The Data Library for Python - Quick Reference Guide (Access layer)

March 29,2024

Raksina Samasiri

This is a quick reference guide for the Data Library for Python on different asset types

If you’re new to the library, a shallow dive into LSEG APIs landscape, including the introduction to data library can be found in article Summary of Common LSEG Refinitiv APIs

In this article, we are focusing on using the access layer, which is the easiest way to get data. It provides simple interfaces allowing you to rapidly prototype solutions within interactive environments such as Jupyter Notebooks. It has been designed for quick experimentation with our data and for Financial Coders specific needs. (To learn more about Data Library for Python’s layers: example codes can be found here)

Here’s what included in this quick reference guide.

Search
Symbology
Convert symbols, Get data - get RIC from bond, Get data - get CUSIP and international CUSIP, Get data with Screener, Get data with Peers, Get data of instruments with all available fields, Get data of instruments with specific fields, Get constituent with Chain object
Timeseries
Get data with field level parameter, Get data with request level parameter (global parameter), Get history
Company guidance
Earnings
Analyst recommendations
Industry Analysis / The Reference data Business Classification (TRBC)
Ownership data
Corporate actions data
Volatility
Volatility, Equity implied volatility, Futures implied volatility
Portfolio and Lists
Portfolio, List, Monitor, Index/exchange constituents as of certain date, Index leavers/joiners
Index and Constituents
Fixed income data
Bond status, Fixed income securities core reference data, Bond pricing, Bond analytics, Historical bond pricing, Historical analytics, Credit default swaps
Pricing streams
Using pricing streams with events, Using pricing streams as a data cache, Get a snapshot for a subset of instruments and fields

Quick Reference Guide

1) Search

The search() function identifies a matching set of documents which satisfy the caller's criteria, sorts it, and selects a subset of the matches to return as the result. The search can be performed easily with the help of Advanced Search tool provided in Workspace / Eikon application
- Find content and functionality using Refinitiv Data Library with Advanced Search
To understand more about the search function, a powerful, search engine covering content such as quotes, instruments, organizations, and many other assets that can be programmatically integrated within your business Workflow, it’s introduced in this article
- Building Search into your Application Workflow

    	
            # Search for Equities with Exchange Name is NYSE Consolidated, descending ordered by Market Cap Company in USD
search_df = rd.discovery.search(
    view = rd.discovery.Views.EQUITY_QUOTES,
    top = 10, # select only top 10 rows of the result
    filter = "(SearchAllCategoryv2 eq 'Equities' and ExchangeName xeq 'NYSE Consolidated')",
    select = "DTSubjectName,ExchangeName,RIC,IssueISIN,MktCapCompanyUsd",
    order_by = "MktCapCompanyUsd desc"
)
 
search_df

2) Symbology

Convert symbols

    	
            # Example 1 - convert_symbols()
from refinitiv.data.discovery import (
    convert_symbols,
    SymbolTypes
)
 
# Convert to all available symbol types
response = convert_symbols(
    symbols=['IBM','LSEG.L','MSFT.O','AAPL.O']
)
response

    	
            # Available SymbolTypes - run help(SymbolTypes)  
    # CUSIP = <SymbolTypes.CUSIP: 'CUSIP'>
    # ISIN = <SymbolTypes.ISIN: 'IssueISIN'>
    # LIPPER_ID = <SymbolTypes.LIPPER_ID: 'FundClassLipperID'>
    # OA_PERM_ID = <SymbolTypes.OA_PERM_ID: 'IssuerOAPermID'>
    # RIC = <SymbolTypes.RIC: 'RIC'>
    # SEDOL = <SymbolTypes.SEDOL: 'SEDOL'>
    # TICKER_SYMBOL = <SymbolTypes.TICKER_SYMBOL: 'TickerSymbol'>
 
# Convert from ticker to RIC, OA Perm ID
response = convert_symbols(
    symbols=['IBM','LSEG','MSFT','AAPL'], 
    from_symbol_type=SymbolTypes.TICKER_SYMBOL,
    to_symbol_types=[SymbolTypes.RIC, SymbolTypes.OA_PERM_ID],
    preferred_country_code="USA"
)
response

