Pipeline API Reference¶
Contents
See also
Quick Reference¶
Pipeline¶
quantopian.pipeline.Pipeline ([columns, ...]) 
A Pipeline object represents a collection of named expressions to be compiled and executed by a PipelineEngine. 
Pipeline Methods¶
quantopian.pipeline.Pipeline.add (self, term, ...) 
Add a column. 
quantopian.pipeline.Pipeline.remove (self, name) 
Remove a column. 
quantopian.pipeline.Pipeline.set_screen (...) 
Set a screen on this Pipeline. 
quantopian.pipeline.Pipeline.show_graph (self) 
Render this Pipeline as a DAG. 
Pipeline Attributes¶
quantopian.pipeline.Pipeline.columns 
The output columns of this pipeline. 
quantopian.pipeline.Pipeline.screen 
The screen of this pipeline. 
Base Classes¶
quantopian.pipeline.CustomFactor 
Base class for userdefined Factors. 
quantopian.pipeline.CustomFilter 
Base class for userdefined Filters. 
zipline.pipeline.Term 
Base class for objects that can appear in the compute graph of a zipline.pipeline.Pipeline . 
zipline.pipeline.LoadableTerm 
A Term that should be loaded from an external resource by a PipelineLoader. 
zipline.pipeline.ComputableTerm 
A Term that should be computed from a tuple of inputs. 
zipline.pipeline.Factor 
Pipeline API expression producing a numerical or datevalued output. 
zipline.pipeline.Filter 
Pipeline expression computing a boolean output. 
zipline.pipeline.Classifier 
A Pipeline expression computing a categorical output. 
zipline.pipeline.data.DataSet 
Base class for Pipeline datasets. 
zipline.pipeline.data.DataSetFamily 
Base class for Pipeline dataset families. 
zipline.pipeline.data.BoundColumn 
A column of data that's been concretely bound to a particular dataset. 
zipline.pipeline.data.Column 
An abstract column of data, not yet associated with a dataset. 
zipline.pipeline.domain.Domain 
A domain represents a set of labels for the arrays computed by a Pipeline. 
Factor Methods¶
Methods That Create Factors¶
zipline.pipeline.Factor.rank ([method, ...]) 
Construct a new Factor representing the sorted rank of each column within each row. 
zipline.pipeline.Factor.demean (self[, mask, ...]) 
Construct a Factor that computes self and subtracts the mean from row of the result. 
zipline.pipeline.Factor.zscore (self[, mask, ...]) 
Construct a Factor that ZScores each day's results. 
zipline.pipeline.Factor.pearsonr (self, ...) 
Construct a new Factor that computes rolling pearson correlation coefficients between target and the columns of self . 
zipline.pipeline.Factor.spearmanr (self, ...) 
Construct a new Factor that computes rolling spearman rank correlation coefficients between target and the columns of self . 
zipline.pipeline.Factor.linear_regression (...) 
Construct a new Factor that performs an ordinary leastsquares regression predicting the columns of self from target. 
zipline.pipeline.Factor.winsorize (self, ...) 
Construct a new factor that winsorizes the result of this factor. 
zipline.pipeline.Factor.downsample (self, ...) 
Make a term that computes from self at lowerthandaily frequency. 
zipline.pipeline.Factor.sin () 
Construct a Factor that computes sin() on each output of self . 
zipline.pipeline.Factor.cos () 
Construct a Factor that computes cos() on each output of self . 
zipline.pipeline.Factor.tan () 
Construct a Factor that computes tan() on each output of self . 
zipline.pipeline.Factor.arcsin () 
Construct a Factor that computes arcsin() on each output of self . 
zipline.pipeline.Factor.arccos () 
Construct a Factor that computes arccos() on each output of self . 
zipline.pipeline.Factor.arctan () 
Construct a Factor that computes arctan() on each output of self . 
zipline.pipeline.Factor.sinh () 
Construct a Factor that computes sinh() on each output of self . 
zipline.pipeline.Factor.cosh () 
Construct a Factor that computes cosh() on each output of self . 
zipline.pipeline.Factor.tanh () 
Construct a Factor that computes tanh() on each output of self . 
zipline.pipeline.Factor.arcsinh () 
Construct a Factor that computes arcsinh() on each output of self . 
zipline.pipeline.Factor.arccosh () 
Construct a Factor that computes arccosh() on each output of self . 
zipline.pipeline.Factor.arctanh () 
Construct a Factor that computes arctanh() on each output of self . 
zipline.pipeline.Factor.log () 
Construct a Factor that computes log() on each output of self . 
zipline.pipeline.Factor.log10 () 
Construct a Factor that computes log10() on each output of self . 
zipline.pipeline.Factor.log1p () 
Construct a Factor that computes log1p() on each output of self . 
zipline.pipeline.Factor.exp () 
Construct a Factor that computes exp() on each output of self . 
zipline.pipeline.Factor.expm1 () 
Construct a Factor that computes expm1() on each output of self . 
zipline.pipeline.Factor.sqrt () 
Construct a Factor that computes sqrt() on each output of self . 
zipline.pipeline.Factor.abs () 
Construct a Factor that computes abs() on each output of self . 
zipline.pipeline.Factor.__add__ (self, other) 
Construct a Factor computing self + other . 
zipline.pipeline.Factor.__sub__ (self, other) 
Construct a Factor computing self  other . 
zipline.pipeline.Factor.__add__ (self, other) 
Construct a Factor computing self + other . 
zipline.pipeline.Factor.__sub__ (self, other) 
Construct a Factor computing self  other . 
zipline.pipeline.Factor.__mul__ (self, other) 
Construct a Factor computing self * other . 
zipline.pipeline.Factor.__div__ (self, other) 
Construct a Factor computing self / other . 
zipline.pipeline.Factor.__mod__ (self, other) 
Construct a Factor computing self % other . 
zipline.pipeline.Factor.__pow__ (self, other) 
Construct a Factor computing self ** other . 
Methods That Create Filters¶
zipline.pipeline.Factor.eq (self, other) 
Construct a Filter computing self == other . 
zipline.pipeline.Factor.top (N[, mask, groupby]) 
Construct a Filter matching the top N asset values of self each day. 
zipline.pipeline.Factor.bottom (N[, mask, ...]) 
Construct a Filter matching the bottom N asset values of self each day. 
zipline.pipeline.Factor.isnull () 
A Filter producing True for values where this Factor has missing data. 
zipline.pipeline.Factor.notnull () 
A Filter producing True for values where this Factor has complete data. 
zipline.pipeline.Factor.isnan (self) 
A Filter producing True for all values where this Factor is NaN. 
zipline.pipeline.Factor.notnan (self) 
A Filter producing True for values where this Factor is not NaN. 
zipline.pipeline.Factor.isfinite (self) 
A Filter producing True for values where this Factor is anything but NaN, inf, or inf. 
zipline.pipeline.Factor.percentile_between (...) 
Construct a Filter matching values of self that fall within the range defined by min_percentile and max_percentile . 
zipline.pipeline.Factor.__lt__ (self, other) 
Construct a Filter computing self < other . 
zipline.pipeline.Factor.__le__ (self, other) 
Construct a Filter computing self <= other . 
zipline.pipeline.Factor.__ne__ (self, other) 
Construct a Filter computing self != other . 
zipline.pipeline.Factor.__ge__ (self, other) 
Construct a Filter computing self >= other . 
zipline.pipeline.Factor.__gt__ (self, other) 
Construct a Filter computing self > other . 
Methods That Create Classifiers¶
zipline.pipeline.Factor.quartiles (self[, mask]) 
Construct a Classifier computing quartiles over the output of self . 
zipline.pipeline.Factor.quintiles (self[, mask]) 
Construct a Classifier computing quintile labels on self . 
zipline.pipeline.Factor.deciles (self[, mask]) 
Construct a Classifier computing decile labels on self . 
zipline.pipeline.Factor.quantiles (self, bins) 
Construct a Classifier computing quantiles of the output of self . 
Filter Methods¶
Methods that Create Filters¶
zipline.pipeline.Filter.__and__ (other) 
Binary Operator: '&' 
zipline.pipeline.Filter.__or__ (other) 
Binary Operator: '' 
zipline.pipeline.Filter.__invert__ () 
Unary Operator: '~' 
Classifier Methods¶
Methods That Create Factors¶
zipline.pipeline.Classifier.peer_count () 
Construct a factor that gives the number of occurrences of each distinct category in a classifier. 
Methods That Create Filters¶
zipline.pipeline.Classifier.isnull () 
A Filter producing True for values where this term has missing data. 
zipline.pipeline.Classifier.notnull () 
A Filter producing True for values where this term has complete data. 
zipline.pipeline.Classifier.eq (other) 
Construct a Filter returning True for asset/date pairs where the output of self matches other . 
zipline.pipeline.Classifier.startswith (self, ...) 
Construct a Filter matching values starting with prefix . 
zipline.pipeline.Classifier.endswith (self, ...) 
Construct a Filter matching values ending with suffix . 
zipline.pipeline.Classifier.has_substring (...) 
Construct a Filter matching values containing substring . 
zipline.pipeline.Classifier.matches (self, ...) 
Construct a Filter that checks regex matches against pattern . 
Methods That Create Classifiers¶
zipline.pipeline.Classifier.relabel (self, ...) 
Convert self into a new classifier by mapping a function over each element produced by self . 
Data¶
Builtin Factors¶
quantopian.pipeline.factors.DailyReturns 
Calculates daily percent change in close price. 
quantopian.pipeline.factors.Returns 
Calculates the percent change in close price over the given window_length. 
quantopian.pipeline.factors.PercentChange 
Calculates the percent change over the given window_length. 
quantopian.pipeline.factors.VWAP 
Volume Weighted Average Price 
quantopian.pipeline.factors.AverageDollarVolume 
Average Daily Dollar Volume 
quantopian.pipeline.factors.AnnualizedVolatility 
Volatility. 
quantopian.pipeline.factors.SimpleBeta 
Factor producing the slope of a regression line between each asset's daily returns to the daily returns of a single "target" asset. 
quantopian.pipeline.factors.SimpleMovingAverage 
Average Value of an arbitrary column 
quantopian.pipeline.factors.Latest 
Factor producing the most recentlyknown value of inputs[0] on each day. 
quantopian.pipeline.factors.MaxDrawdown 
Max Drawdown 
quantopian.pipeline.factors.RSI 
Relative Strength Index 
quantopian.pipeline.factors.ExponentialWeightedMovingAverage 
Exponentially Weighted Moving Average 
quantopian.pipeline.factors.ExponentialWeightedMovingStdDev 
Exponentially Weighted Moving Standard Deviation 
quantopian.pipeline.factors.WeightedAverageValue 
Helper for VWAPlike computations. 
quantopian.pipeline.factors.MovingAverageConvergenceDivergenceSignal 
Moving Average Convergence/Divergence (MACD) Signal line https://en.wikipedia.org/wiki/MACD 
quantopian.pipeline.factors.RollingPearsonOfReturns 
Calculates the Pearson productmoment correlation coefficient of the returns of the given asset with the returns of all other assets. 
quantopian.pipeline.factors.RollingSpearmanOfReturns 
Calculates the Spearman rank correlation coefficient of the returns of the given asset with the returns of all other assets. 
quantopian.pipeline.factors.RollingLinearRegressionOfReturns 
Perform an ordinary leastsquares regression predicting the returns of all other assets on the given asset. 
Builtin Filters¶
quantopian.pipeline.filters.QTradableStocksUS () 
Create the trading universe used in the Quantopian Contest. 
quantopian.pipeline.filters.Q500US ([...]) 
A default universe containing approximately 500 US equities each day. 
quantopian.pipeline.filters.Q1500US ([...]) 
A default universe containing approximately 1500 US equities each day. 
quantopian.pipeline.filters.Q3000US ([...]) 
A default universe containing approximately 3000 US equities each day. 
quantopian.pipeline.filters.make_us_equity_universe (...) 
Create a QUS style universe filter. 
quantopian.pipeline.filters.default_us_equity_universe_mask ([...]) 
Create the base filter used to filter assets from the QUS filters. 
Builtin Classifiers¶
quantopian.pipeline.classifiers.morningstar.Sector 
Classifier that groups assets by Morningstar Sector Code. 
quantopian.pipeline.classifiers.morningstar.SuperSector 
Classifier that groups assets by Morningstar Super Sector. 
Risk Model¶
Style Loadings¶
quantopian.pipeline.experimental.BasicMaterials 
Quantopian Risk Model loadings for the basic materials sector. 
quantopian.pipeline.experimental.ConsumerCyclical 
Quantopian Risk Model loadings for the consumer cyclical sector. 
quantopian.pipeline.experimental.FinancialServices 
Quantopian Risk Model loadings for the financial services sector. 
quantopian.pipeline.experimental.RealEstate 
Quantopian Risk Model loadings for the real estate sector. 
quantopian.pipeline.experimental.ConsumerDefensive 
Quantopian Risk Model loadings for the consumer defensive sector. 
quantopian.pipeline.experimental.HealthCare 
Quantopian Risk Model loadings for the health care sector. 
quantopian.pipeline.experimental.Utilities 
Quantopian Risk Model loadings for the utilities sector. 
quantopian.pipeline.experimental.CommunicationServices 
Quantopian Risk Model loadings for the communication services sector. 
quantopian.pipeline.experimental.Energy 
Quantopian Risk Model loadings for the communication energy sector. 
quantopian.pipeline.experimental.Industrials 
Quantopian Risk Model loadings for the industrials sector. 
quantopian.pipeline.experimental.Technology 
Quantopian Risk Model loadings for the technology sector. 
Sector Loadings¶
quantopian.pipeline.experimental.Momentum 
Quantopian Risk Model loadings for the "momentum" style factor. 
quantopian.pipeline.experimental.ShortTermReversal 
Quantopian Risk Model loadings for the "short term reversal" style factor. 
quantopian.pipeline.experimental.Size 
Quantopian Risk Model loadings for the "size" style factor. 
quantopian.pipeline.experimental.Value 
Quantopian Risk Model loadings for the "value" style factor. 
quantopian.pipeline.experimental.Volatility 
Quantopian Risk Model loadings for the "volatility" style factor. 
Domains¶
Country  Country Code  Pipeline Domain  Supported Exchanges 

