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)

    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)

    def __init__(self, algo, environment):

        self.algo = algo
        self.environment = environment

        self.ordering_client = TransactionSimulator()
        self.perf_tracker = PerformanceTracker(self.environment)

        self.algo_start = self.environment.first_open
        self.algo_sim = AlgorithmSimulator(self.ordering_client, self.algo, self.algo_start)

    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 __init__(self, algo, sim_params):

        self.algo = algo
        self.sim_params = sim_params

        self.ordering_client = TransactionSimulator()
        self.perf_tracker = PerformanceTracker(self.sim_params)

        self.algo_start = self.sim_params.first_open
        self.algo_sim = AlgorithmSimulator(
            self.ordering_client,
            self.perf_tracker,
            self.algo,
            self.algo_start
        )

    def __init__(self, algo, sim_params, blotter=None):

        self.algo = algo
        self.sim_params = sim_params

        if not blotter:
            self.blotter = Blotter()

        self.perf_tracker = PerformanceTracker(self.sim_params)

        self.algo_start = self.sim_params.first_open
        self.algo_sim = AlgorithmSimulator(
            self.blotter,
            self.perf_tracker,
            self.algo,
            self.algo_start
        )

    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.
        """
        # Instantiate perf_tracker
        self.perf_tracker = PerformanceTracker(sim_params)
        self.portfolio_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)

    def __init__(self, *args, **kwargs):
        """Initialize sids and other state variables.

        :Arguments:
        :Optional:
            initialize : function
                Function that is called with a single
                argument at the begninning of the simulation.
            handle_data : function
                Function that is called with 2 arguments
                (context and data) on every bar.
            script : str
                Algoscript that contains initialize and
                handle_data function definition.
            data_frequency : {'daily', 'minute'}
               The duration of the bars.
            capital_base : float <default: 1.0e5>
               How much capital to start with.
            instant_fill : bool <default: False>
               Whether to fill orders immediately or on next bar.
            asset_finder : An AssetFinder object
                A new AssetFinder object to be used in this TradingEnvironment
            equities_metadata : can be either:
                            - dict
                            - pandas.DataFrame
                            - object with 'read' property
                If dict is provided, it must have the following structure:
                * keys are the identifiers
                * values are dicts containing the metadata, with the metadata
                  field name as the key
                If pandas.DataFrame is provided, it must have the
                following structure:
                * column names must be the metadata fields
                * index must be the different asset identifiers
                * array contents should be the metadata value
                If an object with a 'read' property is provided, 'read' must
                return rows containing at least one of 'sid' or 'symbol' along
                with the other metadata fields.
            identifiers : List
                Any asset identifiers that are not provided in the
                equities_metadata, but will be traded by this TradingAlgorithm
        """
        self.sources = []

        # List of trading controls to be used to validate orders.
        self.trading_controls = []

        # List of account controls to be checked on each bar.
        self.account_controls = []

        self._recorded_vars = {}
        self.namespace = kwargs.pop('namespace', {})

        self._platform = kwargs.pop('platform', 'zipline')

        self.logger = None

        self.benchmark_return_source = None

        # default components for transact
        self.slippage = VolumeShareSlippage()
        self.commission = PerShare()

        self.instant_fill = kwargs.pop('instant_fill', False)

        # If an env has been provided, pop it
        self.trading_environment = kwargs.pop('env', None)

        if self.trading_environment is None:
            self.trading_environment = TradingEnvironment()

        # Update the TradingEnvironment with the provided asset metadata
        self.trading_environment.write_data(
            equities_data=kwargs.pop('equities_metadata', {}),
            equities_identifiers=kwargs.pop('identifiers', []),
            futures_data=kwargs.pop('futures_metadata', {}),
        )

