Stock-Market-Analysis

Objectives

• Creating a model to predict next 3 day stock prices using historical data.
• Sentiment analysis on the news/twitter related to a particular stock.
• Comparing two or more companies based on their industry.
• Search engine to make use of information retrieval techniques for searching.
• Making a web application to produce comprehensive reports and compile the findings.

Work Done

• Prediction Module
  • The model takes closing price and volume traded of all four currencies for 60 time periods and suggests if we should buy or sell LITECOIN, 3 time periods into the future.
  • Final model which will take in 5 years of stock data and twitter sentiments as input giving future prices/suggestions on buying or selling for the stock.
• Sentiment Analysis Module
  • First step was to build a model to check for polarity of a single tweet
  • Using the twitter feed of the stock as input
  • The feed is the processed by a classifier (glob) and its polarity is decided
  • The the percentage of positive negative or neutral tweets is plotted in the form of a bar graph.
  • Integrating news
  • Detailed quantitative sentiment analysis (Eg - Innovation for Tech)
• Visualisation Module
  • Create a portal for the investors where they can find analytics, news, about the company.
  • Display a chart showing the time series plot of close price of the company.
  • Show parameters like Market Cap, Book value, sales growth and other detail specific to company.
  • Display fundamental analysis of the company which includes Balance Sheets, P&L balances, Cash Flows of the company.
  • Show recent news / Announcement made by the company
• Comparison Module
  • comparison between two or more stocks based on stock price - visualisation done
  • comparison between two or more stocks based on returns and growth rate
  • Comparison based on capital asset pricing model
  • Comparison dependent on visualisation module
• Search Engine Module
  • The prototype takes query from user and gives it to the IR system.
  • The IR system evaluates the query and output the top results from the database.
  • The key point to note here is that this is not a simple query-result case of RDBMS system but here we have unstructured data and based on the evaluation results of IR system we get the results.
  • Autocomplete using Edit Distance
  • Wildcard queries

Our main project resides in visualization module of the master branch.

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes

Prerequisites

What things you need to install the software

git
Python3
pip3
virtualenv [If no anaconda present]
Good internet connection : For retrieving data from APIs

Installing Anaconda will be better as most of the dependencies will be taken care of.

Installing and running

A step by step series of examples that tell you how to get a development env running

Clonning the repository on your machine

git clone https://github.com/CapstoneProject18/Stock-Market-Analysis.git

Building a virtual environment and starting the environment (If no anaconda installed)

virtualenv env

For windows : env\Scripts\activate.bat
For linux   : source env/bin/activate 

Installing requirements

cd visualization
pip3 install -r requirements.txt

Running the project

python3 manage.py runserver

Open browser window and in new tab go to link http://127.0.0.1:8000

Contributors

• Ayush Dosajh - Sentiment Module
• Ganesh Singh - Prediction Module
• Gulshan Singh - Search Engine Module
• Mayank Singh - Visualization Module
• Sangamesh Kotalwar - Comparison Module

Acknowledgement

We are highly indebted to Mr. Manish Hurkat and Mr. Bhavesh Sangwan for their guidance and constant supervision as well as for providing necessary information regarding the project & also for their support in completing the project. We acknowledge that any work that I submit for assessment at NIIT University:

• Must be all my own work, unless this requirement is specifically excluded when part of a designated group assignment.
• Must not have been prepared with the assistance of any other person, except those permitted within University guidelines or the specific assessment guidelines for the piece of work.
• Has not previously been submitted for assessment at this University or elsewhere.