本文整理汇总了Python中zipline.finance.performance.PerformanceTracker.get_portfolio方法的典型用法代码示例。如果您正苦于以下问题:Python PerformanceTracker.get_portfolio方法的具体用法?Python PerformanceTracker.get_portfolio怎么用?Python PerformanceTracker.get_portfolio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.performance.PerformanceTracker
的用法示例。
在下文中一共展示了PerformanceTracker.get_portfolio方法的5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.performance import PerformanceTracker [as 别名]
# 或者: from zipline.finance.performance.PerformanceTracker import get_portfolio [as 别名]
#.........这里部分代码省略.........
def order_value(self, sid, value,
limit_price=None, stop_price=None, style=None):
"""
Place an order by desired value rather than desired number of shares.
If the requested sid is found in the universe, the requested value is
divided by its price to imply the number of shares to transact.
If the Asset being ordered is a Future, the 'value' calculated
is actually the exposure, as Futures have no 'value'.
value > 0 :: Buy/Cover
value < 0 :: Sell/Short
Market order: order(sid, value)
Limit order: order(sid, value, limit_price)
Stop order: order(sid, value, None, stop_price)
StopLimit order: order(sid, value, limit_price, stop_price)
"""
amount = self._calculate_order_value_amount(sid, value)
return self.order(sid, amount,
limit_price=limit_price,
stop_price=stop_price,
style=style)
@property
def recorded_vars(self):
return copy(self._recorded_vars)
@property
def portfolio(self):
return self.updated_portfolio()
def updated_portfolio(self):
if self.portfolio_needs_update:
self._portfolio = \
self.perf_tracker.get_portfolio(self.performance_needs_update)
self.portfolio_needs_update = False
self.performance_needs_update = False
return self._portfolio
@property
def account(self):
return self.updated_account()
def updated_account(self):
if self.account_needs_update:
self._account = \
self.perf_tracker.get_account(self.performance_needs_update)
self.account_needs_update = False
self.performance_needs_update = False
return self._account
def set_logger(self, logger):
self.logger = logger
def on_dt_changed(self, dt):
"""
Callback triggered by the simulation loop whenever the current dt
changes.
Any logic that should happen exactly once at the start of each datetime
group should happen here.
"""
assert isinstance(dt, datetime), \
"Attempt to set algorithm's current time with non-datetime"
assert dt.tzinfo == pytz.utc, \
"Algorithm expects a utc datetime"
示例2: TradingAlgorithm
# 需要导入模块: from zipline.finance.performance import PerformanceTracker [as 别名]
# 或者: from zipline.finance.performance.PerformanceTracker import get_portfolio [as 别名]
#.........这里部分代码省略.........
value < 0 :: Sell/Short
Market order: order(sid, value)
Limit order: order(sid, value, limit_price)
Stop order: order(sid, value, None, stop_price)
StopLimit order: order(sid, value, limit_price, stop_price)
"""
last_price = self.trading_client.current_data[sid].price
if np.allclose(last_price, 0):
zero_message = "Price of 0 for {psid}; can't infer value".format(
psid=sid
)
if self.logger:
self.logger.debug(zero_message)
# Don't place any order
return
else:
amount = value / last_price
return self.order(sid, amount,
limit_price=limit_price,
stop_price=stop_price,
style=style)
@property
def recorded_vars(self):
return copy(self._recorded_vars)
@property
def portfolio(self):
return self.updated_portfolio()
def updated_portfolio(self):
if self.portfolio_needs_update:
self._portfolio = \
self.perf_tracker.get_portfolio(self.performance_needs_update)
self.portfolio_needs_update = False
self.performance_needs_update = False
return self._portfolio
@property
def account(self):
return self.updated_account()
def updated_account(self):
if self.account_needs_update:
self._account = \
self.perf_tracker.get_account(self.performance_needs_update)
self.account_needs_update = False
self.performance_needs_update = False
return self._account
def set_logger(self, logger):
self.logger = logger
def on_dt_changed(self, dt):
"""
Callback triggered by the simulation loop whenever the current dt
changes.
Any logic that should happen exactly once at the start of each datetime
group should happen here.
"""
assert isinstance(dt, datetime), \
"Attempt to set algorithm's current time with non-datetime"
assert dt.tzinfo == pytz.utc, \
"Algorithm expects a utc datetime"
示例3: TradingAlgorithm
# 需要导入模块: from zipline.finance.performance import PerformanceTracker [as 别名]
# 或者: from zipline.finance.performance.PerformanceTracker import get_portfolio [as 别名]
#.........这里部分代码省略.........
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_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
self.data_gen = self._create_data_generator(source_filter,
sim_params)
self.perf_tracker = PerformanceTracker(sim_params)
self.trading_client = AlgorithmSimulator(self, sim_params)
transact_method = transact_partial(self.slippage, self.commission)
self.set_transact(transact_method)
self.blotter.leverage = leverage_partial(self.leverage, self.perf_tracker.get_portfolio())
return self.trading_client.transform(self.data_gen)
def get_generator(self):
"""
Override this method to add new logic to the construction
of the generator. Overrides can use the _create_generator
method to get a standard construction generator.
