def run(self, source=None, start=None, end=None):
"""Run the algorithm.
:Arguments:
source : can be either:
- pandas.Panel
- pandas.DataFrame
- zipline source
- list of zipline sources
If pandas.DataFrame is provided, it must have the
following structure:
* column names must consist of ints representing the
different sids
* index must be DatetimeIndex
* array contents should be price info.
:Returns:
daily_stats : pandas.DataFrame
Daily performance metrics such as returns, alpha etc.
"""
if source is None:
assert(self.sources)
else:
self.set_sources(source, start, end)
# Create transforms by wrapping them into StatefulTransforms
self.transforms = []
for namestring, trans_descr in self.registered_transforms.iteritems():
sf = StatefulTransform(
trans_descr['class'],
*trans_descr['args'],
**trans_descr['kwargs']
)
sf.namestring = namestring
self.transforms.append(sf)
environment = create_trading_environment(
start=self.start_datetime,
end=self.end_datetime,
capital_base=self.capital_base
)
# create transforms and zipline
self.gen = self._create_generator(environment)
# loop through simulated_trading, each iteration returns a
# perf ndict
perfs = list(self.gen)
# convert perf ndict to pandas dataframe
daily_stats = self._create_daily_stats(perfs)
return daily_stats
def test_returns(self):
# Daily returns.
returns = Returns(1)
transformed = list(returns.transform(self.source))
tnfm_vals = [message.tnfm_value for message in transformed]
# No returns for the first event because we don't have a
# previous close.
expected = [0.0, 0.0, 0.1, 0.0]
assert tnfm_vals == expected
# Two-day returns. An extra kink here is that the
# factory will automatically skip a weekend for the
# last event. Results shouldn't notice this blip.
trade_history = factory.create_trade_history(
133, [10.0, 15.0, 13.0, 12.0, 13.0], [100, 100, 100, 300, 100],
timedelta(days=1), self.trading_environment)
self.source = SpecificEquityTrades(event_list=trade_history)
returns = StatefulTransform(Returns, 2)
transformed = list(returns.transform(self.source))
tnfm_vals = [message.tnfm_value for message in transformed]
expected = [
0.0, 0.0, (13.0 - 10.0) / 10.0, (12.0 - 15.0) / 15.0,
(13.0 - 13.0) / 13.0
]
assert tnfm_vals == expected
def test_tranform_exception(self):
exc_tnfm = StatefulTransform(ExceptionTransform)
self.zipline_test_config['transforms'] = [exc_tnfm]
zipline = simfactory.create_test_zipline(**self.zipline_test_config)
with self.assertRaises(AssertionError) as ctx:
output, _ = drain_zipline(self, zipline)
self.assertEqual(ctx.exception.message, 'An assertion message')
def test_returns(self, name, add_custom_events):
# Daily returns.
returns = Returns(1)
if add_custom_events:
self.source = self.intersperse_custom_events(self.source)
transformed = list(returns.transform(self.source))
tnfm_vals = [
message[returns.get_hash()] for message in transformed
if message.type != DATASOURCE_TYPE.CUSTOM
]
# No returns for the first event because we don't have a
# previous close.
expected = [0.0, 0.0, 0.1, 0.0]
self.assertEquals(tnfm_vals, expected)
# Two-day returns. An extra kink here is that the
# factory will automatically skip a weekend for the
# last event. Results shouldn't notice this blip.
trade_history = factory.create_trade_history(
133, [10.0, 15.0, 13.0, 12.0, 13.0], [100, 100, 100, 300, 100],
timedelta(days=1), self.sim_params)
self.source = SpecificEquityTrades(event_list=trade_history)
returns = StatefulTransform(Returns, 2)
transformed = list(returns.transform(self.source))
tnfm_vals = [message[returns.get_hash()] for message in transformed]
expected = [
0.0, 0.0, (13.0 - 10.0) / 10.0, (12.0 - 15.0) / 15.0,
(13.0 - 13.0) / 13.0
]
self.assertEquals(tnfm_vals, expected)
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
If pandas.DataFrame is provided, it must have the
following structure:
* column names must consist of ints representing the
different sids
* index must be DatetimeIndex
* array contents should be price info.
