def PE_list(i, date_latest, years):
PE_of_years = get_index_pe_history(
symbol=i,
start=date.fromordinal(date_latest.toordinal() - years * 365),
end=date_latest)
return PE_of_years.index.values, PE_of_years['P/E'].tolist(
), PE_of_years['P/B'].tolist(), PE_of_years['Div Yield'].tolist()
def PE(i, date_latest, xdays):
PE_of_xdays = get_index_pe_history(
symbol=i,
start=date.fromordinal(date_latest.toordinal() - xdays),
end=date_latest)
return PE_of_xdays['P/E'].tolist()[0], PE_of_xdays['P/B'].tolist(
)[0], PE_of_xdays['Div Yield'].tolist()[0]
def PEhistory(indexsymbol):
PEhistyear = pd.DataFrame()
years = range(STARTYEAR, ENDYEAR)
for year in years:
PEhistyear = PEhistyear.append(PEhistoryyear(indexsymbol, year))
PEhistyear = PEhistyear.append(
get_index_pe_history(symbol=indexsymbol,
start=date(2020, 3, 31),
end=date(2020, 3, 31)))
return PEhistyear
def PEhistoryyear(indexsymbol, year):
quarter = [3, 6, 9, 12]
PE_history = pd.DataFrame()
for n in quarter:
PE_month_history = get_index_pe_history(
symbol=indexsymbol,
start=date(year, n, 1),
end=date(year, n,
calendar.monthrange(year, n)[1]))
PE_history = PE_history.append(PE_month_history.iloc[[-1]])
return PE_history
nifty['SMA'] = ta.SMA(nifty['Close'], 50)
nifty['EMA'] = ta.EMA(nifty['Close'], timeperiod=5)
nifty['upper_band'], nifty['middle_band'], nifty['lower_band'] = ta.BBANDS(
nifty['Close'], timeperiod=20)
nifty['Percentage Change'] = nifty['Close'].diff() * 100 / nifty['Close']
nifty.to_csv('nifty{0}_{1}.csv'.format(start.year, end.year), sep='\t')
start = date(2010, 1, 1)
end = date(2020, 7, 22)
nifty50Data(start, end)
vix = get_history(symbol="INDIAVIX", start=start, end=end, index=True)
nifty_pe = get_index_pe_history(symbol="NIFTY", start=start, end=end)
vix.to_csv('vix{0}_{1}.csv'.format(start.year, end.year),
sep='\t',
columns=['Close'])
nifty_pe.to_csv('niftyPE{0}_{1}.csv'.format(start.year, end.year),
sep='\t',
columns=['P/E'])
dowJones = pd.read_csv('Dow Jones{0}_{1}.csv'.format(2010, end.year),
thousands=',')
dowJones['Date'] = dowJones['Date'].map(
lambda x: datetime.strptime(x, "%b %d, %Y"))
dowJones.to_csv('DowJones{0}_{1}.csv'.format(start.year, end.year),
sep='\t',
columns=['Date', 'Price'],
def PEhistory(indexsymbol):
pe_history = get_index_pe_history(symbol=indexsymbol,
start=date(2009, 3, 31),
end=date(2009, 3, 31))
pe_history = pe_history.append(
get_index_pe_history(symbol=indexsymbol,
start=date(2010, 3, 31),
end=date(2010, 3, 31)))
pe_history = pe_history.append(
get_index_pe_history(symbol=indexsymbol,
start=date(2011, 3, 31),
end=date(2011, 3, 31)))
pe_history = pe_history.append(
get_index_pe_history(symbol=indexsymbol,
start=date(2012, 3, 30),
end=date(2012, 3, 30)))
pe_history = pe_history.append(
get_index_pe_history(symbol=indexsymbol,
start=date(2013, 3, 28),
end=date(2013, 3, 28)))
pe_history = pe_history.append(
get_index_pe_history(symbol=indexsymbol,
start=date(2014, 3, 31),
end=date(2014, 3, 31)))
pe_history = pe_history.append(
get_index_pe_history(symbol=indexsymbol,
start=date(2015, 3, 31),
end=date(2015, 3, 31)))
pe_history = pe_history.append(
get_index_pe_history(symbol=indexsymbol,
start=date(2016, 3, 31),
end=date(2016, 3, 31)))
pe_history = pe_history.append(
get_index_pe_history(symbol=indexsymbol,
start=date(2017, 3, 31),
end=date(2017, 3, 31)))
pe_history = pe_history.append(
get_index_pe_history(symbol=indexsymbol,
start=date(2018, 3, 28),
end=date(2018, 3, 28)))
pe_history = pe_history.append(
get_index_pe_history(symbol=indexsymbol,
start=date(2019, 3, 29),
end=date(2019, 3, 29)))
pe_history = pe_history.append(
get_index_pe_history(symbol=indexsymbol,
start=date(2020, 3, 31),
end=date(2020, 3, 31)))
print(pe_history)
return pe_history
def PEhistory(indexsymbol):
nifty_pe = get_index_pe_history(symbol=indexsymbol,
start=date(2009, 3, 31),
end=date(2009, 3, 31))
nifty_pe = nifty_pe.append(
