Machine Learning Algorithms for Trading
Revision as of 11:22, 22 July 2015 by Tucker (talk | contribs) (→Lesson 3: Assessing a learning algorithm)
Contents
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
- 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
perhaps include decision trees
Lesson 6: Reinforcement Learning
- Classic view of the problem (from Kaelbling, Littman, Moore)
- Model-based
- Model-free