Chaos Theory in Financial Markets

Overview

This project contains a Jupyter Notebook from a guest lecture given by Dr. Nam Le at the University of Southampton, focusing on the application of Chaos Theory in Financial Markets. The lecture covers a range of topics, including Efficient Market Hypothesis (EMH), Fractal Market Hypothesis, Hurst Exponent, and the implementation of various trading strategies in Python using real-world financial data.

Objectives

• Introduce the concept of chaos theory and its implications for financial markets.
• Explore the limitations of the Efficient Market Hypothesis and introduce alternative models like the Fractal Market Hypothesis.
• Demonstrate the calculation and interpretation of the Hurst Exponent as a measure of market predictability.
• Implement and assess the performance of mean-reversion and trend-following strategies on historical stock data.

Contents

• Chaos_Theory_in_Financial_Market.ipynb: The main notebook containing the lecture material, Python code for analysis and trading strategy implementation, and discussion of results.

How to Use

1. Clone this repository to your local machine.
2. Ensure you have Jupyter Notebook or JupyterLab installed. If not, you can install it using Anaconda or directly via pip:

   pip install notebook
   

Prerequisites

To run the notebook in this project, you will need:

• Python 3.x
• Jupyter Notebook
• Key Python libraries including:
  • Pandas for data manipulation and analysis
  • NumPy for numerical computations
  • Matplotlib for data visualization
  • Statsmodels for statistical modeling
  • Other libraries as specified within the notebook

Installation of these libraries can be done via pip. For example:

pip install pandas numpy matplotlib statsmodels

## License
This project is licensed under the MIT License. For more information, please refer to the LICENSE file.

## Acknowledgements
Special thanks to the University of Southampton for hosting the guest lecture.
Gratitude to all the students and faculty members who participated in the lecture and provided insightful feedback and discussions.