Table of Links
2 Related Works and 2.1 Deep Reinforcement Learning Algorithms
2.2 Deep Reinforcement Learning Libraries and 2.3 Deep Reinforcement Learning in Finance
3 The Proposed FinRL Framework and 3.1 Overview of FinRL Framework
3.5 Training-Testing-Trading Pipeline
4 Hands-on Tutorials and Benchmark Performance and 4.1 Backtesting Module
4.2 Baseline Strategies and Trading Metrics
4.5 Use Case II: Portfolio Allocation and 4.6 Use Case III: Cryptocurrencies Trading
5 Ecosystem of FinRL and Conclusions, and References
5 ECOSYSTEM OF FINRL AND CONCLUSIONS
In this paper, we have developed an open-source framework, FinRL, to help quantitative traders overcome the steep learning curve. Customization is accessible on all layers, from market environments, trading agents up towards trading tasks. FinRL follows a training testing-trading pipeline to reduce the simulation-to-reality gap. Within FinRL, historical market data and live trading platforms are reconfigured into standardized environments in OpenAI gym-style; state-of-the-art DRL algorithms are implemented for users to train trading agents in a pipeline; and an automated backtesting module is provided to evaluate trading performance. Moreover, benchmark schemes on typical trading tasks are provided as practitioners’ stepping stones.
Ecosystem of FinRL Framework. We believe that the rise of the open-source community fostered the development of AI in Finance for both academia and industry side. As the need of utilizing open-source AI for finance ecosystem is imminent in the finance community, FinRL provides a ecosystem that features Deep Reinforcement Learning in finance comprehensively to fulfill such need for all-level users in our open-source community.
FinRL offers an overall framework to utilize DRL agents for various markets, SOTA DRL algorithms, finance tasks (portfolio allocation, cryptocurrency trading, high-frequency trading), live trading support, etc. For entry-level users, FinRL aims to provide a demonstrative and educational atmosphere with hands-on documents to help beginners get familiar with DRL in Finance applications. For intermediate-level users, such as full-stack developers and professionals, FinRL provides ElegantRL [28], a lightweight and scalable DRL library for FinRL with finance-oriented optimizations. For advanced-level users, such as investment banks and hedge funds. FinRL delivers FinRL-Podracer [24, 29], a cloud-native solution for FinRL with high performance and high scalability training.
FinRL also develops other useful tools to support the ecosystem. FinRL-Meta [30] adds financial data engineering for FinRL with unified data processor and hundreds of market environments. Explainable DRL for portfolio management [17] and DRL ensemble strategy for stock trading [50, 51] are also implemented.
Future work. Future research directions would be investiaging DRL’s potential on limit order book [48], hedging [6], market making [16], liquidation [3], and trade execution [27].
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