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train | alter_columns | Alter columns from a table.
Parameters
----------
name : str
The name of the table.
*columns
The new columns to have.
selection_string : str, optional
The string to use in the selection. If not provided, it will select all
of the new columns from the old table.
... | zipline/assets/asset_db_migrations.py | def alter_columns(op, name, *columns, **kwargs):
"""Alter columns from a table.
Parameters
----------
name : str
The name of the table.
*columns
The new columns to have.
selection_string : str, optional
The string to use in the selection. If not provided, it will select ... | def alter_columns(op, name, *columns, **kwargs):
"""Alter columns from a table.
Parameters
----------
name : str
The name of the table.
*columns
The new columns to have.
selection_string : str, optional
The string to use in the selection. If not provided, it will select ... | [
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train | downgrade | Downgrades the assets db at the given engine to the desired version.
Parameters
----------
engine : Engine
An SQLAlchemy engine to the assets database.
desired_version : int
The desired resulting version for the assets database. | zipline/assets/asset_db_migrations.py | def downgrade(engine, desired_version):
"""Downgrades the assets db at the given engine to the desired version.
Parameters
----------
engine : Engine
An SQLAlchemy engine to the assets database.
desired_version : int
The desired resulting version for the assets database.
"""
... | def downgrade(engine, desired_version):
"""Downgrades the assets db at the given engine to the desired version.
Parameters
----------
engine : Engine
An SQLAlchemy engine to the assets database.
desired_version : int
The desired resulting version for the assets database.
"""
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train | downgrades | Decorator for marking that a method is a downgrade to a version to the
previous version.
Parameters
----------
src : int
The version this downgrades from.
Returns
-------
decorator : callable[(callable) -> callable]
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"""Decorator for marking that a method is a downgrade to a version to the
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Parameters
----------
src : int
The version this downgrades from.
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decorator : callable[(callable) -> callable]
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"""Decorator for marking that a method is a downgrade to a version to the
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src : int
The version this downgrades from.
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decorator : callable[(callable) -> callable]
The decorator to apply.
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train | _downgrade_v1 | Downgrade assets db by removing the 'tick_size' column and renaming the
'multiplier' column. | zipline/assets/asset_db_migrations.py | def _downgrade_v1(op):
"""
Downgrade assets db by removing the 'tick_size' column and renaming the
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"""
# Drop indices before batch
# This is to prevent index collision when creating the temp table
op.drop_index('ix_futures_contracts_root_symbol')
op.drop_index('ix_fu... | def _downgrade_v1(op):
"""
Downgrade assets db by removing the 'tick_size' column and renaming the
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"""
# Drop indices before batch
# This is to prevent index collision when creating the temp table
op.drop_index('ix_futures_contracts_root_symbol')
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train | _downgrade_v2 | Downgrade assets db by removing the 'auto_close_date' column. | zipline/assets/asset_db_migrations.py | def _downgrade_v2(op):
"""
Downgrade assets db by removing the 'auto_close_date' column.
"""
# Drop indices before batch
# This is to prevent index collision when creating the temp table
op.drop_index('ix_equities_fuzzy_symbol')
op.drop_index('ix_equities_company_symbol')
# Execute batc... | def _downgrade_v2(op):
"""
Downgrade assets db by removing the 'auto_close_date' column.
"""
# Drop indices before batch
# This is to prevent index collision when creating the temp table
op.drop_index('ix_equities_fuzzy_symbol')
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train | _downgrade_v3 | Downgrade assets db by adding a not null constraint on
``equities.first_traded`` | zipline/assets/asset_db_migrations.py | def _downgrade_v3(op):
"""
Downgrade assets db by adding a not null constraint on
``equities.first_traded``
"""
op.create_table(
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sa.Column(
'sid',
sa.Integer,
unique=True,
nullable=False,
primary_key=True,
... | def _downgrade_v3(op):
"""
Downgrade assets db by adding a not null constraint on
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"""
op.create_table(
'_new_equities',
sa.Column(
'sid',
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unique=True,
nullable=False,
primary_key=True,
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train | _downgrade_v4 | Downgrades assets db by copying the `exchange_full` column to `exchange`,
then dropping the `exchange_full` column. | zipline/assets/asset_db_migrations.py | def _downgrade_v4(op):
"""
Downgrades assets db by copying the `exchange_full` column to `exchange`,
then dropping the `exchange_full` column.
"""
op.drop_index('ix_equities_fuzzy_symbol')
op.drop_index('ix_equities_company_symbol')
op.execute("UPDATE equities SET exchange = exchange_full")... | def _downgrade_v4(op):
"""
Downgrades assets db by copying the `exchange_full` column to `exchange`,
then dropping the `exchange_full` column.
"""
op.drop_index('ix_equities_fuzzy_symbol')
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train | _make_metrics_set_core | Create a family of metrics sets functions that read from the same
metrics set mapping.
