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: | + | ==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
Contents
- 1 Lesson 1: How Machine Learning is used at a hedge fund
- 2 Lesson 2: Regression
- 3 Lesson 3: K Nearest Neighbor (KNN)
- 4 Lesson 2: Q-Learning and Dyna
- 5 Lesson 3: Time series prediction as an ML problem
- 6 Lesson 4: Learner APIs
- 7 Lesson 5: Linear regression
- 8 Lesson 6: KNN
- 9 Lesson 7: Assessing a learning algorithm
- 10 Lesson 8: Overfitting
- 11 Lesson 9: Decision trees
- 12 Lesson 10: Ensemble learners & bagging
- 13 Lesson 11: Random trees & forests
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