So there are two main differences in Research pipeline and algorithm pipeline.
This is the import in research, run_pipeline doesn't work in algo mode.

from quantopian.research import run_pipeline

So I commented that out, and imported
from quantopian.algorithm import attach_pipeline, pipeline_output

Usually I make pipeline a separate function, and return that function to attach pipeline. like
attach_pipeline(make_pipeline(), 'myPipe')

Then I use pipeline_output in, before trading start
myPipe=pipeline_output('myPipe').dropna()

This loads up all the stocks for ordering.

hi Eric thanks for your reply. Looking from your example the pipeline output will only output 1 day information on close, return, and sector. Though what i want is to have a time series for this information (e.g. for the past 4 days up until the current backtest date). Perhaps following notebook will clarify what i mean though it won't compile because i can't append a Hist_Close Custom Factor object to the dataframe object

Finally I've figured out how to get the historical data. The idea is to output any one day information from pipeline and using this information (e.g. the stock universe, sector, etc) to feed into the data.history function. The drawback is you will need to do a bit munging to the resulting frame. Hope this will be helpful for everyone and might be a consideration for a new feature addition as well

I am just curious. What are you trying to do? Why do you need a time series? The data is in a time series for pipeline. I would just change the window length, and make all your computations in pipeline.

For example this will compute the returns over the last 21 days. It uses the times series of the last 21 days to make the computation.

class Returns(CustomFactor):
inputs = [USEquityPricing.close]
window_length=21
def compute(self, today, assets, out, price):
out[:]=np.log(price[0]/price[-1])

Am I missing something?