Returns
-------
metrics_sets : mappingproxy
The mapping of metrics sets to load functions.
register : callable
The function which registers new metrics sets in the ``metrics_sets``
mapping... | zipline/finance/metrics/core.py | def _make_metrics_set_core():
"""Create a family of metrics sets functions that read from the same
metrics set mapping.
Returns
-------
metrics_sets : mappingproxy
The mapping of metrics sets to load functions.
register : callable
The function which registers new metrics sets in... | def _make_metrics_set_core():
"""Create a family of metrics sets functions that read from the same
metrics set mapping.
Returns
-------
metrics_sets : mappingproxy
The mapping of metrics sets to load functions.
register : callable
The function which registers new metrics sets in... | [
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train | validate_column_specs | Verify that the columns of ``events`` can be used by a
EarningsEstimatesLoader to serve the BoundColumns described by
`columns`. | zipline/pipeline/loaders/earnings_estimates.py | def validate_column_specs(events, columns):
"""
Verify that the columns of ``events`` can be used by a
EarningsEstimatesLoader to serve the BoundColumns described by
`columns`.
"""
required = required_estimates_fields(columns)
received = set(events.columns)
missing = required - received
... | def validate_column_specs(events, columns):
"""
Verify that the columns of ``events`` can be used by a
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"""
required = required_estimates_fields(columns)
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missing = required - received
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train | EarningsEstimatesLoader.get_requested_quarter_data | Selects the requested data for each date.
Parameters
----------
zero_qtr_data : pd.DataFrame
The 'time zero' data for each calendar date per sid.
zeroth_quarter_idx : pd.Index
An index of calendar dates, sid, and normalized quarters, for only
the rows... | zipline/pipeline/loaders/earnings_estimates.py | def get_requested_quarter_data(self,
zero_qtr_data,
zeroth_quarter_idx,
stacked_last_per_qtr,
num_announcements,
dates):
"""
Sele... | def get_requested_quarter_data(self,
zero_qtr_data,
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stacked_last_per_qtr,
num_announcements,
dates):
"""
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train | EarningsEstimatesLoader.get_split_adjusted_asof_idx | Compute the index in `dates` where the split-adjusted-asof-date
falls. This is the date up to which, and including which, we will
need to unapply all adjustments for and then re-apply them as they
come in. After this date, adjustments are applied as normal.
Parameters
----------... | zipline/pipeline/loaders/earnings_estimates.py | def get_split_adjusted_asof_idx(self, dates):
"""
Compute the index in `dates` where the split-adjusted-asof-date
falls. This is the date up to which, and including which, we will
need to unapply all adjustments for and then re-apply them as they
come in. After this date, adjustm... | def get_split_adjusted_asof_idx(self, dates):
"""
Compute the index in `dates` where the split-adjusted-asof-date
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need to unapply all adjustments for and then re-apply them as they
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train | EarningsEstimatesLoader.collect_overwrites_for_sid | Given a sid, collect all overwrites that should be applied for this
sid at each quarter boundary.
Parameters
----------
group : pd.DataFrame
The data for `sid`.
dates : pd.DatetimeIndex
The calendar dates for which estimates data is requested.
req... | zipline/pipeline/loaders/earnings_estimates.py | def collect_overwrites_for_sid(self,
group,
dates,
requested_qtr_data,
last_per_qtr,
sid_idx,
columns,
... | def collect_overwrites_for_sid(self,
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train | EarningsEstimatesLoader.merge_into_adjustments_for_all_sids | Merge adjustments for a particular sid into a dictionary containing
adjustments for all sids.
Parameters
----------
all_adjustments_for_sid : dict[int -> AdjustedArray]
All adjustments for a particular sid.
col_to_all_adjustments : dict[int -> AdjustedArray]
... | zipline/pipeline/loaders/earnings_estimates.py | def merge_into_adjustments_for_all_sids(self,
all_adjustments_for_sid,
col_to_all_adjustments):
"""
Merge adjustments for a particular sid into a dictionary containing
adjustments for all sids.
Param... | def merge_into_adjustments_for_all_sids(self,
all_adjustments_for_sid,
col_to_all_adjustments):
"""
Merge adjustments for a particular sid into a dictionary containing
adjustments for all sids.
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train | EarningsEstimatesLoader.get_adjustments | Creates an AdjustedArray from the given estimates data for the given
dates.
Parameters
----------
zero_qtr_data : pd.DataFrame
The 'time zero' data for each calendar date per sid.
requested_qtr_data : pd.DataFrame
The requested quarter data for each calen... | zipline/pipeline/loaders/earnings_estimates.py | def get_adjustments(self,
zero_qtr_data,
requested_qtr_data,
last_per_qtr,
dates,
assets,
columns,
**kwargs):
"""
Creates an AdjustedArr... | def get_adjustments(self,
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last_per_qtr,
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columns,
**kwargs):
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train | EarningsEstimatesLoader.create_overwrites_for_quarter | Add entries to the dictionary of columns to adjustments for the given
sid and the given quarter.
