1. Home
  2. Article Catalog
  3. Historical Implied Volatility and Greeks of Index Options 'At The Money' On Update and in the Past using Python Type Hints

Historical Implied Volatility and Greeks of Index Options 'At The Money' On Update and in the Past using Python Type Hints

Authors:
Jonathan Legrand
Developer Advocate Developer Advocate
In the article below, we will (i) automatically find the Option (of choice) closest to At The Money (ATM) and (ii) calculate its Implied Volatility. We focus below on Future (Monthly) Options on the Index .STOXX50E (EURO STOXX 50 EUR PRICE INDEX) ('EUREX') and .SPX (S&P 500 INDEX), although you can apply the logic below for another index. To find the ATM instrument, we simply and efficiently use the Search API. Usually, the calculation of the Black-Scholes-Merton model's Implied Volatility involves numerical techniques, since it is not a closed equation (unless restricting assumptions that log returns follow a standard normal distribution with mean is zero, $\mu$ = 0, and standard deviation is zero, $\sigma$ = 1, are made). If we used these techniques in calculating each Implied Volatility value on our computer, it would take several seconds - if not minutes - for each data point computed. I have chosen to use the Instrument Pricing Analytics (IPA) service in the Refinitiv Data Platform API Family instead, as this service allows me to send model specifications (and variables) and receive several (up to 100) computed Implied Volatility values in one go - in a few seconds. Not only does this save a great deal of time, but also many lines of code!
    	
            

import refinitiv.data as rd  # This is LSEG's Data and Analytics' API wrapper, called the Refinitiv Data Library for Python.

from refinitiv.data.content import historical_pricing  # We will use this Python Class in `rd` to show the Implied Volatility data already available before our work.

from refinitiv.data.content import search  # We will use this Python Class in `rd` to fid the instrument we are after, closest to At The Money.

 

import numpy as np  # We need `numpy` for mathematical and array manipilations.

import pandas as pd  # We need `pandas` for datafame and array manipilations.

import calendar  # We use `calendar` to identify holidays and maturity dates of intruments of interest.

import pytz  # We use `pytz` to manipulate time values aiding `calendar` library.

import pandas_market_calendars as mcal  # Used to identify holidays. See `https://github.com/rsheftel/pandas_market_calendars/blob/master/examples/usage.ipynb` for info on this market calendar library

from datetime import datetime, timedelta, timezone  # We use these to manipulate time values

from dateutil.relativedelta import relativedelta  # We use `relativedelta` to manipulate time values aiding `calendar` library.

 

# `plotly` is a library used to render interactive graphs:

from plotly.subplots import make_subplots

import plotly.graph_objects as go

import plotly.express as px  # This is just to see the implied vol graph when that field is available

import matplotlib.pyplot as plt  # We use `matplotlib` to just in case users do not have an environment suited to `plotly`.

from IPython.display import clear_output  # We use `clear_output` for users who wish to loop graph production on a regular basis.

 

# Let's authenticate ourseves to LSEG's Data and Analytics service, Refinitiv:

try:  # The following libraries are not available in Codebook, thus this try loop

    rd.open_session(config_name="C:\\Example.DataLibrary.Python-main\\Example.DataLibrary.Python-main\\Configuration\\refinitiv-data.config.json")

    rd.open_session("desktop.workspace")

except:

    rd.open_session()

    	
            print(f"Here we are using the refinitiv Data Library version {rd.__version__}")
        
        
    
Here we are using the refinitiv Data Library version 1.0.0b24
 

EUREX Call Options

In this article, we will attempt to calculate the Implied Volatility (IV) for Future Options on 2 indexes (.STOXX50E & .SPX) trading 'ATM', meaning that the contract's strike price is at (or near - within x%) parity with (equal to) its current treading price (TRDPRC_1). We are also only looking for such Options expiring within a set time window; allowing for the option 'forever', i.e.: that expire whenever after date of calculation. To do so, we 1st have to find the option in question. To find live Options, we best use the Search API. To find Expired Options we will use functions created in Haykaz's amazing articles "Finding Expired Options and Backtesting a Short Iron Condor Strategy" & "Functions to find Option RICs traded on different exchanges"

Finding Live Options (using Search API)

Live Options, in this context, are Options that have not expired at time of computation. To be explicit:

  • 'time of calculation' refers here to the time for which the calculation is done, i.e.: if we compute today an IV for an Option as if it was 3 days ago, 'time of calculation' is 3 days ago.
  • 'time of computation' refers here to the time when we are computing the values, i.e.: if we compute today an IV for an Option as if it was 3 days ago, 'time of computation' is today.

As aforementioned, to find live Options, we best use the Search API: Here we look for options on .STOXX50E that mature on the 3rd friday of July 2023, 2023-07-21:

    	
            

response1 = search.Definition(

    view = search.Views.SEARCH_ALL, # To see what views are available: `help(search.Views)` & `search.metadata.Definition(view = search.Views.SEARCH_ALL).get_data().data.df.to_excel("SEARCH_ALL.xlsx")`

    query=".STOXX50E",

    select="DocumentTitle, RIC, StrikePrice, ExchangeCode, ExpiryDate, UnderlyingAsset, " +

            "UnderlyingAssetName, UnderlyingAssetRIC, ESMAUnderlyingIndexCode, RCSUnderlyingMarket" +

            "UnderlyingQuoteName, UnderlyingQuoteRIC",

    filter="RCSAssetCategoryLeaf eq 'Option' and RIC eq 'STX*' and DocumentTitle ne '*Weekly*'  " +

    "and CallPutOption eq 'Call' and ExchangeCode eq 'EUX' and " +

    "ExpiryDate ge 2022-07-10 and ExpiryDate lt 2023-07-22",  # ge (greater than or equal to), gt (greater than), lt (less than) and le (less than or equal to). These can only be applied to numeric and date properties.

    top=100,

).get_data()

searchDf1 = response1.data.df

    	
            searchDf1
        
        
    

Let's say the current underlying price is 3331.7EUR, now we can pick the option with strike price closest to that, i.e.: the most 'At The Money'; note that this means that the option can be in or out the money, as long as it is the closest to at the money:

    	
            

currentUnderlyingPrc =  rd.get_history(

    universe=[searchDf1.UnderlyingQuoteRIC[0][0]],

    fields=["TRDPRC_1"],

    interval="tick").iloc[-1][0]

    	
            currentUnderlyingPrc
        
        
    
4195.55
    	
            searchDf1.iloc[(searchDf1['StrikePrice']-currentUnderlyingPrc).abs().argsort()[:1]]
        
        
    

In this instance, for this Call Option, 'STXE33500G3.EX', the strike price is 3350, higher than the spot price of our underlying which is 3331.7. The holder of this 'STXE33500G3.EX' option has the right (but not the obligation) to buy the underlying for 3350EUR, which, was the price of the underlying to stay the same till expiry (3331.7EUR on 2023-07-21), means a loss of (3350 - 3331.7 =) 18.3EUR. This option in this instance is 'Out-The-Money'.

 

N.B.: When using the Filter in Search and playing with dates, it is good to read the API Playground Documentation; it mentions that: "Dates are written in ISO datetime format. The time portion is optional, as is the timezone (assumed to be UTC unless otherwise specified). Valid examples include 2012-03-11T17:13:55Z, 2012-03-11T17:13:55, 2012-03-11T12:00-03:30, 2012-03-11.":

empty image

Function for Expiration days

Most of the time, market agents will be interested in the next expiring Option, unless we are too close to it. We would not be interested, for example, in an option expiring in 1 hour, or even tomorrow, because that is so close (in time) that the information reflected in the Option's trades in the market does not represent future expectations of its underlying, but current expectations of it.

To implement such a logic, we need to know what are the expiry dates of the option that we are interested in. We are looking for a Python function narrowing our search to options expiring on the 3rd Friday of any one month. For info on this function, please read articles "Finding Expired Options and Backtesting a Short Iron Condor Strategy" & "Functions to find Option RICs traded on different exchanges"

    	
            

def Get_exp_dates(year, days=True, mcal_get_calendar='EUREX'):

    '''

    Get_exp_dates Version 2.0:

 

    This function gets expiration dates for a year for NDX options, which are the 3rd Fridays of each month.

 

    Changes

    ----------------------------------------------

    Changed from Version 1.0 to 2.0: Jonathan Legrand changed Haykaz Aramyan's original code to allow

        (i) for the function's holiday argument to be changed, and defaulted to 'EUREX' as opposed to 'CBOE_Index_Options' and

        (ii) for the function to output full date objects as opposed to just days of the month if agument days=True.

