-
Clone the repository:
git clone https://github.com/jerrychen04/millennium-data-quality.git cd millennium-data-quality
-
Create a conda environment: If you do not have conda installed, install it here. Make sure you cd into the root of this repository before running this cmd:
conda env create -f environment.yml
-
Activate the conda virtual environment:
Using conda (make sure to activate everytime you create a new shell instance):
conda activate data-quality
To run the main script, execute the following command:
python backtester/main.py
To run a .ipynb, like bab.ipynb
, simply select the data-quality kernel and run the scripts. If not visible, select under > Select Another Kernel > Python Environments. [IMPORTANT] When running on cached data, make sure date ranges align with your order generator.
To run the unit tests, use the following command. Note: tests are not freshly maintained at the moment:
python -m unittest discover -s unit_tests
Go to order_generator.py
and create a new instance of OrderGenerator
in a new file for cleanliness. See example code in main.py to run strategy. Write corresponding unit tests as needed. Run research runs in .ipynb and restart kernel when making package changes.