from quantopian.pipeline import Pipeline  
from quantopian.pipeline.data.factset import Fundamentals, EquityMetadata  
from quantopian.pipeline.data import EquityPricing  
from quantopian.pipeline.factors import RSI,BollingerBands,SimpleMovingAverage,EWMA,EWMSTD  
from quantopian.pipeline.domain import US_EQUITIES


is_share = EquityMetadata.security_type.latest.eq('SHARE')  
is_primary = EquityMetadata.is_primary.latest  
primary_shares = (is_share & is_primary)  
market_cap = Fundamentals.mkt_val.latest

universe = market_cap.top(1000, mask=primary_shares)  
# 1-month (21 trading day) EWMA factor.  
fast_ewma = EWMA.from_span(  
    inputs=[EquityPricing.close],  
    window_length=21,  
    span=15)

# 6-month (126 trading day) EWMA factor.  
slow_ewma = EWMA.from_span(  
    inputs=[EquityPricing.close],  
    window_length=126,  
    span=15)  
# Divide fast_ma by slow_ma to get momentum factor and z-score.  
momentum = fast_ewma / slow_ewma  
momentum_factor = momentum.zscore()

#Now that we defined a universe and a factor, we can choose a market and time period and simulate the factor.  
#The code below creates a Pipeline instance that adds our factor as a column and screens down to equities in our universe. The Pipeline is then run over the US equities market from 2016 to 2019.  
# Create a US equities pipeline with our momentum factor, screening down to our universe.  
pipe = Pipeline(  
    columns={  
        'momentum_factor': momentum_factor,  
    },  
    screen=universe,  
    domain=US_EQUITIES,  
)
# Run the pipeline from 2016 to 2019 and display the first few rows of output.  
from quantopian.research import run_pipeline  
factor_data = run_pipeline(pipe, '2016-01-01', '2019-07-15')  
print("Result contains {} rows of output.".format(len(factor_data)))  
factor_data.head()  
#Step 3 - Test the factor.  
from quantopian.pipeline.factors import Returns  
# Create and run a Pipeline to get day-over-day returns.  
returns_pipe = Pipeline(  
    columns={  
        '1D': Returns(window_length=2),  
    },  
    domain=US_EQUITIES,  
)
returns_data = run_pipeline(returns_pipe, '2016-01-01', '2019-07-15')  
# Import alphalens and pandas.  
import alphalens as al  
import pandas as pd

# Shift the returns so that we can compare our factor data to forward returns.  
shifted_returns = al.utils.backshift_returns_series(returns_data['1D'], 2)

# Merge the factor and returns data.  
al_returns = pd.DataFrame(  
    data=shifted_returns,  
    index=factor_data.index,  
    columns=['1D'],  
)
al_returns.index.levels[0].name = "date"  
al_returns.index.levels[1].name = "asset"

factor_data['momentum_factor'] = - factor_data['momentum_factor']

# Format the factor and returns data so that we can run it through Alphalens.  
al_data = al.utils.get_clean_factor(  
    factor_data['momentum_factor'],  
    al_returns,  
    quantiles=5,  
    bins=None,  
)
from alphalens.tears import create_full_tear_sheet
create_full_tear_sheet(al_data)