 

    Dependencies

    ----------------------------------------------

    Python library 'pandas_market_calendars' version 3.2

 

    Parameters

    -----------------------------------------------

    Input:

        year(int): year for which expiration days are requested

 

        mcal_get_calendar(str): String of the calendar for which holidays have to be taken into account. More on this calendar (link to Github chacked 2022-10-11): https://github.com/rsheftel/pandas_market_calendars/blob/177e7922c7df5ad249b0d066b5c9e730a3ee8596/pandas_market_calendars/exchange_calendar_cboe.py

            Default: mcal_get_calendar='EUREX'

 

        days(bool): If True, only days of the month is outputed, else it's dataeime objects

            Default: days=True

 

    Output:

        dates(dict): dictionary of expiration days for each month of a specified year in datetime.date format.

    '''

 

    # get CBOE market holidays

    EUREXCal = mcal.get_calendar(mcal_get_calendar)

    holidays = EUREXCal.holidays().holidays

 

    # set calendar starting from Saturday

    c = calendar.Calendar(firstweekday=calendar.SATURDAY)

 

    # get the 3rd Friday of each month

    exp_dates = {}

    for i in range(1, 13):

        monthcal = c.monthdatescalendar(year, i)

        date = monthcal[2][-1]

        # check if found date is an holiday and get the previous date if it is

        if date in holidays:

            date = date + timedelta(-1)

        # append the date to the dictionary

        if year in exp_dates:

            ### Changed from original code from here on by Jonathan Legrand on 2022-10-11

            if days: exp_dates[year].append(date.day)

            else: exp_dates[year].append(date)

        else:

            if days: exp_dates[year] = [date.day]

            else: exp_dates[year] = [date]

    return exp_dates

    	
            

fullDates = Get_exp_dates(2022, days=False)

dates = Get_exp_dates(2022)

fullDatesStrDict = {i: [fullDates[i][j].strftime('%Y-%m-%d')

                        for j in range(len(fullDates[i]))]

                    for i in list(fullDates.keys())}

fullDatesDayDict = {i: [fullDates[i][j].day

                        for j in range(len(fullDates[i]))]

                    for i in list(fullDates.keys())}

    	
            print(fullDates)
        
        
    
{2022: [datetime.date(2022, 1, 21), datetime.date(2022, 2, 18), datetime.date(2022, 3, 18), datetime.date(2022, 4, 14), datetime.date(2022, 5, 20), datetime.date(2022, 6, 17), datetime.date(2022, 7, 15), datetime.date(2022, 8, 19), datetime.date(2022, 9, 16), datetime.date(2022, 10, 21), datetime.date(2022, 11, 18), datetime.date(2022, 12, 16)]}
    	
            print(fullDatesStrDict)
        
        
    
{2022: ['2022-01-21', '2022-02-18', '2022-03-18', '2022-04-14', '2022-05-20', '2022-06-17', '2022-07-15', '2022-08-19', '2022-09-16', '2022-10-21', '2022-11-18', '2022-12-16']}
    	
            print(dates)
        
        
    
{2022: [21, 18, 18, 14, 20, 17, 15, 19, 16, 21, 18, 16]}
    	
            print(fullDatesDayDict)
        
        
    
{2022: [21, 18, 18, 14, 20, 17, 15, 19, 16, 21, 18, 16]}

Function to find the next expiring Option outside the next x day window

Most of the time, market agents will be interested in the next expiring Option, unless we are too close to it. We would not be interested, for example, in an option expiring in 1 hour, or even tomorrow, because that is so close (in time) that the information reflected in the Option's trades in the market does not represent future expectations of its underlying, but current expectations of it.

E.g.: I would like to know what is the next Future (Monthly) Option (i) on the Index '.STOXX50E' (ii) closest to ATM (i.e.: with an underlying spot price closest to the option's strike price) (ii) Expiring in more than x days (i.e.: not too close to calculated time 't'), let's say 15 days:

    	
            x = 15
        
        
    
    	
            timeOfCalcDatetime = datetime.now()  # For now, we will focuss on the use-case where we are calculating values for today; later we will allow for it historically for any day going back a few business days.
timeOfCalcStr = datetime.now().strftime('%Y-%m-%d')
timeOfCalcStr
'2023-01-18'
    	
            

fullDatesAtTimeOfCalc = Get_exp_dates(timeOfCalcDatetime.year, days=False)  # `timeOfCalcDatetime.year` here is 2023

fullDatesAtTimeOfCalcDatetime = [

    datetime(i.year, i.month, i.day)

    for i in fullDatesAtTimeOfCalc[list(fullDatesAtTimeOfCalc.keys())[0]]]

    	
            print(fullDatesAtTimeOfCalcDatetime)
        
        
    
[datetime.datetime(2023, 1, 20, 0, 0), datetime.datetime(2023, 2, 17, 0, 0), datetime.datetime(2023, 3, 17, 0, 0), datetime.datetime(2023, 4, 21, 0, 0), datetime.datetime(2023, 5, 19, 0, 0), datetime.datetime(2023, 6, 16, 0, 0), datetime.datetime(2023, 7, 21, 0, 0), datetime.datetime(2023, 8, 18, 0, 0), datetime.datetime(2023, 9, 15, 0, 0), datetime.datetime(2023, 10, 20, 0, 0), datetime.datetime(2023, 11, 17, 0, 0), datetime.datetime(2023, 12, 15, 0, 0)]
    	
            

expiryDateOfInt = [i for i in fullDatesAtTimeOfCalcDatetime

                   if i > timeOfCalcDatetime + relativedelta(days=x)][0]

expiryDateOfInt

datetime.datetime(2023, 2, 17, 0, 0)

Now we can look for the one option we're after:

    	
            

response2 = search.Definition(

    view=search.Views.SEARCH_ALL, # To see what views are available: `help(search.Views)` & `search.metadata.Definition(view = search.Views.SEARCH_ALL).get_data().data.df.to_excel("SEARCH_ALL.xlsx")`

    query=".STOXX50E",

    select="DocumentTitle, RIC, StrikePrice, ExchangeCode, ExpiryDate, UnderlyingAsset, " +

            "UnderlyingAssetName, UnderlyingAssetRIC, ESMAUnderlyingIndexCode, RCSUnderlyingMarket" +

            "UnderlyingQuoteName, UnderlyingQuoteRIC",

    filter="RCSAssetCategoryLeaf eq 'Option' and RIC eq 'STX*' and DocumentTitle ne '*Weekly*' " +

    "and CallPutOption eq 'Call' and ExchangeCode eq 'EUX' and " +

    f"ExpiryDate ge {(expiryDateOfInt - relativedelta(days=1)).strftime('%Y-%m-%d')} " +

    f"and ExpiryDate lt {(expiryDateOfInt + relativedelta(days=1)).strftime('%Y-%m-%d')}",  # ge (greater than or equal to), gt (greater than), lt (less than) and le (less than or equal to). These can only be applied to numeric and date properties.

    top=10000,

).get_data()

searchDf2 = response2.data.df

    	
            searchDf2
        
        
    
  DocumentTitle RIC StrikePrice ExchangeCode ExpiryDate UnderlyingQuoteRIC
0 Eurex Dow Jones EURO STOXX 50 Index Option 400... STXE40000B3.EX 4000 EUX 17/02/2023 [.STOXX50E]
1 Eurex Dow Jones EURO STOXX 50 Index Option 390... STXE39000B3.EX 3900 EUX 17/02/2023 [.STOXX50E]
2 Eurex Dow Jones EURO STOXX 50 Index Option 380... STXE38000B3.EX 3800 EUX 17/02/2023 [.STOXX50E]
3 Eurex Dow Jones EURO STOXX 50 Index Option 395... STXE39500B3.EX 3950 EUX 17/02/2023 [.STOXX50E]
4 Eurex Dow Jones EURO STOXX 50 Index Option 385... STXE38500B3.EX 3850 EUX 17/02/2023 [.STOXX50E]
... ... ... ... ... ... ...
145 Eurex Dow Jones EURO STOXX 50 Index Option 502... STXE50250B3.EX 5025 EUX 17/02/2023 [.STOXX50E]
146 Eurex Dow Jones EURO STOXX 50 Index Option 507... STXE50750B3.EX 5075 EUX 17/02/2023 [.STOXX50E]
147 Eurex Dow Jones EURO STOXX 50 Index Option 505... STXE50500B3.EX 5050 EUX 17/02/2023 [.STOXX50E]
148 Eurex Dow Jones EURO STOXX 50 Index Option 512... STXE51250B3.EX 5125 EUX 17/02/2023 [.STOXX50E]
149 Eurex Dow Jones EURO STOXX 50 Index Option 517... STXE51750B3.EX 5175 EUX 17/02/2023 [.STOXX50E]

