如何在dataframe中使用一列的，然后使用同一日期的另一列数据？[副本]

Question

我正在尝试使用一个振荡器（相对强弱指数）来知道什么时候买入和卖出一只股票。我为RSI和收盘价创建了一个数据帧。我可以绘制这两个图，但我也想添加到我的图，当RSI击中一个买进和卖出信号。因此，为了做到这一点，我需要在RSI降至25以下时对我的RSI列进行比较，这将触发我的买入信号，如果RSI超过85，则触发我的卖出信号。我的问题是，在我的RSI柱下降到25以下的那一天，直到我的RSI柱上升到85以上的那一天，我无法计算出我的收盘价柱。我得到的只是我新的dataframe列中的Nan。

#rsi
import pandas
import warnings
import pandas_datareader.data as web
import datetime
import matplotlib.pyplot as plt
warnings.filterwarnings('ignore')
# Window length for moving average
window_length = 14

# Dates
start = datetime.datetime(2016, 1, 5)
end = datetime.datetime(2016, 12, 31)

# Get data
data = web.DataReader('FB', 'morningstar', start, end)
df= pd.DataFrame(data)

# Get just the close
close = data['Close']
# Get the difference in price from previous step
delta = close.diff()
# Get rid of the first row, which is NaN since it did not have a previous 
# row to calculate the differences
delta = delta[1:] 

# Make the positive gains (up) and negative gains (down) Series
up, down = delta.copy(), delta.copy()
up[up < 0] = 0
down[down > 0] = 0

# Calculate the EWMA
roll_up1 = pandas.stats.moments.ewma(up, window_length)
roll_down1 = pandas.stats.moments.ewma(down.abs(), window_length)

# Calculate the RSI based on EWMA
RS1 = roll_up1 / roll_down1
RSI1 = 100.0 - (100.0 / (1.0 + RS1))

# Calculate the SMA
roll_up2 = pandas.rolling_mean(up, window_length)
roll_down2 = pandas.rolling_mean(down.abs(), window_length)

# Calculate the RSI based on SMA
RS2 = roll_up2 / roll_down2
RSI2 = 100.0 - (100.0 / (1.0 + RS2))
df['RSI2']=RSI2

df=df.dropna(axis=0)

df['RSI2']=df['RSI2'].astype(float)




df['BUY']=df['Close'][df['RSI2'] < 25]
print (df['BUY'])




# Compare graphically
plt.figure()
df['BUY'].plot(title='FB',figsize = (20, 5))
plt.show()
RSI1.plot(title='Relative Strength Index',figsize = (20, 5))
RSI2.plot(figsize = (20, 5))
plt.legend(['RSI via EWMA', 'RSI via SMA'])
plt.show()

Answer 1

如果我答对了你的问题，那么你要找的东西就在pandas（pd.query（）中，就像在SQL中一样，例如

df['rsi_query'] = np.zeros(df.shape[0])
myquery = df.query('RSI>.25 & RSI<.85').index
df.iloc[myquery, -1] = 1(replace it with what you want)

进一步参考