def get_futures_data_LTP(symbol):
current_date = datetime.datetime.now().date()
current_year = current_date.year
current_month = current_date.month
current_expiry_date = get_expiry_date(year=current_year, month=current_month)
if current_date > current_expiry_date:
current_expiry_date = get_expiry_date(year=current_year, month=current_month+1)
next_expiry_date = get_expiry_date(year=current_year, month=current_month + 2)
else:
next_expiry_date = get_expiry_date(year=current_year, month=current_month + 1)
current_exp_quote = get_quote(symbol=symbol, instrument='FUTSTK', expiry=current_expiry_date, strike=450.00)
next_exp_quote = get_quote(symbol=symbol, instrument='FUTSTK', expiry=next_expiry_date, strike=450.00)
print(current_exp_quote)
print(next_exp_quote)
# current_exp_quote_lastPrice = current_exp_quote['lastPrice']
# next_exp_quote_lastPrice = next_exp_quote['lastPrice']
# make current_expiry_ltp = current_month_buyPrice1 from spread
# make next_expiry_ltp = next_month_sellPrice1 from spread
# becasue this will be only the sell spread: Buying current month, selling next month
current_exp_quote_lastPrice = current_exp_quote['sellPrice1']
next_exp_quote_lastPrice = next_exp_quote['buyPrice1']
print(current_exp_quote_lastPrice)
print(next_exp_quote_lastPrice)
if current_exp_quote_lastPrice == '-':
current_exp_quote_lastPrice = 0.0
if next_exp_quote_lastPrice == '-':
next_exp_quote_lastPrice = 0.0
if current_exp_quote_lastPrice > 1 and next_exp_quote_lastPrice > 1:
return current_exp_quote_lastPrice, next_exp_quote_lastPrice;
else:
return 0.0, 0.0;
def getDerivativeHistoryPrice():
df_comp = get_symbol()
for row in df_comp[['CompanyId', 'Symbol']].iterrows():
try:
result = row[1]
symbol_name = str(result.get(key='Symbol'))
company_id = int(result.get(key='CompanyId'))
endDt = datetime.now().date()
previousExipry = max(
get_expiry_date(year=endDt.year, month=(endDt.month - 1)))
currentExpiry = max(
get_expiry_date(year=endDt.year, month=endDt.month))
print(company_id, symbol_name, endDt, previousExipry,
currentExpiry)
df_prices = get_history(symbol='TCS',
start=date(previousExipry.year,
previousExipry.month,
(previousExipry.day + 1)),
end=date(endDt.year, endDt.month,
endDt.month),
futures=True,
expiry_date=date(currentExpiry.year,
currentExpiry.month,
currentExpiry.day))
df_prices['CompanyId'] = company_id
print(df_prices.head())
except Exception as e:
print('error', e)
def fetch_data(sym, expiry, start_date):
df_next_temp = pd.DataFrame()
df_curr_temp = pd.DataFrame()
df_combined_temp = pd.DataFrame()
if sym == "NIFTY" or sym == "BANKNIFTY": # add OR cond. for additional index
index_bool = True
else:
index_bool = False
curr_fut_data = get_history(symbol=sym,
start=start_date,
end=expiry,
index=index_bool,
futures=True,
expiry_date=expiry)
if expiry.month == 12:
next_expiry = get_expiry_date(expiry.year + 1, 1)
else:
next_expiry = get_expiry_date(expiry.year, expiry.month + 1)
# bug in nse site fetching wrong expiry for march, 2018
if next_expiry == date(2018, 3, 29):
next_expiry = date(2018, 3, 28)
next_fut_data = get_history(symbol=sym,
start=start_date,
end=expiry,
index=index_bool,
futures=True,
expiry_date=next_expiry)
df_curr_temp = curr_fut_data[["Last", "Close"]].copy()
df_next_temp = next_fut_data[["Last", "Close"]].copy()
df_curr_temp.reset_index(drop=False, inplace=True)
df_curr_temp['Date'] = pd.to_datetime(df_curr_temp['Date'])
df_curr_temp.set_index('Date', inplace=True)
df_next_temp.reset_index(drop=False, inplace=True)
df_next_temp['Date'] = pd.to_datetime(df_next_temp['Date'])
df_next_temp.set_index('Date', inplace=True)
# you can add/remove columns here
df_combined_temp["Curr Close"] = df_curr_temp["Close"]
# df_combined_temp["Curr Last"] = df_curr_temp["Last"]
df_combined_temp["Next Close"] = df_next_temp["Close"]
# df_combined_temp["Next Last"] = df_next_temp["Last"]
return df_combined_temp
def fetch_nifty_options_data(args):
year = args[0]
month = args[1]
logging.info(f"Start {year}-{month}")
"""
if "Date" in pd.read_csv(f"csv/{year}-{month}.csv").columns:
logging.info(f"Done {year}-{month}")
return
"""
try:
df = pd.DataFrame()
exp_dates = get_expiry_date(year=year, month=month)