And again, we can collect the closest to ATM:

    	
            searchDf2.iloc[(searchDf2['StrikePrice']-currentUnderlyingPrc).abs().argsort()[:1]]
        
        
    
  DocumentTitle RIC StrikePrice ExchangeCode ExpiryDate UnderlyingQuoteRIC
19 Eurex Dow Jones EURO STOXX 50 Index Option 420... STXE42000B3.EX 4200 EUX 17/02/2023 [.STOXX50E]

Now we have our instrument:

    	
            

instrument = searchDf2.iloc[(searchDf2['StrikePrice']-currentUnderlyingPrc).abs().argsort()[:1]].RIC.values[0]

instrument

'STXE42000B3.EX'

Refinitiv-provided Daily Implied Volatility

Refinitiv provides pre-calculated Implied Volatility values, but they are daily, and we will look into calculating them in higher frequencies:

    	
            datetime.now().isoformat(timespec='minutes')
        
        
    
'2023-01-18T15:12'
    	
            

start = (timeOfCalcDatetime - pd.tseries.offsets.BDay(5)).strftime('%Y-%m-%dT%H:%M:%S.%f')  # '2022-10-05T07:30:00.000'

endDateTime = datetime.now()

end = endDateTime.strftime('%Y-%m-%dT%H:%M:%S.%f')  #  e.g.: '2022-09-09T20:00:00.000'

end

'2023-01-18T15:12:02.823222'
    	
            

_RefDailyImpVolDf = historical_pricing.events.Definition(

    instrument, fields=['IMP_VOLT'], count=2000).get_data()

    	
            _RefDailyImpVolDf.data.df.head()
        
        
    
STXE42000B3.EX IMP_VOLT
Timestamp  
54:57.5 20.3118
55:09.8 20.0319
55:10.4 19.7213
55:10.5 19.9746
55:12.7 20.2399
    	
            

try: RefDailyImpVolDf = _RefDailyImpVolDf.data.df.drop(['EVENT_TYPE'], axis=1)  # In codebook, this line is needed

except: RefDailyImpVolDf = _RefDailyImpVolDf.data.df # If outside of codebook

fig = px.line(RefDailyImpVolDf, title = RefDailyImpVolDf.columns.name + " " + RefDailyImpVolDf.columns[0]) # This is just to see the implied vol graph when that field is available

fig.show()

empty image

Option Price

    	
            

_optnMrktPrice = rd.get_history(

    universe=[instrument],

    fields=["TRDPRC_1"],

    interval="10min",

    start=start,  # Ought to always start at 4 am for OPRA exchanged Options, more info in the article below

    end=end)  # Ought to always end at 8 pm for OPRA exchanged Options, more info in the article below

As you can see, there isn't nessesarily a trade every 10 min.:

    	
            _optnMrktPrice.head()
        
        
    
STXE42000B3.EX TRDPRC_1
Timestamp  
11/01/2023 15:30 47
11/01/2023 15:40 45.5
11/01/2023 15:50 41.8
11/01/2023 16:20 42.9
12/01/2023 08:00 49

However, for the statistical inferences that we will make further in the article, when we will calculate Implied Volatilities and therefore implement the Black Scholes model, we will need 'continuous timeseries' with which to deal. There are several ways to go from discrete time series (like ours, even if we go down to tick data), but for this article, we will 1st focus on making 'buckets' of 10 min. If no trade is made in any 10 min. bucket, we will assume the price to have stayed the same as previously, throughout the exchange's trading hours which are:

thankfully this is simple. Let's stick with the EUREX for now:

    	
            

optnMrktPrice = _optnMrktPrice.resample('10Min').mean() # get a datapoint every 10 min

optnMrktPrice = optnMrktPrice[optnMrktPrice.index.strftime('%Y-%m-%d').isin([i for i in _optnMrktPrice.index.strftime('%Y-%m-%d').unique()])]  # Only keep trading days

optnMrktPrice = optnMrktPrice.loc[(optnMrktPrice.index.strftime('%H:%M:%S') >= '07:30:00') & (optnMrktPrice.index.strftime('%H:%M:%S') <= '22:00:00')]  # Only keep trading hours

optnMrktPrice.fillna(method='ffill', inplace=True)  # Forward Fill to populate NaN values

print(f"Our dataframe started at {str(optnMrktPrice.index[0])} and went on continuously till {str(optnMrktPrice.index[-1])}, so out of trading hours rows are removed")

optnMrktPrice

Our dataframe started at 2023-01-11 15:30:00 and went on continuously till 2023-01-18 14:00:00, so out of trading hours rows are removed
STXE42000B3.EX TRDPRC_1
Timestamp  
11/01/2023 15:30 47
11/01/2023 15:40 45.5
11/01/2023 15:50 41.8
11/01/2023 16:00 41.8
11/01/2023 16:10 41.8
... ...
18/01/2023 13:20 66.1
18/01/2023 13:30 67.7
18/01/2023 13:40 67.7
18/01/2023 13:50 67.7
18/01/2023 14:00 67.6

Note also that one may want to only look at 'At Option Trade' datapoints, i.e.: Implied Volatility when a trade is made for the Option, but not when none is made. For this, we will use the 'At Trade' (AT) dataframes:

    	
            

AToptnMrktPrice = _optnMrktPrice

AToptnMrktPrice

STXE42250C3.EX TRDPRC_1
Timestamp  
27/01/2023 13:40 64.7
27/01/2023 13:50 63
... ...
03/02/2023 10:40 86.9
03/02/2023 12:10 88.1

Underlying Asset Price

Now let's get data for the underying, which we need to calculate IV:

    	
            

underlying = searchDf2.iloc[(searchDf2['StrikePrice']-currentUnderlyingPrc).abs().argsort()[:1]].UnderlyingQuoteRIC.values[0][0]

underlying

'.STOXX50E'

If you are interested in the opening times of any one exchange, you can use the following:

    	
            

hoursDf = rd.get_data(universe=["EUREX21"],

                      fields=["ROW80_10"])

display(hoursDf)

hoursDf.iloc[0,1]

Instrument ROW80_10
EUREX21 OGBL/OGBM/OGBS 07:30-08:00 08:0...
'       OGBL/OGBM/OGBS     07:30-08:00     08:00-19:00     19:00-20:00           '
    	
            

_underlyingMrktPrice = rd.get_history(

    universe=[underlying],

    fields=["TRDPRC_1"],

    interval="10min",

    start=start,

    end=end)

_underlyingMrktPrice

.STOXX50E TRDPRC_1
Timestamp  
27/01/2023 13:30 4165.59
27/01/2023 13:40 4162.12
... ...
03/02/2023 12:10 4225.29
03/02/2023 12:20 4224.7
    	
            

ATunderlyingMrktPrice = AToptnMrktPrice.join(

    _underlyingMrktPrice, lsuffix='_OptPr', rsuffix='_UnderlyingPr', how='inner')

ATunderlyingMrktPrice

TRDPRC_1_OptPr TRDPRC_1_UnderlyingPr
Timestamp    
27/01/2023 13:40 64.7 4162.12
27/01/2023 13:50 63 4156.5
03/02/2023 10:40 86.9 4223.69
03/02/2023 12:10 88.1 4225.29

Let's put it all in one data-frame, `df`. Some datasets will have data going from the time we sort for `start` all the way to `end`. Some won't because no trade happened in the past few minutes/hours. We ought to base ourselves on the dataset with values getting closer to `end` and `ffill` for the other column. As a result, the following `if` loop is needed:

    	
            

if optnMrktPrice.index[-1] >= _underlyingMrktPrice.index[-1]:

    df = optnMrktPrice.copy()

    df['underlying ' + underlying + ' TRDPRC_1'] = _underlyingMrktPrice

else:

    df = _underlyingMrktPrice.copy()

    df.rename(columns={"TRDPRC_1": 'underlying ' + underlying + ' TRDPRC_1'}, inplace=True)

    df['TRDPRC_1'] = optnMrktPrice

    df.columns.name = optnMrktPrice.columns.name

df.fillna(method='ffill', inplace=True)  # Forward Fill to populate NaN values

df = df.dropna()

df

Strike Price

    	
            

strikePrice = searchDf2.iloc[

    (searchDf2['StrikePrice']-currentUnderlyingPrc).abs().argsort()[:1]].StrikePrice.values[0]

strikePrice

4225

Risk-Free Interest Rate

    	
            

_EurRfRate = rd.get_history(

    universe=['EURIBOR3MD='],  # USD3MFSR=, USDSOFR=

    fields=['TR.FIXINGVALUE'],

    # Since we will use `dropna()` as a way to select the rows we are after later on in the code, we need to ask for more risk-free data than needed, just in case we don't have enough:

    start=(datetime.strptime(start, '%Y-%m-%dT%H:%M:%S.%f') - timedelta(days=1)).strftime('%Y-%m-%d'),

    end=(datetime.strptime(end, '%Y-%m-%dT%H:%M:%S.%f') + timedelta(days=1)).strftime('%Y-%m-%d'))

