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
Trees to the rescue !
Decision trees are very popular in classification
Can do regression as well !
Simple and efficient
Very intuitive
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 )
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 [email protected]