Get data

    	
            # Example 2 - Get RIC from bond
preferred_ric = rd.get_data('US9128283J70',fields=['TR.PreferredRIC'])
preferred_ric

    	
            # Example 3 - Get CUSIP and international CUSIP
rics = ['IBM.N', 'VOD.L', 'GOOG.O', '0005.HK']
fields = ['TR.CUSIP', 'TR.CUSIPExtended', 'TR.PriceClose']
 
cusip_df = rd.get_data(rics, fields)
cusip_df

Get data with Screener

Article: Find Your Right Companies with SCREENER | Eikon Data APIs(Python)
Remark: TRBC Sector Classification

    	
            # Example 4 - Screener
from refinitiv.data.discovery import Screener
 
screener_request = Screener('U(IN(Equity(active,public,primary))), IN(TR.TRBCEconSectorCode,"51"), TR.CompanyMarketCap>=10000000000,CURN=USD')
#print(list(screener_request))
screener_df = rd.get_data(screener_request, fields=['TR.RIC','TR.CompanyName'])
screener_df

    	
            # Example 5 - Peers
from refinitiv.data.discovery import Peers
 
microsoft_peers = Peers('MSFT.O')
print(list(microsoft_peers))

    	
            ['CRM.N', 'ORCL.N', 'ADBE.OQ', 'AMZN.OQ', 'META.OQ', 'IBM.N', 'OKTA.OQ', 'AAPL.OQ', 'CSCO.OQ', '0700.HK', 'SAPG.DE', 'HPE.N', '6758.T', '7974.T', 'ZTNO.PK', 'SNOW.N', 'NOW.N', 'WDAY.OQ', 'INTU.OQ', 'DDOG.OQ', 'TEAM.OQ', 'DOCU.OQ', 'MDB.OQ', 'HUBS.N', 'CFLT.OQ', 'PANW.OQ', 'PLTR.N', 'PATH.N', 'GOOGL.OQ', 'ZM.OQ', 'ZI.OQ', 'NVDA.OQ', 'TWLO.N', 'CRWD.OQ', 'ZS.OQ', 'DT.N', 'NET.N', 'SHOP.N', 'ADSK.OQ', 'HCP.O', 'FTNT.OQ', 'VEEV.N', 'INFA.N', 'ESTC.N', 'SMAR.N', 'IOT.N', 'MNDY.OQ', 'ASAN.N', 'GTLB.OQ', 'AMD.OQ']

    	
            # Use the microsoft peers with get_history()
peers_df = rd.get_history(microsoft_peers, ['BID', 'ASK'], start = '2024-02-01', end = '2024-02-08')
peers_df

Get Data - EX-1.01.01-GetData.ipynb

    	
            # Example 6 - get data of instruments with all available fields
df = rd.get_data(['LSEG.L', 'VOD.L'])
df

    	
            # Example 7 - get data of instruments with specific fields (add field name in field parameter)
df = rd.get_data(['LSEG.L', 'VOD.L'], ['TR.CommonName', 'TR.Revenue'])
df

Chains - EX-1.01.07-Chains.ipynb

    	
            # Example 8 - get constituent with Chain object
from refinitiv.data.discovery import Chain
fchi = Chain("0#.FCHI")
print(fchi.constituents)

    	
            ['ACCP.PA', 'AIRP.PA', 'AIR.PA', 'MT.AS', 'AXAF.PA', 'BNPP.PA', 'BOUY.PA', 'CAPP.PA', 'CARR.PA', 'CAGR.PA', 'DANO.PA', 'DAST.PA', 'EDEN.PA', 'ENGIE.PA', 'ESLX.PA', 'EUFI.PA', 'HRMS.PA', 'PRTP.PA', 'OREP.PA', 'LVMH.PA', 'LEGD.PA', 'MICP.PA', 'ORAN.PA', 'PERP.PA', 'PUBP.PA', 'RENA.PA', 'SAF.PA', 'SGOB.PA', 'SASY.PA', 'SCHN.PA', 'SOGN.PA', 'STLAM.PA', 'STMPA.PA', 'TEPRF.PA', 'TCFP.PA', 'TTEF.PA', 'URW.PA', 'VIE.PA', 'SGEF.PA', 'VIV.PA']

    	
            print(fchi.summary_links)

