DolphinDB eliminated data silos and fragmented system architectures, providing a solid technical foundation for FICC quantitative trading and market-making.
Introducing DolphinX, an Enterprise-Grade Platform for Agent Development and Governance
This post explores the core building blocks of DolphinDB's distributed computing framework: data sources, the MapReduce model, and iterative computing — along with the architectural concept of compute-storage separation that underpins the whole system.
This article explains how DolphinDB replays historical data into streaming pipelines, and how it enables accurate simulation of real-time trading environments for strategy research, testing, and validation.
In this article, we explore DolphinDB’s streaming framework through practical, real-world examples—from stateless calculations to stateful windowed aggregations—and explain how it unifies stream and batch processing in a single system.
By leveraging LLVM, DolphinDB enables high-performance numerical computing, advanced financial modeling, and iterative algorithms to be written in concise, expressive Dlang.
Can crypto flash wicks be predicted before they happen? Using the October 2025 $19B liquidation event as a case study, this article shows how VPIN, order book imbalance, and market microstructure can uncover early warning signals before extreme price movements occur.
DolphinDB’s high-performance market data replay and Order Matching Simulator Plugin provides a low-latency, high-throughput solution for strategy validation. By integrating the plugin directly into an existing C++ backtesting framework, institutions can reuse their current infrastructure while taking advantage of DolphinDB’s ultra-fast computing capabilities, making it an ideal solution for high-frequency strategy simulation.
This post walks through a complete, production-oriented solution using DolphinDB together with the INSIGHT market data plugin to generate 1-second order book snapshots across all SSE and SZSE stocks and funds in real time. We'll cover everything from plugin installation to post-market batch writes to a distributed database — with the actual scripts you'd run in production.
DolphinDB unifies heterogeneous industrial data (OPC UA, MQTT, Kafka) into one pipeline, with partitioned storage, stream-batch unified computation, and standard APIs. It replaces the slow relational DB underneath MES, cutting query times dramatically without replacing MES itself.
