Python AlgorithmSimulator.transform方法代码示例

# 需要导入模块: from zipline.gens.tradesimulation import AlgorithmSimulator [as 别名]
# 或者: from zipline.gens.tradesimulation.AlgorithmSimulator import transform [as 别名]
    def test_bts_simulation_dt(self):
        code = """
def initialize(context):
    pass
"""
        algo = TradingAlgorithm(script=code,
                                sim_params=self.sim_params,
                                env=self.env)

        algo.perf_tracker = PerformanceTracker(
            sim_params=self.sim_params,
            trading_calendar=self.trading_calendar,
            asset_finder=self.asset_finder,
        )

        dt = pd.Timestamp("2016-08-04 9:13:14", tz='US/Eastern')
        algo_simulator = AlgorithmSimulator(
            algo,
            self.sim_params,
            self.data_portal,
            BeforeTradingStartsOnlyClock(dt),
            algo._create_benchmark_source(),
            NoRestrictions(),
            None
        )

        # run through the algo's simulation
        list(algo_simulator.transform())

        # since the clock only ever emitted a single before_trading_start
        # event, we can check that the simulation_dt was properly set
        self.assertEqual(dt, algo_simulator.simulation_dt)

# 需要导入模块: from zipline.gens.tradesimulation import AlgorithmSimulator [as 别名]
# 或者: from zipline.gens.tradesimulation.AlgorithmSimulator import transform [as 别名]
    def test_bts_simulation_dt(self):
        code = """
def initialize(context):
    pass
"""
        algo = TradingAlgorithm(
            script=code,
            sim_params=self.sim_params,
            env=self.env,
            metrics=metrics.load('none'),
        )

        algo.metrics_tracker = algo._create_metrics_tracker()
        benchmark_source = algo._create_benchmark_source()
        algo.metrics_tracker.handle_start_of_simulation(benchmark_source)

        dt = pd.Timestamp("2016-08-04 9:13:14", tz='US/Eastern')
        algo_simulator = AlgorithmSimulator(
            algo,
            self.sim_params,
            self.data_portal,
            BeforeTradingStartsOnlyClock(dt),
            benchmark_source,
            NoRestrictions(),
            None
        )

        # run through the algo's simulation
        list(algo_simulator.transform())

        # since the clock only ever emitted a single before_trading_start
        # event, we can check that the simulation_dt was properly set
        self.assertEqual(dt, algo_simulator.simulation_dt)

# 需要导入模块: from zipline.gens.tradesimulation import AlgorithmSimulator [as 别名]
# 或者: from zipline.gens.tradesimulation.AlgorithmSimulator import transform [as 别名]
class TradingAlgorithm(object):
    """
    Base class for trading algorithms. Inherit and overload
    initialize() and handle_data(data).

    A new algorithm could look like this:
    ```
    class MyAlgo(TradingAlgorithm):
        def initialize(self, sids, amount):
            self.sids = sids
            self.amount = amount

        def handle_data(self, data):
            sid = self.sids[0]
            amount = self.amount
            self.order(sid, amount)
    ```
    To then to run this algorithm:

    my_algo = MyAlgo([0], 100) # first argument has to be list of sids
    stats = my_algo.run(data)

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

        :Arguments:
            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.
            fill_delay : datetime.timedelta
               Delay between placing an order and filling an order.
            capital_base : float <default: 1.0e5>
               How much capital to start with.
        """
        self._portfolio = None
        self.datetime = None

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

        self._recorded_vars = {}

        self.logger = None

        self.benchmark_return_source = None

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

        self.set_data_frequency(kwargs.pop('data_frequency', 'daily'))

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

        # 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:
            self.sim_params.data_frequency = self.data_frequency

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

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

        # call to user-defined constructor method
        self.initialize(*args, **kwargs)

    def __repr__(self):
        """
        N.B. this does not yet represent a string that can be used
        to instantiate an exact copy of an algorithm.

        However, it is getting close, and provides some value as something
        that can be inspected interactively.
        """
        return """
{class_name}(
    captial_base={capital_base}
    sim_params={sim_params},
    initialized={initialized},
    slippage={slippage},
    commission={commission},
    blotter={blotter},
    recorded_vars={recorded_vars})
""".strip().format(class_name=self.__class__.__name__,
                   capital_base=self.capital_base,
                   sim_params=repr(self.sim_params),
#.........这里部分代码省略.........

# 需要导入模块: from zipline.gens.tradesimulation import AlgorithmSimulator [as 别名]
# 或者: from zipline.gens.tradesimulation.AlgorithmSimulator import transform [as 别名]

#.........这里部分代码省略.........

    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 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)

    # 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, overwrite_sim_params=True,
            benchmark_return_source=None):
        """Run the algorithm.

        :Arguments:
            source : can be either:
                     - pandas.DataFrame
                     - zipline source
                     - list of sources

               If pandas.DataFrame is provided, it must have the
               following structure:
               * column names must be the different asset identifiers
               * index must be DatetimeIndex
               * array contents should be price info.

        :Returns:
            daily_stats : pandas.DataFrame
              Daily performance metrics such as returns, alpha etc.

# 需要导入模块: from zipline.gens.tradesimulation import AlgorithmSimulator [as 别名]
# 或者: from zipline.gens.tradesimulation.AlgorithmSimulator import transform [as 别名]
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.........

# 需要导入模块: from zipline.gens.tradesimulation import AlgorithmSimulator [as 别名]
# 或者: from zipline.gens.tradesimulation.AlgorithmSimulator import transform [as 别名]
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)

    """

    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.
#.........这里部分代码省略.........