Python 使用面向数组编程实现循环

我很难在熊猫/裸体玩具中将头缠绕在环上。以这段代码为例

import pandas as pd

eurusd = pd.read_csv('EURUSD.csv',index_col='Date',parse_dates=True,usecols=['Date','High','Low','Open','Close'])
gbpusd = pd.read_csv('GBPUSD.csv',index_col='Date',parse_dates=True,usecols=['Date','High','Low','Open','Close'])
audusd = pd.read_csv('AUDUSD.csv',index_col='Date',parse_dates=True,usecols=['Date','High','Low','Open','Close'])


eurusd['MovingAvg'] = pd.rolling_mean(eurusd.Close,100)
gbpusd['MovingAvg'] = pd.rolling_mean(gbpusd.Close,100)
audusd['MovingAvg'] = pd.rolling_mean(audusd.Close,100)

我如何实现这样的逻辑

if the eurusd.Close is less than the eurusd.MovingAvg 
AND if gbpusd.Close is less than the gbpusd.MovingAvg
AND if audusd.Close is GREATER than the audusd.MovingAvg
then set some condition to TRUE

---

要使用股票和雅虎金融来说明这一概念：

import pandas.io.data as web

df = web.DataReader(['F', 'AAPL', 'IBM'], 'yahoo', '2015-01-02', '2016-01-01')['Adj Close']

df = pd.concat([df, 
                pd.rolling_mean(df, window=100).rename(
                    columns={col: col + "_100" for col in df})], 
               axis=1)

df['condition'] = False
df.loc[(df.F < df.F_100) & 
       (df.AAPL < df.AAPL_100) & 
       (df.IBM > df.IBM_100), 'condition'] = True

>>> df.tail()
                  AAPL          F         IBM    AAPL_100      F_100     IBM_100 condition
Date                                                                                      
2015-12-24  106.796739  13.692101  135.544053  112.421986  13.616413  140.126056     False
2015-12-28  105.600553  13.567714  134.916580  112.347147  13.611907  139.954141     False
2015-12-29  107.498633  13.615554  137.044105  112.288827  13.607596  139.806220     False
2015-12-30  106.094845  13.558146  136.612715  112.212631  13.602994  139.665642     False
2015-12-31  104.058365  13.481600  134.926379  112.074727  13.595828  139.492368     False

>>> df.condition.sum()
8

将pandas.io.data作为web导入
df=web.DataReader（['F'，'AAPL'，'IBM'，'yahoo'，'2015-01-02'，'2016-01-01'）['Adj Close']
df=局部混凝土（[df，
pd.滚动平均值（df，窗口=100）。重命名(
列={col:col+“\u 100”表示df}中的列]，
轴=1）
df['condition']=False
df.loc[（df.Fdf.IBM_100），“条件”]=True
>>>df.tail（）
AAPL F IBM AAPL_100 F_100 IBM_100条件
日期
2015-12-24106.79673913.692101135.544053112.421986 13.616413140.126056假
2015-12-28 105.600553 13.567714 134.916580 112.347147 13.611907 139.954141假
2015-12-29 107.498633 13.615554 137.044105112.288827 13.607596 139.806220假
2015-12-30106.09484513.558146136.612715112.212631136.602994139.665642假
2015-12-31 104.058365 13.481600 134.926379 112.074727 13.595828 139.492368假
>>>df.condition.sum（）
8.

如果eurusd.Closeaudusd.MovingAvg

返回值错误：序列的真值不明确。使用a.empty、a.bool（）、a.item（）、a.any（）或a.all（）