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B.E. Project Report
B.E. Stock Market Prediction System Final Year Project Report
Get structured B.E. Stock Market Prediction System Final Year Project Report Final Year Project Report and Documentation with project objectives, methodology, system design, diagrams, implementation details, testing and complete project explanations. Suitable as a learning, documentation and project-presentation resource for students working on related final year projects.
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Abstract
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Table of Content
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01
Introduction
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02
Problem Statement
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03
Existing System
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04
Proposed System
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05
Objectives
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06
System Architecture
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07
Major Functional Modules
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08
Hardware Requirements
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09
Software Requirements
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10
Future Enhancement
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11
Conclusion
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12
References
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Abstract
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Table of Content
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01
Chapter 1 — Introduction
- 1.1 Background of the Project
- 1.2 Problem Statement
- 1.3 Objectives of the System
- 1.4 Scope of the Project
- 1.5 Existing System Overview
- 1.6 Proposed System Overview
- 1.7 Technologies Used
- 1.8 Overview
- 1.9 Limitations
- 1.10 Advantages
- 1.11 Disadvantages
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02
Chapter 2 — Literature Review / System Study
- 2.1 Introduction
- 2.2 Review of Similar Systems
- 2.3 Comparative Analysis
- 2.4 Software Development Models
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03
Chapter 3 — System Analysis
- 3.1 Introduction
- 3.2 Functional Requirements
- 3.3 Non-Functional Requirements
- 3.4 User Requirements
- 3.5 Feasibility Study
- 3.5.1 Technical Feasibility
- 3.5.2 Economic Feasibility
- 3.5.3 Operational Feasibility
- 3.6 System Architecture
- 3.7 Data Flow Diagram (Level 0)
- 3.8 Data Flow Diagram (Level 1)
- 3.9 Data Flow Diagram (Level 2)
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04
Chapter 4 — System Design
- 4.1 Introduction
- 4.2 Use Case Diagram
- 4.3 Class Diagram
- 4.4 Sequence Diagram
- 4.5 Activity Diagram
- 4.6 ER Diagram
- 4.7 Database Schema
- 4.8 Table Structures
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05
Chapter 5 — System Implementation
- 5.1 Introduction
- 5.2 Development Environment
- 5.3 Tools and Technologies Used
- 5.4 Hardware Requirements
- 5.5 Software Requirements
- 5.6 Module-wise Implementation
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06
Chapter 6 — Testing
- 6.1 Introduction
- 6.2 Testing Strategy
- 6.3 Unit Testing
- 6.4 Integration Testing
- 6.5 System Testing
- 6.6 Test Cases
- 6.7 Bug Reports
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07
Chapter 7 — Results and Discussion
- All screenshots
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08
Chapter 8 — Conclusion and Future Enhancements
- 8.1 Conclusion
- 8.2 Future Enhancements
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09
Chapter 9 — References
Project's Overview
Stock Market Prediction System is a full-stack stock market intelligence platform designed for Indian NSE-listed stocks. The system combines historical stock data analysis, interactive charts, 30-day LSTM machine learning predictions, financial news, user accounts, personalized watchlists, and an admin console for platform management.
The platform allows users to search NSE stocks, select custom date ranges, view Open/High/Close charts, export stock data to Excel, run LSTM-based prediction, read market news, manage watchlists with notes, update profile details, and delete accounts. Admin can manage users, announcements, featured stocks, platform statistics, active/inactive user status, admin roles, and landing-page content.
This project is suitable for B.Tech, M.Tech, BCA, MCA, BE, ME, BSc, and MSc students who need a practical final year project, major project, or minor project based on stock market prediction, machine learning, LSTM forecasting, MERN stack development, Python ML integration, financial data analysis, JWT authentication, MongoDB database design, and admin dashboard functionality. FileMakr can provide this project with source code, project report, documentation, and setup support for academic submission
Login Credentials
Admin Account
Created by running:
node scripts/seedAdmin.js
| Field | Value |
|---|---|
[email protected] |
|
| Password | Admin@123456 |
| Role | admin |
Admin seed credentials can also be customized using ADMIN_EMAIL, ADMIN_PASSWORD, and ADMIN_NAME in backend/.env.
