For decades, the world of quantitative finance, particularly C++ high-frequency trading bot, has been an exclusive club. It was the domain of elite QI quant institutions with cavernous server rooms, teams of PhDs in physics and mathematics, and budgets stretching into the millions.

For the average retail investor, the stock market is often presented as a meritocracy of ideas—a grand arena where the best analysis and the most disciplined strategies win the day.

The world of high-frequency trading secrets is a realm of shadows and whispers, a digital fortress built on billions of dollars of...

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

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.

The confluence of sophisticated data analytics, the explosive growth of artificial intelligence, and the ever-present dynamism of global markets is reshaping how traders and investors approach future trading strategies development and execution.

The drumbeat of Artificial Intelligence (AI) quant is impossible to ignore, and the world of quantitative finance is listening intently.