:Returns:
daily_stats : pandas.DataFrame
Daily performance metrics such as returns, alpha etc.
"""
if isinstance(source, (list, tuple)):
assert self.sim_params is not None or sim_params is not None, \
"""When providing a list of sources, \
sim_params have to be specified as a parameter
or in the constructor."""
elif isinstance(source, pd.DataFrame):
# if DataFrame provided, wrap in DataFrameSource
source = DataFrameSource(source)
elif isinstance(source, pd.Panel):
source = DataPanelSource(source)
if not isinstance(source, (list, tuple)):
self.sources = [source]
else:
self.sources = source
# Check for override of sim_params.
# If it isn't passed to this function,
# use the default params set with the algorithm.
# Else, we create simulation parameters using the start and end of the
# source provided.
if not sim_params:
if not self.sim_params:
start = source.start
end = source.end
sim_params = create_simulation_parameters(
start=start,
end=end,
capital_base=self.capital_base
)
else:
sim_params = self.sim_params
# Create transforms by wrapping them into StatefulTransforms
self.transforms = []
for namestring, trans_descr in self.registered_transforms.iteritems():
sf = StatefulTransform(
trans_descr['class'],
*trans_descr['args'],
**trans_descr['kwargs']
)
sf.namestring = namestring
self.transforms.append(sf)
# create transforms and zipline
self.gen = self._create_generator(sim_params)
# loop through simulated_trading, each iteration returns a
# perf dictionary
perfs = []
for perf in self.gen:
perfs.append(perf)
# convert perf dict to pandas dataframe
daily_stats = self._create_daily_stats(perfs)
return daily_stats
def run(self, source, start=None, end=None):
"""Run the algorithm.
:Arguments:
source : can be either:
- pandas.DataFrame
- zipline source
- list of zipline sources
If pandas.DataFrame is provided, it must have the
following structure:
* column names must consist of ints representing the
different sids
* index must be DatetimeIndex
* array contents should be price info.
:Returns:
daily_stats : pandas.DataFrame
Daily performance metrics such as returns, alpha etc.
"""
if isinstance(source, (list, tuple)):
assert start is not None and end is not None, \
"""When providing a list of sources, \
start and end date have to be specified."""
elif isinstance(source, pd.DataFrame):
# if DataFrame provided, wrap in DataFrameSource
source = DataFrameSource(source)
elif isinstance(source, pd.Panel):
source = DataPanelSource(source)
# If values not set, try to extract from source.
if start is None:
start = source.start
if end is None:
end = source.end
if not isinstance(source, (list, tuple)):
self.sources = [source]
else:
self.sources = source
# Create transforms by wrapping them into StatefulTransforms
self.transforms = []
for namestring, trans_descr in self.registered_transforms.iteritems():
sf = StatefulTransform(
trans_descr['class'],
*trans_descr['args'],
**trans_descr['kwargs']
)
sf.namestring = namestring
self.transforms.append(sf)
environment = create_trading_environment(
start=start,
end=end,
capital_base=self.capital_base
)
# create transforms and zipline
self.gen = self._create_generator(environment)
# loop through simulated_trading, each iteration returns a
# perf ndict
perfs = list(self.gen)
# convert perf ndict to pandas dataframe
daily_stats = self._create_daily_stats(perfs)
return daily_stats
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 consist of ints representing the
different sids
* index must be DatetimeIndex
* array contents should be price info.
:Returns:
daily_stats : pandas.DataFrame
Daily performance metrics such as returns, alpha etc.
