from nsepy import get_history
import datetime
from datetime import date
import pandas as pd
import numpy as np
s= date(2017,3,31)
nifty_fut = get_history(symbol="NIFTY",
start=s,
end=s,
index=True,
futures=True,
expiry_date=s)
niftydata= nifty_fut
nifty_opt=get_history(symbol="NIFTY",
start=s,
end=s,
index=True,
option_type='CE',
strike_price=14000,
expiry_date=s)
optiondata = nifty_opt
s= s + datetime.timedelta(days=1)
expiry= [date(2017,4,27),
date(2017,5,25),
date(2017,6,29),
date(2017,7,27),
date(2017,8,31),
date(2017,9,28),
date(2017,10,26),
date(2017,11,30),
date(2017,12,28)]
for x in expiry:
expiry=x
nifty_fut= get_history(symbol="NIFTY",
start=s,
end=x,
index=True,
futures=True,
expiry_date=x)
niftydata= niftydata.append(nifty_fut)
high= nifty_fut[['Close']].max()
low= nifty_fut[['Close']].min()
high= int((round(high/100)*100)+100)
low= int((round(low/100)*100)-100)
for z in range (low, high, 100):
nifty_opt= get_history(symbol="NIFTY",
start=s,
end=expiry,
index=True,
option_type='CE',
strike_price=z,
expiry_date=x)
optiondata= optiondata.append(nifty_opt)
nifty_opt= get_history(symbol="NIFTY",
start=s,
end=expiry,
index=True,
option_type='PE',
strike_price=z,
expiry_date=x)
optiondata= optiondata.append(nifty_opt)
s= x + datetime.timedelta(days=1)
NiftyCF = pd.DataFrame({"Nifty": niftydata["Close"]})
NiftyCO= pd.DataFrame ({"Expiry": optiondata['Expiry'],
"Type":optiondata['Option Type'],
"Strike":optiondata['Strike Price'],
"Last":optiondata['Last']})
NiftyCO=NiftyCO.join(NiftyCF)
Opttable = pd.pivot_table(NiftyCO, values= 'Last', index =['Date','Nifty','Type','Expiry'],columns= ['Strike'], aggfunc= np.sum)
Opttable.to_csv("April 2017 OptionsFinal.csv", index=True, encoding='utf8')
Table = pd.read_csv('April 2017 OptionsFinal.csv')
Table['month'] = pd.to_datetime(Table['Date']).dt.strftime('%m')
Table.head()
Table1 = Table.groupby('month').first()
Table1['M'] = pd.to_datetime(Table1['Date']).dt.strftime('%m')
Table1['ATM_Strike'] = round(Table1['Nifty']/100)*100
######Everything is ok till here
Table3 = pd.DataFrame({"Date":['0'], "Close":['0'], "Expiry":['0'],
"Type":['0'], "month":['0'], "ATM":['0'],
"OTM":['0'], "Ratio":['0'], "P&L":['0']})
Table4 = pd.DataFrame({"Date":['0'], "Close":['0'], "Expiry":['0'],
"Type":['0'], "month":['0'], "ATM":['0'],
"OTM":['0'], "Ratio":['0'], "P&L":[0]})
for x in range (0,9):
e=Table1.M
d=Table1.Expiry
a= int((Table1.ATM_Strike))
a=a+200
b = a+200
f1 = str(a)
f2 = str(b)
Table2 = pd.DataFrame({"Date": Table["Date"], "Close": Table["NIFTY"],
"Expiry": Table["Expiry"], "Type": Table["Type"],
"month": Table["month"], "ATM": Table[f1], "OTM": Table[f2]})
Table2= Table2.where((Table2.month == e) & (Table2.Expiry == d) & (Table2.Type == "CE"))
Table2 = Table2.dropna()
Table2['Ratio']=Table2['ATM']-(Table2['OTM']*2)
Table2['P&L']=Table2['Ratio'].diff().cumsum()
Table3 = Table3.append(Table2)
Table4= Table4.append(Table2.where((Table2.Date == d)))
Table4 = Table4.dropna()
Table4.drop(["Date", "Close", "Expiry", "Type", "ATM", "OTM", "Ratio"], axis = 1, inplace = True)
Table4['Total_P&L']=Table4['P&L'].cumsum()
print (Table4)
KeyError: '9500'
During handling of the above exception, another exception occurred: KeyError Traceback (most recent call last) <ipython-input-84-641e305894a7> in <module> 11 "Type": Table["Type"], 12 "month": Table["month"], ---> 13 "ATM": Table[f1], "OTM": Table[f2]})
Hi Mukul,
Kindly elaborate on what you are trying to do here.
As of now, what is happening here is variable a is in form of an integer. When you convert it to string, a is of the form 9500. However, the columns in the Table dataframe has the name 9500.0. Hence you can convert a into type float and then proceed. However, this will further result in an error when dealing with the maximum value. The maximum value of 'Table1.ATM_Strike' is 10500.0. While calculating a and b, you are further adding 200 to the max value, which will again result in the error for '10700.0'. To avoid this, you can consider creating a filter or a condition.
Hope this would help you.