ISO20022 Foreign Exchange Trade Instruction (fxtr.014)

This dataset provides comprehensive records of foreign exchange spot and forward trade instructions between financial counterparties, including currency pairs, trade amounts, rates, and detailed settlement instructions. It is ideal for transaction monitoring, regulatory reporting, and operational reconciliation in FX trading environments.

  • last updated 29 Oct 2025
  • by GoMask
The brief that made it

Automated reconciliation and settlement of FX trades

Sample rows

preview · 8 of 400 rows · all 21 columns
trade_instruction_idstringtrade_typestringbuy_amountfloatbuy_currencystringtrade_datedatevalue_datedatecurrency_pairstringsell_currencystringsell_amountfloatexchange_ratefloatcounterparty_idstringcounterparty_namestringcounterparty_bicstringsettlement_account_numberstringsettlement_bank_namestringsettlement_bank_bicstringsettlement_bank_countrystringinstruction_statusstringcreated_atdatetimeupdated_atdatetimesettlement_instructionsstring
FX-10001spot2500000USD2024-05-232024-05-24USD/EUREUR2298852.460.91954CPTY001Morgan StanleyMSNYUS33789456123Morgan StanleyMSNYUS33USsettled2024-05-23T09:07:08Z2024-05-24T11:20:53ZPay USD to account 789456123 at Morgan Stanley via SWIFT.
FX-10002forward10000000EUR2022-03-142022-03-16EUR/GBPGBP85340000.8534CPTY002Barclays Bank PLCBARCGB22GB12345678Barclays Bank PLCBARCGB22GBconfirmed2022-03-14T13:15:42Z2022-03-16T08:32:10ZSettle EUR to Barclays IBAN GB28BARC123456789.
FX-10003spot750000USD2024-01-032024-01-03USD/JPYJPY118627500158.17CPTY003Sumitomo Mitsui Banking CorporationSMBCJPJT147963258Sumitomo Mitsui Banking CorporationSMBCJPJTJPsettled2024-01-03T10:22:18Z2024-01-03T13:05:41ZPay USD to account 147963258 at SMBC via SWIFT.
FX-10004spot100000GBP2021-09-072021-09-07GBP/USDUSD1398001.398CPTY004HSBC Bank PLCHBUKGB4B987654321HSBC Bank PLCHBUKGB4BGBconfirmed2021-09-07T08:48:11Z2021-09-07T15:16:53ZSettle GBP to HSBC account 987654321.
FX-10005forward5000000USD2023-11-152024-02-15USD/BRLBRL260000005.2CPTY005Banco do BrasilBRASBRRJ22334455Banco do BrasilBRASBRRJBRpending2023-11-15T11:36:02ZblankSettle USD to Banco do Brasil account 22334455.
FX-10006spot500000EUR2024-06-132024-06-14EUR/CHFCHF4850000.97CPTY006Credit SuisseCRESCHZZCH98765432Credit SuisseCRESCHZZCHsettled2024-06-13T09:18:31Z2024-06-14T10:44:29ZPay EUR to Credit Suisse, account CH98765432.
FX-10007spot0.0001AUD2020-07-232020-07-23AUD/USDUSD0.000070.7CPTY007National Australia BankNATAAU33blankblankblankblankrejected2020-07-23T10:09:18Z2020-07-23T10:15:22ZSettle AUD to NAB account 00000001.
FX-10008forward3000000USD2023-03-292023-09-29USD/MXNMXN5400000018CPTY008BBVA BancomerBCMRMXMM11223344BBVA BancomerBCMRMXMMMXconfirmed2023-03-29T14:02:58Z2023-09-29T09:21:03ZPay USD to BBVA Bancomer account 11223344.

What the 400 rows show

from the 400-row sample

Forward (trade type) stands out: mean buy_amount is 12,746,026, against 488,333 for the rest.

