This is a Internship Data Analysis Project on Financial Markets by Career Launcher.
Investment Bankers . CA's . Hedge Fund / Portfolio Managers . Forex traders . Commodities Analysts. These have been historically considered to be among the most coveted professions of all time. Yet, if one fails to keep up with the demands of the day, one would find one's skills to be obsolete in this era of data analysis. Data Science has inarguably been the hottest domain of the decade, asserting its need in every single sphere of corporate life. It was not long agowhen we discovered the massive potential of incorporating ML/AI in the financial world. Now, the very idea of the two being disjointed sounds strange. Data Science has been incremental in providing powerful insights ( which people didn't even know existed ) and helped massively increase the efficiency, helping everyone from a scalp trader to a long term debt investor. Accurate predictions, unbiased analysis, powerful tools that run through millions of rows of data in the blink of an eye have transformed the industry in ways we could've never imagined. The following program is designed to both test your knowledge and to give you the feel and experience of a real world financial world - data science problem.
- Go through the "Basics of Financial Market" pdf to understand the basic terminologies of stock market.
- Go through the instructions in the respective modules to understand the tasks assigned for each module
- Go through the format notebooks for writing the solutions for the respective modules in the correct format.
- Edit the solution jupyter notebooks and add your code for the queries in the respective modules or uplaod your notebook for that module.
Note: Only .ipynb files are supported. Other modules will be uploaded after I get the solutions for the current modules.
Disclaimer before contributing: Only significant contributions to this project would be accepted.
This is a financial marketing Internship offered by Career Launcher and AICTE to the data science skills of the students. This project has been divded into 6 modules covering differnet methods used in financial analuysis of stocks.
In this project students are given stockes prices of various companies listed in the NSE(National Stock Exchange) divided on the basis of their market caps.
Module 1: Importing data and categorising daily calls according to trends.Module 2: This module covers the plotting, basic technical indicators and our own customisation, and making trade calls.Module 3: Calculating Beta of an asset which is a measure of the sensitivity of stocks returns relative to a market benchmark using machine learning (Linear Regression and OLS).Module 4: Making trade calls of whether to buy/hold/sell stocks by analysing previous data and focusing on the trends the stock is having.Module 5: This module explains 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 on investment goals ; that is, the trade off between their desired level of portfolio return vs their desired level of portfolio risk.Module 6: In this module the cluster analysis is a technique used to group sets of objects that share similar characteristics. It is common in statistics, but investors use the approach to build a diversified portfolio. This is so because, stocks that exhibit high correlations in returns fall into one basket, those slightly less correlated in another, and so on, until each stock is placed into a category.
- Name: Vivek Kumar Ghosh
- University: Uttaranchal University, Dehradun
- E-mail: soapmactevis1@gmail.com
- LinkedIn: https://www.linkedin.com/in/itsvivekghosh
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