zipline-crypto 0.2.dev449

Backtesting & live algorithmic trading platform for stocks & crypto

Backtest your Trading Strategies

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Zipline is a Pythonic event-driven system for backtesting, developed and used as the backtesting and live-trading engine by crowd-sourced investment fund Quantopian. Since it closed late 2020, the domain that had hosted these docs expired. The library is used extensively in the book Machine Larning for Algorithmic Trading by Stefan Jansen who is trying to keep the library up to date and available to his readers and the wider Python algotrading community.

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Features

• Ease 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.
• Live trading: Live trading support for Interactive Brokers and Alpaca.
• Multiple Data sources:

Installation

Zipline supports Python >= 3.10 and is compatible with current versions of the relevant NumFOCUS libraries, including pandas and scikit-learn.

If your system meets the pre-requisites described in the installation instructions, you can install Zipline using pip by running:

pip install zipline-crypto

See the installation section of the docs for more detailed instructions.

Note: Installation under Python 3.11 requires building h5py from source until wheels become available.

Quickstart

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. But first, you need to download some market data with historical prices and trading volumes:

$ zipline ingest -b quandl
$ 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 sourced from Quandl (since acquisition hosted by NASDAQ), and stream it through the algorithm over the specified time range. Then, the resulting performance DataFrame is saved as dma.pickle, which you can load and analyze from Python.

You can find other examples in the zipline/examples directory.

Questions, suggestions, bugs?

If you find a bug or have other questions about the library, feel free to open an issue and fill out the template.