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train | densenet121 | r"""Densenet-121 model from
`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>` | pretrainedmodels/models/torchvision_models.py | def densenet121(num_classes=1000, pretrained='imagenet'):
r"""Densenet-121 model from
`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
"""
model = models.densenet121(pretrained=False)
if pretrained is not None:
settings = pretrained_settings['densenet121'][... | def densenet121(num_classes=1000, pretrained='imagenet'):
r"""Densenet-121 model from
`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
"""
model = models.densenet121(pretrained=False)
if pretrained is not None:
settings = pretrained_settings['densenet121'][... | [
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train | inceptionv3 | r"""Inception v3 model architecture from
`"Rethinking the Inception Architecture for Computer Vision" <http://arxiv.org/abs/1512.00567>`_. | pretrainedmodels/models/torchvision_models.py | def inceptionv3(num_classes=1000, pretrained='imagenet'):
r"""Inception v3 model architecture from
`"Rethinking the Inception Architecture for Computer Vision" <http://arxiv.org/abs/1512.00567>`_.
"""
model = models.inception_v3(pretrained=False)
if pretrained is not None:
settings = pretrai... | def inceptionv3(num_classes=1000, pretrained='imagenet'):
r"""Inception v3 model architecture from
`"Rethinking the Inception Architecture for Computer Vision" <http://arxiv.org/abs/1512.00567>`_.
"""
model = models.inception_v3(pretrained=False)
if pretrained is not None:
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train | resnet50 | Constructs a ResNet-50 model. | pretrainedmodels/models/torchvision_models.py | def resnet50(num_classes=1000, pretrained='imagenet'):
"""Constructs a ResNet-50 model.
"""
model = models.resnet50(pretrained=False)
if pretrained is not None:
settings = pretrained_settings['resnet50'][pretrained]
model = load_pretrained(model, num_classes, settings)
model = modify... | def resnet50(num_classes=1000, pretrained='imagenet'):
"""Constructs a ResNet-50 model.
"""
model = models.resnet50(pretrained=False)
if pretrained is not None:
settings = pretrained_settings['resnet50'][pretrained]
model = load_pretrained(model, num_classes, settings)
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train | squeezenet1_0 | r"""SqueezeNet model architecture from the `"SqueezeNet: AlexNet-level
accuracy with 50x fewer parameters and <0.5MB model size"
<https://arxiv.org/abs/1602.07360>`_ paper. | pretrainedmodels/models/torchvision_models.py | def squeezenet1_0(num_classes=1000, pretrained='imagenet'):
r"""SqueezeNet model architecture from the `"SqueezeNet: AlexNet-level
accuracy with 50x fewer parameters and <0.5MB model size"
<https://arxiv.org/abs/1602.07360>`_ paper.
"""
model = models.squeezenet1_0(pretrained=False)
if pretraine... | def squeezenet1_0(num_classes=1000, pretrained='imagenet'):
r"""SqueezeNet model architecture from the `"SqueezeNet: AlexNet-level
accuracy with 50x fewer parameters and <0.5MB model size"
<https://arxiv.org/abs/1602.07360>`_ paper.
"""
model = models.squeezenet1_0(pretrained=False)
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train | vgg11 | VGG 11-layer model (configuration "A") | pretrainedmodels/models/torchvision_models.py | def vgg11(num_classes=1000, pretrained='imagenet'):
"""VGG 11-layer model (configuration "A")
"""
model = models.vgg11(pretrained=False)
if pretrained is not None:
settings = pretrained_settings['vgg11'][pretrained]
model = load_pretrained(model, num_classes, settings)
model = modify... | def vgg11(num_classes=1000, pretrained='imagenet'):
"""VGG 11-layer model (configuration "A")
"""
model = models.vgg11(pretrained=False)
if pretrained is not None:
settings = pretrained_settings['vgg11'][pretrained]
model = load_pretrained(model, num_classes, settings)
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train | adjust_learning_rate | Sets the learning rate to the initial LR decayed by 10 every 30 epochs | examples/imagenet_eval.py | def adjust_learning_rate(optimizer, epoch):
"""Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
lr = args.lr * (0.1 ** (epoch // 30))
for param_group in optimizer.param_groups:
param_group['lr'] = lr | def adjust_learning_rate(optimizer, epoch):
"""Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
lr = args.lr * (0.1 ** (epoch // 30))
for param_group in optimizer.param_groups:
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train | nasnetalarge | r"""NASNetALarge model architecture from the
`"NASNet" <https://arxiv.org/abs/1707.07012>`_ paper. | pretrainedmodels/models/nasnet.py | def nasnetalarge(num_classes=1001, pretrained='imagenet'):
r"""NASNetALarge model architecture from the
`"NASNet" <https://arxiv.org/abs/1707.07012>`_ paper.
