Machine learning for algo trading PDF
Preview Machine learning for algo trading
Machine learning for algo trading An introduction for non- mathematicians Dr. Aly Kassam Quantitative Support Services Overview • High level introduction to machine learning • A machine learning bestiary • What has all this got to do with trading? • Some pitfalls to look out for • What resources exist? • What next? Quantitative Support Services ML – What is it?? “ Machine learning is a scientific discipline that deals with the construction and study of algorithms that can learn from data.[1] Such algorithms operate by building a model based on inputs[2]:2 and using that to make predictions or decisions, rather than following only explicitly programmed ” instructions. Source: Wikipedia Quantitative Support Services A ML bestiary Decision trees Naïve Bayes Bayesian Nets Neural Networks K-means clustering Genetic algorithms Logistic regression K-nearest neighbours Associated rule learning Hidden Markov Models Support Vector Machines Principal component analysis Quantitative Support Services A ML bestiary Supervised learning: • K-nearest neighbours • Decision trees • Logistic regression Unsupervised learning: • Support Vector • K-means clustering Machines • Hidden Markov Models • Bayesian networks • Principal component • Neural networks analysis • Associate rule learning Quantitative Support Services Supervised vs unsupervised Supervised learning Unsupervised learning • Start with a labelled • No labelling on the “training” data set data • Used for producing • Used for producing predictive models descriptive models • Examples are: • Examples are: – Classification – Clustering – Regression – Association learning Quantitative Support Services Supervised learning Quantitative Support Services Support Vector Machines • Used to separate data into different classes • It’s an example of a linear separator • Works in multiple dimensions • Kernels can be used Source: www.stackoverflow.com to add nonlinearity Quantitative Support Services What is the support vector? • The idea behind SVM is to find the hyperplane that results in the greatest margin between the support vectors. Quantitative Support Services Decision trees • Classify data based on a sequence of Viagra? questions • Classification trees Spam: 20 – Separate data into Lottery? Ham: 5 distinct classes • Regression trees – Make real number Spam: 20 Spam: 10 predictions Ham: 40 Ham: 5 • Usually trees are combined into Source: “Machine Learning”, Peter Flach, CUP ensemble models Quantitative Support Services
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