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value | url stringlengths 87 315 | code_tokens listlengths 19 28.4k | sha stringlengths 40 40 |
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train | convert_cols | Adapt OHLCV columns into uint32 columns.
Parameters
----------
cols : dict
A dict mapping each column name (open, high, low, close, volume)
to a float column to convert to uint32.
scale_factor : int
Factor to use to scale float values before converting to uint32.
sid : int
... | zipline/data/minute_bars.py | def convert_cols(cols, scale_factor, sid, invalid_data_behavior):
"""Adapt OHLCV columns into uint32 columns.
Parameters
----------
cols : dict
A dict mapping each column name (open, high, low, close, volume)
to a float column to convert to uint32.
scale_factor : int
Factor ... | def convert_cols(cols, scale_factor, sid, invalid_data_behavior):
"""Adapt OHLCV columns into uint32 columns.
Parameters
----------
cols : dict
A dict mapping each column name (open, high, low, close, volume)
to a float column to convert to uint32.
scale_factor : int
Factor ... | [
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train | BcolzMinuteBarMetadata.write | Write the metadata to a JSON file in the rootdir.
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version : int
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ohlc_ratio : int
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Write the metadata to a JSON file in the rootdir.
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train | BcolzMinuteBarWriter.open | Open an existing ``rootdir`` for writing.
Parameters
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end_session : Timestamp (optional)
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"""
Open an existing ``rootdir`` for writing.
Parameters
----------
end_session : Timestamp (optional)
When appending, the intended new ``end_session``.
"""
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... | def open(cls, rootdir, end_session=None):
"""
Open an existing ``rootdir`` for writing.
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end_session : Timestamp (optional)
When appending, the intended new ``end_session``.
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metadata = BcolzMinuteBarMetadata.read(rootdir)
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train | BcolzMinuteBarWriter.sidpath | Parameters
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sid : int
Asset identifier.
Returns
-------
out : string
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"""
Parameters
----------
sid : int
Asset identifier.
Returns
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out : string
Full path to the bcolz rootdir for the given sid.
"""
sid_subdir = _sid_subdir_path(sid)
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"""
Parameters
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sid : int
Asset identifier.
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Full path to the bcolz rootdir for the given sid.
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train | BcolzMinuteBarWriter.last_date_in_output_for_sid | Parameters
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sid : int
Asset identifier.
Returns
-------
out : pd.Timestamp
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given sid. | zipline/data/minute_bars.py | def last_date_in_output_for_sid(self, sid):
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Parameters
----------
sid : int
Asset identifier.
Returns
-------
out : pd.Timestamp
The midnight of the last date written in to the output for the
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"""
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"""
Parameters
----------
sid : int
Asset identifier.
Returns
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out : pd.Timestamp
The midnight of the last date written in to the output for the
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train | BcolzMinuteBarWriter._init_ctable | Create empty ctable for given path.
Parameters
----------
path : string
The path to rootdir of the new ctable. | zipline/data/minute_bars.py | def _init_ctable(self, path):
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Create empty ctable for given path.
Parameters
----------
path : string
The path to rootdir of the new ctable.
"""
# Only create the containing subdir on creation.
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Create empty ctable for given path.
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train | BcolzMinuteBarWriter._ensure_ctable | Ensure that a ctable exists for ``sid``, then return it. | zipline/data/minute_bars.py | def _ensure_ctable(self, sid):
"""Ensure that a ctable exists for ``sid``, then return it."""
sidpath = self.sidpath(sid)
if not os.path.exists(sidpath):
return self._init_ctable(sidpath)
return bcolz.ctable(rootdir=sidpath, mode='a') | def _ensure_ctable(self, sid):
"""Ensure that a ctable exists for ``sid``, then return it."""
sidpath = self.sidpath(sid)
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train | BcolzMinuteBarWriter.pad | Fill sid container with empty data through the specified date.
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Fill sid container with empty data through the specified date.
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Fill sid container with empty data through the specified date.