"""
if pretrained:
settings = pretrained_settings['nasnetalarge'][pretrained]
assert num_classes == settings['num_classes'], \
... | def nasnetalarge(num_classes=1001, pretrained='imagenet'):
r"""NASNetALarge model architecture from the
`"NASNet" <https://arxiv.org/abs/1707.07012>`_ paper.
"""
if pretrained:
settings = pretrained_settings['nasnetalarge'][pretrained]
assert num_classes == settings['num_classes'], \
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train | adaptive_avgmax_pool2d | Selectable global pooling function with dynamic input kernel size | pretrainedmodels/models/dpn.py | def adaptive_avgmax_pool2d(x, pool_type='avg', padding=0, count_include_pad=False):
"""Selectable global pooling function with dynamic input kernel size
"""
if pool_type == 'avgmaxc':
x = torch.cat([
F.avg_pool2d(
x, kernel_size=(x.size(2), x.size(3)), padding=padding, co... | def adaptive_avgmax_pool2d(x, pool_type='avg', padding=0, count_include_pad=False):
"""Selectable global pooling function with dynamic input kernel size
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if pool_type == 'avgmaxc':
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train | download_url | Download a URL to a local file.
Parameters
----------
url : str
The URL to download.
destination : str, None
The destination of the file. If None is given the file is saved to a temporary directory.
progress_bar : bool
Whether to show a command-line progress bar while downlo... | pretrainedmodels/datasets/utils.py | def download_url(url, destination=None, progress_bar=True):
"""Download a URL to a local file.
Parameters
----------
url : str
The URL to download.
destination : str, None
The destination of the file. If None is given the file is saved to a temporary directory.
progress_bar : bo... | def download_url(url, destination=None, progress_bar=True):
"""Download a URL to a local file.
Parameters
----------
url : str
The URL to download.
destination : str, None
The destination of the file. If None is given the file is saved to a temporary directory.
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train | AveragePrecisionMeter.add | Args:
output (Tensor): NxK tensor that for each of the N examples
indicates the probability of the example belonging to each of
the K classes, according to the model. The probabilities should
sum to one over all classes
target (Tensor): binary NxK ... | pretrainedmodels/datasets/utils.py | def add(self, output, target):
"""
Args:
output (Tensor): NxK tensor that for each of the N examples
indicates the probability of the example belonging to each of
the K classes, according to the model. The probabilities should
sum to one over a... | def add(self, output, target):
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train | AveragePrecisionMeter.value | Returns the model's average precision for each class
Return:
ap (FloatTensor): 1xK tensor, with avg precision for each class k | pretrainedmodels/datasets/utils.py | def value(self):
"""Returns the model's average precision for each class
Return:
ap (FloatTensor): 1xK tensor, with avg precision for each class k
"""
if self.scores.numel() == 0:
return 0
ap = torch.zeros(self.scores.size(1))
rg = torch.arange(1,... | def value(self):
"""Returns the model's average precision for each class
Return:
ap (FloatTensor): 1xK tensor, with avg precision for each class k
"""
if self.scores.numel() == 0:
return 0
ap = torch.zeros(self.scores.size(1))
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train | polynet | PolyNet architecture from the paper
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https://arxiv.org/abs/1611.05725 | pretrainedmodels/models/polynet.py | def polynet(num_classes=1000, pretrained='imagenet'):
"""PolyNet architecture from the paper
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https://arxiv.org/abs/1611.05725
"""
if pretrained:
settings = pretrained_settings['polynet'][pretrained]
assert num_classes... | def polynet(num_classes=1000, pretrained='imagenet'):
"""PolyNet architecture from the paper
'PolyNet: A Pursuit of Structural Diversity in Very Deep Networks'
https://arxiv.org/abs/1611.05725
"""
if pretrained:
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train | CachedObject.unwrap | Get the cached value.
Returns
-------
value : object
The cached value.
Raises
------
Expired
Raised when `dt` is greater than self.expires. | zipline/utils/cache.py | def unwrap(self, dt):
"""
Get the cached value.
