This is the implementation for our FYP titled "Optimal Order Placement in Cryptocurrency Markets" for the COMP4801 course.
- Kritik Satija
- Raghav Agarwal
- Mohammad Muttasif
Component | Tools Used | Contributor(s) |
---|---|---|
Exchange Simulator | Python | Kritik, Raghav |
Deep Q-Network algorithm | Python | Kritik |
Microprice algorithm | Python | Raghav |
Create virtual environment using pipenv, conda, or any other virtual environment creator. The example below is using conda:
conda create -n crypto-market-simulator python=3.x
conda activate crypto-market-simulator
pip install -r requirements.txt
Run the file market-simulator.py
in the market-simulator directory to start the simulator
python market-simulator.py
In the reinforcement-learning directory, run the reinforcement-learning.ipynb file using Jupyter Notebook. Run all the cells
In the microprice directory, run the microprice.ipynb file using Jupyter Notebook. Run all the cells
In the data-analytics directory, run the data_collection.ipynb file using Jupyter Notebook. Run all the cells. This will generate the required csv files for analytics.ipynb. For convenience, a csv file has been provided for analysis