Shapla Street - Dhaka Stock Exchange Analysis and Prediction Platform

Shapla Street is a comprehensive stock market application that scrapes live data from the Dhaka Stock Exchange (DSE). The platform integrates real-time market data with machine learning to provide price predictions and advanced visualization tools for investors.

Features

• Market Dashboard: Real-time monitoring through live scraping of Dhaka Stock Exchange market indices and current stock prices.
• Detailed Stock Analysis: High-performance data visualization including candlestick, area, and line charts.
• Market Price Prediction: Future price forecasting using Long Short-Term Memory (LSTM) neural networks.
• Unified Historical Timeline: Continuous 2-year historical data merged from static archives and live market scraping.
• User Watchlist: Secure user accounts with personalized tools for monitoring favorite stocks.
• Professional Terminal Interface: State-of-the-art financial dashboard optimized for data density and clarity.

Live Data Integration

Shapla Street utilizes a custom-built scraping engine to fetch real-time information from the Dhaka Stock Exchange (DSE). This includes:

• Real-time Price Scraping: Fetches the latest traded prices (LTP) and daily changes directly from the market.
• Market Monitoring: Captures live fluctuations in primary market indices.
• Automated Data Merging: Seamlessly combines scraped live data with historical archives to provide a continuous analysis timeline.

Technology Stack

• Frontend: React, Vite, Lightweight Charts, Tailwind CSS, Axios.
• Backend: Python, Flask, MongoDB (PyMongo), TensorFlow, Scikit-learn, Pandas, BeautifulSoup4.

Project Structure

• backend/: Flask API and machine learning logic.
• frontend/: React application and data visualization components.
• backend/app/stocks/scraper.py: Real-time data extraction engine for DSE.
• backend/app/stocks/ml_logic.py: LSTM model implementation for price forecasting.

Setup Instructions

Backend

1. Navigate to the backend/ directory.
2. Install the required Python packages: pip install -r requirements.txt.
3. Configure the .env file with your database credentials.
4. Start the server: python run.py.
   • The API will be available at http://127.0.0.1:5000.

Frontend

1. Navigate to the frontend/ directory.
2. Install the dependencies: npm install.
3. Start the development server: npm run dev.
   • The application will be accessible at http://localhost:5173.

Credits and Data Sources

• Market Data: Real-time stock prices and market indices are sourced from the Dhaka Stock Exchange (DSE).
• Historical Dataset: Long-term historical data is provided by the Harvard Dataverse.

This project is for educational and analytical purposes.