[Machine Learning for Trading] {ud501} Lesson 21: 03-01 How

发布时间：2020-12-14 00:49:22 所属栏目：Linux 来源：网络整理

导读：a data-centric way to build predictive models ? ? ? The ML problem ? ? Supervised regression learning? ? ? ? Robot navigation example? ? ? ? ? ? ?How it works with stock data ? ? ? ? ? ?Example at a fintech company ? ? ? ? Price forecastin

a data-centric way to build predictive models

The ML problem

Supervised regression learning?

Robot navigation example?

?How it works with stock data

?Example at a fintech company

Price forecasting demo?

QuantDesk

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

?<=?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?

?ML tool in use

orange line => historical value of our portfolio

blue => benchmark (S&P500 here)

?Problems with regression

Problem we will focus on?

Parametric regression?

?K nearest neighbor

?How to predict

?Kernel regression

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

?Parametric vs non parametric

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

typically:?train on older data; test on newer data

look ahead bias occurs if training reversely

Learning APIs?

?Example for linear regression

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

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