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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/โ†’: Step one bar
  • F: Fast forward 24 bars, or until triggers an alert/order
  • Z/โ†: Stepback one bar
  • Hover over the charts:
    • A: Alert
    • B: Buy order
    • S: Sell order
    • D: Toggle price panel

How to deploy locally

  1. Build lightweight-charts with the instructions in lightweight-charts-patch folder
  2. Put your historical data into static/PriceData folder
  3. Install Python (tested with v3.9.2) and dependencies: pip install -r requirements.txt
  4. python manage.py migrate
  5. python manage.py runserver
  6. Browse http://127.0.0.1:8000/

How to deploy to Heroku

Follow the above steps 1 and 2, then:

heroku login
heroku create {my_app_name}
heroku git:remote -a {my_app_name}
heroku config:set SECRET_KEY="my_Pr3c10uSSSsss"
heroku config:set DEBUG=0
git push heroku main
heroku run python manage.py migrate
heroku ps:scale web=1:free

Developer's Note

Backend main logic:

Frontend main logic:

opal's People

Contributors

randalhsu avatar

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