PCR（美元加权看跌期权比率）计算问题

Question

我正在尝试为我自己的目的生成一个看跌/看涨比率计算程序。问题是我被困在一些地方。在

1. 我需要生成所有罢工数量*所有罢工价格的总和
2. 最后一个是生成比率，即总和（所有行权卖出量*所有行权卖出价格）/总和（所有行权买入量*所有行权买入价）。在

到目前为止，我尝试了很多，最后有一个童车程序。不过，我现在提供的代码进一步，以便我可以完成其余部分得到正确的输出。（nse不提供PCR，CBO提供解决方案。）

from nsepy import get_history
from datetime import date
import pandas as pd
import requests
from io import BytesIO 
import certifi
from scipy import stats
from dateutil.relativedelta import relativedelta
import numpy as np
#import matplotlib.pyplot as plt
import datetime
import numpy as np
import matplotlib.colors as colors
import matplotlib.finance as finance
import matplotlib.dates as mdates
import matplotlib.ticker as mticker
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
import matplotlib.font_manager as font_manager
import talib as ta
from talib import MA_Type
import statsmodels as sm



nf_calls=[]
nf_puts=[]


#nf_calls[['VolumeCalls']]=np.nan
#nf_puts[['VolumeCalls']]=np.nan
i=min_avalable_strikes=4850
max_avalable_strike=9400
wPCR=nf_opt_CE=nf_opt_PE=pd.DataFrame()

while i in range(min_avalable_strikes,max_avalable_strike):
    temp_CE = get_history(symbol="NIFTY",
                         start=date(2016,2,1), 
                         end=date(2016,4,24),
                         index=True,
                         option_type="CE",
                         strike_price=i,
                         expiry_date=date(2016,4,28))

    #print(nf_opt_CE.head())
    #if nf_opt_CE['Number of Contracts'].values >0 :
    '''if nf_opt_CE.empty :
        nf_opt_CE.append(0)
    '''

    temp_PE = get_history(symbol="NIFTY",
                         start=date(2016,2,1), 
                         end=date(2016,4,22),
                         index=True,
                         option_type="PE",
                         strike_price=i,
                         expiry_date=date(2016,4,28))
    #nf_opt_PE.rename(columns = {'Number of Contracts':'Volume'}, inplace = True)
    #nf_opt_CE.rename(columns = {'Number of Contracts':'Volume'}, inplace = True)

    #temp_CE=temp_CE.drop(temp_CE[temp_CE['Number of Contracts']>0.0].index)
    #temp_PE=temp_PE.drop(temp_CE[temp_CE['Number of Contracts']>0.0].index)
    nf_opt_CE=pd.concat([nf_opt_CE,temp_CE]).drop_duplicates()
    nf_opt_PE=pd.concat([nf_opt_PE,temp_PE]).drop_duplicates()
    nf_opt_CE.index=pd.to_datetime(nf_opt_CE.index)
    nf_opt_PE.index=pd.to_datetime(nf_opt_PE.index)
    i=i+50
    #print(i)

#print(nf_opt_PE.head())
nf_opt_PE.drop_duplicates(inplace=True)
nf_opt_CE.drop_duplicates(inplace=True)
#print(nf_opt_PE.head(100))

nf_opt_PE.rename(columns = {'Number of Contracts':'Volume'}, inplace = True)
nf_opt_CE.rename(columns = {'Number of Contracts':'Volume'}, inplace = True)

nf_opt_PE.drop(['Symbol','Expiry','Open','High' ,'Low','Last','Settle Price','Turnover','Premium Turnover','Open Interest'  ,'Change in OI','Underlying'],axis=1,inplace=True)

nf_opt_CE.drop(['Symbol','Expiry','Open','High' ,'Low','Last','Settle Price','Turnover','Open Interest','Premium Turnover' ,'Change in OI','Underlying'],axis=1,inplace=True)

nf_opt_PE = nf_opt_PE[nf_opt_PE.Volume > 0]
nf_opt_CE = nf_opt_CE[nf_opt_CE.Volume > 0]
#print(nf_opt_PE.tail())

##priceCrossVolume###
nf_opt_PE['VolCrossPrice']=nf_opt_PE.apply(lambda x: (x['Volume']*x['Close']),axis=1)
nf_opt_CE['VolCrossPrice']=nf_opt_CE.apply(lambda x: (x['Volume']*x['Close']),axis=1)

#nf_puts= nf_opt_CE['Number of Contracts']*nf_opt_CE['Close']
#print(nf_calls.head())


nf_opt_PE.drop(['Volume','Close'],axis=1,inplace=True)
nf_opt_CE.drop(['Volume','Close'],axis=1,inplace=True)
#print(nf_opt_PE.index.Date)
#nf_opt_PE['Summation']=nf_opt_PE.groupby(nf_opt_PE.columns[[0]])['VolCrossPrice'].sum()
wPCR=pd.concat([nf_opt_PE,nf_opt_CE])
#wPCR.drop(['Volume','Close'],axis=1,inplace=True)
#wPCR['Summation']=wPCR.groupby(wPCR.columns[[0,1]]).sum()
wPCR['Summation']=wPCR[['Option Type'] == wPCR['Option Type']].groupby(level=0).sum()

print(nf_opt_PE.head(500))
print(wPCR.tail(500))

