🐊 GatorAI

Quant Lab

A structured pipeline for market data acquisition, cleaning, feature engineering, backtesting strategies, and portfolio optimization with an interactive Streamlit dashboard.

Focus: SPY, QQQ, and IWM

---

📋 Table of Contents

• Project Structure
• Quickstart
• Targets and Tickers
• Testing
• Tech Stack
• Project Goals
• Development Roadmap
• Team
• Contributions
• License
• Contact

---

🧱 Project Structure

GatorAI/
├── data/                 # Raw and processed datasets, test samples
├── src/                  # Core Python modules
│   ├── data/            # Data acquisition and processing
│   ├── backtesting/     # Backtesting engine
│   ├── optimization/    # Portfolio optimization
│   └── dashboard/       # Streamlit application
├── notebooks/           # Jupyter notebooks for exploration
├── tests/               # Unit and integration tests
├── docs/                # Documentation and guides
└── scripts/             # Utility scripts

---

⚙️ Quickstart

1. Set up environment

# Create virtual environment
python -m venv venv

# Activate (Unix/macOS)
source venv/bin/activate

# Activate (Windows)
venv\Scripts\activate

2. Install dependencies

pip install -r requirements.txt

3. Generate sample data (optional)

python scripts/generate_sample_data.py

4. Launch dashboard

streamlit run src/dashboard/app.py

---

🎯 Targets and Tickers

Initial Focus: SPY, QQQ, IWM

Extend ticker universe via:

• src/data/utils.py — Configuration utilities
• src/data/fetch_data.py — Data acquisition functions

---

🧪 Testing

Run all tests:

pytest -q

---

🧩 Tech Stack

Languages: Python

Libraries: pandas, numpy, matplotlib, yfinance, PyPortfolioOpt, scikit-learn, streamlit, plotly

Tools: Git, Jupyter, Streamlit, pytest

---

📈 Project Goals

• Build a modular, reproducible research environment for quantitative portfolio optimization
• Implement AI-assisted risk and return modeling
• Develop an interactive dashboard for real-time portfolio analysis and backtesting
• Deliver a polished, production-ready prototype by end of semester

---

🗓️ Development Roadmap

Phase	Weeks	Focus
Phase 1	1–3	Data collection, cleaning, and pipeline setup
Phase 2	4–6	Backtesting engine and performance metrics
Phase 3	7–8	Portfolio optimization and AI modeling
Phase 4	9–10	Dashboard integration, testing, and documentation

---

🧠 Team

University of Florida — Fall 2025

• Krish Shah — Team Lead / Integration & Architecture
• Neerav Gandhi
• Sparsh Mogha
• Son Tran
• Navaj Sivkumar
• Mahdi Haque
• Muhammad Ismael
• Sidhharth Radhakrishnan
• Pratik Patil

---

🤝 Contributions

We follow a simple Git workflow:

1. Create a new branch for your feature
2. Commit with clear, descriptive messages
3. Open a Pull Request
4. Merge after review and testing

---

📜 License

This project is licensed under the MIT License. See LICENSE for details.

---

📫 Contact

Team Lead: Krish Shah
Institution: University of Florida
Semester: Fall 2025