FinWorld: An All-in-One Open-Source Platform for End-to-End Financial AI Research and Deployment

TL;DR

FinWorld is a comprehensive open-source platform that streamlines end-to-end financial AI research and deployment, integrating diverse data, supporting multiple AI paradigms, and facilitating large language model applications.

Contribution

It introduces an all-in-one platform with native multimodal data integration, unified AI support, and advanced automation, addressing limitations of existing financial AI tools.

Findings

Enhanced reproducibility and benchmarking capabilities.

Supported large-scale data from multiple markets and pools.

Demonstrated effectiveness of RL-based finetuning for LLMs.

Abstract

Financial AI holds great promise for transforming modern finance, with the potential to support a wide range of tasks such as market forecasting, portfolio management, quantitative trading, and automated analysis. However, existing platforms remain limited in task coverage, lack robust multimodal data integration, and offer insufficient support for the training and deployment of large language models (LLMs). In response to these limitations, we present FinWorld, an all-in-one open-source platform that provides end-to-end support for the entire financial AI workflow, from data acquisition to experimentation and deployment. FinWorld distinguishes itself through native integration of heterogeneous financial data, unified support for diverse AI paradigms, and advanced agent automation, enabling seamless development and deployment. Leveraging data from 2 representative markets, 4 stock pools, and over 800 million financial data points, we conduct comprehensive experiments on 4 key financial AI tasks. These experiments systematically evaluate deep learning and reinforcement learning algorithms, with particular emphasis on RL-based finetuning for LLMs and LLM Agents. The empirical results demonstrate that FinWorld significantly enhances reproducibility, supports transparent benchmarking, and streamlines deployment, thereby providing a strong foundation for future research and real-world applications. Code is available at Github~\footnote{https://github.com/DVampire/FinWorld}.