Python PerformanceTracker.transform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 transform [as 别名]
class TradeSimulationClient(object):
    """
    Generator-style class that takes the expected output of a merge, a
    user algorithm, a trading environment, and a simulator slippage as
    arguments.  Pipes the merge stream through a TransactionSimulator
    and a PerformanceTracker, which keep track of the current state of
    our algorithm's simulated universe. Results are fed to the user's
    algorithm, which directly inserts transactions into the
    TransactionSimulator's order book.

    TransactionSimulator maintains a dictionary from sids to the
    as-yet unfilled orders placed by the user's algorithm.  As trade
    events arrive, if the algorithm has open orders against the
    trade's sid, the simulator will fill orders up to 25% of market
    cap.  Applied transactions are added to a txn field on the event
    and forwarded to PerformanceTracker. The txn field is set to None
    on non-trade events and events that do not match any open orders.

    PerformanceTracker receives the updated event messages from
    TransactionSimulator, maintaining a set of daily and cumulative
    performance metrics for the algorithm.  The tracker removes the
    txn field from each event it receives, replacing it with a
    portfolio field to be fed into the user algo. At the end of each
    trading day, the PerformanceTracker also generates a daily
    performance report, which is appended to event's perf_report
    field.

    Fully processed events are fed to AlgorithmSimulator, which
    batches together events with the same dt field into a single
    snapshot to be fed to the algo. The portfolio object is repeatedly
    overwritten so that only the most recent snapshot of the universe
    is sent to the algo.
    """

    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.perf_tracker,
            self.algo,
            self.algo_start
        )

    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 simulate(self, stream_in):
        """
        Main generator work loop.
        """

        # Simulate filling any open orders made by the previous run of
        # the user's algorithm.  Fills the Transaction field on any
        # event that results in a filled order.
        with_filled_orders = self.ordering_client.transform(stream_in)

        # Pipe the events with transactions to perf. This will remove
        # the TRANSACTION field added by TransactionSimulator and replace it
        # with a portfolio field to be passed to the user's
        # algorithm. Also adds a perf_messages field which is usually
        # empty, but contains update messages once per day.
        with_portfolio = self.perf_tracker.transform(with_filled_orders)

        # Pass the messages from perf to the user's algorithm for simulation.
        # Events are batched by dt so that the algo handles all events for a
        # given timestamp at one one go.
        performance_messages = self.algo_sim.transform(with_portfolio)

        # The algorithm will yield a daily_results message (as
        # calculated by the performance tracker) at the end of each
        # day.  It will also yield a risk report at the end of the
        # simulation.
        for message in performance_messages:
            yield message