btc_predictor

This project uses LSTM to predict BTC prices

Overview

After playing with RNN LSTM for synthetic text generation as well as image spoofing I discovered there is a usage for RNN LSTM in prediction. I thought it might be fun to try to build a bitcoin market predictor using RNN LSTM to see how well it does.

My initial hypothesis was it would not do well based on BTC’s market volatility. I was surprised by the results.

To conduct the experiment, I used my NVIDIA Jetson TX2 GPU computer and the software is a combination of python’s, numpy, keras, and tensorflow.

Lucky for me Coindesk has an API for BTC current and historical market data. My training data for the model uses a range from 9/1/2013 -> 12/31/2017 and the test data was from 2/1/2018 -> 3/12/2018. I ensured there was no overlap between the training data and the test data.

It takes ~ 3 1/2 minutes to generate the results which you can see for yourself in the figure below.

Results

This is output from the program

The code

To generate the bitcoin test data, I forked a project that enables access to a number of crypto currency APIs

test_data_btc.py is a program that geneates my test and training data

Bitcoin Price API

Coindesk

The Coindesk class offers a much richer price interface:

get_current_price(currency='USD')
get_past_price(date)
get_historical_data_as_dict(start='2013-09-01', end=None)
get_historical_data_as_list(start='2013-09-01', end=None)

get_current_price and get_past_price both return Decimal objects.

get_current_price takes in an optional parameter specifying the currency.

The dates for all functions must be in the form 'YYYY-MM-DD'.

get_historical_data_as_dict will return a dictionary of the following format:

{'2014-10-20': 400.00, '2014-10-21': 301.99}

Remember that these date/prices will not be in any given order.

get_historical_data_as_list will return a list of dictionaries, correctly sorted by date from start to end.