Merge two ranges with step == 1. Parameters ---------- a : range The first range. b : range The second range.

Normalize datetime.datetime value to midnight. Returns datetime.date as a datetime.datetime at midnight Returns ----

Run the nth empty ingest. Returns ------- wrote_to : str The timestr of the bundle written.

Generates an output dataframe from the given subset of user-provided data, the given column names, and the given default values. Paramet

Make a value with the specified numpy dtype. Only datetime64[ns] and datetime64[D] are supported for datetime dtypes.

Construct a Factor that computes ``self`` and subtracts the mean from row of the result. If ``mask`` is supplied, ignore val

Raise an AccountControlViolation with information about the failure.

Retrieve all of the current open orders. Parameters ---------- asset : Asset If passed and not None, return only

A short repr to use when rendering GraphViz graphs.

Create a term that selects values from one of two choices. Parameters ---------- if_true : zipline.pipeline.term.Com

Parameters ---------- sid : int Asset identifier. Returns ------- out : pd.Timestamp

Construct a new Factor that computes rolling pearson correlation coefficients between ``target`` and the columns of ``self``.

Construct a Filter matching the top N asset values of self each day. If ``groupby`` is supplied, returns a Filter matching the top N

Format the path for final display. Parameters ---------- path : iterable of str The path to the values that are not equal. R

Make a term from ``self`` that names the expression. Parameters ---------- {name} Returns -------

Get the domain for this pipeline. - If an explicit domain was provided at construction time, use it. - Otherwise, infer a do

Make a term that computes from ``self`` at lower-than-daily frequency. Parameters ---------- {frequency}

Elementwise endswith. Parameters ---------- suffix : str Returns ------- matches : np.ndarr

Returns ------- out : A np.ndarray of the equity pricing up to end_ix after adjustments and rounding have been

Like isnan, but checks for locations where we store missing values.

Lookup a future contract by symbol. Parameters ---------- symbol : str The symbol of the desired contract.

Build a block of random numerical data.

Main generator work loop.

Checks whether the fill price is worse than the order's limit price. Parameters ---------- fill_price: float The price to ch

Lookup a futures contract with a given symbol. Parameters ---------- symbol : str The symbol of the desired contr

Get the rolls, i.e. the session at which to hop from contract to contract in the chain. Parameters ----------

Handle a TradingControlViolation, either by raising or logging and error with information about the failure. If dynamic info

Create a 2D numpy array with the given shape containing alternating values of False, True, False, True,... along each row and each column.

Create a numpy array with the given shape containing cascading boolean values, with `first_value` being the top-left value. Examples

Short repr used to render in recursive contexts.

Take a slice of a DataSetFamily to produce a dataset indexed by asset and date. Parameters ---------- *args *

Helpful function to improve readibility.

Adds an event to the manager.

Helper for creating new NumExprFactors. This is just a wrapper around NumericalExpression.__new__ that always forwards `bool

Compute the data query cutoff time for the given sessions. Parameters ---------- sessions : pd.DatetimeIndex The

Enter a context manager that should be exited during tearDown.

Return an 2D array containing cls.expected_value(asset_id, date, colname) for each date/asset pair in the inputs. Missing locs are fille

Retrieve the pricing info for the given sid, dt, and field. Parameters ---------- sid : int Asset identi

A window of pricing data with adjustments applied assuming that the end of the window is the day before the current simulation time.

Check if a value is np.NaT.

Construct a new Factor that performs an ordinary least-squares regression predicting the columns of `self` from `target`. Pa

500 randomly selected days. This is used to make sure our test coverage is unbiased towards any rules. We use a random sample because tes

Track and record values each day. Parameters ---------- **kwargs The names and values to record. Notes

Unspecialize a column to its generic form. This is equivalent to ``column.specialize(GENERIC)``.

Builds the offset argument for event rules.

Return the fully-qualified name (ignoring inner classes) of a type.

Get currencies in which prices are quoted for the requested sids. Parameters ---------- sids : np.array[int64] Ar

Create an empty dataframe with columns of particular types. Parameters ---------- *columns The (column_name, column_dtype) pairs.

Enter a context manager to be exited during the tearDownClass

Construct a currency-converted version of this column. Parameters ---------- currency : str or zipline.currency.Curr

Public API method that returns a dataframe containing the requested history window. Data is fully adjusted. Parameters

Public API method that returns a scalar value representing the value of the desired asset's field at either the given dt. Pa

Retrieve the value at the given coordinates. Parameters ---------- sid : int The asset identifier.

Converts argument strings in key=value or key.namespace=value form to dictionary entries Parameters ---------- arg : str

Build a block of random boolean data.

Resolve a concrete domain for ``pipeline``.

Given a date, align it to the calendar of the pipeline's domain. Parameters ---------- dt : pd.Timestamp Re

Adds a suffix to ``word`` if some sequence has anything other than exactly one element. Parameters ---------- word : str The

Set a restriction on which assets can be ordered. Parameters ---------- restricted_list : Restrictions An object

Set a screen on this Pipeline. Parameters ---------- filter : zipline.pipeline.Filter The filter to apply as a sc

Construct a new Factor that computes rolling spearman rank correlation coefficients between ``target`` and the columns of ``self``.

Compute slopes of linear regressions between columns of ``dependents`` and ``independent``. Parameters ---------- dependents : n

Construct a Factor that Z-Scores each day's results. The Z-Score of a row is defined as:: (row - row.mean()) / row.stdd