"""
return self._create_generator(self.sim_params)
def initialize(self, *args, **kwargs):
pass
# TODO: make a new subclass, e.g. BatchAlgorithm, and move
# the run method to the subclass, and refactor to put the
# generator creation logic into get_generator.
def run(self, source, sim_params=None, benchmark_return_source=None):
"""Run the algorithm.
:Arguments:
source : can be either:
- pandas.DataFrame
- zipline source
- list of zipline sources
If pandas.DataFrame is provided, it must have the
following structure:
* column names must consist of ints representing the
different sids
* index must be DatetimeIndex
* array contents should be price info.
示例4: TradingAlgorithm
# 需要导入模块: from zipline.finance.performance import PerformanceTracker [as 别名]
# 或者: from zipline.finance.performance.PerformanceTracker import get_portfolio [as 别名]
#.........这里部分代码省略.........
def order_value(self, sid, value, limit_price=None, stop_price=None):
"""
Place an order by desired value rather than desired number of shares.
If the requested sid is found in the universe, the requested value is
divided by its price to imply the number of shares to transact.
value > 0 :: Buy/Cover
value < 0 :: Sell/Short
Market order: order(sid, value)
Limit order: order(sid, value, limit_price)
Stop order: order(sid, value, None, stop_price)
StopLimit order: order(sid, value, limit_price, stop_price)
"""
last_price = self.trading_client.current_data[sid].price
if np.allclose(last_price, 0):
zero_message = "Price of 0 for {psid}; can't infer value".format(
psid=sid
)
self.logger.debug(zero_message)
# Don't place any order
return
else:
amount = value / last_price
return self.order(sid, amount, limit_price, stop_price)
@property
def recorded_vars(self):
return copy(self._recorded_vars)
@property
def portfolio(self):
# internally this will cause a refresh of the
# period performance calculations.
return self.perf_tracker.get_portfolio()
def updated_portfolio(self):
# internally this will cause a refresh of the
# period performance calculations.
if self.portfolio_needs_update:
self._portfolio = self.perf_tracker.get_portfolio()
self.portfolio_needs_update = False
return self._portfolio
def set_logger(self, logger):
self.logger = logger
def set_datetime(self, dt):
assert isinstance(dt, datetime), \
"Attempt to set algorithm's current time with non-datetime"
assert dt.tzinfo == pytz.utc, \
"Algorithm expects a utc datetime"
self.datetime = dt
@api_method
def get_datetime(self):
"""
Returns a copy of the datetime.
"""
date_copy = copy(self.datetime)
assert date_copy.tzinfo == pytz.utc, \
"Algorithm should have a utc datetime"
return date_copy
def set_transact(self, transact):
"""
Set the method that will be called to create a
示例5: AlgorithmSimulator
# 需要导入模块: from zipline.finance.performance import PerformanceTracker [as 别名]
# 或者: from zipline.finance.performance.PerformanceTracker import get_portfolio [as 别名]
#.........这里部分代码省略.........
# inject the current algo
# snapshot time to any log record generated.
with self.processor.threadbound():
updated = False
bm_updated = False
for date, snapshot in stream:
self.perf_tracker.set_date(date)
self.algo.blotter.set_date(date)
# If we're still in the warmup period. Use the event to
# update our universe, but don't yield any perf messages,
# and don't send a snapshot to handle_data.
if date < self.algo_start:
for event in snapshot:
if event.type in (DATASOURCE_TYPE.TRADE,
DATASOURCE_TYPE.CUSTOM):
self.update_universe(event)
self.perf_tracker.process_event(event)
else:
for event in snapshot:
if event.type in (DATASOURCE_TYPE.TRADE,
DATASOURCE_TYPE.CUSTOM):
self.update_universe(event)
updated = True
if event.type == DATASOURCE_TYPE.BENCHMARK:
bm_updated = True
txns, orders = self.algo.blotter.process_trade(event)
for data in chain([event], txns, orders):
self.perf_tracker.process_event(data)
# Update our portfolio.
self.algo.set_portfolio(self.perf_tracker.get_portfolio())
# Send the current state of the universe
# to the user's algo.
if updated:
self.simulate_snapshot(date)
updated = False
# run orders placed in the algorithm call
# above through perf tracker before emitting
# the perf packet, so that the perf includes
# placed orders
for order in self.algo.blotter.new_orders:
self.perf_tracker.process_event(order)
self.algo.blotter.new_orders = []
# The benchmark is our internal clock. When it
# updates, we need to emit a performance message.
if bm_updated:
bm_updated = False
yield self.get_message(date)
risk_message = self.perf_tracker.handle_simulation_end()
# When emitting minutely, it is still useful to have a final
# packet with the entire days performance rolled up.
if self.perf_tracker.emission_rate == 'minute':
daily_rollup = self.perf_tracker.to_dict(
emission_type='daily'
)
daily_rollup['daily_perf']['recorded_vars'] = \
self.algo.recorded_vars
yield daily_rollup