Learning to predict future Bitcoin value with RNNs

In this recipe, we will learn how to predict future Bitcoin value with an RNN. The key idea is that the temporal sequence of values observed in the past is a good predictor of future values. For this recipe, we will use the code available at https://github.com/guillaume-chevalier/seq2seq-signal-prediction under the MIT license. The Bitcoin value for a given temporal interval is downloaded via an API from https://www.coindesk.com/api/ . Here is a piece of the API documentation:

We offer historical data from our Bitcoin Price Index through the following endpoint:
https://api.coindesk.com/v1/bpi/historical/close.json
By default, this will return the previous 31 days' worth of data. This endpoint accepts the following optional parameters:
?index=[USD/CNY]The index to return data for. Defaults to USD.
?currency=<VALUE>The currency to return the data in, specified in ISO 4217 format. Defaults to USD.
?start=<VALUE>&end=<VALUE> Allows data to be returned for a specific date range. Must be listed as a pair of start and end parameters, with dates supplied in the YYYY-MM-DD format, e.g. 2013-09-01 for September 1st, 2013.
?for=yesterday Specifying this will return a single value for the previous day. Overrides the start/end parameter.
Sample Request:
https://api.coindesk.com/v1/bpi/historical/close.json?start=2013-09-01&end=2013-09-05
Sample JSON Response:
{"bpi":{"2013-09-01":128.2597,"2013-09-02":127.3648,"2013-09-03":127.5915,"2013-09-04":120.5738,"2013-09-05":120.5333},"disclaimer":"This data was produced from the CoinDesk Bitcoin Price Index. BPI value data returned as USD.","time":{"updated":"Sep 6, 2013 00:03:00 UTC","updatedISO":"2013-09-06T00:03:00+00:00"}}
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