Returns
-------
value : object
The cached value.
Raises
------
Expired
Raised when `dt` is greater than self.expires.
"""
expires = self._expires
if expires i... | def unwrap(self, dt):
"""
Get the cached value.
Returns
-------
value : object
The cached value.
Raises
------
Expired
Raised when `dt` is greater than self.expires.
"""
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train | ExpiringCache.get | Get the value of a cached object.
Parameters
----------
key : any
The key to lookup.
dt : datetime
The time of the lookup.
Returns
-------
result : any
The value for ``key``.
Raises
------
KeyError
... | zipline/utils/cache.py | def get(self, key, dt):
"""Get the value of a cached object.
Parameters
----------
key : any
The key to lookup.
dt : datetime
The time of the lookup.
Returns
-------
result : any
The value for ``key``.
Raises
... | def get(self, key, dt):
"""Get the value of a cached object.
Parameters
----------
key : any
The key to lookup.
dt : datetime
The time of the lookup.
Returns
-------
result : any
The value for ``key``.
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train | ExpiringCache.set | Adds a new key value pair to the cache.
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value : any
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expiration_dt : datetime
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value : any
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train | working_dir.ensure_dir | Ensures a subdirectory of the working directory.
Parameters
----------
path_parts : iterable[str]
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"""Ensures a subdirectory of the working directory.
Parameters
----------
path_parts : iterable[str]
The parts of the path after the working directory.
"""
path = self.getpath(*path_parts)
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The parts of the path after the working directory.
"""
path = self.getpath(*path_parts)
ensure_directory(path)
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train | verify_frames_aligned | Verify that DataFrames in ``frames`` have the same indexing scheme and are
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Parameters
----------
frames : list[pd.DataFrame]
calendar : trading_calendars.TradingCalendar
Raises
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"""
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Parameters
----------
frames : list[pd.DataFrame]
calendar : trading_calendars.TradingCalendar
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----------
frames : list[pd.DataFrame]
calendar : trading_calendars.TradingCalendar
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------
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train | InMemoryDailyBarReader.get_value | Parameters
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sid : int
The asset identifier.
day : datetime64-like
Midnight of the day for which data is requested.
field : string
The price field. e.g. ('open', 'high', 'low', 'close', 'volume')
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----------
sid : int
The asset identifier.
day : datetime64-like
Midnight of the day for which data is requested.
field : string
The price field. e.g. ('open', 'high', 'low', 'close', ... | def get_value(self, sid, dt, field):
"""
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----------
sid : int
The asset identifier.
day : datetime64-like
Midnight of the day for which data is requested.
field : string
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train | InMemoryDailyBarReader.get_last_traded_dt | Parameters
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asset : zipline.asset.Asset
The asset identifier.
dt : datetime64-like
Midnight of the day for which data is requested.
Returns
-------
pd.Timestamp : The last know dt for the asset and dt;
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"""
Parameters
----------
asset : zipline.asset.Asset
The asset identifier.
dt : datetime64-like
Midnight of the day for which data is requested.
Returns
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asset : zipline.asset.Asset
The asset identifier.
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Midnight of the day for which data is requested.
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train | same | Check if all values in a sequence are equal.
Returns True on empty sequences.
Examples
--------
>>> same(1, 1, 1, 1)
True
>>> same(1, 2, 1)
False
>>> same()
True | zipline/utils/functional.py | def same(*values):
"""
Check if all values in a sequence are equal.
Returns True on empty sequences.
Examples
--------
>>> same(1, 1, 1, 1)
True
>>> same(1, 2, 1)
False
>>> same()
True
"""
if not values:
return True
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Check if all values in a sequence are equal.
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Examples
--------
>>> same(1, 1, 1, 1)
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>>> same(1, 2, 1)
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train | dzip_exact | Parameters
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*dicts : iterable[dict]
A sequence of dicts all sharing the same keys.
Returns
-------
zipped : dict
A dict whose keys are the union of all keys in *dicts, and whose values
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*dicts : iterable[dict]
A sequence of dicts all sharing the same keys.
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-------
zipped : dict
A dict whose keys are the union of all keys in *dicts, and whose values
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A sequence of dicts all sharing the same keys.
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train | _gen_unzip | Helper for unzip which checks the lengths of each element in it.