        # set the capital base
        self.capital_base = kwargs.pop('capital_base', DEFAULT_CAPITAL_BASE)
        self.sim_params = kwargs.pop('sim_params', None)
        if self.sim_params is None:
            self.sim_params = create_simulation_parameters(
                capital_base=self.capital_base,
                start=kwargs.pop('start', None),
                end=kwargs.pop('end', None),
                env=self.trading_environment,
            )
        else:
            self.sim_params.update_internal_from_env(self.trading_environment)

        # Build a perf_tracker
        self.perf_tracker = PerformanceTracker(sim_params=self.sim_params,
                                               env=self.trading_environment)

        # Pull in the environment's new AssetFinder for quick reference
        self.asset_finder = self.trading_environment.asset_finder
        self.init_engine(kwargs.pop('ffc_loader', None))

        # Maps from name to Term
#.........这里部分代码省略.........

#.........这里部分代码省略.........
        # default components for transact
        self.slippage = VolumeShareSlippage()
        self.commission = PerShare()

        self.instant_fill = kwargs.pop('instant_fill', False)

        # If an env has been provided, pop it
        self.trading_environment = kwargs.pop('env', None)

        if self.trading_environment is None:
            self.trading_environment = TradingEnvironment()

        # Update the TradingEnvironment with the provided asset metadata
        self.trading_environment.write_data(
            equities_data=kwargs.pop('equities_metadata', {}),
            equities_identifiers=kwargs.pop('identifiers', []),
            futures_data=kwargs.pop('futures_metadata', {}),
        )

        # set the capital base
        self.capital_base = kwargs.pop('capital_base', DEFAULT_CAPITAL_BASE)
        self.sim_params = kwargs.pop('sim_params', None)
        if self.sim_params is None:
            self.sim_params = create_simulation_parameters(
                capital_base=self.capital_base,
                start=kwargs.pop('start', None),
                end=kwargs.pop('end', None),
                env=self.trading_environment,
            )
        else:
            self.sim_params.update_internal_from_env(self.trading_environment)

        # Build a perf_tracker
        self.perf_tracker = PerformanceTracker(sim_params=self.sim_params,
                                               env=self.trading_environment)

        # Pull in the environment's new AssetFinder for quick reference
        self.asset_finder = self.trading_environment.asset_finder
        self.init_engine(kwargs.pop('ffc_loader', None))

        # Maps from name to Term
        self._filters = {}
        self._factors = {}
        self._classifiers = {}

        self.blotter = kwargs.pop('blotter', None)
        if not self.blotter:
            self.blotter = Blotter()

        # Set the dt initally to the period start by forcing it to change
        self.on_dt_changed(self.sim_params.period_start)

        # The symbol lookup date specifies the date to use when resolving
        # symbols to sids, and can be set using set_symbol_lookup_date()
        self._symbol_lookup_date = None

        self.portfolio_needs_update = True
        self.account_needs_update = True
        self.performance_needs_update = True
        self._portfolio = None
        self._account = None

        self.history_container_class = kwargs.pop(
            'history_container_class', HistoryContainer,
        )
        self.history_container = None

    def transaction_sim(self, **params):
        """ This is a utility method that asserts expected
        results for conversion of orders to transactions given a
        trade history"""

        trade_count = params['trade_count']
        trade_interval = params['trade_interval']
        order_count = params['order_count']
        order_amount = params['order_amount']
        order_interval = params['order_interval']
        expected_txn_count = params['expected_txn_count']
        expected_txn_volume = params['expected_txn_volume']
        # optional parameters
        # ---------------------
        # if present, alternate between long and short sales
        alternate = params.get('alternate')
        # if present, expect transaction amounts to match orders exactly.
        complete_fill = params.get('complete_fill')

        sid = 1
        sim_params = factory.create_simulation_parameters()
        blotter = Blotter()
        price = [10.1] * trade_count
        volume = [100] * trade_count
        start_date = sim_params.first_open

        generated_trades = factory.create_trade_history(
            sid,
            price,
            volume,
            trade_interval,
            sim_params,
            env=self.env,
        )

        if alternate:
            alternator = -1
        else:
            alternator = 1

        order_date = start_date
        for i in range(order_count):

            blotter.set_date(order_date)
            blotter.order(sid, order_amount * alternator ** i, MarketOrder())

            order_date = order_date + order_interval
            # move after market orders to just after market next
            # market open.
            if order_date.hour >= 21:
                if order_date.minute >= 00:
                    order_date = order_date + timedelta(days=1)
                    order_date = order_date.replace(hour=14, minute=30)