logging.info(f"Exp dates for {year}-{month}: {str(exp_dates)}")
for exp_date in exp_dates:
start_date = exp_date - timedelta(days=30 * 4)
for i in range(0, 34000, 100):
df_t = get_history(symbol="BANKNIFTY",
start=start_date,
end=exp_date,
index=True,
option_type='CE',
strike_price=i,
expiry_date=exp_date)
df_t["Date"] = df_t.index
df = pd.concat([df, df_t])
df.to_csv(f"csv/banknifty/{year}-{month}.csv", index=False)
logging.info(f"Done {year}-{month}")
except Exception as e:
logging.error(f"Error: {year}-{month}")
logging.error(e, exc_info=True)
time.sleep(1)
fetch_nifty_options_data(args)
def get_data(contract):
df_combined = pd.DataFrame()
month = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
#add year at new year. Check in console whether old data is still fetched
#if not fetched remove it and make suitable logic changes in line 59
year = [2016, 2017, 2018]
#if you have already run this program before,change the filename below to
# avoid overwriting
filename = "_calendar_spread.xlsx"
filename = contract + filename
now_d = date.today()
writer = pd.ExcelWriter(filename, engine='xlsxwriter')
for yy in year:
for mm in month:
expiry = get_expiry_date(year=yy, month=mm)
if expiry == date(2016, 1, 28):
start_date = expiry + timedelta(days=1)
else:
#bug in nse site resulting in wrong expiry for march, 2018
#can be removed if fixed by nse
# run get_expiry_date(2018, 3) in console to verify
if expiry == date(2018, 3, 29):
expiry = date(2018, 3, 28)
df_combined = df_combined.append(
fetch_data(contract, expiry, start_date))
start_date = expiry + timedelta(days=1)
if start_date > now_d:
df_combined.to_excel(writer, contract)
break
writer.save()
def getExpirydate(year,month, week,weekly, symbol):
if symbol =='BANKNIFTY' and weekly:
expiry = date(year,month,17)
else :
expiry = get_expiry_date(year=year, month=month)
return expiry
def get_symbol_futures_price(sym, month_val ):
today=date(datetime.now().year,datetime.now().month,datetime.now().day)
day1=datetime(datetime.now().year,datetime.now().month,1) - timedelta(days=1)
day1=get_expiry_date(datetime.now().year,day1.month)
year_val = datetime.now().year
if month_val == 13 :
month_val = 1
year_val += 1
stock_fut = get_history(symbol=sym,
start=day1,
end=today,
futures=True,
expiry_date=get_expiry_date(year_val,month_val))
return float(stock_fut['Last'][-1])
def get_all_expirty_date_within_range(from_date,to_date): num_months = (to_date.year - from_date.year) * 12 + (to_date.month - from_date.month) + 2 current_date = from_date monthly_expiries = [] for month in range(0,num_months): monthly_expiries.append(get_expiry_date(current_date.year,current_date.month,False,True)) next_month = calendar.nextmonth(current_date.year,current_date.month) current_date = datetime(next_month[0],next_month[1],1).date() flttern_monthly_expiries = list(itertools.chain.from_iterable(monthly_expiries)) return flttern_monthly_expiries
def get_expiry_date_wrapper(year,
month,
index=True,
stock=False,
vix=False,
recursion=0):
return get_expiry_date(year=year,
month=month,
index=index,
stock=stock,
vix=vix,
recursion=recursion)
def get_index_futures(self,expiry_year,expiry_month):
expiry=get_expiry_date(expiry_year,expiry_month)
df= pd.DataFrame()
for i in range(8):
if len(df)==0:
try:
df=history.get_history(self.stock,self.start,self.end,\
futures=True,index=True,\
expiry_date=expiry-timedelta(i))
except:
continue
return df
def get_index_call(self,expiry_year,expiry_month,strike):
expiry=get_expiry_date(expiry_year,expiry_month)
df= pd.DataFrame()
for i in range(8):
if len(df)==0:
try:
df=history.get_history(self.stock,self.start,self.end,\
option_type='CE',index=True,\
expiry_date=expiry-timedelta(i),strike_price=strike)
except:
continue
return df
def get_expiry(year=2018, sm=1, m=12):
'''
:param year: start year for contracts
:param sm: start month for contract
:param m: total month wise contracts
:return: list of expiry till current month + 2
'''
from nsepy.derivatives import get_expiry_date
now = datetime.now()
y_now = now.year
expiry = []
try:
if year < y_now:
expiry.extend(
list(
map(lambda x: get_expiry_date(year=year, month=x),
