This is my implementation of alpha 34. The ranking is done through just assigning each stock a weight from 0 to 1 ,and all the combined weights will equal up to 1. I'm not sure why it dropped off a cliff at the very end of the test.
edit:
Updated backtest to reflect the recommended changes advised by @Vladimir ,and @Tentor Testivis.
Works a lot better now
Line 115
try to replace
combined_alpha = alphas["alpha_34"] / len(alphas["alpha_34"]) to
combined_alpha = alphas["alpha_34"] / abs(alphas["alpha_34"]).sum() Well, subtracting from 1 isn't really ranking. Since you already imported stats from scipy you might as well use it for this:
returns_rank = stats.rankdata(returns_std) and
close_delta_rank = stats.rankdata(np.nan_to_num(close_delta)) To get the result for alpha 34 you should change the cd_ret_rank to something like this:
cd_ret_rank = stats.rankdata((1 - returns_rank) + (1 - close_delta_rank)) But I have to say this alpha doesn't make much sense to me. Take the close delta: say you have 2 stocks, one is traded at $ 10 and the other at $ 1,000 on the second last close. Now both have a close $ 5 higher than before ($ 15 and $ 1,005), then they would get the same rank even though the first is up 50 % and the second only 0.5 %
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