DataSrc step

class DataSrc(object):
    
    def _step(self):
        # get history matrix from dataframe
        data_window = self.data[:, self.step:self.step +
                                self.window_length].copy()

        # (eq.1) prices
        y1 = data_window[:, -1, 0] / data_window[:, -2, 0]
        y1 = np.concatenate([[1.0], y1])  # add cash price

        # (eq 18) X: prices are divided by close price
        nb_pc = len(self.price_columns)
        if self.scale:
            last_close_price = data_window[:, -1, 0]
            data_window[:, :, :nb_pc] /= last_close_price[:,np.newaxis, np.newaxis]

        if self.scale_extra_cols:
            # normalize non price columns
            data_window[:, :, nb_pc:] -= self.stats["mean"][None, None, nb_pc:]
            data_window[:, :, nb_pc:] /= self.stats["std"][None, None, nb_pc:]
            data_window[:, :, nb_pc:] = np.clip(
                data_window[:, :, nb_pc:],
                self.stats["mean"][nb_pc:] - self.stats["std"][nb_pc:] * 10,
                self.stats["mean"][nb_pc:] + self.stats["std"][nb_pc:] * 10
            )

        self.step += 1
        history = data_window
        done = bool(self.step >= self.steps)

        return history, y1, done