invite your input on shaping the direction of upcoming downloadable and affordable programming quant ai courses that I am preparing to release.

concepts into the world of C++, a language synonymous with high-performance computing and the backbone of many institutional quant AI trading systems.

In the relentless digital torrent of modern financial markets, the search for a true signal amidst the deafening noise is the quant AI analyst's grand challenge.

we will dissect the architecture, technology, strategy, and profound implications of this groundbreaking approach to automated AI day trading.

For aspiring and established professionals in banking, AI quant trading, and quantitative finance, the message is stark and unforgiving: adapt or be rendered irrelevant

A dedicated day and swing trader, armed with automated broker platforms like Interactive Brokers and NinjaTrader, finds themselves at a critical juncture.

In the relentless arena of financial markets, the modern trader is besieged by a deluge of data, a cacophony of signals, and the...

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.

The dream of a personal, automated trading system—an AI trading bot-powered alchemist that turns market data into gold—is no longer the exclusive domain of hedge funds and quantitative powerhouses.

The world of finance, often perceived as an impenetrable fortress of arcane knowledge and exclusive access, is in the throes of a...

Quantitative analysis, the engine of modern quant and finance, has evolved dramatically from its origins in simple options pricing to a multifaceted discipline that touches every corner of the global markets.

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

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.

In the sprawling, hyper-fast digital landscape of modern finance, data is more than just information; it is the lifeblood of every...

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.

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?

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!

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.