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most citedDeepScalper: A Risk-Aware Reinforcement Learning Framework to Capture Fleeting Intraday Trading Opportunities

6 citations · 30 across the 12 of their papers we have counts for

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q-fin.TR2024★ 5 cited

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…

q-fin.TR2023★ 4 cited

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…

q-fin.TR2023★ 3 cited

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…

q-fin.TR2022★ 2 cited

Quantitative Stock Investment by Routing Uncertainty-Aware Trading Experts: A Multi-Task Learning Approach

Shuo Sun, Rundong Wang, Bo An

Quantitative investment is a fundamental financial task that highly relies on accurate stock prediction and profitable investment decision making. Despite recent advances in deep l…

q-fin.TR2022★ 6 cited

DeepScalper: A Risk-Aware Reinforcement Learning Framework to Capture Fleeting Intraday Trading Opportunities

Shuo Sun, Wanqi Xue, Rundong Wang +4

Reinforcement learning (RL) techniques have shown great success in many challenging quantitative trading tasks, such as portfolio management and algorithmic trading. Especially, in…