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Founded by passionate advocates of learning and innovation, Learni set out to make professional training accessible to everyone, everywhere in the world. Our team works in the largest cities such as Paris, Lyon, Marseille, and internationally, to support talents and organizations in their skills development.
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The Training Bloomberg API - Leveraging Real-Time Financial Data training is delivered in-person or remotely (blended-learning, e-learning, virtual classroom, remote in-person). At Learni, a Qualiopi-certified training organization, each program is designed to maximize skills acquisition, regardless of the training mode chosen.
The trainer alternates between demonstrative, interrogative, and active methods (through practical exercises and/or real-world scenarios). This pedagogical approach ensures concrete and directly applicable learning in the workplace.
To ensure the quality of the Training Bloomberg API - Leveraging Real-Time Financial Data training, Learni provides the following teaching resources:
For in-house training at a location external to Learni, the client ensures and commits to having all necessary teaching materials (IT equipment, internet connection...) for the proper conduct of the training action in accordance with the prerequisites indicated in the communicated training program.
The assessment of skills acquired during the Training Bloomberg API - Leveraging Real-Time Financial Data training is carried out through:
Learni is committed to the accessibility of its professional training programs. All our training programs are accessible to people with disabilities. Our teams are available to adapt teaching methods to your specific needs. Do not hesitate to contact us for any accommodation request.
Learni training programs are available for inter-company and intra-company settings, both in-person and remote. Registration is possible up to 48 business hours before the start of training. Our programs are eligible for OPCO, Pôle emploi, and FNE-Formation funding. Contact us to discuss your training project and funding possibilities.
Complete installation of the Bloomberg API environment with blpapi Python and C++, configuration of authenticated sessions via OAuth and enterprise certificates, exploration of reference data schemas like securities and fields, first practical exercises on real-time quote requests for stocks and bonds, development of a basic monitoring script with error handling, production of an immediately usable deliverable in the trading desk to validate professional skills.
Deep dive into historical requests via HistoricalDataRequest with override parameters for what-if scenarios, mastery of intraday flows and tick data for high-frequency analysis, exercises on optimized bulk requests to handle entire portfolios, integration of websocket streaming for scalable real-time data, concrete business cases on backtesting quantitative strategies, creation of an interactive dashboard connected to Bloomberg, complete API documentation for deployment in a certifying fintech team.
Design of automated trading applications with Bloomberg API in Dockerized microservices, performance optimization via Redis caching and request throttling to avoid quota limits, setup of monitoring with Prometheus and latency alerts, practical exercises on CI/CD integration for enterprise pipelines, simulation of real risk management cases with derivatives data, production of a deployable red thread project in secure production, expert code review to consolidate professional skills in quantitative finance.
Target audience
Quants, financial analysts, fintech developers, and data scientists seeking professional skill advancement
Prerequisites
Advanced proficiency in Python or C++, REST/Websocket APIs, market finance fundamentals, and databases
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