get_index_pe_history(symbol=indexsymbol,
start=date(2010, 3, 31),
end=date(2010, 3, 31)))
nifty_pe = nifty_pe.append(
get_index_pe_history(symbol=indexsymbol,
start=date(2011, 3, 31),
end=date(2011, 3, 31)))
nifty_pe = nifty_pe.append(
get_index_pe_history(symbol=indexsymbol,
start=date(2012, 3, 30),
end=date(2012, 3, 30)))
nifty_pe = nifty_pe.append(
get_index_pe_history(symbol=indexsymbol,
start=date(2013, 3, 28),
end=date(2013, 3, 28)))
nifty_pe = nifty_pe.append(
get_index_pe_history(symbol=indexsymbol,
start=date(2014, 3, 31),
end=date(2014, 3, 31)))
nifty_pe = nifty_pe.append(
get_index_pe_history(symbol=indexsymbol,
start=date(2015, 3, 31),
end=date(2015, 3, 31)))
nifty_pe = nifty_pe.append(
get_index_pe_history(symbol=indexsymbol,
start=date(2016, 3, 31),
end=date(2016, 3, 31)))
nifty_pe = nifty_pe.append(
get_index_pe_history(symbol=indexsymbol,
start=date(2017, 3, 31),
end=date(2017, 3, 31)))
nifty_pe = nifty_pe.append(
get_index_pe_history(symbol=indexsymbol,
start=date(2018, 3, 28),
end=date(2018, 3, 28)))
nifty_pe = nifty_pe.append(
get_index_pe_history(symbol=indexsymbol,
start=date(2019, 3, 29),
end=date(2019, 3, 29)))
nifty_pe = nifty_pe.append(
get_index_pe_history(symbol=indexsymbol,
start=date(2020, 3, 31),
end=date(2020, 3, 31)))
print(nifty_pe)
return nifty_pe
from nsepy import get_index_pe_history
from datetime import date, datetime, timedelta
import os
dir_path = os.path.dirname(os.path.realpath(__file__))
os.chdir(dir_path)
thisyear = datetime.now().year
thismonth = datetime.now().month
thisday = datetime.now().day
Indices = []
with open("BroadMarketIndices.txt", "rt") as f:
for line in f:
if (line[0] != "#"):
line = line.rstrip('\n')
Indices.append(line)
i = 1
for broadindex in Indices:
broadindex = broadindex.upper()
pe = get_index_pe_history(symbol=broadindex,
start=date(thisyear, thismonth - 1, 1),
end=date(thisyear, thismonth, thisday))
print(pe)
pe.to_csv('data/%d_%s.csv' % (i, broadindex))
i += 1
end=date(2015, 1, 31))
data[['Close']].plot()
sbin = get_history(symbol='SBIN',
start=date(2020, 8, 1),
end=date(2020, 9, 10))
print(sbin)
print(sbin.columns)
ax = plt.gca()
sbin.plot(kind='line', x='%Deliverble', y='Date', ax=ax)
#df.plot(kind='line',x='name',y='num_pets', color='red', ax=ax)
#plt.show()
stock_opt = get_history(symbol="SBIN",
start=date(2020, 9, 1),
end=date(2020, 9, 10),
option_type="CE",
strike_price=300,
expiry_date=date(2015, 1, 29))
prices = get_price_list(dt=date(2020, 9, 8))
print(prices)
nifty_pe = get_index_pe_history(symbol="NIFTY",
start=date(2020, 9, 1),
end=date(2020, 9, 10))
print(nifty_pe)
from nsepy import get_rbi_ref_history
rbi_ref = get_rbi_ref_history(date(2015, 1, 1), date(2020, 10, 10))
print(rbi_ref)
from nsepy import get_index_pe_history from datetime import date nifty_pe = get_index_pe_history(symbol="NIFTY 50", start=date(2009,3,31), end=date(2009,3,31)) print(nifty_pe) nifty_pe = get_index_pe_history(symbol="NIFTY 50", start=date(2010,3,31), end=date(2010,3,31)) print(nifty_pe) nifty_pe = get_index_pe_history(symbol="NIFTY 50", start=date(2011,3,31), end=date(2011,3,31)) print(nifty_pe) nifty_pe = get_index_pe_history(symbol="NIFTY 50", start=date(2012,3,30), end=date(2012,3,30)) print(nifty_pe) nifty_pe = get_index_pe_history(symbol="NIFTY 50", start=date(2013,3,28), end=date(2013,3,28)) print(nifty_pe) nifty_pe = get_index_pe_history(symbol="NIFTY 50", start=date(2014,3,31), end=date(2014,3,31)) print(nifty_pe) nifty_pe = get_index_pe_history(symbol="NIFTY 50", start=date(2015,3,31), end=date(2015,3,31)) print(nifty_pe) nifty_pe = get_index_pe_history(symbol="NIFTY 50", start=date(2016,3,31), end=date(2016,3,31)) print(nifty_pe) nifty_pe = get_index_pe_history(symbol="NIFTY 50", start=date(2017,3,31), end=date(2017,3,31)) print(nifty_pe) nifty_pe = get_index_pe_history(symbol="NIFTY 50", start=date(2018,3,28), end=date(2018,3,28)) print(nifty_pe) nifty_pe = get_index_pe_history(symbol="NIFTY 50", start=date(2019,3,29), end=date(2019,3,29)) print(nifty_pe) nifty_pe = get_index_pe_history(symbol="NIFTY 50", start=date(2020,3,31), end=date(2020,3,31)) print(nifty_pe)