Parameters
----------
col_to_overwrites : dict [column_name -> list of ArrayAdjustment]
A dictionary mapping column names to all overwrites for those
columns.
... | zipline/pipeline/loaders/earnings_estimates.py | def create_overwrites_for_quarter(self,
col_to_overwrites,
next_qtr_start_idx,
last_per_qtr,
quarters_with_estimates_for_sid,
requ... | def create_overwrites_for_quarter(self,
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train | EarningsEstimatesLoader.get_last_data_per_qtr | Determine the last piece of information we know for each column on each
date in the index for each sid and quarter.
Parameters
----------
assets_with_data : pd.Index
Index of all assets that appear in the raw data given to the
loader.
columns : iterable o... | zipline/pipeline/loaders/earnings_estimates.py | def get_last_data_per_qtr(self,
assets_with_data,
columns,
dates,
data_query_cutoff_times):
"""
Determine the last piece of information we know for each column on each
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train | PreviousEarningsEstimatesLoader.get_zeroth_quarter_idx | Filters for releases that are on or after each simulation date and
determines the previous quarter by picking out the most recent
release relative to each date in the index.
Parameters
----------
stacked_last_per_qtr : pd.DataFrame
A DataFrame with index of calendar ... | zipline/pipeline/loaders/earnings_estimates.py | def get_zeroth_quarter_idx(self, stacked_last_per_qtr):
"""
Filters for releases that are on or after each simulation date and
determines the previous quarter by picking out the most recent
release relative to each date in the index.
Parameters
----------
stacked... | def get_zeroth_quarter_idx(self, stacked_last_per_qtr):
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Filters for releases that are on or after each simulation date and
determines the previous quarter by picking out the most recent
release relative to each date in the index.
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----------
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train | SplitAdjustedEstimatesLoader.get_adjustments_for_sid | Collects both overwrites and adjustments for a particular sid.
Parameters
----------
split_adjusted_asof_idx : int
The integer index of the date on which the data was split-adjusted.
split_adjusted_cols_for_group : list of str
The names of requested columns that ... | zipline/pipeline/loaders/earnings_estimates.py | def get_adjustments_for_sid(self,
group,
dates,
requested_qtr_data,
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train | SplitAdjustedEstimatesLoader.get_adjustments | Calculates both split adjustments and overwrites for all sids. | zipline/pipeline/loaders/earnings_estimates.py | def get_adjustments(self,
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train | SplitAdjustedEstimatesLoader.determine_end_idx_for_adjustment | Determines the date until which the adjustment at the given date
index should be applied for the given quarter.
Parameters
----------
adjustment_ts : pd.Timestamp
The timestamp at which the adjustment occurs.
dates : pd.DatetimeIndex
The calendar dates ov... | zipline/pipeline/loaders/earnings_estimates.py | def determine_end_idx_for_adjustment(self,
adjustment_ts,
dates,
upper_bound,
requested_quarter,
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train | SplitAdjustedEstimatesLoader.collect_pre_split_asof_date_adjustments | Collect split adjustments that occur before the
split-adjusted-asof-date. All those adjustments must first be
UN-applied at the first date index and then re-applied on the
appropriate dates in order to match point in time share pricing data.
Parameters
----------
split_a... | zipline/pipeline/loaders/earnings_estimates.py | def collect_pre_split_asof_date_adjustments(
self,
split_adjusted_asof_date_idx,
sid_idx,
pre_adjustments,
requested_split_adjusted_columns
):
"""
Collect split adjustments that occur before the
split-adjusted-asof-date. All those a... | def collect_pre_split_asof_date_adjustments(
self,
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sid_idx,
pre_adjustments,
requested_split_adjusted_columns
):
"""
Collect split adjustments that occur before the
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train | SplitAdjustedEstimatesLoader.collect_post_asof_split_adjustments | Collect split adjustments that occur after the
split-adjusted-asof-date. Each adjustment needs to be applied to all
dates on which knowledge for the requested quarter was older than the
date of the adjustment.
Parameters
----------
post_adjustments : tuple(list(float), l... | zipline/pipeline/loaders/earnings_estimates.py | def collect_post_asof_split_adjustments(self,
post_adjustments,
requested_qtr_data,
sid,
sid_idx,
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train | SplitAdjustedEstimatesLoader.retrieve_split_adjustment_data_for_sid | dates : pd.DatetimeIndex
The calendar dates.
sid : int
The sid for which we want to retrieve adjustments.
split_adjusted_asof_idx : int
The index in `dates` as-of which the data is split adjusted.
