![]() When inspecting stock data, the Close column and the Adjusted Close column can feel a bit misleading if you do not understand how dividends affect them. One aspect of trading that can be slightly confusing is the changes caused by stock splits and dividend adjustments. describe() we can get the overall statistical data for the new dataframe.īy using prepost=True, we can also get the data for before market hours trading. So now it is possible to gather more information about this data. I have saved the dataframe for GE with a period of 5 days and interval of 1 minute. Note that when using interval="1m", the period cannot be longer than "5d". Below, you can see the results for using a period="1mo" and various intervals: interval="1h", interval="30m", interval="5m", and period="5d", interval="1m". For this, we will use the interval parameter. The following is the code for see(), which I use to present most dataframes in my notebooks for more clear labeling, centering, and clearer explanation.įor practical use in trading, it is important to be able to retrieve more than just historical stock prices with Yahoo Finance. Between my specified start and end dates passed to yf.download() there are 2,266 trading days of information. This way we can specify the exact beginning and end of our time range and retrieve all the data for the stock fluctuations within that given range.Īnd since I have saved the data as the dataframe "GE", I am able to get information about my new dataset with Pandas' methods like info(). To get a specific date range, we can use the start and end parameters with yf.download(). Likewise, we can pass any number of months, as long as the company whose ticker is passed had valid trading activity, and get data on those, i.e period = "2mo", and so forth. To get the data from the most recent month of trading, we can pass period = "1mo". To get all the data from the first day of the year to the current day of the year, we can pass period = "ytd". Here we will look at a some general time periods that can be passed to the yf.download() argument, period. Often times we do not want the entire stock exchange history for a ticker symbol, but rather a date range. It is a highly customized wrapper, centered on the pandas. The following is the code for fancy_plot(), which I use for many of my visualizations. Plotting the last 60+ years of stock fluctuations for GE reveals some soaring prices and some unfortunate declines as well. The following is the code for head_tail_vert() and head_tail_horz(), which I use with dataframes extensively to present more easily digestible, labeled data. So with yf.download() and a ticker symbol alone, it is possible to get a great deal of data. By default, if we only pass the ticker name, we receive the data for every trading day in the history of the company, which as you can see below for GE, dates back to January 2, 1962. To dive right in, after importing yfinance as yf, we will create a ticker variable for "GE" and pass it to yf.download(). There is no API key needed, and the yfinance module can return an incredible wealth of data with just one line of code. One of the most beneficial aspects of Yahoo Finance is how quick and easy it is to access data. Special thanks to Alexander Hagmann for his thorough instruction. To view the helpers.py file that I use in this project, please click here for GitHub.Other Links: Yahoo Finance | yfinance module.You can also look over helpers.py, which contains many helper functions that I use extensively throughout this project to streamline the delivery of data and create a more visually appealing experience. For the full code included here, you can view the Jupyter, HTML, and PDF versions. In this article, I will walk through the basic to the advanced and give an overview of the most powerful functionality of this very useful API. And with the convenient and fairly well-maintained yfinance module available for Python, few sources are easier to work with than Yahoo Finance. There is a wealth of information available through the API, including extensive company data, covering not only traded companies but also currency exchange, cryptocurrency, mutual funds, and treasury yields. For data science, Yahoo Finance is an ideal resource for quick, up-to-the-minute financial data, and it is not just for stocks. ![]()
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