This repository contains two projects on quantitative trading, developed as part of an applied finance course. Through these projects, various techniques and tools used in the analysis and prediction of financial markets are explored.
├── LICENSE <- License of the project.
├── README.md <- The top-level README using this project.
└──── Quantitative Trading
└── Project_1_Momentum_Trading.ipynb
└── Project_2_Breakout_Strategy.ipynb
└── Project_2_Breakout_Strategy.html
└── Project_3_Smart_Beta_and_Portfolio_Optimization.ipynb
└── Project_3_Smart_Beta_and_Portfolio_Optimization.html
This project focuses on momentum trading strategies. Using historical data for a selection of stocks, a model is developed to identify momentum signals and generate trades accordingly. The project demonstrates the use of pandas for data manipulation and analysis, and the implementation of a trading algorithm in Python.
This project explores breakout trading strategies. Using a momentum-based approach, the project develops a trading algorithm that buys a stock when it breaks above a certain price threshold and sells when it falls below another threshold. The project demonstrates the use of pandas for data manipulation and analysis, and the implementation of a trading algorithm in Python.
The projects are developed using Python 3.8 and require the following libraries:
- Pandas
- Numpy
- Matplotlib
- Plotly
- Scikit-learn
- Tensorflow
- Seaborn
These libraries can be installed using pip. More details on the requirements for each project can be found in the README file for each project.
In the future, a second folder will be added to this repository containing projects on artificial intelligence for trading. These projects will explore machine learning and deep learning techniques for predicting financial market trends and developing automated trading strategies.
I hope this README is helpful for your Quantitative Trading repository. If you have any questions or need any assistance, please don't hesitate to reach out.
