Difference between revisions of "Learning Algorithms for Trading"
Jump to navigation
Jump to search
| Line 1: | Line 1: | ||
==Lesson 1: How Machine Learning is used at a hedge fund== | ==Lesson 1: How Machine Learning is used at a hedge fund== | ||
| + | *Overview of use and backtesting | ||
| + | **Out of sample | ||
| + | **Roll forward cross validation | ||
*Supervised ML | *Supervised ML | ||
**Use: Regression | **Use: Regression | ||
Revision as of 12:14, 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
- Overview of use and backtesting
- Out of sample
- Roll forward cross validation
- 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
