In this tutorial, we will be discussing how to decompose stocks time-series into different components in order to have a good idea about the complexities of the time-series models and how to accurately capture them and account for them in our model.
Category: DS Concepts
PAM Clustering using R
Hi All!
Today, we will be learning how to perform PAM Clustering using R to achieve customer segmentation. This case-study comes under unsupervised machine learning (PAM or Partition Around Medoids Clustering).
Introduction to NLTK
In this article we will learn about Natural Language Toolkit – “NLTK”. NLTK is one of the key libraries which is widely used for Natural Language Processing in Python. NLTK can be used in a vareity of ways improving your way of looking at text.
Candlestick charts in Python: FA14
In this tutorial, we will be learning how to create candlestick charts along with volume bars and moving average lines in Python.
K-Means Clustering using R
Today, we will be learning how to perform K-Means Clustering using R to achieve customer segmentation. This case-study comes under unsupervised machine learning (K-Means Clustering)
Leverage Effect using VIX and Python: FA13
Leverage Effect using VIX and Python Hi All! Today, in the part 13 of Financial Analytics series, we will be learning about an alternative method of identifying Leverage Effect using VIX and Python. If you’re new to this series, we suggest that you start from here. What is VIX? VIX is also called as […]
Leverage Effect using Python: FA12
In this tutorial, we’ll continue exploring stylized fact and will go through Stylized fact 5: understanding leverage effect using Python.
Auto-correlation in Squared Returns: FA11
In this tutorial, we’ll continue exploring stylized fact and will go through Stylized fact 4: Decreasing auto-correlation trend in squared/absolute returns.
Auto-correlation in Log Returns: FA10
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.