I'll try to answer your questions...
"How can I get rid of the NaNs when getting prices?" In this particular instance, when getting prices to calculate forward returns, it's best to leave the nans there. The reason for most of them is the security hadn't started trading yet. You can do a spot check to verify by looking at the
start_date attribute of the security. In this case let's check WPX
# the above will display the following. WPX started trading 2011-12-12.
Timestamp('2011-12-12 00:00:00+0000', tz='UTC')
get_pricing method was run with a start date of 2010 so this security didn't exist then and therefor returns nan for pricing. Don't worry, Alphalens will simply not look at those securities when there is a nan for price and forward returns. One hint the issue is with the securities not having existed yet... the nans are in the last columns of the
get_pricing dataframe. Why is that? The assets (ie columns) are returned sorted by SID. The Quantopian SIDs are sequentially assigned so as a new security is listed, it is assigned to the next sequential number. Therefore all the columns for securities after WPX will have a SID larger than WPX and therefore must have a start_date after that of WPX.
What is a good value of Alpha?. Like so many things, the answer is 'it depends'. Alpha needs to be viewed in conjunction with beta and general expectation for the market. This is especially true if the beta is very negative. The general assumption is the market goes up, so a negative beta implies ones strategy is going down unless offset by a some alpha. If one had a market neutral strategy with a beta of 0, a good alpha would be over .05. That would imply about 5% return on a strategy with very low correlation to the market. If you are looking for a market like strategy with a beta close to 1, then maybe a smaller alpha would be acceptable since one is assuming some returns come from the market.
Hope that helps. Very nicely laid out code buy the way.
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