From Blackboard to Order Book: Microstructure as a Living Laboratory

Abstract

An optimal trading strategy looks convincing on the blackboard. What happens when it has to place an order, wait for a fill, and compete for liquidity? This two-session minilecture brings the mathematics of algorithmic trading into direct contact with a live experimental exchange. Using FastBoxEx, a programmable trading platform, we follow a practical loop: understand the mechanism, formulate a strategy, deploy it, and investigate what actually happened .The first session builds intuition for the limit order book, order flow, liquidity, and price impact, leading to the central execution dilemma: trade aggressively and pay for immediacy, or wait for a better price and risk not trading. The second puts selected execution and market-making models to work, connecting Almgren–Chriss execution, order-flow feedback, and inventory-aware quoting to observable trading outcomes. By changing market conditions and comparing strategies, we turn familiar models into testable questions. When does reacting to order flow help? When does earning the spread become adverse selection? Why does an apparently optimal policy lose to a simple benchmark? The ambition is both educational and scientific: to make microstructure tangible and enjoyable, while showing how a shared experimental exchange can generate new questions about strategy interaction, model robustness, and market design.

Biography

Samuel Drapeau is a Tenured Associate Professor at the School of Mathematical Sciences, Shanghai Jiao Tong University (SJTU), and Director of the Quantitative Finance Research Center (QFRC). He also holds a joint appointment at the Shanghai Advanced Institute of Finance (SAIF), where he teaches courses on data analysis, stochastic financial modeling, quantitative finance, and fintech. His academic and professional career spans cutting-edge theoretical research and industry practice across interdisciplinary fields including stochastic analysis, quantitative finance and risk management, and AI systems. His work encompasses financial stochastic analysis, high-frequency trading fraud detection, systemic risk modeling, and AI-based high-frequency market simulation and stress testing. He combines stochastic convex analysis, optimal transport theory, and deep learning for market microstructure analysis. He co-developed a high-frequency trading fraud detection model with TMX Group (Toronto Stock Exchange) and proposed regime-switching pricing models and hedging performance optimization frameworks in foreign exchange trading. He has published extensively in journals such as Economics & Politics, Mathematical Finance, SIAM Journal on Financial Mathematics, and Electronic Journal of Probability. Since 2023, Samuel Drapeau has led the development of Fastbox, an open-source financial data platform supporting teaching and research in related disciplines, while mentoring student teams conducting quantitative trading strategy research and collaborating with firms such as Qube Research to train quantitative finance talent.