The Financial Instrument Global Identifier, or FIGI, serves as a critical open standard for identifying financial instruments across global markets. In complex trading ecosystems, having access to high-quality test data is essential for ensuring system reliability and data integrity. The mock-jutsu library provides a dedicated figi function designed to generate these identifiers with precision, allowing developers to simulate real-world capital markets scenarios without relying on restricted or expensive production data feeds.
Every identifier produced by the mock-jutsu figi function adheres strictly to the OpenFIGI standard to ensure technical accuracy. The output consists of a 12-character alphanumeric string structured with a two-letter prefix, followed by the constant letter ‘G’, an eight-character National Securities Identifying Number (NSIN), and a final check digit calculated via the Luhn algorithm. This rigorous adherence to the structural requirements ensures that the generated mock data passes internal validation logic in downstream applications, such as trade execution platforms, order management systems, or risk engines.
Integrating figi generation into your development workflow is seamless across various environments. Python developers can easily invoke jutsu.generate('figi') within their unit test suites, while DevOps engineers might prefer the command-line interface by running mockjutsu generate figi for quick database seeding. Furthermore, the library supports performance testing through a dedicated JMeter plugin, enabling the injection of dynamic identifiers into high-concurrency load tests using the specific function syntax. This versatility makes it an indispensable tool for modern automated testing pipelines in the fintech sector.
Utilizing mock-jutsu for figi generation offers significant benefits during the development of portfolio management systems and regulatory reporting tools. By using realistic test data, engineering teams can verify that their databases correctly index instruments and that their APIs handle identifier lookups with the expected performance. Ultimately, this function reduces the friction of manual data creation, allowing financial engineers to focus on building robust trading infrastructure while maintaining full compliance with data privacy standards by avoiding the use of sensitive production information during the testing phase.
mockjutsu generate figimockjutsu bulk figi --count 10mockjutsu export figi --count 10 --format jsonmockjutsu export figi --count 10 --format csvmockjutsu export figi --count 10 --format sqlfrom mockjutsu import jutsujutsu.generate('figi')jutsu.bulk('figi', count=10)jutsu.template(['figi'], count=5)${__mockjutsu_markets(figi)}# JMeter Function: __mockjutsu_markets# Parameter 1: figi# Parameter 2: (not required for this function)GET /generate/figi# → {"type":"figi","result":"...","status":"ok"}GET /bulk/figi?count=10POST /template {"types":["figi"],"count":1}