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MAINT: One way to set sim_params and data_frequency.
There were sevaral places you could supply sim_params in TradingAlgorithm (__init__, run). This got confusing as its not clear who updated what and which one was the correct one to use at each time. Then there were to ways to define data_frequency, one in __init__() and one in the sim_params which also added code complexity. This refactor makes it explicit that sim_params are to be passed to __init__() only. Moreover, data_frequency is only stored in sim_params. For backwards compatibility, it can still be supplied separately but will link to the one in sim_params. For example, you could create new sim params via: sim_params = create_simulation_parameters(data_frequency='minute') algo = MyAlgo(sim_params) algo.run(data) In addition, perf_tracker only gets initialized in one place: _create_generator() which should also make the various ways of running an algorithm more deterministic. This also fixes a bug with SimulationParameters where you could not change the period_start. Unfortunately, the current implementation still requieres an implicit call to update the internal variables.
This commit is contained in:
Thomas Wiecki
+51
-54
@@ -13,6 +13,7 @@
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from copy import copy
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import warnings
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import pytz
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import pandas as pd
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@@ -147,11 +148,6 @@ class TradingAlgorithm(object):
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self.slippage = VolumeShareSlippage()
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self.commission = PerShare()
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if 'data_frequency' in kwargs:
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self.set_data_frequency(kwargs.pop('data_frequency'))
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else:
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self.data_frequency = None
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self.instant_fill = kwargs.pop('instant_fill', False)
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# Override annualizer if set
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@@ -162,13 +158,13 @@ class TradingAlgorithm(object):
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self.capital_base = kwargs.pop('capital_base', DEFAULT_CAPITAL_BASE)
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self.sim_params = kwargs.pop('sim_params', None)
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if self.sim_params:
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if self.data_frequency is None:
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self.data_frequency = self.sim_params.data_frequency
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else:
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self.sim_params.data_frequency = self.data_frequency
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if self.sim_params is None:
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self.sim_params = create_simulation_parameters(
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capital_base=self.capital_base
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)
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self.perf_tracker = PerformanceTracker(self.sim_params)
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# perf_tacker gets instantiated in ._create_generator()
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self.perf_tracker = None
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self.blotter = kwargs.pop('blotter', None)
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if not self.blotter:
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@@ -209,6 +205,11 @@ class TradingAlgorithm(object):
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if self._initialize is None:
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self._initialize = lambda x: None
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# Alternative way of setting data_frequency for backwards
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# compatibility.
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if 'data_frequency' in kwargs:
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self.data_frequency = kwargs.pop('data_frequency')
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# an algorithm subclass needs to set initialized to True when
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# it is fully initialized.
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self.initialized = False
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@@ -261,7 +262,7 @@ class TradingAlgorithm(object):
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blotter=repr(self.blotter),
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recorded_vars=repr(self.recorded_vars))
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def _create_data_generator(self, source_filter, sim_params):
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def _create_data_generator(self, source_filter, sim_params=None):
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"""
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Create a merged data generator using the sources and
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transforms attached to this algorithm.
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@@ -271,9 +272,12 @@ class TradingAlgorithm(object):
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processed by the zipline, and False for those that should be
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skipped.
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"""
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if sim_params is None:
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sim_params = self.sim_params
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if self.benchmark_return_source is None:
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env = trading.environment
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if (self.data_frequency == 'minute'
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if (sim_params.data_frequency == 'minute'
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or sim_params.emission_rate == 'minute'):
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update_time = lambda date: env.get_open_and_close(date)[1]
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else:
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@@ -315,15 +319,11 @@ class TradingAlgorithm(object):
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processed by the zipline, and False for those that should be
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skipped.
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"""
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sim_params.data_frequency = self.data_frequency
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# perf_tracker will be instantiated in __init__ if a sim_params
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# is passed to the constructor. If not, we instantiate here.