        # there should now be one open order list stored under the sid
        oo = blotter.open_orders
        self.assertEqual(len(oo), 1)
        self.assertTrue(sid in oo)
        order_list = oo[sid][:]  # make copy
        self.assertEqual(order_count, len(order_list))

        for i in range(order_count):
            order = order_list[i]
            self.assertEqual(order.sid, sid)
            self.assertEqual(order.amount, order_amount * alternator ** i)

        tracker = PerformanceTracker(sim_params, env=self.env)

        benchmark_returns = [
            Event({'dt': dt,
                   'returns': ret,
                   'type':
                   zipline.protocol.DATASOURCE_TYPE.BENCHMARK,
                   'source_id': 'benchmarks'})
            for dt, ret in self.env.benchmark_returns.iteritems()
            if dt.date() >= sim_params.period_start.date() and
            dt.date() <= sim_params.period_end.date()
        ]

        generated_events = date_sorted_sources(generated_trades,
                                               benchmark_returns)

        # this approximates the loop inside TradingSimulationClient
        transactions = []
        for dt, events in itertools.groupby(generated_events,
                                            operator.attrgetter('dt')):
            for event in events:
                if event.type == DATASOURCE_TYPE.TRADE:

                    for txn, order in blotter.process_trade(event):
                        transactions.append(txn)
                        tracker.process_transaction(txn)
                elif event.type == DATASOURCE_TYPE.BENCHMARK:
                    tracker.process_benchmark(event)
                elif event.type == DATASOURCE_TYPE.TRADE:
                    tracker.process_trade(event)

        if complete_fill:
            self.assertEqual(len(transactions), len(order_list))

#.........这里部分代码省略.........

class TradingAlgorithm(object):
    """
    Base class for trading algorithms. Inherit and overload
    initialize() and handle_data(data).

    A new algorithm could look like this:
    ```
    from zipline.api import order

    def initialize(context):
        context.sid = 'AAPL'
        context.amount = 100

    def handle_data(self, data):
        sid = context.sid
        amount = context.amount
        order(sid, amount)
    ```
    To then to run this algorithm pass these functions to
    TradingAlgorithm:

    my_algo = TradingAlgorithm(initialize, handle_data)
    stats = my_algo.run(data)

    """

    # If this is set to false then it is the responsibility
    # of the overriding subclass to set initialized = true
    AUTO_INITIALIZE = True

    def __init__(self, *args, **kwargs):
        """Initialize sids and other state variables.

        :Arguments:
        :Optional:
            initialize : function
                Function that is called with a single
                argument at the begninning of the simulation.
            handle_data : function
                Function that is called with 2 arguments
                (context and data) on every bar.
            script : str
                Algoscript that contains initialize and
                handle_data function definition.
            data_frequency : str (daily, hourly or minutely)
               The duration of the bars.
            capital_base : float <default: 1.0e5>
               How much capital to start with.
            instant_fill : bool <default: False>
               Whether to fill orders immediately or on next bar.
            environment : str <default: 'zipline'>
               The environment that this algorithm is running in.
        """
        self.datetime = None

        self.registered_transforms = {}
        self.transforms = []
        self.sources = []

        # List of trading controls to be used to validate orders.
        self.trading_controls = []

        self._recorded_vars = {}
        self.namespace = kwargs.get('namespace', {})

        self._environment = kwargs.pop('environment', 'zipline')

        self.logger = None

        self.benchmark_return_source = None

        # default components for transact
        self.slippage = VolumeShareSlippage()
        self.commission = PerShare()

        self.instant_fill = kwargs.pop('instant_fill', False)

