Package: zipline-crypto
Backtest your Trading StrategiesVersion InfoTestStatusCommunityZipline is a Pythonic event-driven system for backtesting, developed and used as the backtesting and live-trading engine bycrowd-sourced investment fund Quantopian. Since it closed late 2020, the domain that had hosted these docs expired. The library is used extensively in the bookMachine Larning for Algorithmic TradingbyStefan Jansenwho is trying to keep the library up to date and available to his readers and the wider Python algotrading community.Join our Community!DocumentationFeaturesEase of Use:Zipline tries to get out of your way so that you can focus on algorithm development. See below for a code example.Batteries Included:many common statistics like moving average and linear regression can be readily accessed from within a user-written algorithm.PyData Integration:Input of historical data and output of performance statistics are based on Pandas DataFrames to integrate nicely into the existing PyData ecosystem.Statistics and Machine Learning Libraries:You can use libraries like matplotlib, scipy, statsmodels, and scikit-klearn to support development, analysis, and visualization of state-of-the-art trading systems.InstallationZipline supports Python >= 3.9 and is compatible with current versions of the relevantNumFOCUSlibraries, includingpandasandscikit-learn.If your system meets the pre-requisites described in theinstallation instructions, you can install Zipline using pip by running:pipinstallzipline-cryptoSee theinstallationsection of the docs for more detailed instructions.QuickstartSee ourgetting started tutorial.The following code implements a simple dual moving average algorithm.fromzipline.apiimportorder_target,record,symboldefinitialize(context):context.i=0context.asset=symbol('AAPL')defhandle_data(context,data):# Skip first 300 days to get full windowscontext.i+=1ifcontext.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 logicifshort_mavg>long_mavg:# order_target orders as many shares as needed to# achieve the desired number of shares.order_target(context.asset,100)elifshort_mavg<long_mavg:order_target(context.asset,0)# Save values for later inspectionrecord(AAPL=data.current(context.asset,'price'),short_mavg=short_mavg,long_mavg=long_mavg)You can then run this algorithm using the Zipline CLI. But first, you need to download some market data with historical prices and trading volumes:$ziplineingest-bquandl
$ziplinerun-fdual_moving_average.py--start2014-1-1--end2018-1-1-odma.pickle--no-benchmarkThis will download asset pricing data sourced fromQuandl, and stream it through the algorithm over the specified time range. Then, the resulting performance DataFrame is saved asdma.pickle, which you can load and analyze from Python.You can find other examples in thezipline/examplesdirectory.Questions, suggestions, bugs?If you find a bug or have other questions about the library, feel free toopen an issueand fill out the template.