# Filter for primary share equities. IsPrimaryShare is a built-in filter.
primary_share = Filters.morningstar.IsPrimaryShare()

# Equities listed as common stock (as opposed to, say, preferred stock).
# 'ST00000001' indicates common stock.
common_stock = morningstar.share_class_reference.security_type.latest.eq('ST00000001')

# Non-depositary receipts. Recall that the ~ operator inverts filters,
# turning Trues into Falses and vice versa
not_depositary = ~morningstar.share_class_reference.is_depositary_receipt.latest

# Equities not trading over-the-counter.
not_otc = ~morningstar.share_class_reference.exchange_id.latest.startswith('OTC')

# Not when-issued equities.
not_wi = ~morningstar.share_class_reference.symbol.latest.endswith('.WI')

# Equities without LP in their name, .matches does a match using a regular
# expression
not_lp_name = ~morningstar.company_reference.standard_name.latest.matches('.* L[. ]?P.?$')

# Equities with a null value in the limited_partnership Morningstar
# fundamental field.
not_lp_balance_sheet = morningstar.balance_sheet.limited_partnership.latest.isnull()

# Equities whose most recent Morningstar market cap is not null have
# fundamental data and therefore are not ETFs.
have_market_cap = morningstar.valuation.market_cap.latest.notnull()



# Filter for stocks that pass all of our previous filters.
tradeable_stocks = (
    primary_share
    & common_stock
    & not_depositary
    & not_otc
    & not_wi
    & not_lp_name
    & not_lp_balance_sheet
    & have_market_cap
)

original_universe = Factors.AverageDollarVolume(window_length=30, mask=tradeable_stocks).percentile_between(98, 100)

new_universe = Filters.make_us_equity_universe(
        target_size = 60,
        rankby = Factors.AverageDollarVolume(window_length=200),
        mask = Filters.default_us_equity_universe_mask(),
        groupby = Classifiers.morningstar.Sector(),
        max_group_weight = 0.3,
        smoothing_func = lambda f: f.downsample('month_start'),
    )

# a little faster implementation of log returns
class Log_Returns(CustomFactor):
    inputs = [USEquityPricing.close]
    window_length = 2
    
    def compute(self, today, asset_ids, out, close_prices):
        out[:] = np.nan_to_num(np.diff(np.log(close_prices), axis=0))

# Wouldn't bother using Log_Returns as an input. Just do the math here.
class Distribution_Estimation(CustomFactor):
    inputs = [USEquityPricing.close]
    outputs = ['mu','gam','H','GIGparam'] #Mu, Gam is a Nx1 Vector, H is a NxN Matrix and GIGparam is a 3x1
    
    window_length = 2
    
    def specified_function(log_returns, type_model_distribution, mu, gam, H, GIGparam):
        # do whatever logic is needed
        return mu, gam, H, GIGparam
    
    def compute(self, today, asset_ids, out, close_prices):
        # Mu, gam, H, GIGparam
        mu = []; gam = []; H = []; GIGparam = [];
        
        # below will replace any NaNs with zeros. Maybe this isn't desired?
        log_returns = np.nan_to_num(np.diff(np.log(close_prices), axis=0))
        
        type_model_distribution = 'Distribution'; 
        
        mu, gam, H, GIGparam = specified_function(log_returns, type_model_distribution, mu, gam, H, GIGparam)
        out.mu = mu; out.gam = gam; out.H = H; out.GIGparam = GIGparam        

def make_pipeline():
    window = 2
    
    #log Returns are preferred
    log_returns = Log_Returns(window_length = window, mask = new_universe)
    
    #regular Returns
    returns = Factors.Returns(inputs = [USEquityPricing.close], window_length = window, mask = new_universe)
    
    output = Distribution_Estimation(mask = new_universe)
    
    return Pipeline(
        columns = {
            'log_returns': log_returns,
        },
        screen = new_universe
    )

result = run_pipeline(make_pipeline(), '2015-05-05', '2015-05-06')

result