Dynamic Datasets and Market Environments for Financial Reinforcement Learning

• URL: http://arxiv.org/abs/2304.13174v1
• Date: Tue, 25 Apr 2023 22:17:31 GMT
• Title: Dynamic Datasets and Market Environments for Financial Reinforcement Learning
• Authors: Xiao-Yang Liu, Ziyi Xia, Hongyang Yang, Jiechao Gao, Daochen Zha, Ming Zhu, Christina Dan Wang, Zhaoran Wang, Jian Guo
• Abstract summary: FinRL-Meta is a library that processes dynamic datasets from real-world markets into gym-style market environments. We provide examples and reproduce popular research papers as stepping stones for users to design new trading strategies. We also deploy the library on cloud platforms so that users can visualize their own results and assess the relative performance.
• Score: 68.11692837240756
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The financial market is a particularly challenging playground for deep reinforcement learning due to its unique feature of dynamic datasets. Building high-quality market environments for training financial reinforcement learning (FinRL) agents is difficult due to major factors such as the low signal-to-noise ratio of financial data, survivorship bias of historical data, and model overfitting. In this paper, we present FinRL-Meta, a data-centric and openly accessible library that processes dynamic datasets from real-world markets into gym-style market environments and has been actively maintained by the AI4Finance community. First, following a DataOps paradigm, we provide hundreds of market environments through an automatic data curation pipeline. Second, we provide homegrown examples and reproduce popular research papers as stepping stones for users to design new trading strategies. We also deploy the library on cloud platforms so that users can visualize their own results and assess the relative performance via community-wise competitions. Third, we provide dozens of Jupyter/Python demos organized into a curriculum and a documentation website to serve the rapidly growing community. The open-source codes for the data curation pipeline are available at https://github.com/AI4Finance-Foundation/FinRL-Meta

Related papers

• AlphaFin: Benchmarking Financial Analysis with Retrieval-Augmented Stock-Chain Framework [48.3060010653088]
  We release AlphaFin datasets, combining traditional research datasets, real-time financial data, and handwritten chain-of-thought (CoT) data. We then use AlphaFin datasets to benchmark a state-of-the-art method, called Stock-Chain, for effectively tackling the financial analysis task.
  arXiv  Detail & Related papers  (2024-03-19T09:45:33Z)
• Data Acquisition: A New Frontier in Data-centric AI [65.90972015426274]
  We first present an investigation of current data marketplaces, revealing lack of platforms offering detailed information about datasets. We then introduce the DAM challenge, a benchmark to model the interaction between the data providers and acquirers. Our evaluation of the submitted strategies underlines the need for effective data acquisition strategies in Machine Learning.
  arXiv  Detail & Related papers  (2023-11-22T22:15:17Z)
• FinGPT: Democratizing Internet-scale Data for Financial Large Language Models [35.83244096535722]
  Large language models (LLMs) have demonstrated remarkable proficiency in understanding and generating human-like texts. Financial Generative Pre-trained Transformer (FinGPT) automates the collection and curation of real-time financial data from 34 diverse sources on the Internet. FinGPT aims to democratize FinLLMs, stimulate innovation, and unlock new opportunities in open finance.
  arXiv  Detail & Related papers  (2023-07-19T22:43:57Z)
• FinGPT: Open-Source Financial Large Language Models [20.49272722890324]
  We present an open-source large language model, FinGPT, for the finance sector. Unlike proprietary models, FinGPT takes a data-centric approach, providing researchers and practitioners with accessible and transparent resources. We showcase several potential applications as stepping stones for users, such as robo-advising, algorithmic trading, and low-code development.
  arXiv  Detail & Related papers  (2023-06-09T16:52:00Z)
• DataPerf: Benchmarks for Data-Centric AI Development [81.03754002516862]
  DataPerf is a community-led benchmark suite for evaluating ML datasets and data-centric algorithms. We provide an open, online platform with multiple rounds of challenges to support this iterative development. The benchmarks, online evaluation platform, and baseline implementations are open source.
  arXiv  Detail & Related papers  (2022-07-20T17:47:54Z)
• PyRelationAL: A Library for Active Learning Research and Development [0.11545092788508224]
  PyRelationAL is an open source library for active learning (AL) research. It provides access to benchmark datasets and AL task configurations based on existing literature. We perform experiments on the PyRelationAL collection of benchmark datasets and showcase the considerable economies that AL can provide.
  arXiv  Detail & Related papers  (2022-05-23T08:21:21Z)
• FinRL-Meta: A Universe of Near-Real Market Environments for Data-Driven Deep Reinforcement Learning in Quantitative Finance [58.77314662664463]
  FinRL-Meta builds a universe of market environments for data-driven financial reinforcement learning. First, FinRL-Meta separates financial data processing from the design pipeline of DRL-based strategy. Second, FinRL-Meta provides hundreds of market environments for various trading tasks.
  arXiv  Detail & Related papers  (2021-12-13T16:03:37Z)
• FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance [22.808509136431645]
  Deep reinforcement learning (DRL) has been envisioned to have a competitive edge in quantitative finance. In this paper, we present the first open-source framework textitFinRL as a full pipeline to help quantitative traders overcome the steep learning curve.
  arXiv  Detail & Related papers  (2021-11-07T00:34:32Z)
• OSOUM Framework for Trading Data Research [79.0383470835073]
  We supply, to the best of our knowledge, the first open source simulation platform, Open SOUrce Market Simulator (OSOUM) to analyze trading markets and specifically data markets. We describe and implement a specific data market model, consisting of two types of agents: sellers who own various datasets available for acquisition, and buyers searching for relevant and beneficial datasets for purchase. Although commercial frameworks, intended for handling data markets, already exist, we provide a free and extensive end-to-end research tool for simulating possible behavior for both buyers and sellers participating in (data) markets.
  arXiv  Detail & Related papers  (2021-02-18T09:20:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.