An implementation of the 'Bayesian regression for latent source model' method for predicting price variation of Bitcoin. You can read more about the method at https://arxiv.org/pdf/1410.1231.pdf.
- Python 3.5
- MongoDB 3.2
- bigfloat prerequisites
Make sure you have installed all the requirements and created an isolated Python environment for this project (optional). Then follow the installation instructions:
$ git clone https://github.com/stavros0/bitcoin-price-prediction.git
$ cd bitcoin-price-prediction
$ pip install -e .- Use the
okcoin.pyscript to gather market data from the OKCoin Spot Price API at the interval of every ten seconds. Bear in mind that you need at least 721 data points so thatm = len(prices) - n > 0.
$ python okcoin.py- See bitcoin-price-prediction/examples for how to use the
bayesian_regression.pymodule.millionare.pyis intended for tinkering and experimenting only and therefore won't display anything on the screen. That is, you should tinker with my script or write your own script instead. In any case, you have to speak Python.
This project is licensed under the terms of the MIT license. See LICENSE for more information.