Convenience constructor for passing `decay_rate` in terms of center of mass. Forwards `decay_rate` as `1 - (1 / 1 + center_o

Convenience constructor for passing ``decay_rate`` in terms of half life. Forwards ``decay_rate`` as ``exp(log(.5) / halflif

Helper for construction from a pandas.Panel.

Convenience constructor for passing `decay_rate` in terms of `span`. Forwards `decay_rate` as `1 - (2.0 / (1 + span))`. This provid

Convert a tuple into a range with error handling. Parameters ---------- tup : tuple (len 2 or 3) The tuple to turn into a range.

Factory function for producing function application methods for Factor subclasses.

Parameters ---------- group : pd.DataFrame The data for the given sid. dates : pd.DatetimeIndex

Get a Panel that is the current data in view. It is not safe to persist these objects because internal data might change

Get an array of expected FX rates for the given indices.

Determine the last piece of information we know for each column on each date in the index for each sid and quarter. Paramete

Get the latest minute on or before ``dt`` in which ``asset`` traded. If there are no trades on or before ``dt``, returns ``pd.NaT``.

Get the latest day on or before ``dt`` in which ``asset`` traded. If there are no trades on or before ``dt``, returns ``pd.NaT``.

Selects the requested data for each date. Parameters ---------- zero_qtr_data : pd.DataFrame The 'time z

Calculate simulated price impact. Parameters ---------- order : The order being processed. current_price : C

Compute the index in `dates` where the split-adjusted-asof-date falls. This is the date up to which, and including which, we will

Returns all the stock dividends for a specific sid that occur in the given trading range. Parameters ----------

Creates a list of transactions based on the current open orders, slippage model, and commission model. Parameters --

Return the number of shares we would like to order in this minute. Parameters ---------- data : BarData orde

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

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

Perform a chained application of ``getattr`` on ``value`` with the values in ``attrs``. If ``default`` is supplied, return it if any of

Group any overlapping ranges into a single range. Parameters ---------- ranges : iterable[ranges] A sorted sequence of ranges to

Calls the callable only when the rule is triggered.

Extra sources always have a sid column. We expand the given data (by forward filling) to the full range of the simulation da

Handles the close of the given day. Parameters ---------- dt : Timestamp The most recently completed simulation d

Handles the start of each session. Parameters ---------- session_label : Timestamp The label of the session that

Handles the close of the given minute in minute emission. Parameters ---------- dt : Timestamp The minut

When the simulation is complete, run the full period risk report and send it out on the results socket.

The high field's aggregation returns the largest high seen between the market open and the current dt. If there has been no d

Get the index of the first difference between two strings.

Connect to database and create tables. Parameters ---------- txn : sa.engine.Connection, optional The transaction

Construct and store a PipelineEngine from loader. If get_loader is None, constructs an ExplodingPipelineEngine

Call self._initialize with `self` made available to Zipline API functions.

Return a string representation of the data stored in this array.

Return any ranges that intersect. Parameters ---------- ranges : iterable[ranges] A sequence of ranges to check for intersections

Check whether a pair of datetime intervals overlap. Parameters ---------- a : (pd.Timestamp, pd.Timestamp) b : (pd.Timestamp, pd

Invert a dictionary into a dictionary of sets. >>> invert({'a': 1, 'b': 2, 'c': 1}) # doctest: +SKIP {1: {'a', 'c'}, 2: {'b'}}

Do we represent this dtype with LabelArrays rather than ndarrays?

Check if a numpy array is sorted.

Get the items from a dict, sorted by key. Example ------- >>> keysorted({'c': 1, 'b': 2, 'a': 3}) [('a', 3), ('b', 2), ('c', 1)]

Get the argument names of a function >>> def f(x, y=2): ... pass >>> keywords(f) ['x', 'y'] Notes ----- Taken from

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 l

Get the last modified time of path as a Timestamp.

Retrieves a custom class whose name is given. Parameters ---------- interface : type The base class for which to perform thi

Returns ------- adjustments : list[dict[int -> Adjustment]] A list, where each element corresponds to the `column

Parameters ---------- fields : list of str 'open', 'high', 'low', 'close', or 'volume' start_dt: Timestamp

Parameters ---------- columns : list of str 'open', 'high', 'low', 'close', or 'volume' start_date: Timest