Regular User Account
No default regular user exists. Create one using:
- Register page:
http://localhost:3000/register - Admin panel: Admin → Users → Add User
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- Secure admin login in this final year project
- Admin role-based access
- Separate admin console
- Violet/indigo admin theme
- Admin dashboard
- View total users
- View active users
- View inactive users
- View total announcements
- View active featured stocks
- View aggregate watchlist items
- View recent registrations
- Quick links to users, announcements, and featured stocks
- User management
- List all users
- Search users by name
- Search users by email
- Filter users by role
- Filter users by active/inactive status
- Create user
- Add name, email, password, role, and status
- Edit user
- Update name
- Update email
- Update role
- Update status
- Reset user password
- Delete user
- Prevent deletion of own admin account
- Assign user role
- Promote user to admin
- Change admin to user where allowed
- Deactivate user account
- Block login without deleting account
- Announcement management
- Create platform announcement
- Add title and message
- Select announcement type: info, success, warning, critical
- Set announcement priority
- Set announcement active/inactive status
- Edit announcement
- Delete announcement
- Toggle announcement visibility on public landing page
- Featured stock management
- Add featured stock
- Add stock symbol
- Add company name
- Add sector
- Add description
- Control display order on landing page
- Toggle featured stock active/inactive status
- Edit featured stock
- Delete featured stock
- Link featured stock cards to analyzer
- Manage public landing page content through announcements and featured stocks
The admin panel includes dashboard, user management, announcements, and featured stocks modules with platform-level CRUD controls.
- User registration in this final year project
- User login
- JWT token stored in localStorage
- Redirect after login
- Error handling for invalid credentials
- Deactivated account login blocking
- Auto-login after successful registration
- Public stock analyzer access
- Search Indian NSE stocks
- Search by company name
- Search by stock symbol
- Select custom start date
- Select custom end date
- Fetch OHLCV historical data
- View Open, High, and Close price chart
- View historical stock data table
- Highlight minimum and maximum values
- Export stock data to Excel
- Save stock to watchlist after login
- Run LSTM prediction
- View AI prediction page
- Generate 30-day LSTM forecast
- View combined historical and predicted chart
- View educational-use disclaimer
- View financial news feed
- Browse news cards
- View title, author, date, image, and external link
- Use paginated news feed
- Daily local news cache
- User dashboard
- View watchlist count
- View available stocks
- View account status
- Add stock to watchlist
- Add notes to watchlist item
- View watchlist table
- View saved stock symbol and name
- Edit watchlist notes
- Remove stock from watchlist
- Quick analyze saved stocks
- Profile management
- View own profile
- Update name
- Update email
- Change password
- Sign out
- Delete account permanently
The user-side modules include stock analyzer, AI prediction, market news, login, register, dashboard, watchlist CRUD, and profile CRUD.
- MERN-style full-stack architecture in this final year project
- Python ML pipeline integration
- React 18 frontend
- React Router 6 routing
- Tailwind CSS fintech-style interface
- Chart.js 4 interactive charts
- React Toastify notifications
- React Icons
- XLSX Excel export
- Node.js backend
- Express 4 REST API
- MongoDB database
- Mongoose 8 ODM
- JWT authentication
- bcryptjs password hashing
- CORS support
- Python 3.10+ scripts
- TensorFlow/Keras LSTM model
- yfinance data fetch
- pandas data processing
- scikit-learn MinMaxScaler and metrics
- NumPy 1.x compatibility for TensorFlow
- Financial Modeling Prep news support
- Alpha Vantage fallback support
- Yahoo Finance fallback support
- Multi-provider stock data fetch
- 24-hour memory and disk cache for stock data
- Admin-curated landing content
- Inactive user blocking
- Role-based admin access
- Protected routes
- Excel export of stock table
- Watchlist CRUD
- Announcements CRUD
- Featured stocks CRUD
- Educational and research use disclaimer
The technology stack includes React, Tailwind CSS, Chart.js, Node.js, Express, MongoDB, Mongoose, JWT, Python, TensorFlow/Keras, yfinance, pandas, scikit-learn, NumPy, and optional financial data APIs.
