本文整理汇总了Python中zipline.finance.performance.PerformanceTracker.handle_simulation_end方法的典型用法代码示例。如果您正苦于以下问题：Python PerformanceTracker.handle_simulation_end方法的具体用法？Python PerformanceTracker.handle_simulation_end怎么用？Python PerformanceTracker.handle_simulation_end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.handle_simulation_end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: AlgorithmSimulator
# 需要导入模块: from zipline.finance.performance import PerformanceTracker [as 别名]
# 或者: from zipline.finance.performance.PerformanceTracker import handle_simulation_end [as 别名]
#.........这里部分代码省略.........
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
yield risk_message
def get_message(self, date):
rvars = self.algo.recorded_vars
if self.perf_tracker.emission_rate == 'daily':
perf_message = \
self.perf_tracker.handle_market_close()
perf_message['daily_perf']['recorded_vars'] = rvars
return perf_message
elif self.perf_tracker.emission_rate == 'minute':
self.perf_tracker.handle_minute_close(date)
perf_message = self.perf_tracker.to_dict()
perf_message['intraday_perf']['recorded_vars'] = rvars
return perf_message
def update_universe(self, event):
"""
Update the universe with new event information.
"""
# Update our knowledge of this event's sid
sid_data = self.current_data[event.sid]
sid_data.__dict__.update(event.__dict__)
def simulate_snapshot(self, date):
"""
Run the user's algo against our current snapshot and update
the algo's simulated time.
"""
# Needs to be set so that we inject the proper date into algo
# log/print lines.
self.snapshot_dt = date
self.algo.set_datetime(self.snapshot_dt)
# Update the simulation time.
self.simulation_dt = date
self.algo.handle_data(self.current_data)