View source for 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
*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: 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==