本文整理汇总了Python中zipline.finance.slippage.transact_partial函数的典型用法代码示例。如果您正苦于以下问题：Python transact_partial函数的具体用法？Python transact_partial怎么用？Python transact_partial使用的例子？那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。
在下文中一共展示了transact_partial函数的9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: _create_generator
def _create_generator(self, sim_params, source_filter=None):
"""
Create a basic generator setup using the sources 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 not self.initialized:
self.initialize(*self.initialize_args, **self.initialize_kwargs)
self.initialized = True
if self.perf_tracker is None:
# HACK: When running with the `run` method, we set perf_tracker to
# None so that it will be overwritten here.
self.perf_tracker = PerformanceTracker(
sim_params=sim_params, env=self.trading_environment
)
self.portfolio_needs_update = True
self.account_needs_update = True
self.performance_needs_update = True
self.data_gen = self._create_data_generator(source_filter, sim_params)
self.trading_client = AlgorithmSimulator(self, sim_params)
transact_method = transact_partial(self.slippage, self.commission)
self.set_transact(transact_method)
return self.trading_client.transform(self.data_gen)示例2: _create_generator
def _create_generator(self, sim_params, source_filter=None):
"""
Create a basic generator setup 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.
"""
sim_params.data_frequency = self.data_frequency
# perf_tracker will be instantiated in __init__ if a sim_params
# is passed to the constructor. If not, we instantiate here.
if self.perf_tracker is None:
self.perf_tracker = PerformanceTracker(sim_params)
self.data_gen = self._create_data_generator(source_filter,
sim_params)
self.trading_client = AlgorithmSimulator(self, sim_params)
transact_method = transact_partial(self.slippage, self.commission)
self.set_transact(transact_method)
return self.trading_client.transform(self.data_gen)示例3: __init__
def __init__(self):
self.transact = transact_partial(VolumeShareSlippage(), PerShare())
# these orders are aggregated by sid
self.open_orders = defaultdict(list)
# keep a dict of orders by their own id
self.orders = {}
# holding orders that have come in since the last
# event.
self.new_orders = []
self.current_dt = None示例4: __init__
def __init__(self):
self.transact = transact_partial(VolumeShareSlippage(), PerShare())
# these orders are aggregated by sid
self.open_orders = defaultdict(list)
# keep a dict of orders by their own id
self.orders = {}
# track transactions by sid and by order
self.txns_by_sid = defaultdict(list)
self.txns_by_order = defaultdict(list)
# holding orders that have come in since the last
# event.
self.new_orders = []示例5: __init__
def __init__(self, fill_delay=timedelta(minutes=1)):
self.transact = transact_partial(VolumeShareSlippage(), PerShare())
# these orders are aggregated by sid
self.open_orders = defaultdict(list)
# keep a dict of orders by their own id
self.orders = {}
# holding orders that have come in since the last
# event.
self.new_orders = []
self.current_dt = None
self.max_shares = int(1e+11)
self.fill_delay = fill_delay示例6: _create_generator
def _create_generator(self, environment):
"""
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.trading_client = tsc(self, environment)
transact_method = transact_partial(self.slippage, self.commission)
self.set_transact(transact_method)
return self.trading_client.simulate(self.with_tnfms)示例7: _create_generator
def _create_generator(self, environment):
"""
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)
# Group together events with the same dt field. This depends on the
# events already being sorted.
self.grouped_by_date = groupby(self.with_tnfms, attrgetter('dt'))
self.trading_client = tsc(self, environment)
transact_method = transact_partial(self.slippage, self.commission)
self.set_transact(transact_method)
return self.trading_client.simulate(self.grouped_by_date)示例8: _create_generator
def _create_generator(self, sim_params, source_filter=None):
"""
Create a basic generator setup 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.
"""
self.data_gen = self._create_data_generator(source_filter, sim_params)
self.trading_client = AlgorithmSimulator(self, sim_params)
transact_method = transact_partial(self.slippage, self.commission)
self.set_transact(transact_method)
return self.trading_client.transform(self.data_gen)示例9: __init__
def __init__(self):
self.transact = transact_partial(VolumeShareSlippage(), PerShare())
self.open_orders = defaultdict(list)