I took a quick look at your ranking function, and was wonder if you may have accidentally forgotten to apply pct_change() in the denominator of the ranking statement:
stocks[sid] = prices[sid].pct_change(15).dropna()[-1] / np.std(prices[sid].values)
In this case since its just taking the np.std() of the price series, with the numerator being in percentage terms, the ranking function will most likely be driven by the actual stock price rather than the volatility. (e.g. since the np.std() of lower priced stocks will simply always be smaller than np.std() of a high priced stock). In order to rank the stock on a more apples-to-apples basis, apply pct_change() to the prices in the denominator as well when computing the std(). Perhaps try:
stocks[sid] = prices[sid].pct_change(15).dropna()[-1] / (np.std(prices[sid].pct_change().dropna()[-15:].values) * np.sqrt(15))
As well in the revised statement above I transform the standard dev of the daily pct_change returns into a 15-day standard dev by multiplying by sqrt(15). This is because volatility scales at the sqrt of time. Though this scaling by sqrt(15) isn't absolutely necessary I suppose, since I imagine if you just use the basic daily return standard deviation for all the stocks and then you rank them, it should rank in the same order I imagine.
Apologies if perhaps I missed if in the code you already normalized all the stock prices to a standard basis and these it's possible doing it the way you have it will work just fine.
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