Returns
-------
pre_adjustments : tuple(list(f... | zipline/pipeline/loaders/earnings_estimates.py | def retrieve_split_adjustment_data_for_sid(self,
dates,
sid,
split_adjusted_asof_idx):
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dates : pd.DatetimeIndex
The calendar dates.
sid : i... | def retrieve_split_adjustment_data_for_sid(self,
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train | SplitAdjustedEstimatesLoader.merge_split_adjustments_with_overwrites | Merge split adjustments with the dict containing overwrites.
Parameters
----------
pre : dict[str -> dict[int -> list]]
The adjustments that occur before the split-adjusted-asof-date.
post : dict[str -> dict[int -> list]]
The adjustments that occur after the spli... | zipline/pipeline/loaders/earnings_estimates.py | def merge_split_adjustments_with_overwrites(
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overwrites,
requested_split_adjusted_columns
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"""
Merge split adjustments with the dict containing overwrites.
Parameters
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pre : dict[str -> dict[int -> list]]
... | def merge_split_adjustments_with_overwrites(
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post,
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Merge split adjustments with the dict containing overwrites.
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train | PreviousSplitAdjustedEarningsEstimatesLoader.collect_split_adjustments | Collect split adjustments for previous quarters and apply them to the
given dictionary of splits for the given sid. Since overwrites just
replace all estimates before the new quarter with NaN, we don't need to
worry about re-applying split adjustments.
Parameters
----------
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train | NextSplitAdjustedEarningsEstimatesLoader.collect_split_adjustments | Collect split adjustments for future quarters. Re-apply adjustments
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overwrites into the given dictionary of splits for the given sid.
Parameters
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adjustments_for_sid : dict[str -> dict[int -> list]]
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Examples
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.. code-block:: python
# Equivalent to:
# ... | zipline/pipeline/factors/basic.py | def from_span(cls, inputs, window_length, span, **kwargs):
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Convenience constructor for passing `decay_rate` in terms of `span`.
Forwards `decay_rate` as `1 - (2.0 / (1 + span))`. This provides the
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Examples
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train | _ExponentialWeightedFactor.from_halflife | Convenience constructor for passing ``decay_rate`` in terms of half
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Forwards ``decay_rate`` as ``exp(log(.5) / halflife)``. This provides
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.. code-block:: python
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Convenience constructor for passing ``decay_rate`` in terms of half
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Forwards ``decay_rate`` as ``exp(log(.5) / halflife)``. This provides
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Convenience constructor for passing ``decay_rate`` in terms of half
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train | _ExponentialWeightedFactor.from_center_of_mass | Convenience constructor for passing `decay_rate` in terms of center of
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Forwards `decay_rate` as `1 - (1 / 1 + center_of_mass)`. This provides
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.. code-block:: python
# E... | zipline/pipeline/factors/basic.py | def from_center_of_mass(cls,
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"""
Convenience constructor for passing `decay_rate` in terms of center of
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train | tolerant_equals | Check if a and b are equal with some tolerance.
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a, b : float
The floats to check for equality.
atol : float, optional
The absolute tolerance.
rtol : float, optional
The relative tolerance.
equal_nan : bool, optional
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"""Check if a and b are equal with some tolerance.
Parameters
----------
a, b : float
The floats to check for equality.
atol : float, optional
The absolute tolerance.
rtol : float, optional
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a, b : float
The floats to check for equality.
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The absolute tolerance.
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train | round_if_near_integer | Round a to the nearest integer if that integer is within an epsilon
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"""
Round a to the nearest integer if that integer is within an epsilon
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if abs(a - round(a)) <= epsilon:
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Round a to the nearest integer if that integer is within an epsilon
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train | coerce_numbers_to_my_dtype | A decorator for methods whose signature is f(self, other) that coerces
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This is used to make comparison operations between numbers and `Factor`
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"""
A decorator for methods whose signature is f(self, other) that coerces
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train | binop_return_dtype | Compute the expected return dtype for the given binary operator.
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Operator symbol, (e.g. '+', '-', ...).
left : numpy.dtype
Dtype of left hand side.
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"""
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op : str
Operator symbol, (e.g. '+', '-', ...).
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Dtype of left hand side.
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train | binary_operator | Factory function for making binary operator methods on a Factor subclass.
Returns a function, "binary_operator" suitable for implementing functions
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Factory function for making binary operator methods on a Factor subclass.
Returns a function, "binary_operator" suitable for implementing functions
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"""
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Factory function for making binary operator methods on a Factor subclass.
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train | reflected_binary_operator | Factory function for making binary operator methods on a Factor.
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Factory function for making binary operator methods on a Factor.
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assert not is_comparison(op)
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train | unary_operator | Factory function for making unary operator methods for Factors. | zipline/pipeline/factors/factor.py | def unary_operator(op):
"""
Factory function for making unary operator methods for Factors.
