5 citations · 12 across the 7 of their papers we have counts for
7 papers
History Is Not Enough: An Adaptive Dataflow System for Financial Time-Series Synthesis
Haochong Xia, Yao Long Teng, Regan Tan +3
In quantitative finance, the gap between training and real-world performance-driven by concept drift and distributional non-stationarity-remains a critical obstacle for building re…
Bayesian Robust Financial Trading with Adversarial Synthetic Market Data
Haochong Xia, Simin Li, Ruixiao Xu +7
Algorithmic trading relies on machine learning models to make trading decisions. Despite strong in-sample performance, these models often degrade when confronted with evolving real…
FineFT: Efficient and Risk-Aware Ensemble Reinforcement Learning for Futures Trading
Molei Qin, Xinyu Cai, Yewen Li +5
Futures are contracts obligating the exchange of an asset at a predetermined date and price, notable for their high leverage and liquidity and, therefore, thrive in the Crypto mark…
MacroHFT: Memory Augmented Context-aware Reinforcement Learning On High Frequency Trading
Chuqiao Zong, Chaojie Wang, Molei Qin +3
High-frequency trading (HFT) that executes algorithmic trading in short time scales, has recently occupied the majority of cryptocurrency market. Besides traditional quantitative t…
A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalist
Wentao Zhang, Lingxuan Zhao, Haochong Xia +10
Financial trading is a crucial component of the markets, informed by a multimodal information landscape encompassing news, prices, and Kline charts, and encompasses diverse tasks s…
EarnHFT: Efficient Hierarchical Reinforcement Learning for High Frequency Trading
Molei Qin, Shuo Sun, Wentao Zhang +3
High-frequency trading (HFT) uses computer algorithms to make trading decisions in short time scales (e.g., second-level), which is widely used in the Cryptocurrency (Crypto) marke…
PRUDEX-Compass: Towards Systematic Evaluation of Reinforcement Learning in Financial Markets
Shuo Sun, Molei Qin, Xinrun Wang +1
The financial markets, which involve more than $90 trillion market capitals, attract the attention of innumerable investors around the world. Recently, reinforcement learning in fi…