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📊 Quant Research Practice – Python Scripts

This repository contains a collection of practice Python scripts for sample scenarios commonly encountered by quantitative researchers. These exercises are designed to help strengthen my problem-solving, data analysis, and modeling skills using Python.

📌 What’s Included

  • 📈 Statistical analysis and signal processing
  • 🧠 Machine learning models for finance
  • 💡 Factor models and alpha signal generation
  • 🧮 Portfolio optimization techniques
  • 🔁 Backtesting and performance evaluation

Each script is standalone and includes comments for clarity and learning.

🧠 Purpose

The goal of this project is to:

  • Practice solving real-world quant problems using Python
  • Build a reference library for interview prep and future work
  • Improve fluency in tools like NumPy, pandas, scikit-learn, and matplotlib

🛠 Technologies Used

  • Python 3.x
  • NumPy / pandas
  • matplotlib / seaborn
  • scikit-learn
  • statsmodels
  • cvxpy (for optimization problems)

🚀 How to Use

  1. Clone the repo:
git clone https://github.com/your-username/quant-python-practice.git
cd quant-python-practice
  1. Create a virtual environment (optional but recommended):
python -m venv venv
source venv/bin/activate  # or venv\\Scripts\\activate on Windows
  1. Install dependencies:
pip install -r requirements.txt
  1. Run any script from the /scripts directory:
python scripts/mean_variance_optimization.py

📂 Folder Structure

/scripts         → Practice scripts grouped by topic
/data            → Sample CSVs or generated datasets
README.md        → Project overview
requirements.txt → Dependencies (optional)

🎯 Disclaimer

These scripts are for educational and practice purposes only. They are not intended for live trading or production use.

Feel free to explore, fork, or contribute ideas to this project. Happy coding and researching! 🧠📉