range(sm, m + 1))))
year = year + 1
while (year < y_now):
expiry.extend(
list(
map(lambda x: get_expiry_date(year=year, month=x),
range(1, m + 1))))
year = year + 1
if year == y_now:
expiry.extend(
list(
map(
lambda x: get_expiry_date(year=y_now, month=x)
if x <= now.month + 2 else None, range(1, m + 1))))
except Exception as e:
print(e)
if len(expiry) > 0:
while (expiry[-1] == None):
expiry.pop(-1)
return (expiry)
else:
print("not sufficient expiry dates", expiry)
def get_list_of_futures_price_for_next_months(symbol):
global totalprofit
if not nse.is_valid_code(symbol):
print ("Error : " + symbol + "is not valid")
sys.exit(-1)
month={1:"JAN", 2:"FEB",3:"MAR",4:"APR",5:"MAY",6:"JUN",7:"JUL",8:"AUG",9:"SEP",10:"OCT",11:"NOV",12:"DEC"};
lot_size = nse.get_fno_lot_sizes(cached=False)[symbol]
#if lot_size < 1000 or lot_size > 10000:
# return
day_val = get_expiry_date(datetime.now().year,datetime.now().month)
val = get_symbol_futures_price(symbol, datetime.now().month )
val_next_month = get_symbol_futures_price(symbol, datetime.now().month + 1)
spot_price = nse.get_quote(symbol)['lastPrice']
diff_with_spot_price_curr_month = spot_price - val
if diff_with_spot_price_curr_month < 0:
diff_with_spot_price_curr_month = val - spot_price
diff_with_spot_price_next_month = spot_price - val_next_month
if diff_with_spot_price_next_month < 0:
diff_with_spot_price_next_month = val_next_month - spot_price
diff = val - val_next_month
if diff < 0:
diff = val_next_month - val
expectedprofit = diff * lot_size
if expectedprofit < 5000 :
return
totalprofit += int(expectedprofit)
print("Symbol : "+symbol + " Lot Size : " + str(lot_size))
print("===================")
print("Stock Price : "+str(spot_price))
print(month[datetime.now().month] + " Fut Price : " + str(val))
print(month[(datetime.now() + relativedelta( months=+1)).month] + " Fut Price : " + str(val_next_month))
print("Expected Profit: " + str(expectedprofit))
print("************************************************************")
def func_run_parallel(i):
global curr_frames
global near_frames
curr_day = today - relativedelta(days=i)
curr_day_str = curr_day.strftime('%Y-%m-%d')
# print(curr_day_str)
expiry_date_curr_month = list(
get_expiry_date(curr_day.year, curr_day.month))
# try:
# with time_limit(1):
data_curr_month = downloadHistoricDataForFuture(
symbol,
start_date=curr_day,
end_date=curr_day,
expiry_date=max(expiry_date_curr_month),
index_flag=index_flag)
data_curr_month.reset_index(level=0, inplace=True)
# print(curr_frames)
curr_frames.append(data_curr_month)
# except TimeoutException as e:
# print("Timed out!")
near_day = curr_day + relativedelta(months=1)
expiry_date_near_month = list(
get_expiry_date(near_day.year, near_day.month))
# try:
# with time_limit(1):
data_near_month = downloadHistoricDataForFuture(
symbol,
start_date=curr_day,
end_date=curr_day,
expiry_date=max(expiry_date_near_month),
index_flag=index_flag)
data_near_month.reset_index(level=0, inplace=True)
# print(near_frames)
near_frames.append(data_near_month)
def GetCurrentWeekExpiry():
now = datetime.now().date()
curr_year = now.year
curr_month = now.month
month_expiries = get_expiry_date(year=curr_year, month=curr_month)
closest_expiry = datetime(2050, 1, 1).date()
month_expiries.add(datetime(2021, 4, 22).date())
for date in month_expiries:
if date >= now:
closest_expiry = min(date, closest_expiry)
expiry_year = str(closest_expiry.year)[2:]
expiry_month = str(closest_expiry.month).replace('0', '')
expiry_day = str(closest_expiry.day)
expiry = expiry_year + expiry_month + expiry_day
return expiry
def main(self, args):
from_date = Utils.get_date_from_string(args.beginDate)
to_date = Utils.get_date_from_string(args.endDate)
try:
expiry_date = None if args.expiryDate is None else Utils.get_date_from_string(
args.expiryDate)
except ValueError:
expiry_month_year = Utils.get_month_year_from_string(
args.expiryDate)
expiry_date = list(
get_expiry_date(year=expiry_month_year.year,
month=expiry_month_year.month))[0]
strike_price = None if args.strike_price is None else int(
args.strike_price)
if args.optionType is None:
data = get_history(symbol=args.symbol,
start=from_date,