Austria  AT  AT_EQUITIES 
Vienna Stock Exchange 
Australia  AU  AU_EQUITIES 
Australian Securities Exchange, National Stock Exchange of Australia 
Belgium  BE  BE_EQUITIES 
Euronext Brussels 
Brazil  BR  BR_EQUITIES 
Sao Paulo Stock Exchange 
Canada  CA  CA_EQUITIES 
Toronto Stock Exchange, TSX Venture Exchange, Canadian Securities Exchange 
Chile  CL  CL_EQUITIES 
Santiago Stock Exchange 
China  CN  CN_EQUITIES 
Shenzhen Stock Exchange, Shanghai Stock Exchange 
Colombia  CO  CO_EQUITIES 
Colombia Stock Exchange 
Czech Republic  CZ  CZ_EQUITIES 
Prague Stock Exchange 
Denmark  DK  DK_EQUITIES 
NASDAQ OMX Copenhagen 
Finland  FI  FI_EQUITIES 
NASDAQ OMX Helsinki 
France  FR  FR_EQUITIES 
Euronext Paris 
Germany  DE  DE_EQUITIES 
Berlin Stock Exchange, Dusseldorf Stock Exchange, XETRA, Frankfurt Stock Exchange, Hamburg Stock Exchange, Hannover Stock Exchange, Munich Stock Exchange, Stuttgart Stock Exchange, Xetra Indices 
Great Britain  GB  GB_EQUITIES 
London Stock Exchange, ICAP Securities & Derivatives Exchange, Cboe Europe Equities CXE 
Greece  GR  GR_EQUITIES 
Athens Exchange 
Hong Kong  HK  HK_EQUITIES 
Hong Kong Stock Exchange 
Hungary  HU  HU_EQUITIES 
Budapest Stock Exchange 
India  IN  IN_EQUITIES 
Bombay Stock Exchange, National Stock Exchange of India 
Ireland  IE  IE_EQUITIES 
Irish Stock Exchange, Irish Stock Exchange Bonds & Funds 
Italy  IT  IT_EQUITIES 
Milan Stock Exchange 
Japan  JP  JP_EQUITIES 
Tokyo Stock Exchange, JASDAQ, Osaka Exchange, Nagoya Stock Exchange, Fukuoka Stock Exchange, Sapporo Securities Exchange 
Mexico  MX  MX_EQUITIES 
Mexican Stock Exchange 
Netherlands  NL  NL_EQUITIES 
Euronext Amsterdam 
New Zealand  NZ  NZ_EQUITIES 
New Zealand Stock Exchange 
Norway  NO  NO_EQUITIES 
Oslo Exchange 
Peru  PE  PE_EQUITIES 
Lima Stock Exchange 
Poland  PL  PL_EQUITIES 
Warsaw Stock Exchange 
Portugal  PT  PT_EQUITIES 
Euronext Lisbon 
Singapore  SG  SG_EQUITIES 
Singapore Exchange 
South Africa  ZA  ZA_EQUITIES 
Johannesburg Securities Exchange 
South Korea  KR  KR_EQUITIES 
Korea Exchange, Korea KONEX 
Spain  ES  ES_EQUITIES 
Madrid Stock Exchange/Spanish Markets 
Sweden  SE  SE_EQUITIES 
NASDAQ OMX Stockholm, AktieTorget, Nordic Growth Market 
Switzerland  CH  CH_EQUITIES 
SIX Swiss Exchange, BX Swiss AG, Swiss Fund Data 
Turkey  TR  TR_EQUITIES 
Istanbul Stock Exchange 
United States  US  US_EQUITIES 
NYSE, NASDAQ, AMEX 
Detailed Reference¶
Pipeline¶

class
quantopian.pipeline.
Pipeline
(columns=None, screen=None, domain=GENERIC)¶ A Pipeline object represents a collection of named expressions to be compiled and executed by a PipelineEngine.
A Pipeline has two important attributes: 'columns', a dictionary of named
Term
instances, and 'screen', aFilter
representing criteria for including an asset in the results of a Pipeline.To compute a pipeline in the context of a TradingAlgorithm, users must call
attach_pipeline
in theirinitialize
function to register that the pipeline should be computed each trading day. The most recent outputs of an attached pipeline can be retrieved by callingpipeline_output
fromhandle_data
,before_trading_start
, or a scheduled function.Parameters:  columns (dict, optional)  Initial columns.
 screen (zipline.pipeline.Filter, optional)  Initial screen.
Note
The
Pipeline
class is defined inzipline.pipeline
. It is reexported onquantopian.pipeline
to reduce the number of modules that need to be imported by users when working on Quantopian. Most code written on Quantopian should accessPipeline
viaquantopian.pipeline
.
add
(self, term, name, overwrite=False)¶ Add a column.
The results of computing
term
will show up as a column in the DataFrame produced by running this pipeline.Parameters:  column (zipline.pipeline.Term)  A Filter, Factor, or Classifier to add to the pipeline.
 name (str)  Name of the column to add.
 overwrite (bool)  Whether to overwrite the existing entry if we already have a column named name.

remove
(self, name)¶ Remove a column.
Parameters: name (str)  The name of the column to remove. Raises: KeyError
 If name is not in self.columns.Returns: removed  The removed term. Return type: zipline.pipeline.Term

set_screen
(self, screen, overwrite=False)¶ Set a screen on this Pipeline.
Parameters:  filter (zipline.pipeline.Filter)  The filter to apply as a screen.
 overwrite (bool)  Whether to overwrite any existing screen. If overwrite is False and self.screen is not None, we raise an error.

show_graph
(self, format='svg')¶ Render this Pipeline as a DAG.
Parameters: format ({'svg', 'png', 'jpeg'})  Image format to render with. Default is 'svg'.

columns
¶ The output columns of this pipeline.
Returns: columns  Map from column name to expression computing that column's output. Return type: dict[str, zipline.pipeline.ComputableTerm]

screen
¶ The screen of this pipeline.
Returns: screen  Term defining the screen for this pipeline. If screen
is a filter, rows that do not pass the filter (i.e., rows for which the filter computedFalse
) will be dropped from the output of this pipeline before returning results.Return type: zipline.pipeline.Filter or None Notes
Setting a screen on a Pipeline does not change the values produced for any rows: it only affects whether a given row is returned. Computing a pipeline with a screen is logically equivalent to computing the pipeline without the screen and then, as a postprocessingstep, filtering out any rows for which the screen computed
False
.
Base Classes¶

class
quantopian.pipeline.
CustomFactor
¶ Base class for userdefined Factors.
Parameters:  inputs (iterable, optional)  An iterable of BoundColumn instances (e.g. USEquityPricing.close), describing the data to load and pass to self.compute. If this argument is not passed to the CustomFactor constructor, we look for a classlevel attribute named inputs.
 outputs (iterable[str], optional)  An iterable of strings which represent the names of each output this factor should compute and return. If this argument is not passed to the CustomFactor constructor, we look for a classlevel attribute named outputs.
 window_length (int, optional)  Number of rows to pass for each input. If this argument is not passed to the CustomFactor constructor, we look for a classlevel attribute named window_length.
 mask (zipline.pipeline.Filter, optional)  A Filter describing the assets on which we should compute each day.
Each call to
CustomFactor.compute
will only receive assets for whichmask
produced True on the day for which compute is being called.
Notes
Users implementing their own Factors should subclass CustomFactor and implement a method named compute with the following signature:
def compute(self, today, assets, out, *inputs): ...
On each simulation date,
compute
will be called with the current date, an array of sids, an output array, and an input array for each expression passed as inputs to the CustomFactor constructor.The specific types of the values passed to compute are as follows:
today : np.datetime64[ns] Row label for the last row of all arrays passed as `inputs`. assets : np.array[int64, ndim=1] Column labels for `out` and`inputs`. out : np.array[self.dtype, ndim=1] Output array of the same shape as `assets`. `compute` should write its desired return values into `out`. If multiple outputs are specified, `compute` should write its desired return values into `out.<output_name>` for each output name in `self.outputs`. *inputs : tuple of np.array Raw data arrays corresponding to the values of `self.inputs`.
compute
functions should expect to be passed NaN values for dates on which no data was available for an asset. This may include dates on which an asset did not yet exist.For example, if a CustomFactor requires 10 rows of close price data, and asset A started trading on Monday June 2nd, 2014, then on Tuesday, June 3rd, 2014, the column of input data for asset A will have 9 leading NaNs for the preceding days on which data was not yet available.
Examples
A CustomFactor with predeclared defaults:
class TenDayRange(CustomFactor): """ Computes the difference between the highest high in the last 10 days and the lowest low. Predeclares high and low as default inputs and `window_length` as 10. """ inputs = [USEquityPricing.high, USEquityPricing.low] window_length = 10 def compute(self, today, assets, out, highs, lows): from numpy import nanmin, nanmax highest_highs = nanmax(highs, axis=0) lowest_lows = nanmin(lows, axis=0) out[:] = highest_highs  lowest_lows # Doesn't require passing inputs or window_length because they're # predeclared as defaults for the TenDayRange class. ten_day_range = TenDayRange()
A CustomFactor without defaults:
class MedianValue(CustomFactor): """ Computes the median value of an arbitrary single input over an arbitrary window.. Does not declare any defaults, so values for `window_length` and `inputs` must be passed explicitly on every construction. """ def compute(self, today, assets, out, data): from numpy import nanmedian out[:] = data.nanmedian(data, axis=0) # Values for `inputs` and `window_length` must be passed explicitly to # MedianValue. median_close10 = MedianValue([USEquityPricing.close], window_length=10) median_low15 = MedianValue([USEquityPricing.low], window_length=15)
A CustomFactor with multiple outputs:
class MultipleOutputs(CustomFactor): inputs = [USEquityPricing.close] outputs = ['alpha', 'beta'] window_length = N def compute(self, today, assets, out, close): computed_alpha, computed_beta = some_function(close) out.alpha[:] = computed_alpha out.beta[:] = computed_beta # Each output is returned as its own Factor upon instantiation. alpha, beta = MultipleOutputs() # Equivalently, we can create a single factor instance and access each # output as an attribute of that instance. multiple_outputs = MultipleOutputs() alpha = multiple_outputs.alpha beta = multiple_outputs.beta
Note: If a CustomFactor has multiple outputs, all outputs must have the same dtype. For instance, in the example above, if alpha is a float then beta must also be a float.
Note
The
CustomFactor
class is defined inzipline.pipeline
. It is reexported onquantopian.pipeline
to reduce the number of modules that need to be imported by users when working on Quantopian. Most code written on Quantopian should accessCustomFactor
viaquantopian.pipeline
.