#https://swapniljariwala.github.io/2017/11/03/NSEPy-with-backtrader.html
#%matplotlib inline
from datetime import date
import pandas as pd
from nsepy import get_history
from nsepy import get_index_pe_history
try:
nifty = pd.read_csv('data/nifty17years_withPE.csv')
print('Read from disk successful')
except:
print('Downloading from NSE')
nifty = get_history('NIFTY', date(2000, 1, 1), date.today(), index=True)
pe = get_index_pe_history('NIFTY', date(2000, 1, 1), date.today())
nifty['PE'] = pe['P/E']
nifty.to_csv('data/nifty17years_withPE.csv')
print(nifty)
from backtrader.feeds import GenericCSVData
# Define the new parameter
class GenericCSV_PE(GenericCSVData):
# Add a 'pe' line to the inherited ones from the base class
lines = ('pe', )
# add the parameter to the parameters inherited from the base class
params = (('pe', 8), )
# Declare position of each column in csv file
sql = '''
SELECT max(CONVERT(varchar, [Date], 23)) as maxdate
FROM
IndexPEHistory
'''
df = pd.read_sql_query(sql, engine)
lastupdateddate = datetime.strptime(df['maxdate'][0].split()[0],
"%Y-%m-%d").date()
startdate = lastupdateddate + timedelta(days=1)
enddate = datetime.now().date() + timedelta(days=1)
print("Last Updated Date = ", lastupdateddate)
for idx in indexnames:
try:
indexpevals = get_index_pe_history(symbol=idx,
start=startdate,
end=enddate)
indexpevals["Index Name"] = idx
indexpevals.to_sql("IndexPEHistory",
engine,
if_exists='append',
chunksize=1000)
print(idx, " -- COMPLETED")
except Exception as e:
print(idx, " -- NOT COMPLETED")
print(e)
"India VIX is a volatility index which gives a measurement of market volatility based on NIFTY options contract. This servers as important parameter in option pricing"
)
if st.button("Check Volatility"):
vix = get_history(symbol="INDIAVIX",
start=date(sy, sm, sd),
end=date(ey, em, ed),
index=True)
st.write(vix)
st.area_chart(vix[["Close"]])
st.header("Market Valuation")
st.markdown(
"P/E ratio of a security helps to estimate if the security is over-priced or under-priced and further make investment decisions. P/B value helps determine the intrinsic value of a security with regards to its market price"
)
if st.button("Check Valuation"):
pe = get_index_pe_history(symbol="NIFTY",
start=date(sy, sm, sd),
end=date(ey, em, ed))
st.write(pe)
st.area_chart(pe[["P/E"]])
st.line_chart(pe[["P/B"]])
st.header("All stock quotes")
st.markdown("Enter year,date and time to get stock quotes of entire market")
y = int(st.number_input("Enter year"))
m = int(st.number_input("Enter month"))
d = int(st.number_input("Enter date"))
if st.button("Check Quotes"):
prices = get_price_list(dt=date(y, m, d))
st.write(prices)
'''sbin=get_history(symbol="SBIN",start=date(2021,1,1), end=date(2021,1,29))
print(sbin)'''
import pandas as pd
'''try:
nifty = pd.read_csv('C:\\Mydrive\\Study\\workspace\\data\\nifty2020year_withPE.csv')
print('Read from disk successful')
except:'''
datadirectory = "C:\\data\\"
startyear = 2019
endyear = 2020
'''scriptName='NIFTY'
istheScriptIndex=True'''
scriptName = 'SBIN'
istheScriptIndex = False #change this to True if fetch is for Index data
for x in range(startyear, endyear):
print('Downloading from NSE' + str(x))
if (istheScriptIndex):
sciptData = get_history(scriptName,
date(x, 1, 1),
date(x, 12, 31),
index=True)
else:
sciptData = get_history(scriptName, date(x, 1, 1), date(x, 12, 31))
pe = get_index_pe_history(scriptName, date(x, 1, 1), date(x, 12, 31))
sciptData['PE'] = pe['P/E']
sciptData.to_csv(datadirectory + scriptName + str(x) + 'year_withPE.csv')
print('completed for year ' + str(x))
print('completed')