"""
# Only negate is currently supported.
valid_ops = {'-'}
if op not in valid_ops:
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Factory function for making unary operator methods for Factors.
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train | function_application | Factory function for producing function application methods for Factor
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Factory function for producing function application methods for Factor
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"""
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Factory function for producing function application methods for Factor
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train | winsorize | This implementation is based on scipy.stats.mstats.winsorize | zipline/pipeline/factors/factor.py | def winsorize(row, min_percentile, max_percentile):
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This implementation is based on scipy.stats.mstats.winsorize
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nan_count = isnan(row).sum()
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idx = a.argsort()
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train | Factor.demean | Construct a Factor that computes ``self`` and subtracts the mean from
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If ``mask`` is supplied, ignore values where ``mask`` returns False
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"""
Construct a Factor that computes ``self`` and subtracts the mean from
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Construct a Factor that computes ``self`` and subtracts the mean from
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train | Factor.zscore | Construct a Factor that Z-Scores each day's results.
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If ``mask`` is supplied, ignore values where ``mask`` returns False
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"""
Construct a Factor that Z-Scores each day's results.
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when compu... | def zscore(self, mask=NotSpecified, groupby=NotSpecified):
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Construct a Factor that Z-Scores each day's results.
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train | Factor.rank | Construct a new Factor representing the sorted rank of each column
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Parameters
----------
method : str, {'ordinal', 'min', 'max', 'dense', 'average'}
The method used to assign ranks to tied elements. See
`scipy.stats.rankdata` for a full descripti... | zipline/pipeline/factors/factor.py | def rank(self,
method='ordinal',
ascending=True,
mask=NotSpecified,
groupby=NotSpecified):
"""
Construct a new Factor representing the sorted rank of each column
within each row.
Parameters
----------
method : str, {'or... | def rank(self,
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groupby=NotSpecified):
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Construct a new Factor representing the sorted rank of each column
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----------
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train | Factor.pearsonr | Construct a new Factor that computes rolling pearson correlation
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This method can only be called on factors which are deemed safe for use
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"""
Construct a new Factor that computes rolling pearson correlation
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This method can only be called on factors which are deemed safe for use
as inputs to o... | def pearsonr(self, target, correlation_length, mask=NotSpecified):
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Construct a new Factor that computes rolling pearson correlation
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train | Factor.spearmanr | Construct a new Factor that computes rolling spearman rank correlation
coefficients between `target` and the columns of `self`.
This method can only be called on factors which are deemed safe for use
as inputs to other factors. This includes `Returns` and any factors
created from `Facto... | zipline/pipeline/factors/factor.py | def spearmanr(self, target, correlation_length, mask=NotSpecified):
"""
Construct a new Factor that computes rolling spearman rank correlation
coefficients between `target` and the columns of `self`.
This method can only be called on factors which are deemed safe for use
as inpu... | def spearmanr(self, target, correlation_length, mask=NotSpecified):
"""
Construct a new Factor that computes rolling spearman rank correlation
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train | Factor.linear_regression | Construct a new Factor that performs an ordinary least-squares
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This method can only be called on factors which are deemed safe for use
as inputs to other factors. This includes `Returns` and any factors
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Construct a new Factor that performs an ordinary least-squares
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Construct a new Factor that performs an ordinary least-squares
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train | Factor.winsorize | Construct a new factor that winsorizes the result of this factor.
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Construct a new factor that winsorizes the result of this factor.
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train | Factor.quantiles | Construct a Classifier computing quantiles of the output of ``self``.
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If ``mask`` is supplied, ignore data points in locations for which
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"""
Construct a Classifier computing quantiles of the output of ``self``.
Every non-NaN data point the output is labelled with an integer value
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If ``mask`` is supplied, ignore data p... | def quantiles(self, bins, mask=NotSpecified):
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Construct a Classifier computing quantiles of the output of ``self``.
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train | Factor.top | Construct a Filter matching the top N asset values of self each day.
If ``groupby`` is supplied, returns a Filter matching the top N asset
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Parameters
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N : int
Number of assets passing the returned filter each day.
mask : zipl... | zipline/pipeline/factors/factor.py | def top(self, N, mask=NotSpecified, groupby=NotSpecified):
"""
Construct a Filter matching the top N asset values of self each day.
If ``groupby`` is supplied, returns a Filter matching the top N asset
values for each group.
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N : int
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"""
Construct a Filter matching the top N asset values of self each day.
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train | Factor.bottom | Construct a Filter matching the bottom N asset values of self each day.
If ``groupby`` is supplied, returns a Filter matching the bottom N
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Parameters
----------
N : int
Number of assets passing the returned filter each day.
mask ... | zipline/pipeline/factors/factor.py | def bottom(self, N, mask=NotSpecified, groupby=NotSpecified):
"""
Construct a Filter matching the bottom N asset values of self each day.