        # set the capital base
        self.capital_base = kwargs.pop('capital_base', DEFAULT_CAPITAL_BASE)

        self.sim_params = kwargs.pop('sim_params', None)
        if self.sim_params is None:
            self.sim_params = create_simulation_parameters(
                capital_base=self.capital_base
            )
        self.perf_tracker = PerformanceTracker(self.sim_params)

        self.blotter = kwargs.pop('blotter', None)
        if not self.blotter:
            self.blotter = Blotter()

        self.portfolio_needs_update = True
        self.account_needs_update = True
        self.performance_needs_update = True
        self._portfolio = None
        self._account = None

        self.history_container = None
        self.history_specs = {}

#.........这里部分代码省略.........

    def __init__(self, *args, **kwargs):
        """Initialize sids and other state variables.

        :Arguments:
        :Optional:
            initialize : function
                Function that is called with a single
                argument at the begninning of the simulation.
            handle_data : function
                Function that is called with 2 arguments
                (context and data) on every bar.
            script : str
                Algoscript that contains initialize and
                handle_data function definition.
            data_frequency : str (daily, hourly or minutely)
               The duration of the bars.
            capital_base : float <default: 1.0e5>
               How much capital to start with.
            instant_fill : bool <default: False>
               Whether to fill orders immediately or on next bar.
            environment : str <default: 'zipline'>
               The environment that this algorithm is running in.
        """
        self.datetime = None

        self.registered_transforms = {}
        self.transforms = []
        self.sources = []

        # List of trading controls to be used to validate orders.
        self.trading_controls = []

        self._recorded_vars = {}
        self.namespace = kwargs.get('namespace', {})

        self._environment = kwargs.pop('environment', 'zipline')

        self.logger = None

        self.benchmark_return_source = None

        # default components for transact
        self.slippage = VolumeShareSlippage()
        self.commission = PerShare()

        self.instant_fill = kwargs.pop('instant_fill', False)

        # set the capital base
        self.capital_base = kwargs.pop('capital_base', DEFAULT_CAPITAL_BASE)

        self.sim_params = kwargs.pop('sim_params', None)
        if self.sim_params is None:
            self.sim_params = create_simulation_parameters(
                capital_base=self.capital_base
            )
        self.perf_tracker = PerformanceTracker(self.sim_params)

        self.blotter = kwargs.pop('blotter', None)
        if not self.blotter:
            self.blotter = Blotter()

        self.portfolio_needs_update = True
        self.account_needs_update = True
        self.performance_needs_update = True
        self._portfolio = None
        self._account = None

        self.history_container = None
        self.history_specs = {}

        # If string is passed in, execute and get reference to
        # functions.
        self.algoscript = kwargs.pop('script', None)

        self._initialize = None
        self._before_trading_start = None
        self._analyze = None

        self.event_manager = EventManager()

        if self.algoscript is not None:
            exec_(self.algoscript, self.namespace)
            self._initialize = self.namespace.get('initialize')
            if 'handle_data' not in self.namespace:
                raise ValueError('You must define a handle_data function.')
            else:
                self._handle_data = self.namespace['handle_data']

            self._before_trading_start = \
                self.namespace.get('before_trading_start')
            # Optional analyze function, gets called after run
            self._analyze = self.namespace.get('analyze')

        elif kwargs.get('initialize') and kwargs.get('handle_data'):
            if self.algoscript is not None:
                raise ValueError('You can not set script and \
                initialize/handle_data.')
            self._initialize = kwargs.pop('initialize')
            self._handle_data = kwargs.pop('handle_data')
            self._before_trading_start = kwargs.pop('before_trading_start',
#.........这里部分代码省略.........

    def __init__(self, *args, **kwargs):
        """Initialize sids and other state variables.

