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A Project On
Stock Nova
(Stock Prediction Application)
Submitted in Partial Fulfilment for award of the degree of
BACHELOR OF TECHNOLOGY IN COMPUTER SCIENCE & ENGINEERING
UNDER THE GUIDANCE OF
MR. HIMANSHU SARKAR
(ASSISTANT PROFESSOR)
APPROVED BY A.I.C.T.E
Affiliated to
Maulana Abul kalam Azad University of Technology
BIRBHUM INSTITUTE OF ENGINEERING & TECHNOLOGY
Season 2020- 2024
SUBMITTED BY:
SUDAM DAS (11800120001)
DIPAK MAJUMDAR (11800121031)
SRIKRISHNA DAS (118001210 19 )
Submitted To:-
Stock Nova
BIRBHUM INSTITUTE OF ENGINEERING & TECHNOLOGY
Affiliated to the Maulana Abul Kalam Azad University of Technology
Approved by A.I.C.T.E
SURI, BIRBHUM
PROJECT COMPLETION CERTFICATE
The report of the Assigned Project titled “Stock Nova’’ submitted by
Dipak Majumdar (ROLL: 11800121031 ) and Sudam Das
(ROLL: 11800120001 ) of CSE final year 2024 have successfully
completed the project under supervision of Mr.MD.ABUSAFI. They
are pursuing B-Tech from BIRBHUM INSTITUTE OF ENGINEERING &
TECHNOLOGY under Maulana Abul Kalam Azad University of
Technology(MAKAUT) from 2020-2024. The report is hereby
forwarded.
………………………. ….…………………. ...……………………..
DR. DEBASRI CHAKRABORTY MR. HIMANSHU SARKAR DR. SASANKA SEKHAR GHOSH
TEACHER-IN-CHARGE PROJECT GUIDE DIRECTOR (ACTING)
CSE, B.I.E.T, SURI B.I.E.T, SURI B.I.E.T, SURI
Stock Nova
Preface
Excellence is an attitude that the whole of the human race is born with. It is the
environment that makes sure that whether the result of this attitude is visible or otherwise.
The well planned, properly executed and evaluated industrial training help a lot in including
the good work culture. It provides linkage between student and industry in order to develop
the awareness of industrial approach to problem solving based on broad understanding of
process and mode of operation of an organization. During this period, the students get their
real first-hand experience on working in the actual environment. Most of the theoretical
knowledge that they have gained during the course of their studies is put to test here. Apart
from this, the students get opportunity to learn the latest technologies, which immensely
help them in their career. This also benefits the organizations as many students doing their
projects perform very well. I had the opportunity to have the real practical experience,
which has increased my sphere of knowledge to a great extent. Now I am better equipped
to handle the real things than anyone else that has not undergone any such training. I learnt
how an actual project progresses, what sort of problems actually occurs, how to produce
quality products and so on. And being in such a reputed organization I had the best of
experience.
Stock Nova
ACKNOWLEDGEMENT
A project like this takes quite a lot of time to do properly. As is often the case, this project
owes its existence and certainly its quality to a number of people, whose name does not
appear on the cover.
We thank to Mr. Himanshu Sarkar who deserves credit for helping me done the project
and taking care of all the details that most programmer really don’t think about. Errors
and confusions are my responsibility, but the quality of the project is to their credit and
we can only thank them.
We owe my obligation to my friends and our group members for their co-operation and
support.
We thank Dr. Sashanka Sekhar Gosh respected Director of our Institution, for providing us
this Golden opportunity to show our skill in this field.
We thank God for being on our side.
Stock Nova
Stock Nova
Abstract
The Stock Prediction Application is an innovative and comprehensive platform designed to
empower users in making informed decisions in the dynamic realm of stock market
investments. Leveraging a sophisticated blend of technologies, including Django, Prophet,
yfinance, React.js, and Plotly, the application provides a seamless and user-friendly
experience.
The project encompasses a scalable Django backend, offering efficient data management
and secure user authentication. The integration of Prophet and yfinance facilitates accurate
time series forecasting, while React.js ensures a dynamic and interactive user interface.
Plotly charts enhance data visualization, providing users with valuable insights into stock
price predictions.
The Stock Prediction Application's success lies in its ability to adapt and evolve. Future
enhancements could include advanced machine learning techniques, integration of
alternative data sources, and features for user customization and portfolio management.
Mobile application development, risk assessment tools, and continuous model training are
also potential avenues for growth.
By prioritizing community engagement, feedback, and a commitment to user-centric design,
the Stock Prediction Application is poised to be a dynamic and indispensable tool in the
ever-changing landscape of financial technology. This abstract encapsulates the essence of
the project's innovation, versatility, and potential for continuous evolution.
Stock Nova
Chapter 1.
1. Introduction
Welcome to Your Next-Level Investment Experience!
Explore the future of smart investing with our Stock Prediction Web Application. Crafted
using Python, Django, Prophet, Yfinance, Plotly, and other essential libraries, coupled with a
sleek React frontend, this platform redefines the way you engage with financial markets.
