Machine Learning Algorithms for Trading

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

• introduce problem early
• Overview of use and backtesting
  • Out of sample
  • Roll forward cross validation
• Methods
  • Linear regression
  • KNN regression
  • Decision trees Random Forest regression (considering to drop)
• Quiz: which algorithm makes most sense here?
• Supervised ML (intent is that the treatment here is light)
  • Use: Regression
  • Use: Classification
  • Model type: Parametric
  • Model type: Instance-based
• Quiz: What's the next point?
• Problems with regression for finance
  • Hint at reinforcement learning
• 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 two years (2013 2014)
  • It will be "easy" data that has obvious patterns
  • You will create trades.txt and run them through your backtester

Lesson 2: Regression

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

• Overview of how it fits into overall trading process
• Definition of the problem 1
  • Black box diagram
  • training: Xtrain, Ytrain
  • using: Query with X
• Definition of the problem 2: APIs
  • constructor
  • addEvidence(X,Y)
  • query(X)
• How to implement linear regression

Lesson 3: Assessing a learning algorithm

• Now that we have two, (linreg & KNN), let's compare them
  • Pros and cons of LinReg versus KNN
    • LinReg can extrapolate
    • Kernel
    • Piecewise
  • ease of adding new data
• Cross validation,
• roll forward cross validation
  • Use all data versus most recent data
  • Online learning
• How long to take to learn versus query
• Batch versus online
• RMS error
• Scatterplot predict vs actual
• Corrcoef
• Overfitting

Lesson 4: Ensemble learners, bagging and boosting

Mention that this could mean different algorithms.

Bagging is an easy way to do this.

Boosting

perhaps include decision trees.

Lesson 5: Reinforcement Learning

• Classic view of the problem (from Kaelbling, Littman, Moore)
• Model-based
• Model-free