end=to_date,
index=args.index,
futures=args.futures,
expiry_date=expiry_date,
strike_price=strike_price)
else:
data = get_history(symbol=args.symbol,
start=from_date,
end=to_date,
index=args.index,
futures=args.futures,
expiry_date=expiry_date,
option_type=args.optionType,
strike_price=strike_price)
print(data)
if args.no_graph is False and len(data) > 0:
Utils.plot_graph(args.plotType, data)
def get_data():
# try to keep the number of contracts less than 8 for optimal performance
symbols = ["ZEEL"]
month = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
# add year at new year. Check in console whether old data is still fetched
# if not fetched remove it and make suitable logic changes in line 95
year = [2018, 2019]
# if you have already run this program before,change the filename below to
# avoid overwriting
filename = "calendar_spread.xlsx"
now_d = date.today()
writer = pd.ExcelWriter(filename, engine='xlsxwriter')
for sym in symbols:
df_combined = pd.DataFrame()
for yy in year:
for mm in month:
expiry = get_expiry_date(year=yy, month=mm)
if expiry == date(2018, 1, 25):
start_date = expiry + timedelta(days=1)
else:
# bug in nse site resulting in wrong expiry for march, 2018
# can be removed if fixed by nse
# run get_expiry_date(2018, 3) in console to verify
if expiry == date(2018, 3, 29):
expiry = date(2018, 3, 28)
df_combined = df_combined.append(fetch_data(sym, expiry,
start_date))
start_date = expiry + timedelta(days=1)
if start_date > now_d:
df_combined.to_excel(writer, sym)
break
writer.save()
from nsepy.liveurls import quote_eq_url, quote_derivative_url, option_chain_url
from nsepy.derivatives import get_expiry_date
import requests, bs4
import glob, datetime, os, sys
import time
from datetime import datetime
import sqlalchemy
import pyodbc
import urllib
from sqlalchemy.sql import text as sa_text
symbols_stock = ['YESBANK','MARUTI','RELIANCE','BPCL','HDFC','SBIN','HDFCBANK','ICICIBANK','TATAMOTORS','BAJFINANCE','TATASTEEL','AXISBANK','TCS','TITAN','INFY','ITC','LT']
symbols_index = ['NIFTY']
sector_dict = {'YESBANK':'Banking','MARUTI':'Automobile','RELIANCE':'Energy','BPCL':'Energy','HDFC':'Financial Services','SBIN':'Banking','HDFCBANK':'Banking','ICICIBANK':'Banking','TATAMOTORS':'Automobile','BAJFINANCE':'Financial Services','TATASTEEL':'Metals','AXISBANK':'Banking','TCS':'Information Technology','TITAN':'Consumer Goods','INFY':'Information Technology','ITC':'Consumer Goods','LT':'Construction'}
month_list = [10, 11, 12]
expiries = [get_expiry_date(year=2019, month=k) for k in month_list]
eq_quote_referer = "https://www.nseindia.com/live_market/dynaContent/live_watch/get_quote/GetQuote.jsp?symbol={}&illiquid=0&smeFlag=0&itpFlag=0"
derivative_quote_referer = "https://www.nseindia.com/live_market/dynaContent/live_watch/get_quote/GetQuoteFO.jsp?underlying={}&instrument={}&expiry={}&type={}&strike={}"
option_chain_referer = "https://www.nseindia.com/live_market/dynaContent/live_watch/option_chain/optionKeys.jsp?symbolCode=-9999&symbol=NIFTY&symbol=BANKNIFTY&instrument=OPTIDX&date=-&segmentLink=17&segmentLink=17"
quoted = urllib.parse.quote_plus("DRIVER={SQL Server Native Client 11.0};SERVER=NKS\SQLEXPRESS;DATABASE=Derivative Analysis;Trusted_Connection=yes;")
engine = sqlalchemy.create_engine('mssql+pyodbc:///?odbc_connect={}'.format(quoted))
def refresh_table(df, table_name):
df.to_sql(table_name, con = engine, if_exists = 'append', index=False)
print ("Refreshed "+ table_name)
def get_quote(symbol, series='EQ', instrument=None, expiry=None, option_type=None, strike=None):
"""
1. Underlying security (stock symbol or index name)
def plotTheGraph(stock):
today = date.today()
currentExpiryDate = get_expiry_date(today.year, today.month)
# if(today>currentExpiryDate):
# currentExpiryDate = get_expiry_date(today.year,today.month+1)
# prevExpiryDate = get_expiry_date(today.year,today.month)
# futureExpiryDate = get_expiry_date(today.year, today.month + 2)
# else :
# currentExpiryDate = get_expiry_date(today.year, today.month)
# prevExpiryDate = get_expiry_date(today.year, today.month-1)
# if(today.month != 12):
# futureExpiryDate = get_expiry_date(today.year, today.month + 1)
# else:
# futureExpiryDate = get_expiry_date(today.year+1, 1)
currentExpiryDateSet = get_expiry_date(today.year, today.month)
