zoukankan      html  css  js  c++  java
  • [Machine Learning for Trading] {ud501} Lesson 21: 03-01 How Machine Learning is used at a hedge fund | Lesson 22: 03-02 Regression

    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.

  • 相关阅读:
    php设计模式-适配器
    遍历Map的4种方法
    遍历数组
    遍历List的方法
    复选框选中
    单选框选中
    正向代理和反向代理
    对于Dubbo的理解
    python远程控制Linux
    python对中文的处理
  • 原文地址:https://www.cnblogs.com/ecoflex/p/10977432.html
Copyright © 2011-2022 走看看