How MarketReader Processes 3M Trades/Min to Deliver US Market Trading Insights with TimescaleDB
Blog post from Tiger Data
MarketReader, a fintech startup founded in 2021, is dedicated to providing US market trading insights by processing approximately 3 million trades per minute using TimescaleDB and Tiger Data's time-series capabilities. This setup allows them to deliver real-time analytics and reduce market noise for clients like retail brokerages and institutional investors. MarketReader's architecture leverages AWS for elastic compute and orchestration, Redis for messaging, and Supabase for caching, with Tiger Cloud serving as the primary data storage and analytics platform. The system is designed to detect unusual stock behavior and provide contextually rich insights by integrating vector search and large language models, which attach semantically related news and social signals to market movements. This modular and scalable approach not only enhances MarketReader's ability to offer timely and accurate insights but also minimizes operational overhead, allowing the company to focus on expanding its product capabilities and collaborating with investment firms to improve chatbot intelligence and market context for large language models.
| Trend | Post Mentions | Total Month Mentions | Posts | Companies | MoM |
|---|---|---|---|---|---|
| Real-time | 8 | 6,457 | 1,307 | 242 | +28% |
| LLM | 5 | 6,078 | 960 | 218 | +18% |
| Kubernetes | 2 | 1,840 | 308 | 106 | +33% |
| Vector Search | 2 | 2,370 | 415 | 145 | +7% |
| RAG | 1 | 1,806 | 326 | 91 | +5% |
Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.