def test_benchmark_file(self):
"""Test running with a benchmark file.
"""
csv_file_path = self.tmpdir.getpath('b.csv')
with open(csv_file_path, 'w') as csv_file:
csv_file.write("date,return\n"
"2020-01-03 00:00:00+00:00,-0.1\n"
"2020-01-06 00:00:00+00:00,0.333\n"
"2020-01-07 00:00:00+00:00,0.167\n"
"2020-01-08 00:00:00+00:00,0.143\n"
"2020-01-09 00:00:00+00:00,6.375\n")
spec = BenchmarkSpec.from_cli_params(
no_benchmark=False,
benchmark_sid=None,
benchmark_file=csv_file_path,
)
sid, returns = self.resolve_spec(spec)
self.assertIs(sid, None)
expected_dates = pd.to_datetime(
[
'2020-01-03', '2020-01-06', '2020-01-07', '2020-01-08',
'2020-01-09'
],
utc=True,
)
expected_values = [-0.1, 0.333, 0.167, 0.143, 6.375]
expected_returns = pd.Series(index=expected_dates,
data=expected_values)
assert_series_equal(returns, expected_returns, check_names=False)
def run(ctx, algofile, algotext, define, data_frequency, capital_base, bundle,
bundle_timestamp, benchmark_file, benchmark_symbol, benchmark_sid,
no_benchmark, start, end, output, trading_calendar, print_algo,
metrics_set, local_namespace, blotter):
"""Run a backtest for the given algorithm.
"""
# check that the start and end dates are passed correctly
if start is None and end is None:
# check both at the same time to avoid the case where a user
# does not pass either of these and then passes the first only
# to be told they need to pass the second argument also
ctx.fail(
"must specify dates with '-s' / '--start' and '-e' / '--end'", )
if start is None:
ctx.fail("must specify a start date with '-s' / '--start'")
if end is None:
ctx.fail("must specify an end date with '-e' / '--end'")
if (algotext is not None) == (algofile is not None):
ctx.fail(
"must specify exactly one of '-f' / '--algofile' or"
" '-t' / '--algotext'", )
trading_calendar = get_calendar(trading_calendar)
benchmark_spec = BenchmarkSpec.from_cli_params(
no_benchmark=no_benchmark,
benchmark_sid=benchmark_sid,
benchmark_symbol=benchmark_symbol,
benchmark_file=benchmark_file,
)
return _run(
initialize=None,
handle_data=None,
before_trading_start=None,
analyze=None,
algofile=algofile,
algotext=algotext,
defines=define,
data_frequency=data_frequency,
capital_base=capital_base,
bundle=bundle,
bundle_timestamp=bundle_timestamp,
start=start,
end=end,
output=output,
trading_calendar=trading_calendar,
print_algo=print_algo,
metrics_set=metrics_set,
local_namespace=local_namespace,
environ=os.environ,
blotter=blotter,
benchmark_spec=benchmark_spec,
)
def test_benchmark_sid(self, input_sid):
"""Test running with no benchmark provided, with no_benchmark flag.
"""
spec = BenchmarkSpec.from_cli_params(
no_benchmark=False,
benchmark_sid=input_sid,
benchmark_file=None,
)
sid, returns = self.resolve_spec(spec)
assert_equal(sid, input_sid)
self.assertIs(returns, None)
def test_no_benchmark_explicitly_disabled(self):
"""Test running with no benchmark provided, with no_benchmark flag.
"""
spec = BenchmarkSpec.from_cli_params(
no_benchmark=True,
benchmark_sid=None,
benchmark_file=None,
)
sid, returns = self.resolve_spec(spec)
self.assertIs(sid, None)
assert_series_equal(returns, self.zero_returns)
def test_no_benchmark(self):
"""Test running with no benchmark provided.
We should have no benchmark sid and have a returns series of all zeros.
"""
spec = BenchmarkSpec.from_cli_params(
no_benchmark=False,
benchmark_sid=None,
benchmark_file=None,
)
sid, returns = self.resolve_spec(spec)
self.assertIs(sid, None)
self.assertIs(returns, None)
def test_benchmark_sid(self, input_sid):
"""Test running with no benchmark provided, with no_benchmark flag."""
spec = BenchmarkSpec.from_cli_params(
no_benchmark=False,
benchmark_sid=input_sid,
benchmark_symbol=None,
benchmark_file=None,
)
sid, returns = self.resolve_spec(spec)
assert_equal(sid, input_sid)
assert returns is None
warnings = self.logs_at_level(logbook.WARNING)
expected = []
assert_equal(warnings, expected)
def test_no_benchmark_explicitly_disabled(self):
"""Test running with no benchmark provided, with no_benchmark flag."""
spec = BenchmarkSpec.from_cli_params(
no_benchmark=True,
benchmark_sid=None,
benchmark_symbol=None,
benchmark_file=None,
)
sid, returns = self.resolve_spec(spec)
assert sid is None
assert_series_equal(returns, self.zero_returns)
warnings = self.logs_at_level(logbook.WARNING)
expected = []
assert_equal(warnings, expected)
def test_benchmark_symbol(self, case):
"""Test running with no benchmark provided, with no_benchmark flag.
