Stock Dashboard

Overview

The Stock Forecasting Dashboard is a web application that allows users to visualize historical stock data and generate future forecasts using Facebook Prophet. It also supports multiple technical indicators such as Simple Moving Average (SMA), Exponential Moving Average (EMA), Relative Strength Index (RSI), MACD, and Bollinger Bands (BB).

Features

• Historical Data Visualization: Display historical stock price data for certain time intervals (e.g., 1 minute, 5 minutes, 1 hour, 1 day).
• Forecasting: Generate and display future stock price forecasts using Facebook Prophet. Note that the forecast is based on historical data and may not be accurate.
• Technical Indicators: Visualize technical indicators including (14 period) SMA, EMA, RSI, MACD, and Bollinger Bands.
• Interactive UI: Select stock symbols, time intervals, start and end dates, and technical indicators through an intuitive interface.

Screenshots

Technologies Used

• Frontend:

  • Next.js - React framework for server-side rendering.
  • React Chart.js 2 - Charting library for React.
  • Axios - Promise-based HTTP client for the browser and Node.js.
  • React Datepicker - A simple and reusable datepicker component for React.

• Backend:

  • Flask - A lightweight WSGI web application framework for Python.
  • yfinance - Yahoo! Finance market data downloader.
  • Prophet - Forecasting tool from Facebook.
  • TA-Lib - Technical Analysis Library for Python.
  • Redis - In-memory data structure store for caching.

• CI/CD:

  • Docker - Containerization of the frontend, backend, and Redis services.
  • Docker compose - Multi-container Docker applications.
  • GitHub Actions: Automated deployment testing.

Installation

Prerequisites

• Docker
• Node.js and npm (if running locally without Docker)
• Python 3.8+

Running with Docker (recommended)

1. Clone the repository:

   git clone https://github.com/aritrakar/stock_dashboard.git
   cd stock_dashboard

2. Build and start the containers:

   docker-compose up --build

3. Access the application at http://localhost:3000.

Running Locally

Backend

1. Navigate to the backend directory:

   cd backend

2. Create a virtual environment and activate it:

   python -m venv .venv
   source .venv/bin/activate  # On Windows use .venv\Scripts\activate

3. Install the dependencies:

   pip install -r requirements.txt

4. Start the Flask server:

   source .env.local
   flask run

Frontend

1. Navigate to the frontend directory:

   cd ../frontend

2. Install the dependencies:

   npm install

3. Start the Next.js development server:

   npm run dev

4. Access the application at http://localhost:3000.

Redis

1. Install and start Redis:

   sudo apt-get install redis-server
   sudo systemctl start redis-server

2. Start the Redis container:

   docker run -d -p 6379:6379 redis

Usage

1. Enter Stock Symbol: Type the stock symbol (e.g., AAPL for Apple Inc.) in the input box (AAPL by default).
2. Select Interval: Choose the desired time interval (e.g., 1 minute, 1 hour, 1 day) from the dropdown menu (1d or 1 day by default).
3. Select Date Range: Pick the start and end dates using the date pickers.
4. Select Indicators: Choose the technical indicators you want to visualize.
5. Forecast Period: Enter the number of periods (not necessarily days) to forecast. Note that simply entering a number is not enough; you must also click the "Generate" button to generate the forecast.
6. Generate Forecast: Click the "Generate" button to fetch historical data and generate the forecast.

API Endpoints

GET /api/historical

Fetch historical stock data.

Query Parameters

• symbol (string): Stock symbol.
• interval (string): Time interval (e.g., 1m, 1d).
• start_date (string): Start date in YYYY-MM-DD format.
• end_date (string): End date in YYYY-MM-DD format.
• indicators (array): List of technical indicators to include.

POST /api/forecast

Generate stock price forecast.

Request Body

• symbol (string): Stock symbol.
• interval (string): Time interval (e.g., 1m, 1d).
• start_date (string): Start date in YYYY-MM-DD format.
• end_date (string): End date in YYYY-MM-DD format.
• indicators (array): List of technical indicators to include.
• forecast_period (int): Number of periods (not necessarily days) to forecast.

GET /api/stock-info

Fetch stock information.

Query Parameters

• symbol (string): Stock symbol.

Request Body

• symbol (string): Stock symbol.
• interval (string): Time interval (e.g., 1m, 1d).
• start_date (string): Start date in YYYY-MM-DD format.
• end_date (string): End date in YYYY-MM-DD format.
• indicators (array): List of technical indicators to include.

Future Improvements

• Hosting: Deploy the application to a cloud platform like Digital Ocean, AWS, GCP, or Heroku.
  • Configure production server (Gunicorn, Nginx).
• Error Handling: Implement proper error handling and display error messages to the user.
• User Authentication: Implement user authentication to save user preferences and forecasts.
• Custom Indicators: Allow users to customize technical indicators, such as being able to specify the period, color, and other parameters.
• Multiple Stock Symbols: Support multiple stock symbols in a single chart.
• Custom Forecasting Models: Allow users to choose different forecasting models (e.g., ARIMA, LSTM).
• Real-time Data: Fetch real-time stock data and update the charts in real-time. This requires a WebSocket connection to the backend and a data source that provides real-time data.
• Responsive Design: Make the application responsive for mobile devices.

License

This project is licensed under the MIT License. See the LICENSE file for details.