prevExpiryDateSet = get_expiry_date(today.year, today.month - 1)
if (today.month != 12):
futureExpiryDateSet = get_expiry_date(today.year, today.month + 1)
else:
futureExpiryDateSet = get_expiry_date(today.year + 1, 1)
currentExpiryDate = sorted(currentExpiryDateSet, reverse=True)[0]
prevExpiryDate = sorted(prevExpiryDateSet, reverse=True)[0]
futureExpiryDate = sorted(futureExpiryDateSet, reverse=True)[0]
start = prevExpiryDate - timedelta(15)
end = today #date(2018,12,27)
print('start', start)
print('end', end)
end2 = today
index = False
future = True
instrument = 'FUTSTK'
instrumentArray = ['NIFTY', 'BANKNIFTY']
if stock in instrumentArray:
instrument = 'FUTIDX'
index = True
future = False
# Live Data
liveData = get_quote(symbol=stock,
instrument=instrument,
expiry=currentExpiryDate,
option_type='-',
strike=700)
#Pivot table code
liveData = pd.DataFrame.from_dict(liveData, orient='index')
# liveData = pd.DataFrame(liveData,columns=['column','values'])
# liveData['index'] = np.arange(len(liveData))
# liveData = liveData.reset_index().set_index('index')
# liveData.pivot(columns='level_0',values='0')
# print(liveData)
ltp = liveData.get('ltp', 0)
volume = liveData.get('numberOfContractsTraded', 0)
# print("Today's volume: ",volume)
chngOI = liveData.get('changeinOpenInterest', 0)
# print("Today's OI: ",chngOI)
# print("live: ",liveData)
# print(">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>")
data_fut = get_history(symbol=stock,
index=index,
futures=future,
start=start,
end=end,
expiry_date=currentExpiryDate)
data_fut2 = get_history(symbol=stock,
index=index,
futures=future,
start=start,
end=end2,
expiry_date=futureExpiryDate)
# pd.DataFrame(data_fut, index=data_fut['Date'].strftime("%b %d")).style.format("{:.2}")
# pd.DataFrame(data_fut2, index=data_fut..strftime("%b %d")).style.format("{:.2}")
if instrument == 'FUTSTK':
OI_combined = pd.concat(
[data_fut2['Open Interest'], data_fut['Open Interest']],
axis=1,
sort=False)
OI_combined['OI_Combined'] = OI_combined.sum(axis=1) + int(chngOI)
# print("Open Interest: ",OI_combined['OI_Combined'])
# OI_combined['OI_Combined'] = OI_combined['OI_Combined'] + chngOI
# No. of contracts
volume_combined = pd.concat([
data_fut2['Number of Contracts'], data_fut['Number of Contracts']
],
axis=1,
sort=False)
volume_combined['volume_combined'] = volume_combined.sum(
axis=1) + int(volume)
# print("Volume : ",volume_combined['volume_combined'])
# print("volume_combined['volume_combined'] = ",volume_combined['volume_combined'])
# volume_combined['volume_combined'] = volume_combined['volume_combined'] + volume
else:
# No. of contracts
volume_combined = pd.concat([data_fut2['Volume'], data_fut['Volume']],
axis=1,
sort=False)
volume_combined['volume_combined'] = volume_combined.sum(axis=1)
# print("volume_combined['volume_combined'] = ",volume_combined['volume_combined'])
# volume_combined['volume_combined'] = volume_combined['volume_combined'] + volume
# Rule to throw an alert
# Display Underlying
plt.subplot(222)
plt.title('Underlying')
# priceCombined= pd.concat([data_fut['Underlying'],data_fut2['Underlying']],axis=1,sort=False)
# plt.plot(priceCombined,color='green')
plt.plot(data_fut['Last'], color='green')
# plt.plot(pd.concat(data_fut['Last'],liveData.get('ltp',0)), color='green')
plt.legend(['Last'])
plt.xlabel('Date')
plt.ylabel('Price')
# plt.xlabel.set_major_formatter(mdates.DateFormatter('%b %d'))
# Display Volumes
plt.subplot(224)
plt.title('Volume')
plt.plot(volume_combined.volume_combined, label='Volume', color='blue')
plt.plot(volume_combined.volume_combined.rolling(5).mean(),
label='Volume',
color='orange')
plt.legend(['Volume', 'Volume_mean'])
plt.xlabel('Date')
plt.ylabel('Volume')
# plt.xlabel.set_major_formatter(mdates.DateFormatter('%b %d'))
# Display Cumulative Open Interest
plt.figure(1, figsize=(10, 9))
plt.subplot(221)
plt.title('Open Interest')
plt.plot(OI_combined.OI_Combined, label='OI')
plt.plot(OI_combined.OI_Combined.rolling(5).mean(), label='OI')
plt.xlabel('Date')
plt.ylabel('Open Interest')