    	
            ['.DJI', 'EUR=', '/.STOXX50E', '.FCHI', '.AD.FCHI']

    	
            # Example 8.1 - use the chain with get_data()
rd.get_data(fchi, ['BID', 'ASK', 'TR.Revenue'])

    	
            # Example 8.2 - use the chain with get_history()
rd.get_history(fchi, ['BID', 'ASK'])

3) Timeseries

3.1) Get data with field level parameters

Available parameters of each field can be found in Parameters tab in CodeCreator app of Workspace.

    	
            # Example 1 - field level parameters are applied into each field
rics = ['AAPL.O','IBM','MSFT.O','LSEG.L']
 
# Parameters used are
    # SDate=2024-01-15 # start date
    # EDate=2024-01-17 # end date
    # Frq=D # frequency = daily
 
df = rd.get_data(rics,
                   ['TR.PriceClose(SDate=2024-01-15,EDate=2024-01-17,Frq=D).date',
                    'TR.PriceClose(SDate=2024-01-15,EDate=2024-01-17,Frq=D)',
                    'TR.TotalReturn1Mo(SDate=2024-01-15,EDate=2024-01-17,Frq=D)'])
df

3.2) Get data with request level parameter (global parameter)

    	
            #Example 2 - request level parameters are applied to all fields in the request
rics = ['AAPL.O','IBM','MSFT.O','LSEG.L']
 
# Parameters used are
    # SDate=2024-01-15 # start date
    # EDate=2024-01-17 # end date
    # Frq=D # frequency = daily
 
df = rd.get_data(rics,
                 ['TR.PriceClose.date','TR.PriceClose','TR.TotalReturn1Mo'],
                 {'SDate':'2024-01-15','EDate':'2024-01-17','Frq':'D'}
                )
df

3.3) Get history

    	
            #Example 3 - retrieve timeseries data with get_history() function
 
rics = ['MSFT.O','LSEG.L']
 
# no field specified -> all available field will be returned
df = rd.get_history(rics,
                    start = '2024-01-15',
                    end = '2024-01-17'
                   )
df

    	
            # pick only interested fields, add them into the 'fields' parameter
 
# Supported intervals are:
    # tick, tas, taq, minute, 1min, 5min, 10min, 30min, 60min, hourly, 1h, daily,
    # 1d, 1D, 7D, 7d, weekly, 1W, monthly, 1M, quarterly, 3M, 6M, yearly, 1Y
        
df = rd.get_history(universe = rics,
                    fields = ['BID', 'ASK'],
                    start = '2024-01-15',
                    end = '2024-01-17',
                    interval = 'daily'
                   )
df

4) Company Guidance

    	
            fields = ['TR.GuidanceMeasure',
          'TR.GuidancePeriodYear',
          'TR.GuidancePeriodMonth',
          'TR.GuidanceDate',
          'TR.GuidanceText',
          'TR.GuidanceSpeaker',
          'TR.GuidanceDocType']
 
guidance = rd.get_data('TSLA.O', fields, {'Period': 'FY1'})
guidance

5) Earnings

    	
            rics = ['TSLA.O','AAPL.O','IBM','AMZN.O','JPM','GS']
fields = ['TR.EpsSmartEst','TR.EPSMean','TR.EpsPreSurprisePct','TR.EpsPreSurprise',
          'TR.EpsPreSurpriseFlag','TR.EPSMedian','TR.EPSLow','TR.EPSHigh']
 
earnings_df = rd.get_data(rics, fields)
earnings_df

6) Analyst Recommendations

    	
            fields = ['TR.AnalystName','TR.RecLabelEstBrokerName','TR.RecEstValue',
          'TR.BrkRecLabel','TR.TPEstValue','TR.EPSLTGEstValue',
          'TR.SingleStockRatingRecommendation1to5T24M',
          'TR.SingleStockRatingRecommendation1to100T24M',
          'TR.RecMean','TR.PriceTargetMean','TR.LTGMean']
 
