How to optimise my data frame processing? S&P Highs and Lows
I want to get daily if a stock price is in it’s 52-weeks low or high and for that I wrote this piece of code:
prices = yf.download(tickers, start=start, end=end)["Adj Close"] max_min = pd.DataFrame(0, index=np.arange(len(prices)), columns=list(prices.columns)) for i in range(251, len(prices)): for j in range(prices.shape[1]): if prices.iloc[i, j] >= prices.iloc[:i].max()[j]: max_min.loc[i, prices.columns[j]] = 1 elif prices.iloc[i, j] <= prices.iloc[:i].min()[j]: max_min.loc[i, prices.columns[j]] = -1 Where "tickers" is a list with all the tickers of the S&P 500 and the start date is oct from 2006 and end date is today.
prices.shape = (3451, 505)
It’s taking forever and I need to get this data daily. How can I optimise my code?
P.D: I ran an example with this code.
a = pd.DataFrame(np.random.randint(0,200, size=(10,10)), index=np.arange(10), columns=list("ABCDEFGHIJ")) max_min = pd.DataFrame(0, index=np.arange(len(prices)), columns=list(prices.columns)) for i in range(4, len(a)): for j in range(a.shape[1]): if a.iloc[i, j] >= a.iloc[:i].max()[j]: max_min.loc[i, a.columns[j]] = 1 elif a.iloc[i, j] <= a.iloc[:i].min()[j]: max_min.loc[i, a.columns[j]] = -1 
