EODML usage examples

Here are representative usage examples of EODML. Please email us (info@undocumentedmatlab.com) if you have any questions.
EODML's Matlab connectivity
The following examples are listed below:

  1. Get market data (delayed snapshot)
  2. Get historic/intra-day data
  3. Get fundamental data
  4. Get historic and upcoming earning events
  5. Get historic and upcoming split events
  6. Get historic and upcoming dividend events
  7. Get historic IPO events
  8. Get historic technical indicators
  9. Get historic short interests
  10. Search for a symbol across all exchangesEODML download
  11. Search for all symbols in an exchange
  12. Get an options chain
  13. Get EODML version and query limits

  0) General usage

The general format of EODML queries is:

>> [data, errMsg, limits] = EODML(action, parameter1Name,parameter1Value, parameter2Name,parameter2Value, ...);

where:

  • data – the returned data (usually from from EODHistoricalData’s servers)
  • errMsg – error message (if any)
  • limits – the updated query limits. These can be used to ensure you do not surpass your license’s allowed limits

  • action – a string that specifies the query type. One of: ‘license’, ‘limits’, ‘version’, ‘update’, ‘revert’, ‘doc’, ‘prices’, ‘fundamentals’, ‘technicals’, ‘options’, ‘dividends’, ‘splits’, ‘earnings’, ‘shorts’, ‘ipo’, ‘lookup’
  • parameterName – a case-insensitive string that specifies the parameter name. Each query action has its own set of acceptable parameter names.

In your very first EODML command, you need to specify the API token that you received from EODHistoricalData.com, via the 'API_Token' parameter (you can get a free token here). EODML will reuse this token in all subsequent commands, so you only need to specify it once (or when you change your token key). For example:

>> data = EODML('prices', ..., 'API_Token','123456.abcdef');  % '123456.abcdef' is a dummy (invalid) token, use your own personal token

Note: all parameter names are case in-sensitive. i.e., you can use ‘API_Token’ or ‘API_TOKEN’ or ‘api_token’ as you wish.

List of general parameters that are relevant to all query types:

  • API_Token – string; no default. This is the API token that is provided by EODHistoricalData.com (you can get a free token here)
  • Timeout – numeric; default=5. Max number of seconds to wait for EOD’s response
  • Symbol or Symbols – string or cell-array of strings, e.g., ‘IBM’ or ‘IBM,GOOG’ or {‘IBM’,’GOOG’}
  • UseParallel – logical true/false; default=false (only available in Analyst/Pro licenses, for multiple symbols)
  • RaiseErrorMsgs – logical true/false; default=false. If false and the errMsg output arg is requested, then errMsg is updated upon error rather than raise an error
  • Order – string; default=’asc’; either ‘asc’ or ‘desc’. Affects the order of returned data array(s), where relevant

  1) Get market data (delayed snapshot)

>> data = EODML('prices', 'symbol','IBM', 'datatype','live')
data = 
  struct with fields:
           symbol: 'IBM.US'
        timestamp: 1577204520
        gmtoffset: 0
             open: 135.61
             high: 135.62
              low: 134.61
            close: 134.7425
           volume: 593912
    previousClose: 135.55
           change: -0.808
         change_p: -0.596
      datestr_GMT: '24-Dec-2019 16:22:00'
      datenum_GMT: 737783.681944444

Available parameters that affect this query (in addition to the standard general parameters):

  • DataType – string; must be ‘live’ to get the current (delayed) market snapshot
  • SecType – string; default=’equity’; one of ‘equity’,’index’,’bond’
  • Filter – string; default=”; coma-delimited fields e.g. ‘close,change,volume’

  2) Get historic/intra-day data

>> data = EODML('prices', 'symbol','IBM', 'FromDate',20191203, 'ToDate','2019/12/13', 'DataType','day')
data = 
  9×1 struct array with fields:
    symbol
    date
    datenum
    open
    high
    low
    close
    adjusted_close
    volume
 
>> data(1)
ans = 
  struct with fields:
 
            symbol: 'IBM.US'
              date: '2019-12-03'
           datenum: 737762
              open: 132
              high: 132.44
               low: 130.69
             close: 132.12
    adjusted_close: 132.12
            volume: 3642500

Available parameters that affect this query (in addition to the standard general parameters):

