This article explores a groundbreaking development in this field, demonstrating how the latest generation of quant AI, specifically Anthropic's Claude 4.1 Opus

concepts into the world of C++, a language synonymous with high-performance computing and the backbone of many institutional quant AI trading systems.

ion of a presentation delivered by Brian from quantlabset.com at Moomoo Financial Canada. The original event, hosted for his meetup groups, delved into the cutting-edge intersection of artificial intelligence and quant AI trading. This article aims to reconstruct that presentation,

In this article, we will explore Retrieval-Augmented Generation (RAG), a powerful AI technique that combines database retrieval with large language models (LLMs) to enhance AI-generated responses. We will walk through a basic implementation using LangChain, OpenAI’s GPT, and a PostgreSQL/SQLite database for a trading application.