{"id":199012,"date":"2023-11-16T10:20:30","date_gmt":"2023-11-16T15:20:30","guid":{"rendered":"https:\/\/ibkrcampus.com\/?p=199012"},"modified":"2023-11-17T15:52:14","modified_gmt":"2023-11-17T20:52:14","slug":"stock-market-data-obtaining-data-visualization-analysis-in-python-part-ii","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/stock-market-data-obtaining-data-visualization-analysis-in-python-part-ii\/","title":{"rendered":"Stock Market Data: Obtaining Data, Visualization & Analysis in Python – Part II"},"content":{"rendered":"\n

Learn how to get historical data for stocks with Part <\/a>I in this tutorial.<\/em><\/p>\n\n\n\n

S&P 500 Stock Tickers<\/h2>\n\n\n\n

If you want to analyse the stock market data for all the stocks which make up S&P 500 then below code will help you. It gets the list of stocks from the Wikipedia page and then fetches the stock market data from yahoo finance.<\/p>\n\n\n\n

# Import packages\nimport yfinance as yf\nimport pandas as pd\n\n# Read and print the stock tickers that make up S&P500\ntickers = pd.read_html(\n    'https:\/\/en.wikipedia.org\/wiki\/List_of_S%26P_500_companies')[0]\nprint(tickers.head())\n\n# Get the data for this tickers from yahoo finance\ndata = yf.download(tickers.Symbol.to_list(),'2021-1-1','2021-7-12', auto_adjust=True)['Close']\nprint(data.head())<\/pre>\n\n\n\n
SP500_tickers_data.py <\/a>hosted with \u2764 by GitHub<\/a><\/p>\n\n\n\n
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Intraday or Minute Frequency Stock Data<\/strong><\/h2>\n\n\n\n
yfinance module can be used to fetch the minute level stock market data. It returns the stock market data for the last 7 days.<\/p>\n\n\n\n
If yfinance is not installed on your computer, then run the below line of code from your Jupyter Notebook to install yfinance.<\/p>\n\n\n\n
!pip install yfinance<\/pre>\n\n\n\n
install yfinance.py <\/a>hosted with \u2764 by GitHub<\/a><\/p>\n\n\n\n
The yfinance module has the download method which can be used to download the stock market data.<\/p>\n\n\n\n
It takes the following parameters:<\/p>\n\n\n\n
    \n
  1. ticker<\/code>: The name of the tickers you want the stock market data for. If you want stock market data for multiple tickers then separate them by space<\/li>\n\n\n\n
  2. period<\/code>: The number of days\/month of stock market data required. The valid frequencies are 1d, 5d, 1mo, 3mo, 6mo, 1y, 2y, 5y, 10y, ytd, max<\/li>\n\n\n\n
  3. interval<\/code>: The frequency of the stock market data. The valid intervals are 1m, 2m, 5m, 15m, 30m, 60m, 90m, 1h, 1d, 5d, 1wk, 1mo, 3mo<\/li>\n<\/ol>\n\n\n\n
    The below code fetches the stock market data for MSFT for the past 5 days of 1-minute frequency.<\/p>\n\n\n\n
    import yfinance as yf\nintraday_data = yf.download(tickers=\"MSFT\",\n                            period=\"5d\",\n                            interval=\"1m\",\n                            auto_adjust=True)\nintraday_data.head()<\/pre>\n\n\n\n
    minute_data.py <\/a>hosted with \u2764 by GitHub<\/a><\/p>\n\n\n\n
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    Resample Stock Data<\/h2>\n\n\n\n
    Convert 1-minute data to 1-hour data or Resample Stock Data<\/h3>\n\n\n\n
    During strategy modelling, you might be required to work with a custom frequency of stock market data such as 15 minutes or 1 hour or even 1 month.<\/p>\n\n\n\n
    If you have minute level data, then you can easily construct the 15 minutes, 1 hour or daily candles by resampling them. Thus, you don’t have to buy them separately.

    In this case, you can use the pandas     resample<\/a> method to convert the stock market data to the frequency of your choice. The implementation of these is shown below where a 1-minute frequency data is converted to 10-minute frequency data.<\/p>\n\n\n\n
    The first step is to define the dictionary with the conversion logic. For example, to get the open value the first value will be used, to get the high value the maximum value will be used and so on.<\/p>\n\n\n\n
    The name Open, High, Low, Close and Volume should match the column names in your dataframe.<\/p>\n\n\n\n
    ohlcv_dict = {\n 'Open': 'first',\n 'High': 'max',\n 'Low': 'min',\n 'Close': 'last',\n 'Volume': 'sum'\n}<\/pre>\n\n\n\n
    ohlcv_dict.py <\/a>hosted with \u2764 by GitHub<\/a><\/p>\n\n\n\n
    Convert the index to datetime timestamp as by default string is returned. Then call the resample method with the frequency such as:<\/p>\n\n\n\n