        :Arguments:
        :Optional:
            initialize : function
                Function that is called with a single
                argument at the begninning of the simulation.
            handle_data : function
                Function that is called with 2 arguments
                (context and data) on every bar.
            script : str
                Algoscript that contains initialize and
                handle_data function definition.
            data_frequency : str (daily, hourly or minutely)
               The duration of the bars.
            annualizer : int <optional>
               Which constant to use for annualizing risk metrics.
               If not provided, will extract from data_frequency.
            capital_base : float <default: 1.0e5>
               How much capital to start with.
            instant_fill : bool <default: False>
               Whether to fill orders immediately or on next bar.
        """
        self.datetime = None

        self.registered_transforms = {}
        self.transforms = []
        self.sources = []

        self._recorded_vars = {}

        self.logger = None

        self.benchmark_return_source = None
        self.perf_tracker = None

        # default components for transact
        self.slippage = VolumeShareSlippage()
        self.commission = PerShare()

        if 'data_frequency' in kwargs:
            self.set_data_frequency(kwargs.pop('data_frequency'))
        else:
            self.data_frequency = None

        self.instant_fill = kwargs.pop('instant_fill', False)

        # Override annualizer if set
        if 'annualizer' in kwargs:
            self.annualizer = kwargs['annualizer']

        # set the capital base
        self.capital_base = kwargs.pop('capital_base', DEFAULT_CAPITAL_BASE)

        self.sim_params = kwargs.pop('sim_params', None)
        if self.sim_params:
            if self.data_frequency is None:
                self.data_frequency = self.sim_params.data_frequency
            else:
                self.sim_params.data_frequency = self.data_frequency

            self.perf_tracker = PerformanceTracker(self.sim_params)

        self.blotter = kwargs.pop('blotter', None)
        if not self.blotter:
            self.blotter = Blotter()

        self.portfolio_needs_update = True
        self._portfolio = None

        # If string is passed in, execute and get reference to
        # functions.
        self.algoscript = kwargs.pop('script', None)

        if self.algoscript is not None:
            self.ns = {}
            exec_(self.algoscript, self.ns)
            if 'initialize' not in self.ns:
                raise ValueError('You must define an initialze function.')
            if 'handle_data' not in self.ns:
                raise ValueError('You must define a handle_data function.')
            self._initialize = self.ns['initialize']
            self._handle_data = self.ns['handle_data']

        # If two functions are passed in assume initialize and
        # handle_data are passed in.
        elif kwargs.get('initialize', False) and kwargs.get('handle_data'):
            if self.algoscript is not None:
                raise ValueError('You can not set script and \
                initialize/handle_data.')
            self._initialize = kwargs.pop('initialize')
            self._handle_data = kwargs.pop('handle_data')

        # an algorithm subclass needs to set initialized to True when
        # it is fully initialized.
        self.initialized = False

        self.initialize(*args, **kwargs)

    def transaction_sim(self, **params):
        """ This is a utility method that asserts expected
        results for conversion of orders to transactions given a
        trade history"""

        trade_count = params['trade_count']
        trade_interval = params['trade_interval']
        trade_delay = params.get('trade_delay')
        order_count = params['order_count']
        order_amount = params['order_amount']
        order_interval = params['order_interval']
        expected_txn_count = params['expected_txn_count']
        expected_txn_volume = params['expected_txn_volume']
        # optional parameters
        # ---------------------
        # if present, alternate between long and short sales
        alternate = params.get('alternate')
        # if present, expect transaction amounts to match orders exactly.
        complete_fill = params.get('complete_fill')

        sid = 1
        sim_params = factory.create_simulation_parameters()
        trade_sim = TransactionSimulator()
        price = [10.1] * trade_count
        volume = [100] * trade_count
        start_date = sim_params.first_open

        generated_trades = factory.create_trade_history(
            sid,
            price,
            volume,
            trade_interval,
            sim_params
        )

        if alternate:
            alternator = -1
        else:
            alternator = 1

        order_date = start_date
        for i in xrange(order_count):
            order = ndict({
                'sid': sid,
                'amount': order_amount * alternator ** i,
                'dt': order_date
            })

            trade_sim.place_order(order)

            order_date = order_date + order_interval
            # move after market orders to just after market next
            # market open.
            if order_date.hour >= 21:
                    if order_date.minute >= 00:
                        order_date = order_date + timedelta(days=1)
                        order_date = order_date.replace(hour=14, minute=30)

