Backtesting

Old Zipline users know the command line tool that used to run backtests. e.g:

zipline --start 2014-1-1 --end 2018-1-1 -o dma.pickle

This is still supported but not recommended. One does not have much power when running a backtest that way.

The recommended way is to run inside a python file, preferably using an IDE so you could debug your code with breakpoints and memory view.

I will show you exactly how to do so, providing a template that you could just copy and develop your code in.

Important notes before we start

You of course need to have everything already installed, so go to the install part if you don’t

You need to have an ingested data bundle. You could use the Alpaca Data Bundle or user your own.

You need to have some understanding on how a zipline algo is used. (Beginner Tutorial)

You need to set the ZIPLINE_ROOT env variable to point to your ingested data bundle.

Algo Template

This next code snippet is a simple algorithm that is self contained. You could copy that into a python file and just execute it

import pytz
import pandas as pd
from datetime import datetime
import matplotlib.pyplot as plt
import pandas_datareader.data as yahoo_reader

from zipline.utils.calendars import get_calendar
from zipline.api import order_target, symbol
from zipline.data import bundles
from zipline import run_algorithm


def get_benchmark(symbol=None, start=None, end=None):
    bm = yahoo_reader.DataReader(symbol,
                                 'yahoo',
                                 pd.Timestamp(start),
                                 pd.Timestamp(end))['Close']
    bm.index = bm.index.tz_localize('UTC')
    return bm.pct_change(periods=1).fillna(0)


def initialize(context):
    context.equity = symbol("AMZN")


def handle_data(context, data):
    order_target(context.equity, 100)


def before_trading_start(context, data):
    pass


def analyze(context, perf):
    ax1 = plt.subplot(211)
    perf.portfolio_value.plot(ax=ax1)
    ax2 = plt.subplot(212, sharex=ax1)
    perf.sym.plot(ax=ax2, color='r')
    plt.gcf().set_size_inches(18, 8)
    plt.legend(['Algo', 'Benchmark'])
    plt.ylabel("Returns", color='black', size=25)


if __name__ == '__main__':
    bundle_name = 'alpaca_api'
    bundle_data = bundles.load(bundle_name)

    # Set the trading calendar
    trading_calendar = get_calendar('NYSE')

    start = pd.Timestamp(datetime(2020, 1, 1, tzinfo=pytz.UTC))
    end = pd.Timestamp(datetime(2020, 11, 1, tzinfo=pytz.UTC))

    r = run_algorithm(start=start,
                      end=end,
                      initialize=initialize,
                      capital_base=100000,
                      handle_data=handle_data,
                      benchmark_returns=get_benchmark(symbol="SPY",
                                                      start=start.date().isoformat(),
                                                      end=end.date().isoformat()),
                      bundle='alpaca_api',
                      broker=None,
                      state_filename="./demo.state",
                      trading_calendar=trading_calendar,
                      before_trading_start=before_trading_start,
                      #                   analyze=analyze,
                      data_frequency='daily'
                      )
    fig, axes = plt.subplots(1, 1, figsize=(16, 5), sharex=True)
    r.algorithm_period_return.plot(color='blue')
    r.benchmark_period_return.plot(color='red')

    plt.legend(['Algo', 'Benchmark'])
    plt.ylabel("Returns", color='black', size=20)
    plt.show()