Overview

FinRL Contest 2023 Website; Starter Kit

Financial reinforcement learning (FinRL), as an interdisciplinary field of finance and reinforcement learning, has been recognized as a promising approach to financial tasks. Over the past decade, deep reinforcement learning (DRL) has achieved remarkable success in solving complex problems across a variety of domains, including: robotics, gaming, and large language models like ChatGPT and GPT-4. The success of DRL has also led to its application in finance, where it has demonstrated great potential for enhancing the performance of financial tasks, such as: portfolio management, option pricing, and algorithmic trading.

The FinRL contest is a competition that explores and evaluates the capability of machine learning methods in finance. However, developing machine learning algorithms for financial data presents unique challenges:

  • Dynamic Transitions and Data Noise: Financial datasets are constantly evolving, making it difficult to capture the underlying dynamics. Moreover, financial data is often susceptible to noise and anomalies.

  • Partial Observability: No traders can access complete market-influencing information due to the gigantic, complex, and unpredictable nature of financial systems, where unforeseen events like natural disasters, policy changes, and consumer behavior shifts can have hard-to-anticipate effects.

  • Other Complex Behaviors: Many other factors can cause complicated and unpredictable behaviors, such as the market’s decentralized nature and the large number of financial instruments available.

The FinRL Contest 2023 presents two tasks: data-centric stock trading and real-time order execution, aiming to foster innovations toward addressing the above challenges.

We welcome students, researchers, and engineers who are passionate about finance and machine learning. We encourage the development of:

  • Tailored data processing strategies

  • Novel features

  • Innovative algorithms

These approaches should adapt to changing market conditions and aim to generate superior returns for investors.