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train | BcolzMinuteBarWriter.set_sid_attrs | Write all the supplied kwargs as attributes of the sid's file. | zipline/data/minute_bars.py | def set_sid_attrs(self, sid, **kwargs):
"""Write all the supplied kwargs as attributes of the sid's file.
"""
table = self._ensure_ctable(sid)
for k, v in kwargs.items():
table.attrs[k] = v | def set_sid_attrs(self, sid, **kwargs):
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train | BcolzMinuteBarWriter.write | Write a stream of minute data.
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data : iterable[(int, pd.DataFrame)]
The data to write. Each element should be a tuple of sid, data
where data has the following format:
columns : ('open', 'high', 'low', 'close', 'volume')
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Parameters
----------
data : iterable[(int, pd.DataFrame)]
The data to write. Each element should be a tuple of sid, data
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The data to write. Each element should be a tuple of sid, data
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train | BcolzMinuteBarWriter.write_sid | Write the OHLCV data for the given sid.
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If the length of the bcolz ctable is not exactly to the date before
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train | BcolzMinuteBarWriter.write_cols | Write the OHLCV data for the given sid.
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train | BcolzMinuteBarWriter._write_cols | Internal method for `write_cols` and `write`.
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The asset identifier for the data being written.
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train | BcolzMinuteBarWriter.data_len_for_day | Return the number of data points up to and including the
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"""
Return the number of data points up to and including the
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"""
day_ix = self._session_labels.get_loc(day)
# Add one to the 0-indexed day_ix to get the number of days.
num_days = day_ix + 1
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train | BcolzMinuteBarWriter.truncate | Truncate data beyond this date in all ctables. | zipline/data/minute_bars.py | def truncate(self, date):
"""Truncate data beyond this date in all ctables."""
truncate_slice_end = self.data_len_for_day(date)
glob_path = os.path.join(self._rootdir, "*", "*", "*.bcolz")
sid_paths = sorted(glob(glob_path))
for sid_path in sid_paths:
file_name = os... | def truncate(self, date):
"""Truncate data beyond this date in all ctables."""
truncate_slice_end = self.data_len_for_day(date)
glob_path = os.path.join(self._rootdir, "*", "*", "*.bcolz")
sid_paths = sorted(glob(glob_path))
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train | BcolzMinuteBarReader._minutes_to_exclude | Calculate the minutes which should be excluded when a window
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Returns
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"""
Calculate the minutes which should be excluded when a window
occurs on days which had an early close, i.e. days where the close
based on the regular period of minutes per day and the market close
do not match.
Returns
-------
... | def _minutes_to_exclude(self):
"""
Calculate the minutes which should be excluded when a window
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based on the regular period of minutes per day and the market close
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train | BcolzMinuteBarReader._minute_exclusion_tree | Build an interval tree keyed by the start and end of each range
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"""
Build an interval tree keyed by the start and end of each range
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train | BcolzMinuteBarReader._exclusion_indices_for_range | Returns
-------
List of tuples of (start, stop) which represent the ranges of minutes
which should be excluded when a market minute window is requested. | zipline/data/minute_bars.py | def _exclusion_indices_for_range(self, start_idx, end_idx):
"""
Returns
-------
List of tuples of (start, stop) which represent the ranges of minutes
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"""
itree = self._minute_exclusion_tree
... | def _exclusion_indices_for_range(self, start_idx, end_idx):
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Returns
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List of tuples of (start, stop) which represent the ranges of minutes
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train | BcolzMinuteBarReader.get_value | Retrieve the pricing info for the given sid, dt, and field.
Parameters
----------
sid : int
Asset identifier.
dt : datetime-like
The datetime at which the trade occurred.
field : string
The type of pricing data to retrieve.
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"""
Retrieve the pricing info for the given sid, dt, and field.
Parameters
----------
sid : int
Asset identifier.
dt : datetime-like
The datetime at which the trade occurred.
field : string
... | def get_value(self, sid, dt, field):
"""
Retrieve the pricing info for the given sid, dt, and field.
