optionchain.py

import os
import datetime
import json
import matplotlib.pyplot as plt
import requests
import numpy as np
import math
import scipy.integrate as integrate
import multiprocessing

# today's date
today = str(datetime.date.today())

# making directories
def make_directory(name):
    parent_dir = "./data"
    path = os.path.join(parent_dir, name)
    try:  
        os.mkdir(path)  
    except OSError as error:  
        print(error)

make_directory(today + '-data')
make_directory('output')

request_headers = {
    'Host':'www.nseindia.com', 
    'User-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:82.0) Gecko/20100101 Firefox/82.0',
    'Accept':'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8', 
    'Accept-Language':'en-US,en;q=0.5', 
    'Accept-Encoding':'gzip, deflate, br',
    'DNT':'1', 
    'Connection':'keep-alive', 
    'Upgrade-Insecure-Requests':'1',
    'Pragma':'no-cache',
    'Cache-Control':'no-cache',    
}

nse_url = 'https://www.nseindia.com/'

# get name of all stocks

# fno_list_url = "https://www.nseindia.com/api/equity-stockIndices?index=SECURITIES%20IN%20F%26O"
# resp = requests.get(url=nse_url, headers=request_headers)
# if resp.ok:
#     req_cookies = dict(nsit=resp.cookies['nsit'], nseappid=resp.cookies['nseappid'], ak_bmsc=resp.cookies['ak_bmsc'])
#     response = requests.get(url=fno_list_url, headers=request_headers, cookies=req_cookies).json()
#     with open('fno_list.json', 'w') as f:
#         json.dump(response, f)

# with open('fno_list.json') as g:
#     fno_list = json.load(g)

# fno_scrips = []
# for i in fno_list['data']:
#     fno_scrips.append(i['symbol'])

fno_scrips = ['DEEPAKNTR', 'IRCTC', 'GUJGASLTD', 'ZEEL', 'MOTHERSUMI', 'BEL', 'POWERGRID', 'GRANULES', 'ONGC', 'BERGEPAINT', 'MRF', 'GRASIM', 'APOLLOTYRE', 'NAM-INDIA', 'UPL', 'NAVINFLUOR', 'SHREECEM', 'ALKEM', 'SRTRANSFIN', 'ULTRACEMCO', 'ASIANPAINT', 'HEROMOTOCO', 'TVSMOTOR', 'KOTAKBANK', 'NMDC', 'TITAN', 'DIVISLAB', 'INDIGO', 'JINDALSTEL', 'RECLTD', 'VEDL', 'SIEMENS', 'ADANIPORTS', 'TATAPOWER', 'AARTIIND', 'TATACONSUM', 'BHEL', 'IOC', 'VOLTAS', 'JSWSTEEL', 'PEL', 'HDFC', 'PFC', 'TRENT', 'AUROPHARMA', 'EICHERMOT', 'SUNTV', 'HCLTECH', 'UBL', 'BATAINDIA', 'MARUTI', 'L&TFH', 'RAMCOCEM', 'ICICIPRULI', 'CUB', 'MCDOWELL-N', 'HINDPETRO', 'TATASTEEL', 'ACC', 'MINDTREE', 'SAIL', 'LT', 'BOSCHLTD', 'EXIDEIND', 'GAIL', 'TECHM', 'AUBANK', 'PFIZER', 'ICICIBANK', 'ADANIENT', 'BIOCON', 'AMARAJABAT', 'SUNPHARMA', 'BALKRISIND', 'LALPATHLAB', 'JUBLFOOD', 'AMBUJACEM', 'DLF', 'IBULHSGFIN', 'HDFCBANK', 'COALINDIA', 'M&M', 'ITC', 'TORNTPOWER', 'TATAMOTORS', 'BAJAJFINSV', 'CUMMINSIND', 'BRITANNIA', 'CONCOR', 'TCS', 'M&MFIN', 'INFY', 'HINDALCO', 'WIPRO', 'BPCL', 'BHARATFORG', 'SBIN', 'NESTLEIND', 'MARICO', 'CHOLAFIN', 'HAVELLS', 'PIDILITIND', 'SBILIFE', 'APOLLOHOSP', 'HINDUNILVR', 'NTPC', 'HDFCLIFE', 'RELIANCE', 'ASHOKLEY', 'AXISBANK', 'GMRINFRA', 'DRREDDY', 'LICHSGFIN', 'DABUR', 'BAJAJ-AUTO', 'TATACHEM', 'IGL', 'INDUSINDBK', 'LUPIN', 'PVR', 'GLENMARK', 'LTTS', 'COLPAL', 'LTI', 'APLLTD', 'CIPLA', 'RBLBANK', 'BAJFINANCE', 'MUTHOOTFIN', 'NAUKRI', 'ICICIGI', 'GODREJCP', 'PNB', 'TORNTPHARM', 'MGL', 'HDFCAMC', 'IDFCFIRSTB', 'PAGEIND', 'GODREJPROP', 'CADILAHC', 'PIIND', 'CANBK', 'NATIONALUM', 'SRF', 'FEDERALBNK', 'MFSL', 'MANAPPURAM', 'MPHASIS', 'COFORGE', 'PETRONET', 'IDEA', 'BANDHANBNK', 'INDUSTOWER', 'ESCORTS', 'BANKBARODA', 'BHARTIARTL']

