his is the realm of ultra-low latency trading analysis and high-frequency trading (HFT), a domain where victory and defeat are measured not in minutes or seconds, but in microseconds and nanoseconds—millionths and billionths of a second

The engine of this transformation is Generative Quant AI aka Artificial Intelligence. We are moving beyond an era where AI was merely a tool for data analysis into a new paradigm where AI is the architect, the engineer, and the coder of the entire trading infrastructure.

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

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.

On June 12th, a new frontier was unveiled—a system that represents the culmination of this evolution, demonstrating the incredible power of modern AI tools for crypto arbitrage trading with HFT market making. T

The cryptocurrency markets, with their hallmark of most volatile crypto, pervasive regulatory uncertainties, and relentless 24/7 trading cycles, present a uniquely challenging environment for human traders susceptible to emotional decision-making.

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.

Large Language Models (LLMs), to transform dense academic research into the skeleton of a C++ HFT code (High-Frequency Trading) application.