  • DataType – string; default=’day’; one of ‘intraday’,’day’,’week’,’month’,’bulk’ (bulk only available in the Pro license)
  • SecType – string; default=’equity’; one of ‘equity’,’index’,’bond’
  • FromDate – integer or string; default=[]; earliest data date (GMT timezone). Examples: 20191203, ‘2019-12-03’, 201912031535, ‘2019/12/03 15:35’
  • ToDate – integer or string; default=[]; latest data date (GMT timezone). Examples: 20191203, ‘2019-12-03’, 201912031535, ‘2019/12/03 15:35’
  • Filter – string; default=”; coma-delimited fields e.g. ‘close,change,volume’
  • Interval – string; default=’5m’; one of ‘5m’,’1m’,’1hr’; only relevant when DataType=’intraday’
  • Exchange – string; default=’US’; exchange name (e.g. ‘NYSE’,’NASDAQ’ etc.), only relevant when DataType=’bulk’ (Pro license)

  3) Get fundamental data

% Fundamental data for an equity:
>> data = EODML('fundamental', 'symbol','IBM')
data = 
  struct with fields:
               symbol: 'IBM.US'
              General: [1×1 struct]
           Highlights: [1×1 struct]
            Valuation: [1×1 struct]
          SharesStats: [1×1 struct]
           Technicals: [1×1 struct]
      SplitsDividends: [1×1 struct]
            ESGScores: [1×1 struct]
    outstandingShares: [1×1 struct]
             Earnings: [1×1 struct]
           Financials: [1×1 struct]
 
>> data.General
ans = 
  struct with fields:
                     Code: 'IBM'
                     Type: 'Common Stock'
                     Name: 'International Business Machines Corporation'
                 Exchange: 'NYSE'
             CurrencyCode: 'USD'
             CurrencyName: 'US Dollar'
           CurrencySymbol: '$'
              CountryName: 'USA'
               CountryISO: 'US'
                     ISIN: 'US4592001014'
                    CUSIP: '459200101'
                      CIK: 51143
         EmployerIdNumber: '13-0871985'
            FiscalYearEnd: 'December'
                  IPODate: []
    InternationalDomestic: 'International/Domestic'
                   Sector: 'Technology'
                 Industry: 'Information Technology Services'
                GicSector: 'Information Technology'
                 GicGroup: 'Software & Services'
              GicIndustry: 'IT Services'
           GicSubIndustry: 'IT Consulting & Other Services'
              Description: 'International Business Machines Corporation operates as an integrated technology and services company worldwide...'
                  Address: '1 New Orchard Road↵10504,Armonk,USA↵'
                    Phone: '914-499-1900'
                   WebURL: 'www.ibm.com'
                  LogoURL: 'https://eodhistoricaldata.com/img/logos/US/IBM.png'
        FullTimeEmployees: 350600
                UpdatedAt: '2019-12-24'
 
>> data.Highlights
ans = 
  struct with fields:
          MarketCapitalization: 120048099328
       MarketCapitalizationMln: 120048.0993
                        EBITDA: 16664000512
                       PERatio: 15.7525
                      PEGRatio: 4.92
         WallStreetTargetPrice: 148.3
                     BookValue: 20.275
                 DividendShare: 6.48
                 DividendYield: 0.0478
                 EarningsShare: 8.605
        EPSEstimateCurrentYear: 12.8
           EPSEstimateNextYear: []
        EPSEstimateNextQuarter: 2.17
     EPSEstimateCurrentQuarter: []
             MostRecentQuarter: '2019-09-30'
                  ProfitMargin: 0.1
            OperatingMarginTTM: 0.1445
             ReturnOnAssetsTTM: 0.0513
             ReturnOnEquityTTM: 0.4061
                    RevenueTTM: 77130997760
            RevenuePerShareTTM: 86.587
     QuarterlyRevenueGrowthYOY: -0.039
                GrossProfitTTM: 36936000000
                 DilutedEpsTTM: 8.605
    QuarterlyEarningsGrowthYOY: -0.365
 