# get data of all stocks

for scrip in fno_scrips:
    fno_data_url = 'https://www.nseindia.com/api/quote-derivative?symbol=' + requests.utils.quote(scrip)
    resp = requests.get(url=nse_url, headers=request_headers)
    if resp.ok:
        req_cookies = dict(nsit=resp.cookies['nsit'], nseappid=resp.cookies['nseappid'], ak_bmsc=resp.cookies['ak_bmsc'])
        response = requests.get(url=fno_data_url, headers=request_headers, cookies=req_cookies).json()
        with open('./data/'+ today +'-data/fno_data_' + scrip + '.json', 'w') as f:
            json.dump(response, f)


def output(scrip) :
    make_directory('output/' + scrip)
    with open('./data/'+ today +'-data/fno_data_' + scrip + '.json') as g:
        perf = json.load(g)

    # all expiry dates
    dates = list(dict.fromkeys(perf['expiryDates']))
    for m in range(0,1):
        try:
            expiry = dates[m]
        except:
            break

        labels = []
        maxpain = []

        for i in perf['stocks']:
            if i['metadata']['expiryDate'] == expiry:
                labels.append(i['metadata']['strikePrice'])

        labels = list(dict.fromkeys(labels))
        labels.sort()

        def oi_count(type,value) :

            oi = []

            for i in labels:
                temp = len(oi)
                for j in perf['stocks']:
                    if j['metadata']['expiryDate'] == expiry and j['metadata']['optionType'] == type:
                        if str(j['metadata']['strikePrice']) == str(i):
                            if value != 'lastPrice':
                                oi.append(j['marketDeptOrderBook']['tradeInfo'][value])
                            else:
                                oi.append(j['metadata'][value])

                if temp == len(oi):
                    oi.append(0)

            return oi

        call = oi_count('Call','openInterest')
        put = oi_count('Put','openInterest')

        change_call = oi_count('Call','changeinOpenInterest')
        change_put = oi_count('Put','changeinOpenInterest')

        ltp_call = oi_count('Call','lastPrice')
        ltp_put = oi_count('Put','lastPrice')

        underlyingValue = perf['underlyingValue']

        # if perf['underlyingValue'] < 1000:
        #     value = round(perf['underlyingValue'], -1)
        # elif perf['underlyingValue'] >= 1000:
        #     value = round(perf['underlyingValue'], -2)
        # elif perf['underlyingValue'] >= 10000:
        #     value = round(perf['underlyingValue'], -3)

        strikePrices  = perf['strikePrices']
        strikePrices = list(dict.fromkeys(strikePrices))
        
        nearest = []
        l = 0
        for i in strikePrices:
            nearest.append(i - underlyingValue)
        for i in nearest:
            if i < 0:
                nearest[l] = nearest[l] * -1
                l = l + 1
        
        value = strikePrices[nearest.index(min(nearest))]
        for j in perf['stocks']:
            if j['metadata']['expiryDate'] == expiry and j['metadata']['strikePrice'] == value:
                if j['metadata']['optionType'] == 'Call':
                    iv_call = j['marketDeptOrderBook']['otherInfo']['impliedVolatility']
                if j['metadata']['optionType'] == 'Put':
                    iv_put = j['marketDeptOrderBook']['otherInfo']['impliedVolatility']


        prob = []
        daysexpiry = (datetime.datetime.strptime(expiry,'%d-%b-%Y') - datetime.datetime.strptime(today,'%Y-%m-%d')).days

