This process, for all its technological sophistication, has remained fundamentally artisanal: slow, painstaking, and limited by the cognitive bandwidth of its human practitioners via this new Quant AI.

A recent trend has emerged, with a notable outflow of engineering talent from the renowned hedge fund DE Shaw careers to firms like Hudson River Trading (HRT) and OpenAI.

Now, as large language models (LLMs) like ChatGPT trading demonstrate increasingly sophisticated capabilities, a pressing question emerges: can these AI systems effectively serve as quantitative analysts for trading?

Optimization can achieve superior speedups in low-level program asm code, challenging the supremacy of traditional compilers like GCC and heralding a new era for software performance engineering.

Jeremy Sosabowski is a prominent figure in the fintech world, particularly known for his expertise in AI-driven financial forecasting. His f

A Visual Guide to How Diffusion Models Work for any Quant Researcher: Understanding the Key Intuitions Behind Text-to-Image Generative AI fr

These agents, guided by structured workflows, represent a significant leap forward in AI capabilities, and many believe 2025 will be the ye

High Flyer and touch the face of God seems so powerful with the rise of what was developed in Deepseek.

LLM model tools have taken the world by storm, demonstrating an uncanny ability to generate human-quality text, translate languages, and eve