"""
if isinstance(source, list):
if overwrite_sim_params:
warnings.warn("""List of sources passed, will not attempt to extract sids, and start and end
dates. Make sure to set the correct fields in sim_params passed to
__init__().""", UserWarning)
overwrite_sim_params = False
elif isinstance(source, pd.DataFrame):
# if DataFrame provided, wrap in DataFrameSource
source = DataFrameSource(source)
elif isinstance(source, pd.Panel):
source = DataPanelSource(source)
if isinstance(source, list):
self.set_sources(source)
else:
self.set_sources([source])
# Override sim_params if params are provided by the source.
if overwrite_sim_params:
if hasattr(source, 'start'):
self.sim_params.period_start = source.start
if hasattr(source, 'end'):
self.sim_params.period_end = source.end
all_sids = [sid for s in self.sources for sid in s.sids]
self.sim_params.sids = set(all_sids)
# Changing period_start and period_close might require updating
# of first_open and last_close.
self.sim_params._update_internal()
# Create history containers
if len(self.history_specs) != 0:
self.history_container = HistoryContainer(
self.history_specs,
self.sim_params.sids,
self.sim_params.first_open)
# Create transforms by wrapping them into StatefulTransforms
self.transforms = []
for namestring, trans_descr in iteritems(self.registered_transforms):
sf = StatefulTransform(
trans_descr['class'],
*trans_descr['args'],
**trans_descr['kwargs']
)
sf.namestring = namestring
self.transforms.append(sf)
# force a reset of the performance tracker, in case
# this is a repeat run of the algorithm.
self.perf_tracker = None
# create transforms and zipline
self.gen = self._create_generator(self.sim_params)
with ZiplineAPI(self):
# loop through simulated_trading, each iteration returns a
# perf dictionary
perfs = []
for perf in self.gen:
perfs.append(perf)
# convert perf dict to pandas dataframe
daily_stats = self._create_daily_stats(perfs)
self.analyze(daily_stats)
return daily_stats
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 consist of ints representing the
different sids
* index must be DatetimeIndex
* array contents should be price info.
:Returns:
daily_stats : pandas.DataFrame
Daily performance metrics such as returns, alpha etc.
"""
if isinstance(source, list):
if overwrite_sim_params:
warnings.warn("""List of sources passed, will not attempt to extract sids, and start and end
dates. Make sure to set the correct fields in sim_params passed to
__init__().""", UserWarning)
overwrite_sim_params = False
elif isinstance(source, pd.DataFrame):
# if DataFrame provided, wrap in DataFrameSource
source = DataFrameSource(source)
elif isinstance(source, pd.Panel):
source = DataPanelSource(source)
if isinstance(source, list):
self.set_sources(source)
else:
self.set_sources([source])
# Override sim_params if params are provided by the source.
if overwrite_sim_params:
if hasattr(source, 'start'):
self.sim_params.period_start = source.start
if hasattr(source, 'end'):
self.sim_params.period_end = source.end
all_sids = [sid for s in self.sources for sid in s.sids]
self.sim_params.sids = set(all_sids)
# Changing period_start and period_close might require updating
# of first_open and last_close.
self.sim_params._update_internal()
# Create history containers
if len(self.history_specs) != 0:
self.history_container = HistoryContainer(
self.history_specs,
self.sim_params.sids,
self.sim_params.first_open)
# Create transforms by wrapping them into StatefulTransforms
self.transforms = []
for namestring, trans_descr in iteritems(self.registered_transforms):
sf = StatefulTransform(
trans_descr['class'],
*trans_descr['args'],
**trans_descr['kwargs']
)
sf.namestring = namestring
self.transforms.append(sf)
# force a reset of the performance tracker, in case
# this is a repeat run of the algorithm.
self.perf_tracker = None
# create transforms and zipline
self.gen = self._create_generator(self.sim_params)
with ZiplineAPI(self):
# loop through simulated_trading, each iteration returns a
# perf dictionary
perfs = []
for perf in self.gen:
perfs.append(perf)
# convert perf dict to pandas dataframe
daily_stats = self._create_daily_stats(perfs)
self.analyze(daily_stats)
return daily_stats
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
If pandas.DataFrame is provided, it must have the
following structure:
* column names must consist of ints representing the
different sids
* index must be DatetimeIndex
* array contents should be price info.