If ``groupby`` is supplied, returns a Filter matching the bottom N
asset values for each group.
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Construct a Filter matching the bottom N asset values of self each day.
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train | Factor.percentile_between | Construct a new Filter representing entries from the output of this
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Parameters
----------
min_percentile : float [0.0, 100.0]
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Construct a new Filter representing entries from the output of this
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train | Rank._validate | Verify that the stored rank method is valid. | zipline/pipeline/factors/factor.py | def _validate(self):
"""
Verify that the stored rank method is valid.
"""
if self._method not in _RANK_METHODS:
raise UnknownRankMethod(
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return super(Rank, self)._validate() | def _validate(self):
"""
Verify that the stored rank method is valid.
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train | Rank._compute | For each row in the input, compute a like-shaped array of per-row
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"""
For each row in the input, compute a like-shaped array of per-row
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train | _time_to_micros | Convert a time into microseconds since midnight.
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----------
time : datetime.time
The time to convert.
Returns
-------
us : int
The number of microseconds since midnight.
Notes
-----
This does not account for leap seconds or daylight savings. | zipline/utils/pandas_utils.py | def _time_to_micros(time):
"""Convert a time into microseconds since midnight.
Parameters
----------
time : datetime.time
The time to convert.
Returns
-------
us : int
The number of microseconds since midnight.
Notes
-----
This does not account for leap seconds or... | def _time_to_micros(time):
"""Convert a time into microseconds since midnight.
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time : datetime.time
The time to convert.
Returns
-------
us : int
The number of microseconds since midnight.
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train | mask_between_time | Return a mask of all of the datetimes in ``dts`` that are between
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Parameters
----------
dts : pd.DatetimeIndex
The index to mask.
start : time
Mask away times less than the start.
end : time
Mask away times greater than the end.
include_start : ... | zipline/utils/pandas_utils.py | def mask_between_time(dts, start, end, include_start=True, include_end=True):
"""Return a mask of all of the datetimes in ``dts`` that are between
``start`` and ``end``.
Parameters
----------
dts : pd.DatetimeIndex
The index to mask.
start : time
Mask away times less than the sta... | def mask_between_time(dts, start, end, include_start=True, include_end=True):
"""Return a mask of all of the datetimes in ``dts`` that are between
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dts : pd.DatetimeIndex
The index to mask.
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train | find_in_sorted_index | Find the index of ``dt`` in ``dts``.
This function should be used instead of `dts.get_loc(dt)` if the index is
large enough that we don't want to initialize a hash table in ``dts``. In
particular, this should always be used on minutely trading calendars.
Parameters
----------
dts : pd.Datetime... | zipline/utils/pandas_utils.py | def find_in_sorted_index(dts, dt):
"""
Find the index of ``dt`` in ``dts``.
This function should be used instead of `dts.get_loc(dt)` if the index is
large enough that we don't want to initialize a hash table in ``dts``. In
particular, this should always be used on minutely trading calendars.
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"""
Find the index of ``dt`` in ``dts``.
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train | nearest_unequal_elements | Find values in ``dts`` closest but not equal to ``dt``.
Returns a pair of (last_before, first_after).
When ``dt`` is less than any element in ``dts``, ``last_before`` is None.
When ``dt`` is greater any element in ``dts``, ``first_after`` is None.
``dts`` must be unique and sorted in increasing order... | zipline/utils/pandas_utils.py | def nearest_unequal_elements(dts, dt):
"""
Find values in ``dts`` closest but not equal to ``dt``.
Returns a pair of (last_before, first_after).
When ``dt`` is less than any element in ``dts``, ``last_before`` is None.
When ``dt`` is greater any element in ``dts``, ``first_after`` is None.
``... | def nearest_unequal_elements(dts, dt):
"""
Find values in ``dts`` closest but not equal to ``dt``.
Returns a pair of (last_before, first_after).
When ``dt`` is less than any element in ``dts``, ``last_before`` is None.
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train | categorical_df_concat | Prepare list of pandas DataFrames to be used as input to pd.concat.
Ensure any columns of type 'category' have the same categories across each
dataframe.
Parameters
----------
df_list : list
List of dataframes with same columns.
inplace : bool
True if input list can be modified.... | zipline/utils/pandas_utils.py | def categorical_df_concat(df_list, inplace=False):
"""
Prepare list of pandas DataFrames to be used as input to pd.concat.
Ensure any columns of type 'category' have the same categories across each
dataframe.
Parameters
----------
df_list : list
List of dataframes with same columns.... | def categorical_df_concat(df_list, inplace=False):
"""
Prepare list of pandas DataFrames to be used as input to pd.concat.
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train | required_event_fields | Compute the set of resource columns required to serve
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Split requested columns into columns that should load the next known
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Split requested columns into columns that should load the next known
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The codes for the label array.
