SOL/GMT Candlestick Chart

This web application displays a live, interactive candlestick chart of the SOL/GMT exchange rate. It's built using Python, Flask, Plotly, and yfinance, and is deployed on Render (or Heroku) for 24/7 availability.

Features

• Interactive Candlestick Chart: Uses Plotly to create a dynamic chart that allows users to zoom, pan, and hover over data points for details.
• Automated Data Fetching: Fetches historical price data for SOL-USD and GMT-USD from Yahoo Finance using the yfinance library.
• SOL/GMT Calculation: Calculates the SOL/GMT exchange rate from the fetched data.
• Web Application: Built with Flask, a lightweight Python web framework, to serve the chart as a web page.
• Cloud Deployment: Deployed on Render (recommended) or Heroku for continuous availability.

Live Demo

View the live chart here!

Project Structure

sol-gmt-candlestick-chart/ ├── app.py # Main Flask application ├── requirements.txt # Python dependencies ├── templates/ │ └── index.html # HTML template for displaying the chart └── static/ └── style.css # (Optional) CSS for styling

Setup and Deployment

Prerequisites

• Python 3.7+
• pip (Python package installer)
• Git
• A Render account (recommended) or a Heroku account. A GitHub account is also highly recommended for easier deployment.

Local Development (Running the app on your computer)

1. Clone the repository:

   git clone https://github.com/YOUR_GITHUB_USERNAME/sol-gmt-candlestick-chart.git  # Replace with your repo URL
   cd sol-gmt-candlestick-chart

2. Create a virtual environment (recommended):

   python3 -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Run the application:

   python app.py

   The app will be accessible at http://127.0.0.1:5000 (or a similar local address) in your web browser.

Deployment to Render (Recommended)

1. Create a Render Account: Sign up at https://render.com/.

2. Push your code to GitHub.

3. Create a New Web Service on Render:

   • Connect your GitHub repository.
   • Name: Choose a name (e.g., sol-gmt-chart).
   • Environment: Python 3.
   • Branch: Your main branch (e.g., main).
   • Build Command: pip install -r requirements.txt
   • Start Command: gunicorn app:app
   • Instance Type: Free
   • Advanced:
   • Add an environment variable: FLASK_ENV with a value of production.
   • Add a second environment variable: PORT with value of 8080.

4. Deploy: Click "Create Web Service". Render will provide you with a URL.

Deployment to Heroku (Alternative)

1. Create a Heroku Account: Sign up at https://www.heroku.com/.
2. Install Heroku CLI: https://devcenter.heroku.com/articles/heroku-cli. 3 Login to Heroku CLI: heroku login
3. Create a Procfile: (If not already present) In your project's root, create Procfile with: web: gunicorn app:app
4. Initialize git: git init, git add ., git commit -m "Initial"
5. Create a Heroku App: heroku create your-app-name
6. Set Environment Variable: heroku config:set FLASK_ENV=production
7. Deploy: git push heroku main
8. Open the App: heroku open

Contributing

Feel free to open issues or submit pull requests for bug fixes, enhancements, or new features.

License

This project is licensed under the MIT License - see the LICENSE file for details. (You'll need to create a LICENSE file and put the MIT License text in it.)