A fast orderbook implementation, in C, for Python

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  • License: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
  • Author: Bryant Moscon
  • Tags market data, trading
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Project description

Orderbook

License Python PyPi

A fast orderbook implementation, in C, for Python

Basic Usage

from decimal import Decimal

import requests
from order_book import OrderBook

ob = OrderBook()

# get some orderbook data
data = requests.get("https://api-public.sandbox.pro.coinbase.com/products/BTC-USD/book?level=2").json()

ob.bids = {Decimal(price): size for price, size, _ in data['bids']}
ob.asks = {Decimal(price): size for price, size, _ in data['asks']}

# OR

for side in data:
    # there is additional data we need to ignore
    if side in {'bids', 'asks'}:
        ob[side] = {Decimal(price): size for price, size, _ in data[side]}


# Data is accessible by .index(), which returns a tuple of (price, size) at that level in the book
price, size = ob.bids.index(0)
print(f"Best bid price: {price} size: {size}")

price, size = ob.asks.index(0)
print(f"Best ask price: {price} size: {size}")

print(f"The spread is {ob.asks.index(0)[0] - ob.bids.index(0)[0]}\n\n")

# Data is accessible via iteration

print("Bids")
for price in ob.bids:
    print(f"Price: {price} Size: {ob.bids[price]}")


print("\n\nAsks")
for price in ob.asks:
    print(f"Price: {price} Size: {ob.asks[price]}")


# Data can be exported to a sorted dictionary
# in Python3.7+ dictionaries remain in insertion ordering, the
# dict returned by .to_dict() has had its keys inserted in sorted order
print("\n\nRaw asks dictionary")
print(ob.asks.to_dict())

Installation

The preferable way to install is via pip - pip install order-book. Installing from source will require a compiler and can be done with setuptools: python setup.py install.

Running code coverage

The script coverage.sh will compile the source using the -coverage CFLAG, run the unit tests, and build a coverage report in HTML. The script contains some dependencies that may need to be installed (coverage, lcov, genhtml).

Running the performance tests

You can run the performance tests like so: python perf/performance_test.py. The program will profile the time to run for random data samples of various sizes as well as the construction of a sorted orderbook using live L2 orderbook data from Coinbase.

The performance of constructing a sorted orderbook (using live data from Coinbase) using this C library, versus a pure Python sorted dictionary library:

Library Time, in seconds
C Library 0.00021767616271
Python Library 0.00043988227844

The performance of constructing sorted dictionaries using the same libraries, as well as the cost of building unsorted, python dictionaies for dictionaries of random floating point data:

Library Number of Keys Time, in seconds
C Library 100 0.00021600723266
Python Library 100 0.00044703483581
Python Dict 100 0.00022006034851
C Library 500 0.00103306770324
Python Library 500 0.00222206115722
Python Dict 500 0.00097918510437
C Library 1000 0.00202703475952
Python Library 1000 0.00423812866210
Python Dict 1000 0.00176715850830

This represents a roughly 2x speedup compared to a pure python implementation, and in many cases is close to the performance of an unsorted python dictionary.

For other performance metrics, run performance_test.py

Changelog

0.0.2 (2020-12-27)

  • Bugfix: Fix sorted dictionary arg parsing
  • Feature: Coverage report generation for C library
  • Bugfix: Fix reference counting in index method in SortedDict
  • Feature: New unit tests to improve SortedDict coverage
  • Feature: Modularize files
  • Feature: Add ability to set bids/asks to dictionaries via attributes or [ ]
  • Docs: Update README with simple usage example

0.0.1 (2020-12-26)

  • Initial Release

Project details

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  • License: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
  • Author: Bryant Moscon
  • Tags market data, trading
Classifiers

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