Bitcoin Price Data
This tutorial demonstrates how to populate a feature store with a timeseries of bitcoin price data, and then compute some transformations on it.
Run this tutorial as a Colab notebook.
As with the quick-start guide, start by creating a blank feature store and namespace.
Now download some data from the CoinDesk API and save it to the feature store.
When creating the tutorial/bitcoin.close
feature, we specified partition="year"
. ByteHub allows you to choose with year
or date
partitioning, which will result in the saved data being split into separate folders when it is saved. Choose date
if you are working with data that has a very high time-resolution, e.g. updated every second, otherwise choose year
, as this will create few files and result in better performance.
We can now query and resample this data using load_dataframe
:
Now create transform features to compute the exponentially-weighted moving averages of the bitcoin price over different time windows, along with a momentum indicator.
These new, transformed features are now available to query from the load_dataframe
method. They are calculated on-the-fly, and therefore reflect any changes to the underlying bitcoin price data.
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