{"id":117502,"date":"2022-01-03T12:09:00","date_gmt":"2022-01-03T17:09:00","guid":{"rendered":"https:\/\/ibkrcampus.com\/?p=117502"},"modified":"2022-11-21T09:50:30","modified_gmt":"2022-11-21T14:50:30","slug":"historical-options-futures-data-using-tws-api-part-ii","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/historical-options-futures-data-using-tws-api-part-ii\/","title":{"rendered":"Historical Options & Futures Data using TWS API \u2013 Part II"},"content":{"rendered":"\n

In the previous article<\/a>, we learnt to build a trading application that fetches historical data for future contracts from the IB servers. We will now see how we can extract historical data for options contracts. We will first cover the easy part which is getting data for live option contracts. Following this, we will discuss a possible approach to arrange<\/em> historical data for expired option contracts. As discussed in the previous article, I am assuming that you are subscribed to the relevant market data packs. <\/p>\n\n\n\n

Trading Application<\/strong> <\/p>\n\n\n\n

Our trading application need not be changed from what we used to extract futures data. However, in the last article, we simply printed out the historical data and at that time I had promised that I will discuss how to store the extracted data. Therefore, in this article, we will tweak our trading application slightly to store data in Pandas dataframe instead of printing it out on the console. I have used Pandas dataframe and not native python data structures to store data because dataframes allow us some very convenient data handling and processing functions which will help us later in this article. If you are not conversant with Pandas Dataframes then I would highly recommend that you pick up at least some basic proficiency in this data structure. <\/p>\n\n\n\n

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The first change in the trading application is adding a class variable hist_data<\/em>. In Object Oriented Programming, Classes can have variables which define specific attribute of that class. Our TradeApp class is going to have hist_data<\/em> as an attribute which will be a dataframe having the columns \u201cDate\u201d, \u201cOpen\u201d, \u201cHigh\u201d,\u201d Low\u201d,\u201d Close\u201d and \u201cVolume\u201d. The idea is that as and when TWS return us the historical bar data, we append each bar as a row to this class attribute. <\/p>\n\n\n\n

We have also changed the historicalData<\/em> Ewrapper function and instead of printing out the bar information, we are using each returned bar\u2019s data to create a python dictionary and then append that to the hist_data<\/em> class variable. I would like to clarify here that appending dictionaries to a dataframe is not the most efficient way of handling dataframes and that the TWS API has provisions to achieve the same thing more efficiently. However, I have kept that out of scope of this article. <\/p>\n\n\n\n

Creating Option Contract<\/strong> <\/p>\n\n\n\n

Option contracts have a number of properties which are required to identify the exact option contract. In addition to the symbol of the underlying stock, exchange, currency and expiry data we also need to know the strike price of the option and whether the option is a Call or a Put. Therefore, you may have figured out already that we will have to change how we define the parameters of the contract object. Refer the below code for the option contract that I have used in this demo. <\/p>\n\n\n\n

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

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In the above code, I have created the contract object for the call option on Amazon\u2019s stock having a strike price of 3400 and expiry date of 31st<\/sup> December 2021 (which is a week from when I am writing this article). You can also see that I have defined the exchange as BOX which should work for most US listed option contracts.  <\/p>\n\n\n\n

It should be noted that if we define the option contract parameters loosely then TWS will return details for a number of option contracts. For example, if we do not define the strike parameter or define the lastTradeDateorContractMonth<\/em> as \u201c202201\u201d (i.e. only the year and month) then we will get a number of option contracts satisfying the defined parameters. Therefore, please ensure that you are defining all the relevant parameters which will help TWS pin point the exact option that you are interested in. <\/p>\n\n\n\n

Storing Historical Data<\/strong> <\/p>\n\n\n\n

Finally let us execute the reqHistoricalData<\/em> function of our Trading Application to extract historical data for the required option contract. TWS will then send us data through the historicalData<\/em> EWrapper function where we are updating the hist_data <\/em>class variable. <\/p>\n\n\n\n

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

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There may be some lag between when the function is executed and when the entire data is received and therefore I am adding a 5 second delay before printing out the class variable hist_data<\/em>. <\/p>\n\n\n\n

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

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Historical Data of Expired Option Contracts<\/strong> <\/p>\n\n\n\n

Before we commence our discussion on this topic please note that the date as of writing of this article is 24<\/em>th<\/sup><\/em> December 2021. You will need to keep this date in mind and consider the dates presented below in reference to this date. <\/em> <\/p>\n\n\n\n

Unlike future contracts, where we could use the parameter includeExpired <\/em>to get historical data for expired futures, <\/em>there is no such provision for expired option contracts. Therefore, we will have to use a hack which will get us reasonably good historical data for expired options. This hack is based on the Black Scholes Formula which calculates option price based on: <\/p>\n\n\n\n