def _create_data_generator(self, source_filter, sim_params):
"""
Create a merged data generator using the sources and
transforms 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.
"""
benchmark_return_source = [
Event({'dt': ret.date,
'returns': ret.returns,
'type': zipline.protocol.DATASOURCE_TYPE.BENCHMARK,
'source_id': 'benchmarks'})
for ret in trading.environment.benchmark_returns
if ret.date.date() >= sim_params.period_start.date()
and ret.date.date() <= sim_params.period_end.date()
]
date_sorted = date_sorted_sources(*self.sources)
if source_filter:
date_sorted = ifilter(source_filter, date_sorted)
with_tnfms = sequential_transforms(date_sorted,
*self.transforms)
with_alias_dt = alias_dt(with_tnfms)
with_benchmarks = date_sorted_sources(benchmark_return_source,
with_alias_dt)
# Group together events with the same dt field. This depends on the
# events already being sorted.
return groupby(with_benchmarks, attrgetter('dt'))
def _create_data_generator(self, source_filter):
"""
Create a merged data generator using the sources and
transforms 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.
"""
benchmark_return_source = [
Event({'dt': ret.date,
'returns': ret.returns,
'type': zipline.protocol.DATASOURCE_TYPE.BENCHMARK,
'source_id': 'benchmarks'})
for ret in trading.environment.benchmark_returns
if ret.date.date() >= self.sim_params.period_start.date()
and ret.date.date() <= self.sim_params.period_end.date()
]
date_sorted = date_sorted_sources(*self.sources)
if source_filter:
date_sorted = ifilter(source_filter, date_sorted)
with_tnfms = sequential_transforms(date_sorted,
*self.transforms)
with_alias_dt = alias_dt(with_tnfms)
with_benchmarks = date_sorted_sources(benchmark_return_source,
with_alias_dt)
# Group together events with the same dt field. This depends on the
# events already being sorted.
return groupby(with_benchmarks, attrgetter('dt'))
def _create_data_generator(self, source_filter, sim_params):
"""
Create a merged data generator using the sources and
transforms 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 self.benchmark_return_source is None:
env = trading.environment
if (self.data_frequency == 'minute'
or sim_params.emission_rate == 'minute'):
update_time = lambda date: env.get_open_and_close(date)[1]
else:
update_time = lambda date: date
benchmark_return_source = [
Event({
'dt': update_time(dt),
'returns': ret,
'type': zipline.protocol.DATASOURCE_TYPE.BENCHMARK,
'source_id': 'benchmarks'
})
for dt, ret in trading.environment.benchmark_returns.iterkv()
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_tnfms = sequential_transforms(date_sorted, *self.transforms)
with_alias_dt = alias_dt(with_tnfms)
with_benchmarks = date_sorted_sources(benchmark_return_source,
with_alias_dt)
# 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):
"""
Create a basic generator setup using the sources and
transforms attached to this algorithm.
"""
self.date_sorted = date_sorted_sources(*self.sources)
self.with_tnfms = sequential_transforms(self.date_sorted, *self.transforms)
self.with_alias_dt = alias_dt(self.with_tnfms)
# Group together events with the same dt field. This depends on the
# events already being sorted.
self.grouped_by_dt = groupby(self.with_alias_dt, attrgetter("dt"))
self.trading_client = tsc(self, sim_params)
transact_method = transact_partial(self.slippage, self.commission)
self.set_transact(transact_method)
return self.trading_client.simulate(self.grouped_by_dt)
def _create_data_generator(self, source_filter, sim_params):
"""
Create a merged data generator using the sources and
transforms 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 self.benchmark_return_source is None:
env = trading.environment
if (self.data_frequency == 'minute'
or sim_params.emission_rate == 'minute'):
update_time = lambda date: env.get_open_and_close(date)[1]
else:
update_time = lambda date: date
benchmark_return_source = [
Event({'dt': update_time(dt),
'returns': ret,
'type': zipline.protocol.DATASOURCE_TYPE.BENCHMARK,
'source_id': 'benchmarks'})
for dt, ret in trading.environment.benchmark_returns.iterkv()
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_tnfms = sequential_transforms(date_sorted,
*self.transforms)
with_alias_dt = alias_dt(with_tnfms)
with_benchmarks = date_sorted_sources(benchmark_return_source,
with_alias_dt)
# 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):
"""
Create a basic generator setup using the sources and
transforms attached to this algorithm.
"""
self.date_sorted = date_sorted_sources(*self.sources)
self.with_tnfms = sequential_transforms(self.date_sorted,
*self.transforms)
self.with_alias_dt = alias_dt(self.with_tnfms)
# Group together events with the same dt field. This depends on the
# events already being sorted.
self.grouped_by_dt = groupby(self.with_alias_dt, attrgetter('dt'))
self.trading_client = tsc(self, sim_params)
transact_method = transact_partial(self.slippage, self.commission)
self.set_transact(transact_method)
return self.trading_client.simulate(self.grouped_by_dt)
def _create_data_generator(self, source_filter):
"""
Create a merged data generator using the sources and
transforms 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.
"""
date_sorted = date_sorted_sources(*self.sources)
if source_filter:
date_sorted = ifilter(source_filter, date_sorted)
with_tnfms = sequential_transforms(date_sorted, *self.transforms)
with_alias_dt = alias_dt(with_tnfms)
# Group together events with the same dt field. This depends on the
# events already being sorted.
return groupby(with_alias_dt, attrgetter('dt'))
def _create_data_generator(self, source_filter):
"""
Create a merged data generator using the sources and
transforms 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.
"""
date_sorted = date_sorted_sources(*self.sources)
if source_filter:
date_sorted = ifilter(source_filter, date_sorted)
with_tnfms = sequential_transforms(date_sorted,
*self.transforms)
with_alias_dt = alias_dt(with_tnfms)
# Group together events with the same dt field. This depends on the
# events already being sorted.
return groupby(with_alias_dt, attrgetter('dt'))