 

_EurRfRate

EURIBOR3MD= Fixing Value
Date  
03/02/2023 2.545
02/02/2023 2.54
01/02/2023 2.483
31/01/2023 2.512
30/01/2023 2.482
27/01/2023 2.492
26/01/2023 2.468

Euribor values are released daily at 11am CET, and it is published as such on Refinitiv:

empty image
    	
            

EurRfRate = _EurRfRate.resample('10Min').mean().fillna(method='ffill')

df['EurRfRate'] = EurRfRate

You might be running your code after the latest Risk Free Rate published, so the most accurate such value after taht would be the latest value, thus the use of `ffill`:

    	
            

df = df.fillna(method='ffill')

df

STXE42250C3.EX underlying .STOXX50E TRDPRC_1 TRDPRC_1 EurRfRate
Timestamp      
27/01/2023 13:40 4162.12 64.7 2.492
27/01/2023 13:50 4156.5 63 2.492
... ... ... ...
03/02/2023 12:10 4225.29 88.1 2.54
03/02/2023 12:20 4224.7 88.1 2.54

Now for the At Trade dataframe:

    	
            

pd.options.mode.chained_assignment = None  # default='warn'

ATunderlyingMrktPrice['EurRfRate'] = [pd.NA for i in ATunderlyingMrktPrice.index]

for i in _EurRfRate.index:

    _i = str(i)[:10]

    for n, j in enumerate(ATunderlyingMrktPrice.index):

        if _i in str(j):

            if len(_EurRfRate.loc[i].values) == 2:

                ATunderlyingMrktPrice['EurRfRate'].iloc[n] = _EurRfRate.loc[i].values[0][0]

            elif len(_EurRfRate.loc[i].values) == 1:

                ATunderlyingMrktPrice['EurRfRate'].iloc[n] = _EurRfRate.loc[i].values[0]

ATdf = ATunderlyingMrktPrice.copy()

Again, you might be running your code after the latest Risk Free Rate published, so the most accurate such value after that would be the latest value, thus the use of `ffill`:

    	
            

ATdf = ATdf.fillna(method='ffill')

ATdf

 

 

 

TRDPRC_1_OptPr TRDPRC_1_UnderlyingPr EurRfRate
Timestamp      
27/01/2023 13:40 64.7 4162.12 2.492
27/01/2023 13:50 63 4156.5 2.492
03/02/2023 10:40 86.9 4223.69 2.545
03/02/2023 12:10 88.1 4225.29 2.545

Annualized Continuous Dividend Rate

We are going to assume no dividends.

 

Calculating IV

On the Developer Portal, one can see documentation about the Instrument Pricing Analytics service that allows access to calculating functions (that use to be called 'AdFin'). This service is accessible via several RESTful endpoints (in a family of endpoints called 'Quantitative Analytics') which can be used via RD.

Data returned this far was time-stamped in the GMT Time Zone, we need to re-calibrate it to the timezone of our machine

    	
            

dfGMT = df.copy()

dfLocalTimeZone = df.copy()

dfLocalTimeZone.index = [

    df.index[i].replace(

        tzinfo=pytz.timezone(

            'GMT')).astimezone(

        tz=datetime.now().astimezone().tzinfo)

    for i in range(len(df))]

dfGMT

STXE42250C3.EX underlying .STOXX50E TRDPRC_1 TRDPRC_1 EurRfRate
Timestamp      
27/01/2023 13:40 4162.12 64.7 2.492
27/01/2023 13:50 4156.5 63 2.492
27/01/2023 14:00 4160.8 63.2 2.492
... ... ... ...
03/02/2023 12:00 4224.96 86.9 2.54
03/02/2023 12:10 4225.29 88.1 2.54
03/02/2023 12:20 4224.7 88.1 2.54
    	
            dfLocalTimeZone
        
        
    
STXE42250C3.EX underlying .STOXX50E TRDPRC_1 TRDPRC_1 EurRfRate
2023-01-27 14:40:00+01:00 4162.12 64.7 2.492
2023-01-27 14:50:00+01:00 4156.5 63 2.492
2023-01-27 15:00:00+01:00 4160.8 63.2 2.492
... ... ... ...
2023-02-03 13:00:00+01:00 4224.96 86.9 2.54
2023-02-03 13:10:00+01:00 4225.29 88.1 2.54
2023-02-03 13:20:00+01:00 4224.7 88.1 2.54

Now for the At Trade dataframe:

    	
            

ATdfGMT = ATdf.copy()

ATdfLocalTimeZone = ATdf.copy()

ATdfLocalTimeZone.index = [

    ATdf.index[i].replace(

        tzinfo=pytz.timezone(

            'GMT')).astimezone(

        tz=datetime.now().astimezone().tzinfo)

    for i in range(len(ATdf))]

ATdfGMT

 

 

 

TRDPRC_1_OptPr TRDPRC_1_UnderlyingPr EurRfRate
Timestamp      
27/01/2023 13:40 64.7 4162.12 2.492
27/01/2023 13:50 63 4156.5 2.492
03/02/2023 10:40 86.9 4223.69 2.545
03/02/2023 12:10 88.1 4225.29 2.545
    	
            ATdfLocalTimeZone
        
        
    

 

 

 

TRDPRC_1_OptPr TRDPRC_1_UnderlyingPr EurRfRate
2023-01-27 14:40:00+01:00 64.7 4162.12 2.492
2023-01-27 14:50:00+01:00 63 4156.5 2.492
2023-02-03 11:40:00+01:00 86.9 4223.69 2.545
2023-02-03 13:10:00+01:00 88.1 4225.29 2.545
    	
            

universeL = [

        {

          "instrumentType": "Option",

          "instrumentDefinition": {

            "buySell": "Buy",

            "underlyingType": "Eti",

            "instrumentCode": instrument,

            "strike": str(strikePrice),

          },

          "pricingParameters": {

            "marketValueInDealCcy": str(dfLocalTimeZone['TRDPRC_1'][i]),

            "riskFreeRatePercent": str(dfLocalTimeZone['EurRfRate'][i]),

            "underlyingPrice": str(dfLocalTimeZone['underlying ' + underlying + ' TRDPRC_1'][i]),

            "pricingModelType": "BlackScholes",

            "dividendType": "ImpliedYield",

            "volatilityType": "Implied",

            "underlyingTimeStamp": "Default",

            "reportCcy": "EUR"

          }

        }

      for i in range(len(dfLocalTimeZone.index))]

    	
            

ATuniverseL = [

        {

          "instrumentType": "Option",

          "instrumentDefinition": {

            "buySell": "Buy",

            "underlyingType": "Eti",

            "instrumentCode": instrument,

            "strike": str(strikePrice),

          },

          "pricingParameters": {

            "marketValueInDealCcy": str(ATdfLocalTimeZone['TRDPRC_1_OptPr'][i]),

            "riskFreeRatePercent": str(ATdfLocalTimeZone['EurRfRate'][i]),

            "underlyingPrice": str(ATdfLocalTimeZone['TRDPRC_1_UnderlyingPr'][i]),

            "pricingModelType": "BlackScholes",

            "dividendType": "ImpliedYield",

            "volatilityType": "Implied",

            "underlyingTimeStamp": "Default",

            "reportCcy": "EUR"

          }

        }

      for i in range(len(ATdfLocalTimeZone.index))]

    	
            

def Chunks(lst, n):

    """Yield successive n-sized chunks from lst."""

    for i in range(0, len(lst), n):

        yield lst[i:i + n]

    	
            

requestFields = [

    "MarketValueInDealCcy", "RiskFreeRatePercent",

    "UnderlyingPrice", "PricingModelType",

    "DividendType", "VolatilityType",

    "UnderlyingTimeStamp", "ReportCcy",

    "VolatilityType", "Volatility",

    "DeltaPercent", "GammaPercent",

    "RhoPercent", "ThetaPercent",

    "VegaPercent"]

    	
            

for i, j in enumerate(Chunks(universeL, 100)):

    print(f"Batch of (100 or fewer) requests no.: {str(i+1)}/{str(len([i for i in Chunks(universeL, 100)]))}")

    # Example request with Body Parameter - Symbology Lookup

    request_definition = rd.delivery.endpoint_request.Definition(

        method=rd.delivery.endpoint_request.RequestMethod.POST,

        url='https://api.refinitiv.com/data/quantitative-analytics/v1/financial-contracts',

        body_parameters={"fields": requestFields,

                         "outputs": ["Data", "Headers"],

                         "universe": j})

