Impossible to follow change in fundamentals (e.g. change in inventory / sales) over time with get

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Impossible to follow change in fundamentals (e.g. change in inventory / sales) over time with get_fundamentals?

posted Feb 3, 2016

Hi,

I think the answer to this is "it's impossible", but I wanted to confirm:

Is it possible to rank stocks / select stocks based on changes in their fundamental data over time? For example, I want to look at stocks whose own ratio of inventory / sales has changed (i.e. not the stocks with the highest inventory / sales at a given point in time, but stocks with a high change in their own inv / sales). From what I understand we can't yet do this with fundamentals.

I'm relatively new to Python but thought maybe it would be possible to create a list of, say, sales over time. Then run get_fundamentals in before_trading_start to pull the sales figure for the stock and .append it to the list. Would that work?

Thanks!!

3 responses

Simon Thornington

Feb 3, 2016

Yes, or you could query fundamentals within Pipeline and inside your CustomFactor code, you have access to a numpy array the N previous values of any number, including the fundamental number you care about...

Luca

Feb 4, 2016

As Simon said, you have to build your own CustomFactor to be used with Pipeline (code not tested):


class RatioChange(CustomFactor):  
    """  
    Calculates the percent change of the ratio of the two inputs over the given window_length.  
    """  
    inputs = [morningstar.something1,  
                      morningstar.something2]

    def compute(self, today, assets, out, something1_data, something2_data):  
        last  = something1_data[-1] / something2_data[-1]  
        first  = something1_data[0] / something2_data[0]  
        out[:] = (last - first) / first        # we divide by first to normalize the change (percentage): easier to rank

Luca

Feb 4, 2016

You should also be able to easily calculate the moving average and exponential moving average of the ratio change (as usual, code not tested):

from quantopian.pipeline.factors import SimpleMovingAverage, ExponentialWeightedMovingAverage

class SimpleMovingAverageRatioPercentChange(SimpleMovingAverage):

    inputs = [morningstar.something1,  
                      morningstar.something2]

    def compute(self, today, assets, out, something1_data, something2_data):  
        data = something1_data / something2_data  
        prct_change = np.diff(data, axis = 0)   # absolute change in the ratio  
        prct_change /= data[:-1]                        # percentage change  
        SimpleMovingAverage.compute(self, today, assets, out, prct_change)      


class ExponentialWeightedMovingAverageRatioPercentChange(ExponentialWeightedMovingAverage):

    inputs = [morningstar.something1,  
                      morningstar.something2]

    def compute(self, today, assets, out, something1_data, something2_data, decay_rate):  
        data = something1_data / something2_data  
        prct_change = np.diff(data, axis = 0)   # absolute change  
        prct_change /= data[:-1]                # percentage change  
        ExponentialWeightedMovingAverage.compute(self, today, assets, out, prct_change, decay_rate)

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