Parameters
----------
sid : int
Asset identifier.
dt : datetime-like
The datetime at which the trade occurred.
field : string
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train | BcolzMinuteBarReader._find_position_of_minute | Internal method that returns the position of the given minute in the
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ex. this method would return 1 for 2002-01-02 9:32 AM Eastern, if
2002-01-02 is the first trading d... | zipline/data/minute_bars.py | def _find_position_of_minute(self, minute_dt):
"""
Internal method that returns the position of the given minute in the
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day. Adjusts non market minutes to the last close.
ex. this method would return 1 for 2002-... | def _find_position_of_minute(self, minute_dt):
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Internal method that returns the position of the given minute in the
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train | BcolzMinuteBarReader.load_raw_arrays | Parameters
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start_dt: Timestamp
Beginning of the window range.
end_dt: Timestamp
End of the window range.
sids : list of int
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Parameters
----------
fields : list of str
'open', 'high', 'low', 'close', or 'volume'
start_dt: Timestamp
Beginning of the window range.
end_dt: Timestamp
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train | H5MinuteBarUpdateWriter.write | Write the frames to the target HDF5 file, using the format used by
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Parameters
----------
frames : iter[(int, DataFrame)] or dict[int -> DataFrame]
An iterable or other mapping of sid to the corresponding OHLCV
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"""
Write the frames to the target HDF5 file, using the format used by
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Parameters
----------
frames : iter[(int, DataFrame)] or dict[int -> DataFrame]
An iterable or other mapping of sid to the corresponding OHLCV
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Write the frames to the target HDF5 file, using the format used by
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frames : iter[(int, DataFrame)] or dict[int -> DataFrame]
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train | next_event_indexer | Construct an index array that, when applied to an array of values, produces
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sid at each moment in time.
Locations where no next event was known will be filled with -1.
Parameters
----------
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data_query_cutoff,
all_sids,
event_dates,
event_timestamps,
event_sids):
"""
Construct an index array that, when applied to an array of values, produces
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train | previous_event_indexer | Construct an index array that, when applied to an array of values, produces
a 2D array containing the values associated with the previous event for
each sid at each moment in time.
Locations where no previous event was known will be filled with -1.
Parameters
----------
data_query_cutoff : pd.... | zipline/pipeline/loaders/utils.py | def previous_event_indexer(data_query_cutoff_times,
all_sids,
event_dates,
event_timestamps,
event_sids):
"""
Construct an index array that, when applied to an array of values, produces
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train | last_in_date_group | Determine the last piece of information known on each date in the date
index for each group. Input df MUST be sorted such that the correct last
item is chosen from each group.
Parameters
----------
df : pd.DataFrame
The DataFrame containing the data to be grouped. Must be sorted so that
... | zipline/pipeline/loaders/utils.py | def last_in_date_group(df,
data_query_cutoff_times,
assets,
reindex=True,
have_sids=True,
extra_groupers=None):
"""
Determine the last piece of information known on each date in the date
index... | def last_in_date_group(df,
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extra_groupers=None):
"""
Determine the last piece of information known on each date in the date
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train | ffill_across_cols | Forward fill values in a DataFrame with special logic to handle cases
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Parameters
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df : pd.DataFrame
The DataFrame to do forward-filling on.
columns : list of BoundColumn
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Forward fill values in a DataFrame with special logic to handle cases
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Parameters
----------
df : pd.DataFrame
The DataFrame to do forward-filling on.
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Forward fill values in a DataFrame with special logic to handle cases
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train | shift_dates | Shift dates of a pipeline query back by `shift` days.
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Shift dates of a pipeline query back by `shift` days.
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train | format_docstring | Template ``formatters`` into ``docstring``.
Parameters
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owner_name : str
The name of the function or class whose docstring is being templated.
Only used for error messages.
docstring : str
The docstring to template.
formatters : dict[str -> str]
Parameters ... | zipline/utils/sharedoc.py | def format_docstring(owner_name, docstring, formatters):
"""
Template ``formatters`` into ``docstring``.
Parameters
----------
owner_name : str
The name of the function or class whose docstring is being templated.
Only used for error messages.
docstring : str
The docstri... | def format_docstring(owner_name, docstring, formatters):
"""
Template ``formatters`` into ``docstring``.
