Headless algorithmic trading bot using market data from Alpaca API for real time data subscription/a paper trading account, and implemented using a bespoke Django wrapper for Alpaca's Python client.

Trades are placed with Alpaca Paper trading platform, with market data periodically sourced using Docker containers running Celery and Redis, and stored using a Postgresql database.

Current supported strategies:

• 14 day moving average
• 7 day moving average
• // TODO

• James Dalton

• Django - Django is a high-level Python Web framework that encourages rapid development and clean, pragmatic design.
• Django-Rest-Framework - Django REST framework is a powerful and flexible toolkit for building Web APIs.
• Docker - Docker is a set of platform as a service products that use OS-level virtualization to deliver software in packages called containers.
• Celery - Celery is a simple, flexible, and reliable distributed system to process vast amounts of messages, while providing operations with the tools required to maintain such a system. It’s a task queue with focus on real-time processing, while also supporting task scheduling.
• Redis - Redis is an open source (BSD licensed), in-memory data structure store, used as a database, cache and message broker
• Visual Studio Code - IDE

1. Install Docker Desktop. Create your own Alpaca API Account.

2. Navigate to your server directory, and create a .env file. Copy the included .env.example in this repo for a starter file;

$ cd server
$ cp .env.example .env

Note: setting POSTGRES_HOST_AUTH_METHOD=trust means Postgresql does not require a password. This is used only in development mode, being run on localhost

3. Activate your virtual environment, and navigate back into your project folder if necessary

$ pipenv shell

4. Run docker compose up to initialise django server, postgres for databasing, and celery/celery-beat/redis for handling of background tasks.

$ (server) docker compose up

Note: you may have to give wait-for-it.sh and docker-entrypoint.sh executable permissions

$ (server) chmod +x ./wait-for-it.sh && chmod +x ./docker-entrypoint.sh

5. To run the server for the first time, you will have to run database migrations. and create a superuser, and test data (refer core.management.bootstrap.py for detail on these commands). To do this you can use docker compose and the web container

# Run migrations
$ (server) docker compose exec web python manage.py migrate
# Create superuser account
$ (server) docker compose exec web python manage.py bootstrap -cs
# Create test data
$ (server) docker compose exec web python manage.py bootstrap -ctd

6. Create a strategy via POST to http://localhost:8000/v1/strategies/ or through your browser using Django's admin at http://localhost:8000/admin/core/strategy/

7. Keep docker and your local server running for periodic tasks to perform the given strategy created in 6.

$ (server) docker compose exec web python manage.py test

• Optionally add --keepdb to persist database between tests, and --verbosity=2 to receive verbose output of tests

$ (server) docker compose exec web python manage.py test --keepdb --verbosity=2

[packages]
django
djangorestframework
django-environ
alpaca-trade-api
celery
django-celery-beat
psycopg2
psycopg2-binary

[dev-packages]
django-debug-toolbar
freezegun

To make running docker compose commands on your local machine easier, use alias' in .bashrc/.zshrc configuration files eg.

# ~/.zshrc file
[...]
alias pm="docker compose exec web python manage.py"

Then the command docker compose exec web python manage.py test becomes pm test

This project is licensed under the terms of the MIT license