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from nsepython import *
import seaborn as sns
import pandas as pd
import streamlit as st
from datetime import datetime
from time import gmtime, strftime
from IPython.display import clear_output
import matplotlib.pyplot as plt
from pytz import timezone
def get_data():
a=(nse_fno("BANKNIFTY"))
last_prices=round(nse_quote_ltp("BANKNIFTY"))
exp=list(set(a['expiryDates']))
exp.sort(key = lambda date: datetime.strptime(date, '%d-%b-%Y'))
if last_prices%100>50:
x=(last_prices-last_prices%100+100)
strike=[x-200,x-100,x,x+100,x+200]
elif last_prices%100<50:
x=(last_prices-last_prices%100)
strike=[x-200,x-100,x,x+100,x+200]
d={'call change op':[],
'call vwap':[],
'% change op':[],
'strike':[],
'put change op':[],
'put vwap':[],
'% change op put':[]
}
for i in a['stocks']:
for sp in strike:
if i['metadata']['expiryDate']==exp[0] and i['metadata']['optionType']=='Call' and i['metadata']['strikePrice']==sp:
d['strike'].append(sp)
d['call change op'].append(i['marketDeptOrderBook']['tradeInfo']['changeinOpenInterest'])
d['% change op'].append(i['marketDeptOrderBook']['tradeInfo']['pchangeinOpenInterest'])
d['call vwap'].append(i['marketDeptOrderBook']['tradeInfo']['vmap'])
elif i['metadata']['expiryDate']==exp[0] and i['metadata']['optionType']=='Put' and i['metadata']['strikePrice']==sp:
d['put change op'].append(i['marketDeptOrderBook']['tradeInfo']['changeinOpenInterest'])
d['% change op put'].append(i['marketDeptOrderBook']['tradeInfo']['pchangeinOpenInterest'])
d['put vwap'].append(i['marketDeptOrderBook']['tradeInfo']['vmap'])
out=pd.json_normalize(d)
out=out.explode(list(out.columns)).reset_index(drop = True)
out.fillna(0,inplace=True)
x=out.astype(float).round(2)
return x
def get_info(dataset):
df= pd.DataFrame(columns=['value', 'pcr', 'cal_per','put_per'])
value= dataset['put change op'].sum() - dataset['call change op'].sum()
pcr= dataset['put change op'].sum()/dataset['call change op'].sum()
cal_per= dataset['% change op'].mean()
put_per= dataset['% change op put'].mean()
new_row={'time':datetime.now(timezone("Asia/Kolkata")).strftime('%I.%M %p'),'value':value, 'pcr':round(pcr,2), 'cal_per':round(cal_per,2), 'put_per':round(put_per,2)}
df = df.append(new_row,ignore_index=True, verify_integrity=False, sort=None)
return df
def ploting():
try:
global final
except:
df = pd.DataFrame(columns=['value', 'pcr', 'cal_per','put_per'])
dataset= get_data()
main= get_info(dataset)
final =final.append(main,ignore_index=True, verify_integrity=False, sort=None)
return dataset,final
final = pd.DataFrame(columns=['value', 'pcr', 'cal_per','put_per','time'])
if __name__=='__main__':
st.title('WELCOME BULLS CARTEL')
today_date =strftime("%d %b %Y", gmtime()),datetime.now(timezone("Asia/Kolkata")).strftime('%I.%M %p')
st.markdown(f"as at {today_date}")
option= st.selectbox(
'How would you like to be contacted?',
('5', '10', '15'))
st.write('You selected:', option)
st.header('Important Information')
st.markdown(""" CALL % INCREASE MEANS MARKET GOES DOWN
PUT % INCREASE MEANS MARKET GOES UP
""")
dataset,final=ploting()
p1=st.empty()
p2=st.empty()
p3=st.empty()
p1.dataframe(dataset.style.highlight_max(['% change op put','% change op'],axis=0)) #Column hightlight
p2.dataframe(final.style.highlight_max(['cal_per','put_per'],axis=1)) # row highlight
fig, ax = plt.subplots(figsize=(6, 2))
ax.plot(final['time'],final['pcr'])
ax.axhline(y=0, color='black', linestyle='solid') # 0 line graph
fig.autofmt_xdate(rotation=70)
p3.pyplot(fig)
time.sleep(3*60) # how to the start again code check upper condition min * sec
p1.empty() # then clean all data frame
p2.empty()
p3.empty()
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