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# Instantiate perf_tracker
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if self.perf_tracker is None:
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self.perf_tracker = PerformanceTracker(sim_params)
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self.data_gen = self._create_data_generator(source_filter,
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sim_params)
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self.data_gen = self._create_data_generator(source_filter, sim_params)
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self.trading_client = AlgorithmSimulator(self, sim_params)
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@@ -343,14 +343,15 @@ class TradingAlgorithm(object):
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# TODO: make a new subclass, e.g. BatchAlgorithm, and move
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# the run method to the subclass, and refactor to put the
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# generator creation logic into get_generator.
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def run(self, source, sim_params=None, benchmark_return_source=None):
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def run(self, source, overwrite_sim_params=True,
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benchmark_return_source=None):
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"""Run the algorithm.
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:Arguments:
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source : can be either:
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- pandas.DataFrame
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- zipline source
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- list of zipline sources
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- list of sources
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If pandas.DataFrame is provided, it must have the
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following structure:
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@@ -364,44 +365,34 @@ class TradingAlgorithm(object):
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Daily performance metrics such as returns, alpha etc.
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"""
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if isinstance(source, (list, tuple)):
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assert self.sim_params is not None or sim_params is not None, \
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"""When providing a list of sources, \
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sim_params have to be specified as a parameter
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or in the constructor."""
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if isinstance(source, list):
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if overwrite_sim_params:
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warnings.warn("""List of sources passed, will not attempt to extract sids, and start and end
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dates. Make sure to set the correct fields in sim_params passed to
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__init__().""", UserWarning)
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overwrite_sim_params = False
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elif isinstance(source, pd.DataFrame):
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# if DataFrame provided, wrap in DataFrameSource
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source = DataFrameSource(source)
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elif isinstance(source, pd.Panel):
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source = DataPanelSource(source)
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if not isinstance(source, (list, tuple)):
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self.sources = [source]
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if isinstance(source, list):
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self.set_sources(source)
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else:
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self.sources = source
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self.set_sources([source])
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# Check for override of sim_params.
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# If it isn't passed to this function,
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# use the default params set with the algorithm.
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# Else, we create simulation parameters using the start and end of the
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# source provided.
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if sim_params is None:
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if self.sim_params is None:
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start = source.start
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end = source.end
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sim_params = create_simulation_parameters(
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start=start,
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end=end,
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capital_base=self.capital_base,
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)
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else:
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sim_params = self.sim_params
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# update sim params to ensure it's set
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self.sim_params = sim_params
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if self.sim_params.sids is None:
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# Override sim_params if params are provided by the source.
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if overwrite_sim_params:
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if hasattr(source, 'start'):
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self.sim_params.period_start = source.start
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if hasattr(source, 'end'):
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self.sim_params.period_end = source.end
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all_sids = [sid for s in self.sources for sid in s.sids]
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self.sim_params.sids = set(all_sids)
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# Changing period_start and period_close might require updating
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# of first_open and last_close.
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self.sim_params._update_internal()
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# Create history containers
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if len(self.history_specs) != 0:
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@@ -427,7 +418,7 @@ class TradingAlgorithm(object):
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self.perf_tracker = None
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# create transforms and zipline
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self.gen = self._create_generator(sim_params)
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self.gen = self._create_generator(self.sim_params)
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with ZiplineAPI(self):
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# loop through simulated_trading, each iteration returns a
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@@ -677,10 +668,16 @@ class TradingAlgorithm(object):
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assert isinstance(transforms, list)
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self.transforms = transforms
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def set_data_frequency(self, data_frequency):
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assert data_frequency in ('daily', 'minute')
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self.data_frequency = data_frequency
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self.annualizer = ANNUALIZER[self.data_frequency]
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# Remain backwards compatibility
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@property
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def data_frequency(self):
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return self.sim_params.data_frequency
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@data_frequency.setter
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def data_frequency(self, value):
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assert value in ('daily', 'minute')
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self.sim_params.data_frequency = value
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self.annualizer = ANNUALIZER[self.sim_params.data_frequency]
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@api_method
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def order_percent(self, sid, percent,
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