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Ensemble methods for modeling financial data

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Gaurav Chakravorty
Gaurav Chakravorty

We use ensemble methods in creating models on financial data and we find better out of sample performance.

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info@tworoads.co.in Ensemble methods for algorithmic trading
Background ● What is algorithmic trading? ● What is the relevance of machine learning? ● Where does the current topic fit in ?
Trading Traders trade via open outcrying Close to the conventional notion of “trading” Slow and inefficient Manual Algorithmic People like you and I design algorithms to predict like human traders Computer algorithms trade with each other Blazingly fast with high trade volumes
Machine learning How does an algorithm make money?
Let’s make it more interesting !
Linear regression! (Why is the relationship linear ?) (Any more problems ?) Standard ML technique
How about this graph ?
Trees to the rescue ! Decision trees are very popular in classification Can do regression as well ! Simple and efficient Very intuitive
Walk through
Hang on p Phew!
Which brings us to the discussion of the day What is an ensemble method? How is it relevant to finance? Two very common ( but remarkably powerful) ensemble methods
Ensemble Wikipedia says: “In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms” Begin with a weak learner ( Tree in our case ) Train several of them Combine their output ( Bagging and Boosting )
Bagging How do you naturally expand the idea of a tree? ( Hint : think real world )
Random forest ● Training ○ Sample a subset of the input ( Bootstrapping ) ○ Build a regression tree on top of it ○ Repeat till “convergence” ● Prediction ○ Pass the input to each tree in the forest ○ Take a weighted combination
In random forests, the trees are built independently Possibility of redundancy Is there a way to not isolate our training subsets? Potential issues?
Boosting ● Training ○ Sample a subset of the input ○ Build a tree on top of it ○ Obtain an error statistic on the WHOLE input ○ Use this statistic to generate the next input subset Median heavy training instead of mean heavy training
Why use this in finance ? i.i.d assumption goes for a toss Noise filtering is a challenge Sophisticated methods often fail ( and are miserably slow) We need to rely on simple methods and yet guarantee high accuracy
Thanks for coming! Abhijit Sharang abhijit.sharang@tworoads.co.in www.tworoads.co.in info@tworoads.co.in

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Ensemble methods for modeling financial data

  • 2. Background ● What is algorithmic trading? ● What is the relevance of machine learning? ● Where does the current topic fit in ?
  • 3. Trading Traders trade via open outcrying Close to the conventional notion of “trading” Slow and inefficient Manual Algorithmic People like you and I design algorithms to predict like human traders Computer algorithms trade with each other Blazingly fast with high trade volumes
  • 4. Machine learning How does an algorithm make money?
  • 5. Let’s make it more interesting !
  • 6. Linear regression! (Why is the relationship linear ?) (Any more problems ?) Standard ML technique
  • 7. How about this graph ?
  • 8. Trees to the rescue ! Decision trees are very popular in classification Can do regression as well ! Simple and efficient Very intuitive
  • 11. Which brings us to the discussion of the day What is an ensemble method? How is it relevant to finance? Two very common ( but remarkably powerful) ensemble methods
  • 12. Ensemble Wikipedia says: “In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms” Begin with a weak learner ( Tree in our case ) Train several of them Combine their output ( Bagging and Boosting )
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  • 14. Bagging How do you naturally expand the idea of a tree? ( Hint : think real world )
  • 15. Random forest ● Training ○ Sample a subset of the input ( Bootstrapping ) ○ Build a regression tree on top of it ○ Repeat till “convergence” ● Prediction ○ Pass the input to each tree in the forest ○ Take a weighted combination
  • 16.
  • 17. In random forests, the trees are built independently Possibility of redundancy Is there a way to not isolate our training subsets? Potential issues?
  • 18.
  • 19. Boosting ● Training ○ Sample a subset of the input ○ Build a tree on top of it ○ Obtain an error statistic on the WHOLE input ○ Use this statistic to generate the next input subset Median heavy training instead of mean heavy training
  • 20.
  • 21. Why use this in finance ? i.i.d assumption goes for a toss Noise filtering is a challenge Sophisticated methods often fail ( and are miserably slow) We need to rely on simple methods and yet guarantee high accuracy
  • 22. Thanks for coming! Abhijit Sharang abhijit.sharang@tworoads.co.in www.tworoads.co.in info@tworoads.co.in