        # there should now be one open order list stored under the sid
        oo = trade_sim.open_orders
        self.assertEqual(len(oo), 1)
        self.assertTrue(sid in oo)
        order_list = oo[sid]
        self.assertEqual(order_count, len(order_list))

        for i in xrange(order_count):
            order = order_list[i]
            self.assertEqual(order.sid, sid)
            self.assertEqual(order.amount, order_amount * alternator ** i)

        tracker = PerformanceTracker(sim_params)

        # this approximates the loop inside TradingSimulationClient
        transactions = []
        for trade in generated_trades:
            if trade_delay:
                trade.dt = trade.dt + trade_delay
            trade_sim.update(trade)
            if trade.TRANSACTION:
                transactions.append(trade.TRANSACTION)

            tracker.process_event(trade)

        if complete_fill:
            self.assertEqual(len(transactions), len(order_list))

        total_volume = 0
        for i in xrange(len(transactions)):
            txn = transactions[i]
            total_volume += txn.amount
            if complete_fill:
                order = order_list[i]
                self.assertEqual(order.amount, txn.amount)

        self.assertEqual(total_volume, expected_txn_volume)
        self.assertEqual(len(transactions), expected_txn_count)

        cumulative_pos = tracker.cumulative_performance.positions[sid]
        self.assertEqual(total_volume, cumulative_pos.amount)

#.........这里部分代码省略.........

#.........这里部分代码省略.........
                            "close": [10.1] * len(days),
                            "volume": [100] * len(days),
                            "day": [day.value for day in days],
                        },
                        index=days,
                    )
                }

                path = os.path.join(tempdir.path, "testdata.bcolz")
                BcolzDailyBarWriter(path, days).write(assets.items())

                equity_daily_reader = BcolzDailyBarReader(path)

                data_portal = DataPortal(
                    env,
                    first_trading_day=equity_daily_reader.first_trading_day,
                    equity_daily_reader=equity_daily_reader,
                )

            if "default_slippage" not in params or not params["default_slippage"]:
                slippage_func = FixedSlippage()
            else:
                slippage_func = None

            blotter = Blotter(sim_params.data_frequency, self.env.asset_finder, slippage_func)

            start_date = sim_params.first_open

            if alternate:
                alternator = -1
            else:
                alternator = 1

            tracker = PerformanceTracker(sim_params, self.env)

            # replicate what tradesim does by going through every minute or day
            # of the simulation and processing open orders each time
            if sim_params.data_frequency == "minute":
                ticks = minutes
            else:
                ticks = days

            transactions = []

            order_list = []
            order_date = start_date
            for tick in ticks:
                blotter.current_dt = tick
                if tick >= order_date and len(order_list) < order_count:
                    # place an order
                    direction = alternator ** len(order_list)
                    order_id = blotter.order(
                        blotter.asset_finder.retrieve_asset(sid), order_amount * direction, MarketOrder()
                    )
                    order_list.append(blotter.orders[order_id])
                    order_date = order_date + order_interval
                    # move after market orders to just after market next
                    # market open.
                    if order_date.hour >= 21:
                        if order_date.minute >= 00:
                            order_date = order_date + timedelta(days=1)
                            order_date = order_date.replace(hour=14, minute=30)
                else:
                    bar_data = BarData(data_portal, lambda: tick, sim_params.data_frequency)
                    txns, _, closed_orders = blotter.get_transactions(bar_data)
                    for txn in txns:

#.........这里部分代码省略.........
        else:
            benchmark_return_source = self.benchmark_return_source

        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_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

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.........