Stock Market Analysis

Project source can be downloaded from https://github.com/SBZed/Stock-Market-Analysis.git

Author & Contributor List

Saurabh Zinjad

All other known bugs and fixes can be sent to "zinjadsaurabh1997@gmail.com" with the subject "stock market analysis Suggestion". Reported bugs/fixes will be submitted to correction.

Introduction:

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 ago when 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 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.

Topics:

1. Data Analysis using Pandas

2. Data Visualization and Technical Analysis

3. Fundamental analysis using Regression

4. Trade Call Prediction using Classification

5. Modern Portfolio Theory

6. Clustering for Diversification analysis

Prerequisites:

1. Python

If you are new to Python,This YouTube series is best for you.

2. Anaconda(Optional)

• We are using Anaconda, links for installation:
  • For windows
  • For Ubuntu
  • For Mac

3. Jupyter Notebook:

• Jupyter notebook is a very popular and flexible development environment which lets us write and execute python code, display the output and any kind of visualization or plot, etc. in the same document. however, it is always advisable to install Python with Anaconda environment on your system as well.
• Link for Introduction to Jupyter notebook:
  • Quick Beginner Guide
  • Introduction to Jupyter notebook
  • Jupyter Notebook Users Manual
  • Markdown for Jupyter notebooks cheatsheet

4. Financial Markets:

• In the coming modules, we will be doing various tasks to analyze and make predictions on allotted selected company stock. You may need to learn about the underlying markets.

  • Basics of Financial Markets:
    The very first step in solving a problem is understanding the problem. To tackle the questions and problem statements that await us, it's suggested that you first go through above link (it's a PDF "Basics of Financial Markets" documentation) and get introduced to the basic concepts that we'd coming across in the following modules to come.

  • Introduction to Stock Markets:
    Now read and understand Chapter 6,7 and 8 from this link.

  • The Stock Market Documentary:
    If you Don't know what is stock markets, how it works and want to know about it for fun. Then check out this documentary.

Steps to Follow Before Start:

1. Select an Industry: Go to the All Stock Data directory.
2. In this directory, you'll be able to browse through the different industries. You can choose an industry which is related to your discipline or any industry of your liking.
3. On doing so, download a CSV file of stock data of a company belonging to the industry you chose. I have chosen Infosys Stock from Large-cap.
4. To know more about this large-cap, mid-cap and small-cap data go to Readme_all_stock_data.md file.

Modules Structure:

.
├── ...
├── Module			# Each Module/Topic/Chapter
│   ├── Questions           # Queries/Questions related to chapter 
|    |   ├── .ipynb         # Questions Jupyter notebook
│   ├── Solutions           # Solved Queries - Answers to all Questions
|    |   ├── .ipynb         # Questions Jupyter notebook
|    |   ├── .csv           # CSV file / Dataset for stocks
|   ├── images              # Images used in Jupyter notebook, Just for seek of representation
│   └── Resource Matrial    # Different reference matrial(txt, doc, pdf)
└── ...