Welcome, members of the Quant Programming Elite community. In the digital forge of modern finance, where alpha is measured in microseconds and risk is quantified in terabytes of data, the code you write is not merely a tool—it is your weapon, your shield, and your compass

A stark new dividing line is being drawn, not by a market crash or a regulatory shift, but by a version number: Cpp 26.

From fundamental changes in core programming languages to the emergence of new tools and methodologies, the collective insights offer a snapshot of the current technological frontier, with future of Python continuing to play a central and expanding role.

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.

https://www.youtube.com/watch?v=TrCfhizFZdw

This article reviews one such experience with Financial Modeling Prep API (FMP), highlighting recent issues and offering alternative solutions for those seeking reliable financial data.

Instead, they’re deliberately obscure, quant programming proprietary languages and systems designed to do two things:

Citadel, the multi-trillion dollar hedge fund and market-making behemoth, has hired William Dealtry, a key architect of Man Group's prized database, ArcticDB. This is a high speed data base.

Fenix AI Stock Trading Bot Free, an open-source framework for algorithmic cryptocurrency trading built entirely within the Python ecosystem.

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.

The Simplicity Advantage: Why Streamlit Outshines Grafana and QuestDB for Trading Algorithm Simulations. There is a reason why you should...

Diversification Low Correlation Sharpe Ratio. Diversification spreads risk. Holding positions that react to different economic forces smooths out the bumps in your equity curve.

As reported by eFinancialCareers, a new contender, Rust vs C#, is beginning to challenge C#'s position, not in a full-frontal assault, but through a series of strategic, targeted replacements in performance-critical areas.

Based on volatility, recent price momentum, and cash-and-carry options arbitrage strategy opportunities, we identify the five top instruments—BRR, CC, ES, GC, and ALI—with the greatest potential for 10–30-day profits.

For traders utilizing the powerful and globally recognized platform of Interactive Brokers (IBKR), gaining efficient access to this wealth of IBKR options chain data is not merely a convenience; it represents a fundamental strategic advantage.  

his article delves into the path leading to flux 1.0, exploring its motivations, core principles, key features, and the anticipated evolution that a 1.0 release represents.

This article delves into the design and components of a Python-based AI agent project, specifically focusing on its application as a forex trading system.

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.

I’m thrilled to share with you an intriguing quantitative finance project that I’ve been working on. This particular project is coded in Python, and while I’ll be rolling out more advanced projects and demos in C++ as we dive deeper into advanced AI with tools like Anthropic, today’s focus is a simpler yet insightful example of pure quant finance. So, let’s check it out!