class
quantopian.pipeline.
CustomFilter
¶ Base class for userdefined Filters.
Parameters:  inputs (iterable, optional)  An iterable of BoundColumn instances (e.g. USEquityPricing.close),
describing the data to load and pass to
self.compute
. If this argument is passed to the CustomFilter constructor, we look for a classlevel attribute namedinputs
.  window_length (int, optional)  Number of rows to pass for each input. If this argument is not passed to the CustomFilter constructor, we look for a classlevel attribute named window_length.
Notes
Users implementing their own Filters should subclass CustomFilter and implement a method named
compute
with the following signature:def compute(self, today, assets, out, *inputs): ...
On each simulation date,
compute
will be called with the current date, an array of sids, an output array, and an input array for each expression passed as inputs to the CustomFilter constructor.The specific types of the values passed to
compute
are as follows:today : np.datetime64[ns] Row label for the last row of all arrays passed as `inputs`. assets : np.array[int64, ndim=1] Column labels for `out` and`inputs`. out : np.array[bool, ndim=1] Output array of the same shape as `assets`. `compute` should write its desired return values into `out`. *inputs : tuple of np.array Raw data arrays corresponding to the values of `self.inputs`.
See the documentation for
CustomFactor
for more details on implementing a customcompute
method.See also
Note
The
CustomFactor
class is defined inzipline.pipeline
. It is reexported onquantopian.pipeline
to reduce the number of modules that need to be imported by users when working on Quantopian. Most code written on Quantopian should accessCustomFilter
viaquantopian.pipeline
. inputs (iterable, optional)  An iterable of BoundColumn instances (e.g. USEquityPricing.close),
describing the data to load and pass to

class
zipline.pipeline.
Term
¶ Base class for objects that can appear in the compute graph of a
zipline.pipeline.Pipeline
.Notes
Most Pipeline API users only interact with
Term
via subclasses:Instances of
Term
are memoized. If you call a Term's constructor with the same arguments twice, the same object will be returned from both calls:Example:
>>> from zipline.pipeline.data import EquityPricing >>> from zipline.pipeline.factors import SimpleMovingAverage >>> x = SimpleMovingAverage(inputs=[EquityPricing.close], window_length=5) >>> y = SimpleMovingAverage(inputs=[EquityPricing.close], window_length=5) >>> x is y True
Warning
Memoization of terms means that it's generally unsafe to modify attributes of a term after construction.

inputs
¶ A tuple of other Terms needed as inputs for
self
.


class
zipline.pipeline.
ComputableTerm
¶ A Term that should be computed from a tuple of inputs.
This is the base class for
zipline.pipeline.Factor
,zipline.pipeline.Filter
, andzipline.pipeline.Classifier
.

class
zipline.pipeline.
LoadableTerm
¶ A Term that should be loaded from an external resource by a PipelineLoader.
This is the base class for
zipline.pipeline.data.BoundColumn
.

class
zipline.pipeline.
Factor
¶ Pipeline API expression producing a numerical or datevalued output.
Factors are the most commonlyused Pipeline term, representing the result of any computation producing a numerical result.
Factors can be combined, both with other Factors and with scalar values, via any of the builtin mathematical operators (
+
,
,*
, etc).This makes it easy to write complex expressions that combine multiple Factors. For example, constructing a Factor that computes the average of two other Factors is simply:
>>> f1 = SomeFactor(...) >>> f2 = SomeOtherFactor(...) >>> average = (f1 + f2) / 2.0
Factors can also be converted into
zipline.pipeline.Filter
objects via comparison operators: (<
,<=
,!=
,eq
,>
,>=
).There are many natural operators defined on Factors besides the basic numerical operators. These include methods for identifying missing or extremevalued outputs (
isnull()
,notnull()
,isnan()
,notnan()
), methods for normalizing outputs (rank()
,demean()
,zscore()
), and methods for constructing Filters based on rankorder properties of results (top()
,bottom()
,percentile_between()
).
rank
(method='ordinal', ascending=True, mask=sentinel('NotSpecified'), groupby=sentinel('NotSpecified'))¶ Construct a new Factor representing the sorted rank of each column within each row.
Parameters:  method (str, {'ordinal', 'min', 'max', 'dense', 'average'})  The method used to assign ranks to tied elements. See scipy.stats.rankdata for a full description of the semantics for each ranking method. Default is 'ordinal'.
 ascending (bool, optional)  Whether to return sorted rank in ascending or descending order. Default is True.
 mask (zipline.pipeline.Filter, optional)  A Filter representing assets to consider when computing ranks. If mask is supplied, ranks are computed ignoring any asset/date pairs for which mask produces a value of False.
 groupby (zipline.pipeline.Classifier, optional)  A classifier defining partitions over which to perform ranking.
Returns: ranks  A new factor that will compute the ranking of the data produced by self.
Return type: Notes
The default value for method is different from the default for scipy.stats.rankdata. See that function's documentation for a full description of the valid inputs to method.
Missing or nonexistent data on a given day will cause an asset to be given a rank of NaN for that day.
See also

demean
(self, mask=sentinel('NotSpecified'), groupby=sentinel('NotSpecified'))¶ Construct a Factor that computes
self
and subtracts the mean from row of the result.If
mask
is supplied, ignore values wheremask
returns False when computing row means, and output NaN anywhere the mask is False.If
groupby
is supplied, compute by partitioning each row based on the values produced bygroupby
, demeaning the partitioned arrays, and stitching the subresults back together.Parameters:  mask (zipline.pipeline.Filter, optional)  A Filter defining values to ignore when computing means.
 groupby (zipline.pipeline.Classifier, optional)  A classifier defining partitions over which to compute means.
Examples
Let
f
be a Factor which would produce the following output:AAPL MSFT MCD BK 20170313 1.0 2.0 3.0 4.0 20170314 1.5 2.5 3.5 1.0 20170315 2.0 3.0 4.0 1.5 20170316 2.5 3.5 1.0 2.0
Let
c
be a Classifier producing the following output:AAPL MSFT MCD BK 20170313 1 1 2 2 20170314 1 1 2 2 20170315 1 1 2 2 20170316 1 1 2 2
Let
m
be a Filter producing the following output:AAPL MSFT MCD BK 20170313 False True True True 20170314 True False True True 20170315 True True False True 20170316 True True True False
Then
f.demean()
will subtract the mean from each row produced byf
.AAPL MSFT MCD BK 20170313 1.500 0.500 0.500 1.500 20170314 0.625 0.375 1.375 1.125 20170315 0.625 0.375 1.375 1.125 20170316 0.250 1.250 1.250 0.250
f.demean(mask=m)
will subtract the mean from each row, but means will be calculated ignoring values on the diagonal, and NaNs will written to the diagonal in the output. Diagonal values are ignored because they are the locations where the maskm
produced False.AAPL MSFT MCD BK 20170313 NaN 1.000 0.000 1.000 20170314 0.500 NaN 1.500 1.000 20170315 0.166 0.833 NaN 0.666 20170316 0.166 1.166 1.333 NaN
f.demean(groupby=c)
will subtract the groupmean of AAPL/MSFT and MCD/BK from their respective entries. The AAPL/MSFT are grouped together because both assets always produce 1 in the output of the classifierc
. Similarly, MCD/BK are grouped together because they always produce 2.AAPL MSFT MCD BK 20170313 0.500 0.500 0.500 0.500 20170314 0.500 0.500 1.250 1.250 20170315 0.500 0.500 1.250 1.250 20170316 0.500 0.500 0.500 0.500
f.demean(mask=m, groupby=c)
will also subtract the groupmean of AAPL/MSFT and MCD/BK, but means will be calculated ignoring values on the diagonal , and NaNs will be written to the diagonal in the output.AAPL MSFT MCD BK 20170313 NaN 0.000 0.500 0.500 20170314 0.000 NaN 1.250 1.250 20170315 0.500 0.500 NaN 0.000 20170316 0.500 0.500 0.000 NaN
Notes
Mean is sensitive to the magnitudes of outliers. When working with factor that can potentially produce large outliers, it is often useful to use the
mask
parameter to discard values at the extremes of the distribution:>>> base = MyFactor(...) >>> normalized = base.demean( ... mask=base.percentile_between(1, 99), ... )
demean()
is only supported on Factors of dtype float64.See also

zscore
(self, mask=sentinel('NotSpecified'), groupby=sentinel('NotSpecified'))¶ Construct a Factor that ZScores each day's results.
The ZScore of a row is defined as:
(row  row.mean()) / row.stddev()
If
mask
is supplied, ignore values wheremask
returns False when computing row means and standard deviations, and output NaN anywhere the mask is False.If
groupby
is supplied, compute by partitioning each row based on the values produced bygroupby
, zscoring the partitioned arrays, and stitching the subresults back together.Parameters:  mask (zipline.pipeline.Filter, optional)  A Filter defining values to ignore when ZScoring.
 groupby (zipline.pipeline.Classifier, optional)  A classifier defining partitions over which to compute ZScores.
Returns: zscored  A Factor producing that zscores the output of self.
Return type: Notes
Mean and standard deviation are sensitive to the magnitudes of outliers. When working with factor that can potentially produce large outliers, it is often useful to use the
mask
parameter to discard values at the extremes of the distribution:>>> base = MyFactor(...) >>> normalized = base.zscore( ... mask=base.percentile_between(1, 99), ... )
zscore()
is only supported on Factors of dtype float64.Examples
See
demean()
for an indepth example of the semantics formask
andgroupby
.See also

pearsonr
(self, target, correlation_length, mask=sentinel('NotSpecified'))¶ Construct a new Factor that computes rolling pearson correlation coefficients between
target
and the columns ofself
.Parameters:  target (zipline.pipeline.Term)  The term used to compute correlations against each column of data produced by self. This may be a Factor, a BoundColumn or a Slice. If target is twodimensional, correlations are computed assetwise.
 correlation_length (int)  Length of the lookback window over which to compute each correlation coefficient.
 mask (zipline.pipeline.Filter, optional)  A Filter describing which assets should have their correlation with the target slice computed each day.
Returns: correlations  A new Factor that will compute correlations between
target
and the columns ofself
.Return type: Notes
This method can only be called on expressions which are deemed safe for use as inputs to windowed
Factor
objects. Examples of such expressions include This includesBoundColumn
Returns
and any factors created fromrank()
orzscore()
.Examples
Suppose we want to create a factor that computes the correlation between AAPL's 10day returns and the 10day returns of all other assets, computing each correlation over 30 days. This can be achieved by doing the following:
returns = Returns(window_length=10) returns_slice = returns[sid(24)] aapl_correlations = returns.pearsonr( target=returns_slice, correlation_length=30, )
This is equivalent to doing:
aapl_correlations = RollingPearsonOfReturns( target=sid(24), returns_length=10, correlation_length=30, )

spearmanr
(self, target, correlation_length, mask=sentinel('NotSpecified'))¶ Construct a new Factor that computes rolling spearman rank correlation coefficients between
target
and the columns ofself
.Parameters:  target (zipline.pipeline.Term)  The term used to compute correlations against each column of data produced by self. This may be a Factor, a BoundColumn or a Slice. If target is twodimensional, correlations are computed assetwise.
 correlation_length (int)  Length of the lookback window over which to compute each correlation coefficient.
 mask (zipline.pipeline.Filter, optional)  A Filter describing which assets should have their correlation with the target slice computed each day.
Returns: correlations  A new Factor that will compute correlations between
target
and the columns ofself
.Return type: Notes
This method can only be called on expressions which are deemed safe for use as inputs to windowed
Factor
objects. Examples of such expressions include This includesBoundColumn
Returns
and any factors created fromrank()
orzscore()
.Examples
Suppose we want to create a factor that computes the correlation between AAPL's 10day returns and the 10day returns of all other assets, computing each correlation over 30 days. This can be achieved by doing the following:
returns = Returns(window_length=10) returns_slice = returns[sid(24)] aapl_correlations = returns.spearmanr( target=returns_slice, correlation_length=30, )
This is equivalent to doing:
aapl_correlations = RollingSpearmanOfReturns( target=sid(24), returns_length=10, correlation_length=30, )
See also