# plt.xlabel.set_major_formatter(mdates.DateFormatter('%b %d'))
fig = pylab.gcf()
fig.canvas.set_window_title(stock)
plt.legend(['OI', 'OI_mean'])
plt.show()
stock = input("Enter the Future Symbol here: ")
if stock == "":
plt.close(fig)
return
else:
plt.close(fig)
plotTheGraph(stock=stock)
from datetime import date, datetime, timedelta
from nsepy import get_history
import matplotlib.pyplot as plt
from nsepy.derivatives import get_expiry_date
now = datetime.now()
today = now.date()
today = today + timedelta(days=30)
# options expire on last thursday of every month
p1_expiry = get_expiry_date(year=today.year, month=today.month - 1)
p2_expiry = get_expiry_date(
year=today.year, month=today.month - 2) + timedelta(days=1)
print(p1_expiry, p2_expiry)
symbol = "NIFTY"
close = 'Close'
print(symbol)
df = get_history(
symbol=symbol, index=True,
strike_price=9600, option_type='CE',
start=p2_expiry, end=p1_expiry, expiry_date=p1_expiry,
)
df = df.sort_index(ascending=True)
print(df[[close, 'Underlying']])
print(data_spot_14_days_back.Deliverble[count])
count_this_week = count_this_week +1
print("Coun this week=", count_this_week)
thisWeekAvgDeliverble = sum_this_week/count_this_week
print(thisWeekAvgDeliverble)
data_spot_7_days_back = get_history(symbol=str(sys.argv[1]),start=start_7,end=date(today.year,today.month,today.day))
dataSpotLength = data_spot_7_days_back.size/15
dataSpotRecords = int(dataSpotLength) -1
data_futures= get_history(symbol=str(sys.argv[1]),start=date(today.year,10,10),end=date(today.year,today.month,today.day),futures=True,expiry_date=get_expiry_date(year = today.year,month = today.month))
Length = data_futures.size
print(Length)
print(data_futures.columns)
numRecords = Length/13 #number of fields in column = 13
NumOfRecords = int(numRecords) -1
print(NumOfRecords)
if data_futures.Open[NumOfRecords] > data_futures.Close[NumOfRecords -1] and data_futures.Open[NumOfRecords] > data_futures.High[NumOfRecords -1]:
print(data_futures.Symbol, " closed above yesterdays close and also breached yesterdays high")
else:
print(data_futures.Symbol, " closed above yesterdays high")
#f data.Open() > data
def numberofDaysToExpiery(year, month):
today = datetime.date.today()
expiry = get_expiry_date(year=year, month=month)
someday = expiry
diff = someday - today
return diff.days
### columns for the data frame which will store the below ADF statitics for all co-integrated pairs
cols = ['Pair(y)','Pair(x)','ADF Test Statistics','ADF p-value',
'ADF at 1%','ADF at 5%','ADF at 10%', 'Status']
lst =[]
### loop to download the data and then check for co-integration for each symbol-pair
for i in symbol_pairs:
try:
symb = i
print("FOR THE PAIR => ",symb)
# downloading the historical data from NSE:
y= ns.get_history(symbol= symb[0], start= date(2016,10,10),
end= date(2018,10,10), futures= True,
expiry_date= nsd.get_expiry_date(2018,10))
x= ns.get_history(symbol= symb[1], start= date(2016,10,10),
end= date(2018,10,10), futures= True,
expiry_date= nsd.get_expiry_date(2018,10))
#to make sure Dataframes are the same length
min_date= max(df.dropna().index[0] for df in [y,x])
max_date= min(df.dropna().index[-1] for df in [y,x])
y= y[(y.index>= min_date) & (y.index <= max_date)]
x= x[(x.index>= min_date) & (x.index <= max_date)]
# to run ordinary Least Squares regression to fing hedge ratio and then to create spread series
df1= pd.DataFrame({'y':y['Close'],'x':x['Close']})
est= sm.OLS(df1.y,df1.x)
est= est.fit()
def get_futures_data_continous(symbol, start_date, end_date):
# try to keep the number of contracts less than 8 for optimal performance
month = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
start_month = start_date.month
end_month = end_date.month
start_year = start_date.year
end_year = end_date.year
# add year at new year. Check in console whether old data is still fetched
# if not fetched remove it and make suitable logic changes in line 95
year = [2018, 2019]
# if you have already run this program before,change the filename below to
# avoid overwriting
path_local = "../data/data_calendarSpreads/" + symbol + ".xlsx"
path_heroku = "/app/data/data_calendarSpreads/" + symbol + ".xlsx"
