Ensemble methods of algorithmic trading, it's background and other details. By Abhijit Sharang, presented at Data Science Meetup at InMobi http://technology.inmobi.com/events/data-science-meetup

1. info@tworoads.co.in
Ensemble methods for
algorithmic trading

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

9. Walk through

10. Hang on
p
Phew!

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 )

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

17. In random forests, the trees are built
independently
Possibility of redundancy
Is there a way to not isolate our training
subsets?
Potential issues?

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

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