Thank you soooo much! 👍👍👍

Null records notebook used in the video

Alphalens tearsheet used in the video

Example Estimates algorithm backtest

Estimates EOD tearsheet

Thank you very much for your video and notebooks that you have attached!

Thanks a lot for this contribution @Vedran!
And @Thomas for the initiative!

Thank you Vedran.

Thank you so much!

Hi @Vedran,

When treating the alpha factor, you first winsorize it (to remove outliers), then rank and then zscore (to get the long and shorts). Why it's not enough to just windsorize and zscore? Do you rank to control the target weight for each stock?

Thanks in advance.

In this specific case I just used default insider competition algorithm template. Personally, I do not rank factors. Zscoring and winsorizing are finishing touches after alpha factor discovery phase is done. I just use them to squeeze most of already great alpha factor.

I see, thanks @Vedran!

Hi Vedran, nice interview. What is the short-term fast moving alpha?

Thank you Tien. When you discover that kind of alpha factor you need to act on it on the same moment in time/day. If you wait for next day or longer, performance will drop drastically. If it takes too long for a portfolio strategy to complete rebalancing cycle that type of behavior becomes the issue.

Ah ok, thanks for the explanation.

Very interesting, thank you both for sharing!

Thanks Vedran. Nice and informative!

Thanks Vedran for sharing!
Do you think that reducing the number of stocks traded to the two extreme percentiles would improve the performance of the algorithm?
Or any positive effect is already carried by the alpha that you have chosen?

You`re welcome.
I think that reduction of the number of stocks traded to the two extreme percentiles would increase volatility since the positions in portfolio end up larger so both maximum return and maximum drawdown would be amplified.

Could you please explain me a little better this fragment of code found in your second posted notebook?

THANKS

result = None

for name, (f,w) in factors.iteritems(): #.items() in Python 3.5  
        if result  == None:  
            result  = w*f(mask=universe)  
        else:  
            result  += w*f(mask=universe)  

This for loop iterates over factors dictionary with .iteritems() method (name of the method got changed in Python 3 version to .items()) by each stock as individual key and calculates corresponding multiplication between values for stocks in defined universe (f - factor value for stock, w - hardcoded weight which in this example was set to 1) into result zipline NumExprFactor variable .

Hi Vedra, Thank You very much for educating us and sharing the material. Could you please advise how we can remove Sector - 'Misc' from the trading universe? As the IC is negative for this group, perhaps removing that sector will improve the returns. It will be interesting to see.

I tried using this but it didnt work.

qtu = QTradableStocksUS() & (~Sector().eq(-1)))  

@Nadeem - I do not know what is the best optimal way to achieve that since that sector filter returns false because of NaN values.
What you can do is try to use explicit filter like this:

qtu = QTradableStocksUS() &
(sector.eq(101) |
sector.eq(102) |
sector.eq(103) |
sector.eq(104) |
sector.eq(205) |
sector.eq(206) |
sector.eq(207) |
sector.eq(308) |
sector.eq(309) |
sector.eq(310) |
sector.eq(311))

Hi Vedran in minute 13:11 you mention "basically there is one factor which is already known to you (Thomas) and the community, the short term fast moving alpha". I have some students participating in the challenges they are asking me about that "known short term fast moving alpha" and I can´t find any post. about. Could you please give us info about that known short term fast moving alpha factor.

Hello German,

That factor is known to some of the algorithm authors who also found it but not to the entire community.
I cannot give you information about that factor since that is 'secret sauce' which is already licensed and under intellectual property protection.

great stuff !

This is really impressive :)

I'm new to Quantopian, and I am trying to make some changes in your code. But I'm facing some difficult in trying to make your Long/Short strategy to become a Long Only strategy. Which part of the code do I have to change in the algorithm to make it ONLY buy the best ones, but not selling the worst ones?

@Leonardo

Try to add following code to rebalance function:

MAX_LONG_POSITION_SIZE = 0.05 #or any other positive position weight

constraints.append(
opt.PositionConcentration.with_equal_bounds(
min=0,
max=MAX_LONG_POSITION_SIZE
))