Stock Market Analytics App

Overview

Web dashboard built with Plotly Dash for analyzing stock market data and managing a custom portfolio. Data is fetched from the BigQuery dataset prepared using the StockMarketETL data pipeline

Features

• Market Dashboard: Explore stock market insights with interactive line charts, candlestick charts, volume charts, and correlation heatmaps.
• Portfolio Dashboard: View key performance indicators (KPIs), portfolio distribution, and sector allocation.
• Portfolio Form: Add, edit, and delete stocks with an intuitive user interface.
• User Guide: Access a glossary of terms, data source information, and an overview of the dashboard sections.
• Automated Data Updates: Daily updates of stock data from Yahoo Finance stored in BigQuery
• Responsive Design: Built with Dash and Bootstrap for a user-friendly experience across devices.

File Structure

• app.py: Initializes the Dash app, sets up routes, and registers callbacks.
• config.py: Contains configuration settings such as the SQLite database path and CSV file path for sector data.
• components/: Reusable UI components (alerts, charts, inputs, tables, etc.).
• guide/: Layout for the User Guide section.
• market_dashboard/: Layout and callback definitions for the Market Dashboard.
• portfolio_dashboard/: Layout and callback definitions for the Portfolio Dashboard.
• portfolio_form/: Layout and callback definitions for managing the custom portfolio.
• services/: Modules for database operations (db.py) and portfolio management (portfolio.py).
• tests/: Contains unit tests for portfolio services.
• utils/: Utility modules for callback handling, database utilities, and figure styling.

⚙️ Installation

Local Installation

1. Clone the repository:

   git clone https://github.com/Alfredomg7/StockMarketAnalytics.git
   cd StockMarketAnalytics

2. Install dependencies:

   pip install -r requirements.txt

3. Configure your BigQuery credentials in .env file:

# Set up your BigQuery credential variables in .env
TYPE = 'service_account'
PROJECT_ID = 'your-project-id'
PRIVATE_KEY_ID = 'your-private-key-id'
PRIVATE_KEY = 'your-private-key'
CLIENT_EMAIL = 'your-client-email@example.com'
CLIENT_ID = 'your-client-id'
AUTH_URI = 'https://accounts.google.com/o/oauth2/auth'
TOKEN_URI = 'https://oauth2.googleapis.com/token'
AUTH_PROVIDER_X509_CERT_URL = 'https://www.googleapis.com/oauth2/v1/certs'
CLIENT_X509_CERT_URL = 'your-client-cert-url'
UNIVERSE_DOMAIN = 'googleapis.com'
DATASET_ID = 'your_dataset_name'
STOCKS_TABLE_ID = 'your_stocks_table_name'
SECTORS_TABLE_ID = 'your_sectors_table_name's

Docker Installation

1. Build the Docker image:

   docker build -t stock-market-etl .

2. Run the container with environment variables:

   docker run -it --env-file .env stock-market-etl

Acknowledgments

• This project uses prepared data from yfinance library stored in a BigQuery dataset feeded by the StockMarketETL data pipeline

• Thanks to the developers of Dash, Plotly, and other open-source libraries that made this project possible.