An End-To-End LLM Enhanced Trading System
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866910811182596096 |
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| author | Zhou, Ziyao Mehra, Ronitt |
| author_facet | Zhou, Ziyao Mehra, Ronitt |
| contents | This project introduces an end-to-end trading system that leverages Large Language Models (LLMs) for real-time market sentiment analysis. By synthesizing data from financial news and social media, the system integrates sentiment-driven insights with technical indicators to generate actionable trading signals. FinGPT serves as the primary model for sentiment analysis, ensuring domain-specific accuracy, while Kubernetes is used for scalable and efficient deployment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_01574 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An End-To-End LLM Enhanced Trading System Zhou, Ziyao Mehra, Ronitt Trading and Market Microstructure This project introduces an end-to-end trading system that leverages Large Language Models (LLMs) for real-time market sentiment analysis. By synthesizing data from financial news and social media, the system integrates sentiment-driven insights with technical indicators to generate actionable trading signals. FinGPT serves as the primary model for sentiment analysis, ensuring domain-specific accuracy, while Kubernetes is used for scalable and efficient deployment. |
| title | An End-To-End LLM Enhanced Trading System |
| topic | Trading and Market Microstructure |
| url | https://arxiv.org/abs/2502.01574 |