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()

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

        
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            searchDf1
        
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currentUnderlyingPrc =  rd.get_history(
    universe=[searchDf1.UnderlyingQuoteRIC[0][0]],
    fields=["TRDPRC_1"],
    interval="tick").iloc[-1][0]

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

        
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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())}

        
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            print(fullDates)
        
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            print(fullDatesStrDict)
        
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            print(dates)
        
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            print(fullDatesDayDict)
        
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            x = 15
        
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            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
        
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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]]]

        
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            print(fullDatesAtTimeOfCalcDatetime)
        
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expiryDateOfInt = [i for i in fullDatesAtTimeOfCalcDatetime
                   if i > timeOfCalcDatetime + relativedelta(days=x)][0]
expiryDateOfInt

        
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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

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

        
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            datetime.now().isoformat(timespec='minutes')
        
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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

        
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_RefDailyImpVolDf = historical_pricing.events.Definition(
    instrument, fields=['IMP_VOLT'], count=2000).get_data()

        
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            _RefDailyImpVolDf.data.df.head()
        
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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()

        
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_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

        
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            _optnMrktPrice.head()
        
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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

        
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AToptnMrktPrice = _optnMrktPrice
AToptnMrktPrice

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

        
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hoursDf = rd.get_data(universe=["EUREX21"],
                      fields=["ROW80_10"])
display(hoursDf)
hoursDf.iloc[0,1]

        
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_underlyingMrktPrice = rd.get_history(
    universe=[underlying],
    fields=["TRDPRC_1"],
    interval="10min",
    start=start,
    end=end)
_underlyingMrktPrice

        
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ATunderlyingMrktPrice = AToptnMrktPrice.join(
    _underlyingMrktPrice, lsuffix='_OptPr', rsuffix='_UnderlyingPr', how='inner')
ATunderlyingMrktPrice

        
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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

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

        
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_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

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

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

        
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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()

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

        
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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

        
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            dfLocalTimeZone
        
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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

        
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            ATdfLocalTimeZone
        
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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))]

        
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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))]

        
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def Chunks(lst, n):
    """Yield successive n-sized chunks from lst."""
    for i in range(0, len(lst), n):
        yield lst[i:i + n]

        
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requestFields = [
    "MarketValueInDealCcy", "RiskFreeRatePercent",
    "UnderlyingPrice", "PricingModelType",
    "DividendType", "VolatilityType",
    "UnderlyingTimeStamp", "ReportCcy",
    "VolatilityType", "Volatility",
    "DeltaPercent", "GammaPercent",
    "RhoPercent", "ThetaPercent",
    "VegaPercent"]

        
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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)

        
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            response3df
        
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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)

        
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            ATresponse3df
        
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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)

        
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            IPADf
        
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            ATIPADf
        
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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()

        
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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]]

        
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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()

        
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            ATIPADfSimpleGraph
        
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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`")

        
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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)

        
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