[Machine Learning for Trading] {ud501} Lesson 21: 03-01 How Machine Learning is used at a hedge fund

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a data-centric way to build predictive models

 

 

 

The ML problem

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Supervised regression learning 

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Robot navigation example 

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 How it works with stock data

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 Example at a fintech company

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Price forecasting demo 

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QuantDesk

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factors we are using now <= choices of these factors are from another genetic algorithm 

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技术图片 <= roll back time, and we look over all this last three months and look forward one month, see how accurate all those predictions were

 

 

https://lucenaresearch.com/#register

https://quantdesk.lucenaresearch.com/#login

 

 

Backtesting 

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 ML tool in use

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orange line => historical value of our portfolio

blue => benchmark (S&P500 here)

 

 

 

 

 

 Problems with regression

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Problem we will focus on 

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Parametric regression 

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 K nearest neighbor

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 How to predict

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 Kernel regression

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Kernel regression is different from KNN, because it uses kernel to weight the contribution of each nearest point

 

 

 

 

 Parametric vs non parametric

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Yes, the cannon ball distance can be best estimated using a parametric model, as it follows a well-defined trajectory.

On the other hand, the behavior of honey bees can be hard to model mathematically. Therefore, a non-parametric approach would be more suitable.

 

 

 

 Training and testing

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typically: train on older data; test on newer data

look ahead bias occurs if training reversely

 

 

 

Learning APIs 

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 Example for linear regression

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 Note: This is intended to be pseudo-code only, although some Python-specific syntax has been shown.

 

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