Blog Posts

Designing an institutional trading system with both strategic and tactical asset allocation components, along with sophisticated forms of analysis and a comprehensive risk management system, requires a multi-layered approach. Each component ensures the system can adapt to market conditions, manage risk effectively, and strive for optimal returns.   Key Takeaways – How to Design an […]

Numerical methods in finance are computational techniques used to solve mathematical problems that arise in financial modeling. These methods are important because many financial models lead to equations that: can’t be solved analytically, or require simulation for prediction and risk assessment. These methods are used in various areas such as option pricing, risk management, portfolio […]

Complex analysis is a branch of mathematics focusing on functions of complex numbers. Complex numbers, blending real and imaginary parts, are important because they allow us to solve equations (x^2 = -1) that can’t be solved with just real numbers, which expands our understanding and capabilities in various fields (e.g., finance, machine learning, quant trading). […]

Artificial General Intelligence (AGI) in the context of finance, markets, and trading refers to the development of AI systems that possess the ability to understand, learn, and apply intelligence across a wide range of financial activities in a manner similar or better to human intelligence. AGI would differ significantly from the current state of AI, […]

Setting up a trading algorithm in C++ and connecting it to a broker for live trading involves several steps and considerations. In the first part of the article, we’ll include a high-level overview of the process. In the second part, we’ll go into more specifics on where to write the algorithm/trading system and how to […]

Tensor theory, originating in mathematics and physics, finds its application in finance through its ability to represent and analyze complex, multi-dimensional data. A tensor is a generalization of scalars (zero-order tensors), vectors (first-order tensors), and matrices (second-order tensors) to higher dimensions.   Key Takeaways – Tensor Theory in Finance, Markets & Trading Multidimensional Data Analysis […]

Riemannian manifolds are mathematical constructs from the field of differential geometry, which have applications in various disciplines, including finance and quant trading. We’ll cover both the fundamental concepts of Riemannian manifolds and their practical applications in financial contexts.   Key Takeaways – Riemannian Manifolds in Finance, Markets & Trading Geometric Framework Riemannian manifolds provide a […]

Geometric mechanics is a branch of mathematics, most typically applied to theoretical physics, that applies geometric methods to problems in mechanics and dynamics. It combines differential geometry, the study of smooth manifolds, with the principles of classical and quantum mechanics. The key concepts and applications of geometric mechanics in finance offer a different perspective on […]

Statistical Mechanics, also known as Statistical Physics, looks into the microscopic details of systems to predict macroscopic behaviors. Beyond traditional physics, Statistical Mechanics finds applications in finance, modeling complex systems from particles to assets and broader physical systems to portfolios.   Key Takeaways – Statistical Mechanics & Applications to Finance Statistical mechanics can provide new […]

Manifold learning is a branch of machine learning that involves the analysis and understanding of high-dimensional data by finding low-dimensional representations without losing meaningful properties.   Key Takeaways – Manifold Learning in Finance, Markets & Trading Dimensionality Reduction Manifold learning can simplify complex financial datasets. Reveal underlying structures and relationships critical for informed trading decisions. […]