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Running Code for Options/Stocks- open/high/low/close/ltp

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  • S

    Hi All,

    Just shairng a running code to get Options and Stocks live prices. As i have already invested time to build this, you may leverage on this and get a head start :-)

    Step 1: Get universe of tickers (stock/options) using this code:

    import pandas as pd
import requests
import numpy as np

# Get Nifty 50 Tickers
nifty50df = pd.read_excel (r'C:\Users\sukhw\OneDrive\Documents\Options\Staticdata.xlsx', sheet_name= 'IndexMembers')  

# -----------------------------------Get the list of Options tickers as per expiry date
urlk = "https://margincalculator.angelbroking.com/OpenAPI_File/files/OpenAPIScripMaster.json"
resp = requests.get(urlk)
data1 = resp.json()
tickerlist = pd.json_normalize(data1,errors='ignore')

# select Option Tickers from the list
optiontickerlist = tickerlist[(tickerlist['exch_seg'] == 'NFO') & (tickerlist['instrumenttype'] == 'OPTSTK')]

# select Stock Tickers from the list
stocktickerlist = tickerlist[(tickerlist['exch_seg'] == 'NSE') & (tickerlist['symbol'].str.contains('-EQ', regex = True) == True)]
stocktickerlist['expiry'] = np.where(stocktickerlist.index%2==0, 'stock', 'stock') 

#append the above tables
alltickers = pd.concat([optiontickerlist,stocktickerlist], sort =False).reset_index()

# Assign columns to Dataframe 
alltickers = alltickers[['symbol', 'token','name','expiry','strike','lotsize','instrumenttype','exch_seg','tick_size']]

# Join the tables
tickerAll = pd.merge(alltickers, nifty50df, left_on=['name'], right_on=['Symbol'], how='inner')

# Drop the columns
tickerAll = tickerAll.drop(['Symbol'], axis = 1)

# Export the data
tickerAll.to_excel (r'C:\Users\sukhw\OneDrive\Documents\Options\OptionTickers.xlsx', index = None, header=True)

    Step 2: Now get Prices for select tickers you want to monitor on daily basis:

    import pandas as pd
import datetime as dt
from smartapi import SmartConnect
from datetime import date, timedelta

# Now get Prices for select Tickers as per your requirement

def getprices():
    
    vdate = (dt.datetime.today() - dt.timedelta(days=0)).strftime("%d%m%Y")
    
    
    # Get Tickers (either Stock or Options) you want to focus on for your daily monitoring
    tickerdf = pd.read_excel (r'C:\Users\sukhw\OneDrive\Documents\Options\SpecificTickers.xlsx', sheet_name= 'Sheet1')  
    
    
    # Setting the access to get  Open, high, Low, Close, LTP of options
    
    smartApi =SmartConnect(api_key="YourKey")
    login = smartApi.generateSession('YourClientID', 'YourPassword') # Angel broking
    refreshToken = login['data']['refreshToken']
    feedToken = smartApi.getfeedToken()
    smartApi.getProfile(refreshToken)
    smartApi.generateToken(refreshToken)
    
    
    
    prices_df = pd.DataFrame()
    
    # ------------------------------------ get Open, high, Low, Close, LTP of the Option Tikcers
    
    for index,row in tickerdf.iterrows():
            
        exchange = row['exch_seg']
        tradingsymbol = row['Ticker']
        symboltoken = int(row['Token'])
         
        data = smartApi.ltpData(exchange, tradingsymbol, symboltoken)
        prices = pd.json_normalize(data['data'],errors='ignore')
        
        prices['BussDate'] = vdate
        
        prices_df = prices_df.append(prices).reset_index(drop=True)
    
    # Export the data
    prices_df.to_excel (r'C:\Users\sukhw\OneDrive\Documents\Options\OptionPrices.xlsx', index = None, header=True)
    prices_df.to_excel (r'C:\Users\sukhw\OneDrive\Documents\Options\OptionPrices\OptionPrices{}.xlsx'.format(vdate), index = None, header=True)

    Any questions, feel free

    thanks
    Sukhwant

    0
  • A

    Dear Sukhwant,
    Could you send me the statistics.xlsx file for the tokens or let me know the source from where you have downloaded it. Because it is little difficult for me to identify token from the .json link that is provided by the smartapi.

    Thank you for the code.

    Regards,
    Anvesh Raja

    0