Python & Django: A Solid Technological Backbone:
Powered by Python and Django, our application boasts a robust backend that ensures
reliability, scalability, and security. Navigate seamlessly through a wealth of financial data,
knowing that the foundation supporting your investment decisions is strong.
React Frontend: Elevating User Interaction:
Our frontend, driven by React, delivers a dynamic and responsive interface. Experience real-
time updates and an intuitive design that puts powerful analytics at your fingertips, making
your investment journey both efficient and enjoyable.
Prophet Forecasting: Precision in Predictions:
Unleash the power of Prophet, Facebook's advanced forecasting tool. This feature provides
accurate predictions through sophisticated time series analysis, offering you insights that
form the bedrock of informed investment decisions.
Yfinance Integration: Real-Time Market Insights:
Stay ahead with real-time market data seamlessly integrated from Yfinance. Access up-to-
the-minute stock information, enabling you to spot trends, identify opportunities, and make
timely decisions.
Plotly Visualizations: Transforming Data into Insights:
Visualize complex financial data effortlessly with Plotly. Our application utilizes Plotly's
interactive charts to present intricate trends and patterns in a clear and actionable format,
making financial analysis a breeze.
Security at the Core:
We prioritize the security of your financial data. With robust security measures in place, rest
assured that your information is safeguarded as you explore, analyze, and strategize your
investment portfolio.
Stock Nova
**1. 3 The Proposed Syatem:
- 3 .1 Objective of the proposed system:**
In the realm of financial markets, predicting stock prices remains a significant
challenge due to the inherent complexity and volatility of market behaviors. This
project aims to develop a web application that leverages machine learning
techniques to predict stock prices. The application will be built using React.js for the
front end and a Python-based framework for the back end. The project's core focus
will be on delivering accurate predictions and providing users with an intuitive and
interactive interface for exploring these predictions.
Objectives:
1. Data Collection and Preprocessing:
Gather historical stock price data for selected companies.
Preprocess and clean the data by handling missing values, outliers, and normalizing
features.
2. Machine Learning Model Development:
Explore various machine learning algorithms suitable for time series prediction, such
as LSTM (Long
Short-Term Memory) networks or ARIMA (AutoRegressive Integrated Moving
Average) models.
Train and validate the chosen model using historical stock price data.
3. Backend Development:
Python web framework Django to create the backend infrastructure.
Develop APIs to handle data requests from the front end and serve predictions from
the machine learning model.
4. Frontend Development:
Utilize React.js to design and implement an engaging user interface.
Design interactive charts and graphs to display historical stock prices, model
predictions, and evaluation metrics.
Stock Nova
5. Integration:
Establish a seamless connection between the frontend and backend to facilitate data
flow and model inference.
Enable real-time updates of predictions based on new data.
6. User Experience:
Ensure a easy to use user interface that allows users to input preferences, select
stocks, and view predicted prices.
Implement visual cues for uncertainty levels in predictions.
7. Performance Evaluation:
Assess the accuracy of the machine learning model's predictions using appropriate
evaluation metrics and compare them with baseline models.
8. Deployment and Testing:
Deploy the web application on a chosen hosting platform.
Conduct rigorous testing to identify and fix bugs, security vulnerabilities, and
performance bottlenecks.
The scope of the project includes:
1. Developing a web application that predicts stock prices using machine learning
techniques.
2. Collecting and preprocessing historical stock price data.
3. Implementing a machine learning model for time series prediction.
4. Creating an interactive frontend using React.js.
5. Establishing a backend using a Python framework for serving predictions and
handling data requests.
6. Evaluating the accuracy of predictions and comparing them with baseline models.
7. Ensuring real-time updates and a user-friendly interface.
Stock Nova
Chapter 2.
Methodology
2.1 System Requirements
Hardware Requirements:
- PROCESSOR: PENTIUM IV
- RAM: 2 GB PROCESSOR: 2.4 GHZ
- PROCESSING SPEED: 600 MHZ
- HARD DISK DRIVE: 50GB
- KEYBOARD :104 KEYS
Software Requirements:
- Python 3.10:
Python 3.10.0 is the newest major release of the Python programming language, and ef
locotion a it contains many new features and optimizations.
- Visual Studio Code:
Visual Studio Code, also commonly referred to as VS Code, is a source-code editor made by
Microsoft for Windows, Linux and macOS. Features include class p,it support for debugging, syntax
highlighting, intelligent code completion, snippets, code refactoring, and embedded Git.
- PyCharm:
PyCharm is an integrated development environment used in computer programming, specifically for
the Python programming language. It is developed by the Czech company JetBrains.
- Sublime Text:
Sublime Text is a shareware cross-platform source code editor. It natively supports many
programming languages and markup languages. Users can expand its functionality with plugins,
typically community-built and maintained under free-software licenses. To facilitate plugins, Sublime
Text features a Python API.
