本文整理汇总了Python中zipline.finance.trading.TradingEnvironment.get_open_and_close方法的典型用法代码示例。如果您正苦于以下问题：Python TradingEnvironment.get_open_and_close方法的具体用法？Python TradingEnvironment.get_open_and_close怎么用？Python TradingEnvironment.get_open_and_close使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类zipline.finance.trading.TradingEnvironment的用法示例。
在下文中一共展示了TradingEnvironment.get_open_and_close方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: TradingAlgorithm
# 需要导入模块: from zipline.finance.trading import TradingEnvironment [as 别名]
# 或者: from zipline.finance.trading.TradingEnvironment import get_open_and_close [as 别名]
#.........这里部分代码省略.........
capital_base={capital_base}
sim_params={sim_params},
initialized={initialized},
slippage={slippage},
commission={commission},
blotter={blotter},
recorded_vars={recorded_vars})
""".strip().format(class_name=self.__class__.__name__,
capital_base=self.capital_base,
sim_params=repr(self.sim_params),
initialized=self.initialized,
slippage=repr(self.slippage),
commission=repr(self.commission),
blotter=repr(self.blotter),
recorded_vars=repr(self.recorded_vars))
def _create_data_generator(self, source_filter, sim_params=None):
"""
Create a merged data generator using the sources attached to this
algorithm.
::source_filter:: is a method that receives events in date
sorted order, and returns True for those events that should be
processed by the zipline, and False for those that should be
skipped.
"""
if sim_params is None:
sim_params = self.sim_params
if self.benchmark_return_source is None:
if sim_params.data_frequency == 'minute' or \
sim_params.emission_rate == 'minute':
def update_time(date):
return self.trading_environment.get_open_and_close(date)[1]
else:
def update_time(date):
return date
benchmark_return_source = [
Event({'dt': update_time(dt),
'returns': ret,
'type': zipline.protocol.DATASOURCE_TYPE.BENCHMARK,
'source_id': 'benchmarks'})
for dt, ret in
self.trading_environment.benchmark_returns.iteritems()
if dt.date() >= sim_params.period_start.date() and
dt.date() <= sim_params.period_end.date()
]
else:
benchmark_return_source = self.benchmark_return_source
date_sorted = date_sorted_sources(*self.sources)
if source_filter:
date_sorted = filter(source_filter, date_sorted)
with_benchmarks = date_sorted_sources(benchmark_return_source,
date_sorted)
# Group together events with the same dt field. This depends on the
# events already being sorted.
return groupby(with_benchmarks, attrgetter('dt'))
def _create_generator(self, sim_params, source_filter=None):
"""
Create a basic generator setup using the sources to this algorithm.