 

    response3 = request_definition.get_data()

    headers_name = [h['name'] for h in response3.data.raw['headers']]

 

    if i == 0:

        response3df = pd.DataFrame(data=response3.data.raw['data'], columns=headers_name)

    else:

        _response3df = pd.DataFrame(data=response3.data.raw['data'], columns=headers_name)

        response3df = response3df.append(_response3df, ignore_index=True)

Batch of (100 or fewer) requests no.: 1/3
Batch of (100 or fewer) requests no.: 2/3
Batch of (100 or fewer) requests no.: 3/3
    	
            response3df
        
        
    

 

 

 

MarketValueInDealCcy RiskFreeRatePercent UnderlyingPrice PricingModelType DividendType VolatilityType UnderlyingTimeStamp ReportCcy VolatilityType Volatility DeltaPercent GammaPercent RhoPercent ThetaPercent VegaPercent
0 64.7 2.492 4162.12 BlackScholes ImpliedYield Calculated Default EUR Calculated 15.991558 0.409959 0.00172 1.888962 -1.090608 5.4819
1 63 2.492 4156.5 BlackScholes ImpliedYield Calculated Default EUR Calculated 16.097524 0.401092 0.001701 1.845857 -1.089352 5.444814
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
256 88.1 2.54 4225.29 BlackScholes ImpliedYield Calculated Default EUR Calculated 14.926027 0.520198 0.001859 2.427816 -1.075951 5.701125
257 88.1 2.54 4224.7 BlackScholes ImpliedYield Calculated Default EUR Calculated 14.979799 0.519102 0.001853 2.422134 -1.079447 5.701135
    	
            

for i, j in enumerate(Chunks(ATuniverseL, 100)):

    print(f"Batch of (100 or fewer) requests no.: {str(i+1)}/{str(len([i for i in Chunks(ATuniverseL, 100)]))}")

    # Example request with Body Parameter - Symbology Lookup

    ATrequest_definition = rd.delivery.endpoint_request.Definition(

        method=rd.delivery.endpoint_request.RequestMethod.POST,

        url='https://api.refinitiv.com/data/quantitative-analytics/v1/financial-contracts',

        body_parameters={"fields": requestFields,

                         "outputs": ["Data", "Headers"],

                         "universe": j})

 

    ATresponse3 = ATrequest_definition.get_data()

    try:

        ATheaders_name = [h['name'] for h in ATresponse3.data.raw['headers']]

    except:

        print(ATresponse3.errors)

 

    if i == 0:

        ATresponse3df = pd.DataFrame(data=ATresponse3.data.raw['data'], columns=ATheaders_name)

    else:

        _ATresponse3df = pd.DataFrame(data=ATresponse3.data.raw['data'], columns=ATheaders_name)

        ATresponse3df = ATresponse3df.append(_ATresponse3df, ignore_index=True)

Batch of (100 or fewer) requests no.: 1/1
    	
            ATresponse3df
        
        
    

 

 

 

MarketValueInDealCcy RiskFreeRatePercent UnderlyingPrice PricingModelType DividendType VolatilityType UnderlyingTimeStamp ReportCcy VolatilityType Volatility DeltaPercent GammaPercent RhoPercent ThetaPercent VegaPercent
0 64.7 2.492 4162.12 BlackScholes ImpliedYield Calculated Default EUR Calculated 15.991558 0.409959 0.00172 1.888962 -1.090608 5.4819
1 63 2.492 4156.5 BlackScholes ImpliedYield Calculated Default EUR Calculated 16.097524 0.401092 0.001701 1.845857 -1.089352 5.444814
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
37 86.9 2.545 4223.69 BlackScholes ImpliedYield Calculated Default EUR Calculated 14.85904 0.517257 0.001869 2.413944 -1.071334 5.701031
38 88.1 2.545 4225.29 BlackScholes ImpliedYield Calculated Default EUR Calculated 14.923951 0.520242 0.00186 2.42803 -1.076091 5.701092
    	
            

IPADf, ATIPADf = response3df.copy(), ATresponse3df.copy()  # IPA here stands for the service we used to get all the calculated valuse, Instrument Pricint Analitycs.

IPADf.index, ATIPADf.index = dfLocalTimeZone.index, ATdfLocalTimeZone.index

IPADf.columns.name = dfLocalTimeZone.columns.name

ATIPADf.columns.name = ATdfLocalTimeZone.columns.name

IPADf.rename(columns={"Volatility": 'ImpliedVolatility'}, inplace=True)

ATIPADf.rename(columns={"Volatility": 'ImpliedVolatility'}, inplace=True)

    	
            IPADf
        
        
    
STXE42250C3.EX MarketValueInDealCcy RiskFreeRatePercent UnderlyingPrice PricingModelType DividendType VolatilityType UnderlyingTimeStamp ReportCcy VolatilityType ImpliedVolatility DeltaPercent GammaPercent RhoPercent ThetaPercent VegaPercent
2023-01-27 14:40:00+01:00 64.7 2.492 4162.12 BlackScholes ImpliedYield Calculated Default EUR Calculated 15.991558 0.409959 0.00172 1.888962 -1.090608 5.4819
2023-01-27 14:50:00+01:00 63 2.492 4156.5 BlackScholes ImpliedYield Calculated Default EUR Calculated 16.097524 0.401092 0.001701 1.845857 -1.089352 5.444814
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2023-02-03 13:10:00+01:00 88.1 2.54 4225.29 BlackScholes ImpliedYield Calculated Default EUR Calculated 14.926027 0.520198 0.001859 2.427816 -1.075951 5.701125
2023-02-03 13:20:00+01:00 88.1 2.54 4224.7 BlackScholes ImpliedYield Calculated Default EUR Calculated 14.979799 0.519102 0.001853 2.422134 -1.079447 5.701135
    	
            ATIPADf
        
        
    

 

 

 

MarketValueInDealCcy RiskFreeRatePercent UnderlyingPrice PricingModelType DividendType VolatilityType UnderlyingTimeStamp ReportCcy VolatilityType ImpliedVolatility DeltaPercent GammaPercent RhoPercent ThetaPercent VegaPercent
2023-01-27 14:40:00+01:00 64.7 2.492 4162.12 BlackScholes ImpliedYield Calculated Default EUR Calculated 15.991558 0.409959 0.00172 1.888962 -1.090608 5.4819
2023-01-27 14:50:00+01:00 63 2.492 4156.5 BlackScholes ImpliedYield Calculated Default EUR Calculated 16.097524 0.401092 0.001701 1.845857 -1.089352 5.444814
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2023-02-03 11:40:00+01:00 86.9 2.545 4223.69 BlackScholes ImpliedYield Calculated Default EUR Calculated 14.85904 0.517257 0.001869 2.413944 -1.071334 5.701031
2023-02-03 13:10:00+01:00 88.1 2.545 4225.29 BlackScholes ImpliedYield Calculated Default EUR Calculated 14.923951 0.520242 0.00186 2.42803 -1.076091 5.701092

With out-of-trading hours

From now on we will not show AT dataframe equivalents because it is... equivalent!

    	
            

display(searchDf2.iloc[(searchDf2['StrikePrice']-currentUnderlyingPrc).abs().argsort()[:1]])

 

IPADfGraph = IPADf[['ImpliedVolatility', 'MarketValueInDealCcy',

                    'RiskFreeRatePercent', 'UnderlyingPrice', 'DeltaPercent',

                    'GammaPercent', 'RhoPercent', 'ThetaPercent', 'VegaPercent']]

 

fig = px.line(IPADfGraph)  # This is just to see the implied vol graph when that field is available

# fig.layout = dict(xaxis=dict(type="category"))

 

# Format Graph: https://plotly.com/python/tick-formatting/

fig.update_layout(

    title=instrument,

    template='plotly_dark')

 

# Make it so that only one line is shown by default: # https://stackoverflow.com/questions/73384807/plotly-express-plot-subset-of-dataframe-columns-by-default-and-the-rest-as-opt

fig.for_each_trace(

    lambda t: t.update(

        visible=True if t.name in IPADfGraph.columns[:1] else "legendonly"))

 

# fig.update_xaxes(autorange=True)

# fig.update_layout(yaxis=IPADf.index[0::10])

 

fig.show()

 

 

 

 

 

 

DocumentTitle

 

 

 

 

 

RIC StrikePrice ExchangeCode ExpiryDate UnderlyingQuoteRIC
22 Eurex Dow Jones EURO STOXX 50 Index Option 422... STXE42250C3.EX 4225 EUX 17/03/2023 [.STOXX50E]
empty image

3 Graphs

    	
            

fig = plotly.subplots.make_subplots(rows=3, cols=1)

