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https://github.com/bmoscon/orderbook
A fast L2/L3 orderbook data structure, in C, for Python
https://github.com/bmoscon/orderbook
c finance orderbook python python-c-extension python-extension trading
Last synced: 7 days ago
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A fast L2/L3 orderbook data structure, in C, for Python
- Host: GitHub
- URL: https://github.com/bmoscon/orderbook
- Owner: bmoscon
- License: gpl-3.0
- Created: 2020-11-25T00:09:54.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2024-11-09T00:14:46.000Z (about 2 months ago)
- Last Synced: 2024-12-13T16:48:55.741Z (14 days ago)
- Topics: c, finance, orderbook, python, python-c-extension, python-extension, trading
- Language: Python
- Homepage:
- Size: 228 KB
- Stars: 259
- Watchers: 13
- Forks: 52
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGES.md
- License: LICENSE
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README
# Orderbook
[](LICENSE)
[](https://pypi.python.org/pypi/order-book)
A ***fast*** L2/L3 orderbook data structure, in C, for Python
### Basic Usage
```python
from decimal import Decimal
import requests
from order_book import OrderBook
ob = OrderBook()
# get some orderbook data
data = requests.get("https://api.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
# Note: bids/asks are iterators
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())
# Data can also be exported as an ordered list
# .to_list() returns a list of (price, size) tuples
print("Top 5 Asks")
print(ob.asks.to_list()[:5])
print("\nTop 5 Bids")
print(ob.bids.to_list()[:5])
```
### Main Features
* Sides maintained in correct order
* Can perform orderbook checksums
* Supports max depth and depth truncation
### 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 uses tools 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` as well as the other performance tests in [`perf/`](perf/)