An enhanced deep reinforcement learning ensemble empowered by large language model
The domain of financial trading operates at the intersection of diverse informational inputs, including asset pricing and technical indicators. These elements inform critical financial activities such as quantitative trading across varied asset classes. Despite the growing use of advanced Artific...
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sg-ntu-dr.10356-1752002024-04-19T15:42:57Z An enhanced deep reinforcement learning ensemble empowered by large language model Li, Xinyi Bo An School of Computer Science and Engineering boan@ntu.edu.sg Computer and Information Science Deep reinforcement learning Large language model Prompt engineering Financial trading Multimodal data processing The domain of financial trading operates at the intersection of diverse informational inputs, including asset pricing and technical indicators. These elements inform critical financial activities such as quantitative trading across varied asset classes. Despite the growing use of advanced Artificial Intelligence (AI) in finance, applying these technologies to trading tasks often runs into challenges due to insufficient analysis of diverse formats of data and the ability to adapt to diverse trading scenarios. This study introduces a framework that synergizes an ensemble of deep reinforcement learning algorithms with the explanatory power of a Large Language Model. The framework termed ”Large Language Model-Enhanced Deep Reinforcement Learning Ensemble” (LLM-DRE), stands at this crossroad, ingeniously combining the robust decision-making capabilities of DRL with the nuanced contextual understanding of LLMs. At its core, LLM-DRE effectively processes and synthesizes disparate forms of market data to fine-tune trading strategies, enabling an enriched assessment of financial environments. Performance-wise, LLM-DRE not only holds its ground but excels against traditional baselines, exhibiting substantial enhancements in profitability and risk-adjusted returns. It systematically outperforms the benchmarks across various assets, demonstrating particularly impressive results on volatile markets, where its adaptive strategies foresee and leverage rapid price movements to secure profitable gains. Bachelor's degree 2024-04-19T13:27:16Z 2024-04-19T13:27:16Z 2024 Final Year Project (FYP) Li, X. (2024). An enhanced deep reinforcement learning ensemble empowered by large language model. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/175200 https://hdl.handle.net/10356/175200 en SCSE23-0063 application/pdf Nanyang Technological University |
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Computer and Information Science Deep reinforcement learning Large language model Prompt engineering Financial trading Multimodal data processing Li, Xinyi An enhanced deep reinforcement learning ensemble empowered by large language model |
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The domain of financial trading operates at the intersection of diverse informational
inputs, including asset pricing and technical indicators. These elements inform critical
financial activities such as quantitative trading across varied asset classes. Despite
the growing use of advanced Artificial Intelligence (AI) in finance, applying these
technologies to trading tasks often runs into challenges due to insufficient analysis of
diverse formats of data and the ability to adapt to diverse trading scenarios.
This study introduces a framework that synergizes an ensemble of deep reinforcement
learning algorithms with the explanatory power of a Large Language Model. The
framework termed ”Large Language Model-Enhanced Deep Reinforcement Learning
Ensemble” (LLM-DRE), stands at this crossroad, ingeniously combining the robust
decision-making capabilities of DRL with the nuanced contextual understanding of
LLMs. At its core, LLM-DRE effectively processes and synthesizes disparate forms of
market data to fine-tune trading strategies, enabling an enriched assessment of financial
environments.
Performance-wise, LLM-DRE not only holds its ground but excels against traditional
baselines, exhibiting substantial enhancements in profitability and risk-adjusted returns.
It systematically outperforms the benchmarks across various assets, demonstrating particularly
impressive results on volatile markets, where its adaptive strategies foresee
and leverage rapid price movements to secure profitable gains. |
author2 |
Bo An |
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Bo An Li, Xinyi |
format |
Final Year Project |
author |
Li, Xinyi |
author_sort |
Li, Xinyi |
title |
An enhanced deep reinforcement learning ensemble empowered by large language model |
title_short |
An enhanced deep reinforcement learning ensemble empowered by large language model |
title_full |
An enhanced deep reinforcement learning ensemble empowered by large language model |
title_fullStr |
An enhanced deep reinforcement learning ensemble empowered by large language model |
title_full_unstemmed |
An enhanced deep reinforcement learning ensemble empowered by large language model |
title_sort |
enhanced deep reinforcement learning ensemble empowered by large language model |
publisher |
Nanyang Technological University |
publishDate |
2024 |
url |
https://hdl.handle.net/10356/175200 |
_version_ |
1814047216079208448 |