analysts_df = rd.get_data(['TSLA.O'], fields)
analysts_df

7) Industry Analysis / The Reference data Business Classification (TRBC)

    	
            rics = ['AIRP.PA','BASFn.DE','1COV.DE','AKZO.AS','DSMN.AS','LXSG.DE','SOLB.BR','CLN.S']
fields = ['TR.CommonName','TR.TRBCEconomicSector','TR.TRBCBusinessSector',
          'TR.TRBCIndustryGroup','TR.TRBCIndustry','TR.TRBCActivity',
          'TR.TRBCEconSectorCode','TR.TRBCBusinessSectorCode','TR.TRBCIndustryGroupCode',
          'TR.TRBCIndustryCode','TR.TRBCActivityCode']
 
trbc_df = rd.get_data(rics, fields)
trbc_df

8) Ownership Data

    	
            # Example 1
fields = ['TR.InsiderFullName','TR.InsiderFullName.date',
          'TR.AdjSharesTraded','TR.TransactionDate','TR.AdjSharesHeld']
 
own = rd.get_data('LSEG.L', fields ,{'SDate':'2022-02-16','EDate':'2024-02-15','Frq':'Q'})
own

    	
            # Example 2
fields = ['TR.FundPortfolioName','TR.FundInvestorType','TR.FdAdjPctOfShrsOutHeld',
          'TR.FundAdjShrsHeld','TR.FdAdjSharesHeldValue','TR.FundHoldingsDate']
 
own = rd.get_data('MSFT.O', fields,{'SDate':-25,'EDate':-24,'Frq':'D'})
own = own[own["Fund Shares Held (Adjusted)"]!=0].reset_index(drop=True)
own

9) Corporate Actions Data

    	
            fields = ['TR.EventType','TR.EventTitle','TR.EventStartDate','TR.EventLastUpdate']
parameters = {'SDate':'2021-08-01','EventType':'ALL','EDate':'2023-11-20'}
 
corax = rd.get_data('AAPL.O', fields, parameters)
corax

10) Volatility

    	
            # Example 1
vols_profile=['TR.Volatility2D','TR.Volatility5D','TR.Volatility10D',
              'TR.Volatility15D','TR.Volatility20D','TR.Volatility25D',
              'TR.Volatility30D','TR.Volatility40D','TR.Volatility50D',
              'TR.Volatility60D','TR.Volatility90D','TR.Volatility100D',
              'TR.Volatility120D','TR.Volatility150D','TR.Volatility160D',
              'TR.Volatility180D','TR.Volatility240D','TR.Volatility250D',
              'TR.Volatility260D']
 
vols = rd.get_data(['AAPL.O','IBM.N','AOT.BK'], vols_profile)
vols.transpose()

    	
            # Example 2
# EQUITY IMPLIED VOLS  = Ticker + "ATMIV.U" (Reference the IMPLIEDVOL speed_guide within Eikon)
RIC = 'WMT' + 'ATMIV.U'
 
fields = ['TR.30DAYATTHEMONEYIMPLIEDVOLATILITYINDEXFORCALLOPTIONS',
          'TR.30DAYATTHEMONEYIMPLIEDVOLATILITYINDEXFORPUTOPTIONS',
          'TR.60DAYATTHEMONEYIMPLIEDVOLATILITYINDEXFORCALLOPTIONS',
          'TR.60DAYATTHEMONEYIMPLIEDVOLATILITYINDEXFORPUTOPTIONS',
          'TR.90DAYATTHEMONEYIMPLIEDVOLATILITYINDEXFORCALLOPTIONS',
          'TR.90DAYATTHEMONEYIMPLIEDVOLATILITYINDEXFORCALLOPTIONS']
 
implied_vol = rd.get_data(RIC, fields,{'SDate':0,'EDate':-10,'Frq':'D'})
implied_vol

    	
            # Example 3
# Futures Implied Vols = Ticker + 'ATMIV' (refernce FUT/IMPLIEDVOL1 Speed Guide in Eikon)
# Examples,  Corn (1CATMIV), Wheat (1WATMIV), Crude Oil (CLATMIV), 10Y UST(2TYATMIV)...
 
