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Universe selection in Research environment

Hi,

I am new to quantconnect. I did not find any documentation on how I can select symbols from the 8,000 stocks quantconnect offers in the research environment.

I would like that to be time dependent as well. That means, if I look for the 500 stocks with the highest market cap from 2001 until today, then the list should automatically update with the correct symbols.

Is that possible in quantconnect research?

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Okay, since nobody answers, let me rephrase my question - if it is not possible to load the whole universe via a command like qd.add_universe, can I at least load the csv file you provide (see link below)?

A research environment does not make sense, if I can't load all symbols. When I trade stocks I wanna create portfolios of stocks that meet my criteria, not select arbitrary stocks. Therefore I need all 8,000 stocks that have fundamental data. 

https://www.quantconnect.com/data#equity/usa/price
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i would recommend to take a look on Arons strategies:

https://www.quantconnect.com/forum/discussion/3214/piotroski-f-score-fundamental-filter--value-screen

 

https://www.quantconnect.com/forum/discussion/3152/implementation-of-keller-and-keunig039s-protective-asset-allocation-paa

also:

 

https://www.quantconnect.com/tutorials/simple-capm-alpha-ranking-strategy-dow-30-stocks/

and this

 

https://www.quantconnect.com/forum/discussion/3327/enhanced-short-term-mean-reversion-algorithm
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Just my 2 cents here.  I've been using the platform for a about a year now off and on.  I haven't found a way to do this yet in research environment although I would like to as well.  In the backtesting this is called "Universe Selection".  It seems you just use research environment to debug your code then you should put in a backtest, and the universe selection will work just like you have described.  I don't think the research environment is designed to be so dynamic to completely simulate portfolio selection, that is what the backtester is for.  Again there is probably a better answer out there.  Cheers.  

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