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An iterable of tuples. ``unzip`` should map ensure that these are
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An iterable of tuples. ``unzip`` should map ensure that these are
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The expected element length. I... | def _gen_unzip(it, elem_len):
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seq : iterable[iterable]
The sequence to unzip.
elem_len : int, optional
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The sequence to unzip.
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train | getattrs | Perform a chained application of ``getattr`` on ``value`` with the values
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If ``default`` is supplied, return it if any of the attribute lookups fail.
Parameters
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value : object
Root of the lookup chain.
attrs : iterable[str]
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"""
Perform a chained application of ``getattr`` on ``value`` with the values
in ``attrs``.
If ``default`` is supplied, return it if any of the attribute lookups fail.
Parameters
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value : object
Root of the lookup chain.
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Perform a chained application of ``getattr`` on ``value`` with the values
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train | set_attribute | Decorator factory for setting attributes on a function.
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Examples
--------
>>> @set_attribute('__name__', 'foo')
... def bar():
... return 3
...
>>> bar()
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'foo' | zipline/utils/functional.py | def set_attribute(name, value):
"""
Decorator factory for setting attributes on a function.
Doesn't change the behavior of the wrapped function.
Examples
--------
>>> @set_attribute('__name__', 'foo')
... def bar():
... return 3
...
>>> bar()
3
>>> bar.__name__
... | def set_attribute(name, value):
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Examples
--------
>>> @set_attribute('__name__', 'foo')
... def bar():
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...
>>> bar()
3
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train | foldr | Fold a function over a sequence with right associativity.
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The function to reduce the sequence with.
The first argument will be the element of the sequence; the second
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"""Fold a function over a sequence with right associativity.
Parameters
----------
f : callable[any, any]
The function to reduce the sequence with.
The first argument will be the element of the sequence; the second
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train | invert | Invert a dictionary into a dictionary of sets.
>>> invert({'a': 1, 'b': 2, 'c': 1}) # doctest: +SKIP
{1: {'a', 'c'}, 2: {'b'}} | zipline/utils/functional.py | def invert(d):
"""
Invert a dictionary into a dictionary of sets.
>>> invert({'a': 1, 'b': 2, 'c': 1}) # doctest: +SKIP
{1: {'a', 'c'}, 2: {'b'}}
"""
out = {}
for k, v in iteritems(d):
try:
out[v].add(k)
except KeyError:
out[v] = {k}
return out | def invert(d):
"""
Invert a dictionary into a dictionary of sets.
>>> invert({'a': 1, 'b': 2, 'c': 1}) # doctest: +SKIP
{1: {'a', 'c'}, 2: {'b'}}
"""
out = {}
for k, v in iteritems(d):
try:
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out[v] = {k}
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train | simplex_projection | r"""Projection vectors to the simplex domain
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train | vectorized_beta | Compute slopes of linear regressions between columns of ``dependents`` and
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Array with columns of data to be regressed against ``independent``.
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Compute slopes of linear regressions between columns of ``dependents`` and
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dependents : np.array[N, M]
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train | _format_url | Format a URL for loading data from Bank of Canada. | zipline/data/treasuries_can.py | def _format_url(instrument_type,
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Format a URL for loading data from Bank of Canada.
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train | load_frame | Load a DataFrame of data from a Bank of Canada site. | zipline/data/treasuries_can.py | def load_frame(url, skiprows):
"""
Load a DataFrame of data from a Bank of Canada site.
"""
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skiprows=skiprows,
skipinitialspace=True,
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train | check_known_inconsistencies | There are a couple quirks in the data provided by Bank of Canada.
Check that no new quirks have been introduced in the latest download. | zipline/data/treasuries_can.py | def check_known_inconsistencies(bill_data, bond_data):
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There are a couple quirks in the data provided by Bank of Canada.
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There are a couple quirks in the data provided by Bank of Canada.
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train | earliest_possible_date | The earliest date for which we can load data from this module. | zipline/data/treasuries_can.py | def earliest_possible_date():
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The earliest date for which we can load data from this module.
"""
today = pd.Timestamp('now', tz='UTC').normalize()
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train | fill_price_worse_than_limit_price | Checks whether the fill price is worse than the order's limit price.
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fill_price: float
The price to check.
order: zipline.finance.order.Order
The order whose limit price to check.
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fill_price: float
The price to check.
order: zipline.finance.order.Order
The order whose limit price to check.
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... | def fill_price_worse_than_limit_price(fill_price, order):
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Checks whether the fill price is worse than the order's limit price.
