Pipelines

Pipelines are an advanced way to compute cross-sectional factors and filters for many assets before your trading logic runs. They are useful for universe selection, ranking, and factor-based strategies. Pipeline support depends on having the right data loaders configured. The standard run path wires pricing data for EquityPricing; custom datasets require custom loaders.

Basic idea

Build a Pipeline object.
Attach it in initialize.
Read its output after initialization, usually in before_trading_start.
Use the output to choose assets or target weights.

from ziplime.pipeline import Pipeline
from ziplime.pipeline.data import EquityPricing
from ziplime.pipeline.terms.factors import SimpleMovingAverage


def make_pipeline():
    sma_20 = SimpleMovingAverage(
        inputs=[EquityPricing.close],
        window_length=20,
    )
    return Pipeline(columns={"sma_20": sma_20})


async def initialize(context):
    context.attach_pipeline(make_pipeline(), "signals")


def before_trading_start(context, data):
    context.signals = context.pipeline_output("signals")

Building a pipeline

from ziplime.pipeline import Pipeline

pipe = Pipeline(
    columns={
        "close": EquityPricing.close.latest,
        "volume": EquityPricing.volume.latest,
    }
)

You can add columns after construction:

pipe = Pipeline()
pipe.add(EquityPricing.close.latest, "close")
pipe.add(EquityPricing.volume.latest, "volume")

Screens

A screen filters which assets appear in the output.

from ziplime.pipeline.terms.factors import AverageDollarVolume

adv = AverageDollarVolume(window_length=20)
screen = adv.top(100)

pipe = Pipeline(
    columns={"adv": adv},
    screen=screen,
)

You can also set a screen after construction:

pipe.set_screen(screen, overwrite=True)

Common factor examples

Latest OHLCV values

pipe = Pipeline(columns={
    "close": EquityPricing.close.latest,
    "volume": EquityPricing.volume.latest,
})

Moving average

from ziplime.pipeline.terms.factors import SimpleMovingAverage

sma_50 = SimpleMovingAverage(
    inputs=[EquityPricing.close],
    window_length=50,
)

Returns

from ziplime.pipeline.terms.factors import Returns

returns_20 = Returns(window_length=20)

Average dollar volume

from ziplime.pipeline.terms.factors import AverageDollarVolume

adv_20 = AverageDollarVolume(window_length=20)

Ranking and filtering

Factors support methods such as:

Method	Meaning
`factor.top(N)`	Keep the top N assets by factor value
`factor.bottom(N)`	Keep the bottom N assets
`factor.percentile_between(min, max)`	Keep assets between percentile bounds
`factor.rank()`	Rank assets by value
`factor.zscore()`	Cross-sectional z-score
`factor.demean()`	Subtract cross-sectional mean

Example:

momentum = Returns(window_length=60)
liquid = AverageDollarVolume(window_length=20).top(500)
top_momentum = momentum.top(50, mask=liquid)

pipe = Pipeline(
    columns={"momentum": momentum},
    screen=top_momentum,
)

Reading pipeline output

context.pipeline_output(name) is available after initialization.

def before_trading_start(context, data):
    output = context.pipeline_output("signals")
    context.todays_assets = list(output.index)

The output type follows the pipeline engine and loader path. In Zipline-compatible paths it is a pandas DataFrame indexed by asset for the current session.

Custom factors

Use CustomFactor for calculations that are not covered by built-in factors.

import numpy as np

from ziplime.pipeline import CustomFactor
from ziplime.pipeline.data import EquityPricing


class CloseRange(CustomFactor):
    inputs = [EquityPricing.close]
    window_length = 20

    def compute(self, today, assets, out, close):
        out[:] = np.nanmax(close, axis=0) - np.nanmin(close, axis=0)

Add it to a pipeline:

pipe = Pipeline(columns={"close_range": CloseRange()})

When to use pipelines

Use pipelines when:

You need a daily ranked universe.
You compute the same cross-sectional factors for many assets.
You want to keep expensive universe-selection logic outside handle_data.

Use data.current and data.history directly when:

You trade only a small fixed list of assets.
Your signal is simple and per-asset.
You do not need cross-sectional ranking.

Introduction

Writing Strategies

Strategy Language

Migrating to Ziplime

Limitations

Basic idea

Building a pipeline

Screens

Common factor examples

Latest OHLCV values

Moving average

Returns

Average dollar volume

Ranking and filtering

Reading pipeline output

Custom factors

When to use pipelines

​Basic idea

​Building a pipeline

​Screens

​Common factor examples

​Latest OHLCV values

​Moving average

​Returns

​Average dollar volume

​Ranking and filtering

​Reading pipeline output

​Custom factors

​When to use pipelines

Basic idea

Building a pipeline

Screens

Common factor examples

Latest OHLCV values

Moving average

Returns

Average dollar volume

Ranking and filtering

Reading pipeline output

Custom factors

When to use pipelines