linear_regression
(self, target, regression_length, mask=sentinel('NotSpecified'))¶ Construct a new Factor that performs an ordinary leastsquares regression predicting the columns of self from target.
Parameters:  target (zipline.pipeline.Term)  The term to use as the predictor/independent variable in each regression. This may be a Factor, a BoundColumn or a Slice. If target is twodimensional, regressions are computed assetwise.
 regression_length (int)  Length of the lookback window over which to compute each regression.
 mask (zipline.pipeline.Filter, optional)  A Filter describing which assets should be regressed with the target slice each day.
Returns: regressions  A new Factor that will compute linear regressions of target against the columns of self.
Return type: Notes
This method can only be called on expressions which are deemed safe for use as inputs to windowed
Factor
objects. Examples of such expressions include This includesBoundColumn
Returns
and any factors created fromrank()
orzscore()
.Examples
Suppose we want to create a factor that regresses AAPL's 10day returns against the 10day returns of all other assets, computing each regression over 30 days. This can be achieved by doing the following:
returns = Returns(window_length=10) returns_slice = returns[sid(24)] aapl_regressions = returns.linear_regression( target=returns_slice, regression_length=30, )
This is equivalent to doing:
aapl_regressions = RollingLinearRegressionOfReturns( target=sid(24), returns_length=10, regression_length=30, )
See also

winsorize
(self, min_percentile, max_percentile, mask=sentinel('NotSpecified'), groupby=sentinel('NotSpecified'))¶ Construct a new factor that winsorizes the result of this factor.
Winsorizing changes values ranked less than the minimum percentile to the value at the minimum percentile. Similarly, values ranking above the maximum percentile are changed to the value at the maximum percentile.
Winsorizing is useful for limiting the impact of extreme data points without completely removing those points.
If
mask
is supplied, ignore values wheremask
returns False when computing percentile cutoffs, and output NaN anywhere the mask is False.If
groupby
is supplied, winsorization is applied separately separately to each group defined bygroupby
.Parameters:  min_percentile (float, int)  Entries with values at or below this percentile will be replaced with the (len(input) * min_percentile)th lowest value. If low values should not be clipped, use 0.
 max_percentile (float, int)  Entries with values at or above this percentile will be replaced with the (len(input) * max_percentile)th lowest value. If high values should not be clipped, use 1.
 mask (zipline.pipeline.Filter, optional)  A Filter defining values to ignore when winsorizing.
 groupby (zipline.pipeline.Classifier, optional)  A classifier defining partitions over which to winsorize.
Returns: winsorized  A Factor producing a winsorized version of self.
Return type: Examples
price = USEquityPricing.close.latest columns={ 'PRICE': price, 'WINSOR_1: price.winsorize( min_percentile=0.25, max_percentile=0.75 ), 'WINSOR_2': price.winsorize( min_percentile=0.50, max_percentile=1.0 ), 'WINSOR_3': price.winsorize( min_percentile=0.0, max_percentile=0.5 ), }
Given a pipeline with columns, defined above, the result for a given day could look like:
'PRICE' 'WINSOR_1' 'WINSOR_2' 'WINSOR_3' Asset_1 1 2 4 3 Asset_2 2 2 4 3 Asset_3 3 3 4 3 Asset_4 4 4 4 4 Asset_5 5 5 5 4 Asset_6 6 5 5 4

downsample
(self, frequency)¶ Make a term that computes from
self
at lowerthandaily frequency.Parameters: frequency ({'year_start', 'quarter_start', 'month_start', 'week_start'})  A string indicating desired sampling dates:
 'year_start' > first trading day of each year
 'quarter_start' > first trading day of January, April, July, October
 'month_start' > first trading day of each month
 'week_start' > first trading_day of each week

sin
()¶ Construct a Factor that computes
sin()
on each output ofself
.Returns: factor Return type: zipline.pipeline.Factor

cos
()¶ Construct a Factor that computes
cos()
on each output ofself
.Returns: factor Return type: zipline.pipeline.Factor

tan
()¶ Construct a Factor that computes
tan()
on each output ofself
.Returns: factor Return type: zipline.pipeline.Factor

arcsin
()¶ Construct a Factor that computes
arcsin()
on each output ofself
.Returns: factor Return type: zipline.pipeline.Factor

arccos
()¶ Construct a Factor that computes
arccos()
on each output ofself
.Returns: factor Return type: zipline.pipeline.Factor

arctan
()¶ Construct a Factor that computes
arctan()
on each output ofself
.Returns: factor Return type: zipline.pipeline.Factor

sinh
()¶ Construct a Factor that computes
sinh()
on each output ofself
.Returns: factor Return type: zipline.pipeline.Factor

cosh
()¶ Construct a Factor that computes
cosh()
on each output ofself
.Returns: factor Return type: zipline.pipeline.Factor

tanh
()¶ Construct a Factor that computes
tanh()
on each output ofself
.Returns: factor Return type: zipline.pipeline.Factor

arcsinh
()¶ Construct a Factor that computes
arcsinh()
on each output ofself
.Returns: factor Return type: zipline.pipeline.Factor

arccosh
()¶ Construct a Factor that computes
arccosh()
on each output ofself
.Returns: factor Return type: zipline.pipeline.Factor

arctanh
()¶ Construct a Factor that computes
arctanh()
on each output ofself
.Returns: factor Return type: zipline.pipeline.Factor

log
()¶ Construct a Factor that computes
log()
on each output ofself
.Returns: factor Return type: zipline.pipeline.Factor

log10
()¶ Construct a Factor that computes
log10()
on each output ofself
.Returns: factor Return type: zipline.pipeline.Factor

log1p
()¶ Construct a Factor that computes
log1p()
on each output ofself
.Returns: factor Return type: zipline.pipeline.Factor

exp
()¶ Construct a Factor that computes
exp()
on each output ofself
.Returns: factor Return type: zipline.pipeline.Factor

expm1
()¶ Construct a Factor that computes
expm1()
on each output ofself
.Returns: factor Return type: zipline.pipeline.Factor

sqrt
()¶ Construct a Factor that computes
sqrt()
on each output ofself
.Returns: factor Return type: zipline.pipeline.Factor

abs
()¶ Construct a Factor that computes
abs()
on each output ofself
.Returns: factor Return type: zipline.pipeline.Factor

eq
(self, other)¶ Construct a
Filter
computingself == other
.Parameters: other (zipline.pipeline.Factor, float)  Righthand side of the expression. Returns: filter  Filter computing self == other
with the outputs ofself
andother
.Return type: zipline.pipeline.Filter

top
(N, mask=sentinel('NotSpecified'), groupby=sentinel('NotSpecified'))¶ Construct a Filter matching the top N asset values of self each day.
If
groupby
is supplied, returns a Filter matching the top N asset values for each group.Parameters:  N (int)  Number of assets passing the returned filter each day.
 mask (zipline.pipeline.Filter, optional)  A Filter representing assets to consider when computing ranks. If mask is supplied, top values are computed ignoring any asset/date pairs for which mask produces a value of False.
 groupby (zipline.pipeline.Classifier, optional)  A classifier defining partitions over which to perform ranking.
Returns: filter
Return type:

bottom
(N, mask=sentinel('NotSpecified'), groupby=sentinel('NotSpecified'))¶ Construct a Filter matching the bottom N asset values of self each day.
If
groupby
is supplied, returns a Filter matching the bottom N asset values for each group defined bygroupby
.Parameters:  N (int)  Number of assets passing the returned filter each day.
 mask (zipline.pipeline.Filter, optional)  A Filter representing assets to consider when computing ranks. If mask is supplied, bottom values are computed ignoring any asset/date pairs for which mask produces a value of False.
 groupby (zipline.pipeline.Classifier, optional)  A classifier defining partitions over which to perform ranking.
Returns: filter
Return type:

isnull
()¶ A Filter producing True for values where this Factor has missing data.
Equivalent to self.isnan() when
self.dtype
is float64. Otherwise equivalent toself.eq(self.missing_value)
.Returns: filter Return type: zipline.pipeline.Filter

notnull
()¶ A Filter producing True for values where this Factor has complete data.
Equivalent to
~self.isnan()` when ``self.dtype
is float64. Otherwise equivalent to(self != self.missing_value)
.

isnan
(self)¶ A Filter producing True for all values where this Factor is NaN.
Returns: nanfilter Return type: zipline.pipeline.Filter

notnan
(self)¶ A Filter producing True for values where this Factor is not NaN.
Returns: nanfilter Return type: zipline.pipeline.Filter

isfinite
(self)¶ A Filter producing True for values where this Factor is anything but NaN, inf, or inf.

percentile_between
(min_percentile, max_percentile, mask=sentinel('NotSpecified'))¶ Construct a Filter matching values of self that fall within the range defined by
min_percentile
andmax_percentile
.Parameters:  min_percentile (float [0.0, 100.0])  Return True for assets falling above this percentile in the data.
 max_percentile (float [0.0, 100.0])  Return True for assets falling below this percentile in the data.
 mask (zipline.pipeline.Filter, optional)  A Filter representing assets to consider when percentile
calculating thresholds. If mask is supplied, percentile cutoffs
are computed each day using only assets for which
mask
returns True. Assets for whichmask
produces False will produce False in the output of this Factor as well.
Returns: out  A new filter that will compute the specified percentilerange mask.
Return type:

quartiles
(self, mask=sentinel('NotSpecified'))¶ Construct a Classifier computing quartiles over the output of
self
.Every nonNaN data point the output is labelled with a value of either 0, 1, 2, or 3, corresponding to the first, second, third, or fourth quartile over each row. NaN data points are labelled with 1.
If
mask
is supplied, ignore data points in locations for whichmask
produces False, and emit a label of 1 at those locations.Parameters: mask (zipline.pipeline.Filter, optional)  Mask of values to ignore when computing quartiles. Returns: quartiles  A classifier producing integer labels ranging from 0 to 3. Return type: zipline.pipeline.Classifier

quintiles
(self, mask=sentinel('NotSpecified'))¶ Construct a Classifier computing quintile labels on
self
.Every nonNaN data point the output is labelled with a value of either 0, 1, 2, or 3, 4, corresonding to quintiles over each row. NaN data points are labelled with 1.
If
mask
is supplied, ignore data points in locations for whichmask
produces False, and emit a label of 1 at those locations.Parameters: mask (zipline.pipeline.Filter, optional)  Mask of values to ignore when computing quintiles. Returns: quintiles  A classifier producing integer labels ranging from 0 to 4. Return type: zipline.pipeline.Classifier

deciles
(self, mask=sentinel('NotSpecified'))¶ Construct a Classifier computing decile labels on
self
.Every nonNaN data point the output is labelled with a value from 0 to 9 corresonding to deciles over each row. NaN data points are labelled with 1.
If
mask
is supplied, ignore data points in locations for whichmask
produces False, and emit a label of 1 at those locations.Parameters: mask (zipline.pipeline.Filter, optional)  Mask of values to ignore when computing deciles. Returns: deciles  A classifier producing integer labels ranging from 0 to 9. Return type: zipline.pipeline.Classifier

quantiles
(self, bins, mask=sentinel('NotSpecified'))¶ Construct a Classifier computing quantiles of the output of
self
.Every nonNaN data point the output is labelled with an integer value from 0 to (bins  1). NaNs are labelled with 1.
If
mask
is supplied, ignore data points in locations for whichmask
produces False, and emit a label of 1 at those locations.Parameters:  bins (int)  Number of bins labels to compute.
 mask (zipline.pipeline.Filter, optional)  Mask of values to ignore when computing quantiles.
Returns: quantiles  A classifier producing integer labels ranging from 0 to (bins  1).
Return type:

__add__
(self, other)¶ Construct a
Factor
computingself + other
.Parameters: other (zipline.pipeline.Factor, float)  Righthand side of the expression. Returns: factor  Factor computing self + other
with outputs ofself
andother
.Return type: zipline.pipeline.Factor

__sub__
(self, other)¶ Construct a
Factor
computingself  other
.Parameters: other (zipline.pipeline.Factor, float)  Righthand side of the expression. Returns: factor  Factor computing self  other
with outputs ofself
andother
.Return type: zipline.pipeline.Factor

__add__
(self, other) Construct a
Factor
computingself + other
.Parameters: other (zipline.pipeline.Factor, float)  Righthand side of the expression. Returns: factor  Factor computing self + other
with outputs ofself
andother
.Return type: zipline.pipeline.Factor

__sub__
(self, other) Construct a
Factor
computingself  other
.Parameters: other (zipline.pipeline.Factor, float)  Righthand side of the expression. Returns: factor  Factor computing self  other
with outputs ofself
andother
.Return type: zipline.pipeline.Factor

__mul__
(self, other)¶ Construct a
Factor
computingself * other
.Parameters: other (zipline.pipeline.Factor, float)  Righthand side of the expression. Returns: factor  Factor computing self * other
with outputs ofself
andother
.Return type: zipline.pipeline.Factor

__div__
(self, other)¶ Construct a
Factor
computingself / other
.Parameters: other (zipline.pipeline.Factor, float)  Righthand side of the expression. Returns: factor  Factor computing self / other
with outputs ofself
andother
.Return type: zipline.pipeline.Factor

__mod__
(self, other)¶ Construct a
Factor
computingself % other
.Parameters: other (zipline.pipeline.Factor, float)  Righthand side of the expression. Returns: factor  Factor computing self % other
with outputs ofself
andother
.Return type: zipline.pipeline.Factor

__pow__
(self, other)¶ Construct a
Factor
computingself ** other
.Parameters: other (zipline.pipeline.Factor, float)  Righthand side of the expression. Returns: factor  Factor computing self ** other
with outputs ofself
andother
.Return type: zipline.pipeline.Factor

__lt__
(self, other)¶ Construct a
Filter
computingself < other
.Parameters: other (zipline.pipeline.Factor, float)  Righthand side of the expression. Returns: filter  Filter computing self < other
with the outputs ofself
andother
.Return type: zipline.pipeline.Filter

__le__
(self, other)¶ Construct a
Filter
computingself <= other
.Parameters: other (zipline.pipeline.Factor, float)  Righthand side of the expression. Returns: filter  Filter computing self <= other
with the outputs ofself
andother
.Return type: zipline.pipeline.Filter

__ne__
(self, other)¶ Construct a
Filter
computingself != other
.Parameters: other (zipline.pipeline.Factor, float)  Righthand side of the expression. Returns: filter  Filter computing self != other
with the outputs ofself
andother
.Return type: zipline.pipeline.Filter

__ge__
(self, other)¶ Construct a
Filter
computingself >= other
.Parameters: other (zipline.pipeline.Factor, float)  Righthand side of the expression. Returns: filter  Filter computing self >= other
with the outputs ofself
andother
.Return type: zipline.pipeline.Filter

__gt__
(self, other)¶ Construct a
Filter
computingself > other
.Parameters: other (zipline.pipeline.Factor, float)  Righthand side of the expression. Returns: filter  Filter computing self > other
with the outputs ofself
andother
.Return type: zipline.pipeline.Filter


class
zipline.pipeline.
Filter
¶ Pipeline expression computing a boolean output.
Filters are most commonly useful for describing sets of assets to include or exclude for some particular purpose. Many Pipeline API functions accept a
mask
argument, which can be supplied a Filter indicating that only values passing the Filter should be considered when performing the requested computation. For example,zipline.pipeline.Factor.top()
accepts a mask indicating that ranks should be computed only on assets that passed the specified Filter.The most common way to construct a Filter is via one of the comparison operators (
<
,<=
,!=
,eq
,>
,>=
) ofFactor
. For example, a natural way to construct a Filter for stocks with a 10day VWAP less than $20.0 is to first construct a Factor computing 10day VWAP and compare it to the scalar value 20.0:>>> from zipline.pipeline.factors import VWAP >>> vwap_10 = VWAP(window_length=10) >>> vwaps_under_20 = (vwap_10 <= 20)
Filters can also be constructed via comparisons between two Factors. For example, to construct a Filter producing True for asset/date pairs where the asset's 10day VWAP was greater than it's 30day VWAP:
>>> short_vwap = VWAP(window_length=10) >>> long_vwap = VWAP(window_length=30) >>> higher_short_vwap = (short_vwap > long_vwap)
Filters can be combined via the
&
(and) and
(or) operators.&
ing together two filters produces a new Filter that produces True if both of the inputs produced True.
ing together two filters produces a new Filter that produces True if either of its inputs produced True.The
~
operator can be used to invert a Filter, swapping all True values with Falses and viceversa.Filters may be set as the
screen
attribute of a Pipeline, indicating asset/date pairs for which the filter produces False should be excluded from the Pipeline's output. This is useful both for reducing noise in the output of a Pipeline and for reducing memory consumption of Pipeline results.
__and__
(other)¶ Binary Operator: '&'

__or__
(other)¶ Binary Operator: ''

__invert__
()¶ Unary Operator: '~'


class
zipline.pipeline.
Classifier
¶ A Pipeline expression computing a categorical output.
Classifiers are most commonly useful for describing grouping keys for complex transformations on Factor outputs. For example, Factor.demean() and Factor.zscore() can be passed a Classifier in their
groupby
argument, indicating that means/standard deviations should be computed on assets for which the classifier produced the same label.
element_of
(choices)¶ Construct a Filter indicating whether values are in
choices
.Parameters: choices (iterable[str or int])  An iterable of choices. Returns: matches  Filter returning True for all sid/date pairs for which self
produces an entry inchoices
.Return type: Filter

endswith
(self, suffix)¶ Construct a Filter matching values ending with
suffix
.Parameters: suffix (str)  String suffix against which to compare values produced by self
.Returns: matches  Filter returning True for all sid/date pairs for which self
produces a string ending withprefix
.Return type: Filter

eq
(other)¶ Construct a Filter returning True for asset/date pairs where the output of
self
matchesother
.

has_substring
(self, substring)¶ Construct a Filter matching values containing
substring
.Parameters: substring (str)  Substring against which to compare values produced by self
.Returns: matches  Filter returning True for all sid/date pairs for which self
produces a string containingsubstring
.Return type: Filter

isnull
()¶ A Filter producing True for values where this term has missing data.

matches
(self, pattern)¶ Construct a Filter that checks regex matches against
pattern
.Parameters: pattern (str)  Regex pattern against which to compare values produced by self
.Returns: matches  Filter returning True for all sid/date pairs for which self
produces a string matched bypattern
.Return type: Filter See also

notnull
()¶ A Filter producing True for values where this term has complete data.

peer_count
()¶ Construct a factor that gives the number of occurrences of each distinct category in a classifier.
Examples
Let
c
be a Classifier which would produce the following output:AAPL MSFT MCD BK AMZN FB 20150505 'a' 'a' None 'b' 'a' None 20150506 'b' 'a' 'c' 'b' 'b' 'b' 20150507 None 'a' 'aa' 'aa' 'aa' None 20150508 'c' 'c' 'c' 'c' 'c' 'c'
Then
c.peer_count()
will count, for each row, the total number of assets in each classifier category produced byc
. Missing data will be evaluated to NaN.AAPL MSFT MCD BK AMZN FB 20150505 3.0 3.0 NaN 1.0 3.0 NaN 20150506 4.0 1.0 1.0 4.0 4.0 4.0 20150507 NaN 1.0 3.0 3.0 3.0 NaN 20150508 6.0 6.0 6.0 6.0 6.0 6.0
Returns: factor  A CustomFactor that counts, for each asset, the total number of assets with the same classifier category label. Return type: CustomFactor

relabel
(self, relabeler)¶ Convert
self
into a new classifier by mapping a function over each element produced byself
.Parameters: relabeler (function[str > str or None])  A function to apply to each unique value produced by self
.Returns: relabeled  A classifier produced by applying relabeler
to each unique value produced byself
.Return type: Classifier

startswith
(self, prefix)¶ Construct a Filter matching values starting with
prefix
.Parameters: prefix (str)  String prefix against which to compare values produced by self
.Returns: matches  Filter returning True for all sid/date pairs for which self
produces a string starting withprefix
.Return type: Filter


class
zipline.pipeline.data.
DataSet
¶ Base class for Pipeline datasets.
A
DataSet
is defined by two parts: A collection of
Column
objects that describe the queryable attributes of the dataset.  A
Domain
describing the assets and calendar of the data represented by theDataSet
.
To create a new Pipeline dataset, define a subclass of
DataSet
and set one or moreColumn
objects as classlevel attributes. Each column requires anp.dtype
that describes the type of data that should be produced by a loader for the dataset. Integer columns must also provide a "missing value" to be used when no value is available for a given asset/date combination.By default, the domain of a dataset is the special singleton value,
GENERIC
, which means that they can be used in a Pipeline running on any domain.In some cases, it may be preferable to restrict a dataset to only allow support a single domain. For example, a DataSet may describe data from a vendor that only covers the US. To restrict a dataset to a specific domain, define a domain attribute at class scope.
You can also define a domainspecific version of a generic DataSet by calling its
specialize
method with the domain of interest.Examples
The builtin EquityPricing dataset is defined as follows:
class EquityPricing(DataSet): open = Column(float) high = Column(float) low = Column(float) close = Column(float) volume = Column(float)
The builtin USEquityPricing dataset is a specialization of EquityPricing. It is defined as:
from zipline.pipeline.domain import US_EQUITIES USEquityPricing = EquityPricing.specialize(US_EQUITIES)
Columns can have types other than float. A dataset containing assorted company metadata might be defined like this:
class CompanyMetadata(DataSet): # Use float for semanticallynumeric data, even if it's always # integral valued (see Notes section below). The default missing # value for floats is NaN. shares_outstanding = Column(float) # Use object for string columns. The default missing value for # objectdtype columns is None. ticker = Column(object) # Use integers for integervalued categorical data like sector or # industry codes. Integerdtype columns require an explicit missing # value. sector_code = Column(int, missing_value=1) # Use bool for booleanvalued flags. Note that the default missing # value for booldtype columns is False. is_primary_share = Column(bool)
Notes
Because numpy has no native support for integers with missing values, users are strongly encouraged to use floats for any data that's semantically numeric. Doing so enables the use of NaN as a natural missing value, which has useful propagation semantics.

columns
¶ Get all the columns of this dataset. :returns: frozenset[zipline.pipeline.data.BoundColumn]

classmethod
get_column
(name)¶ Look up a column by name.
Parameters: name (str)  Name of the column to look up. Returns: column  Column with the given name. Return type: zipline.pipeline.data.BoundColumn Raises: AttributeError
 If no column with the given name exists.
 A collection of

class
zipline.pipeline.data.
DataSetFamily
¶ Base class for Pipeline dataset families.
Dataset families are used to represent data where the unique identifier for a row requires more than just asset and date coordinates. A
DataSetFamily
can also be thought of as a collection ofDataSet
objects, each of which has the same columns, domain, and ndim.DataSetFamily
objects are defined with by one or moreColumn
objects, plus one additional field:extra_dims
.The
extra_dims
field defines coordinates other than asset and date that must be fixed to produce a logical timeseries. The column objects determine columns that will be shared by slices of the family.extra_dims
are represented as an ordered dictionary where the keys are the dimension name, and the values are a set of unique values along that dimension.To work with a
DataSetFamily
in a pipeline expression, one must choose a specific value for each of the extra dimensions using theslice()
method. For example, given aDataSetFamily
:class SomeDataSet(DataSetFamily): extra_dims = [ ('dimension_0', {'a', 'b', 'c'}), ('dimension_1', {'d', 'e', 'f'}), ] column_0 = Column(float) column_1 = Column(bool)
This dataset might represent a table with the following columns:
sid :: int64 asof_date :: datetime64[ns] timestamp :: datetime64[ns] dimension_0 :: str dimension_1 :: str column_0 :: float64 column_1 :: bool
Here we see the implicit
sid
,asof_date
andtimestamp
columns as well as the extra dimensions columns.This
DataSetFamily
can be converted to a regularDataSet
with:DataSetSlice = SomeDataSet.slice(dimension_0='a', dimension_1='e')
This sliced dataset represents the rows from the higher dimensional dataset where
(dimension_0 == 'a') & (dimension_1 == 'e')
.
classmethod
slice
(*args, **kwargs)¶ Take a slice of a DataSetFamily to produce a dataset indexed by asset and date.
Parameters:  *args 
 **kwargs  The coordinates to fix along each extra dimension.
Returns: dataset  A regular pipeline dataset indexed by asset and date.
Return type: Notes
The extra dimensions coords used to produce the result are available under the
extra_coords
attribute.