  • 650,000median buy_amount
  • 4instruction statuses
  • 54sell currencies
  • 56settlement bank countries
  • 79currency pairs
  • 8,169,900median sell_amount
Mean buy_amount by trade_type400 rows
010M20M488.3kspot249 rows12.7Mforward151 rows
buy_amount400 rows, in bands of 20M
019038037413822001080M160Mbuy_amount →

Median 650,000, from 0.00 to 159,800,000.

buy_currency400 rows · 9 values
  1. USD310
  2. EUR49
  3. GBP18
  4. AUD10
  5. CAD5
  6. NZD3
  7. JPY2
  8. CHF2
  9. ZAR1
21 columns by typefrom the column list below
  • string 14
  • float 3
  • date 2
  • datetime 2

Columns

21 columns in three groups
blueprint · 21 columns
columntypedescriptionexample
Text 14 columns
trade_instruction_idstringUnique identifier for each FX trade instructionuniqueFX-10001
trade_typestringType of FX trade (spot or forward)spot · forwardspot
currency_pairstringISO currency codes for the traded pair (e.g., EUR/USD)USD/EUR
buy_currencystringISO currency code being bought9 currenciesUSD
sell_currencystringISO currency code being soldEUR
counterparty_idstringUnique identifier for the counterparty institutionCPTY001
counterparty_namestringName of the counterparty institutionMorgan Stanley
counterparty_bicstringBank Identifier Code (BIC/SWIFT) of the counterpartyoptionalMSNYUS33
settlement_instructionsstringInstructions for settlement (e.g., account details, payment method)USD payment to NCB KSA.
settlement_account_numberstringAccount number for settlement of the tradeoptional789456123
settlement_bank_namestringName of the bank where settlement will occuroptionalMorgan Stanley
settlement_bank_bicstringBIC/SWIFT code of the settlement bankoptionalMSNYUS33
settlement_bank_countrystringCountry of the settlement bank (ISO 3166-1 alpha-2 code)optionalUS
instruction_statusstringCurrent status of the trade instructionpending · confirmed · settled · cancelled · rejectedsettled
Numbers 3 columns
buy_amountfloatAmount of buy currency in the trade0 or more2500000
sell_amountfloatAmount of sell currency in the trade0 or more2298852.46
exchange_ratefloatAgreed FX rate for the currency pair0 or more0.91954
Dates and times 4 columns
trade_datedateDate the FX trade was agreed upon2024-05-23
value_datedateSettlement date for the FX trade2024-05-24
created_atdatetimeTimestamp when the instruction was created2024-05-23T09:07:08Z
updated_atdatetimeTimestamp when the instruction was last updatedoptional2024-05-24T11:20:53Z

Use it for

  • median buy amo…650,000400 rowsmean buy amount by tr…488.3kspot12.7Mforward

    A finance dashboard

    Buy_amount by trade_type and a breakdown of buy_currency. Excel, Power BI or Tableau.

  • Why do the 151 forward rows have a mean buy_amount of 12,746,026?

    A root-cause class exercise

    Hand out the rows and one question. The answer is in the data, not in the brief.

  • A software demo

    Believable trade instructions with trade_date, value_date and trade_type to fill a screen in front of a buyer.

Not quite right?

Make it yours.

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This dataset400 rows21 columns
Yours1,000 rows21 columnssettlement_bank_country: UK only

blueprint · iso20022-foreign-exchange-trade-instruction-fxtr014

Behind this dataset

Same schema. As many rows as you need.

These 400 rows came out of a blueprint — 21 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.

Rules it was built with
  • Trade reference unique per deal
  • Currency pair specified (e.g., EUR/USD)
  • Exchange rate agreed
  • Buy amount and sell amount calculated
  • Value date for settlement
  • Counterparty identification
  • Settlement instructions for both currencies
  • Trade type: spot, forward, swap
Rows
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Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
iso20022-foreign-exchange-trade-instruction-fxtr014

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