% Fundamental data for a bond (note that we can search by CUSIP or ISIN in the Symbol parameter):
>> data = EODML('fundamental', 'symbol','910047AG4', 'sectype','bond')
data = 
  struct with fields:
                     symbol: '910047AG4'
                       ISIN: 'US910047AG49'
                      CUSIP: '910047AG4'
                       Name: 'UNITED AIRLS HLDGS INC 6% 01Dec2020'
                 UpdateDate: '2019-11-27'
                        WKN: 'A1HS3T'
                      Sedol: 'BFV4Y03'
                       FIGI: 'BBG005J8GHT4'
                   Currency: 'USD'
                     Coupon: 6
                      Price: 103.03
              LastTradeDate: '2019-11-27'
              Maturity_Date: '2020-11-30'
            YieldToMaturity: 2.833
                   Callable: 'No'
               NextCallDate: []
    MinimumSettlementAmount: '1000 USD'
        ParIntegralMultiple: '1000 USD'
         ClassificationData: [1×1 struct]
                     Rating: [1×1 struct]
                  IssueData: [1×1 struct]
 
>> data.ClassificationData
ans = 
  struct with fields:
               BondType: 'Unternehmensanleihen Welt Rest'
               DebtType: 'Senior Unsecured Note'
          IndustryGroup: 'Industrial'
       IndustrySubGroup: 'Transportation'
        SubProductAsset: 'CORP'
    SubProductAssetType: 'Corporate Bond'
 
>> data.Rating
ans = 
  struct with fields:
              MoodyRating: 'Ba3'
    MoodyRatingUpdateDate: '2019-11-27'
                 SPRating: 'BB'
       SPRatingUpdateDate: '2018-04-16'
 
>> data.IssueData
ans = 
  struct with fields:
                 IssueDate: '2013-11-08'
              OfferingDate: '2013-11-01'
           FirstCouponDate: '2014-06-01'
           FirstTradingDay: '2013-11-08'
    CouponPaymentFrequency: []
                    Issuer: 'United Airlines Holdings Inc.'
         IssuerDescription: 'United Continental Holdings Inc. is an airline holding company. The Company owns and operates airlines that transports persons, property and mail throughout the United States and abroad.'
             IssuerCountry: 'USA'
                 IssuerURL: []

Available parameters that affect this query (in addition to the standard general parameters):

  • DataType – string; default=’standard’; one of ‘standard’,’insiderTrading’,’marketCap’,’bulk’ (bulk only available in the Pro license)
  • SecType – string; default=’equity’; one of ‘equity’,’index’,’bond’
  • FromDate – integer or string; default=[]; earliest data date (GMT timezone). Examples: 20191203, ‘2019-12-03’, 201912031535, ‘2019/12/03 15:35’
  • ToDate – integer or string; default=[]; latest data date (GMT timezone). Examples: 20191203, ‘2019-12-03’, 201912031535, ‘2019/12/03 15:35’
  • Filter – string; default=”; coma-delimited groups/fields e.g. ‘General,Financials::Balance_Sheet::yearly’
  • Exchange – string; default=’US’; exchange name (e.g. ‘NYSE’,’NASDAQ’ etc.), only relevant when DataType=’bulk’ (Pro license)

  4) Get historic and upcoming earning events

% Historic earning reports (requested on Dec. 24, 2019):
>> data = EODML('earnings', 'symbol','IBM', 'fromdate',20190101)
data = 
  4×1 struct array with fields:
    symbol
    report_date
    date
    datenum
    actual
    estimate
    difference
    percent
 
>> data(end)
ans = 
  struct with fields:
         symbol: 'IBM.US'
    report_date: '2019-10-16'
           date: '2019-09-30'
        datenum: 737698
         actual: 2.68
       estimate: 2.67
     difference: 0.01
        percent: 0.37
 
% Upcoming earning reports (requested on Dec. 24, 2019):
>> data = EODML('earnings', 'symbol','IBM')
data =
     []
 
>> data = EODML('earnings', 'symbol','JOB.US')
data = 
  struct with fields:
         symbol: 'JOB.US'
    report_date: '2019-12-30'
           date: '2019-12-30'
        datenum: 737789
         actual: []
       estimate: -0.2
     difference: 0
        percent: []
 
% All upcoming (announced) earnings reports:
>> data = EODML('earn')
data = 
  38×1 struct array with fields:
    symbol
    report_date
    date
    datenum
    actual
    estimate
    difference
    percent
 
