Construct a Filter matching values ending with ``suffix``. Parameters ---------- suffix : str String suf

Ensure that the data root exists.

Ensure that the data root exists.

Ensure that an object gets doctested. This is useful for instances of objects like curry or partial which are not discovered by default. Para

Argument preprocessor that converts the input into a numpy dtype. Examples -------- >>> import numpy as np >>> from zipline.util

Ensure that a file exists. This will create any parent directories needed and create an empty file if it does not exist. Parameters

Argument preprocessor that converts the input into a pandas Timestamp object. Examples -------- >>> from zipline.utils.preprocess imp

Argument preprocessor that converts the input into a tzinfo object. Examples -------- >>> from zipline.utils.preprocess import preprocess

The exchange to which this order should be routed.

Build an exchange_info suitable for passing to an AssetFinder from a list of EquityCalendarDomain.

A list of canonical exchange names for all exchanges in this suite.

Preprocessing decorator verifying that inputs fall INCLUSIVELY between bounds. Bounds should be passed as a pair of ``(min_value, max_va

Preprocessing decorator that verifies inputs are numpy arrays with a specific dimensionality. Examples -------- >>> from numpy i

Preprocessing decorator that verifies inputs have expected numpy dtypes. Examples -------- >>> from numpy import dtype, arange, int8

Preprocessing decorator that verifies inputs have expected dtype kinds. Examples -------- >>> from numpy import int64, int32, float3

Preprocessing decorator verifying that inputs fall EXCLUSIVELY between bounds. Bounds should be passed as a pair of ``(min_value, max_va

Preprocessing decorator that verifies inputs have expected types. Examples -------- >>> @expect_types(x=int, y=str) ... def foo(

Resizes the buffer to hold a new window with a new cap_multiple. If cap_multiple is None, then the old cap_multiple is used.

A dict mapping `term` -> `# of extra rows to load/compute of `term`. Notes ---- This value depends on the other term

Fetch a csv from a remote url and register the data so that it is queryable from the ``data`` object. Parameters ----------

Returns ------- dt : pd.Timestamp The first trading day (session) for which the reader can provide da

Returns ------- dt : pd.Timestamp The first trading day (session) for which the reader can provide da

Returns ------- dt : pd.Timestamp The first trading day (session) for which the reader can provide da

Returns ------- dt : pd.Timestamp The first trading day (session) for which the reader can provide da

Returns ------- dt : pd.Timestamp The first trading day (session) for which the reader can provide da

Create a LabelArray from a pandas categorical. Parameters ---------- categorical : pd.Categorical The ca

Construct from an h5py.File. Parameters ---------- h5_file : h5py.File An HDF5 daily pricing file.

Construct from a file path and a country code. Parameters ---------- path : str The path to an HDF5 dail

Construct from a file path. Parameters ---------- path : str Path to an HDF5 daily pricing file.

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