With a single function call, DolphinDB can write analysis results directly to your Obsidian knowledge base as structured Markdown notes—making them immediately available for AI to search, reason over, and build on.
On July 15, we hosted a webinar introducing DolphinX, our enterprise AI Agent development and governance platform. Presented by DolphinDB kernel engineer Qiwei Zhu, the session focused on the practical challenges of deploying AI Agents in production, including permissions, security, governance, and enterprise integration.
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
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.
This article walks through exactly that transformation, using DolphinDB as the computing layer. We’ll cover both the batch path (historical data, processed in parallel) and the streaming path (real-time data, processed with sub-millisecond latency), with the same core logic running in both.
Using simulated tick-by-tick trade data from the Shanghai Stock Exchange on a single trading day in 2020, we’ll calculate minute-level capital flow — splitting buy and sell orders into “large” and “small” buckets based on a 50,000-share threshold — and stream the results live into a dashboard.
