OPAL: Price Action Learning aids

Traders can practice and refine their price action skills with this Django web app.

Deployed site: Desktop layout / mobile layout

Features

• Historical data bar-by-bar replaying and fast forwarding
  • Implemented with WebSocket and auto prefetch mechanism for smoother experience
  • Utilize zlib compression for lower bandwidth usage
• Dual time frame charts (H1 and M5) with synced status
• Draw Daily open price (as an important potential support/resistance)
• Select between different tickers
• Jump to a specified time
• Alerts
• Buy/Sell orders
• Positions calculation
• Customizable chart options (timezone, colors, etc.)

Hotkeys

• Space/→: Step one bar
• F: Fast forward 24 bars, or until triggers an alert/order
• Z/←: Stepback one bar
• Hover over the charts:
  • A: Alert
  • B: Buy order
  • S: Sell order
  • D: Toggle price panel
  • G: Go to hovered time
• Scales:
  • Q: Fit to left chart
  • W/E: Fit to right chart
  • R: Reset all scales

How to deploy locally

1. (Optional) Put your historical data into static/PriceData folder
2. Install Python (tested with v3.11.4) and dependencies: pip install -r requirements.txt
3. python manage.py migrate
4. python manage.py runserver
5. Browse http://127.0.0.1:8000/

How to deploy to render.com

1. Create a Web Service with this repo
2. Set Start Command as: daphne mysite.asgi:application --port $PORT --bind 0.0.0.0 -v2
3. Add Environment Variables:
   • PYTHON_VERSION: 3.11.4
   • ALLOWED_HOSTS: (deployed service url, e.g. xxxx-xxxx.onrender.com)
   • SECRET_KEY: my_Pr3c10uSSSsss
   • DEBUG: 0
4. Manual Deploy -> Clear build cache & deploy

Developer's Note

Backend main logic:

• ChartExerciser/consumers.py

Frontend main logic:

• ChartExerciser/templates/index.html
• ChartExerciser/static/main.js