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fill_price: float
The price to check.
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The order whose limit price to check.
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Internal utility method to return the trailing mean volume over the
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train | _assert_valid_categorical_missing_value | Check that value is a valid categorical missing_value.
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kwargs : dict
The kwargs passed to cls.__new__.
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params : list[(str, object)]
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kwargs : dict
The kwargs passed to cls.__new__.
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kwargs : dict
The kwargs passed to cls.__new__.
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The domain of this term.
dtype : np.dtype
Dtype of this term's output.
missing_value : object
Missing value for this term.
ndim : 1 or 2
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The domain of this term.
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Dtype of this term's output.
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The domain of this term.
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Dtype of this term's output.
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train | Position.adjust_commission_cost_basis | A note about cost-basis in zipline: all positions are considered
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Used to mark a function as deprecated.
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The message to display in the deprecation warning.
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How far up the stack the warning needs to go, before
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train | HistoryLoader.history | A window of pricing data with adjustments applied assuming that the
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assets : iterable of Assets
The assets in the window.
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assets : iterable of Assets
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Efficient parsing for a 1d Pandas/numpy object containing string
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Efficient parsing for a 1d Pandas/numpy object containing string
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train | PandasCSV._lookup_unconflicted_symbol | Attempt to find a unique asset whose symbol is the given string.
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train | AlgorithmSimulator.transform | Main generator work loop. | zipline/gens/tradesimulation.py | def transform(self):
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train | AlgorithmSimulator._cleanup_expired_assets | Clear out any assets that have expired before starting a new sim day.
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train | AlgorithmSimulator._get_daily_message | Get a perf message for the given datetime. | zipline/gens/tradesimulation.py | def _get_daily_message(self, dt, algo, metrics_tracker):
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train | AlgorithmSimulator._get_minute_message | Get a perf message for the given datetime. | zipline/gens/tradesimulation.py | def _get_minute_message(self, dt, algo, metrics_tracker):
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train | SQLiteAdjustmentReader.load_adjustments | Load collection of Adjustment objects from underlying adjustments db.
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dates : pd.DatetimeIndex
Dates for which adjustments are needed.
assets : pd.Int64Index
Assets for which adjustments are needed.
should_include_splits : bool
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train | SQLiteAdjustmentReader._df_dtypes | Get dtypes to use when unpacking sqlite tables as dataframes. | zipline/data/adjustments.py | def _df_dtypes(self, table_name, convert_dates):
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train | SQLiteAdjustmentWriter.calc_dividend_ratios | Calculate the ratios to apply to equities when looking back at pricing
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Calculate the ratios to apply to equities when looking back at pricing
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Calculate the ratios to apply to equities when looking back at pricing
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train | SQLiteAdjustmentWriter.write_dividend_data | Write both dividend payouts and the derived price adjustment ratios. | zipline/data/adjustments.py | def write_dividend_data(self, dividends, stock_dividends=None):
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train | SQLiteAdjustmentWriter.write | Writes data to a SQLite file to be read by SQLiteAdjustmentReader.
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splits : pandas.DataFrame, optional
Dataframe containing split data. The format of this dataframe is:
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Writes data to a SQLite file to be read by SQLiteAdjustmentReader.
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Writes data to a SQLite file to be read by SQLiteAdjustmentReader.
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train | CustomTermMixin.compute | Override this method with a function that writes a value into `out`. | zipline/pipeline/mixins.py | def compute(self, today, assets, out, *arrays):
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train | AliasedMixin.make_aliased_type | Factory for making Aliased{Filter,Factor,Classifier}. | zipline/pipeline/mixins.py | def make_aliased_type(cls, other_base):
"""
Factory for making Aliased{Filter,Factor,Classifier}.
"""
docstring = dedent(
"""
A {t} that names another {t}.
Parameters
----------
term : {t}
{{name}}
"""
... | def make_aliased_type(cls, other_base):
"""
Factory for making Aliased{Filter,Factor,Classifier}.
"""
docstring = dedent(
"""
A {t} that names another {t}.
Parameters
----------
term : {t}
{{name}}
"""
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train | DownsampledMixin.compute_extra_rows | Ensure that min_extra_rows pushes us back to a computation date.
Parameters
----------
all_dates : pd.DatetimeIndex
The trading sessions against which ``self`` will be computed.
start_date : pd.Timestamp
The first date for which final output is requested.
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min_extra_rows):
"""
Ensure that min_extra_rows pushes us back to a computation date.
