NumPy convolve实现VWAP与循环实现结果存在微小差异的问题排查

我实现了两种股票成交量加权平均价格（VWAP）计算方法：一种是可读性强但速度极慢的传统循环方法，另一种是使用NumPy的convolve方法实现的高效版本。后者性能提升显著，但输出结果与循环方法存在微小差异。我已排除整数除法的影响，目前无法确定差异原因，特此求助。

VWAP实现代码 #

import numpy as np

def calc_vwap(price, volume, period_lookback):
    """
    Calculates the volume-weighted average price (VWAP) for a given period of time.
    The VWAP is calculated by taking the sum of the product of each price and volume over a given period, 
    and dividing by the sum of the volume over that period.
    
    Parameters:
        price (numpy.ndarray): A list or array of prices.
        volume (numpy.ndarray): A list or array of volumes, corresponding to the prices.
        period_lookback (int): The number of days to look back when calculating VWAP.
        
    Returns:
        numpy.ndarray: An array of VWAP values, one for each day in the input period.
    """
    vwap = np.zeros(len(price))
    for i in range(period_lookback, len(price)):
        lb = i - period_lookback  # lower bound
        ub = i + 1  # upper bound
        volume_sum = volume[lb:ub].sum()
        if volume_sum > 0:
            vwap[i] = (price[lb:ub] * volume[lb:ub]).sum() / volume_sum
        else:
            vwap[i] = np.nan
    return vwap

def calc_vwap_speedy(price, volume, period_lookback):
    # Calculate product of price and volume
    price_volume = price * volume
    # Use convolve to get the rolling sum of product of price and volume and volume array
    price_volume_conv = np.convolve(price_volume, np.ones(period_lookback), mode='valid')
    volume_conv = np.convolve(volume, np.ones(period_lookback), mode='valid')
    # Create a mask to check if the volume sum is greater than 0
    mask = volume_conv > 0
    # Initialize the vwap array
    vwap = np.zeros(len(price))
    # Use the mask to check if volume sum is greater than zero, if it is, proceed with the division and store the result in vwap array, otherwise store NaN
    vwap[period_lookback-1:] = np.where(mask, price_volume_conv / volume_conv, np.nan)
    return vwap

测试结果 #

RUN TIME
standard -> 8.046217331999998
speedy ->   0.09436071299998616

OUTPUT
standard -> [0.         0.         0.         ... 0.49073531 0.48826866 0.49220622]
speedy ->   [0.         0.         0.         ... 0.49525183 0.48842067 0.49092021]

内容的提问来源于stack exchange，提问作者Hofbr