>> struct2table(data)
ans =
  38×8 table
      symbol    report_date       date      datenum     actual       estimate    difference    percent
    __________  ____________  ____________  _______  ____________  ____________  __________  ____________
    '3174.TSE'  '2019-12-24'  '2019-12-24'  737783   {0×0 double}  {0×0 double}      0       {0×0 double}
    '6196.TSE'  '2019-12-24'  '2019-12-24'  737783   {0×0 double}  {0×0 double}      0       {0×0 double}
    'HURC.US'   '2019-12-24'  '2019-12-24'  737783   {0×0 double}  {0×0 double}      0       {0×0 double}
    'JRJC.US'   '2019-12-24'  '2019-12-24'  737783   {0×0 double}  {0×0 double}      0       {0×0 double}
    'MLR.V'     '2019-12-24'  '2019-12-24'  737783   {0×0 double}  {0×0 double}      0       {0×0 double}
    'ROYT.US'   '2019-12-24'  '2019-12-24'  737783   {0×0 double}  {[    0.05]}      0       {0×0 double}
    ...

Available parameters that affect this query (in addition to the standard general parameters):

  • DataType – string; default=’standard’; either ‘standard’ or ‘trends’
  • FromDate – integer or string; default=[]; earliest data date (GMT timezone). Examples: 20191203, ‘2019-12-03’
  • ToDate – integer or string; default=[]; latest data date (GMT timezone). Examples: 20191203, ‘2019-12-03’

  5) Get historic and upcoming split events

% 2 splits for AAPL since 1/1/2000:
>> data = EODML('splits', 'symbol','AAPL', 'fromdate',20010101)
data = 
  2×1 struct array with fields:
    symbol
    date
    datenum
    split
    split_ratio
 
>> struct2table(data)
ans =
  2×5 table
     symbol         date     datenum         split         split_ratio
    _________  ____________  _______  ___________________  ___________
    'AAPL.US'  '2005-02-28'  732371   '2.000000/1.000000'       2
    'AAPL.US'  '2014-06-09'  735759   '7.000000/1.000000'       7
 
% No splits for IBM since 1/1/2000:
>> data = EODML('splits', 'symbol','IBM', 'fromdate',20100101)
data =
     []
 
% Upcoming (future) splits:
>> data = EODML('splits')
data = 
  60×1 struct array with fields:
    symbol
    split_date
    split_datenum
    optionable
    old_shares
    new_shares
    split_ratio
 
>> struct2table(data)
ans =
  60×7 table
         symbol        split_date   split_datenum  optionable  old_shares  new_shares      split_ratio
    ________________  ____________  _____________  __________  __________  __________  ___________________
    '5DP.SG'          '2019-12-24'     737783         'N'           2           3                      1.5
    'DCTH.US'         '2019-12-24'     737783         'N'         700           1      0.00142857142857143
    'LZRFY.US'        '2019-12-24'     737783         'N'         100         105                     1.05
    'QKL.F'           '2019-12-24'     737783         'N'          14           1       0.0714285714285714
    'QKL.STU'         '2019-12-24'     737783         'N'          14           1       0.0714285714285714
    'QUIK.US'         '2019-12-24'     737783         'N'          14           1       0.0714285714285714
    'BALMLAWRIE.NSE'  '2019-12-26'     737785         'N'           2           3                      1.5
    '000100.KO'       '2019-12-27'     737786         'N'           1           1                        1
    ...

Available parameters that affect this query (in addition to the standard general parameters):

  • DataType – string; default=’standard’; either ‘standard’ or ‘bulk’ (bulk in Pro license only)
  • FromDate – integer or string; default=[]; earliest data date (GMT timezone). Examples: 20191203, ‘2019-12-03’
  • ToDate – integer or string; default=[]; latest data date (GMT timezone). Examples: 20191203, ‘2019-12-03’
  • Exchange – string; default=’US’; exchange name (e.g. ‘NYSE’,’NASDAQ’ etc.), only relevant when DataType=’bulk’ (Pro license)

  6) Get historic and upcoming dividend events

% Historic dividend events:
>> data = EODML('dividend', 'symbol','IBM', 'fromdate',20190101)
data = 
  4×1 struct array with fields:
    symbol
    date
    datenum
    declarationDate
    recordDate
    paymentDate
    value
 