 

fig.add_trace(go.Scatter(x=IPADf.index, y=IPADf.ImpliedVolatility, name='Op Imp Volatility'), row=1, col=1)

fig.add_trace(go.Scatter(x=IPADf.index, y=IPADf.MarketValueInDealCcy, name='Op Mk Pr'), row=2, col=1)

fig.add_trace(go.Scatter(x=IPADf.index, y=IPADf.UnderlyingPrice, name=underlying+' Undrlyg Pr'), row=3, col=1)

 

 

fig.update(layout_xaxis_rangeslider_visible=False)

fig.update_layout(title=IPADf.columns.name)

fig.update_layout(

    template='plotly_dark',

    autosize=False,

    width=1300,

    height=500)

fig.show()

searchDf2.iloc[(searchDf2['StrikePrice']-currentUnderlyingPrc).abs().argsort()[:1]]

empty image

 

 

 

 

 

 

 

 

 

DocumentTitle RIC StrikePrice ExchangeCode ExpiryDate UnderlyingQuoteRIC
22 Eurex Dow Jones EURO STOXX 50 Index Option 422... STXE42250C3.EX 4225 EUX 17/03/2023 [.STOXX50E]

Simple Graph

Certain companies are slow to update libraries, dependencies or Python versions. They/You may thus not have access to plotly (the graph library we used above). Matplotlib is rather light and should work, even on machines with old setups:

    	
            

display(searchDf2.iloc[(searchDf2['StrikePrice']-currentUnderlyingPrc).abs().argsort()[:1]])

ATIPADfSimpleGraph = ATIPADf[['ImpliedVolatility']]

 

fig, axes = plt.subplots(ncols=1)

 

ax = axes

ax.plot(ATIPADfSimpleGraph.ImpliedVolatility, '.-')

# ax.xaxis.set_major_formatter(ticker.FuncFormatter(format_date))

ax.set_title(f"{searchDf2.iloc[(searchDf2['StrikePrice']-currentUnderlyingPrc).abs().argsort()[:1]].RIC.values[0]} Implied Volatility At Trade Only")

fig.autofmt_xdate()

 

plt.show()

empty image

 

 

 

 

 

 

 

 

 

 

 

 

 

 

DocumentTitle RIC StrikePrice ExchangeCode ExpiryDate UnderlyingQuoteRIC
22 Eurex Dow Jones EURO STOXX 50 Index Option 422... STXE42250C3.EX 4225 EUX 17/03/2023 [.STOXX50E]
    	
            ATIPADfSimpleGraph
        
        
    
 

 

 

 

 

 

ImpliedVolatility

 

 

 

 

 

2023-01-27 14:40:00+01:00 15.991558
2023-01-27 14:50:00+01:00 16.097524
2023-02-03 11:40:00+01:00 14.85904
2023-02-03 13:10:00+01:00 14.923951

EUREX, or SPX Call or Put Options

Let's put it all together into a single function. This ImpVolatilityCalcIPA function will allow anyone to:

(I) find the option (i) with the index of your choice (SPX or EUREX) as underlying, (ii) closest to strike price right now (i.e.: At The Money) and (iii) with the next, closest expiry date past x days after today,

(II) calculate the Implied Volatility for that option either (i) only at times when the option itself is traded or (ii) at any time the option or the underlying is being traded.

    	
            

def ImpVolatilityCalcIPA(x=15,

                         indexUnderlying=".STOXX50E",

                         callOrPut='Put',

                         dateBack=3,

                         expiryYearOfInterest=datetime.now().year,

                         riskFreeRate=None, riskFreeRateField=None,

                         timeZoneInGraph=datetime.now().astimezone(),

                         maxColwidth=200,

                         graphStyle='without out of trading hours',  # 'with out of trading hours', '3 graphs', 'simple'

                         simpleGraphLineStyle='.-',  # 'o-'

                         simpleGraphSize=(15, 5),

                         graphTemplate='plotly_dark',

                         debug=False,

                         returnDfGraph=False,

                         AtOptionTradeOnly=False):

 

 

    if indexUnderlying == ".STOXX50E":

        exchangeC, exchangeRIC, mcalGetCalendar = 'EUX', 'STX', 'EUREX'

    elif indexUnderlying == '.SPX':

        exchangeC, exchangeRIC, mcalGetCalendar = 'OPQ', 'SPX', 'CBOE_Futures'# 'CBOE_Index_Options'  # should be 'CBOE_Index_Options'... CBOT_Equity

 

 

    def Get_exp_dates(year=expiryYearOfInterest,

                      days=True,

                      mcal_get_calendar=mcalGetCalendar):

        '''

        Get_exp_dates Version 3.0:

 

        This function gets expiration dates for a year for NDX options, which are the 3rd Fridays of each month.

 

        Changes

        ----------------------------------------------

        Changed from Version 1.0 to 2.0: Jonathan Legrand changed Haykaz Aramyan's original code to allow

            (i) for the function's holiday argument to be changed, and defaulted to 'EUREX' as opposed to 'CBOE_Index_Options' and

            (ii) for the function to output full date objects as opposed to just days of the month if agument days=True.

 

        Changed from Version 2.0 to 3.0: Jonathan Legrand changed this function to reflec tthe fact that it can be used for indexes other than EUREX.

 

        Dependencies

        ----------------------------------------------

        Python library 'pandas_market_calendars' version 3.2

 

        Parameters

        -----------------------------------------------

        Input:

            year(int): year for which expiration days are requested

 

            mcal_get_calendar(str): String of the calendar for which holidays have to be taken into account. More on this calendar (link to Github chacked 2022-10-11): https://github.com/rsheftel/pandas_market_calendars/blob/177e7922c7df5ad249b0d066b5c9e730a3ee8596/pandas_market_calendars/exchange_calendar_cboe.py

                Default: mcal_get_calendar='EUREX'

 

            days(bool): If True, only days of the month is outputed, else it's dataeime objects

                Default: days=True

 

        Output:

            dates(dict): dictionary of expiration days for each month of a specified year in datetime.date format.

        '''

 

        # get CBOE market holidays

        Cal = mcal.get_calendar(mcal_get_calendar)

        holidays = Cal.holidays().holidays

 

        # set calendar starting from Saturday

        c = calendar.Calendar(firstweekday=calendar.SATURDAY)

 

        # get the 3rd Friday of each month

        exp_dates = {}

        for i in range(1, 13):

            monthcal = c.monthdatescalendar(year, i)

            date = monthcal[2][-1]

            # check if found date is an holiday and get the previous date if it is

            if date in holidays:

                date = date + timedelta(-1)

            # append the date to the dictionary

            if year in exp_dates:

                ### Changed from original code from here on by Jonathan Legrand on 2022-10-11

                if days: exp_dates[year].append(date.day)

                else: exp_dates[year].append(date)

            else:

                if days: exp_dates[year] = [date.day]

                else: exp_dates[year] = [date]

        return exp_dates

 

    timeOfCalcDatetime = datetime.now()  # For now, we will focuss on the use-case where we are calculating values for today; later we will allow for it historically for any day going back a few business days.

    timeOfCalcStr = datetime.now().strftime('%Y-%m-%d')

    fullDatesAtTimeOfCalc = Get_exp_dates(timeOfCalcDatetime.year, days=False)  # `timeOfCalcDatetime.year` here is 2023

    fullDatesAtTimeOfCalcDatetime = [

        datetime(i.year, i.month, i.day)

        for i in fullDatesAtTimeOfCalc[list(fullDatesAtTimeOfCalc.keys())[0]]]

    expiryDateOfInt = [i for i in fullDatesAtTimeOfCalcDatetime

                       if i > timeOfCalcDatetime + relativedelta(days=x)][0]

 

    if debug: print(f"expiryDateOfInt: {expiryDateOfInt}")

 

    response = search.Definition(

        view = search.Views.SEARCH_ALL, # To see what views are available: `help(search.Views)` & `search.metadata.Definition(view = search.Views.SEARCH_ALL).get_data().data.df.to_excel("SEARCH_ALL.xlsx")`

        query=indexUnderlying,

        select="DocumentTitle, RIC, StrikePrice, ExchangeCode, ExpiryDate, UnderlyingAsset, " +

                "UnderlyingAssetName, UnderlyingAssetRIC, ESMAUnderlyingIndexCode, RCSUnderlyingMarket" +

