如何更有效地计算滚动比

Question

1. 我的数据长度超过 3000。
2. 以下是 20 天价值的代码（股票市场的交易量比率）
3. 耗时超过 2 分钟。

4. 有什么好的方法可以减少运行时间。

   import pandas as pd
   import numpy as np
   from pandas.io.data import DataReader
   import matplotlib.pylab as plt
   data = DataReader('047040.KS','yahoo',start='2010')
     data['vr']=0
     data['Volume Ratio']=0
     data['acend']=0
    data['vr'] = np.sign(data['Close']-data['Open']) 
    data['vr'] = np.where(data['vr']==0,0.5,data['vr']) 
    data['vr'] = np.where(data['vr']<0,0,data['vr']) 
    data['acend'] = np.multiply(data['Volume'],data['vr']) 
   
    for i in range(len(data['Open'])):
        if i<19:
            data['Volume Ratio'][i]=0
        else:
            data['Volume Ratio'][i] = ((sum(data['acend'][i-19:i]))/((sum(data['Volume'][i-19:i])-sum(data['acend'][i-19:i]))))*100
   

Answer 1

考虑使用条件行选择和rolling.sum()：

data.loc[data.index[:20], 'Volume Ratio'] = 0
data.loc[data.index[20:], 'Volume Ratio'] = (data.loc[:20:, 'acend'].rolling(window=20).sum() / (data.loc[:20:, 'Volume'].rolling(window=20).sum() - data.loc[:20:, 'acend'].rolling(window=20).sum()) * 100

或者，简化 - .rolling.sum() 将为前 20 个值创建 np.nan，因此只需使用 .fillna(0)：

data['new_col'] = data['acend'].rolling(window=20).sum().div(data['Volume'].rolling(window=20).sum().subtract(data['acend'].rolling(window=20).sum()).mul(100).fillna(0)