Optimizing grid bot trading strategy with RL.
For now to run in you can simple do (right it uses the best parameters we were able to find by default)
grid_bot = GridBot()
grid_bot.trade(df_path)
It will contain all the relevant info inside.
To reproduce mentioned results with RL application, use venv (we used python3.9) defined by requirements.txt and run all cels in src/models/grid_bot_rl/test.ipynb
├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── processed <- Market data with features.
│ └── raw <- Market data.
│
├── docs <- A default Sphinx project; see sphinx-doc.org for details
│
├── models <- Regular grid bot
│
├── notebooks <- Jupyter notebooks. (Visualization & metrics)
│
├── 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 market data
│ │ └── make_dataset.py
│ │
│ ├── features <- Scripts to turn raw data into features for modeling
│ │ └── build_features.py
│ │
│ ├── models <- Scripts to train models and then use trained models to make
│ │ │ predictions
│ │ └── grid_bot_rl <- Grid bot with adaptive orders.
│ │ │
│ │ └── test.ipynb <- Notebook with experiment setup
│ │
│ └── visualization <- Scripts to create exploratory and results oriented visualizations
│ └── visualize.py
│
└── tox.ini <- tox file with settings for running tox; see tox.readthedocs.io
Project based on the cookiecutter data science project template. #cookiecutterdatascience