StockDataView

StockDataView is a Streamlit application designed to display and visualize stock data fetched from the Financial Modeling Prep (FMP) API. It provides users with bar charts, tables of stock fundamentals, and ratings for a given stock ticker.

Features

• Stock Data Visualization: Presents stock data in clear and interactive bar charts.
• Fundamental Data Tables: Displays key stock fundamentals in easy-to-read tables.
• Stock Ratings: Shows ratings information for selected stocks.
• Containerized Application: Easily deployable via Docker.
• API Key Input: Requires users to provide their FMP API key for data retrieval.
• Automated CI/CD: Uses GitHub Actions for continuous integration and deployment.
• Package Management: Uses uv and pyproject.toml for dependency management.
• Makefile Automation: Includes a Makefile for streamlined development and deployment workflows.

Prerequisites

• Docker: Required to build and run the application container
• FMP API Key: Obtain a free or paid API key from Financial Modeling Prep
• Git: To clone the repository (optional)

Installation

Option 1: Use the Pre-Built Image

1. Pull the Image from Docker Hub:

   docker pull skytics/stockdataview:latest

2. Run the Container:

   • Replace your-api-key with your FMP API key:

   docker run -p 8501:8501 -e FMP_API_KEY=your-api-key skytics/stockdataview:latest

3. Open http://localhost:8501 in your browser

Option 2: Build and Run Locally

1. Clone the Repository:

   git clone https://github.com/your-username/StockDataView.git
   cd StockDataView

2. Build the Production Image:

   make docker-build-prod

   • This builds only the production stage of the multi-stage Dockerfile

3. Run the Container:

   • Replace your-api-key with your FMP API key:

   docker run -p 8501:8501 -e FMP_API_KEY=your-api-key skytics/stockdataview:latest

4. Open http://localhost:8501 in your browser

Usage

• Once the app is running, enter a stock ticker (e.g., AAPL) in the Streamlit interface.
• View the displayed bar charts, tables, stock fundamentals, and ratings fetched from the FMP API.

Development

Package Management (uv)

• This project uses uv for package management. Dependencies are defined in pyproject.toml.
• Sync dependencies from pyproject.toml.

To install dependencies:

uv sync

Makefile Commands

• make docker-build-prod: Build the production image
• make docker-run: Run the production image
• make docker-build-dev: Build the dev image (for linting/testing)
• make docker-check: Run linting in the dev image
• make docker-test: Run tests in the dev image
• make docker-clean: Remove all images

Multi-Stage Dockerfile

• builder stage: Used for development (linting, testing)
• production stage: Final app image for running StockDataView

CI/CD Workflow

• GitHub Actions automates building the production image and pushing it to Docker Hub (skytics/stockdataview:latest) on main branch updates
• The builder stage is used for linting and testing in CI
• See .github/workflows/ci-cd.yml for details

Data Source

StockDataView project utilizes data from the free tier of the Financial Modeling Prep (FMP) API. Due to the limitations of the free plan, the primary focus is on US-listed stocks. Data for international markets and more advanced features may not be available in this version.

Limitations due to Free FMP API Plan:

• Data Coverage: Primarily focused on US stock market data.
• Historical Data: Limited historical data availability (typically up to 5 years for most endpoints).
• Real-time Data: Data may be end-of-day or delayed real-time.
• API Call Limits: Functionality might be limited by the daily API call restrictions of the free plan.

Potential Future Enhancements:

With access to a paid FMP API plan, future versions of this dashboard could include:

• Support for international stock markets.
• Access to more extensive historical data.
• Real-time data updates.
• Integration of additional financial data and features.

Contributing

Contributions are welcome! Please feel free to submit a pull request.

License

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