Search Results for author: Hongyang Yang

Found 12 papers, 11 papers with code

FinGPT: Instruction Tuning Benchmark for Open-Source Large Language Models in Financial Datasets

1 code implementation7 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.

Benchmarking named-entity-recognition +3

FinGPT: Democratizing Internet-scale Data for Financial Large Language Models

1 code implementation19 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.

Algorithmic Trading Sentiment Analysis

Instruct-FinGPT: Financial Sentiment Analysis by Instruction Tuning of General-Purpose Large Language Models

1 code implementation22 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.

Sentiment Analysis

FinGPT: Open-Source Financial Large Language Models

2 code implementations9 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.

Algorithmic Trading Language Modelling +1

Dynamic Datasets and Market Environments for Financial Reinforcement Learning

4 code implementations25 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.

reinforcement-learning

FinRL-Meta: Market Environments and Benchmarks for Data-Driven Financial Reinforcement Learning

4 code implementations6 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.

reinforcement-learning Reinforcement Learning (RL)

FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance

no code implementations7 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.

Friction reinforcement-learning +1

FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance

6 code implementations19 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.

reinforcement-learning Reinforcement Learning (RL) +1

DP-LSTM: Differential Privacy-inspired LSTM for Stock Prediction Using Financial News

4 code implementations20 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.

Stock Prediction Stock Price Prediction

Practical Deep Reinforcement Learning Approach for Stock Trading

9 code implementations19 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.

reinforcement-learning Reinforcement Learning (RL)

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