R&D

At Horizon Trading Solutions, Research, Development, and Innovation are at the core of everything we do. Each year, we dedicate substantial resources to advancing our technology, ensuring we remain at the forefront of trading innovation. Our relentless focus on R&D drives the creation of cutting-edge, high-performance solutions that empower our clients to stay ahead in dynamic markets.

Our platform offers a comprehensive framework for scripting and backtesting strategies, seamlessly integrated with innovative execution algorithms and advanced trading methodologies developed through our rigorous R&D efforts.

Within Horizon’s team, we are proud to have Yadh Hafsi, a doctoral researcher whose insights and contributions play a key role in shaping our technological advancements. Yadh regularly shares his expertise, and you can explore his latest contributions below.

Optimal Execution under Incomplete Information

Horizon’s OEMS gives you the control you need. The platform provides an extensive permission layer coupled with trading desk permissioning and handover workflows.

This latest research paper, written by Yadh Hafsi, in collaboration with Etienne Chevalier and Vathana Ly Vath concentrates on studying optimal liquidation strategies under partial information for a single asset within a finite time horizon. It introduces a model specifically designed for high-frequency trading, where price formation is driven solely by order flow dynamics, captured through mutually stimulating marked Hawkes processes. Operating within a limit order book framework, the model accounts for permanent price impact and transient market impact, while incorporating liquidity as a hidden Markov process that influences the intensities of bid and ask price processes. The optimal liquidation problem is formulated as an impulse control problem, with the dynamics of the hidden Markov chain addressed through normalized filtering equations. The value function is characterized as the limit of a sequence of auxiliary continuous functions, defined recursively. This formulation facilitates the application of a dynamic programming principle to derive optimal strategies and supports the development of an implementable algorithm to approximate the problem. The study is enriched with numerical results and visualizations of candidate optimal strategies, offering actionable insights for practical implementation.

Uncovering Market Disorder and Liquidity Trends Detection

This research focuses on a comprehensive examination of market microstructure, specifically targeting the optimization of execution strategies. In the realm of practical application, the focus primarily revolves around high-frequency trading, aiming to dissect market behaviors, study liquidity dynamics, and analyze the resilience of limit order books. On the theoretical front, the core emphasis lies in stochastic control theory and point processes.

The research involves understanding the non-Markovian nature inherent in price processes. Utilizing Hawkes processes, the analysis aims to model the patterns of order arrivals within a limit order book. This collaborative paper titled “Uncovering Market Disorder and Liquidity Trends Detection”, co-authored with Vathana Ly Vath and Etienne Chevalier, epitomizes this effort, seeking to identify and characterize significant liquidity shifts within order-driven markets. Leveraging Marked Hawkes processes to treat trades-through as a proxy for liquidity allows for the precise identification of fluctuations in liquidity intensity. Leveraging the insights gleaned from my preliminary research, my current focus lies in formulating an optimal execution strategy adept at navigating through diverse liquidity regimes. This endeavor is directed toward identifying effective solutions to curb market impact and minimize transaction costs. This research pursues a practical application of deciphering market microstructure complexities. By enhancing comprehension of high-frequency trading dynamics, it aims to contribute to the development of more efficient trading strategies, potentially applicable in real-time trading scenarios. Moreover, this research aims to offer theoretical contributions in the field of stochastic control, bridging practical insights with advancements in theoretical frameworks.