在python中查找价格动能的有效方法:平均一列的最后n个条目 [英] Efficient way to find price momentum in python: averaging last n entries of a column

问题描述

我定义的价格动量是过去 n 天内给定股票动量的平均值.

I'm defining price momentum is an average of the given stock’s momentum over the past n days.

动量又是一个分类:如果当天的收盘价高于前一天，则每天标记为1，如果价格低于前一天，则标记为-1.

Momentum, in turn, is a classification: each day is labeled 1 if closing price that day is higher than the day before, and −1 if the price is lower than the day before.

我的库存变化百分比如下:

I have stock change percentages as follows:

df['close in percent'] = np.array([0.27772152, 1.05468772, 
                                   0.124156 , -0.39298394, 
                                   0.56415267,  1.67812005])

momentum = df['close in percent'].apply(lambda x: 1 if x > 0 else -1).values

Momentum应该是:[1,1,1,-1,1,1].

因此，如果我找到过去 n = 3天的平均动量，我希望价格动量为:

So if I'm finding the average momentum for the last n = 3 days, I want my price momentum to be:

Price_momentum = [Nan, Nan, 1, 1/3, 1/3, 1/3]

我设法使用以下代码使其正常工作，但这非常慢(数据集有5000多个行，执行需要10分钟).

I managed to use the following code to get it working, but this is extremely slow (the dataset is 5000+ rows and it takes 10 min to execute).

for i in range(3,len(df)+1,1):
    data = np.array(momentum[i-3:i])
    df['3_day_momentum'].iloc[i-1]=data.mean()

推荐答案

您可以创建

You can create a rolling object:

df = pd.DataFrame()
df['close_in_percent'] = np.array([0.27772152, 1.05468772, 
                                   0.124156 , -0.39298394, 
                                   0.56415267,  1.67812005])
df['momentum'] = np.where(df['close_in_percent'] > 0, 1, -1)
df['3_day_momentum'] = df.momentum.rolling(3).mean()

在这里，np.where是apply()的替代方法，它通常很慢，应该作为最后的手段.

Here, np.where is an alternative to apply(), which is generally slow and should be used as a last resort.

   close_in_percent  momentum  3_day_momentum
0            0.2777         1             NaN
1            1.0547         1             NaN
2            0.1242         1          1.0000
3           -0.3930        -1          0.3333
4            0.5642         1          0.3333
5            1.6781         1          0.3333

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