遇到错误：

^{pr2}$

我假设错误在：

wPCR['Summation']=wPCR[['Option Type'] == wPCR['Option Type']].groupby(level=0).sum()

有人能提出一个简单的方法来找到合适的加权PCR吗？在

Answer 1

    from nsepy import get_history
from datetime import date
import pandas as pd
import requests
from io import BytesIO 
import certifi
from scipy import stats
from dateutil.relativedelta import relativedelta
import numpy as np
#import matplotlib.pyplot as plt
import datetime
import numpy as np
import matplotlib.colors as colors
import matplotlib.finance as finance
import matplotlib.dates as mdates
import matplotlib.ticker as mticker
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
import matplotlib.font_manager as font_manager
import talib as ta
from talib import MA_Type
import statsmodels as sm



nf_calls=[]
nf_puts=[]


#nf_calls[['VolumeCalls']]=np.nan
#nf_puts[['VolumeCalls']]=np.nan
i=min_avalable_strikes=4850
max_avalable_strike=9400
nf_opt_CE=nf_opt_PE=pd.DataFrame()

while i in range(min_avalable_strikes,max_avalable_strike):
    temp_CE = get_history(symbol="NIFTY",
                         start=date(2016,2,1), 
                         end=date(2016,4,24),
                         index=True,
                         option_type="CE",
                         strike_price=i,
                         expiry_date=date(2016,4,28))

    #print(nf_opt_CE.head())
    #if nf_opt_CE['Number of Contracts'].values >0 :
    '''if nf_opt_CE.empty :
        nf_opt_CE.append(0)
    '''

    temp_PE = get_history(symbol="NIFTY",
                         start=date(2016,2,1), 
                         end=date(2016,4,22),
                         index=True,
                         option_type="PE",
                         strike_price=i,
                         expiry_date=date(2016,4,28))
    #nf_opt_PE.rename(columns = {'Number of Contracts':'Volume'}, inplace = True)
    #nf_opt_CE.rename(columns = {'Number of Contracts':'Volume'}, inplace = True)

    #temp_CE=temp_CE.drop(temp_CE[temp_CE['Number of Contracts']>0.0].index)
    #temp_PE=temp_PE.drop(temp_CE[temp_CE['Number of Contracts']>0.0].index)
    nf_opt_CE=pd.concat([nf_opt_CE,temp_CE]).drop_duplicates()
    nf_opt_PE=pd.concat([nf_opt_PE,temp_PE]).drop_duplicates()
    nf_opt_CE.index=pd.to_datetime(nf_opt_CE.index)
    nf_opt_PE.index=pd.to_datetime(nf_opt_PE.index)
    i=i+50
    #print(i)

#print(nf_opt_PE.head())
nf_opt_PE.drop_duplicates(inplace=True)
nf_opt_CE.drop_duplicates(inplace=True)
#print(nf_opt_PE.head(100))

nf_opt_PE.rename(columns = {'Number of Contracts':'Volume'}, inplace = True)
nf_opt_CE.rename(columns = {'Number of Contracts':'Volume'}, inplace = True)

nf_opt_PE.drop(['Symbol','Expiry','Open','High' ,'Low','Last','Settle Price','Turnover','Open Interest'  ,'Change in OI','Underlying'],axis=1,inplace=True)

nf_opt_CE.drop(['Symbol','Expiry','Open','High' ,'Low','Last','Settle Price','Turnover','Open Interest','Change in OI','Underlying'],axis=1,inplace=True)

nf_opt_PE = nf_opt_PE[nf_opt_PE.Volume > 0]
nf_opt_CE = nf_opt_CE[nf_opt_CE.Volume > 0]
#print(nf_opt_PE.tail())

##priceCrossVolume###
nf_opt_PE['PESum']=nf_opt_PE.groupby(level=0)['Premium Turnover'].sum()
nf_opt_CE['CESum']=nf_opt_CE.groupby(level=0)['Premium Turnover'].sum()

#nf_puts= nf_opt_CE['Number of Contracts']*nf_opt_CE['Close']
#print(nf_calls.head())


nf_opt_PE.drop(['Volume','Close'],axis=1,inplace=True)
nf_opt_CE.drop(['Volume','Close'],axis=1,inplace=True)
#print(nf_opt_PE.index.Date)
#nf_opt_PE['Summation']=

wPCR= (nf_opt_PE['PESum']/nf_opt_CE['CESum'])
#wPCR.rename(columns = {'0':'wPCR'}, inplace = True)
wPCR.plot()
plt.show()

#print(wPCR.head(500))
#print(nf_opt_PE.tail(500))