1 code implementation • 7 Oct 2023 • Neng Wang, Hongyang Yang, Christina Dan Wang
This paper introduces a distinctive approach anchored in the Instruction Tuning paradigm for open-source large language models, specifically adapted for financial contexts.
2 code implementations • 6 Oct 2023 • Boyu Zhang, Hongyang Yang, Tianyu Zhou, Ali Babar, Xiao-Yang Liu
Financial sentiment analysis is critical for valuation and investment decision-making.
1 code implementation • 19 Jul 2023 • Xiao-Yang Liu, Guoxuan Wang, Hongyang Yang, Daochen Zha
In light of this, we aim to democratize Internet-scale financial data for LLMs, which is an open challenge due to diverse data sources, low signal-to-noise ratio, and high time-validity.
1 code implementation • 22 Jun 2023 • Boyu Zhang, Hongyang Yang, Xiao-Yang Liu
Sentiment analysis is a vital tool for uncovering insights from financial articles, news, and social media, shaping our understanding of market movements.
2 code implementations • 9 Jun 2023 • Hongyang Yang, Xiao-Yang Liu, Christina Dan Wang
While proprietary models like BloombergGPT have taken advantage of their unique data accumulation, such privileged access calls for an open-source alternative to democratize Internet-scale financial data.
4 code implementations • 25 Apr 2023 • Xiao-Yang Liu, Ziyi Xia, Hongyang Yang, Jiechao Gao, Daochen Zha, Ming Zhu, Christina Dan Wang, Zhaoran Wang, Jian Guo
The financial market is a particularly challenging playground for deep reinforcement learning due to its unique feature of dynamic datasets.
4 code implementations • 6 Nov 2022 • Xiao-Yang Liu, Ziyi Xia, Jingyang Rui, Jiechao Gao, Hongyang Yang, Ming Zhu, Christina Dan Wang, Zhaoran Wang, Jian Guo
However, establishing high-quality market environments and benchmarks for financial reinforcement learning is challenging due to three major factors, namely, low signal-to-noise ratio of financial data, survivorship bias of historical data, and model overfitting in the backtesting stage.
1 code implementation • 13 Dec 2021 • Xiao-Yang Liu, Jingyang Rui, Jiechao Gao, Liuqing Yang, Hongyang Yang, Zhaoran Wang, Christina Dan Wang, Jian Guo
In this paper, we present a FinRL-Meta framework that builds a universe of market environments for data-driven financial reinforcement learning.
no code implementations • 7 Nov 2021 • Xiao-Yang Liu, Hongyang Yang, Jiechao Gao, Christina Dan Wang
In this paper, we present the first open-source framework \textit{FinRL} as a full pipeline to help quantitative traders overcome the steep learning curve.
6 code implementations • 19 Nov 2020 • Xiao-Yang Liu, Hongyang Yang, Qian Chen, Runjia Zhang, Liuqing Yang, Bowen Xiao, Christina Dan Wang
In this paper, we introduce a DRL library FinRL that facilitates beginners to expose themselves to quantitative finance and to develop their own stock trading strategies.
4 code implementations • 20 Dec 2019 • Xinyi Li, Yinchuan Li, Hongyang Yang, Liuqing Yang, Xiao-Yang Liu
In this paper, we propose a novel deep neural network DP-LSTM for stock price prediction, which incorporates the news articles as hidden information and integrates difference news sources through the differential privacy mechanism.
9 code implementations • 19 Nov 2018 • Xiao-Yang Liu, Zhuoran Xiong, Shan Zhong, Hongyang Yang, Anwar Walid
We explore the potential of deep reinforcement learning to optimize stock trading strategy and thus maximize investment return.