Stock-Market-Analysis-Model

This is a guided project under Career Launcher's Machine Learning Internship that I worked upon. Kindly refer the modules as reference.

Table of contents

• Introduction
• Technologies
• Module 1- Anaylyzing stock using Python
• Module 2- Data visualization and Technical Analysis
• Module 3- Fundamental analysis using Regression
• Module 4-Trade Call Prediction using Classification
• Module 5- Modern Portfolio Theory
• Module 6- Clustering for Diversification analysis

Introduction

The aim of the guided project is to use data analysis and machine learning in python to solve various problem statements. There are various datasets used for the analysis. With a combination of mid, small and large companies. Each Module uses these datasets which are stored in the data folder. The outcome of this guided project is to analyze stock datasets using python, data visualization, analysis using regression, making trade calls using classification, clustering for diversification analysis and coming up with Modern Portfolio

Technologies

• Jupyter Notebook- 6.0.2
• Python- 3.7.1

Module 1- Anaylyzing stock using Python

In Module 1, you are going to get familiar with pandas, the python module which is used is to process and analyze the data. Processing could include removing unknown values from the data or replacing unknown values with values that makes sense, maybe 0. Analysing the data may include finding out the stock price, e.g how the stock prices change with Nifty 50 basket of stocks.

Module 2- Data visualization and Technical Analysis

'A picture speaks a thousand words' has never been truer in financial markets. Absolutely no one goes through the millions of rows of numbers, we always prefer the data in a plotted form to draw better inferences. This module would cover the plotting, basic technical indicators and our own customisation, and making our own trade calls! You should target to finish module 2

Module 3- Fundamental analysis using Regression

This module would introduce us to the Regression related inferences to be drawn from the data. Regression is basically a statistical approach to find the relationship between variables. In machine learning, this is used to predict the outcome of an event based on the relationship between variables obtained from the data-set. More often than not, we utilize linear regression to come up with an ideal inference. We'd be using the regression model to solve the problem statements

Module 4-Trade Call Prediction using Classification

Trade Call Prediction using Classification In this module, we'd be covering the concept of classification and utilize our skills.

Module 5- Modern Portfolio Theory

In this module, We’ll be looking at investment portfolio optimization with python, the fundamental concept of diversification and the creation of an efficient frontier that can be used by investors to choose specific mixes of assets based oninvestment goals; that is, the trade off between their desired level of portfolio return vs their desired level of portfolio risk.

Modern Portfolio Theory (https://www.investopedia.com/terms/m/modernportfoliotheory.asp) suggests that it is possible toconstruct an "efficient frontier" of optimal portfolios, offering the maximum possible expected return for a given level of risk.It suggests that it is not enough to look at the expected risk and return of one particular stock. By investing in more than onestock, an investor can reap the benefits of diversification, particularly a reduction in the riskiness of the portfolio. MPTquantifies the benefits of diversification, also known as not putting all of your eggs in one basket.

Module 6- Clustering for Diversification analysis

Clustering is a method of unsupervised learning and is a common technique for statistical data analysis used in many fields.

Clustering is a Machine Learning technique that involves the grouping of data points. Given a set of data points, we can usea clustering algorithm to classify each data point into a specific group. In theory, data points that are in the same groupshould have similar properties and/or features, while data points in different groups should have highly dissimilar properties and/or features.

In financial Markets, Cluster analysis is a technique used to group sets of objects that share similar characteristics. It iscommon in statistics, but investors will use the approach to build a diversified portfolio. Stocks that exhibit high correlationsin returns fall into one basket, those slightly less correlated in another, and so on, until each stock is placed into a category.