| title | authors |
|---|---|
Finding the Optimal Cryptocurrency Trading Strategy using Deep Learning |
Nathan Waltz, Funso Oje, Hongjin Zhang |
Finding the Optimal Cyptocurrency Trading Strategy using Deep Learning
Description
We are training a deep learning model to find the optimal trading strategy on the BTC dataset 1. An important assumption that we are making is that there are no fees.
How to run
Conda
Make sure that you have Anaconda or Miniconda installed, or something similar.
$ git clone https://github.com/TorNATO-PRO/crypto-forecasting.git
$ cd crypto-forecasting
$ conda env create -f environment.yml
$ conda activate crypto
(crypto) $ python main.py
Docker
Ensure that Docker is installed.
$ git clone https://github.com/TorNATO-PRO/crypto-forecasting.git
$ cd crypto-forecasting
$ docker build -it .
$ docker run crypto
TODO
- Create basic directory structure
- Implement Baseline
- Implement Oracle
- Added NNI for hyperparameter tuning
- Create custom model(s) for data
- Develop loss function(s) for comparing performance
- Figure out a way to measure power consumption and execution time
- Train on all datasets
- Train on more datasets
Project Structure
.
├── LICENSE
├── README.md
├── assets
│ └── datasets
│ ├── BTC-USD.csv
│ └── ETH-USD.csv
├── environment.yml
├── main.py
├── requirements.txt
└── src
├── __init__.py
├── data_loader
│ ├── __init__.py
│ └── load.py
├── models
│ ├── __init__.py
│ ├── baseline
│ │ └── baseline.py
│ ├── custom
│ │ └── custom.py
│ ├── loss.py
│ ├── oracle
│ │ ├── data
│ │ │ └── oracle_best.pth
│ │ └── oracle.py
│ └── utils.py
└── tuning
├── __init__.py
├── custom_hyp_conf.yml
├── custom_trial.py
├── oracle_hyp_conf.yml
├── oracle_trial.py
├── search_space_custom.json
└── search_space_oracle.json