Assert that two objects are equal using the ``==`` operator. Parameters ---------- result : object The result that came from the

Retrieve the Asset for a given sid.

Lookup an equity by symbol. Parameters ---------- symbol : str The ticker symbol to resolve. as_of_date :

Get the value of a cached object. Parameters ---------- key : any The key to lookup. dt : datetime

Copy an adjusted array, deep-copying the ``data`` array.

Add a column. The results of computing ``term`` will show up as a column in the DataFrame produced by running this pipeline.

Place an order for a fixed number of shares. Parameters ---------- asset : Asset The asset to be ordered.

Retrieve all assets in `sids`. Parameters ---------- sids : iterable of int Assets to retrieve.

Helper for constructing an AssetExists mask from a boolean-coercible array.

The data stored in this array.

Compile the given terms into a TermGraph, compute it with initial_workspace, and compare the results with ``expected``.

A Filter producing True for all values where this Factor is NaN. Returns ------- nanfilter : zipline.pipeline.Filter

Write data to the file. Parameters ---------- dts : pd.DatetimeIndex Index of row labels for rates to be written.

Wrapper around np.testing.assert_array_equal that also verifies that inputs are ndarrays. See Also -------- np.assert_array_equa

Create and run an TradingAlgorithm in memory.

Specialize ``self`` to a concrete domain.

Construct a new Factor representing the sorted rank of each column within each row. Parameters ---------- me

Assert that some exception is raised in a context and that the message exactly matches some string. Parameters ---------- exc : type

Create a new term that fills missing values of this term's output with ``fill_value``. Parameters ----------

Convert an array of shape (N,) into an array of shape (N, 1). This is equivalent to `a[:, np.newaxis]`. Parameters ---------- a

Convert a pandas-intelligible string to (integer) seconds since UTC. >>> from pandas import Timestamp >>> (Timestamp('2014-01-01') - Tim

A Filter producing True for values where this Factor has missing data. Equivalent to self.isnan() when ``self.dtype`` is float64.

Check that each term in ``terms`` computes an expected number of extra rows for the given parameters.

Make a random array of shape (len(dates), len(sids)) with ``dtype``.

Checks the callable_ to make sure that it satisfies the given expectations. expected_args should be an iterable of Arguments in the order

Element-wise startswith. Parameters ---------- prefix : str Returns ------- matches : np.nd

Create a 1-dimensional factor computing the {} of self, each day. Parameters ---------- mask : zipline.pipeline.Filter, optio

The dtype of the data stored in this array.

DEPRECATED: use ``data.history`` instead.

Lookup an Equity by its ticker symbol. Parameters ---------- symbol_str : str The ticker symbol for the equity to

Write the OHLCV data for the given sid. If there is no bcolz ctable yet created for the sid, create it. If the length of the

Create a Pipeline API object from a blaze expression. Parameters ---------- expr : Expr The blaze expression to use. deltas :

Convert self back into an array of strings. This is an O(N) operation.

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

Construct a Classifier computing quantiles of the output of ``self``. Every non-NaN data point the output is labelled with an intege

Produce an iterator rolling windows rows over our data. Each emitted window will have `window_length` rows. Parameters

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

Construct an object from a registered factory. Parameters ---------- name : str Name with which the factory was r

Place an order. Parameters ---------- asset : zipline.assets.Asset The asset that this order is for. amou

Make a fixture attribute an alias of another fixture's attribute by default. Parameters ---------- attr_name : str The name o

Convert self into a regular ndarray of ints. This is an O(1) operation. It does not copy the underlying data.

Get a path relative to the working directory. Parameters ---------- path_parts : iterable[str] The parts of the p

Set the slippage models for the simulation. Parameters ---------- us_equities : EquitySlippageModel The

Construct a Filter returning True for asset/date pairs where the output of ``self`` matches ``other``.

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

A Filter producing True for values where this Factor has complete data. Equivalent to ``~self.isnan()` when ``self.dtype`` is float6

Sets the commission models for the simulation. Parameters ---------- us_equities : EquityCommissionModel The comm

Format the message for final display. Parameters ---------- msg : str The message to show to the user to provide additional conte

Create a DataFrame representing assets that exist for the full duration between `start_date` and `end_date`. Parameters ----------

Map a function from str -> str element-wise over ``self``. ``f`` will be applied exactly once to each non-missing unique value in

Decorator that applies pre-processors to the arguments of a function before calling the function. Parameters ---------- **proces

Read and return pricing update data. Parameters ---------- dts : DatetimeIndex The minutes for which to

Construct a new factor that winsorizes the result of this factor. Winsorizing changes values ranked less than the minimum percentile

Apply a function. Parameters ---------- f : callable The function to call. args : tuple, optional

Checks for a version value in the version table. Parameters ---------- conn : sa.Connection The connection to use to perform

Load a 2D array of fx rates. Parameters ---------- rate : str Name of the rate to load. quote :