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Question

从雅虎财经下载的nvidia的ohlcv上读到，当我试图定义哪一个通过了avg>volume测试时，我正在为signal /dontbuy创建一个列，所有的东西要么全部‘购买’，要么不买。

df=pd.read_csv('NVDA.csv',dtype={'label':str})
df['Price%delta']=((df['Close']/df['Open'])*100)                       

df['Avg_volume']=df['Volume'].rolling(7).mean()

df['Signal']=0

for index, row in df.iterrows():
    if row['Volume'] > row['Avg_volume']:
    df['Signal']='Buy'
    else:
        df['Signal']='Dont Buy'

Answer 1

您根本不需要for循环：

mask = df["Volume"] > df["Avg_volume"] 

df.loc[mask, "Signal"] = "Buy"
df.loc[~mask, "Signal"] = 'Don't buy'

Answer 2

您没有指定要分配'Buy'或'Don't buy'的任何索引。使用loc代替：

for index, row in df.iterrows(): 
    if row['Volume'] > row['Avg_volume']:
        df.loc[index, 'Signal']='Buy'
    else:
        df.loc[index, 'Signal']='Dont Buy'

Answer 3

基于np.where()的矢量化解决方案

df['Signal'] = np.where(df['Volume'] > df['Avg_volume'], 'Buy', 'Dont Buy')