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----------
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Ensure that min_extra_rows pushes us back to a computation date.
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train | DownsampledMixin._compute | Compute by delegating to self._wrapped_term._compute on sample dates.
On non-sample dates, forward-fill from previously-computed samples. | zipline/pipeline/mixins.py | def _compute(self, inputs, dates, assets, mask):
"""
Compute by delegating to self._wrapped_term._compute on sample dates.
On non-sample dates, forward-fill from previously-computed samples.
"""
to_sample = dates[select_sampling_indices(dates, self._frequency)]
assert to... | def _compute(self, inputs, dates, assets, mask):
"""
Compute by delegating to self._wrapped_term._compute on sample dates.
On non-sample dates, forward-fill from previously-computed samples.
"""
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train | DownsampledMixin.make_downsampled_type | Factory for making Downsampled{Filter,Factor,Classifier}. | zipline/pipeline/mixins.py | def make_downsampled_type(cls, other_base):
"""
Factory for making Downsampled{Filter,Factor,Classifier}.
"""
docstring = dedent(
"""
A {t} that defers to another {t} at lower-than-daily frequency.
Parameters
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Factory for making Downsampled{Filter,Factor,Classifier}.
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A {t} that defers to another {t} at lower-than-daily frequency.
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train | preprocess | Decorator that applies pre-processors to the arguments of a function before
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Parameters
----------
**processors : dict
Map from argument name -> processor function.
A processor function takes three arguments: (func, argname, argvalue).
`func` is the the fu... | zipline/utils/preprocess.py | def preprocess(*_unused, **processors):
"""
Decorator that applies pre-processors to the arguments of a function before
calling the function.
Parameters
----------
**processors : dict
Map from argument name -> processor function.
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Decorator that applies pre-processors to the arguments of a function before
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train | call | Wrap a function in a processor that calls `f` on the argument before
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Useful for creating simple arguments to the `@preprocess` decorator.
Parameters
----------
f : function
Function accepting a single argument and returning a replacement.
Examples
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>>... | zipline/utils/preprocess.py | def call(f):
"""
Wrap a function in a processor that calls `f` on the argument before
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Useful for creating simple arguments to the `@preprocess` decorator.
Parameters
----------
f : function
Function accepting a single argument and returning a replacement.
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Wrap a function in a processor that calls `f` on the argument before
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train | _build_preprocessed_function | Build a preprocessed function with the same signature as `func`.
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train | get_benchmark_returns | Get a Series of benchmark returns from IEX associated with `symbol`.
Default is `SPY`.
Parameters
----------
symbol : str
Benchmark symbol for which we're getting the returns.
The data is provided by IEX (https://iextrading.com/), and we can
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"""
Get a Series of benchmark returns from IEX associated with `symbol`.
Default is `SPY`.
Parameters
----------
symbol : str
Benchmark symbol for which we're getting the returns.
The data is provided by IEX (https://iextrading.com/), and we can
... | def get_benchmark_returns(symbol):
"""
Get a Series of benchmark returns from IEX associated with `symbol`.
Default is `SPY`.
Parameters
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symbol : str
Benchmark symbol for which we're getting the returns.
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train | delimit | Surround `content` with the first and last characters of `delimiters`.
>>> delimit('[]', "foo") # doctest: +SKIP
'[foo]'
>>> delimit('""', "foo") # doctest: +SKIP
'"foo"' | zipline/pipeline/visualize.py | def delimit(delimiters, content):
"""
Surround `content` with the first and last characters of `delimiters`.
>>> delimit('[]', "foo") # doctest: +SKIP
'[foo]'
>>> delimit('""', "foo") # doctest: +SKIP
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"""
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>>> delimit('[]', "foo") # doctest: +SKIP
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train | roots | Get nodes from graph G with indegree 0 | zipline/pipeline/visualize.py | def roots(g):
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return set(n for n, d in iteritems(g.in_degree()) if d == 0) | def roots(g):
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train | _render | Draw `g` as a graph to `out`, in format `format`.
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g : zipline.pipeline.graph.TermGraph
Graph to render.
out : file-like object
format_ : str {'png', 'svg'}
Output format.
include_asset_exists : bool
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Draw `g` as a graph to `out`, in format `format`.
Parameters
----------
g : zipline.pipeline.graph.TermGraph
Graph to render.
out : file-like object
format_ : str {'png', 'svg'}
Output format.