Parameters
----------
owner_name : str
The name of the function or class whose docstring is being templated.
Only used for error messages.
docstring : str
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train | templated_docstring | Decorator allowing the use of templated docstrings.
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column : zipline.pipeline.Term
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The results of computing `term` will show up as a column in the
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Add a column.
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train | Pipeline.set_screen | Set a screen on this Pipeline.
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The filter to apply as a screen.
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train | Pipeline.to_execution_plan | Compile into an ExecutionPlan.
Parameters
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Domain on which the pipeline will be executed.
default_screen : zipline.pipeline.term.Term
Term to use as a screen if self.screen is None.
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domain : zipline.pipeline.domain.Domain
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train | Pipeline._prepare_graph_terms | Helper for to_graph and to_execution_plan. | zipline/pipeline/pipeline.py | def _prepare_graph_terms(self, default_screen):
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train | Pipeline.show_graph | Render this Pipeline as a DAG.
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Image format to render with. Default is 'svg'. | zipline/pipeline/pipeline.py | def show_graph(self, format='svg'):
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Render this Pipeline as a DAG.
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format : {'svg', 'png', 'jpeg'}
Image format to render with. Default is 'svg'.
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Render this Pipeline as a DAG.
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Image format to render with. Default is 'svg'.
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train | Pipeline._output_terms | A list of terms that are outputs of this pipeline.
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A list of terms that are outputs of this pipeline.
Includes all terms registered as data outputs of the pipeline, plus the
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A list of terms that are outputs of this pipeline.
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train | Pipeline.domain | Get the domain for this pipeline.
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- If no domain can be inferred, return ``default``.
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Get the domain for this pipeline.
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Get the domain for this pipeline.
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train | _ensure_element | Create a tuple containing all elements of tup, plus elem.
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"""
Create a tuple containing all elements of tup, plus elem.
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train | NumericalExpression._validate | Ensure that our expression string has variables of the form x_0, x_1,
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Ensure that our expression string has variables of the form x_0, x_1,
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train | NumericalExpression._compute | Compute our stored expression string with numexpr. | zipline/pipeline/expression.py | def _compute(self, arrays, dates, assets, mask):
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train | NumericalExpression._rebind_variables | Return self._expr with all variables rebound to the indices implied by
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"""
Return self._expr with all variables rebound to the indices implied by
new_inputs.
"""
expr = self._expr
# If we have 11+ variables, some of our variable names may be
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train | NumericalExpression._merge_expressions | Merge the inputs of two NumericalExpressions into a single input tuple,
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Merge the inputs of two NumericalExpressions into a single input tuple,
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resolve correctly.
Returns a tuple of (new_self_expr, new_other_expr, new_inputs)
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... | def _merge_expressions(self, other):
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Merge the inputs of two NumericalExpressions into a single input tuple,
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train | NumericalExpression.build_binary_op | Compute new expression strings and a new inputs tuple for combining
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"""
Compute new expression strings and a new inputs tuple for combining
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Compute new expression strings and a new inputs tuple for combining
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train | NumericalExpression.graph_repr | Short repr to use when rendering Pipeline graphs. | zipline/pipeline/expression.py | def graph_repr(self):
"""Short repr to use when rendering Pipeline graphs."""
# Replace any floating point numbers in the expression
# with their scientific notation
final = re.sub(r"[-+]?\d*\.\d+",
lambda x: format(float(x.group(0)), '.2E'),
... | def graph_repr(self):
"""Short repr to use when rendering Pipeline graphs."""
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train | last_modified_time | Get the last modified time of path as a Timestamp. | zipline/utils/paths.py | def last_modified_time(path):
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Get the root directory for all zipline-managed files.
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Get the root directory for all zipline-managed files.
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train | DataFrameLoader.load_adjusted_array | Load data from our stored baseline. | zipline/pipeline/loaders/frame.py | def load_adjusted_array(self, domain, columns, dates, sids, mask):
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train | Column.bind | Bind a `Column` object to its name. | zipline/pipeline/data/dataset.py | def bind(self, name):
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train | BoundColumn.specialize | Specialize ``self`` to a concrete domain. | zipline/pipeline/data/dataset.py | def specialize(self, domain):
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train | DataSet.get_column | Look up a column by name.