                "UnderlyingQuoteName, UnderlyingQuoteRIC",

        filter=f"RCSAssetCategoryLeaf eq 'Option' and RIC eq '{exchangeRIC}*' and DocumentTitle ne '*Weekly*' " +

        f"and CallPutOption eq '{callOrPut}' and ExchangeCode eq '{exchangeC}' and " +

        f"ExpiryDate ge {(expiryDateOfInt - relativedelta(days=1)).strftime('%Y-%m-%d')} " +

        f"and ExpiryDate lt {(expiryDateOfInt + relativedelta(days=1)).strftime('%Y-%m-%d')}",  # ge (greater than or equal to), gt (greater than), lt (less than) and le (less than or equal to). These can only be applied to numeric and date properties.

        top=10000,

    ).get_data()

    searchDf = response.data.df

 

    if debug: display(searchDf)

 

    try:

        underlyingPrice =  rd.get_history(

            universe=[indexUnderlying],

            fields=["TRDPRC_1"],

            interval="tick").iloc[-1][0]

    except:

        print("Function failed at the search strage, returning the following dataframe: ")

        display(searchDf)

 

    if debug:

        print(f"Underlying {indexUnderlying}'s price recoprded here was {underlyingPrice}")

        display(searchDf.iloc[(searchDf['StrikePrice']-underlyingPrice).abs().argsort()[:10]])

 

    instrument = searchDf.iloc[(searchDf['StrikePrice']-underlyingPrice).abs().argsort()[:1]].RIC.values[0]

 

    start = (timeOfCalcDatetime - pd.tseries.offsets.BDay(dateBack)).strftime('%Y-%m-%dT%H:%M:%S.%f')  # '2022-10-05T07:30:00.000'

    endDateTime = datetime.now()

    end = endDateTime.strftime('%Y-%m-%dT%H:%M:%S.%f')  #  e.g.: '2022-09-09T20:00:00.000'

 

    _optnMrktPrice = rd.get_history(

        universe=[instrument],

        fields=["TRDPRC_1"],

        interval="10min",

        start=start,  # Ought to always start at 4 am for OPRA exchanged Options, more info in the article below

        end=end)  # Ought to always end at 8 pm for OPRA exchanged Options, more info in the article below

 

    if debug:

        print(instrument)

        display(_optnMrktPrice)

 

    ## Data on certain options are stale and do not nessesarily show up on Workspace, in case that happens, we will pick the next ATM Option, which probably will have the same strike, but we will only do so once, any more and we could get too far from strike:

    if _optnMrktPrice.empty:

        if debug: print(f"No data could be found for {instrument}, so the next ATM Option was chosen")

        instrument = searchDf.iloc[(searchDf['StrikePrice']-underlyingPrice).abs().argsort()[1:2]].RIC.values[0]

        if debug: print(f"{instrument}")

        _optnMrktPrice = rd.get_history(universe=[instrument],

                                        fields=["TRDPRC_1"], interval="10min",

                                        start=start, end=end)

        if debug: display(_optnMrktPrice)

    if _optnMrktPrice.empty:  # Let's try one more time, as is often nessesary

        if debug: print(f"No data could be found for {instrument}, so the next ATM Option was chosen")

        instrument = searchDf.iloc[(searchDf['StrikePrice']-underlyingPrice).abs().argsort()[2:3]].RIC.values[0]

        if debug: print(f"{instrument}")

        _optnMrktPrice = rd.get_history(universe=[instrument],

                                        fields=["TRDPRC_1"], interval="10min",

                                        start=start, end=end)

        if debug: display(_optnMrktPrice)

    if _optnMrktPrice.empty:

        print(f"No data could be found for {instrument}, please check it on Refinitiv Workspace")

 

    optnMrktPrice = _optnMrktPrice.resample('10Min').mean() # get a datapoint every 10 min

    optnMrktPrice = optnMrktPrice[optnMrktPrice.index.strftime('%Y-%m-%d').isin([i for i in _optnMrktPrice.index.strftime('%Y-%m-%d').unique()])]  # Only keep trading days

    optnMrktPrice = optnMrktPrice.loc[(optnMrktPrice.index.strftime('%H:%M:%S') >= '07:30:00') & (optnMrktPrice.index.strftime('%H:%M:%S') <= '22:00:00')]  # Only keep trading hours

    optnMrktPrice.fillna(method='ffill', inplace=True)  # Forward Fill to populate NaN values

 

    # Note also that one may want to only look at 'At Option Trade' datapoints,

    # i.e.: Implied Volatility when a trade is made for the Option, but not when

    # none is made. For this, we will use the 'At Trade' (`AT`) dataframes:

    if AtOptionTradeOnly: AToptnMrktPrice = _optnMrktPrice

 

    underlying = searchDf.iloc[(searchDf['StrikePrice']).abs().argsort()[:1]].UnderlyingQuoteRIC.values[0][0]

 

    _underlyingMrktPrice = rd.get_history(

        universe=[underlying],

        fields=["TRDPRC_1"],

        interval="10min",

        start=start,

        end=end)

    # Let's put it al in one data-frame, `df`. Some datasets will have data

    # going from the time we sert for `start` all the way to `end`. Some won't

    # because no trade happened in the past few minutes/hours. We ought to base

    # ourselves on the dataset with values getting closer to `end` and `ffill`

    # for the other column. As a result, the following `if` loop is needed:

    if optnMrktPrice.index[-1] >= _underlyingMrktPrice.index[-1]:

        df = optnMrktPrice.copy()

        df['underlying ' + underlying + ' TRDPRC_1'] = _underlyingMrktPrice

    else:

        df = _underlyingMrktPrice.copy()

        df.rename(

            columns={"TRDPRC_1": 'underlying ' + underlying + ' TRDPRC_1'},

            inplace=True)

        df['TRDPRC_1'] = optnMrktPrice

        df.columns.name = optnMrktPrice.columns.name

    df.fillna(method='ffill', inplace=True)  # Forward Fill to populate NaN values

    df = df.dropna()

 

    if AtOptionTradeOnly:

        ATunderlyingMrktPrice = AToptnMrktPrice.join(

            _underlyingMrktPrice, lsuffix='_OptPr', rsuffix=' Underlying ' + underlying + ' TRDPRC_1', how='inner')

        ATdf = ATunderlyingMrktPrice

 

    strikePrice = searchDf.iloc[(searchDf['StrikePrice']-underlyingPrice).abs().argsort()[:1]].StrikePrice.values[0]

 

    if riskFreeRate is None and indexUnderlying == ".SPX":

        _riskFreeRate = 'USDCFCFCTSA3M='

        _riskFreeRateField = 'TR.FIXINGVALUE'

    elif riskFreeRate is None and indexUnderlying == ".STOXX50E":

        _riskFreeRate = 'EURIBOR3MD='

        _riskFreeRateField = 'TR.FIXINGVALUE'

    else:

        _riskFreeRate, _riskFreeRateField = riskFreeRate, riskFreeRateField

 

    _RfRate = rd.get_history(

        universe=[_riskFreeRate],  # USD3MFSR=, USDSOFR=

        fields=[_riskFreeRateField],

        # Since we will use `dropna()` as a way to select the rows we are after later on in the code, we need to ask for more risk-free data than needed, just in case we don't have enough:

        start=(datetime.strptime(start, '%Y-%m-%dT%H:%M:%S.%f') - timedelta(days=1)).strftime('%Y-%m-%d'),

        end=(datetime.strptime(end, '%Y-%m-%dT%H:%M:%S.%f') + timedelta(days=1)).strftime('%Y-%m-%d'))

 

    RfRate = _RfRate.resample('10Min').mean().fillna(method='ffill')

    df['RfRate'] = RfRate

    df = df.fillna(method='ffill')

 

    if AtOptionTradeOnly:

        pd.options.mode.chained_assignment = None  # default='warn'

        ATunderlyingMrktPrice['RfRate'] = [pd.NA for i in ATunderlyingMrktPrice.index]

        for i in RfRate.index:

            _i = str(i)[:10]

            for n, j in enumerate(ATunderlyingMrktPrice.index):

                if _i in str(j):

                    if len(RfRate.loc[i].values) == 2:

                        ATunderlyingMrktPrice['RfRate'].iloc[n] = RfRate.loc[i].values[0][0]

                    elif len(RfRate.loc[i].values) == 1:

                        ATunderlyingMrktPrice['RfRate'].iloc[n] = RfRate.loc[i].values[0]

        ATdf = ATunderlyingMrktPrice.copy()

    ATdf = ATdf.fillna(method='ffill')  # This is in case there were no Risk Free datapoints released after a certain time, but trades on the option still went through.

