# Imports needed to use pipeline and custom factors
from quantopian.pipeline import Pipeline, CustomFactor
from quantopian.research import run_pipeline

# Import to use static assets so we can test 
from quantopian.pipeline.filters import StaticAssets

# Import some data
from quantopian.pipeline.data.builtin import USEquityPricing

# Define our Custom Factor

class PreviousClose(CustomFactor):  
    # Define inputs  
    inputs = [USEquityPricing.close]  


    # Set window_length to whatever number of days to lookback  
    # The default is 2 days ago.  
    # This can be set/over-ridden as shown below:  
    # my_open_10 = PreviousOpen(window_length = 10)  
    # would return the open 10 days ago  
    window_length = 2  


    def compute(self, today, assets, out, close):  
        out[:] = close[0]

# Create an asset that we want to get data for
my_asset = symbols('SPY')

# Make a filter to get just spy data
my_filter = StaticAssets([my_asset])

# Make our factors
# Using a mask limits the computation to only the specified asset.
close_yesterday = PreviousClose(window_length = 1, mask = my_filter) 
close_2_day_ago = PreviousClose(window_length = 2, mask = my_filter) 
close_3_day_ago = PreviousClose(window_length = 3, mask = my_filter) 
close_4_day_ago = PreviousClose(window_length = 4, mask = my_filter) 
close_5_day_ago = PreviousClose(window_length = 5, mask = my_filter) 


# Now create our pipeline object which does the heavy lifting to get the data we specify
# Add a screen to just return specific assets. Otherwise all assets in the Q database are returned
# Defining the pipeline is really just defining the columns we want in the returned dataframe and
# limiting the rows (if desired) to a particular subset of assets.
my_pipe = Pipeline(
        screen = my_filter,
        columns = {
            'close_yesterday': close_yesterday,
            'close_2_day_ago': close_2_day_ago,
            'close_3_day_ago': close_3_day_ago,
            'close_4_day_ago': close_4_day_ago,
            'close_5_day_ago': close_5_day_ago,

        }
    )

# With the factors and pipeline defined in the previous cell we can now run it.
# In this case we want to get the data that the pipelne would have returned from1-5-2017 to 2-5-2017
start_date = '1-5-2017'
end_date = '2-5-2017'
results = run_pipeline(my_pipe, '1-5-2017', '2-5-2017')

# Let's print the dataframe to see what we have...
results

# Let's see if this is correct
# Let's fetch pricing

# run the get_pricing method to pull in actual data
# the result is a Pandas dataframe
my_prices = get_pricing(symbols=my_asset, 
                        start_date=start_date, 
                        end_date=end_date, 
                        frequency='daily', 
                        fields=['close_price'])

# Now lets print it
my_prices