>> struct2table(data)
ans =
  4×7 table
     symbol       date      datenum  declarationDate   recordDate   paymentDate   value
    ________  ____________  _______  _______________  ____________  ____________  _____
    'IBM.US'  '2019-02-07'  737463     '2019-01-29'   '2019-02-08'  '2019-03-09'  1.57
    'IBM.US'  '2019-05-09'  737554     '2019-04-30'   '2019-05-10'  '2019-06-10'  1.62
    'IBM.US'  '2019-08-08'  737645     '2019-07-30'   '2019-08-09'  '2019-09-10'  1.62
    'IBM.US'  '2019-11-07'  737736     '2019-10-29'   '2019-11-08'  '2019-12-10'  1.62
 
>> data(1)
ans = 
  struct with fields:
             symbol: 'IBM.US'
               date: '2019-02-07'
            datenum: 737463
    declarationDate: '2019-01-29'
         recordDate: '2019-02-08'
        paymentDate: '2019-03-09'
              value: 1.57
 
% Upcoming (future) dividend events:
>> data = EODML('dividend', 'symbol','IBM')
data = 
  199×1 struct array with fields:
    symbol
    date
    datenum
    declarationDate
    recordDate
    paymentDate
    value
 
>> data(1)
ans = 
  struct with fields:
             symbol: 'IBM.US'
               date: '1970-05-01'
            datenum: 719649
    declarationDate: []
         recordDate: []
        paymentDate: []
              value: 0.06

Available parameters that affect this query (in addition to the standard general parameters):

  • DataType – string; default=’standard’; either ‘standard’ or ‘bulk’ (bulk in Pro license only)
  • FromDate – integer or string; default=[]; earliest data date (GMT timezone). Examples: 20191203, ‘2019-12-03’
  • ToDate – integer or string; default=[]; latest data date (GMT timezone). Examples: 20191203, ‘2019-12-03’
  • Exchange – string; default=’US’; exchange name (e.g. ‘NYSE’,’NASDAQ’ etc.), only relevant when DataType=’bulk’ (Pro license)

  7) Get historic IPO events

% Historic IPO events:
>> data = EODML('IPO', 'fromdate',20191201)
data = 
  132×1 struct array with fields:
    symbol
    name
    exchange
    currency
    start_date
    filing_date
    amended_date
    price_from
    price_to
    offer_price
    shares
    deal_type
 
>> struct2table(data)
ans =
  132×12 table
     symbol            name             exchange      currency   start_date   filing_date   amended_date  price_from  price_to  offer_price   shares   deal_type
    _________  _____________________  ______________  ________  ____________  ____________  ____________  __________  ________  ___________  ________  __________
    'AMX.AU'   'Aerometrex Ltd'       'ASX'           'AUD'     '2019-12-10'  '2019-11-01'  '2019-11-01'           0         0        1      25000000  'Priced'
    'N/A'      'Air Baltic Corp AS'   'Riga'          'EUR'     {0×0 double}  '2018-07-25'  '2019-12-09'           0         0        0             0  'Amended'
    'N/A'      'Akeso Inc'            'HKSE'          'USD'     {0×0 double}  '2019-12-03'  {0×0 double}           0         0        0             0  'Filed'
    '9966.HK'  'Alphamab Oncology'    'HKSE'          'HKD'     '2019-12-05'  '2018-10-23'  '2019-12-02'           0         0        0             0  'Expected'
    '9966.HK'  'Alphamab Oncology'    'HKSE'          'HKD'     {0×0 double}  '2018-10-23'  '2019-12-02'           0         0     10.2      89701000  'Amended'
    '9966.HK'  'Alphamab Oncology'    'HKSE'          'HKD'     {0×0 double}  '2018-10-23'  '2019-12-02'           0         0     10.2      89702000  'Amended'
    '9966.HK'  'Alphamab Oncology'    'HKSE'          'HKD'     '2019-12-05'  '2018-10-23'  '2019-12-02'           0         0        0             0  'Expected'
    'NA.CN'    'Altum Resource Corp'  'Canadian Sec'  'CAD'     {0×0 double}  '2019-12-12'  {0×0 double}      0.0759    0.0759        0       3500000  'Filed'
    ...