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Name of the column to look up.
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Column with the given name.
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train | DataSetFamily._make_dataset | Construct a new dataset given the coordinates. | zipline/pipeline/data/dataset.py | def _make_dataset(cls, coords):
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The coordinates to fix along each extra dimension.
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train | expected_bar_value | Check that the raw value for an asset/date/column triple is as
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Used by tests to verify data written by a writer. | zipline/pipeline/loaders/synthetic.py | def expected_bar_value(asset_id, date, colname):
"""
Check that the raw value for an asset/date/column triple is as
expected.
Used by tests to verify data written by a writer.
"""
from_asset = asset_id * 100000
from_colname = OHLCV.index(colname) * 1000
from_date = (date - PSEUDO_EPOCH)... | def expected_bar_value(asset_id, date, colname):
"""
Check that the raw value for an asset/date/column triple is as
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Used by tests to verify data written by a writer.
"""
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train | expected_bar_values_2d | Return an 2D array containing cls.expected_value(asset_id, date,
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holes=None):
"""
Return an 2D array containing cls.expected_value(asset_id, date,
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train | PrecomputedLoader.load_adjusted_array | Load by delegating to sub-loaders. | zipline/pipeline/loaders/synthetic.py | def load_adjusted_array(self, domain, columns, dates, sids, mask):
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out = {}
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train | SeededRandomLoader.values | Make a random array of shape (len(dates), len(sids)) with ``dtype``. | zipline/pipeline/loaders/synthetic.py | def values(self, dtype, dates, sids):
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shape = (len(dates), len(sids))
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train | SeededRandomLoader._float_values | Return uniformly-distributed floats between -0.0 and 100.0. | zipline/pipeline/loaders/synthetic.py | def _float_values(self, shape):
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Return uniformly-distributed floats between -0.0 and 100.0.
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train | SeededRandomLoader._datetime_values | Return uniformly-distributed dates in 2014. | zipline/pipeline/loaders/synthetic.py | def _datetime_values(self, shape):
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Return uniformly-distributed dates in 2014.
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start = Timestamp('2014', tz='UTC').asm8
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train | quantiles | Compute rowwise array quantiles on an input. | zipline/lib/quantiles.py | def quantiles(data, nbins_or_partition_bounds):
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Compute rowwise array quantiles on an input.
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train | MetricsTracker.handle_minute_close | Handles the close of the given minute in minute emission.
Parameters
----------
dt : Timestamp
The minute that is ending
Returns
-------
A minute perf packet. | zipline/finance/metrics/tracker.py | def handle_minute_close(self, dt, data_portal):
"""
Handles the close of the given minute in minute emission.
Parameters
----------
dt : Timestamp
The minute that is ending
Returns
-------
A minute perf packet.
"""
self.sync_l... | def handle_minute_close(self, dt, data_portal):
"""
Handles the close of the given minute in minute emission.
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----------
dt : Timestamp
The minute that is ending
Returns
-------
A minute perf packet.
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train | MetricsTracker.handle_market_open | Handles the start of each session.
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----------
session_label : Timestamp
The label of the session that is about to begin.
data_portal : DataPortal
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Parameters
----------
session_label : Timestamp
The label of the session that is about to begin.
data_portal : DataPortal
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Parameters
----------
session_label : Timestamp
The label of the session that is about to begin.
data_portal : DataPortal
The current data portal.
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train | MetricsTracker.handle_market_close | Handles the close of the given day.
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dt : Timestamp
The most recently completed simulation datetime.
data_portal : DataPortal
The current data portal.
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-------
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dt : Timestamp
The most recently completed simulation datetime.
data_portal : DataPortal
The current data portal.
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data_portal : DataPortal
The current data portal.
