Dedicated to sharing the hottest FinTech practices
The article examines how containerization, micro‑services, and cloud‑native patterns have reshaped software architecture, challenged traditional IOE stacks, and forced enterprises to rethink organization, tooling, and development practices to stay competitive in an accelerating technological landscape.
The GF Fintech team visited Tencent to share and learn about cutting‑edge front‑end and back‑end technologies, covering Angular 2 with RxJS, React Native, ClojureScript functional programming, and Node.js microservices, followed by lively Q&A and networking.
This article explains how a financial e‑commerce platform uses the Event Sourcing architecture pattern, an asynchronous message bus, and a Go‑based domain‑specific language to build a flexible, exactly‑once points system that decouples business rules from application code and simplifies operations.
Google's gRPC 1.0 release brings high‑performance, multi‑language RPC support built on Protocol Buffers and HTTP/2, enabling loosely coupled, scalable backend systems for data centers, mobile apps, IoT, and microservices with improved efficiency, security, and ecosystem adoption.
This article explains how GF Securities built a customized Lambda/Kappa‑style big‑data platform that integrates CEP, Spark, Flink and Kafka to deliver low‑latency stock price alerts, real‑time news, and capital‑flow trading strategies for the finance industry.
At DockerCon 2016, Aaron Grattafiori outlined a comprehensive security framework for container‑based microservices, emphasizing user namespaces, custom AppArmor/SELinux policies, sec‑comp whitelists, hardened host OS, limited host access, network security, immutable containers, and secret management to achieve high‑assurance deployments.
Exploring how a securities firm processes billions of daily stock‑market indicators in real time, this article compares an in‑process Redis + Lua solution with an out‑of‑process Goroutine‑based architecture, detailing data flow, performance trade‑offs, and scalability considerations for high‑frequency time‑series workloads.
Facing massive transaction volumes and stringent regulatory demands, GF Securities re‑engineered its legacy monolithic trading platform using container and micro‑service technologies, adopting cloud‑native principles to achieve high reliability, low latency, auto‑scaling, and proactive, business‑level monitoring for a resilient, anti‑fragile system.
Google Brain’s open‑source TensorFlow model tackles long‑text summarization by extracting key information and generating concise headlines, demonstrating state‑of‑the‑art extractive and abstractive techniques, with released code, hyper‑parameter details, and examples that illustrate its performance on news articles.
This article explains how a securities firm adopted Docker and cloud‑native practices to overcome scaling, upgrade, and reliability challenges, detailing its four‑layer platform architecture, massive container deployments, orchestration strategies, and immutable DevOps best practices for high‑frequency trading environments.
