from breeze_connect import BreezeConnect
from datetime import datetime,date,time,timedelta
import pandas as pd
import numpy as np
from credentials001 import *
import requests
import datetime
import time
from nsepython import *
from datetime import datetime
requests.packages.urllib3.util.connection.HAS_IPV6 = False
currentExpiry = nse_expirydetails(nse_optionchain_scrapper('BANKNIFTY'), 0)[0]
# print(currentExpiry)
breeze = BreezeConnect(api_key=api_key)
breeze.generate_session(api_secret=api_secret, session_token=session_token)
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', None)
pd.set_option('display.max_colwidth', None)
bn = breeze.get_quotes(stock_code="CNXBAN",
exchange_code="NSE",
expiry_date="",
product_type="cash",
right="others",
strike_price="0")
df = pd.DataFrame(bn['Success'])
# print(df)
actual_ltp = df[df['exchange_code'] == 'NSE']['ltp']
# print(actual_ltp)
rounded_ltp = round(actual_ltp, -2).to_string(index=False)
rounded_ltp = pd.to_numeric(rounded_ltp).astype(int)
# print("NIFTY BANK LTP:",rounded_ltp)
p =100
# Call - Premium of 100 - Strikeprice
dfce=[]
for i in range(rounded_ltp, rounded_ltp + 2000, 100):
bnc = breeze.get_quotes(stock_code="CNXBAN",
exchange_code="NFO",
expiry_date=currentExpiry.strftime('%Y-%m-%dT06:00:00.000Z'),
product_type="options",
right="call",
strike_price=i)
df = pd.DataFrame(bnc['Success'])
# print(df)
dfce.append(df)
dfce = pd.concat(dfce)
# print(dfce)
# print(dfce['ltp'])
dfce['sq.ltp'] = (dfce['ltp'] - p) * (dfce['ltp'] - p)
# print(df1)
dfce['sq.ltp.min']=dfce['sq.ltp'].min()
cestrikeprice = dfce.loc[dfce['sq.ltp'] == dfce['sq.ltp.min'], 'strike_price'].to_string(index=False)
ltpCE100 = dfce.loc[dfce['sq.ltp'] == dfce['sq.ltp.min'], 'ltp'].to_string(index=False)
# print("CE Price of LTP 100:",strikepriceCE100,ltpCE100)
# Put - Premium of 100 - Strikeprice
dfpe=[]
for j in range(rounded_ltp-2000, rounded_ltp, 100):
bnp = breeze.get_quotes(stock_code="CNXBAN",
exchange_code="NFO",
expiry_date=currentExpiry.strftime('%Y-%m-%dT06:00:00.000Z'),
product_type="options",
right="put",
strike_price=j)
df = pd.DataFrame(bnp['Success'])
# print(df)
dfpe.append(df)
dfpe = pd.concat(dfpe)
# print(dfpe)
dfpe['sq.ltp'] = (dfpe['ltp'] - p) * (dfpe['ltp'] - p)
# print(df1)
dfpe['sq.ltp.min']=dfpe['sq.ltp'].min()
pestrikeprice = dfpe.loc[dfpe['sq.ltp'] == dfpe['sq.ltp.min'], 'strike_price'].to_string(index=False)
ltpPE100 = dfpe.loc[dfpe['sq.ltp'] == dfpe['sq.ltp.min'], 'ltp'].to_string(index=False)
# print("PE Price of LTP 100:",strikepricePE100,ltpPE100)
bnce = breeze.get_historical_data(interval="1minute", from_date=datetime.now().strftime('%Y-%m-%dT09:00:00.000Z'),
to_date=datetime.now().strftime('%Y-%m-%dT16:00:00.000Z'), stock_code="CNXBAN", exchange_code="NFO",
product_type="options",expiry_date=currentExpiry.strftime('%Y-%m-%dT06:00:00.000Z'),
right="call",strike_price=cestrikeprice)
bnpe = breeze.get_historical_data(interval="1minute", from_date=datetime.now().strftime('%Y-%m-%dT09:00:00.000Z'),
to_date=datetime.now().strftime('%Y-%m-%dT16:00:00.000Z'), stock_code="CNXBAN", exchange_code="NFO",
product_type="options",expiry_date=currentExpiry.strftime('%Y-%m-%dT06:00:00.000Z'),
right="put",strike_price=pestrikeprice)
dfce100 = pd.DataFrame(bnce['Success'])
# print(dfce100)
dfpe100 = pd.DataFrame(bnpe['Success'])
# print(dfpe100)
def rma(x, n, y0):
a = (n-1) / n
ak = a**np.arange(len(x)-1, -1, -1)
return np.r_[np.full(n, np.nan), y0, np.cumsum(ak * x) / ak / n + y0 * a**np.arange(1, len(x)+1)]
#
dfce100['change'] = pd.to_numeric(dfce100['close']).diff()
dfce100['gain'] = dfce100.change.mask(dfce100.change < 0, 0.0)