"""
symbol, expected_sid = case
spec = BenchmarkSpec.from_cli_params(
no_benchmark=False,
benchmark_sid=None,
benchmark_symbol=symbol,
benchmark_file=None,
)
sid, returns = self.resolve_spec(spec)
assert_equal(sid, expected_sid)
self.assertIs(returns, None)
warnings = self.logs_at_level(logbook.WARNING)
expected = []
assert_equal(warnings, expected)
def test_benchmark_file(self):
"""Test running with a benchmark file."""
csv_file_path = self.tmpdir.getpath("b.csv")
with open(csv_file_path, "w") as csv_file:
csv_file.write("date,return\n"
"2020-01-03 00:00:00+00:00,-0.1\n"
"2020-01-06 00:00:00+00:00,0.333\n"
"2020-01-07 00:00:00+00:00,0.167\n"
"2020-01-08 00:00:00+00:00,0.143\n"
"2020-01-09 00:00:00+00:00,6.375\n")
spec = BenchmarkSpec.from_cli_params(
no_benchmark=False,
benchmark_sid=None,
benchmark_symbol=None,
benchmark_file=csv_file_path,
)
sid, returns = self.resolve_spec(spec)
assert sid is None
expected_dates = pd.to_datetime(
[
"2020-01-03", "2020-01-06", "2020-01-07", "2020-01-08",
"2020-01-09"
],
utc=True,
)
expected_values = [-0.1, 0.333, 0.167, 0.143, 6.375]
expected_returns = pd.Series(index=expected_dates,
data=expected_values)
assert_series_equal(returns, expected_returns, check_names=False)
warnings = self.logs_at_level(logbook.WARNING)
expected = []
assert_equal(warnings, expected)
def test_no_benchmark(self):
"""Test running with no benchmark provided.
We should have no benchmark sid and have a returns series of all zeros.
"""
spec = BenchmarkSpec.from_cli_params(
no_benchmark=False,
benchmark_sid=None,
benchmark_symbol=None,
benchmark_file=None,
)
sid, returns = self.resolve_spec(spec)
self.assertIs(sid, None)
self.assertIs(returns, None)
warnings = self.logs_at_level(logbook.WARNING)
expected = [
'No benchmark configured. Assuming algorithm calls set_benchmark.',
'Pass --benchmark-sid, --benchmark-symbol, or --benchmark-file to set a source of benchmark returns.', # noqa
"Pass --no-benchmark to use a dummy benchmark of zero returns.",
]
assert_equal(warnings, expected)
def run(ctx,
algofile,
algotext,
define,
data_frequency,
capital_base,
bundle,
bundle_timestamp,
benchmark_file,
benchmark_symbol,
benchmark_sid,
no_benchmark,
start,
end,
output,
trading_calendar,
print_algo,
metrics_set,
local_namespace,
blotter,
broker,
broker_uri,
state_file,
realtime_bar_target,
list_brokers):
"""Run a backtest for the given algorithm.
"""
if list_brokers:
click.echo("Supported brokers:")
for _, name, _ in pkgutil.iter_modules(brokers.__path__):
if name != 'broker':
click.echo(name)
return
# check that the start and end dates are passed correctly
if not broker and start is None and end is None:
# check both at the same time to avoid the case where a user
# does not pass either of these and then passes the first only
# to be told they need to pass the second argument also
ctx.fail(
"must specify dates with '-s' / '--start' and '-e' / '--end'",
)
if not broker and start is None:
ctx.fail("must specify a start date with '-s' / '--start'")
if not broker and end is None:
ctx.fail("must specify an end date with '-e' / '--end'")
if broker and broker_uri is None:
ctx.fail("must specify broker-uri if broker is specified")
if broker and state_file is None:
ctx.fail("must specify state-file with live trading")
if broker and realtime_bar_target is None:
ctx.fail("must specify realtime-bar-target with live trading")
brokerobj = None
if broker:
mod_name = 'zipline.gens.brokers.%s_broker' % broker.lower()
try:
bmod = import_module(mod_name)
except ImportError:
ctx.fail("unsupported broker: can't import module %s" % mod_name)
cl_name = '%sBroker' % broker.upper()
try:
bclass = getattr(bmod, cl_name)
except AttributeError:
ctx.fail("unsupported broker: can't import class %s from %s" %
(cl_name, mod_name))
brokerobj = bclass(broker_uri)
if end is None:
end = pd.Timestamp.utcnow() + pd.Timedelta(days=1, seconds=1) # Add 1-second to assure that end is > 1day
if (algotext is not None) == (algofile is not None):
ctx.fail(