 

    if timeZoneInGraph != 'GMT':

        df.index = [

            df.index[i].replace(

                tzinfo=pytz.timezone(

                    'GMT')).astimezone(

                tz=timeZoneInGraph.tzinfo)

            for i in range(len(df))]

        if AtOptionTradeOnly:

            ATdf.index = [

                ATdf.index[i].replace(

                    tzinfo=pytz.timezone(

                        'GMT')).astimezone(

                    tz=datetime.now().astimezone().tzinfo)

                for i in range(len(ATdf))]

 

    if AtOptionTradeOnly:

        universeL = [

            {

              "instrumentType": "Option",

              "instrumentDefinition": {

                "buySell": "Buy",

                "underlyingType": "Eti",

                "instrumentCode": instrument,

                "strike": str(strikePrice),

              },

              "pricingParameters": {

                "marketValueInDealCcy": str(ATdf['TRDPRC_1_OptPr'][i]),

                "riskFreeRatePercent": str(ATdf['RfRate'][i]),

                "underlyingPrice": str(ATdf['TRDPRC_1 Underlying ' + underlying + ' TRDPRC_1'][i]),

                "pricingModelType": "BlackScholes",

                "dividendType": "ImpliedYield",

                "volatilityType": "Implied",

                "underlyingTimeStamp": "Default",

                "reportCcy": "EUR"

              }

            }

            for i in range(len(ATdf.index))]

    else:

        universeL = [

            {

              "instrumentType": "Option",

              "instrumentDefinition": {

                "buySell": "Buy",

                "underlyingType": "Eti",

                "instrumentCode": instrument,

                "strike": str(strikePrice),

              },

              "pricingParameters": {

                "marketValueInDealCcy": str(df['TRDPRC_1'][i]),

                "riskFreeRatePercent": str(df['RfRate'][i]),

                "underlyingPrice": str(df['underlying ' + underlying + ' TRDPRC_1'][i]),

                "pricingModelType": "BlackScholes",

                "dividendType": "ImpliedYield",

                "volatilityType": "Implied",

                "underlyingTimeStamp": "Default",

                "reportCcy": "EUR"

              }

            }

            for i in range(len(df.index))]

 

    def Chunks(lst, n):

        """Yield successive n-sized chunks from lst."""

        for i in range(0, len(lst), n):

            yield lst[i:i + n]

 

    requestFields = [

        "MarketValueInDealCcy", "RiskFreeRatePercent",

        "UnderlyingPrice", "PricingModelType",

        "DividendType", "VolatilityType",

        "UnderlyingTimeStamp", "ReportCcy",

        "VolatilityType", "Volatility",

        "DeltaPercent", "GammaPercent",

        "RhoPercent", "ThetaPercent", "VegaPercent"]

 

    for i, j in enumerate(Chunks(universeL, 100)):

        # Example request with Body Parameter - Symbology Lookup

        request_definition = rd.delivery.endpoint_request.Definition(

            method=rd.delivery.endpoint_request.RequestMethod.POST,

            url='https://api.refinitiv.com/data/quantitative-analytics/v1/financial-contracts',

            body_parameters={

                "fields": requestFields,

                "outputs": ["Data", "Headers"],

                "universe": j})

        response2 = request_definition.get_data()

        headers_name = [h['name'] for h in response2.data.raw['headers']]

        _IPADf = pd.DataFrame(data=response2.data.raw['data'], columns=headers_name)

        if i == 0: IPADf = _IPADf

        else: IPADf = IPADf.append(_IPADf, ignore_index=True)

 

    if AtOptionTradeOnly:

        IPADf.index = ATdf.index

        IPADf.columns.name = ATdf.columns.name

    else:

        IPADf.index = df.index

        IPADf.columns.name = df.columns.name

    IPADf.rename(columns={"Volatility": 'ImpliedVolatility'}, inplace=True)

 

    # We are going to want to show details about data retreived in a dataframe in the output of this function. The one line below allows us to maximise the width (column) length of cells to see all that is written within them.

    pd.options.display.max_colwidth = maxColwidth

 

    if graphStyle == 'simple':

        display(searchDf.iloc[(searchDf['StrikePrice']-underlyingPrice).abs().argsort()[:1]])

        IPADfSimpleGraph = IPADf[['ImpliedVolatility']]

        fig, axes = plt.subplots(ncols=1, figsize=simpleGraphSize)

        axes.plot(IPADf[['ImpliedVolatility']].ImpliedVolatility, simpleGraphLineStyle)

        if AtOptionTradeOnly: axes.set_title(f"{instrument} Implied Volatility At Trade Only")

        else: axes.set_title(f"{instrument} Implied Volatility")

        plt.show()

 

    else:

 

        display(searchDf.iloc[(searchDf['StrikePrice']-underlyingPrice).abs().argsort()[:1]])

 

        IPADfGraph = IPADf[

            ['ImpliedVolatility', 'MarketValueInDealCcy',

             'RiskFreeRatePercent', 'UnderlyingPrice', 'DeltaPercent',

             'GammaPercent', 'RhoPercent', 'ThetaPercent', 'VegaPercent']]

 

        if debug: display(IPADfGraph)

 

        try:  # This is needed in case there is not enough data to calculate values for all timestamps , see https://stackoverflow.com/questions/67244912/wide-format-csv-with-plotly-express

            fig = px.line(IPADfGraph)

        except:

            if returnDfGraph:

                return IPADfGraph

            else:

                IPADfGraph = IPADfGraph[

                    ["ImpliedVolatility", "MarketValueInDealCcy",

                     "RiskFreeRatePercent", "UnderlyingPrice"]]

                fig = px.line(IPADfGraph)

 

        if graphStyle == 'with out of trading hours':

            fig.update_layout(

                title=instrument,

                template=graphTemplate)

            fig.for_each_trace(

                lambda t: t.update(

                    visible=True if t.name in IPADfGraph.columns[:1] else "legendonly"))

            fig.show()

 

        elif graphStyle == '3 graphs':

            fig = plotly.subplots.make_subplots(rows=3, cols=1)

 

            fig.add_trace(go.Scatter(

                x=IPADf.index, y=IPADfGraph.ImpliedVolatility,

                name='Op Imp Volatility'), row=1, col=1)

            fig.add_trace(go.Scatter(

                x=IPADf.index, y=IPADfGraph.MarketValueInDealCcy,

                name='Op Mk Pr'), row=2, col=1)

            fig.add_trace(go.Scatter(

                x=IPADf.index, y=IPADfGraph.UnderlyingPrice,

                name=underlying+' Undrlyg Pr'), row=3, col=1)

 

            fig.update(layout_xaxis_rangeslider_visible=False)

            fig.update_layout(title=IPADfGraph.columns.name)

            fig.update_layout(

                title=instrument,

                template=graphTemplate,

                autosize=False,

                width=1300,

                height=500)

            fig.show()

 

        else:

 

            print("Looks like the agrument `graphStyle` used is incorrect. Try `simple`, `with out of trading hours` or `3 graphs`")

    	
            

ImpVolatilityCalcIPA(  # This will pick up 10 min data

    x=15,

    indexUnderlying=".STOXX50E",  # ".SPX" or ".STOXX50E"

    callOrPut='Call',  # 'Put' or 'Call'

    dateBack=3,

    expiryYearOfInterest=datetime.now().year,

    riskFreeRate=None,

    riskFreeRateField=None,  # 'TR.FIXINGVALUE'

    timeZoneInGraph=datetime.now().astimezone(),

    maxColwidth=200,

    graphStyle='3 graphs',  # 'with out of trading hours', '3 graphs', 'simple'

    simpleGraphLineStyle='.-',  # 'o-'

    simpleGraphSize=(15, 5),

    graphTemplate='plotly_dark',

    debug=False,

    returnDfGraph=False,

    AtOptionTradeOnly=True)

 

 

 

 

 

 

DocumentTitle

 

 

 

 

 

RIC StrikePrice ExchangeCode ExpiryDate UnderlyingQuoteRIC
22 Eurex Dow Jones EURO STOXX 50 Index Option 4225 Call Mar 2023 , Stock Index Cash Option, Call 4225 EUR 17-Mar-2023, Eurex STXE42250C3.EX 4225 EUX 17/03/2023 [.STOXX50E]
empty image

Creating a class with PEP 3107 (a.k.a.: Type Hints)

Stay tuned! As soon I will update this article using PEP 3107 (and PEP 484) (and some decorators)!

 

 

 

Conclusion

As you can see, not only can we use IPA to gather large amounts of bespoke, calculated, values, but be can also portray this insight in a simple, quick and relevent way. The last cell in particular loops through our built fundction to give an updated graph every 5 seconds using 'legacy' technologies that would work in most environments (e.g.: Eikon Codebook).

 

References

Brilliant: Black-Scholes-Merton

What is the RIC syntax for options in Refinitiv Eikon?

Functions to find Option RICs traded on different exchanges

Eikon Calc Help Page

Making your code faster: Cython and parallel processing in the Jupyter Notebook

 

Q&A

RIC nomenclature for expired Options on Futures

Expiration Dates for Expired Options API