Difference between revisions of "Learning Algorithms for Trading"
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| Line 1: | Line 1: | ||
| − | ==Supervised machine learning== | + | ==Lesson 1: Supervised machine learning== |
*Regression | *Regression | ||
*Classification | *Classification | ||
| Line 6: | Line 6: | ||
*Overview: LinReg, KNN, Decision Trees, ANN | *Overview: LinReg, KNN, Decision Trees, ANN | ||
| − | ==How ML fits into the computing at a hedge fund== | + | ==Lesson 2: How ML fits into the computing at a hedge fund== |
*Long/short | *Long/short | ||
| − | ==Time series prediction as an ML problem== | + | ==Lesson 3: Time series prediction as an ML problem== |
[note: need to create fake stock data that has embedded patterns] | [note: need to create fake stock data that has embedded patterns] | ||
| − | ==Learner APIs== | + | ==Lesson 4: Learner APIs== |
| − | ==Linear regression== | + | ==Lesson 5: Linear regression== |
| − | ==KNN== | + | ==Lesson 6: KNN== |
| − | ==Assessing a learning algorithm== | + | ==Lesson 7: Assessing a learning algorithm== |
*Now that we have two, (linreg & KNN), let's compare them | *Now that we have two, (linreg & KNN), let's compare them | ||
*RMS error | *RMS error | ||
| Line 24: | Line 24: | ||
*Corrcoef | *Corrcoef | ||
| − | ==Overfitting== | + | ==Lesson 8: Overfitting== |
| − | ==Decision trees== | + | ==Lesson 9: Decision trees== |
| − | ==Ensemble learners & bagging== | + | ==Lesson 10: Ensemble learners & bagging== |
| − | ==Random trees & forests== | + | ==Lesson 11: Random trees & forests== |
Revision as of 00:10, 4 March 2015
Contents
- 1 Lesson 1: Supervised machine learning
- 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: Supervised machine learning
- Regression
- Classification
- Parametric
- Instance-based
- Overview: LinReg, KNN, Decision Trees, ANN
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
