9 of 9 Backtesting Jobs in London

Developer to join its high-frequency trading team. This role involves owning and expanding the Python research and analytics stack that supports strategy research, backtesting, and live trading in crypto markets. You will work closely with quant researchers and traders to build tools and infrastructure used daily in production. What

Developer to join its high-frequency trading team. This role involves owning and expanding the Python research and analytics stack that supports strategy research, backtesting, and live trading in crypto markets. You will work closely with quant researchers and traders to build tools and infrastructure used daily in production. What

disrupt the Index space and participate in the next phase of our growth. What will you do? You will be responsible for modeling, creating, backtesting, and launching a wide range of new index strategies across various asset classes. Your exposure will include (but of course not limited to) equities, futures

whether customer performance is good or poor and why Implementing logic changes within a Java-based algorithmic framework Conducting Python-based research, prototyping, and backtesting Collaborating closely with global quant and engineering teams (including Asia) Explaining model behaviour and insights to internal stakeholders and clients What we're looking

whether customer performance is good or poor and why Implementing logic changes within a Java-based algorithmic framework Conducting Python-based research, prototyping, and backtesting Collaborating closely with global quant and engineering teams (including Asia) Explaining model behaviour and insights to internal stakeholders and clients What we're looking

planet. The opportunity to work with tech years ahead of competitors! Responsibilities: Develop and Maintain Research Platforms, building Python-based tools and libraries for backtesting strategies, analyzing data, and simulating trading logic to support quant researchers. Implement Quantitative Models and Signals, translating trading ideas or academic models into production-ready

Ensure data integrity, latency minimization, and fault tolerance in streaming environments using q and kdb+ capabilities. Collaborate with quantitative researchers to develop research tools, backtesting frameworks, and analytics in q. Provide fast-access APIs and utilities to extract and manipulate data for strategy testing and signal generation. Key Requirements: Degree

series feature storage, retrieval, and versioning optimised for ML workloads. MLOps Pipelines: Design end-to-end workflows for data ingestion, feature engineering, model training, backtesting, and deployment. Data Ingestion Layer: Connect raw data streams into structured, queryable formats (Parquet/Delta Lake). Production Serving: Deploy feature computation and model

Job Description As a world leading bank, our culture is all about thinking outside the box, challenging the status quo