Amazon FinSpace Spark time series library - Amazon FinSpace

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Amazon FinSpace Spark time series library

Important

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Amazon FinSpace PySpark Kernel delivers a time series analytics library to prepare and analyze historical financial time series data using FinSpace managed Spark clusters. You can use the time series library to analyze high-density data like US options historical Options Price Reporting Authority (OPRA) with billions of daily events or sparse time series data such as quotes for fixed income instruments. The time series library is available to use in the FinSpace notebook environment.

The time-series library is logically organized in four stages for a conceptual framework. Every stage provides a set of functions and you can plug your own functions.

  1. Collect – The objective of this stage is to collect the series of events that arrive at an irregular frequency into uniform intervals called bars. You can perform collection with your functions or use the FinSpace functions to calculate bars such as time bars.

  2. Summarize – The objective of this stage is to take collected data in bars from previous stage and summarize it using the events captures within a bar.

  3. Fill and Filter – The data produced in the previous stage could have missing bars where no data was collected or contain data that is not desired to be used in the next stage. The objective of this stage is to prepare a dataset of features with evenly spaced intervals and filter out any data outside desired time window.

  4. Analytics – At this stage, a prepared dataset of features is ready for application of technical and statistical indicators. You can bring your own indicator functions or choose one of the FinSpace functions for this stage.

A diagram that shows the time series framework.

See the following sections to learn more about supported functions in the time series library.

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