In this project, the task is to predict the price variations of bitcoin, a virtual cryptographic currency, based on this paper. These predictions could be used as the foundation of a bitcoin trading strategy. To make these predictions, first take we will have to familiarize yourself with a machine learning technique, Bayesian Regression, and implement this technique in Python.
This project requires numpy, pandas, sklearn, and statsmodels. To install these, simply use the pip installer sudo pip install X or, if using Anaconda, conda install X, where X is the package name.
The datasets to use are in the data folder and are pre-cleaned. The original raw data can be found here.
The datasets from this site have three attributes:
(1) time in epoch
(2) price in USD per bitcoin
(3) bitcoin amount in a transaction (buy/sell)
However, only the first two attributes are relevant to this project.
To make the data to have evenly space records, we took all the records within a 20 second window and replaced it by a single record as the average of all the transaction prices in that window. Not every 20 second window had a record; therefore those missing entries were filled using the prices of the previous 20 observations and assuming a Gaussian distribution. The raw data that has been cleaned is given in the file dataset.csv.
Finally, as discussed in the paper, the data was divided into a total of 9 different datasets. The whole dataset is partitioned into three equally sized (50 price variations in each) subsets: train1, train2, and test. The train sets are used for training a linear model, while the test set is for evaluation of the model. There are three csv files associated with each subset of data: *_90.csv, *_180.csv, and *_360.csv. In *_90.csv, for example, each line represents a vector of length 90 where the elements are 30 minute worth of bitcoin price variations (since we have 20 second intervals) and a price variation in the 91st column. Similarly, the *_180.csv represents 60 minutes of prices and *_360.csv represents 120 minutes of prices.
Implement the Bayesian Regression model to predict the future price variation of bitcoin as described in the reference paper.
python bitcoin.py data/