This project provides a Python implementation of Bayesian regression for latent source modeling, aiming to predict Bitcoin price variations. It is intended for researchers and developers interested in applying advanced statistical methods to cryptocurrency market analysis.

How It Works

The core of the project is a Bayesian regression model designed to capture latent factors influencing Bitcoin prices. This approach offers a probabilistic framework for prediction, allowing for uncertainty quantification and potentially more robust forecasting compared to traditional deterministic methods.

Quick Start & Requirements

• Install via pip install -e . after cloning the repository.
• Requires Python 3.5 and MongoDB 3.2.
• Data gathering is handled by okcoin.py, which fetches data from the OKCoin Spot Price API. A minimum of 721 data points is needed for the model to function.

Highlighted Details

• Implements Bayesian regression for latent source modeling.
• Includes scripts for data collection (okcoin.py) and model usage (bayesian_regression.py).
• millionare.py is provided for experimentation.

Maintenance & Community

No specific information on maintainers, community channels, or roadmap is provided in the README.

Licensing & Compatibility

Licensed under the MIT license, permitting commercial use and integration with closed-source projects.

Limitations & Caveats

The project requires specific, older versions of Python (3.5) and MongoDB (3.2), which may pose compatibility challenges with modern systems. The millionare.py script is explicitly for tinkering and does not provide output.