In this tutorial, we’ll continue exploring stylized fact and will go through Stylized fact 3: Is auto-correlation absent in returns? and will see if there is decreasing auto-correlation trend in squared/absolute returns using Python.

# Category: DS Languages

## Volatility Check in Returns charts: FA9

Volatility Check in Returns charts Hi All! In our previous tutorial, we had introduced stylized facts, what the five stylized facts are and had also covered stylized fact 1 – Distribution of returns – Is it non-Gaussian? In this tutorial, we will be covering stylized fact 2 – “Are Volatility clusters formed in returns […]

## Exploring Log Returns Distributions: FA8

Exploring Log Returns Distributions Hi All! In our previous tutorial, we learnt how to consider inflation rate in the return series of a stock and obtaining the adjusted return series. In this tutorial, we will start exploring stylized facts of asset returns from technical and analytical point of view and exploring log returns distributions […]

## Adjusted Returns including Inflation: FA7

In this tutorial, we will learn the ways of considering inflation in a return series and implementing them using Python.

## Different Financial Data Sources: FA6

In this post, we will learn about other well-known sources from where we can get financial data and how to use them to fetch data in Python.

## partition() – Partial Sort in NumPy

partition() – Partial Sort in NumPy. Sorting arrays partisally, getting smallest values till given index in unordered way. Partition an array.

## Markowitz Theory using Python: FA5

In this part 5 of our Financial Analytics series, we will learn how we can apply this theory to obtain Efficient Frontier and we will also learn how we can implement this in Python.

## lexsort() – Indirect Sort in NumPy

lexsort() – indirect sort in NumPy while sorting values of one array it used values or keys of another array for tie-breaking if same values are encountered.

## Sort Arrays in NumPy

To sort arrays in NumPy, it has provided us many in built functions for doing that. Advantage to sort arrays in NumPy is huge.