        

        for i in labels:
            #STRIKE<VALUE WIN PROB => NORMSDIST(LN(STRIKEPRICE/VALUE)/CALLIV*SQRT(DAYSTOEXPIRATION/365))
            #normsdist = (1/2pi)^2 * e^(-(z^2)/2)
            if i < value and i > 0:
                try:
                    normsdist = math.log(i/underlyingValue)/((iv_call/100)*math.sqrt(daysexpiry/365))
                    result = integrate.quad(lambda q: ((1/math.sqrt(2 * math.pi))*((math.e)**(q**2/-2.0))), -np.inf, normsdist)
                    prob.append(100 - result[0]*100)
                except:
                    prob.append(0)
            elif i > value and i > 0:
                try:
                    normsdist = math.log(i/underlyingValue)/((iv_put/100)*math.sqrt(daysexpiry/365))
                    result = integrate.quad(lambda q: ((1/math.sqrt(2 * math.pi))*((math.e)**(q**2/-2.0))), -np.inf, normsdist)
                    prob.append(result[0]*100)
                except:
                    prob.append(0)
            else:
                prob.append(0)



        for i in labels:
            max_l = 0
            
            for k in range(0, labels.index(i) + 1):
                max_l = max_l + (i - labels[k])*call[k]
            for k in range(labels.index(i) , len(labels)):
                max_l = max_l + (labels[k] - i)*put[k]

            maxpain.append(max_l)

        minpos = maxpain.index(min(maxpain)) 

        x = np.arange(len(labels))

        pcr = []
        j = 0
        for i in labels:
            if call[j] == 0:
                pcr.append(1)
            else:
                if put[j]/call[j] > 10:
                    pcr.append(-1)
                else:
                    pcr.append(put[j]/call[j])
            j = j + 1


        width = 0.5

        fig, (ax1, ax2, ax3, ax4, ax5) = plt.subplots(nrows=5, ncols=1)
        fig.set_figheight(25)
        fig.set_figwidth(30)

        rects1 = ax1.bar(x - width/2, call, width, label='call')
        rects2 = ax1.bar(x + width/2, put, width, label='put')

        rects3 = ax3.bar(x - width/2, change_call, width, label='change_call')
        rects4 = ax3.bar(x + width/2, change_put, width, label='change_put')

        rects5 = ax4.bar(x - width/2, ltp_call, width, label='change_call')
        rects6 = ax4.bar(x + width/2, ltp_put, width, label='change_put')

        title = "".join(['Underlying Value - ', str(perf['underlyingValue']), ' - - Lot Size - ', str(perf['stocks'][0]['marketDeptOrderBook']['tradeInfo']['marketLot'])])
        
        props = {
            'title': title, 
            'xticks': x,
            'xticklabels': labels,
        } 
        ax1.set(**props)
        ax2.set(**props)
        ax3.set(**props)
        ax4.set(**props)
        ax5.set(**props)
        ax1.tick_params(labelsize=8, rotation=90)
        ax2.tick_params(labelsize=8, rotation=90)
        ax3.tick_params(labelsize=8, rotation=90)
        ax4.tick_params(labelsize=8, rotation=90)
        ax5.tick_params(labelsize=8, rotation=90)

        def autolabel(rects,axis):
            for rect in rects:
                height = rect.get_height()
                axis.annotate('{}'.format(height),
                            xy=(rect.get_x() + rect.get_width() / 2, height),
                            xytext=(0, 3),
                            textcoords="offset points",
                            ha='center', va='bottom', fontsize = 6)

        autolabel(rects1,ax1)
        autolabel(rects2,ax1)

        autolabel(rects3,ax3)
        autolabel(rects4,ax3)

        autolabel(rects5,ax4)
        autolabel(rects6,ax4)


        def lines(lineinfo) :
            for i in lineinfo:
                for j in i['xline']:
                    i['axis'].axvline(x=j,color = 'green')
                for j in i['hline']:
                    i['axis'].axhline(y=j,color = 'red')
        
        lines([{'axis':ax1, 'xline':{minpos,labels.index(value)}, 'hline':{}},
                {'axis':ax2, 'xline':{minpos,labels.index(value)}, 'hline':{0}},
                {'axis':ax3, 'xline':{minpos,labels.index(value)}, 'hline':{}},
                {'axis':ax4, 'xline':{minpos,labels.index(value)}, 'hline':{}},
                {'axis':ax5, 'xline':{minpos,labels.index(value)}, 'hline':{25,50,75}}
        ])
        

        ax2.plot(x, pcr)
        ax5.plot(x, prob)
        
        plt.savefig('./data/output/' + scrip + '/' + expiry + '-oi-' + today + '.png')

        plt.clf()
        plt.close('all')


if __name__ == '__main__' :
    pool = multiprocessing.Pool()
    inputs = fno_scrips
    outputs = pool.map(output,inputs)