dfce100['loss'] = -dfce100.change.mask(dfce100.change > 0, -0.0)
n = 14
dfce100['avg_gain'] = rma(dfce100.gain[n+1:].to_numpy(), n, np.nansum(dfce100.gain.to_numpy()[:n+1])/n)
dfce100['avg_loss'] = rma(dfce100.loss[n+1:].to_numpy(), n, np.nansum(dfce100.loss.to_numpy()[:n+1])/n)
dfce100['rs'] = dfce100.avg_gain / dfce100.avg_loss
dfce100['rsi_14'] = 100 - (100 / (1 + dfce100.rs))
# print(dfce100)
# dfce100['sma'] = dfce100['close'].rolling(10).mean()
dfce100['lowp'] = dfce100['low'].iloc[-2]
dfce100['highp'] = dfce100['high'].iloc[-2]
dfce100ltp = pd.to_numeric(dfce100['close'].iloc[-1])
dfceltp100tp = round(dfce100ltp * 1.1, -1)
dfpe100ltp = pd.to_numeric(dfpe100['close'].iloc[-1])
dfpeltp100tp = round(dfpe100ltp * 1.1, -1)
rsi14ceprev = dfce100['rsi_14'].iloc[-2]
dfce100['lowc'] = dfce100['low'].iloc[-1]
dfce100['highc'] = dfce100['high'].iloc[-1]
rsi14cecur = dfce100['rsi_14'].iloc[-1]
# print(dfce100[["open","high","low","close","datetime","rsi_14"]])
# print(rsi14cecur,rsi14ceprev)
dfpe100['change'] = pd.to_numeric(dfpe100['close']).diff()
dfpe100['gain'] = dfpe100.change.mask(dfpe100.change < 0, 0.0)
dfpe100['loss'] = -dfpe100.change.mask(dfpe100.change > 0, -0.0)
n = 14
dfpe100['avg_gain'] = rma(dfpe100.gain[n+1:].to_numpy(), n, np.nansum(dfpe100.gain.to_numpy()[:n+1])/n)
dfpe100['avg_loss'] = rma(dfpe100.loss[n+1:].to_numpy(), n, np.nansum(dfpe100.loss.to_numpy()[:n+1])/n)
dfpe100['rs'] = dfpe100.avg_gain / dfpe100.avg_loss
dfpe100['rsi_14'] = 100 - (100 / (1 + dfpe100.rs))
# print(dfce100)
# dfce100['sma'] = dfce100['close'].rolling(10).mean()
dfpe100['lowp'] = dfpe100['low'].iloc[-2]
dfpe100['highp'] = dfpe100['high'].iloc[-2]
rsi14peprev = dfpe100['rsi_14'].iloc[-2]
dfpe100['lowc'] = dfpe100['low'].iloc[-1]
dfpe100['highc'] = dfpe100['high'].iloc[-1]
rsi14pecur = dfpe100['rsi_14'].iloc[-1]
# print(dfce100[["open","high","low","close","datetime","rsi_14"]])
print(rsi14cecur,rsi14ceprev,rsi14pecur,rsi14peprev,dfce100ltp,dfpe100ltp,dfceltp100tp,dfpeltp100tp)
if rsi14ceprev < 30 and rsi14cecur > 30:
print(breeze.place_order(stock_code="CNXBAN",
exchange_code="NFO",
product="options",
action="buy",
order_type="market",
stoploss="",
quantity="25",
price="",
validity="day",
validity_date=datetime.now().strftime('%Y-%m-%dT06:00:00.000Z'),
disclosed_quantity="0",
expiry_date=currentExpiry.strftime('%Y-%m-%dT06:00:00.000Z'),
right="call",
strike_price=cestrikeprice))
print(breeze.place_order(stock_code="CNXBAN",
exchange_code="NFO",
product="options",
action="sell",
order_type="limit",
stoploss="",
quantity="25",
price=dfceltp100tp,
validity="day",
validity_date=datetime.now().strftime('%Y-%m-%dT06:00:00.000Z'),
disclosed_quantity="0",
expiry_date=currentExpiry.strftime('%Y-%m-%dT06:00:00.000Z'),
right="call",
strike_price=cestrikeprice))
if rsi14peprev < 30 and rsi14pecur > 30:
print(breeze.place_order(stock_code="CNXBAN",
exchange_code="NFO",
product="options",
action="buy",
order_type="market",
stoploss="",
quantity="25",
price="",
validity="day",
validity_date=datetime.now().strftime('%Y-%m-%dT06:00:00.000Z'),
disclosed_quantity="0",
expiry_date=currentExpiry.strftime('%Y-%m-%dT06:00:00.000Z'),
right="put",
strike_price=pestrikeprice))
print(breeze.place_order(stock_code="CNXBAN",
exchange_code="NFO",
product="options",
action="sell",
order_type="limit",
stoploss="",
quantity="25",
price=dfpeltp100tp,
validity="day",
validity_date=datetime.now().strftime('%Y-%m-%dT06:00:00.000Z'),
disclosed_quantity="0",
expiry_date=currentExpiry.strftime('%Y-%m-%dT06:00:00.000Z'),
right="put",
strike_price=pestrikeprice))
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