RICS = ['1CATMIV','1WATMIV','CLATMIV']
fields = ['TR.30DAYATTHEMONEYIMPLIEDVOLATILITYINDEXFORCALLOPTIONS',
          'TR.30DAYATTHEMONEYIMPLIEDVOLATILITYINDEXFORPUTOPTIONS',
          'TR.60DAYATTHEMONEYIMPLIEDVOLATILITYINDEXFORCALLOPTIONS',
          'TR.60DAYATTHEMONEYIMPLIEDVOLATILITYINDEXFORPUTOPTIONS',
          'TR.90DAYATTHEMONEYIMPLIEDVOLATILITYINDEXFORCALLOPTIONS',
          'TR.90DAYATTHEMONEYIMPLIEDVOLATILITYINDEXFORCALLOPTIONS']
 
fut_implied_vol = rd.get_data(RICS, fields)
fut_implied_vol

11) Portfolios and Lists

    	
            # Example 1 - Portfolio
fields = ['TR.PortfolioStatementDate','TR.PortfolioModifiedDate','TR.PortfolioConstituentName',
          'TR.PortfolioShares','TR.PortfolioWeight','TR.PortfolioDate',
          'TR.PriceClose','TR.CompanyMarketCap']
 
port_data = rd.get_data(["Portfolio('TEST-PORTFOLIO','20240221')"], fields)
port_data

    	
            # Example 2 - List
list_data = rd.get_data("List('test-list')",['TR.RIC','CF_LAST'])
list_data

    	
            # Example 3 - Monitor
monitor_data = rd.get_data("Monitor('TEST-MONITOR')",['TR.RIC','CF_LAST'])
monitor_data

12) Index and Constituents

There's an article Building historical index constituents, which explain the process of building historical constituents of a market index, underscoring its importance for investors, researchers, and various participants in the financial markets.

    	
            # Example 1 - Index/Exchange Constituents as of certain date
index_data = rd.get_data('0#.IXIC(20230102)',['TR.RIC'])
index_data

    	
            # Example 2 - Index Leavers/Joiners
fields = ['TR.IndexJLConstituentRIC','TR.IndexJLConstituentName','TR.IndexJLConstituentChangeDate.change']
 
leavers_joiners = rd.get_data('.SPX', fields,{'SDate':'20190101','EDate':'20191201','IC':'B'})
leavers_joiners

13) Fixed Income Data

    	
            # Example 1 - Bond Status
rics = ['841338AA4=','717081EP4=','US10YT=RR','FR201534887=','931142BF9=','909279AG6=']
fields = ['TR.FiIssuerName','TR.FiNetCoupon','TR.FiMaturityDate','TR.FIAssetStatus','TR.FiAssetStatusDescription']
 
bond_status = rd.get_data(rics, fields)
bond_status

    	
            # Example 2 - Fixed Income Securities Core Reference Data
rics = ['717081EP4=','US10YT=RR','FR201534887=','931142BF9=','DE182350257=']
fields = ['TR.FiIssuerName','TR.FiParentTicker','TR.ISIN','TR.CUSIP','TR.FiCurrency',
          'TR.FiNetCoupon','TR.FiMaturityDate','TR.FiIndustrySubSectorDescription',
          'TR.FiSPRating','TR.FiMoodysRating','TR.FiFitchsRating','TR.FiFaceOutstanding']
 
fi_sec_master = rd.get_data(rics, fields)
fi_sec_master

    	
            # Example 3 - Bond Pricing
bond_rics=['US931142CB75=TE','931142CB7=RRPS','931142CB7=','931142CB7=2M','931142CB7=1M']
fields = ['CF_SOURCE','TR.BIDPRICE','TR.ASKPRICE','TR.MIDPRICE','TR.BIDYIELD','TR.MIDYIELD','TR.ASKYIELD']
 
bond_prices = rd.get_data(bond_rics, fields)
bond_prices.sort_values(['Mid Price'])

