VR Quantfolio Intro

Interactive Quantitative Finance Toolkit with Python

Overview

This project provides an interactive web application for exploring quantitative finance concepts, including:

• Stock Data Analysis: Fetch and visualize historical stock data
• AutoML Stock Prediction: Train ML models using PyCaret
• Time Series Forecasting: ARIMA and NeuralProphet implementations
• Portfolio Optimization: Efficient frontier and risk-adjusted returns

Features

Streamlit App

• Data Selection: Choose stocks and date ranges
• Visualization: Interactive Plotly charts
• AutoML Training: One-click model comparison
• ARIMA Forecasting: Walk-forward validation
• Portfolio Optimization: Mean-variance optimization

Jupyter Tutorials

• ARIMA Fundamentals: Step-by-step time series forecasting guide
• Detailed explanations of stationarity, differencing, and cumsum reversal

Installation

Prerequisites

• Python 3.9+
• pip or conda

Setup

# Clone the repository
git clone https://github.com/vedanthr5/vr-quantfolio-intro.git
cd vr-quantfolio-intro

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

Running the App

Streamlit App

streamlit run home.py

Then open http://localhost:8501 in your browser.

Jupyter Tutorials

jupyter notebook tutorials/

📁 Project Structure

vr-quantfolio-intro/
├── home.py                 # Main Streamlit entry point
├── requirements.txt           # Python dependencies
├── _quarto.yml               # Quarto config for GitHub Pages
│
├── .streamlit/
│   └── config.toml           # Streamlit theme configuration
│
├── pages/                     # Streamlit multipage app
│   ├── 1_Data_Selection.py
│   ├── 2_Visualization.py
│   ├── 3_AutoML_Training.py
│   ├── 4_Export_Model.py
│   ├── 5_ARIMA_Prediction.py
│   ├── 6_Portfolio_Optimization.py
│   └── 7_Resources.py
│
├── utils/                     # Shared utilities
│   ├── __init__.py
│   ├── data_fetcher.py       # yfinance data utilities
│   └── styles.py             # CSS and styling
│
├── tutorials/                 # Jupyter notebooks
│   └── arima_fundamentals.ipynb
│
└── docs/                      # GitHub Pages output
    └── index.html

Tutorials

ARIMA Time Series Forecasting

The arima_fundamentals.ipynb notebook covers:

1. Data loading and exploration
2. Stationarity testing (ADF test)
3. Differencing transformation
4. Walk-forward ARIMA training
5. Cumulative sum reversal
6. Error metrics (MSE, SMAPE)

Technologies

Category	Tools
Web App	Streamlit
Data	pandas, yfinance
ML	PyCaret, scikit-learn
Time Series	statsmodels, NeuralProphet
Portfolio	Riskfolio-Lib
Visualization	Plotly, matplotlib
Documentation	Quarto, GitHub Pages

Deployment

Streamlit Cloud

1. Push to GitHub
2. Connect repo to Streamlit Cloud
3. Set main file to home.py

GitHub Pages (Quarto)

# Install Quarto
# https://quarto.org/docs/get-started/

# Render notebooks to HTML
quarto render

# Push docs/ folder to GitHub
git add docs/
git commit -m "Update GitHub Pages"
git push

Contributing

Contributions welcome! Please open an issue or PR.

License

MIT License - see LICENSE file.

Author

Vedanth R

• GitHub: @vedanthr5

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