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train | LabelArray.as_categorical | Coerce self into a pandas categorical.
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Coerce self into a pandas categorical.
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Coerce self into a pandas categorical.
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train | LabelArray.as_categorical_frame | Coerce self into a pandas DataFrame of Categoricals. | zipline/lib/labelarray.py | def as_categorical_frame(self, index, columns, name=None):
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Coerce self into a pandas DataFrame of Categoricals.
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The value to assign at the given locations.
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Set scalar value into the array.
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The value to assign at the given locations.
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Set scalar value into the array.
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train | LabelArray.empty_like | Make an empty LabelArray with the same categories as ``self``, filled
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Make an empty LabelArray with the same categories as ``self``, filled
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Asymmetric rounding function for adjusting prices to the specified number
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Asymmetric rounding function for adjusting prices to the specified number
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train | check_stoplimit_prices | Check to make sure the stop/limit prices are reasonable and raise
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train | BenchmarkSource._initialize_precalculated_series | Internal method that pre-calculates the benchmark return series for
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train | DataPortal.handle_extra_source | Extra sources always have a sid column.
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train | DataPortal.get_spot_value | Public API method that returns a scalar value representing the value
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assets : Asset, ContinuousFuture, or iterable of same.
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train | DataPortal.get_adjusted_value | Returns a scalar value representing the value
of the desired asset's field at the given dt with adjustments applied.
Parameters
----------
asset : Asset
The asset whose data is desired.
field : {'open', 'high', 'low', 'close', 'volume', \
'price', 'l... | zipline/data/data_portal.py | def get_adjusted_value(self, asset, field, dt,
perspective_dt,
data_frequency,
spot_value=None):
"""
Returns a scalar value representing the value
of the desired asset's field at the given dt with adjustments applie... | def get_adjusted_value(self, asset, field, dt,
perspective_dt,
data_frequency,
spot_value=None):
"""
Returns a scalar value representing the value
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train | DataPortal._get_history_daily_window | Internal method that returns a dataframe containing history bars
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end_dt,
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Internal method that returns a dataf... | def _get_history_daily_window(self,
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train | DataPortal._get_history_minute_window | Internal method that returns a dataframe containing history bars
of minute frequency for the given sids. | zipline/data/data_portal.py | def _get_history_minute_window(self, assets, end_dt, bar_count,
field_to_use):
"""
Internal method that returns a dataframe containing history bars
of minute frequency for the given sids.
"""
# get all the minutes for this window
try:
... | def _get_history_minute_window(self, assets, end_dt, bar_count,
field_to_use):
"""
Internal method that returns a dataframe containing history bars
of minute frequency for the given sids.
"""
# get all the minutes for this window
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train | DataPortal.get_history_window | Public API method that returns a dataframe containing the requested
history window. Data is fully adjusted.
Parameters
----------
assets : list of zipline.data.Asset objects
The assets whose data is desired.
bar_count: int
The number of bars desired.
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Public A... | def get_history_window(self,
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train | DataPortal._get_minute_window_data | Internal method that gets a window of adjusted minute data for an asset
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minute bars.
Missing bars are filled with NaN.
Parameters
----------
assets : iterable[Asset]
The assets whose data ... | zipline/data/data_portal.py | def _get_minute_window_data(self, assets, field, minutes_for_window):
"""
Internal method that gets a window of adjusted minute data for an asset
and specified date range. Used to support the history API method for
minute bars.
Missing bars are filled with NaN.
Paramet... | def _get_minute_window_data(self, assets, field, minutes_for_window):
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Internal method that gets a window of adjusted minute data for an asset
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Missing bars are filled with NaN.
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train | DataPortal._get_daily_window_data | Internal method that gets a window of adjusted daily data for a sid
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daily bars.
Parameters
----------
asset : Asset
The asset whose data is desired.
start_dt: pandas.Timestamp
... | zipline/data/data_portal.py | def _get_daily_window_data(self,
assets,
field,
days_in_window,
extra_slot=True):
"""
Internal method that gets a window of adjusted daily data for a sid
and specified date... | def _get_daily_window_data(self,
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train | DataPortal._get_adjustment_list | Internal method that returns a list of adjustments for the given sid.
Parameters
----------
asset : Asset
The asset for which to return adjustments.
adjustments_dict: dict
A dictionary of sid -> list that is used as a cache.
table_name: string
... | zipline/data/data_portal.py | def _get_adjustment_list(self, asset, adjustments_dict, table_name):
"""
Internal method that returns a list of adjustments for the given sid.
Parameters
----------
asset : Asset
The asset for which to return adjustments.
adjustments_dict: dict
A... | def _get_adjustment_list(self, asset, adjustments_dict, table_name):
"""
Internal method that returns a list of adjustments for the given sid.
