/OrderBookMatchingEngine

Simple Python implementation of order book matching engine

Primary LanguagePythonMIT LicenseMIT

Order Book Matching Engine

pytest Documentation Status !pypi !python-versions pre-commit Ruff

Overview

This package is a simple order book matching engine implementation in Python. Its main features are:

  • price-time priority
  • limit and market orders
  • order cancellation and expiration
  • conversion into pandas DataFrame of orders, executed trades, order book summary

Install

pip install order-matching

Documentation

order-book-matching-engine.readthedocs.io

Usage

>>> from datetime import datetime, timedelta
>>> from pprint import pp
>>> import pandas as pd

>>> from order_matching.matching_engine import MatchingEngine
>>> from order_matching.order import LimitOrder
>>> from order_matching.side import Side
>>> from order_matching.orders import Orders

>>> matching_engine = MatchingEngine(seed=123)
>>> timestamp = datetime(2023, 1, 1)
>>> transaction_timestamp = timestamp + timedelta(days=1)
>>> buy_order = LimitOrder(side=Side.BUY, price=1.2, size=2.3, timestamp=timestamp, order_id="a", trader_id="x")
>>> sell_order = LimitOrder(side=Side.SELL, price=0.8, size=1.6, timestamp=timestamp, order_id="b", trader_id="y")
>>> executed_trades = matching_engine.match(orders=Orders([buy_order, sell_order]), timestamp=transaction_timestamp)

>>> pp(executed_trades.trades)
[Trade(side=SELL,
       price=1.2,
       size=1.6,
       incoming_order_id='b',
       book_order_id='a',
       execution=LIMIT,
       trade_id='c4da537c-1651-4dae-8486-7db30d67b366',
       timestamp=datetime.datetime(2023, 1, 2, 0, 0))]

Related Projects

Contribute

Create a virtual environment and activate it:

python -m venv venv
source venv/bin/activate

Install development dependencies:

pip install -e .[dev]

and use pre-commit to make sure that your code is formatted using black and isort automatically:

pre-commit install

Run tests:

pip install -e .[test]
pytest

Run benchmark and see the result either in the terminal or as a plot in benchmark_history.svg:

./benchmark.sh

Build and serve documentation website:

pip install -e .[doc]
mkdocs serve