Scrape option chain data using yfinance Python script in Google Colab

Google Colab link:

We start by installing the yfinance library in our Python Colab notebook, which allows us to access financial data from Yahoo Finance. You may have to install the library, using the pip package manager directly in our notebook. More typically for Google Colab you may find the yfinance library pre-installed.

In fetching options data, we create an instance of a stock ticker by passing a stock symbol, for example, 'AAPL' for Apple Inc., to the Ticker method of yfinance.

We can then explore the available options data. First, you can retrieve all possible expiration dates for options associated with our chosen ticker. These are the dates on which different sets of options will expire.

We then select one of these expiration dates, not necessarily the first one in the list, to fetch detailed options data for that particular date. The data includes two types of options: calls and puts. Each option type comes with several pieces of crucial information such as the strike price, the last traded price, bid and ask prices, the volume of trades, open interest, and the implied volatility.

This setup not only allows us to scrape current options data but also provides insights into how options are priced and traded in the market, making it a great tool for those looking to explore further the microstructure of the market.

Yahoo Finance provides a comprehensive set of data for both call and put options. Here’s what you can typically expect:

Expiration Date: The date on which the option contract expires.

Strike Price: The price at which the holder of the option can buy (call) or sell (put) the underlying stock.

Last Price: The last price at which the option was traded.

Bid Price: The highest price that a buyer is willing to pay for the option.

Ask Price: The lowest price at which a seller is willing to sell the option.

Change: The change in the price of the option from the previous day’s close.

% Change: The percentage change in the price of the option from the previous day’s close.

Volume: The number of options contracts traded during the trading session.

Open Interest: The total number of outstanding options contracts that have not been settled.

Implied Volatility: A measure of the expected volatility of the stock price, derived from the option price.

These data points are are useful to build an implied volatility surface. The yfinance library provides an accessible way to scrape and analyze this data, which can be used for various financial analyses and modeling directly in Google Colab. Implied Volatility may be important for understanding tail risk and and developing measures of CVaR.

[MichaelXiong-rv]

Do you think HasData's scraping API could be utilized for more custom options data needs beyond what yfinance provides?

[ashihtaka]

I had no clue you could use Python scripts in Google

[TcePhone]

Thank You! I hope you continue the option series using the Colab, Maybe something on valuing and selecting options based on fundamental and technical criteria would be cool.

[DanBrewster-yw]

Nice work! Just wondering, can HasData scrape options data in real-time or is it better for past data?

[kummertkein]

What type of options are these, European, American (or both or something else)? Is there a way to get only the data for, say, European options?

[mj]

Thank you for posting this Brian! Does this work for an arbitrary date? For example September 29, 2008?

[jbond]

how about scrab data from page directly instead of using yfinace?