'Towards Open Options Chains: A Data Pipeline Solution for Options Data - Part I' finance optionstrading
The use case for the data is fairly straightforward: to run backtests of options trading strategies. Therefore, the data pipeline must collect sufficiently fine-grained data. We will need options prices, volumes, and the options greeks , collected at sufficiently high frequency. In case you've never seen options data before, see the screenshot below for a sample.
While I only intend to run this on a limited set of 10-20 stocks, we have to consider that some users out there may use this solution for collecting data on a huge scale, say 1,000 stocks. We assume that the small-scale scenario is for individual use by retail investors, and the large-scale scenario is for serving the data to a wider group of users. In designing the solution, we focus on the small-scale scenario but keep the large-scale scenario in mind.
! After parsing the data, we see that the contract and time features are contained in the innermost object:.We see that the data has the potential to grow in size really quickly. The sample contained 297 rows and 47 columns of data. If we narrow this down to about 30 columns and collect the data, say, every 30 minutes, that amounts to approximately 30MB per month, per ticker .
For the small-scale use case, 600MB will be added to the repository every month. For the large-scale use case, this figure is a whopping 30GB. The amount of storage we need to provide depends on how much data we want to analyze at a time:
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