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Draw `g` as a graph to `out`, in format `format`.
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g : zipline.pipeline.graph.TermGraph
Graph to render.
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Output format.
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train | display_graph | Display a TermGraph interactively from within IPython. | zipline/pipeline/visualize.py | def display_graph(g, format='svg', include_asset_exists=False):
"""
Display a TermGraph interactively from within IPython.
"""
try:
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train | format_attrs | Format key, value pairs from attrs into graphviz attrs format
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--------
>>> format_attrs({'key1': 'value1', 'key2': 'value2'}) # doctest: +SKIP
'[key1=value1, key2=value2]' | zipline/pipeline/visualize.py | def format_attrs(attrs):
"""
Format key, value pairs from attrs into graphviz attrs format
Examples
--------
>>> format_attrs({'key1': 'value1', 'key2': 'value2'}) # doctest: +SKIP
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"""
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The function to call.
args : tuple, optional
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kwargs : dict, optional
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The function to call.
args : tuple, optional
The positional arguments.
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The function to call.
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train | maybe_show_progress | Optionally show a progress bar for the given iterator.
Parameters
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it : iterable
The underlying iterator.
show_progress : bool
Should progress be shown.
**kwargs
Forwarded to the click progress bar.
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it : iterable
The underlying iterator.
show_progress : bool
Should progress be shown.
**kwargs
Forwarded to the click progress bar.
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The underlying iterator.
show_progress : bool
Should progress be shown.
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train | main | Top level zipline entry point. | zipline/__main__.py | def main(extension, strict_extensions, default_extension, x):
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create_args(x, zipline.extension_args)
load_extensions(
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A click.option decorator.
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train | zipline_magic | The zipline IPython cell magic. | zipline/__main__.py | def zipline_magic(line, cell=None):
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train | ingest | Ingest the data for the given bundle. | zipline/__main__.py | def ingest(bundle, assets_version, show_progress):
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train | clean | Clean up data downloaded with the ingest command. | zipline/__main__.py | def clean(bundle, before, after, keep_last):
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train | bundles | List all of the available data bundles. | zipline/__main__.py | def bundles():
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train | binary_operator | Factory function for making binary operator methods on a Filter subclass.
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Factory function for making binary operator methods on a Filter subclass.
Returns a function "binary_operator" suitable for implementing functions
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"""
# When combining a Filter with a NumericalExpression, we use this
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Factory function for making binary operator methods on a Filter subclass.
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train | unary_operator | Factory function for making unary operator methods for Filters. | zipline/pipeline/filters/filter.py | def unary_operator(op):
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Factory function for making unary operator methods for Filters.
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valid_ops = {'~'}
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train | NumExprFilter.create | Helper for creating new NumExprFactors.
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train | NumExprFilter._compute | Compute our result with numexpr, then re-apply `mask`. | zipline/pipeline/filters/filter.py | def _compute(self, arrays, dates, assets, mask):
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Compute our result with numexpr, then re-apply `mask`.
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train | PercentileFilter._validate | Ensure that our percentile bounds are well-formed. | zipline/pipeline/filters/filter.py | def _validate(self):
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Ensure that our percentile bounds are well-formed.
"""
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train | PercentileFilter._compute | For each row in the input, compute a mask of all values falling between
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train | parse_treasury_csv_column | Parse a treasury CSV column into a more human-readable format.
Columns start with 'RIFLGFC', followed by Y or M (year or month), followed
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Parse a treasury CSV column into a more human-readable format.
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train | get_daily_10yr_treasury_data | Download daily 10 year treasury rates from the Federal Reserve and
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train | _sid_subdir_path | Format subdir path to limit the number directories in any given
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sid : int
Asset identifier.
Returns
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out : string
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Format subdir path to limit the number directories in any given
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The number in each directory is designed to support at least 100000
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Parameters
----------
sid : int
Asset identifier.
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-------
out :... | def _sid_subdir_path(sid):
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Format subdir path to limit the number directories in any given
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":",
"padded_sid",
"=",
"format",
"(",
"sid",
",",
"'06'",
")",
"return",
"os",
".",
"path",
".",
"join",
"(",
"# subdir 1 00/XX",
"padded_sid",
"[",
"0",
":",
"2",
"]",
",",
"# subdir 2 XX/00",
"padded_sid",
"... | 77ad15e6dc4c1cbcdc133653bac8a63fc704f7fe |
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