Available parameters that affect this query (in addition to the standard general parameters):

  • FromDate – integer or string; default=[]; earliest data date (GMT timezone). Examples: 20191203, ‘2019-12-03’
  • ToDate – integer or string; default=[]; latest data date (GMT timezone). Examples: 20191203, ‘2019-12-03’

  8) Get historic technical indicators

>> data = EODML('technical', 'symbol','IBM', 'fromdate',20190701, 'period',40, 'function','rsi')
data = 
  83×1 struct array with fields:
    symbol
    date
    datenum
    rsi
 
>> data(1)
ans = 
  struct with fields:
 
     symbol: 'IBM.US'
       date: '2019-08-27'
    datenum: 737664
        rsi: 42.92
 
>> struct2table(data)
ans =
  83×4 table
     symbol         date        datenum     rsi
    ________    ____________    _______    ______
    'IBM.US'    '2019-08-27'    737664      42.92
    'IBM.US'    '2019-08-28'    737665     44.394
    'IBM.US'    '2019-08-29'    737666     46.291
    'IBM.US'    '2019-08-30'    737667     46.861
    'IBM.US'    '2019-09-03'    737671     45.765
    'IBM.US'    '2019-09-04'    737672     47.712
    'IBM.US'    '2019-09-05'    737673     51.457
    'IBM.US'    '2019-09-06'    737674     51.134
    'IBM.US'    '2019-09-09'    737677      52.68
    'IBM.US'    '2019-09-10'    737678     54.464
    ...

Available parameters that affect this query (in addition to the standard general parameters):

  • FromDate – integer or string; default=[]; earliest data date (GMT timezone). Examples: 20191203, ‘2019-12-03’
  • ToDate – integer or string; default=[]; latest data date (GMT timezone). Examples: 20191203, ‘2019-12-03’
  • Function – string; default=’SMA’; one of ‘SMA’,’EMA’,’WMA’,’RSI’,’StdDev’,’AvgVol’,’AvgVolCcy’,’Volatility’,’SplitAdjusted’,’AvgVolCcy’,’Stochastic’,’StochRSI’,’Slope’,’DMI’,’ADX’,’MACD’,’ATR’,’CCI’,’SAR’,’BBands’
  • Period – number; default=50; number of data points used to calculate the function (2-100k)
  • FastPeriod – number; default=12; used by MACD
  • SlowPeriod – number; default=26; used by MACD
  • SignalPeriod – number; default=9; used by MACD
  • FastKPeriod – number; default=14; used by Stochastic, StochRSI
  • FastDPeriod – number; default=14; used by StochRSI
  • SlowKPeriod – number; default=3; used by Stochastic
  • SlowDPeriod – number; default=3; used by Stochastic
  • AGGPeriod – string; default=’day’; one of ‘day’,’week’,’month’; used by SplitAdjusted function
  • AdjustDividends – logical; default=true; if false, close prices are only adjusted for splits, not dividends

  9) Get historic short interests

% Historic short interests for AAPL (note the 'desc' order)
>> data = EODML('shorts', 'symbol','AAPL', 'fromdate',20190101, 'todate',20191231, 'order','desc')
data = 
  17×1 struct array with fields:
    symbol
    date
    datenum
    short
    volume
 
>> data(1)
ans = 
  struct with fields:
 
     symbol: 'AAPL.US'
       date: '2019-09-13'
    datenum: 737681
      short: 42651634
     volume: 28651684
 
>> struct2table(data)
ans =
  17×5 table
     symbol          date        datenum     short       volume
    _________    ____________    _______    ________    ________
    'AAPL.US'    '2019-09-13'    737681     42651634    28651684
    'AAPL.US'    '2019-08-30'    737667     39517330    25536624
    'AAPL.US'    '2019-08-15'    737652     45594258    36615393
    'AAPL.US'    '2019-07-31'    737637     43005960    23525795
    'AAPL.US'    '2019-07-15'    737621     42428971    19134090
    'AAPL.US'    '2019-06-28'    737604     43448528    24985287
    'AAPL.US'    '2019-06-14'    737590     47003511    26629536
    'AAPL.US'    '2019-05-31'    737576     51257104    29778281
    'AAPL.US'    '2019-05-15'    737560     49550348    37444374
    'AAPL.US'    '2019-04-30'    737545     52669476    24561618
    'AAPL.US'    '2019-04-15'    737530     61003851    23755423
    'AAPL.US'    '2019-03-29'    737513     67786010    35017255
    'AAPL.US'    '2019-03-15'    737499     72751656    27347457
    'AAPL.US'    '2019-02-28'    737484     96832513    22065273
    'AAPL.US'    '2019-02-15'    737471     39903215    26937971
    'AAPL.US'    '2019-01-31'    737456     40360782    34248007
    'AAPL.US'    '2019-01-15'    737440     46579709    45191034

