5 papers
FinFlowRL: An Imitation-Reinforcement Learning Framework for Adaptive Stochastic Control in Finance
Yang Li, Zhi Chen, Steve Y. Yang +1
Traditional stochastic control methods in finance rely on simplifying assumptions that often fail in real world markets. While these methods work well in specific, well defined sce…
FinFlowRL: An Imitation-Reinforcement Learning Framework for Adaptive Stochastic Control in Finance
Yang Li, Zhi Chen
Traditional stochastic control methods in finance struggle in real world markets due to their reliance on simplifying assumptions and stylized frameworks. Such methods typically pe…
ByteGen: A Tokenizer-Free Generative Model for Orderbook Events in Byte Space
Yang Li, Zhi Chen
Generative modeling of high-frequency limit order book (LOB) dynamics is a critical yet unsolved challenge in quantitative finance, essential for robust market simulation and strat…
FlowOE: Imitation Learning with Flow Policy from Ensemble RL Experts for Optimal Execution under Heston Volatility and Concave Market Impacts
Yang Li, Zhi Chen
Optimal execution in financial markets refers to the process of strategically transacting a large volume of assets over a period to achieve the best possible outcome by balancing t…
FlowHFT: Imitation Learning via Flow Matching Policy for Optimal High-Frequency Trading under Diverse Market Conditions
Yang Li, Zhi Chen, Steve Yang
High-frequency trading (HFT) is an investing strategy that continuously monitors market states and places bid and ask orders at millisecond speeds. Traditional HFT approaches fit m…