Multi-asset trading platform for capital markets execution and risk management
Trading Technologies operates a SaaS platform connecting global exchanges and liquidity venues for derivatives, fixed income, FX, and crypto trading. The tech stack is heavily AWS-native (Kubernetes, Lambda, RDS, MSK, EKS) with Python and C++ at the core, and the company is actively migrating away from VMware while adopting Backstage—signaling infrastructure modernization. Engineering dominates the 22-person hiring pipeline (16 roles, mostly senior), paired with concurrent investment in telemetry, monitoring, and incident automation, revealing a push toward operational resilience in a latency-critical domain.
Trading Technologies provides a SaaS platform for trade execution, order management, market data, analytics, risk management, clearing, and post-trade operations across listed derivatives, fixed income, FX, and cryptocurrencies. The customer base spans Tier 1 banks, brokers, hedge funds, proprietary traders, and exchanges globally. The platform connects to major international exchanges and liquidity venues and integrates with complementary technology partners through an ecosystem model. Founded in 1994 and headquartered in Chicago, the company serves as the operational backbone for end-to-end trading workflows at leading sell-side and buy-side institutions.
Core: Python, C++, Go, React. Infrastructure: AWS (EKS, ECS, RDS, Lambda, MSK, Kafka, CloudWatch). DevOps: Kubernetes, Docker, Terraform, Chef. Observability: Slack, Confluence. Testing: Selenium, Playwright, Pact. Currently adopting Backstage and migrating off VMware.
Core focus: performance and memory optimization of the trading platform; real-time telemetry, logging, and monitoring tools; incident response automation and remediation workflows; Backstage internal developer platform adoption; compliance decision systems; AWS footprint modernization and VMware datacenter removal.
Trading Technologies's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.