:Returns:
daily_stats : pandas.DataFrame
Daily performance metrics such as returns, alpha etc.
"""
if isinstance(source, (list, tuple)):
assert self.sim_params is not None or sim_params is not None, \
"""When providing a list of sources, \
sim_params have to be specified as a parameter
or in the constructor."""
elif isinstance(source, pd.DataFrame):
# if DataFrame provided, wrap in DataFrameSource
source = DataFrameSource(source)
elif isinstance(source, pd.Panel):
source = DataPanelSource(source)
if not isinstance(source, (list, tuple)):
self.sources = [source]
else:
self.sources = source
# Check for override of sim_params.
# If it isn't passed to this function,
# use the default params set with the algorithm.
# Else, we create simulation parameters using the start and end of the
# source provided.
if sim_params is None:
if self.sim_params is None:
start = source.start
end = source.end
sim_params = create_simulation_parameters(
start=start,
end=end,
capital_base=self.capital_base,
)
else:
sim_params = self.sim_params
# update sim params to ensure it's set
self.sim_params = sim_params
if self.sim_params.sids is None:
all_sids = [sid for s in self.sources for sid in s.sids]
self.sim_params.sids = set(all_sids)
# Create history containers
if len(self.history_specs) != 0:
self.history_container = HistoryContainer(
self.history_specs, self.sim_params.sids,
self.sim_params.first_open)
# Create transforms by wrapping them into StatefulTransforms
self.transforms = []
for namestring, trans_descr in iteritems(self.registered_transforms):
sf = StatefulTransform(trans_descr['class'], *trans_descr['args'],
**trans_descr['kwargs'])
sf.namestring = namestring
self.transforms.append(sf)
# force a reset of the performance tracker, in case
# this is a repeat run of the algorithm.
self.perf_tracker = None
# create transforms and zipline
self.gen = self._create_generator(sim_params)
with ZiplineAPI(self):
# loop through simulated_trading, each iteration returns a
# perf dictionary
perfs = []
for perf in self.gen:
perfs.append(perf)
# convert perf dict to pandas dataframe
daily_stats = self._create_daily_stats(perfs)
self.analyze(daily_stats)
return daily_stats
def run(self, source, start=None, end=None):
"""Run the algorithm.
:Arguments:
source : can be either:
- pandas.DataFrame
- zipline source
- list of zipline sources
If pandas.DataFrame is provided, it must have the
following structure:
* column names must consist of ints representing the
different sids
* index must be DatetimeIndex
* array contents should be price info.
:Returns:
daily_stats : pandas.DataFrame
Daily performance metrics such as returns, alpha etc.
"""
if isinstance(source, (list, tuple)):
assert start is not None and end is not None, \
"""When providing a list of sources, \
start and end date have to be specified."""
elif isinstance(source, pd.DataFrame):
assert isinstance(source.index, pd.tseries.index.DatetimeIndex)
# if DataFrame provided, wrap in DataFrameSource
source = DataFrameSource(source)
# If values not set, try to extract from source.
if start is None:
start = source.start
if end is None:
end = source.end
if not isinstance(source, (list, tuple)):
self.sources = [source]
else:
self.sources = source
# Create transforms by wrapping them into StatefulTransforms
self.transforms = []
for namestring, trans_descr in self.registered_transforms.iteritems():
sf = StatefulTransform(trans_descr['class'], *trans_descr['args'],
**trans_descr['kwargs'])
sf.namestring = namestring
self.transforms.append(sf)
environment = create_trading_environment(start=start, end=end)
# create transforms and zipline
self.gen = self._create_generator(environment)
# loop through simulated_trading, each iteration returns a
# perf ndict
perfs = list(self.gen)
# convert perf ndict to pandas dataframe
daily_stats = self._create_daily_stats(perfs)
return daily_stats
