quanta_lib.py 文件源码

def compute(self, today, assets, out, close):

            # prepare X matrix (x_is - x_bar)
            X = range(self.window_length)
            X_bar = np.nanmean(X)
            X_vector = X - X_bar
            X_matrix = np.tile(X_vector, (len(close.T), 1)).T

            # prepare Y vectors (y_is - y_bar)
            Y_bar = np.nanmean(close, axis=0)
            Y_bars = np.tile(Y_bar, (self.window_length, 1))
            Y_matrix = close - Y_bars

            # multiply X matrix an Y matrix and sum (dot product)
            # then divide by variance of X
            # this gives the MLE of Beta
            betas = (np.sum((X_matrix * Y_matrix), axis=0) / X_var) / (self.window_length)

            # prepare variance of X
            X_var = np.nanvar(X)

            # now use to get to MLE of alpha
            out[:] = Y_bar - (betas * X_bar)