Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
---|---|---|---|---|---|---|---|---|---|---|
Freqtrade | 23,607 | 2 | 2 days ago | 59 | November 30, 2023 | 52 | gpl-3.0 | Python | ||
Free, open source crypto trading bot | ||||||||||
Vnpy | 22,295 | 4 days ago | 1 | March 29, 2016 | 9 | mit | Python | |||
基于Python的开源量化交易平台开发框架 | ||||||||||
Zipline | 16,309 | 79 | 6 | 5 months ago | 30 | October 05, 2020 | 357 | apache-2.0 | Python | |
Zipline, a Pythonic Algorithmic Trading Library | ||||||||||
Awesome Quant | 14,317 | a day ago | 8 | Python | ||||||
A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance) | ||||||||||
Abu | 10,540 | a month ago | 2 | gpl-3.0 | Python | |||||
阿布量化交易系统(股票,期权,期货,比特币,机器学习) 基于python的开源量化交易,量化投资架构 | ||||||||||
Hummingbot | 6,569 | 18 hours ago | 6 | December 05, 2023 | 342 | apache-2.0 | Python | |||
Open source software that helps you create and deploy high-frequency crypto trading bots | ||||||||||
Stocksharp | 6,198 | 171 | a day ago | 176 | November 28, 2023 | 8 | apache-2.0 | C# | ||
Algorithmic trading and quantitative trading open source platform to develop trading robots (stock markets, forex, crypto, bitcoins, and options). | ||||||||||
Financial Machine Learning | 5,112 | a day ago | 5 | Python | ||||||
A curated list of practical financial machine learning tools and applications. | ||||||||||
Jesse | 4,945 | 2 | 5 days ago | 209 | December 04, 2023 | 4 | mit | Python | ||
An advanced crypto trading bot written in Python | ||||||||||
Crypto Signal | 4,541 | 19 days ago | 65 | mit | Python | |||||
Github.com/CryptoSignal - Trading & Technical Analysis Bot - 4,100+ stars, 1,100+ forks |
Zipline is a Pythonic algorithmic trading library. It is an event-driven system for backtesting. Zipline is currently used in production as the backtesting and live-trading engine powering Quantopian -- a free, community-centered, hosted platform for building and executing trading strategies. Quantopian also offers a fully managed service for professionals that includes Zipline, Alphalens, Pyfolio, FactSet data, and more.
Zipline currently supports Python 2.7, 3.5, and 3.6, and may be installed via either pip or conda.
Note: Installing Zipline is slightly more involved than the average Python package. See the full Zipline Install Documentation for detailed instructions.
For a development installation (used to develop Zipline itself), create and
activate a virtualenv, then run the etc/dev-install
script.
See our getting started tutorial.
The following code implements a simple dual moving average algorithm.
from zipline.api import order_target, record, symbol
def initialize(context):
context.i = 0
context.asset = symbol('AAPL')
def handle_data(context, data):
# Skip first 300 days to get full windows
context.i += 1
if context.i < 300:
return
# Compute averages
# data.history() has to be called with the same params
# from above and returns a pandas dataframe.
short_mavg = data.history(context.asset, 'price', bar_count=100, frequency="1d").mean()
long_mavg = data.history(context.asset, 'price', bar_count=300, frequency="1d").mean()
# Trading logic
if short_mavg > long_mavg:
# order_target orders as many shares as needed to
# achieve the desired number of shares.
order_target(context.asset, 100)
elif short_mavg < long_mavg:
order_target(context.asset, 0)
# Save values for later inspection
record(AAPL=data.current(context.asset, 'price'),
short_mavg=short_mavg,
long_mavg=long_mavg)
You can then run this algorithm using the Zipline CLI. First, you must download some sample pricing and asset data:
$ zipline ingest
$ zipline run -f dual_moving_average.py --start 2014-1-1 --end 2018-1-1 -o dma.pickle --no-benchmark
This will download asset pricing data data sourced from Quandl, and stream it through the algorithm over the specified time range.
Then, the resulting performance DataFrame is saved in dma.pickle
, which you can load and analyze from within Python.
You can find other examples in the zipline/examples
directory.
If you find a bug, feel free to open an issue and fill out the issue template.
All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome. Details on how to set up a development environment can be found in our development guidelines.
If you are looking to start working with the Zipline codebase, navigate to the GitHub issues tab and start looking through interesting issues. Sometimes there are issues labeled as Beginner Friendly or Help Wanted.
Feel free to ask questions on the mailing list or on Gitter.
Note
Please note that Zipline is not a community-led project. Zipline is maintained by the Quantopian engineering team, and we are quite small and often busy.
Because of this, we want to warn you that we may not attend to your pull request, issue, or direct mention in months, or even years. We hope you understand, and we hope that this note might help reduce any frustration or wasted time.