VR Quantfolio

Interactive Quantitative Finance Tutorials

Welcome

This site hosts interactive tutorials on quantitative finance with Python.

Available Tutorials

ARIMA Time Series Forecasting

Learn how to forecast stock prices using the ARIMA model:

  • Stationarity testing with the ADF test
  • Differencing transformations
  • Walk-forward validation
  • Cumulative sum reversal
  • Error metrics (MSE, SMAPE)

Interactive App

Want to try these concepts hands-on? Check out the Streamlit app:

Open in Streamlit

Features include:

  • Stock data fetching and visualization
  • AutoML model training with PyCaret
  • ARIMA and NeuralProphet forecasting
  • Portfolio optimization

What You’ll Learn

Topic Description
Time Series Analysis Stationarity, differencing, autocorrelation
ARIMA Modeling AR, I, and MA components explained
Machine Learning AutoML for stock prediction
Portfolio Theory Mean-variance optimization, efficient frontier

Technologies Used

  • Python: pandas, numpy, scipy
  • Visualization: Plotly, matplotlib
  • ML/AI: PyCaret, statsmodels, NeuralProphet
  • Portfolio: Riskfolio-Lib
  • Web: Streamlit

Source Code

All code is available on GitHub:

GitHub

Made by Vedanth Ramanathan

Documentation · GitHub

Back to top