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train | MetricsTracker.handle_simulation_end | When the simulation is complete, run the full period risk report
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When the simulation is complete, run the full period risk report
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log.info(
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Parameters
----------
args : list
A list of strings representing arguments i... | zipline/extensions.py | def create_args(args, root):
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namespace : Namespace
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train | create_registry | Create a new registry for an extensible interface.
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----------
interface : type
The abstract data type for which to create a registry,
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Returns
-------
interface : type
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"""
Create a new registry for an extensible interface.
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----------
interface : type
The abstract data type for which to create a registry,
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Returns
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Create a new registry for an extensible interface.
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train | Registry.load | Construct an object from a registered factory.
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Name with which the factory was registered.
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train | calculate_per_unit_commission | If there is a minimum commission:
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train | PerDollar.calculate | Pay commission based on dollar value of shares. | zipline/finance/commission.py | def calculate(self, order, transaction):
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train | _ClassicRiskMetrics.risk_metric_period | Creates a dictionary representing the state of the risk report.
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start_session : pd.Timestamp
Start of period (inclusive) to produce metrics on
end_session : pd.Timestamp
End of period (inclusive) to produce metrics on
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train | RollFinder._get_active_contract_at_offset | For the given root symbol, find the contract that is considered active
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"""
For the given root symbol, find the contract that is considered active
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"""
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train | RollFinder.get_contract_center | Parameters
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root_symbol : str
The root symbol for the contract chain.
dt : Timestamp
The datetime for which to retrieve the current contract.
offset : int
The offset from the primary contract.
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The root symbol for the contract chain.
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The datetime for which to retrieve the current contract.
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----------
root_symbol : str
The root symbol for which to calculate rolls.
start : Timestamp
Start of the date range.
end : Timestamp
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Get the rolls, i.e. the session at which to hop from contract to
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----------
root_symbol : str
The root symbol for which to calculate rolls.
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Get the rolls, i.e. the session at which to hop from contract to
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The root symbol for which to calculate rolls.
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train | VolumeRollFinder._active_contract | r"""
Return the active contract based on the previous trading day's volume.
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| +++++ _____
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Return the active contract based on the previous trading day's volume.
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train | VolumeRollFinder.get_contract_center | Parameters
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The root symbol for the contract chain.
dt : Timestamp
The datetime for which to retrieve the current contract.
offset : int
The offset from the primary contract.
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The datetime for which to retrieve the current contract.
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train | _normalize_array | Coerce buffer data for an AdjustedArray into a standard scalar
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train | _merge_simple | Merge lists of new and existing adjustments for a given index by appending
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train | _check_window_params | Check that a window of length `window_length` is well-defined on `data`.
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data : np.ndarray[ndim=2]
The array of data to check.
window_length : int
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data : np.ndarray[ndim=2]
The array of data to check.
window_length : int
Length of the desired window.
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train | AdjustedArray.update_adjustments | Merge ``adjustments`` with existing adjustments, handling index
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The mapping of row indices to lists of adjustments that should be
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"""
Merge ``adjustments`` with existing adjustments, handling index
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Parameters
----------
adjustments : dict[int -> list[Adjustment]]
The mapping of row indices to lists of... | def update_adjustments(self, adjustments, method):
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Merge ``adjustments`` with existing adjustments, handling index
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----------
adjustments : dict[int -> list[Adjustment]]
The mapping of row indices to lists of... | [
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window_length : int
The number of rows in each emitted window.
offset : int, optional
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"""
Produce an iterator rolling windows rows over our data.
Each emitted window will have `window_length` rows.
Parameters
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train | AdjustedArray.inspect | Return a string representation of the data stored in this array. | zipline/lib/adjusted_array.py | def inspect(self):
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Return a string representation of the data stored in this array.
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Data:
{data!r}
Adjustments:
{adjustments}
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Return a string representation of the data stored in this array.
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Data:
{data!r}
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train | AdjustedArray.update_labels | Map a function over baseline and adjustment values in place.
Note that the baseline data values must be a LabelArray. | zipline/lib/adjusted_array.py | def update_labels(self, func):
"""
Map a function over baseline and adjustment values in place.
Note that the baseline data values must be a LabelArray.
