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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() | |