Bitcoin Bro

Time-series prediction for Bitcoin (BTCUSDT) price using data scraped from Binance API

Real-time Dashboard | Blogpost

Installation

1. Create a conda environment and pip install dependencies.

conda create -n bitcoin-bro python=3.8
conda activate bitcoin-bro
pip install -r requirements.txt

2. Run notebooks in the /notebooks directory.

jupyter notebook

3. Run the streamlit application to see the real-time predictions

streamlit run streamlit_app.py

Project Organization

---

├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── assets             <- Contains static assets such as images.
├── data
│   ├── external       <- Data from third party sources.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── docs               <- A default Sphinx project; see sphinx-doc.org for details
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks showing experiments and analysis
│   ├── 1.0-Downloading-Data                <- Shows how raw data is downloaded from Binance API
│   ├── 2.0-Feature-Engineering             <- Shows how raw data is transformed by feature engineering
│   ├── 3.0-Modelling-XGB-Baseline          <- Shows the training and hyperparam tuning of XGBoost baseline
│   └── 4.0-Generate-Real-Time-Predictions  <- Shows how to generate real-time predictions
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── data           <- Scripts to download or generate data
│   │   └── binance_downloader.py           <- Contains functions to download data from Binance API
│   │
│   ├── features       <- Scripts to turn raw data into features for modeling
│   │   ├── feature_generator.py            <- Contains functions to generate features
|   |   └── utilities.py                    <- Contains utility functions such as time converting
│   │
│   ├── models         <- Scripts to train models and then use trained models to make
│   │   │                 predictions
│   │   └── metrics.py                      <- Contains functions to compute model performance metrics 
│   │
│   └── visualization  <- Scripts to create exploratory and results oriented visualizations
│       └── plot_generator.py               <- Contains functions to generate plots
│
└── tox.ini            <- tox file with settings for running tox; see tox.readthedocs.io

---

Citation

If used this work or you found this work useful, please use this BibTeX to cite this repository in your publications or works:

@software{jonathan_lim_siu_chi_2023_7535204,
  author       = {Jonathan Lim Siu Chi},
  title        = {jonathanlimsc/bitcoin-bro: Bitcoin Bro v1.0},
  month        = jan,
  year         = 2023,
  publisher    = {Zenodo},
  version      = {v1.0},
  doi          = {10.5281/zenodo.7535204},
  url          = {https://doi.org/10.5281/zenodo.7535204}
}

Project based on the cookiecutter data science project template. #cookiecutterdatascience