll be using mostly structured historical data for our examples here. In most cases, Real-Time data isn’t available from public APIs and must be purchased. Unstructured Data: News articles, Social Media, Sentiment Analysis, etc.Īdditionally, financial data can be further categorized as either Historical or Real-Time.Structured Data: Closing prices, financials, market performance, etc.Generally, financial data comes in one of 2 primary types: This type of data is available from many sources such as, Quandl, Alpha Vantage, and many brokerages.įinancial data can be bought, manually scraped from the web, or obtained from public APIs. The canonical format is tabular data (think spreadsheets) which can be formatted as rows and columns. Getting data from various sources via Python including Yahoo Finance, Quandl, and Alpha Vantageįinancial data comes in many forms.Readers should be familiar with basic Python syntax but needn’t have obtained a level of skill mistakable as guru. We’ll be using the Pandas library, the yfinance library, and a handful of useful helper methods. In this article, you’ll learn how to easily get, read, and interpret financial data using Python. 7.2 pandas-datareader Alpha Vantage API.7.1 Unofficial Python alpha_vantage API.
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