Difference between revisions of "Learning Algorithms for Trading"
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| Line 3: | Line 3: | ||
**Out of sample | **Out of sample | ||
**Roll forward cross validation | **Roll forward cross validation | ||
| − | *Supervised ML | + | *Supervised ML (intent is that the treatment here is light) |
**Use: Regression | **Use: Regression | ||
**Use: Classification | **Use: Classification | ||
**Model type: Parametric | **Model type: Parametric | ||
**Model type: Instance-based | **Model type: Instance-based | ||
| − | *Reinforcement Learning | + | *Reinforcement Learning (light) |
**Use: Find a policy | **Use: Find a policy | ||
*Introduce the problem we will focus on in the rest of the class, namely: | *Introduce the problem we will focus on in the rest of the class, namely: | ||
**Example data, will learn on over a particular year (2012) | **Example data, will learn on over a particular year (2012) | ||
| − | **Will test on over the next | + | **Will test on over the next two years (2013 2014) |
**It will be "easy" data that has obvious patterns | **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 | *Overview: LinReg, KNN, Decision Trees, Q-Learning | ||
Revision as of 12:25, 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 (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 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
