Zipline 3.0 中文文档（一）（2）

$  sudo  apt  install  libatlas-base-dev  python-dev  gfortran  pkg-config  libfreetype6-dev  hdf5-tools 

$  sudo  dnf  install  atlas-devel  gcc-c++  gcc-gfortran  libgfortran  python-devel  redhat-rpm-config  hdf5 

$  pacman  -S  lapack  gcc  gcc-fortran  pkg-config  hdf5 

$  pacman  -S  python3 

$  wget  http://prdownloads.sourceforge.net/ta-lib/ta-lib-0.4.0-src.tar.gz
$  tar  -xzf  ta-lib-0.4.0-src.tar.gz
$  cd  ta-lib/
$  sudo  ./configure
$  sudo  make
$  sudo  make  install 

$  brew  install  freetype  pkg-config  gcc  openssl  hdf5  ta-lib 

$  conda  create  -n  env_zipline  python=3.10 

$  conda  activate  env_zipline 

(env_zipline)  $  conda  install  -c  ml4t  zipline-reloaded 

(env_zipline)  $  conda  deactivate 

$  conda  create  -n  env_zipline  python=3.10 

$  conda  activate  env_zipline 

(env_zipline)  $  conda  install  -c  ml4t  zipline-reloaded 

(env_zipline)  $  conda  deactivate 

from zipline.examples import buyapple 

from zipline.api import order, record, symbol
def initialize(context):
    pass
def handle_data(context, data):
    order(symbol('AAPL'), 10)
    record(AAPL=data.current(symbol('AAPL'), 'price')) 

$  zipline  ingest  -b  <bundle> 

$  zipline  run  --help 

Usage: zipline run [OPTIONS]
Run a backtest for the given algorithm.
Options:
 -f, --algofile FILENAME         The file that contains the algorithm to run.
 -t, --algotext TEXT             The algorithm script to run.
 -D, --define TEXT               Define a name to be bound in the namespace
                                 before executing the algotext. For example
                                 '-Dname=value'. The value may be any python
                                 expression. These are evaluated in order so
                                 they may refer to previously defined names.
 --data-frequency [daily|minute]
                                 The data frequency of the simulation.
                                 [default: daily]
 --capital-base FLOAT            The starting capital for the simulation.
                                 [default: 10000000.0]
 -b, --bundle BUNDLE-NAME        The data bundle to use for the simulation.
                                 [default: quandl]
 --bundle-timestamp TIMESTAMP    The date to lookup data on or before.
                                 [default: <current-time>]
 -s, --start DATE                The start date of the simulation.
 -e, --end DATE                  The end date of the simulation.
 -o, --output FILENAME           The location to write the perf data. If this
                                 is '-' the perf will be written to stdout.
                                 [default: -]
 --trading-calendar TRADING-CALENDAR
                                 The calendar you want to use e.g. LSE. NYSE
                                 is the default.
 --print-algo / --no-print-algo  Print the algorithm to stdout.
 --benchmark-file                The csv file that contains the benchmark
                                 returns (date, returns columns)
 --benchmark-symbol              The instrument's symbol to be used as
                                 a benchmark.
                                 (should exist in the ingested bundle)
 --benchmark-sid                 The sid of the instrument to be used as a
                                 benchmark.
                                 (should exist in the ingested bundle)
 --no-benchmark                  This flag is used to set the benchmark to
                                 zero. Alpha, beta and benchmark metrics
                                 are not calculated
 --help                          Show this message and exit. 

zipline  run  -f  ../zipline/examples/buyapple.py  --start  2016-1-1  --end  2018-1-1  -o  buyapple_out.pickle  --no-benchmark 

AAPL
[2018-01-03 04:30:51.843465] INFO: Performance: Simulated 503 trading days out of 503.
[2018-01-03 04:30:51.843598] INFO: Performance: first open: 2016-01-04 14:31:00+00:00
[2018-01-03 04:30:51.843672] INFO: Performance: last close: 2017-12-29 21:00:00+00:00 

import pandas as pd
perf = pd.read_pickle('buyapple_out.pickle') # read in perf DataFrame
perf.head() 

%pylab inline
figsize(12, 12)
import matplotlib.pyplot as plt
ax1 = plt.subplot(211)
perf.portfolio_value.plot(ax=ax1)
ax1.set_ylabel('Portfolio Value')
ax2 = plt.subplot(212, sharex=ax1)
perf.AAPL.plot(ax=ax2)
ax2.set_ylabel('AAPL Stock Price') 

Populating the interactive namespace from numpy and matplotlib 

<matplotlib.text.Text at 0x10c48c198> 

%load_ext zipline 

%%zipline --start 2016-1-1 --end 2018-1-1
from zipline.api import symbol, order, record
def initialize(context):
    pass
def handle_data(context, data):
    order(symbol('AAPL'), 10)
    record(AAPL=data.current(symbol('AAPL'), "price") 

_.head() 

from zipline import run_algorithm
import pandas as pd
import pandas_datareader.data as web
def initialize(context):
    ...
def handle_data(context, data):
    ...
start = pd.Timestamp('2014')
end = pd.Timestamp('2018')
sp500 = web.DataReader('SP500', 'fred', start, end).SP500
benchmark_returns = sp500.pct_change()
result = run_algorithm(start=start.tz_localize('UTC'),
                       end=end.tz_localize('UTC'),
                       initialize=initialize,
                       handle_data=handle_data,
                       capital_base=100000,
                       benchmark_returns=benchmark_returns,
                       bundle='quandl',
                       data_frequency='daily')