file = path_local if os.path.isfile(path_local) else path_heroku
exists = os.path.isfile(file)
if exists:
writerA = pd.ExcelWriter(file, engine='openpyxl')
stock_data_df = pd.read_excel(file, sheet_name=symbol)
last_record = stock_data_df['Date'].tail(1)
last_record_date_time = last_record.iloc[0]
last_record_date = last_record_date_time.to_pydatetime().date()
current_date = datetime.datetime.now().date()
current_year = current_date.year
current_month = current_date.month
print(last_record_date, current_date- timedelta(days=1))
if last_record_date < current_date - timedelta(days=1):
# if current_date > current_expiry_date:
# current_expiry_date = get_expiry_date(year=current_year, month=current_month+1)
expiry = get_expiry_date(year=current_year, month=current_month)
if expiry < last_record_date:
expiry = get_expiry_date(year=current_year, month=current_month + 1)
print(symbol, expiry, last_record_date + timedelta(days=1))
new_data = fetch_data(symbol, expiry, last_record_date + timedelta(days=1))
# stock_data_df.append(fetch_data(symbol, expiry, last_record_date + timedelta(days=1)))
if (len(new_data.index) > 0):
append_df_to_excel(file, new_data, sheet_name=symbol)
else:
print(symbol, expiry, last_record_date + timedelta(days=1))
new_data = fetch_data(symbol, expiry, last_record_date + timedelta(days=1))
# stock_data_df.append(fetch_data(symbol, expiry, last_record_date + timedelta(days=1)))
if (len(new_data.index) > 0):
append_df_to_excel(file, new_data, sheet_name=symbol)
else:
writer = pd.ExcelWriter(file, engine='xlsxwriter')
df_combined = pd.DataFrame()
for yy in year:
for mm in month:
if ((mm >= start_month and yy == start_year) or yy > start_year):
expiry = get_expiry_date(year=yy, month=mm)
if expiry == date(2018, 1, 25):
start_date = expiry + timedelta(days=1)
else:
# bug in nse site resulting in wrong expiry for march, 2018
# can be removed if fixed by nse
# run get_expiry_date(2018, 3) in console to verify
if expiry == date(2018, 3, 29):
expiry = date(2018, 3, 28)
df_combined = df_combined.append(fetch_data(symbol, expiry,
start_date))
start_date = expiry + timedelta(days=1)
if start_date > end_date:
df_combined.to_excel(writer, symbol)
break
# writer.save()
if (len(df_combined.index) > 200):
writer.save()
niftyCodes['index'] = np.arange(
len(niftyCodes)) # adding a column to assign row number
# print(niftyCodes)
niftyCodes = niftyCodes.reset_index().set_index('index')
# print(niftyCodes)
niftyCodes = niftyCodes.iloc[:, [0, 3,
4]] # Choosing 0th, 3rd, 4th column of df
# print(niftyCodes)
return niftyCodes
nifty_codes = get_nifty_scrips(niftyCodesPath)
print(nifty_codes[2:])
today = date.today()
currentExpiryDateSet = get_expiry_date(today.year, today.month)
currentExpiryDate = sorted(currentExpiryDateSet, reverse=True)[0]
def fetch_periodic_data(nifty_codes):
sourceFile = open(dyn_file_name, 'a+')
source_file_consolidate = open('LiveDataConsolidated.txt', 'w')
print(fmt.format('UNDERLYING', 'TIMESTAMP', 'OPEN', 'HIGH', 'LOW', 'LAST',
'ULAST', 'VOL', 'AVGVOL', 'VOLSGNL', 'CHOI', '%PREM',
'%PR', '%OI', '%VOL', 'MON_INV_MLL'),
file=source_file_consolidate)
sourceFile.flush()
ts = time.time()
st = datetime.fromtimestamp(ts).strftime('%Y-%m-%d %H:%M:%S')
print("fetchPeriodicData started @ ", st)
from datetime import date, datetime, timedelta
from nsepy import get_history
import matplotlib.pyplot as plt
from nsepy.derivatives import get_expiry_date
now = datetime.now()
today = now.date()
today = today + timedelta(days=30)
# options expire on last thursday of every month
p1_expiry = get_expiry_date(year=today.year, month=today.month - 1)
p2_expiry = get_expiry_date(year=today.year,
month=today.month - 2) + timedelta(days=1)
print(p1_expiry, p2_expiry)
symbol = "NIFTY"
close = 'Close'
print(symbol)
df = get_history(
symbol=symbol,
index=True,
strike_price=9600,
option_type='CE',
start=p2_expiry,
end=p1_expiry,
expiry_date=p1_expiry,
)
from nsepy import get_history
from datetime import date
import pandas as pd
from nsepy.derivatives import get_expiry_date
expiry = get_expiry_date(year=2015, month=12)
print expiry
tcs = get_history(symbol='TCS',start=date(2015,1,1),end=date(2015,12,31))
