We implement a ML-based trading strategy using M2, CPI, and P/E ratio data to forecast equity performance.
├── LICENSE
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── external <- Data from third party sources.
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks.
│
├── 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
Project based on the cookiecutter data science project template. #cookiecutterdatascience