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!

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.

High-Frequency Trading strategies represents a sophisticated and often complex segment of algorithmic trading, characterized by the rapid execution of a large volume of orders. At its core

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.

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?

Algorithmic trading, once a niche domain, has become a dominant force, accounting for a significant portion of daily trading volume in major financial markets. What are the best platforms for high-frequency trading?

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

The financial markets are a complex, ever-shifting landscape. For traders seeking an edge, the allure of algorithmic trading course – using computer programs to execute trading strategies – has never been stronger. It promises discipline, speed, and the ability to analyze vast amounts of data far beyond human capacity.

On May 10th, Brian unveiled a fascinating, fully AI-driven process(e.g. for a potential gold trading platform) that takes raw market data, transforms it into insightful reports, synthesizes these into actionable strategies, and even generates the code for a live trading dashboard.

C++ programming Bjarne Stroustrup with modern AIBjarne Stroustrup on Modern C++: Writing Better Code in the 21st Century

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

However, the reality of implementing the Rithmic API often falls short of its promise, primarily due to the significant challenges

didn't mince words, calling the IBKR Algo Trading API a "piece of garbage" and questioning why IBKR continues to offer such a subpar product

Sierra Chart has emerged as a leading solution. This article delves into the various data services offered by Sierra Chart, exploring its r

This begs the question: what does this unanimous lack of enthusiasm signify? This helps which technology to use for Options Futures and Oth

hree prominent contenders in this arena are MotiveWave, Sierra Chart, and TradingView, each offering a unique blend of features and catering

 These tasks, while crucial, can be time-consuming and tedious. Cellm aims to use AI Excel spreadsheet generation usage by integrating the