[Machine Learning for Trading] {ud501} Lesson 25: 03-05 Reinforcement learning | Lesson 26: 03-06 Q-Learning | Lesson 27: 03-07 Dyna

原文地址：https://www.cnblogs.com/ecoflex/p/10977470.html

时间： 2024-11-05 16:36:04

[Machine Learning for Trading] {ud501} Lesson 25: 03-05 Reinforcement learning | Lesson 26: 03-06 Q-Learning | Lesson 27: 03-07 Dyna的相关文章

[Machine Learning for Trading] {ud501} Lesson 3: 01-02 Working with multiple stocks

Lesson outline Lesson outline Here's an overview of what you'll learn to do in this lesson. Documentation links are for reference. Read in multiple stocks: Create an empty pandas.DataFrame with dates as index: pandas.date_range Drop missing date rows

[Machine Learning for Trading] {ud501} Lesson 19: 02-09 The Fundamental Law of active portfolio management | Lesson 20: 02-10 Portfolio optimization and the efficient frontier

this lesson => Buffet said two things => (1) investor skill => (2) breadth / the number of investments Grinold's Fundamental Law breadth => more opportunities to applying that skill => eg. how many stocks you invest in IC => information

[Machine Learning for Trading] {ud501} Lesson 7: 01-06 Histograms and scatter plots

A closer look at daily returns Histogram of daily returns gaussian => kurtosis = 0 How to plot a histogram Computing histogram statistics Select the option that best describes the relationship between XYZ and SPY. Note: These are histograms of daily

[Machine Learning for Trading] {ud501} Lesson 9: 01-08 Optimizers: Building a parameterized model | Lesson 10: 01-09 Optimizers: How to optimize a portfolio

What is an optimizer? Minimization example How to defeat a minimizer Convex problems Building a parameterized model Minimizer finds coefficients What is portfolio optimization? The difference optimization can make Which criteria is easiest to solve f

[Machine Learning for Trading] {ud501} Lesson 21: 03-01 How Machine Learning is used at a hedge fund | Lesson 22: 03-02 Regression

原文地址:https://www.cnblogs.com/ecoflex/p/10977432.html

[Machine Learning for Trading] {ud501} Lesson 23: 03-03 Assessing a learning algorithm | Lesson 24: 03-04 Ensemble learners, bagging and boosting

A closer look at KNN solutions What happens as K varies What happens as D varies Metric 1 RMS Error In Sample vs out of sample Which is worse? Cross validation 5-fold cross validation Roll forward cross validation Metric 2: correlation Correlation an

[Machine Learning for Trading] {ud501} Lesson 25: 03-05 Reinforcement learning | Lesson 26: 03-06 Q-Learning | Lesson 27: 03-07 Dyna