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 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 […]
In this tutorial, we will learn the ways of considering inflation in a return series and implementing them using Python.
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.
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.
In this part of our financial analytics series, we will be studying Markowitz portfolio theory. Portfolio theory published by Harry Markowitz in 1952.
Boolean Operations in NumPy includes everything that we need to do manipulation on provided data or arrays — it is the foundation of data manipulation in NumPy.
Hi Enthusiastic Learners! In this article we learn about many Universal Functions or Built-In functions of NumPy. Universal Functions plays a very crucial role in getting best performance out of NumPy and if you want to know advantages and effects on performance while using Universal Functions (UFuncs), you should go through our article Why use […]
In this Article we will discuss that Why you SHOULD use Universal Functions or Built-in Functions in NumPy? While answering this question we will show you that how much efficient NumPy built-in functions are over standard loops and other approaches.