Python for Finance

This Mean-Reversion Python Algorithm was created and developed as part of my independent study with the department chair of Finance at the University of Texas at San Antonio, Dr. Bhanot.

To complete this financial algorithm, I researched computational finance theory and practical applications by reading and applying the tools learned using the book, “Python for Finance” by Yves Hilpisch.

After thorough research, analysis, programming, trial & error, I developed a mean-reversion algorithm in Python, deployed in the Quantopian Virtual trading environment utilizing real-time data and backtested using historical time-series data. The algorithm generated 28.5% returns over the preceding 12-month period and beat the S&P 500 by 20.91% during this period.

The Mean Reversion financial algorithm in Python included:

  • Simple Moving Average (SMA)
  • Portfolio Management
  • Algorithmic Trading
  • Leverage
  • Backtesting
  • Risk Mitigation
  • Lookback Period
  • Alpha Generating
  • Computational Finance
  • Time-Series Data
  • Data Integration
  • Rebalancing

Below is the completed Mean-Reversion Algorithm final report, Python script & GitHub repository:

Final Report  File Document Doc Icon  |  Python Script  Python Logo Brand Icon  |  GitHub  Github Social Media Icon

If you have any questions and would like to learn more about this or any project, are interested in networking, or ready to discuss why I should be your next financial analyst, please don’t hesitate to reach out directly through the contact form, by email at codyhspicer@gmail.com, by phone at (210) 272-7534, or let’s connect through LinkedIntoday. I look forward to hearing from you!

Kind regards,