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,

The world of finance, often perceived as an impenetrable fortress of arcane knowledge and exclusive access, is in the throes of a...

In the ever-evolving landscape of financial markets, the quest for "alpha"—the elusive excess return on an investment above a benchmark...

Today, we stand at the precipice of a new revolution, one driven by (Artificial Intelligence) AI Quant  that doesn't just execute pre-programmed commands but actively analyzes, strategizes, and even builds its own operational dashboards.

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.