ml-market is a software for creating online machine learning or prediction markets written in Python using the Django web framework. In comparison to existing solutions it provides a flexible market maker architecture and a RESTful API for market interaction.
Machine learning markets are a type of prediction markets where a number of traders interact with a central market authority (a market maker) with the purpose of making predictions concerning the possible outcomes of some unknown event. Markets should be made in such a way as to incentivise participants to bet according to their true beliefs in order to obtain a reliable estimate on the outcome of the event.
You will need Python 3.3+ and the following components from your favourite package manager for Python (e.g. pip):
| Component Name | Package Name |
|---|---|
| Django | django |
| Django REST Framework | djangorestframework |
| Django Extensions | django-extensions |
| Django Enum Fields | django-enumfields |
Once you have those, grab the latest source code from Github.
Once inside the root directory of the project you can create the required database models by running:
python manage.py makemigrations
followed by:
python manage.py migrate
This should create the underlying SQLite database tables using the default provider. If you want to change the database back-end to something more suitable for use in production please refer to the Django documentation on this topic.
To then start the Django test server run:
python manage.py runserver
from inside the main project directory.
You can then browse to http://127.0.0.1:8000/admin/ to create or manage markets or to http://127.0.0.1:8000/ to visit the main site.
To run the existing unit tests navigate to the root directory of the project and run:
python manage.py test market.tests
Visit http://ixtreon.github.io/ml-market/
The markets module defines the markets' core structure and its web views; it also includes a simple admin interface for their management.
In a market participants place orders (subject to the funds in their account) with their predictions on the possible outcomes of a set of events. In other words they choose the amount of contracts associated with an outcome to purchase, where each contract promises a payment of a credit if its associated outcome happens to be the actual result of the event.
Note that this module does not handle in any way the orders it receives but simply raises the order_received signal to announce their arrival. It is up to another module to hook to the signal and e.g. modify the user's funds or the published prices. (actually it creates its own market maker and hooks it)
The msr-maker module is an implementation of the logarithmic market scoring rule (Hanson et al.) market maker. It listens for orders and instantly matches them calculating a price based on the current holdings of the market maker and the liquidity constant.
The order_book module contains a bare-bones order book maker which is considerably simpler than the msr-maker.
- Support for structured input (xml or json)