print tcs
tcs.insert(0, 'Date', pd.to_datetime(tcs.index,format='%Y-%m-%d') )
type(tcs.index)
c = type(tcs.Date)
print c
tcs.to_csv('tcs_stock.csv', encoding='utf-8', index=False)
#############################################################
infy = get_history(symbol='INFY',start=date(2015,1,1),end=date(2015,12,31))
print infy
infy.insert(0,'Date',pd.to_datetime(infy.index,format='%Y-%m-%d'))
print(type(infy.index))
print(type(infy.Date))
infy.Date.dt
infy.to_csv('infy_stock.csv', encoding='utf-8', index=False)
#############################################################
nifty_it = get_history(symbol="NIFTYIT",start=date(2015,1,1),end=date(2015,12,31),index=True)
print nifty_it
nifty_it.insert(0, 'Date', pd.to_datetime(nifty_it.index,format='%Y-%m-%d'))
print(type(nifty_it.index))
print(type(nifty_it.Date))
nifty_it.Date.dt
nifty_it.to_csv('nifty_it_index.csv', encoding='utf-8', index=False)
def data_downloader_FnO_daily(day_range):
with open('/home/parallax/algo_trading/app/wishlist.csv', 'r') as wishlist:
symbol_count = 0
csv_reader = csv.reader(wishlist)
for element in csv_reader:
symbol = element[0]
symbol.replace('_', " ")
print(symbol)
today = date.today()
symbol_count = symbol_count + 1
first_day = today
i = 0
curr_frames = []
near_frames = []
existing_current_month_data = pd.read_csv(
'/home/parallax/algo_trading/app/F&O_data/' + symbol +
'_curr_month.csv')
existing_near_month_data = pd.read_csv(
'/home/parallax/algo_trading/app/F&O_data/' + symbol +
'_near_month.csv')
while (i < day_range):
curr_day = today - relativedelta(days=i)
curr_day_str = curr_day.strftime('%Y-%m-%d')
if ((curr_day_str
not in existing_near_month_data['Date'].tolist()) or
(curr_day_str
not in existing_current_month_data['Date'].tolist())):
expiry_date_curr_month = list(
get_expiry_date(curr_day.year, curr_day.month))
data_curr_month = downloadHistoricDataForFuture(
symbol,
start_date=curr_day,
end_date=curr_day,
expiry_date=max(expiry_date_curr_month))
data_curr_month.reset_index(level=0, inplace=True)
curr_frames.append(data_curr_month)
near_day = curr_day + relativedelta(months=1)
expiry_date_near_month = list(
get_expiry_date(near_day.year, near_day.month))
data_near_month = downloadHistoricDataForFuture(
symbol,
start_date=curr_day,
end_date=curr_day,
expiry_date=max(expiry_date_near_month))
data_near_month.reset_index(level=0, inplace=True)
near_frames.append(data_near_month)
# break
i = i + 1
curr_frames.append(existing_current_month_data)
near_frames.append(existing_near_month_data)
curr_month_data = pd.concat(curr_frames)
curr_filename = "/home/parallax/algo_trading/app/F&O_data/" + symbol + "_curr_month.csv"
curr_month_data.to_csv(curr_filename, index=False)
near_month_data = pd.concat(near_frames)
near_filename = "/home/parallax/algo_trading/app/F&O_data/" + symbol + "_near_month.csv"
near_month_data.to_csv(near_filename, index=False)
print("data downloaded for " + symbol)
# break;
return "success"
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Apr 28 14:31:44 2018
@author: sanjotraibagkar
"""
from datetime import date
from nsepy.history import get_price_list
from nsepy import get_history
from datetime import date, timedelta
import pandas as pd
from matplotlib import pyplot as plt
import datetime
from nsepy.derivatives import get_expiry_date
import numpy as np
from utility import *
prices = get_price_list(dt=date(2018, 4, 27))
print(prices)
from nsepy.derivatives import get_expiry_date
expiry = get_expiry_date(year=2018, month=3)
print(expiry)
li.get_option_chain('NIFTY', instrument='OPTIDX', expiry=expiry)
Option_data_NIFTY.csv
import os
from datetime import date
from datetime import datetime
from nsepy import get_history
from nsepy.derivatives import get_expiry_date
today = datetime.today()
data = []
file = open('scripts.txt', 'r')
for line in file:
#int (line)
data.append(
get_history(symbol=line,
start=date(today.year, today.month, today.day),
end=date(today.year, today.month, today.day),
futures=True,
expiry_date=get_expiry_date(year=today.year,
month=today.month + 1)))
print(len(data))
print(data.pop(0), data.pop(1))
for line in file:
print(line in data)
