{"id":96425,"date":"2021-07-27T11:22:00","date_gmt":"2021-07-27T15:22:00","guid":{"rendered":"https:\/\/ibkrcampus.com\/?p=96425"},"modified":"2022-11-21T09:47:48","modified_gmt":"2022-11-21T14:47:48","slug":"stock-market-data-and-analysis-in-python-part-i","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/stock-market-data-and-analysis-in-python-part-i\/","title":{"rendered":"Stock Market Data And Analysis In Python – Part I"},"content":{"rendered":"\n

Excerpt<\/em><\/strong><\/p>\n\n\n\n

How to get Stock Market Data in Python?<\/strong><\/h2>\n\n\n\n

Yahoo! Finance<\/strong><\/h3>\n\n\n\n

One of the first sources from which you can get historical daily price-volume stock market data is Yahoo! Finance. You can use\u00a0pandas_datareader<\/code>\u00a0or\u00a0yfinance<\/code>\u00a0module to get the data and then can download or store in a csv file by using pandas.to_csv method.<\/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

\n!pip install yfinance\n<\/p>\n

view raw<\/a>install yfinance.py<\/a> hosted with \u2764 by GitHub<\/a><\/p>\n\n\n\n

\"Stock<\/figure>\n\n\n\n

\n# Import yfinance package
\nimport yfinance as yf

\n\n# Set the start and end date
\nstart_date = ‘1990-01-01’
\nend_date = ‘2021-07-12’

\n\n# Set the ticker
\nticker = ‘AMZN’

\n\n# Get the data
\ndata = yf.download(ticker, start_date, end_date)

\n\n# Print 5 rows
\ndata.tail()\n<\/p>\n

\namazon_data.py<\/a>\u00a0hosted with \u2764 by\u00a0GitHub<\/a>\n\n\n\n

\"\"<\/figure>\n\n\n\n

To visualize the adjusted close price data, you can use the matplotlib library and plot method as shown below.<\/p>\n\n\n\n

\n# Import matplotlib for plotting
\nimport matplotlib.pyplot as plt
\n%matplotlib inline

\n\n# Plot adjusted close price data
\ndata[‘Adj Close’].plot()
\nplt.show()\n<\/p>\n\n

\nplot_amazn_data.py<\/a> hosted with \u2764 by GitHub<\/a>\n<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

Let us improve the plot by resizing, giving appropriate labels and adding grid lines for better readability.<\/p>\n\n\n\n

\n# Plot the adjusted close price
\ndata[‘Adj Close’].plot(figsize=(10, 7))

\n\n# Define the label for the title of the figure
\nplt.title(“Adjusted Close Price of %s” % ticker, fontsize=16)

\n\n# Define the labels for x-axis and y-axis
\nplt.ylabel(‘Price’, fontsize=14)
\nplt.xlabel(‘Year’, fontsize=14)

\n\n# Plot the grid lines
\nplt.grid(which=”major”, color=’k’, linestyle=’-.’, linewidth=0.5)

\n\n# Show the plot
\nplt.show()\n<\/p>\n\n

\nplot_amzn_data_with_grids.py<\/a> hosted with \u2764 by GitHub<\/a>\n<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

<\/p>\n\n\n\n

<\/p>\n\n\n\n

Advantages<\/strong><\/p>\n\n\n\n

  1. Adjusted close price stock market data is available<\/li>
  2. Most recent stock market data<\/a> is available<\/li>
  3. Doesn’t require API key to fetch the stock market data<\/li><\/ol>\n\n\n\n

    See https:\/\/blog.quantinsti.com\/stock-market-data-analysis-python\/<\/a> for additional insight on this topic and for a tutorial from Nitesh Khandelwal, Co-Founder and CEO, QuantInsti, that answers questions related to getting Data for Algo Trading.<\/em><\/p>\n\n\n\n

    Past performance is not indicative of future results.<\/em><\/p>\n\n\n\n

    Any stock, options or futures symbols displayed are for illustrative purposes only and are not intended to portray recommendations.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

    Join Ishan Shah for a tutorial on how to use pandas_datareader or yfinance module to get historical daily price-volume stock market data from Yahoo finance.<\/p>\n","protected":false},"author":517,"featured_media":81558,"comment_status":"closed","ping_status":"open","sticky":true,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[339,343,349,338,350,341,344],"tags":[10057,865,10053,6614,4659,1224,10054,10056,10055],"contributors-categories":[13654],"class_list":{"0":"post-96425","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-data-science","8":"category-programing-languages","9":"category-python-development","10":"category-ibkr-quant-news","11":"category-quant-asia-pacific","12":"category-quant-development","13":"category-quant-regions","14":"tag-amazon_data-py","15":"tag-github","16":"tag-install-yfinance","17":"tag-jupyter-notebook","18":"tag-matplotlib","19":"tag-pandas","20":"tag-pandas_datareader","21":"tag-plot_amazn_data-py","22":"tag-plot_amzn_data_with_grids-py","23":"contributors-categories-quantinsti"},"pp_statuses_selecting_workflow":false,"pp_workflow_action":"current","pp_status_selection":"publish","acf":[],"yoast_head":"\nStock Market Data And Analysis In Python – Part I<\/title>\n<meta name=\"description\" content=\"Join Ishan Shah for a tutorial on how to use pandas_datareader or yfinance module to get historical daily price-volume stock market data from Yahoo...\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.interactivebrokers.com\/campus\/wp-json\/wp\/v2\/posts\/96425\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Stock Market Data And Analysis In Python - 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