Parameters
----------
asset : Asset
The asset for which to return adjustments.
adjustments_dict: dict
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train | DataPortal.get_splits | Returns any splits for the given sids and the given dt.
Parameters
----------
assets : container
Assets for which we want splits.
dt : pd.Timestamp
The date for which we are checking for splits. Note: this is
expected to be midnight UTC.
Retu... | zipline/data/data_portal.py | def get_splits(self, assets, dt):
"""
Returns any splits for the given sids and the given dt.
Parameters
----------
assets : container
Assets for which we want splits.
dt : pd.Timestamp
The date for which we are checking for splits. Note: this is
... | def get_splits(self, assets, dt):
"""
Returns any splits for the given sids and the given dt.
Parameters
----------
assets : container
Assets for which we want splits.
dt : pd.Timestamp
The date for which we are checking for splits. Note: this is
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train | DataPortal.get_stock_dividends | Returns all the stock dividends for a specific sid that occur
in the given trading range.
Parameters
----------
sid: int
The asset whose stock dividends should be returned.
trading_days: pd.DatetimeIndex
The trading range.
Returns
------... | zipline/data/data_portal.py | def get_stock_dividends(self, sid, trading_days):
"""
Returns all the stock dividends for a specific sid that occur
in the given trading range.
Parameters
----------
sid: int
The asset whose stock dividends should be returned.
trading_days: pd.Dateti... | def get_stock_dividends(self, sid, trading_days):
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Returns all the stock dividends for a specific sid that occur
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----------
sid: int
The asset whose stock dividends should be returned.
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train | DataPortal.get_fetcher_assets | Returns a list of assets for the current date, as defined by the
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Returns
-------
list: a list of Asset objects. | zipline/data/data_portal.py | def get_fetcher_assets(self, dt):
"""
Returns a list of assets for the current date, as defined by the
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Returns
-------
list: a list of Asset objects.
"""
# return a list of assets for the current date, as defined by the
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Returns a list of assets for the current date, as defined by the
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list: a list of Asset objects.
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train | DataPortal.get_current_future_chain | Retrieves the future chain for the contract at the given `dt` according
the `continuous_future` specification.
Returns
-------
future_chain : list[Future]
A list of active futures, where the first index is the current
contract specified by the continuous future ... | zipline/data/data_portal.py | def get_current_future_chain(self, continuous_future, dt):
"""
Retrieves the future chain for the contract at the given `dt` according
the `continuous_future` specification.
Returns
-------
future_chain : list[Future]
A list of active futures, where the firs... | def get_current_future_chain(self, continuous_future, dt):
"""
Retrieves the future chain for the contract at the given `dt` according
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Returns
-------
future_chain : list[Future]
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train | make_kind_check | Make a function that checks whether a scalar or array is of a given kind
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"""
Make a function that checks whether a scalar or array is of a given kind
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"""
def check(value):
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Make a function that checks whether a scalar or array is of a given kind
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"""
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if hasattr(value, 'dtype'):
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train | coerce_to_dtype | Make a value with the specified numpy dtype.
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train | repeat_first_axis | Restride `array` to repeat `count` times along the first axis.
Parameters
----------
array : np.array
The array to restride.
count : int
Number of times to repeat `array`.
Returns
-------
result : array
Array of shape (count,) + array.shape, composed of `array` repe... | zipline/utils/numpy_utils.py | def repeat_first_axis(array, count):
"""
Restride `array` to repeat `count` times along the first axis.
Parameters
----------
array : np.array
The array to restride.
count : int
Number of times to repeat `array`.
Returns
-------
result : array
Array of shape... | def repeat_first_axis(array, count):
"""
Restride `array` to repeat `count` times along the first axis.
Parameters
----------
array : np.array
The array to restride.
count : int
Number of times to repeat `array`.
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train | repeat_last_axis | Restride `array` to repeat `count` times along the last axis.
Parameters
----------
array : np.array
The array to restride.
count : int
Number of times to repeat `array`.
Returns
-------
result : array
Array of shape array.shape + (count,) composed of `array` repeat... | zipline/utils/numpy_utils.py | def repeat_last_axis(array, count):
"""
Restride `array` to repeat `count` times along the last axis.
Parameters
----------
array : np.array
The array to restride.
count : int
Number of times to repeat `array`.
Returns
-------
result : array
Array of shape a... | def repeat_last_axis(array, count):
"""
Restride `array` to repeat `count` times along the last axis.
Parameters
----------
array : np.array
The array to restride.
count : int
Number of times to repeat `array`.
Returns
-------
result : array
Array of shape a... | [
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"to",
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"count",
"times",
"along",
"the",
"last",
"axis",
"."
] | quantopian/zipline | python | https://github.com/quantopian/zipline/blob/77ad15e6dc4c1cbcdc133653bac8a63fc704f7fe/zipline/utils/numpy_utils.py#L216-L256 | [
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