Difference between revisions of "Machine Learning Algorithms for Trading"

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*How to implement linear regression
 
*How to implement linear regression
  
==Lesson 3: Regression learning & KNN==
+
==Lesson 3: K Nearest Neighbor (KNN)==
  
 
==Lesson 2: Q-Learning and Dyna==
 
==Lesson 2: Q-Learning and Dyna==

Revision as of 13:52, 14 July 2015

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

  • Overview of use and backtesting
    • Out of sample
    • Roll forward cross validation
  • ML methods we will use in this class
    • Linear regression
    • KNN regression
    • Random Forest regression (considering to drop)
    • Q-Learning
  • Supervised ML (intent is that the treatment here is light)
    • Use: Regression
    • Use: Classification
    • Model type: Parametric
    • Model type: Instance-based
  • 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]

  • Definition of the problem 1
    • 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: K Nearest Neighbor (KNN)

Lesson 2: Q-Learning and Dyna

  • Long/short
  • Overview of use and backtesting
    • Out of sample
    • Roll forward cross validation
  • Supervised ML (intent is that the treatment here is light)
    • Use: Regression
    • Use: Classification
    • Model type: Parametric
    • Model type: Instance-based
  • Reinforcement Learning (light)
    • 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 two years (2013 2014)
    • It will be "easy" data that has obvious patterns
    • You will create trades.txt and run them through your backtester
  • Overview: LinReg, KNN, Decision Trees, Q-Learning

Lesson 3: Time series prediction as an ML problem

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

Lesson 4: Learner APIs

Lesson 5: Linear regression

Lesson 6: KNN

Lesson 7: Assessing a learning algorithm

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

Lesson 8: Overfitting

Lesson 9: Decision trees

Lesson 10: Ensemble learners & bagging

Lesson 11: Random trees & forests