Available parameters that affect this query (in addition to the standard general parameters):

  • FromDate – integer or string; default=[]; earliest data date (GMT timezone). Examples: 20191203, ‘2019-12-03’
  • ToDate – integer or string; default=[]; latest data date (GMT timezone). Examples: 20191203, ‘2019-12-03’

  10) Search for a symbol across all exchanges

>> data = EODML('lookup', 'symbol','IBM')
data = 
  18×1 struct array with fields:
    symbol
    Exchange
    Name
    Country
    Currency
    ISIN
 
>> struct2table(data)
ans =
  18×6 table
    symbol    Exchange                        Name                          Country     Currency         ISIN
    ______    ________    _____________________________________________    _________    ________    ______________
    'IBM'     'US'        'International Business Machines Corporation'    'USA'         'USD'      'US4592001014'
    'IBM'     'XETRA'     'International Business Machines Corporation'    'Germany'     'EUR'      {0×0 double}
    'IBM'     'MX'        'International Business Machines Corporation'    'Mexico'      'MXN'      {0×0 double}
    'IBM'     'F'         'International Business Machines Corporation'    'Germany'     'EUR'      {0×0 double}
    'IBM'     'STU'       'IBM (IBM.SG)'                                   'Germany'     'EUR'      {0×0 double}
    'IBM'     'MU'        'IBM'                                            'Germany'     'EUR'      {0×0 double}
    'IBM'     'HM'        'IBM'                                            'Germany'     'EUR'      {0×0 double}
    'IBM'     'BE'        'IBM'                                            'Germany'     'EUR'      {0×0 double}
    'IBM'     'DU'        'IBM - Dusseldorf Stock Exchang'                 'Germany'     'EUR'      {0×0 double}
    'IBMK'    'US'        'iShares iBonds Dec 2022 Term Muni Bond ETF'     'USA'         'USD'      'US46435G7557'
    'IBMI'    'US'        'iShares iBonds Sep 2020 Term Muni Bond ETF'     'USA'         'USD'      'US46434V5710'
    'IBMJ'    'US'        'iShares iBonds Dec 2021 Term Muni Bond ETF'     'USA'         'USD'      'US46435G7896'
    'IBML'    'US'        'iShares iBonds Dec 2023 Term Muni Bond ETF'     'USA'         'USD'      'US46435G3184'
    'IBMM'    'US'        'iShares iBonds Dec 2024 Term Muni Bond ETF'     'USA'         'USD'      {0×0 double}
    'IBMN'    'US'        'iShares iBonds Dec 2025 Term Muni Bond ETF'     'USA'         'USD'      {0×0 double}
    'IBMO'    'US'        'iShares iBonds Dec 2026 Term Muni Bond ETF'     'USA'         'USD'      {0×0 double}
    'IBMP'    'US'        'iShares iBonds Dec 2027 Term Muni Bond ETF'     'USA'         'USD'      {0×0 double}
    'IBMQ'    'US'        'iShares iBonds Dec 2028 Term Muni Bond ETF'     'USA'         'USD'      {0×0 double}

Available parameters that affect this query (in addition to the standard general parameters):

  • DataType – string; must be ‘symbol’ for this query type

  11) Search for all symbols in an exchange

>> data = EODML('lookup', 'symbol','LSE', 'dataType','exchange')
data = 
  3444×1 struct array with fields:
    symbol
    Name
    Country
    Exchange
    Currency
    Type
 