classmethod

class
zipline.pipeline.data.
BoundColumn
¶ A column of data that's been concretely bound to a particular dataset.

dtype
¶ The dtype of data produced when this column is loaded.
Type: numpy.dtype

latest
¶ A
Filter
,Factor
, orClassifier
computing the most recently known value of this column on each date. Seezipline.pipeline.mixins.LatestMixin
for more details.Type: zipline.pipeline.LoadableTerm

dataset
¶ The dataset to which this column is bound.
Type: zipline.pipeline.data.DataSet
Notes
Instances of this class are dynamically created upon access to attributes of
DataSet
. For example,close
is an instance of this class. Pipeline API users should never construct instances of this directly.

class
zipline.pipeline.data.
Column
¶ An abstract column of data, not yet associated with a dataset.

class
zipline.pipeline.domain.
Domain
¶ A domain represents a set of labels for the arrays computed by a Pipeline.
A domain defines two things:
 A calendar defining the dates to which the pipeline's inputs and outputs should be aligned. The calendar is represented concretely by a pandas DatetimeIndex.
 The set of assets that the pipeline should compute over. Right now, the only supported way of representing this set is with a twocharacter country code describing the country of assets over which the pipeline should compute. In the future, we expect to expand this functionality to include more general concepts.

zipline.pipeline.domain.
GENERIC
¶ Special sentinel domain used for pipeline terms that can be computed on any domain.
Pricing Data¶

class
quantopian.pipeline.data.
EquityPricing
¶ DataSet
containing daily trading prices and volumes.
close
= EquityPricing.close::float64¶

high
= EquityPricing.high::float64¶

low
= EquityPricing.low::float64¶

open
= EquityPricing.open::float64¶

volume
= EquityPricing.volume::float64¶


class
quantopian.pipeline.data.
USEquityPricing
¶ Backwardscompat alias for
EquityPricing.specialize(US_EQUITIES)
.
FactSet Data¶

class
quantopian.pipeline.data.factset.
Fundamentals
¶ DataSet
containing fundamental data sourced from FactSet.Notes
See the Data Reference for more info.

class
quantopian.pipeline.data.factset.
GeoRev
¶ DataSetFamily
containing company revenue, broken down by source country or region.Slices of this family allow users to query for revenue sourced from a particular country or collection of countries.
GeoRev.slice('US')
, for example, produces data on each asset's revenue from the United States, whileGeoRev.slice('CN')
produces data on each asset's revenue from China.Notes
See the Data Reference for more info.

class
quantopian.pipeline.data.factset.
RBICSFocus
¶ DataSet
providing information about companies' areas of business focus.

class
quantopian.pipeline.data.factset.estimates.
PeriodicConsensus
¶ DataSetFamily
for quarterly, semiannual, and annual consensus estimates.Slices of this family allow users to query for consensus estimates of quarterly, semiannual, and annual financial items.
Examples
# Earnings estimates for next fiscal quarter. fq1_eps = PeriodicConsensus.slice('EPS', 'qf', 1) # Earnings estimates for most recently announced quarter. fq0_eps = PeriodicConsensus.slice('EPS', 'qf', 0) # Earnings estimates for two quarters out. fq2_eps = PeriodicConsensus.slice('EPS', 'qf', 2) # Earnings estimates for next fiscal year. fy1_eps = PeriodicConsensus.slice('EPS', 'af', 1) # Cash flow estimates for next quarter. fq1_dps = PeriodicConsensus.slice('CFPS', 'qf', 1)
Notes
See the Data Reference for more info.

class
quantopian.pipeline.data.factset.estimates.
Actuals
¶ DataSetFamily
for "actual" reports of estimated values.Slices of this family allow users to query for actual results of estimated quarterly, semiannual, and annual financial items.
Examples
# Most recently reported quarterly earnings. fq0_eps = Actuals.slice('EPS', 'qf', 0) # EPS reported two quarters ago. fqm1_eps = Actuals.slice('EPS', 'qf', 1) # Most recently reported annual earnings. fy0_eps = Actuals.slice('EPS', 'af', 0) # Most recently reported quarterly cash flow. fq0_cfps = Actuals.slice('CFPS', 'qf', 0)
Notes
See the Data Reference for more info.

class
quantopian.pipeline.data.factset.estimates.
ConsensusRecommendations
¶ DataSet
containing consensus broker recommendations.Notes
See the Data Reference for more info.

class
quantopian.pipeline.data.factset.estimates.
LongTermConsensus
¶ DataSetFamily
for long term consensus estimates.Examples
# Long term estimates for eps growth. lt_eps_growth = LongTermConsensus.slice('EPS_LTG')
# Long term estimates for price target. lt_price_target = LongTermConsensus.slice('PRICE_TGT')
Notes
See the Data Reference for more info.
Morningstar Data¶

class
quantopian.pipeline.data.morningstar.
Fundamentals
¶ DataSet
containing fundamental data sourced from Morningstar.Notes
See the Data Reference for more info.
Builtin Factors¶

class
quantopian.pipeline.factors.
DailyReturns
¶ Calculates daily percent change in close price.
Default Inputs: [EquityPricing.close]

class
quantopian.pipeline.factors.
Returns
¶ Calculates the percent change in close price over the given window_length.
Default Inputs: [EquityPricing.close]

class
quantopian.pipeline.factors.
PercentChange
¶ Calculates the percent change over the given window_length.
Default Inputs: None
Default Window Length: None
Notes
Percent change is calculated as
(new  old) / abs(old)
.

class
quantopian.pipeline.factors.
VWAP
¶ Volume Weighted Average Price
Default Inputs: [EquityPricing.close, EquityPricing.volume]
Default Window Length: None

class
quantopian.pipeline.factors.
AverageDollarVolume
¶ Average Daily Dollar Volume
Default Inputs: [EquityPricing.close, EquityPricing.volume]
Default Window Length: None

class
quantopian.pipeline.factors.
AnnualizedVolatility
¶ Volatility. The degree of variation of a series over time as measured by the standard deviation of daily returns. https://en.wikipedia.org/wiki/Volatility_(finance)
Default Inputs: [Returns(window_length=2)]
Parameters: annualization_factor (float, optional)  The number of time units per year. Defaults is 252, the number of NYSE trading days in a normal year.

class
quantopian.pipeline.factors.
SimpleBeta
¶ Factor producing the slope of a regression line between each asset's daily returns to the daily returns of a single "target" asset.
Parameters:  target (zipline.Asset)  Asset against which other assets should be regressed.
 regression_length (int)  Number of days of daily returns to use for the regression.
 allowed_missing_percentage (float, optional)  Percentage of returns observations (between 0 and 1) that are allowed to be missing when calculating betas. Assets with more than this percentage of returns observations missing will produce values of NaN. Default behavior is that 25% of inputs can be missing.

class
quantopian.pipeline.factors.
SimpleMovingAverage
¶ Average Value of an arbitrary column
Default Inputs: None
Default Window Length: None

class
quantopian.pipeline.factors.
Latest
¶ Factor producing the most recentlyknown value of inputs[0] on each day.
The .latest attribute of DataSet columns returns an instance of this Factor.

class
quantopian.pipeline.factors.
MaxDrawdown
¶ Max Drawdown
Default Inputs: None
Default Window Length: None

class
quantopian.pipeline.factors.
RSI
¶ Relative Strength Index
Default Inputs: [EquityPricing.close]
Default Window Length: 15

class
quantopian.pipeline.factors.
ExponentialWeightedMovingAverage
¶ Exponentially Weighted Moving Average
Default Inputs: None
Default Window Length: None
Parameters:  inputs (length1 list/tuple of BoundColumn)  The expression over which to compute the average.
 window_length (int > 0)  Length of the lookback window over which to compute the average.
 decay_rate (float, 0 < decay_rate <= 1) 
Weighting factor by which to discount past observations.
When calculating historical averages, rows are multiplied by the sequence:
decay_rate, decay_rate ** 2, decay_rate ** 3, ...
Notes
 This class can also be imported under the name
EWMA
.
See also
Alternate Constructors

classmethod
from_span
(cls, inputs, window_length, span, **kwargs)¶ Convenience constructor for passing decay_rate in terms of span.
Forwards decay_rate as 1  (2.0 / (1 + span)). This provides the behavior equivalent to passing span to pandas.ewma.
Examples
# Equivalent to: # my_ewma = EWMA( # inputs=[EquityPricing.close], # window_length=30, # decay_rate=(1  (2.0 / (1 + 15.0))), # ) my_ewma = EWMA.from_span( inputs=[EquityPricing.close], window_length=30, span=15, )
Notes
This classmethod is provided by both
ExponentialWeightedMovingAverage
andExponentialWeightedMovingStdDev
.

classmethod
from_center_of_mass
(inputs, window_length, center_of_mass, **kwargs)¶ Convenience constructor for passing decay_rate in terms of center of mass.
Forwards decay_rate as 1  (1 / 1 + center_of_mass). This provides behavior equivalent to passing center_of_mass to pandas.ewma.
Examples
# Equivalent to: # my_ewma = EWMA( # inputs=[EquityPricing.close], # window_length=30, # decay_rate=(1  (1 / 15.0)), # ) my_ewma = EWMA.from_center_of_mass( inputs=[EquityPricing.close], window_length=30, center_of_mass=15, )
Notes
This classmethod is provided by both
ExponentialWeightedMovingAverage
andExponentialWeightedMovingStdDev
.

classmethod
from_halflife
(cls, inputs, window_length, halflife, **kwargs)¶ Convenience constructor for passing
decay_rate
in terms of half life.Forwards
decay_rate
asexp(log(.5) / halflife)
. This provides the behavior equivalent to passing halflife to pandas.ewma.Examples
# Equivalent to: # my_ewma = EWMA( # inputs=[EquityPricing.close], # window_length=30, # decay_rate=np.exp(np.log(0.5) / 15), # ) my_ewma = EWMA.from_halflife( inputs=[EquityPricing.close], window_length=30, halflife=15, )
Notes
This classmethod is provided by both
ExponentialWeightedMovingAverage
andExponentialWeightedMovingStdDev
.

class
quantopian.pipeline.factors.
ExponentialWeightedMovingStdDev
¶ Exponentially Weighted Moving Standard Deviation
Default Inputs: None
Default Window Length: None
Parameters:  inputs (length1 list/tuple of BoundColumn)  The expression over which to compute the average.
 window_length (int > 0)  Length of the lookback window over which to compute the average.
 decay_rate (float, 0 < decay_rate <= 1) 
Weighting factor by which to discount past observations.
When calculating historical averages, rows are multiplied by the sequence:
decay_rate, decay_rate ** 2, decay_rate ** 3, ...
Notes
 This class can also be imported under the name
EWMSTD
.
See also
pandas.DataFrame.ewm()
Alternate Constructors

classmethod
from_span
(cls, inputs, window_length, span, **kwargs)¶ Convenience constructor for passing decay_rate in terms of span.
Forwards decay_rate as 1  (2.0 / (1 + span)). This provides the behavior equivalent to passing span to pandas.ewma.
Examples
# Equivalent to: # my_ewma = EWMA( # inputs=[EquityPricing.close], # window_length=30, # decay_rate=(1  (2.0 / (1 + 15.0))), # ) my_ewma = EWMA.from_span( inputs=[EquityPricing.close], window_length=30, span=15, )
Notes
This classmethod is provided by both
ExponentialWeightedMovingAverage
andExponentialWeightedMovingStdDev
.

