The purpose of the project is to use tools like python and scipy to detect technical patterns in the forex and stock market. It is important to note that the above example is not limited to any asset market, as long as it is in the OHLCV format.
To get started, make sure you have all the dependencies mentioned in the notebook. Read the corresponding documentation of each library to learn how to install them and how they work.
In notebook#1 (Technical Patterns) running the test gives us our first dataset in the CSV format. This dataset will be used in notebook#2 (ML Technical Patterns) for training a classifier.
- Jupyter Notebook - The web framework used
- Scipy - Classifier deployment
- Pandas - Used to generate and manipulate Data
- Robin Dhillon - Robinbeatrix
This project is licensed under the MIT License - see the LICENSE.md file for details
- Hat tip to ANDREW W. LO, HARRY MAMAYSKY, AND JIANG WANG for their amazing contributiuons in Journal of Finance
- Special thanks to Sam Chakerian (https://medium.com/@samchaaa)
- And a huge shoutout to the community on www.quantopian.com