def test_bts_simulation_dt(self):
code = """
def initialize(context):
pass
"""
algo = self.make_algo(script=code, 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
assert dt == algo_simulator.simulation_dt
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)
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 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)
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 = CustomPerfTracker(
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)
# Zipline uses a lot of composition object oriented design.
# How does composition help keep the system flexible and allow for object to be
# substituted at run-time.
# Zipline is a backtester that runs on historical data but it's also
# the engine that drives Quantopion's live trading.
# What objects might be modified to allow for live trading and greater functionality?
# What Are the other key objects? (hint, look at the blotter)
self.trading_client = AlgorithmSimulator(self, sim_params)
transact_method = transact_partial(self.slippage, self.commission)
self.set_transact(transact_method)
# The transform method does the heavy lifting of the main zipline event loop
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.
"""
# 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)