"""
if not isinstance(self.data, LabelArray):
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train | TradingControl.handle_violation | Handle a TradingControlViolation, either by raising or logging and
error with information about the failure.
If dynamic information should be displayed as well, pass it in via
`metadata`. | zipline/finance/controls.py | def handle_violation(self, asset, amount, datetime, metadata=None):
"""
Handle a TradingControlViolation, either by raising or logging and
error with information about the failure.
If dynamic information should be displayed as well, pass it in via
`metadata`.
"""
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Handle a TradingControlViolation, either by raising or logging and
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train | MaxOrderCount.validate | Fail if we've already placed self.max_count orders today. | zipline/finance/controls.py | def validate(self,
asset,
amount,
portfolio,
algo_datetime,
algo_current_data):
"""
Fail if we've already placed self.max_count orders today.
"""
algo_date = algo_datetime.date()
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Fail if we've already placed self.max_count orders today.
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train | RestrictedListOrder.validate | Fail if the asset is in the restricted_list. | zipline/finance/controls.py | def validate(self,
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portfolio,
algo_datetime,
algo_current_data):
"""
Fail if the asset is in the restricted_list.
"""
if self.restrictions.is_restricted(asset, algo_datetime):
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Fail if the asset is in the restricted_list.
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train | MaxOrderSize.validate | Fail if the magnitude of the given order exceeds either self.max_shares
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asset,
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portfolio,
algo_datetime,
algo_current_data):
"""
Fail if the magnitude of the given order exceeds either self.max_shares
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Fail if the magnitude of the given order exceeds either self.max_shares
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train | MaxPositionSize.validate | Fail if the given order would cause the magnitude of our position to be
greater in shares than self.max_shares or greater in dollar value than
self.max_notional. | zipline/finance/controls.py | def validate(self,
asset,
amount,
portfolio,
algo_datetime,
algo_current_data):
"""
Fail if the given order would cause the magnitude of our position to be
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Fail if the given order would cause the magnitude of our position to be
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train | LongOnly.validate | Fail if we would hold negative shares of asset after completing this
order. | zipline/finance/controls.py | def validate(self,
asset,
amount,
portfolio,
algo_datetime,
algo_current_data):
"""
Fail if we would hold negative shares of asset after completing this
order.
"""
if portfolio.positions[asset].a... | def validate(self,
asset,
amount,
portfolio,
algo_datetime,
algo_current_data):
"""
Fail if we would hold negative shares of asset after completing this
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"""
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train | AssetDateBounds.validate | Fail if the algo has passed this Asset's end_date, or before the
Asset's start date. | zipline/finance/controls.py | def validate(self,
asset,
amount,
portfolio,
algo_datetime,
algo_current_data):
"""
Fail if the algo has passed this Asset's end_date, or before the
Asset's start date.
"""
# If the order is for ... | def validate(self,
asset,
amount,
portfolio,
algo_datetime,
algo_current_data):
"""
Fail if the algo has passed this Asset's end_date, or before the
Asset's start date.
"""
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train | MaxLeverage.validate | Fail if the leverage is greater than the allowed leverage. | zipline/finance/controls.py | def validate(self,
_portfolio,
_account,
_algo_datetime,
_algo_current_data):
"""
Fail if the leverage is greater than the allowed leverage.
"""
if _account.leverage > self.max_leverage:
self.fail() | def validate(self,
_portfolio,
_account,
_algo_datetime,
_algo_current_data):
"""
Fail if the leverage is greater than the allowed leverage.
"""
if _account.leverage > self.max_leverage:
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train | MinLeverage.validate | Make validation checks if we are after the deadline.
Fail if the leverage is less than the min leverage. | zipline/finance/controls.py | def validate(self,
_portfolio,
account,
algo_datetime,
_algo_current_data):
"""
Make validation checks if we are after the deadline.
Fail if the leverage is less than the min leverage.
"""
if (algo_datetime > sel... | def validate(self,
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account,
algo_datetime,
_algo_current_data):
"""
Make validation checks if we are after the deadline.
Fail if the leverage is less than the min leverage.
"""
if (algo_datetime > sel... | [
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