Search Results for author:
Found 12 papers, 11 papers with code
Practical Deep Reinforcement Learning Approach for Stock Trading
We explore the potential of deep reinforcement learning to optimize stock trading strategy and thus maximize investment return.
DP-LSTM: Differential Privacy-inspired LSTM for Stock Prediction Using Financial News
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.
FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance
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.
FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance
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.
FinRL-Meta: A Universe of Near-Real Market Environments for Data-Driven Deep Reinforcement Learning in Quantitative Finance
In this paper, we present a FinRL-Meta framework that builds a universe of market environments for data-driven financial reinforcement learning.
FinRL-Meta: Market Environments and Benchmarks for Data-Driven Financial Reinforcement Learning
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.
Dynamic Datasets and Market Environments for Financial Reinforcement Learning
The financial market is a particularly challenging playground for deep reinforcement learning due to its unique feature of dynamic datasets.
FinGPT: Open-Source Financial Large Language Models
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.
Instruct-FinGPT: Financial Sentiment Analysis by Instruction Tuning of General-Purpose Large Language Models
Sentiment analysis is a vital tool for uncovering insights from financial articles, news, and social media, shaping our understanding of market movements.
FinGPT: Democratizing Internet-scale Data for Financial Large Language Models
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.
Enhancing Financial Sentiment Analysis via Retrieval Augmented Large Language Models
Financial sentiment analysis is critical for valuation and investment decision-making.
FinGPT: Instruction Tuning Benchmark for Open-Source Large Language Models in Financial Datasets
This paper introduces a distinctive approach anchored in the Instruction Tuning paradigm for open-source large language models, specifically adapted for financial contexts.