2.2 Technology Used
As we know python is a High-Level Programming language and we have used python and its
libraries. During this project we have learnt so many things in this field, here some of the main
libraries are discussed below, otherwise there are some more libraries (Like: Pandas, Numpy,
Matplotlib and etc ) that have been used as dependencies of a main library and some more small
functionalities.
Stock Nova
Django:
Django is a high-level Python web framework that simplifies the process of building web
applications. It follows the Model-View-Controller (MVC) architectural pattern and encourages the
use of reusable code. Django is equipped with a robust set of tools for handling tasks related to
backend development, such as managing databases, handling user authentication, and routing HTTP
requests. It promotes a clean and pragmatic design, making it easier for developers to create
scalable and maintainable web applications.
Prophet:
Prophet is a time series forecasting tool developed by Facebook's Core Data Science team. It
is designed to handle time-dependent data and is particularly well-suited for predicting trends in
scenarios where data exhibits seasonality and other complex patterns. Prophet utilizes an additive
model that includes components for trend, seasonality, holidays, and special events. It is known for
its ease of use, automatic detection of changepoints, and the ability to provide uncertainty intervals
for forecasted values, making it valuable for applications like stock price predictions.
yfinance:
yfinance is a Python library that facilitates the retrieval of historical stock data from Yahoo
Finance. It provides a convenient interface for accessing financial information, including stock prices,
trading volumes, and other relevant data. yfinance simplifies the process of gathering historical data,
which is crucial for training and validating predictive models in financial applications. Its integration
allows the Stock Prediction Application to access up-to-date and accurate historical stock
information, enhancing the accuracy of predictions.
Plotly:
Plotly is a graphing library for creating interactive and visually appealing plots and charts. It
supports a wide range of chart types, including line charts, scatter plots, and heatmaps. Plotly is
particularly useful for presenting predictions and trends in a user-friendly manner. Its interactive
features, such as zooming, panning, and hover tooltips, enhance the user experience by allowing
users to explore and analyze the presented data. The library supports integration with various
programming languages, making it versatile for different web development frameworks like Django.
React.js:
React.js is a JavaScript library for building user interfaces, developed by Facebook. It follows
a component-based architecture, allowing developers to create reusable and modular UI
components. React.js facilitates the development of dynamic and responsive frontend components,
enabling the creation of single-page applications (SPAs) where the user interface updates efficiently
in response to user interactions. It utilizes a virtual DOM for optimal rendering performance. React.js
is widely used for building modern, interactive, and efficient web applications. Its integration into
the Stock Prediction Application will enhance the overall user interface and interactivity.
Stock Nova
- Admin Portal:
- Admins can manage user accounts.
- Monitor application activity and logs.
- Configure backend settings.
- React.js Integration:
- The frontend should be built using React.js.
- Interactive and responsive components for a seamless user experience.
- Additional Predictive Models:
- Explore and integrate alternative predictive models for stock price forecasting.
- Historical Data Analysis:
- Users can explore and analyze historical stock data trends.
- Tools for visualizing historical data using Plotly.
2. 5. 1 Non-Functional Requirements:
1. Performance:
● The system should handle concurrent user requests efficiently.
● Response time for predictions and data retrieval should be optimized.
2. Scalability:
● The application should be scalable to accommodate a growing number of users.
● Backend infrastructure should support increased data processing demands.
3. Reliability:
● The application should be robust and reliable, with minimal downtime.
● Predictions should be accurate and reliable for user trust.
4. Security:
● User authentication and data transmission should be secure.
● Implement best practices for securing user data.
5. Usability:
● The user interface should be intuitive and easy to navigate.
● Visualizations using Plotly should be user-friendly and informative.
6. Compatibility:
● The application should be compatible with modern web browsers.
● Ensure compatibility with different devices, such as desktops, tablets, and mobile
phones.
- Documentation:
Stock Nova
● Provide comprehensive documentation for developers and users.
● Include installation instructions, user guides, and API documentation.
8. Testing:
● Implement unit testing for backend functionalities.
● Conduct system testing to ensure end-to-end functionality.
9. Future-Proofing:
● Design the system to be easily extensible for future features and enhancements.
These requirements lay the foundation for a robust and user-friendly Stock Prediction Application,
with a focus on both current functionality and future development.
2. 6 Architecture of The Proposed System
The architecture of the proposed Stock Prediction Application involves both the
frontend and backend components. Here's a high-level overview of the system architecture:
2.6.1 Frontend Architecture (React.js):
1. React Components:
The user interface is built using React.js components.
Components include input forms for stock symbols, visualizations for displaying
predictions using Plotly, and interactive elements for user engagement.
2. State Management:
Utilize React.js state management to handle dynamic changes in the UI.
Stateful components manage user input, predictions, and other relevant data.
3. React Router:
Implement React Router for client-side routing, enabling navigation between
different views within the application.
4. User Authentication:
Integrate a user authentication system, potentially using JWT (JSON Web Tokens)
or OAuth, to secure user-specific functionalities.
5. Responsive Design:
Ensure a responsive design that adapts to various screen sizes and devices for a
consistent user experience.