    	
            # Example 4 - Bond Analytics
RICS = ['717081EP4=','US10YT=RR','FR201534887=','931142BF9=','DE182350257=']
fields = ['TR.YieldToMaturityAnalytics','TR.YieldToWorstAnalytics','TR.DurationAnalytics',
          'TR.ModifiedDurationAnalytics','TR.ConvexityAnalytics','TR.GovernmentSpreadAnalytics',
          'TR.SwapSpreadAnalytics','TR.AssetSwapSpreadAnalytics','TR.ZSpreadAnalytics','TR.OASAnalytics']
 
fi_analytics = rd.get_data(RICS, fields)
fi_analytics

    	
            # Example 5 - Historical Bond Pricing
point_in_time = rd.get_data(['717081EP4='],
                                ['TR.YieldToMaturityAnalytics'],
                                {'SDate':'2024-01-17','EDate':'2024-01-17','Frq':'D'})
point_in_time

    	
            # Example 6 - Historical Analytics
ytm = rd.get_data(['717081EP4='],
                     ['TR.YieldToMaturityAnalytics(ValuationDate=2024-02-10)',
                      'TR.ModifiedDurationAnalytics(ValuationDate=2024-02-10)',
                      'TR.ConvexityAnalytics(ValuationDate=2024-02-10)'])
ytm

    	
            # Example 7 - Credit Default Swaps
import time
 
# Expanding the chain with function (in case the Chain RIC is not supported by the library)
def get_underlying(base_ric):
    LONGNEXTLR = base_ric
    #For LONGLING1 to LONGLINK15 and LONGNEXTLR fields
    fields = ['LONGLINK{}'.format(x) for x in range(1, 15)]
    fields.append('LONGNEXTLR')
 
    all_underlying_rics = []
 
    #if LONGNEXTLR is not empty, try to retrieve the data fields
    while LONGNEXTLR!='':
        df = rd.get_data(LONGNEXTLR,fields)
        LONGNEXTLR = df.iloc[0]['LONGNEXTLR'] if pd.notnull(df.iloc[0]['LONGNEXTLR']) else ''
        
        #If LONGLINK<x> field is not null, append its value to all_underlying_rics list
        for x in range(1, 15):
            currentField = 'LONGLINK{}'.format(x)
            all_underlying_rics.append(df.iloc[0][currentField]) if pd.notnull(df.iloc[0][currentField]) else None
        #delay between each API call for 1 second
        time.sleep(1)
    return all_underlying_rics
 
cdx = get_underlying('0#CDXIG5YC=R')
print(cdx)

