• 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.

• 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.

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

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.

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

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

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.