Learning Algorithms for Trading

Lesson 1: How Machine Learning is used at a hedge fund

• Overview of use and backtesting
  • Out of sample
  • Roll forward cross validation
• Supervised ML
  • Use: Regression
  • Use: Classification
  • Model type: Parametric
  • Model type: Instance-based
• Reinforcement Learning
  • Use: Find a policy
• Introduce the problem we will focus on in the rest of the class, namely:
  • Example data, will learn on over a particular year (2012)
  • Will test on over the next year (2013)
  • It will be "easy" data that has obvious patterns
  • Will create trades.txt and run them through your backtester
• Overview: LinReg, KNN, Decision Trees, Q-Learning

Lesson 2: Q-Learning and Dyna

• Long/short

Lesson 3: Time series prediction as an ML problem

[note: need to create fake stock data that has embedded patterns]

Lesson 7: Assessing a learning algorithm

• Now that we have two, (linreg & KNN), let's compare them
• RMS error
• Scatterplot predict vs actual
• Corrcoef