Python PerformanceTracker.set_date方法代码示例

# 需要导入模块: from zipline.finance.performance import PerformanceTracker [as 别名]
# 或者: from zipline.finance.performance.PerformanceTracker import set_date [as 别名]

#.........这里部分代码省略.........
            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"

        self.datetime = dt
        self.perf_tracker.set_date(dt)
        self.blotter.set_date(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
        transaction from open orders and trade events.
        """
        self.blotter.transact = transact

    def update_dividends(self, dividend_frame):
        """
        Set DataFrame used to process dividends.  DataFrame columns should
        contain at least the entries in zp.DIVIDEND_FIELDS.
        """
        self.perf_tracker.update_dividends(dividend_frame)

    @api_method
    def set_slippage(self, slippage):
        if not isinstance(slippage, SlippageModel):
            raise UnsupportedSlippageModel()
        if self.initialized:
            raise OverrideSlippagePostInit()

# 需要导入模块: from zipline.finance.performance import PerformanceTracker [as 别名]
# 或者: from zipline.finance.performance.PerformanceTracker import set_date [as 别名]

#.........这里部分代码省略.........
            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"

        self.datetime = dt
        self.perf_tracker.set_date(dt)
        self.blotter.set_date(dt)

    @api_method
    def get_datetime(self, tz=None):
        """
        Returns the simulation datetime.
        """
        dt = self.datetime
        assert dt.tzinfo == pytz.utc, "Algorithm should have a utc datetime"

        if tz is not None:
            # Convert to the given timezone passed as a string or tzinfo.
            if isinstance(tz, string_types):
                tz = pytz.timezone(tz)
            dt = dt.astimezone(tz)

        return dt  # datetime.datetime objects are immutable.

    def set_transact(self, transact):
        """
        Set the method that will be called to create a
        transaction from open orders and trade events.
        """
        self.blotter.transact = transact

    def update_dividends(self, dividend_frame):
        """
        Set DataFrame used to process dividends.  DataFrame columns should
        contain at least the entries in zp.DIVIDEND_FIELDS.
        """
        self.perf_tracker.update_dividends(dividend_frame)

# 需要导入模块: from zipline.finance.performance import PerformanceTracker [as 别名]
# 或者: from zipline.finance.performance.PerformanceTracker import set_date [as 别名]
class AlgorithmSimulator(object):

    EMISSION_TO_PERF_KEY_MAP = {
        'minute': 'intraday_perf',
        'daily': 'daily_perf'
    }

    def get_hash(self):
        """
        There should only ever be one TSC in the system, so
        we don't bother passing args into the hash.
        """
        return self.__class__.__name__ + hash_args()

    def __init__(self, algo, sim_params):

        # ==============
        # Simulation
        # Param Setup
        # ==============
        self.sim_params = sim_params

        # ==============
        # Perf Tracker
        # Setup
        # ==============
        self.perf_tracker = PerformanceTracker(self.sim_params)

        self.perf_key = self.EMISSION_TO_PERF_KEY_MAP[
            self.perf_tracker.emission_rate]

        # ==============
        # Algo Setup
        # ==============
        self.algo = algo
        self.algo_start = self.sim_params.first_open
        self.algo_start = self.algo_start.replace(hour=0, minute=0,
                                                  second=0,
                                                  microsecond=0)

        # ==============
        # Snapshot Setup
        # ==============

        # The algorithm's data as of our most recent event.
        # We want an object that will have empty objects as default
        # values on missing keys.
        self.current_data = BarData()

        # We don't have a datetime for the current snapshot until we
        # receive a message.
        self.simulation_dt = None
        self.snapshot_dt = None

        # =============
        # Logging Setup
        # =============

        # Processor function for injecting the algo_dt into
        # user prints/logs.
        def inject_algo_dt(record):
            if not 'algo_dt' in record.extra:
                record.extra['algo_dt'] = self.snapshot_dt
        self.processor = Processor(inject_algo_dt)

    def transform(self, stream_in):
        """
        Main generator work loop.
        """
        # Set the simulation date to be the first event we see.
        peek_date, peek_snapshot = next(stream_in)
        self.simulation_dt = peek_date

        # Stitch back together the generator by placing the peeked
        # event back in front
        stream = itertools.chain([(peek_date, peek_snapshot)],
                                 stream_in)

        # 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:
#.........这里部分代码省略.........