Unveiling the Power of Macroeconomic Data for Systematic Trading Strategies
While economic information undeniably influences financial markets, incorporating it into systematic trading strategies has faced limitations.
While economic information undeniably influences financial markets, incorporating it into systematic trading strategies has faced limitations.
The financial world mourned the loss of a titan this week with the passing of James Simons, better known as Jim Simons Quant King, at the age of 86.
This article delves into the world and power of KDB Plus, a powerful database solution for handling massive time-series data, through an insightful conversation with KX’s Developer Advocate,
Analyzing this data efficiently is the key to making informed decisions, identifying trends, and staying ahead of the curve. This is where Kx Kdb+ and Q the query language, come into play.
This article chronicles my experience, exploring the motivations behind this choice and the unique advantages offers for algorithmic trading in Rust.
But where do you begin your kdb+ and q journey? Don’t worry, aspiring q programmers! This guide will equip you with the best learning resources to kickstart your kdb+ and q expertise.
The world of high-frequency trading (HFT) thrives on speed and precision, and high-performance computing (HPC) plays a pivotal role in its success.
This article explores the exciting opportunities for Python which powers algorithmic trading, equipping you with strategies for navigating the job market, continuous learning, and leveraging valuable educational resources.
Here, we delve into the potential limitations of relying solely on open-source projects for youropen-source algorithmic trading system endeavors.
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As you know with the crazy restrictive emergency laws being introduced in the last week, many moving their savings out of FIAT banks into cryptocurrency trading.
Here are some questions to get your started: