[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 return values, i.e. X-axis is +/- change (%), and Y-axis is the number of occurrences.
We are considering two general properties indicated by the histogram for each stock: return and volatility (or risk).

Plot two histograms together

Scatterplots

Fitting a line to data points

Slope does not equal correlation

Correlation vs slope

Scatterplots in python

Real world use of kurtosis

In early 2000s investment banks built bonds based on mortgages（ morgage: 抵押） => assume these mortgages was normally distributed

=> on that basis, they were able to show that these bonds had low probability of fault => 2 mistakes

=> (1) return of each mortagage was independent

=> (2) using gassian distrubution discribing the return

(1) and (2) were proved to be wrong => precipitated the great recession of 2008

原文地址：https://www.cnblogs.com/ecoflex/p/10972290.html

时间： 2024-07-30 09:30:32

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

[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