• Education Board

    Co-Chair
      Elizabeth Hawthorne  
      Christine  Stephenson  
    Past Chair
      Jane Prey  
      Mehran  Sahami  
    Member-at-Large
      Diana Burley  
      Michelle  Craig  
      Alison  Derbenwick Miller  
      Paul  Leidig  
      Andrew  McGettrick  
      Briana  Morrison  
      Fay Cobb  Payton  
      Mihaela  Sabin  
      Cara  Tang  
      R.  Venkatesh  
    CSTA, ex officio
      Jake Baskin  
    D&I Council Liaison
      Lisa Smith
  • Education Advisory Committee

    ACM India Representative
      Abhiram Ranade  
      R.  Venkatesh  
    Member
      Thomas Cortina  
      Leigh Ann  Delyser  
      Daryl  Detrick
      Judith  Gal-Ezer  
      Christina  Gardner-Mccune  
      Michael  Goldweber  
      Steven  Gordon  
      David  Joyner  
      Amruth  Kumar  
      Alvaro  Monge  
      Tamara  Pearson  
      Chris  Piech  
      Susan  Reiser  
      Christian  Servin  
      Olivier  St-Cyr  
      Peter  Thiemann  
      Jodi  Tims  
      Cindy  Tucker  
      Ellen  Walker  
      Andrew  Williams  
      Pat  Yongpradit  
      Aimin  Zhu
      Stuart  Zweben  
    CSAB Representative
      Paul Leidig  
    Chair, Committee for Computing Education in Community Colleges
      Cara Tang  
    Headquarters Liaison
      Yan Timanovsky  
    Advisor
      Alison Clear  
      Robert  Schnabel  
    SIGCSE Representative
      Mary Anne Egan  

Learning Path: Hands-On Algorithmic Trading with Python

The pace of automation in the investment management industry has become frenetic in the last decade because of algorithmic trading and machine learning technologies. Industry experts estimate that as much as 75% of the daily trading volume in US equity markets is executed algorithmically, i.e. by computer programs following a set of pre-defined rules. In the 20th century, algorithmic trading was used by sell-side brokers to get the best execution of large trades for their clients. In the 21st century, algorithms are used in the entire trading process, from idea generation to execution and portfolio management. While all algorithmic trading is executed by computers, the rules for generating trades may be designed by humans or discovered by machine learning algorithms from training data.

Discipline in the face of grueling markets is a key success factor in trading and investing. Emotional irrationality, behavioral biases, inability to multitask effectively and slow execution speeds put manual trading by retail investors at a massive disadvantage. Retail investors are aware of these disadvantages and there is considerable interest in algorithmic trading, especially using Python. This learning path is about taking the first step in leveling the playing field for retail equity investors. It provides the concepts, process, and technological tools for developing algorithmic trading strategies. Note that live trading is out of scope for this learning path.

Featured Resource: Learning Path: Hands-On Algorithmic Trading with Python