Execution algorithms are vital to modern trading, they enable market participants to execute large orders while minimising market impact and transaction costs. As these algorithms grow more sophisticated, optimising them becomes increasingly challenging. In this work, we present a reinforcement learning (RL) framework for discovering optimal execution strategies, evaluated within a reactive agent-based market simulator. This simulator creates reactive order flow and allows us to decompose slippage into its constituent components: market impact and execution risk. We assess the RL agent's performance using the efficient frontier based on work by Almgren and Chriss, measuring its ability to balance risk and cost. Results show that the RL-derived strategies consistently outperform baselines and operate near the efficient frontier, demonstrating a strong ability to optimise for risk and impact. These findings highlight the potential of reinforcement learning as a powerful tool in the trader's toolkit.

Order book imbalance (OBI) - buy orders minus sell orders near the best quote - measures supply-demand imbalance that can move prices. OBI is positively correlated with returns, and some investors try to use it to improve performance. Large orders placed at once can reveal intent, invite front-running, raise volatility, and cause losses. Execution algorithms therefore split parent orders into smaller lots to limit price distortion. In principle, using OBI inside such algorithms could improve execution, but prior evidence is scarce because isolating OBI's effect in real markets is nearly impossible amid many external factors. Multi-agent simulation offers a way to study this. In an artificial market, individual actors are agents whose rules and interactions form the model. This study builds an execution algorithm that accounts for OBI, tests it across several market patterns in artificial markets, and analyzes mechanisms, comparing it with a conventional (OBI-agnostic) algorithm. Results: (i) In stable markets, the OBI strategy's performance depends on the number of order slices; outcomes vary with how the parent order is partitioned. (ii) In markets with unstable prices, the OBI-based algorithm outperforms the conventional approach. (iii) Under spoofing manipulation, the OBI strategy is not significantly worse than the conventional algorithm, indicating limited vulnerability to spoofing. Overall, OBI provides a useful signal for execution. Incorporating OBI can add value - especially in volatile conditions - while remaining reasonably robust to spoofing; in calm markets, benefits are sensitive to slicing design.

This work is about optimal order execution, where a large order is split into several small orders to maximize the implementation shortfall. Based on the diversity of cryptocurrency exchanges, we attempt to extract cross-exchange signals by aligning data from multiple exchanges for the first time. Unlike most previous studies that focused on using single-exchange information, we discuss the impact of cross-exchange signals on the agent's decision-making in the optimal execution problem. Experimental results show that cross-exchange signals can provide additional information for the optimal execution of cryptocurrency to facilitate the optimal execution process.

Henri Waelbroeck seems to fit the popular image of the scientist transplanted into the world of high finance and hedge fund trading, the sort of stereotype found in books like "The Fear Index" by Robert Harris. Waelbroeck, director of research at machine learning-enhanced trade execution system Portware, was previously a professor at the Institute of Nuclear Sciences at the National University of Mexico (UNAM). His areas of expertise include: complex systems science, quantum gravity theories, genetic algorithms, artificial neural networks, chaos theory. The impression Waelbroeck conveys is one of precision. He explains that algorithms have grown in complexity since being introduced to the world of trading around 2000. This has made it increasingly difficult for traders to understand each vendor's full algorithm platform and how to optimally select an algorithm for each particular trade that comes in from a portfolio manager.

Dynamic executio n is a flexible plan execution technique in which a plan executive schedules and executes tasks dynamically at runtime in response to disturbances in order to satisfy plan constraints. In this paper, we extend dynamic execution to temporally and spatially flexible plans which, 1) execute tasks conditionally based on runtime state, and 2) support error recovery for anticipated runtime constraint violations. To accomplish these goals, we broaden our focus from dynamic execution of flexible plans to dynamic execution of flexible reactive programs. First, we introduce the Reactive Model-based Programming Language (RMPL) which, in addition to modeling temporal and spatial flexibility, includes three reactive programming language constructs: conditional execution, iteration, and exception handling. Then, we develop a probabilistic particle-sampling based dynamic execution algorithm which reasons efficiently over future program states to schedule tasks dynamically at runtime in order to satisfy program constraints. In addition, the algorithm monitors its own progress and notifies the executive if at any time the likelihood of successful program execution drops below a specified probability bound, δ.