Technology Used
| Layer | Technology |
|---|---|
| Frontend | React 18 |
| Routing | React Router 6 |
| Styling | Tailwind CSS |
| Charts | Chart.js 4 |
| Notifications | React Toastify |
| Icons | React Icons |
| Excel Export | XLSX |
| Backend | Node.js, Express 4 |
| Database | MongoDB |
| ODM | Mongoose 8 |
| Authentication | JWT, bcryptjs |
| ML Runtime | Python 3.10+ |
| ML Model | TensorFlow/Keras LSTM |
| Data Fetch | yfinance |
| Data Processing | pandas, NumPy |
| ML Scaling / Metrics | scikit-learn |
| Optional APIs | Alpha Vantage, Financial Modeling Prep, Yahoo Finance |
Data Sources
The project uses a multi-provider fallback chain for stock data:
- Python
yfinanceas the primary source for NSE.NStickers - Alpha Vantage
.BSEfallback when API key is configured - Financial Modeling Prep when API key is configured
- Yahoo Finance as a last-resort fallback through
yahoo-finance2
Stock data results are cached for 24 hours, and LSTM predictions use get_stockdata.py with TensorFlow/Keras on historical close prices.
Step 1: Open the Project
cd stockMarketPrediction
Step 2: Configure backend environment
cd backend
copy .env.example .env
For Mac/Linux:
cp .env.example .env
Step 3: Add backend environment variables
Edit backend/.env:
MONGO_URI=mongodb://127.0.0.1:27017/stockvision
JWT_SECRET=your_strong_jwt_secret
JWT_EXPIRES_IN=7d
CLIENT_URL=http://localhost:3000
PORT=3001
ADMIN_PASSWORD=Admin@123456
ADMIN_NAME=Platform Admin
ALPHA_VANTAGE_API_KEY=
FINANCIALMODELAPI=
Backend environment variables include MongoDB URI, JWT secret, token expiry, frontend URL, backend port, admin seed credentials, and optional stock/news API keys.
Step 4: Start MongoDB
For Windows:
net start MongoDB
For Mac:
brew services start mongodb-community
For Linux:
sudo systemctl start mongod
Or use MongoDB Atlas and set MONGO_URI in .env.
Step 5: Install and run backend
cd backend
npm install
node scripts/seedAdmin.js
node index.js
Expected output:
Server started on port 3001
MongoDB connected
Step 6: Install Python ML dependencies
cd backend
pip install -r requirements.txt
Verify installation:
python -c "import tensorflow; import yfinance; print('Python ML OK')"
NumPy must be below version 2.0 for TensorFlow compatibility, and the requirements file pins numpy>=1.26.0,<2.0.0.
Step 7: Install and run frontend
Open a new terminal:
cd frontend
npm install
npm start
Frontend opens at:
http://localhost:3000
Backend API runs at:
http://localhost:3001
The local ports are frontend 3000, backend API 3001, and MongoDB 27017
README Included
Setup Support Available
Admin Account
Created by running:
node scripts/seedAdmin.js
| Field | Value |
|---|---|
[email protected] |
|
| Password | Admin@123456 |
| Role | admin |
Admin seed credentials can also be customized using ADMIN_EMAIL, ADMIN_PASSWORD, and ADMIN_NAME in backend/.env.
Regular User Account
No default regular user exists. Create one using:
- Register page:
http://localhost:3000/register - Admin panel: Admin → Users → Add User