>> head(struct2table(data))
ans =
  8×6 table
    symbol                                         Name                                          Country   Exchange   Currency       Type
    ______   _________________________________________________________________________________   _______   ________   ________   ______________
    '0KBI'   'Knorr-Bremse Aktiengesellschaft'                                                   'UK'      'LSE'      'EUR'      'Common Stock'
    '100H'   'MULTI-UNITS LUXEMBOURG - Lyxor FTSE 100 UCITS ETF - Monthly Hedged to USD - Acc'   'UK'      'LSE'      'USD'      'ETF'
    '1MCS'   'WisdomTree FTSE 250 1x Daily Short'                                                'UK'      'LSE'      'GBX'      'ETF'
    '1PAS'   'WisdomTree Palladium 1x Daily Short'                                               'UK'      'LSE'      'USD'      'ETF'
    '2MCL'   'WisdomTree FTSE 250 2x Daily Leveraged'                                            'UK'      'LSE'      'GBX'      'ETF'
    '2PAL'   'WisdomTree Palladium 2x Daily Leveraged'                                           'UK'      'LSE'      'USD'      'ETF'
    '2UKL'   'WisdomTree FTSE 100 2x Daily Leveraged'                                            'UK'      'LSE'      'GBX'      'ETF'
    '2UKS'   'WisdomTree FTSE 100 2x Daily Short'                                                'UK'      'LSE'      'GBX'      'ETF'

Available parameters that affect this query (in addition to the standard general parameters):

  • DataType – string; must be ‘exchange’ for this query type

  12) Get an options chain

>> data = EODML('options', 'symbol','IBM')
data = 
  struct with fields:
      symbol: 'IBM.US'
        code: 'IBM'
    exchange: 'US'
        data: [15×1 struct]
 
>> data.data(1)
ans = 
  struct with fields:
       expirationDate: '2019-12-27'
    expirationDatenum: 737786
                 puts: [39×1 struct]
                calls: [39×1 struct]
 
>> struct2table(data.data)
ans =
  15×4 table
    expirationDate  expirationDatenum      puts           calls
    ______________  _________________  _____________  _____________
    {'2019-12-27'}       737786        {39×1 struct}  {39×1 struct}
    {'2020-01-03'}       737793        {38×1 struct}  {38×1 struct}
    {'2020-01-10'}       737800        {35×1 struct}  {35×1 struct}
    {'2020-01-17'}       737807        {55×1 struct}  {55×1 struct}
    ...
 
>> data.data(1).puts(6)
ans = 
  struct with fields:
 
         contractName: 'IBM191227P00120000'
         contractSize: 'REGULAR'
             currency: 'USD'
                 type: 'PUT'
           inTheMoney: 'FALSE'
    lastTradeDateTime: '2019-12-23 10:01:34'
       expirationDate: '2019-12-27'
    expirationDatenum: 737786
               strike: 120
            lastPrice: 0.01
                  bid: []
                  ask: 0.03
               change: -0.21
        changePercent: -0.9545
               volume: 2
         openInterest: 3
    impliedVolatility: 45.512
                delta: -0.0048
                gamma: 0.0022
                theta: -0.0113
                 vega: 0.002
                  rho: -0.0001
          theoretical: 0.01
       intrinsicValue: 0
            timeValue: 0
            updatedAt: '2019-12-23 19:32:03'
 
>> struct2table(data.data(1).puts)
ans =
  39×26 table
    ...

Available parameters that affect this query (in addition to the standard general parameters):

  • FromExpiryDate – integer or string; default=[]; earliest data date (GMT timezone). Examples: 20191203, ‘2019-12-03’
  • ToExpiryDate – integer or string; default=[]; latest data date (GMT timezone). Examples: 20191203, ‘2019-12-03’
  • FromTradeDate – integer or string; default=[]; earliest data date (GMT timezone). Examples: 20191203, ‘2019-12-03’
  • ToTradeDate – integer or string; default=[]; latest data date (GMT timezone). Examples: 20191203, ‘2019-12-03’
  • ContractName – string; default=”; query only the specified contract e.g. ‘AAPL180420P00002500’

  13) Get EODML version and query limits

>> data = EODML('version')
data = 
  struct with fields:
    Version: 1.03
    Release: '24-Dec-2019'
    License: 'Free'
     Expiry: '08-Aug-2020'
 
>> limits = EODML('limits')
limits = 
  struct with fields:
          licenseType: 'Free'
      numQueriesToday: 27
    numQueriesThisMin: 0
     maxQueriesPerDay: 30
     maxQueriesPerMin: 3

Legal disclaimer

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(detailed disclaimer is available in EODML’s License Agreement)

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