    	
            ['CDXIG5Y=R', 'MSPRDHEADER=', 'AEP5YUSAX=R', 'AESC5YUSAX=R', 'AIG5YUSAX=R', 'ALL5YUSAX=R', 'GMAC5YUSAX=R', 'MO5YUSAX=R', 'AMD5YUSAX=R', 'AXP5YUSAX=R', 'AMGN5YUSAX=R', 'APA5YUSAX=R', 'ARW5YUSAX=R', 'FSFF5YUSAX=R', 'T5YUSAX=R', 'AZO5YUSAX=R', 'AVT5YUSAX=R', 'ABX5YUSAX=R', 'BAX5YUSAX=R', 'BRK5YUSAX=R', 'BBY5YUSAX=R', 'BLCK5YUSAX=R', 'BA5YUSAX=R', 'BWA5YUSAX=R', 'BSX5YUSAX=R', 'BMY5YUSAX=R', 'CPB5YUSAX=R', 'COF5YUSAX=R', 'CAH5YUSAX=R', 'CAT5YUSAX=R', 'CNQ5YUSAX=R', 'CSCF5YUSAX=R', 'CMCS5YUSAX=R', 'CAG5YUSAX=R', 'COX5YUSAX=R', 'CSX5YUSAX=R', 'CVS5YUSAX=R', 'DHR5YUSAX=R', 'DRI5YUSAX=R', 'DE5YUSAX=R', 'DELL5YUSAX=R', 'DVN5YUSAX=R', 'D5YUSAX=R', 'DOW5YUSAX=R', 'DHI5YUSAX=R', 'DXC5YUSAX=R', 'EMN5YUSAX=R', 'ENB5YUSAX=R', 'ETE5YUSAX=R', 'EXC5YUSAX=R', 'EXPE5YUSAX=R', 'FDX5YUSAX=R', 'FE5YUSAX=R', 'F5YUSAX=R', 'FCX5YUSAX=R', 'GE5YUSAX=R', 'GIS5YUSAX=R', 'GMA5YUSAX=R', 'HAL5YUSAX=R', 'HCA5YUSAX=R', 'HES5YUSAX=R', 'HD5YUSAX=R', 'HON5YUSAX=R', 'HSTL5YUSAX=R', 'HPQ5YUSAX=R', 'IBM5YUSAX=R', 'IP5YUSAX=R', 'TYCH5YUSAX=R', 'JNJ5YUSAX=R', 'KINE5YUSAX=R', 'HNZ5YUSAX=R', 'KR5YUSAX=R', 'LEN5YUSAX=R', 'LNC5YUSAX=R', 'LMT5YUSAX=R', 'LTR5YUSAX=R', 'LOW5YUSAX=R', 'MPCA5YUSAX=R', 'MAR5YUSAX=R', 'MCD5YUSAX=R', 'MCK5YUSAX=R', 'MDC5YUSAX=R', 'MET5YUSAX=R', 'KFT5YUSAX=R', 'MOT5YUSAX=R', 'NRUF5YUSAX=R', 'NFLX5YUSAX=R', 'NEM5YUSAX=R', 'FPLG5YUSAX=R', 'NSC5YUSAX=R', 'NORV5YUSAX=R', 'OXY5YUSAX=R', 'OMC5YUSAX=R', 'ORCL5YUSAX=R', 'PKG5YUSAX=R', 'CBS5YUSAX=R', 'PFE5YUSAX=R', 'PG5YUSAX=R', 'PRUX5YUSAX=R', 'PHM5YUSAX=R', 'DGX5YUSAX=R', 'RDN5YUSAX=R', 'UTX5YUSAX=R', 'R5YUSAX=R', 'SRE5YUSAX=R', 'SHW5YUSAX=R', 'SPGL5YUSAX=R', 'SO5YUSAX=R', 'LUV5YUSAX=R', 'DTEI5YUSAX=R', 'TGT5YUSAX=R', 'TECK5YUSAX=R', 'TOL5YUSAX=R', 'TCA5YUSAX=R', 'TSN5YUSAX=R', 'UNP5YUSAX=R', 'UNH5YUSAX=R', 'UPS5YUSAX=R', 'VLO5YUSAX=R', 'VZ5YUSAX=R', 'WMT5YUSAX=R', 'DISY5YUSAX=R', 'WY5YUSAX=R', 'WHR5YUSAX=R', 'WMB5YUSAX=R', 'AVGE5YUSAX=R', 'OVV5YUSAX=R']

    	
            fields = ['TR.CDSOrgShortName','TR.CDSTenor','TR.PARMIDSPREAD','TR.SPREADOVERRATING',
          'TR.SPREADOVERSECTOR','TR.SPREADOVERSUBSECTOR','TR.DEFAULTPROBABILITY']
 
cds = rd.get_data(cdx, fields)
cds.dropna()

14) Pricing streams

EX-1.01.03-PricingStream.ipynb

    	
            # Example 1 - Using pricing streams with events
 
import datetime
from IPython.display import display, clear_output
 
# Define a callback to receive data events
def display_data(data, instrument, stream):
    clear_output(wait=True)
    current_time = datetime.datetime.now().time()
    print(current_time, "- Data received for", instrument)
    display(data)
 
# Open the stream and register the callback
stream = rd.open_pricing_stream(
    universe=['GBP=', 'EUR=', 'JPY='],
    fields=['BID', 'ASK'],
    on_data=display_data
)

    	
            # Close the stream
stream.close()

    	
            <OpenState.Closed: 'Closed'>

    	
            # Example 2 - Using pricing streams as a data cache
# Create and open a Pricing stream object
stream = rd.open_pricing_stream(
    universe=['GBP=', 'EUR=', 'JPY='],
    fields=['BID', 'ASK']
)
# Extract snapshot data from the streaming cache
stream.get_snapshot()

    	
            # Example 3 - Get a snapshot for a subset of instruments and fields
stream.get_snapshot(
    universe = ['EUR=', 'GBP='], 
    fields = ['BID', 'ASK']
)