Difference between revisions of "Learning Algorithms for Trading"
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*Overview: LinReg, KNN, Decision Trees, Q-Learning | *Overview: LinReg, KNN, Decision Trees, Q-Learning | ||
| − | ==Lesson 2: | + | ==Lesson 2: Q-Learning and Dyna== |
*Long/short | *Long/short | ||
Revision as of 12:12, 14 July 2015
Contents
- 1 Lesson 1: How Machine Learning is used at a hedge fund
- 2 Lesson 2: Q-Learning and Dyna
- 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: Q-Learning and Dyna
- 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