classmethod
from_center_of_mass
(inputs, window_length, center_of_mass, **kwargs)¶ Convenience constructor for passing decay_rate in terms of center of mass.
Forwards decay_rate as 1  (1 / 1 + center_of_mass). This provides behavior equivalent to passing center_of_mass to pandas.ewma.
Examples
# Equivalent to: # my_ewma = EWMA( # inputs=[EquityPricing.close], # window_length=30, # decay_rate=(1  (1 / 15.0)), # ) my_ewma = EWMA.from_center_of_mass( inputs=[EquityPricing.close], window_length=30, center_of_mass=15, )
Notes
This classmethod is provided by both
ExponentialWeightedMovingAverage
andExponentialWeightedMovingStdDev
.

classmethod
from_halflife
(cls, inputs, window_length, halflife, **kwargs)¶ Convenience constructor for passing
decay_rate
in terms of half life.Forwards
decay_rate
asexp(log(.5) / halflife)
. This provides the behavior equivalent to passing halflife to pandas.ewma.Examples
# Equivalent to: # my_ewma = EWMA( # inputs=[EquityPricing.close], # window_length=30, # decay_rate=np.exp(np.log(0.5) / 15), # ) my_ewma = EWMA.from_halflife( inputs=[EquityPricing.close], window_length=30, halflife=15, )
Notes
This classmethod is provided by both
ExponentialWeightedMovingAverage
andExponentialWeightedMovingStdDev
.

class
quantopian.pipeline.factors.
WeightedAverageValue
¶ Helper for VWAPlike computations.
Default Inputs: None
Default Window Length: None

compute
(today, assets, out, base, weight)¶ Override this method with a function that writes a value into out.


class
quantopian.pipeline.factors.
BollingerBands
¶ Bollinger Bands technical indicator. https://en.wikipedia.org/wiki/Bollinger_Bands
Default Inputs:
zipline.pipeline.data.EquityPricing.close
Parameters:  inputs (length1 iterable[BoundColumn])  The expression over which to compute bollinger bands.
 window_length (int > 0)  Length of the lookback window over which to compute the bollinger bands.
 k (float)  The number of standard deviations to add or subtract to create the upper and lower bands.

compute
(today, assets, out, close, k)¶ Override this method with a function that writes a value into out.

class
quantopian.pipeline.factors.
MovingAverageConvergenceDivergenceSignal
(*args, **kwargs)¶ Moving Average Convergence/Divergence (MACD) Signal line https://en.wikipedia.org/wiki/MACD
A technical indicator originally developed by Gerald Appel in the late 1970's. MACD shows the relationship between two moving averages and reveals changes in the strength, direction, momentum, and duration of a trend in a stock's price.
Default Inputs:
zipline.pipeline.data.EquityPricing.close
Parameters:  fast_period (int > 0, optional)  The window length for the "fast" EWMA. Default is 12.
 slow_period (int > 0, > fast_period, optional)  The window length for the "slow" EWMA. Default is 26.
 signal_period (int > 0, < fast_period, optional)  The window length for the signal line. Default is 9.
Notes
Unlike most pipeline expressions, this factor does not accept a
window_length
parameter.window_length
is inferred fromslow_period
andsignal_period
.
compute
(today, assets, out, close, fast_period, slow_period, signal_period)¶ Override this method with a function that writes a value into out.

class
quantopian.pipeline.factors.
RollingPearsonOfReturns
(*args, **kwargs)¶ Calculates the Pearson productmoment correlation coefficient of the returns of the given asset with the returns of all other assets.
Pearson correlation is what most people mean when they say "correlation coefficient" or "Rvalue".
Parameters:  target (zipline.assets.Asset)  The asset to correlate with all other assets.
 returns_length (int >= 2)  Length of the lookback window over which to compute returns. Daily returns require a window length of 2.
 correlation_length (int >= 1)  Length of the lookback window over which to compute each correlation coefficient.
 mask (zipline.pipeline.Filter, optional)  A Filter describing which assets should have their correlation with the target asset computed each day.
Notes
Computing this factor over many assets can be time consuming. It is recommended that a mask be used in order to limit the number of assets over which correlations are computed.
Examples
Let the following be example 10day returns for three different assets:
SPY MSFT FB 20170313 .03 .03 .04 20170314 .02 .03 .02 20170315 .01 .02 .01 20170316 0 .02 .01 20170317 .01 .04 .01 20170320 .02 .03 .02 20170321 .03 .01 .02 20170322 .04 .02 .02
Suppose we are interested in SPY's rolling returns correlation with each stock from 20170317 to 20170322, using a 5day look back window (that is, we calculate each correlation coefficient over 5 days of data). We can achieve this by doing:
rolling_correlations = RollingPearsonOfReturns( target=sid(8554), returns_length=10, correlation_length=5, )
The result of computing
rolling_correlations
from 20170317 to 20170322 gives:SPY MSFT FB 20170317 1 .15 .96 20170320 1 .10 .96 20170321 1 .16 .94 20170322 1 .16 .85
Note that the column for SPY is all 1's, as the correlation of any data series with itself is always 1. To understand how each of the other values were calculated, take for example the .15 in MSFT's column. This is the correlation coefficient between SPY's returns looking back from 20170317 (.03, .02, .01, 0, .01) and MSFT's returns (.03, .03, .02, .02, .04).

class
quantopian.pipeline.factors.
RollingSpearmanOfReturns
(*args, **kwargs)¶ Calculates the Spearman rank correlation coefficient of the returns of the given asset with the returns of all other assets.
Parameters:  target (zipline.assets.Asset)  The asset to correlate with all other assets.
 returns_length (int >= 2)  Length of the lookback window over which to compute returns. Daily returns require a window length of 2.
 correlation_length (int >= 1)  Length of the lookback window over which to compute each correlation coefficient.
 mask (zipline.pipeline.Filter, optional)  A Filter describing which assets should have their correlation with the target asset computed each day.
Notes
Computing this factor over many assets can be time consuming. It is recommended that a mask be used in order to limit the number of assets over which correlations are computed.

class
quantopian.pipeline.factors.
RollingLinearRegressionOfReturns
(*args, **kwargs)¶ Perform an ordinary leastsquares regression predicting the returns of all other assets on the given asset.
Parameters:  target (zipline.assets.Asset)  The asset to regress against all other assets.
 returns_length (int >= 2)  Length of the lookback window over which to compute returns. Daily returns require a window length of 2.
 regression_length (int >= 1)  Length of the lookback window over which to compute each regression.
 mask (zipline.pipeline.Filter, optional)  A Filter describing which assets should be regressed against the target asset each day.
Notes
Computing this factor over many assets can be time consuming. It is recommended that a mask be used in order to limit the number of assets over which regressions are computed.
This factor is designed to return five outputs:
 alpha, a factor that computes the intercepts of each regression.
 beta, a factor that computes the slopes of each regression.
 r_value, a factor that computes the correlation coefficient of each regression.
 p_value, a factor that computes, for each regression, the twosided pvalue for a hypothesis test whose null hypothesis is that the slope is zero.
 stderr, a factor that computes the standard error of the estimate of each regression.
For more help on factors with multiple outputs, see
zipline.pipeline.CustomFactor
.Examples
Let the following be example 10day returns for three different assets:
SPY MSFT FB 20170313 .03 .03 .04 20170314 .02 .03 .02 20170315 .01 .02 .01 20170316 0 .02 .01 20170317 .01 .04 .01 20170320 .02 .03 .02 20170321 .03 .01 .02 20170322 .04 .02 .02
Suppose we are interested in predicting each stock's returns from SPY's over rolling 5day look back windows. We can compute rolling regression coefficients (alpha and beta) from 20170317 to 20170322 by doing:
regression_factor = RollingRegressionOfReturns( target=sid(8554), returns_length=10, regression_length=5, ) alpha = regression_factor.alpha beta = regression_factor.beta
The result of computing
alpha
from 20170317 to 20170322 gives:SPY MSFT FB 20170317 0 .011 .003 20170320 0 .004 .004 20170321 0 .007 .006 20170322 0 .002 .008
And the result of computing
beta
from 20170317 to 20170322 gives:SPY MSFT FB 20170317 1 .3 1.1 20170320 1 .2 1 20170321 1 .3 1 20170322 1 .3 .9
Note that SPY's column for alpha is all 0's and for beta is all 1's, as the regression line of SPY with itself is simply the function y = x.
To understand how each of the other values were calculated, take for example MSFT's
alpha
andbeta
values on 20170317 (.011 and .3, respectively). These values are the result of running a linear regression predicting MSFT's returns from SPY's returns, using values starting at 20170317 and looking back 5 days. That is, the regression was run with x = [.03, .02, .01, 0, .01] and y = [.03, .03, .02, .02, .04], and it produced a slope of .3 and an intercept of .011.
Builtin Filters¶

class
quantopian.pipeline.filters.
StaticAssets
(assets)¶ A Filter that computes True for a specific set of predetermined assets.
StaticAssets
is mostly useful for debugging or for interactively computing pipeline terms for a fixed set of assets that are known ahead of time.Parameters: assets (iterable[Asset])  An iterable of assets for which to filter.

class
quantopian.pipeline.filters.
StaticSids
(sids)¶ A Filter that computes True for a specific set of predetermined sids.
StaticSids
is mostly useful for debugging or for interactively computing pipeline terms for a fixed set of sids that are known ahead of time.Parameters: sids (iterable[int])  An iterable of sids for which to filter.

quantopian.pipeline.filters.
QTradableStocksUS
()¶ Create the trading universe used in the Quantopian Contest.
Returns:  universe (
Filter
defining the contest universe.)  Equities are filtered in three passes. Each pass operates only on equities
 that survived the previous pass.
 First Pass
 Filter based on infrequentlychanging attributes using the following rules
 1. The stock must be a common (i.e. not preferred) stock.
 2. The stock must not be a depository receipt.
 3. The stock must not be for a limited partnership.
 4. The stock must not be traded over the counter (OTC).
 Second Pass
 For companies with more than one share class, choose the most liquid share
 class. Share classes belonging to the same company are indicated by a
 common
primary_share_class_id
.  Liquidity is measured using the 200day median daily dollar volume.
 Equities without a
primary_share_class_id
are automatically excluded.  Third Pass
 Filter based on dynamic attributes using the following rules
 1. The stock must have a 200day median daily dollar volume exceeding  2.5 Million USD.
 2. The stock must have a moving average market capitalization of at least  350 Million USD over the last 20 days.
 3. The stock must not have more than 20 days of missing close price in the  last 200 and must not have any missing close price in the last 20 days.
 4. The stock must not be an active M&A target; equities that pass the  filter
IsAnnouncedAcquisitionTarget()
are screened out.
Notes
 ETFs are not included in this universe.
 Unlike the
Q500US()
andQ1500US()
, this universe has no size cutoff. All equities that match the required criteria are included.  If the most liquid share class of a company passes the static pass but fails the dynamic pass, then no share class for that company is included.
 universe (

quantopian.pipeline.filters.
Q500US
(minimum_market_cap=500000000)¶ A default universe containing approximately 500 US equities each day.
Constituents are chosen at the start of each calendar month by selecting the top 500 "tradeable" stocks by 200day average dollar volume, capped at 30% of equities allocated to any single sector
A stock is considered "tradeable" if it meets the following criteria:
 The stock must be the primary share class for its company.
 The company issuing the stock must have known market capitalization.
 The stock must not be a depository receipt.
 The stock must not be traded over the counter (OTC).
 The stock must not be for a limited partnership.
 The stock must have a known previousday close price.
 The stock must have had nonzero volume on the previous trading day.

quantopian.pipeline.filters.
Q1500US
(minimum_market_cap=500000000)¶ A default universe containing approximately 1500 US equities each day.
Constituents are chosen at the start of each month by selecting the top 1500 "tradeable" stocks by 200day average dollar volume, capped at 30% of equities allocated to any single sector.
A stock is considered "tradeable" if it meets the following criteria:
 The stock must be the primary share class for its company.
 The company issuing the stock must have known market capitalization.
 The stock must not be a depository receipt.
 The stock must not be traded over the counter (OTC).
 The stock must not be for a limited partnership.
 The stock must have a known previousday close price.
 The stock must have had nonzero volume on the previous trading day.

