arXiv · arXiv · 2025
In the highly volatile and uncertain global financial markets, traditional quantitative trading models relying on statistical modeling or empirical rules often fail to adapt to dynamic market changes and black swan events due to rigid assumptions and limited generalization. To address these issues, this paper proposes QTMRL (Quantitative Trading Multi-Indicator Reinforcement Learning), an intelligent trading agent co…
Jingfeng Pan, Jiahao Chen
arXiv · arXiv · 2026
LLM agents are increasingly cast as autonomous portfolio managers, and benchmarks have moved from financial question-answering to sequential trading. Yet most still rank agents by returns over a fixed window, a weak proxy: the market path dominates a period's return, and apparent alpha can dissolve once look-ahead leakage is controlled. We introduce CLQT, which reframes closed-loop trading evaluation as diagnosis bef…
Bo Qu, Mingguang Chen
arXiv · arXiv q-fin · 2023
Artificial Intelligence (AI) and Machine Learning (ML) are transforming the domain of Quantitative Trading (QT) through the deployment of advanced algorithms capable of sifting through extensive financial datasets to pinpoint lucrative investment openings. AI-driven models, particularly those employing ML techniques such as deep learning and reinforcement learning, have shown great prowess in predicting market trends…
Maochun Xu, Zixun Lan, Zheng Tao, Jiawei Du, Zongao Ye
arXiv · arXiv q-fin · 2022
In recent years, many practitioners in quantitative finance have attempted to use Deep Reinforcement Learning (DRL) to build better quantitative trading (QT) strategies. Nevertheless, many existing studies fail to address several serious challenges, such as the non-stationary financial environment and the bias and variance trade-off when applying DRL in the real financial market. In this work, we proposed Safe-FinRL,…
Zitao Song, Xuyang Jin, Chenliang Li
arXiv · arXiv q-fin · 2021
Quantitative trading (QT), which refers to the usage of mathematical models and data-driven techniques in analyzing the financial market, has been a popular topic in both academia and financial industry since 1970s. In the last decade, reinforcement learning (RL) has garnered significant interest in many domains such as robotics and video games, owing to its outstanding ability on solving complex sequential decision …
Shuo Sun, Rundong Wang, Bo An
arXiv · arXiv q-fin · 2019
Recent years have witnessed the successful marriage of finance innovations and AI techniques in various finance applications including quantitative trading (QT). Despite great research efforts devoted to leveraging deep learning (DL) methods for building better QT strategies, existing studies still face serious challenges especially from the side of finance, such as the balance of risk and return, the resistance to e…
Jingyuan Wang, Yang Zhang, Ke Tang, Junjie Wu, Zhang Xiong