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 • 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.
1 code implementation • 9 Apr 2023 • Shuai Li, Ziqi Chen, Hongtu Zhu, Christina Dan Wang, Wang Wen
The CRT assumes that the conditional distribution of X given Z is known under the null hypothesis and then it is compared to the distribution of the observed samples of the original data.
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 • 12 Sep 2022 • Berend Jelmer Dirk Gort, Xiao-Yang Liu, Xinghang Sun, Jiechao Gao, Shuaiyu Chen, Christina Dan Wang
Designing profitable and reliable trading strategies is challenging in the highly volatile cryptocurrency market.
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.
no code implementations • 3 Aug 2019 • Xinyi Li, Yinchuan Li, Xiao-Yang Liu, Christina Dan Wang
In this paper, we propose a novel deep neural network Mid-LSTM for midterm stock prediction, which incorporates the market trend as hidden states.