quantopian.pipeline.filters.
Q3000US
(minimum_market_cap=500000000)¶ A default universe containing approximately 3000 US equities each day. Used for generating a universe of tradeable stocks at the start of each trading day.
Constituents are chosen at the start of each month by selecting the top 3000 "tradeable" stocks by 200day average dollar volume, capped at 30% of equities allocated to any single sector.
A stock is considered "tradeable" if it meets the following criteria:
 The stock must be the primary share class for its company.
 The company issuing the stock must have known market capitalization.
 The stock must not be a depository receipt.
 The stock must not be traded over the counter (OTC).
 The stock must not be for a limited partnership.
 The stock must have a known previousday close price.
 The stock must have had nonzero volume on the previous trading day.

quantopian.pipeline.filters.
make_us_equity_universe
(target_size, rankby, groupby, max_group_weight, mask, smoothing_func=<function downsample_monthly>, exclude_ipos=False)¶ Create a
QUS
style universe filter.The constructed
Filter
accepts approximately the toptarget_size
assets ranked byrankby
, subject to tradeability, weighting, and turnover constraints.The selection algorithm implemented by the generated Filter is as follows:
Look at all known stocks and eliminate stocks for which
mask
returns False.Partition the remaining stocks into buckets based on the labels computed by
groupby
.Choose the top
target_size
stocks, sorted byrankby
, subject to the constraint that the percentage of stocks accepted in any single group in (2) is less than or equal tomax_group_weight
.Pass the resulting "naive" filter to
smoothing_func
, which must return a new Filter.Smoothing is most often useful for applying transformations that reduce turnover at the boundary of the universe's rankinclusion criterion. For example, a smoothing function might require that an asset pass the naive filter for 5 consecutive days before acceptance, reducing the number of assets that tooregularly enter and exit the universe.
Another common smoothing technique is to reduce the frequency at which we recalculate using
Filter.downsample
. The default smoothing behavior is to downsample to monthly frequency.&
the result of smoothing withmask
, ensuring that smoothing does not reintroduce maskedout assets.
Parameters:  target_size (int > 0)  The target number of securities to accept each day.
Exactly
target_size
assets will be accepted by the Filter supplied tosmoothing_func
, but more or fewer may be accepted in the final output depending on the smoothing function applied.  rankby (zipline.pipeline.Factor)  The Factor by which to rank all assets each day.
The top
target_size
assets that passmask
will be accepted, subject to the constraint that no single group receives greater thanmax_group_weight
as a percentage of the total number of accepted assets.  mask (zipline.pipeline.Filter)  An initial filter used to ignore securities deemed "untradeable".
Assets for which
mask
returns False on a given day will always be rejected by the final output filter, and will be ignored when calculating ranks.  groupby (zipline.pipeline.Classifier)  A classifier that groups assets into buckets. Each bucket will
receive at most
max_group_weight
as a percentage of the total number of accepted assets.  max_group_weight (float)  A float between 0.0 and 1.0 indicating the maximum percentage of
assets that should be accepted in any single bucket returned by
groupby
.  smoothing_func (callable[Filter > Filter], optional) 
A function accepting a Filter and returning a new Filter.
This is generally used to apply 'stickiness' to the output of the "naive" filter. Adding stickiness helps reduce turnover of the final output by preventing assets from entering or exiting the final universe too frequently.
The default smoothing behavior is to downsample at monthly frequency. This means that the naive universe is recalculated at the start of each month, rather than continuously every day, reducing the impact of spurious turnover.
Example
The algorithm for the builtin Q500US universe is defined as follows:
At the start of each month, choose the top 500 assets by average dollar volume over the last year, ignoring hardtotrade assets, and choosing no more than 30% of the assets from any single market sector.
The Q500US is implemented as:
from quantopian.pipeline import factors, filters, classifiers def Q500US(): return filters.make_us_equity_universe( target_size=500, rankby=factors.AverageDollarVolume(window_length=200), mask=filters.default_us_equity_universe_mask(), groupby=classifiers.fundamentals.Sector(), max_group_weight=0.3, smoothing_func=lambda f: f.downsample('month_start'), )
See also
quantopian.pipeline.filters.default_us_equity_universe_mask()
,quantopian.pipeline.filters.Q500US()
,quantopian.pipeline.filters.Q1500US()
,quantopian.pipeline.filters.Q3000US()
Returns: universe  A Filter representing the final universe Return type: zipline.pipeline.Filter

quantopian.pipeline.filters.
default_us_equity_universe_mask
(minimum_market_cap=500000000)¶ Create the base filter used to filter assets from the
QUS
filters.The criteria required to pass the resulting filter are as follows:
 The stock must be the primary share class for its company.
 The company issuing the stock must have a minimum market capitalization of 'minimum_market_cap', defaulting to 500 Million.
 The stock must not be a depository receipt.
 The stock must not be traded over the counter (OTC).
 The stock must not be for a limited partnership.
 The stock must have a known previousday close price.
 The stock must have had nonzero volume on the previous trading day.
Notes
We previously had an additional limited partnership check using Fundamentals.limited_partnership, but this provided only false positives beyond those already captured by not_lp_by_name, so it has been removed.

class
quantopian.pipeline.filters.morningstar.
IsDepositaryReceipt
¶ A Filter indicating whether a given asset is a depositary receipt

inputs
= (Fundamentals<US>.is_depositary_receipt::bool,)¶

A Filter indicating whether a given asset class is a primary share.

quantopian.pipeline.filters.morningstar.
is_common_stock
()¶ Construct a Filter indicating whether an asset is common (as opposed to preferred) stock.
Builtin Classifiers¶

class
quantopian.pipeline.classifiers.morningstar.
SuperSector
¶ Classifier that groups assets by Morningstar Super Sector.
There are three possible classifications:
 1  Cyclical
 2  Defensive
 3  Sensitive
These values are provided as integer constants on the class.
For more information on morningstar classification codes, see: https://www.quantopian.com/help/fundamentals#industrysector.

inputs
= (Fundamentals<US>.morningstar_economy_sphere_code::int64,)¶

dtype
= dtype('int64')¶

missing_value
= 1¶

CYCLICAL
= 1¶

DEFENSIVE
= 2¶

SENSITIVE
= 3¶

SUPER_SECTOR_NAMES
= {1: 'CYCLICAL', 2: 'DEFENSIVE', 3: 'SENSITIVE'}¶

class
quantopian.pipeline.classifiers.morningstar.
Sector
¶ Classifier that groups assets by Morningstar Sector Code.
There are 11 possible classifications:
 101  Basic Materials
 102  Consumer Cyclical
 103  Financial Services
 104  Real Estate
 205  Consumer Defensive
 206  Healthcare
 207  Utilities
 308  Communication Services
 309  Energy
 310  Industrials
 311  Technology
These values are provided as integer constants on the class.
For more information on morningstar classification codes, see: https://www.quantopian.com/help/fundamentals#industrysector.

inputs
= (Fundamentals<US>.morningstar_sector_code::int64,)¶

dtype
= dtype('int64')¶

missing_value
= 1¶

BASIC_MATERIALS
= 101¶

CONSUMER_CYCLICAL
= 102¶

FINANCIAL_SERVICES
= 103¶

REAL_ESTATE
= 104¶

CONSUMER_DEFENSIVE
= 205¶

HEALTHCARE
= 206¶

UTILITIES
= 207¶

COMMUNICATION_SERVICES
= 308¶

ENERGY
= 309¶

INDUSTRIALS
= 310¶

TECHNOLOGY
= 311¶

SECTOR_NAMES
= {101: 'BASIC_MATERIALS', 102: 'CONSUMER_CYCLICAL', 103: 'FINANCIAL_SERVICES', 104: 'REAL_ESTATE', 205: 'CONSUMER_DEFENSIVE', 206: 'HEALTHCARE', 207: 'UTILITIES', 308: 'COMMUNICATION_SERVICES', 309: 'ENERGY', 310: 'INDUSTRIALS', 311: 'TECHNOLOGY'}¶
Risk Model Factors (Experimental)¶
Functions and classes listed here provide access to the outputs of the
Quantopian Risk Model via the Pipeline API. They are currently importable
from quantopian.pipeline.experimental
.
We expect to eventually stabilize and move these features to
quantopian.pipeline
.

quantopian.pipeline.experimental.
risk_loading_pipeline
()¶ Create a pipeline with all risk loadings for the Quantopian Risk Model.
Returns: pipeline  A Pipeline containing risk loadings for each factor in the Quantopian Risk Model. Return type: quantopian.pipeline.Pipeline
Sector Loadings¶
These classes provide access to sector loadings computed by the Quantopian Risk Model.

class
quantopian.pipeline.experimental.
BasicMaterials
¶ Quantopian Risk Model loadings for the basic materials sector.

class
quantopian.pipeline.experimental.
ConsumerCyclical
¶ Quantopian Risk Model loadings for the consumer cyclical sector.

class
quantopian.pipeline.experimental.
FinancialServices
¶ Quantopian Risk Model loadings for the financial services sector.

class
quantopian.pipeline.experimental.
RealEstate
¶ Quantopian Risk Model loadings for the real estate sector.

class
quantopian.pipeline.experimental.
ConsumerDefensive
¶ Quantopian Risk Model loadings for the consumer defensive sector.

class
quantopian.pipeline.experimental.
HealthCare
¶ Quantopian Risk Model loadings for the health care sector.

class
quantopian.pipeline.experimental.
Utilities
¶ Quantopian Risk Model loadings for the utilities sector.

class
quantopian.pipeline.experimental.
CommunicationServices
¶ Quantopian Risk Model loadings for the communication services sector.

class
quantopian.pipeline.experimental.
Energy
¶ Quantopian Risk Model loadings for the communication energy sector.

class
quantopian.pipeline.experimental.
Industrials
¶ Quantopian Risk Model loadings for the industrials sector.

class
quantopian.pipeline.experimental.
Technology
¶ Quantopian Risk Model loadings for the technology sector.
Style Loadings¶
These classes provide access to style loadings computed by the Quantopian Risk Model.

class
quantopian.pipeline.experimental.
Momentum
¶ Quantopian Risk Model loadings for the "momentum" style factor.
This factor captures differences in returns between stocks that have had large gains in the last 11 months and stocks that have had large losses in the last 11 months.

class
quantopian.pipeline.experimental.
ShortTermReversal
¶ Quantopian Risk Model loadings for the "short term reversal" style factor.
This factor captures differences in returns between stocks that have experienced short term losses and stocks that have experienced short term gains.

class
quantopian.pipeline.experimental.
Size
¶ Quantopian Risk Model loadings for the "size" style factor.
This factor captures difference in returns between stocks with high market capitalizations and stocks with low market capitalizations.

class
quantopian.pipeline.experimental.
Value
¶ Quantopian Risk Model loadings for the "value" style factor.
This factor captures differences in returns between "expensive" stocks and "inexpensive" stocks, measured by the ratio between each stock's book value and its market cap.

class
quantopian.pipeline.experimental.
Volatility
¶ Quantopian Risk Model loadings for the "volatility" style factor.
This factor captures differences in returns between stocks that experience large price fluctuations and stocks that have relatively stable prices.
Domains¶

quantopian.pipeline.domain.
US_EQUITIES
¶ zipline.pipeline.domain.Domain
for equities traded in the United States.
Miscellaneous¶

class
zipline.pipeline.mixins.
LatestMixin
¶ Common behavior for
zipline.pipeline.data.BoundColumn.latest
.Given a
DataSet
namedMyData
with a columncol
of numeric dtype, the following expression:factor = MyData.col.latest
is equivalent to:
class Latest(CustomFactor): inputs = [MyData.col] window_length = 1 def compute(self, today, assets, out, data): out[:] = data[1] factor = Latest()
The behavior is the same for columns of boolean or string dtype, except the resulting expression will be a
CustomFilter
for boolean columns, and the resulting object will be aCustomClassifier
for string or integer columns.

class
zipline.pipeline.
CustomClassifier
¶ Base class for userdefined Classifiers.
Does not suppport multiple outputs.
Note
Custom classifiers are rarely used. Almost all userdefined classifiers are created via
latest
on aBoundColumn
with string/int dtype, or viazipline.pipeline.Factor.quantiles()
.