"must specify exactly one of '-f' / '--algofile' or"
" '-t' / '--algotext'",
)
trading_calendar = get_calendar(trading_calendar)
benchmark_spec = BenchmarkSpec.from_cli_params(
no_benchmark=no_benchmark,
benchmark_sid=benchmark_sid,
benchmark_symbol=benchmark_symbol,
benchmark_file=benchmark_file,
)
return _run(
initialize=None,
handle_data=None,
before_trading_start=None,
analyze=None,
teardown=None,
algofile=algofile,
algotext=algotext,
defines=define,
data_frequency=data_frequency,
capital_base=capital_base,
bundle=bundle,
bundle_timestamp=bundle_timestamp,
start=start,
end=end,
output=output,
trading_calendar=trading_calendar,
print_algo=print_algo,
metrics_set=metrics_set,
local_namespace=local_namespace,
environ=os.environ,
blotter=blotter,
benchmark_spec=benchmark_spec,
broker=brokerobj,
state_filename=state_file,
realtime_bar_target=realtime_bar_target,
performance_callback=None,
stop_execution_callback=None,
execution_id=None
)
def run_algorithm(start,
end,
initialize,
capital_base=5e+6,
handle_data=None,
before_trading_start=None,
analyze=None,
data_frequency='daily',
bundle='mydb',
bundle_timestamp=None,
trading_calendar=None,
metrics_set='default',
benchmark_returns=None,
default_extension=True,
extensions=(),
strict_extensions=True,
environ=os.environ,
blotter='cn_blotter'):
"""
运行交易策略算法.
Parameters
----------
start : datetime
回测的开始时间.
end : datetime
回测的结束时间.
initialize : callable[context -> None]
策略算法的初始化函数, 每个回测开始前只运行一次, 用来设置策略算法的全局变量.
capital_base : float
回测的初始资金.
handle_data : callable[(context, BarData) -> None], optional
策略算法处理行情/执行下单操作的函数, 按照设置的策略频率执行.
before_trading_start : callable[(context, BarData) -> None], optional
每个交易日开盘前执行的函数(在第一天的初始化函数结束后执行)
analyze : callable[(context, pd.DataFrame) -> None], optional
在回测结束以后执行的分析函数. 传入参数为 ``context`` 和回测结果数据.
data_frequency : {'daily', 'minute'}, optional
策略的执行频率.
bundle : str, optional
*zipline* 专用的数据格式组成的数据包.
bundle_timestamp : datetime, optional
数据包的时间戳, 用来区分不同时间点的数据.
trading_calendar : TradingCalendar, optional
回测用的交易日历.
metrics_set : iterable[Metric] or str, optional
记录的回测性能指标集, :func:`zipline.finance.metrics.load` 根据名称调用.
default_extension : bool, optional
Should the default zipline extension be loaded. This is found at
``$ZIPLINE_ROOT/extension.py``
extensions : iterable[str], optional
The names of any other extensions to load. Each element may either be
a dotted module path like ``a.b.c`` or a path to a python file ending
in ``.py`` like ``a/b/c.py``.
strict_extensions : bool, optional
Should the run fail if any extensions fail to load. If this is false,
a warning will be raised instead.
environ : mapping[str -> str], optional
The os environment to use. Many extensions use this to get parameters.
This defaults to ``os.environ``.
blotter : str or zipline.finance.blotter.Blotter, optional
交易信息记录类, 调用前需要用 ``zipline.extensions.register`` 先注册.
默认为 :class:`zipline.finance.blotter.SimulationBlotter`, 永远不会取消订单.
Returns
-------
perf : pd.DataFrame
交易策略的每日回测结果.
See Also
--------
zipline.data.bundles.bundles : 可用的数据包.
"""
from trading_calendars import register_calendar
from zipline_extensions_cn.utils import AShareCalendar
from trading_calendars.errors import CalendarNameCollision
try:
register_calendar('AShare', AShareCalendar())
except CalendarNameCollision:
pass
#load_extensions(default_extension, extensions, strict_extensions, environ)
if benchmark_returns is not None:
benchmark_spec = BenchmarkSpec.from_returns(benchmark_returns)
else:
benchmark_spec = BenchmarkSpec(None, None, None, None, True)
return _run(
handle_data=handle_data,
initialize=initialize,
before_trading_start=before_trading_start,
analyze=analyze,
algofile=None,
algotext=None,
defines=(),
data_frequency=data_frequency,
capital_base=capital_base,
bundle=bundle,
bundle_timestamp=bundle_timestamp,
start=start,
end=end,
output=os.devnull,
trading_calendar=trading_calendar,
print_algo=False,
metrics_set=metrics_set,
local_namespace=False,
environ=environ,
blotter=blotter,
benchmark_spec=benchmark_spec,
)