def initialize(context):
    attach_pipeline(make_pipeline(), 'my_pipeline')

    # Rebalance each day.  In daily mode, this is equivalent to putting
    # `rebalance` in our handle_data, but in minute mode, it's equivalent to
    # running at the start of the day each day.
    schedule_function(rebalance, date_rules.every_day())

def initialize(context):

    pipe = Pipeline()
    attach_pipeline(pipe, 'example')

    # Note that we don't call add_factor on these Factors.
    # We don't need to store intermediate values if we're not going to use them
    sma_short = SimpleMovingAverage(inputs=[USEquityPricing.close], window_length=30)
    sma_long = SimpleMovingAverage(inputs=[USEquityPricing.close], window_length=100)

    sma_val = sma_short/sma_long

    # Construct the custom factor
    mkt_cap = MarketCap()

    # Create and apply a filter representing the top 500 equities by MarketCap
    # every day.
    mkt_cap_top_500 = mkt_cap.top(500)

    remove_penny_stocks = sma_short > 1.0

    pipe.add(sma_val, 'sma_val')
    pipe.add(mkt_cap, 'mkt_cap')
    # Use mkt_cap_top_500 as a mask on rank
    pipe.add(sma_val.rank(mask=mkt_cap_top_500), 'sma_rank')

    # Use multiple screens to narrow the universe
    pipe.set_screen(mkt_cap.top(500) & remove_penny_stocks)

def initialize(context):

    # Create, register and name a pipeline in initialize.
    pipe = Pipeline()
    attach_pipeline(pipe, 'example')

    # Construct a simple moving average factor and add it to the pipeline.
    sma_short = SimpleMovingAverage(inputs=[USEquityPricing.close], window_length=10)
    pipe.add(sma_short, 'sma_short')

    # Set a screen on the pipelines to filter out securities.
    pipe.set_screen(sma_short > 1.0)

def initialize(context):
    attach_pipeline(make_pipeline(), 'my_pipeline')

    # Rebalance each day.  In daily mode, this is equivalent to putting
    # `rebalance` in our handle_data, but in minute mode, it's equivalent to
    # running at the start of the day each day.
    schedule_function(rebalance, date_rules.every_day())

    # Explicitly set the commission to the "old" value until we can
    # rebuild example data.
    # github.com/quantopian/zipline/blob/master/tests/resources/
    # rebuild_example_data#L105
    context.set_commission(commission.PerShare(cost=.0075, min_trade_cost=1.0))

        def initialize(context):
            pipeline = Pipeline()
            context.vwaps = []
            for length in vwaps:
                name = vwap_key(length)
                factor = VWAP(window_length=length)
                context.vwaps.append(factor)
                pipeline.add(factor, name=name)

            filter_ = USEquityPricing.close.latest > 300
            pipeline.add(filter_, "filter")
            if set_screen:
                pipeline.set_screen(filter_)

            attach_pipeline(pipeline, "test")

def initialize(context):

    pipe = Pipeline()
    attach_pipeline(pipe, "example")

    sma_short = SimpleMovingAverage(inputs=[USEquityPricing.close], window_length=30)
    sma_long = SimpleMovingAverage(inputs=[USEquityPricing.close], window_length=100)

    # Combined factors to create new factors
    sma_val = sma_short / sma_long

    # Create and apply a screen to remove penny stocks
    remove_penny_stocks = sma_short > 1.0
    pipe.set_screen(remove_penny_stocks)

    pipe.add(sma_short, "sma_short")
    pipe.add(sma_long, "sma_long")
    pipe.add(sma_val, "sma_val")
    # Rank a factor using a mask to ignore the values we're
    # filtering out by passing mask=remove_penny_stocks to rank.
    pipe.add(sma_val.rank(mask=remove_penny_stocks), "sma_rank")

 def late_attach(context, data):
     attach_pipeline(Pipeline(), "test")
     raise AssertionError("Shouldn't make it past attach_pipeline!")

        def initialize(context):
            p = Pipeline('test')
            p.add(USEquityPricing.close.latest, 'close')

            attach_pipeline(p)

 def initialize(context):
     pipeline = attach_pipeline(Pipeline(), 'my_pipeline')
     test_factor = TestFactor()
     pipeline.add(test_factor, 'test_factor')