Hi,

I'm new to Quantopian. Here's my first code contribution: an easy to use TTM CustomFactor. For good performance, it has a single expression Python list comprehension. Results are accurate for all sample cases I have tested so far.

class TrailingTwelveMonths(CustomFactor):  
    window_length=400  
    window_safe = True  
    mask=symbols  
    outputs="""  
        factor  
        asof_date  
        """.split()  
    def compute(self, today, assets, out, values, dates):  
        out.factor[:] = [  
            (v[d + np.timedelta64(52, 'W') > d[-1]])[  
                np.unique(  
                    d[d + np.timedelta64(52, 'W') > d[-1]],  
                    return_index=True  
                )[1]  
            ].sum()  
            for v, d in zip(values.T, dates.T)  
        ]  
        out.asof_date[:] = dates[-1]  
def make_pipeline():  
    (  
        total_revenue_ttm,  
        total_revenue_ttm_asof_date  
    ) = TrailingTwelveMonths(  
        inputs=[  
            Fundamentals.total_revenue,  
            Fundamentals.total_revenue_asof_date,  
        ]  
    )  
    return Pipeline(  
        columns={  
            'total_revenue_ttm': total_revenue_ttm,  
            'total_revenue_ttm_asof_date': total_revenue_ttm_asof_date  
        },  
        screen=symbols  
    )  
df = run_pipeline(make_pipeline(), '2017-11-03', '2017-11-03')  
df['total_revenue_ttm_asof_date'] = df['total_revenue_ttm_asof_date'].astype('datetime64[ns]')  
df