Learning Algorithms for Trading
Revision as of 12:10, 14 July 2015 by 75.137.163.19 (talk) (→Lesson 1: Supervised machine learning)
Contents
- 1 Lesson 1: How Machine Learning is used at a hedge fund
- 2 Lesson 2: How ML fits into the computing at a hedge fund
- 3 Lesson 3: Time series prediction as an ML problem
- 4 Lesson 4: Learner APIs
- 5 Lesson 5: Linear regression
- 6 Lesson 6: KNN
- 7 Lesson 7: Assessing a learning algorithm
- 8 Lesson 8: Overfitting
- 9 Lesson 9: Decision trees
- 10 Lesson 10: Ensemble learners & bagging
- 11 Lesson 11: Random trees & forests
Lesson 1: How Machine Learning is used at a hedge fund
- Supervised ML
- Use: Regression
- Use: Classification
- Model type: Parametric
- Model type: Instance-based
- Reinforcement Learning
- Use: Find a policy
- Overview: LinReg, KNN, Decision Trees, Q-Learning
Lesson 2: How ML fits into the computing at a hedge fund
- Long/short
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
