ion of a presentation delivered by Brian from quantlabset.com at Moomoo Financial Canada. The original event, hosted for his meetup groups, delved into the cutting-edge intersection of artificial intelligence and quant AI trading. This article aims to reconstruct that presentation,

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

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.

algorithmic trading and automated systems dominate the landscape, a simple yet powerful README file can serve as the gateway to efficient IBKR API stock and ETF trading.

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.

A Fresh Wave of Innovation Hits the Desktops of Top Quant Programming, Promising Enhanced Alpha and Deeper Insights

Large Language Models (LLMs), to transform dense academic research into the skeleton of a C++ HFT code (High-Frequency Trading) application.

. These reports represent distilled knowledge about market behavior, volatility, pricing anomalies, and risk-reward profiles. However, this knowledge remains inert until translated into a system capable of interpreting it and executing trades at machine speed. This introduces you to a way an HFT tracker can be built rapidly from my lastest rounds of reports I have showcased.

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

The world of options analysis and trading is characterized by its complexity and speed. Traders grapple with multi-dimensional data – str

Google AI's introduction of PlanGEN, a multi-agent AI framework designed to enhance planning and reasoning in Large Language Models (LLMs),

Interactive Brokers' Trader Workstation (TWS) has long been a stalwart platform for serious traders, particularly those delving into the c

Democratizing Open-Source Algorithmic Trading: An Open-Source Framework for Instant Backtests and Live Deployment. This is a real Reddit alg

While traditional data providers offer snapshots of market activity, they often lack the depth and speed required for sophisticated analysis

This article provides a comprehensive guide to building a real-time HFT system using Python for an aspiring quant developer.

This program goes beyond traditional theoretical approaches by incorporating the power of source code, enabling participants to leverage alg

This article delves into the inherent dangers of copy trading, shedding light on the risks that lurk behind the promise of effortless profit

This article explores the world of free AI trading tools, examining their features, limitations, and how they can ben