The rise of quantitative approaches to trading has created a clear divide between retail traders relying on intuition and institutional players leveraging sophisticated mathematical models and quantative trading firms and high-frequency trading (HFT) systems.

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?

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.

Minimalism in software development, particularly when leveraging the versatility of Python and its rich standard library, offers a powerful avenue for learning, rapid prototyping, and building specialized tools.

QuantLabs, a prominent name in providing resources and tools for quantitative traders and developers, has recently augmented its public collection of open-source tools with four sophisticated Python A.I. trading scripts.

An in-depth exploration of experimental quant C and C++ projects, from implementing Ehlers' Cybernetic Oscillator and probing option theory to simulating mean-reverting processes and LPI volatility.

The announcement, centered around the power of newly released Large Language Models (LLMs), showcases the rapidly evolving capabilities of AI coding in generating complex, high-performance C++ code for institutional-level trading strategies.

The world of quantitative finance and algorithmic trading or quant developer is in a perpetual state of evolution.

The question is simple yet profound: Are you primarily interested in market deep analysis reports on all asset classes, or in the programming code behind these reports using quantitative techniques?

Is the greater interest in consuming and understanding comprehensive market deep analysis, or in developing the technical prowess to construct these analyses through programming and quantitative techniques?

  The core currency in this world is latency – the delay between an event (like a market data update) and the system's reaction (like placing an order).

Now, Citadel Securities, arguably one of the most sophisticated users of the language in finance, has signaled its deep commitment and forward-looking strategy by bringing aboard a C++ guru titan, Herb Sutter, who is already encouraging the firm—and implicitly, the industry—to get ready for C++26, the next iteration of the language standard.

The Quantitative Compass: How Probability and Statistics Navigate the World of Finance Finance, at its heart, is the science of managing money, encompassing activities like investing, borrowing, lending, budgeting, saving, and forecasting.

Building Intelligent Market Navigators: An Exploration of AI Trading Agents in Python

The world of quantitative finance, or with quant trading firms (HFT)  operates at the intersection of sophisticated mathematics, vast datasets, and high-speed computation.

Forget the smooth, predictable curves of backtested trend following fund models – the market is throwing a full-blown tantrum, and the sophisticated algorithms of some of the biggest players are getting KO'd.

Amplified Risks: Analyzing the Role of Leverage in Renaissance Technologies' Losses Introduction: The Double-Edged Sword of Financial Engin

When they set out to build OpenAlgo, an open-source algorithmic trading framework tailored for Indian traders, we faced a barrage of questio

The financial markets are a dynamic landscape, constantly evolving with shifts in global economies, geopolitical events, and investor sentim

HFT Tracker: Call-Put Parity and the Value of Futures Option Chain Data In the frenetic world of high-frequency trading  or HFT tracker, wh