Plug-and-Play with FinRL's DRL Algorithms in the Agent Layer
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Authors:
(1) Xiao-Yang Liu, Hongyang Yang, Columbia University (xl2427,[email protected]);
(2) Jiechao Gao, University of Virginia ([email protected]);
(3) Christina Dan Wang (Corresponding Author), New York University Shanghai ([email protected]).
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Table of Links
Abstract and 1 Introduction
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
3.2 Application Layer
3.3 Agent Layer
3.4 Environment Layer
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
4.3 Hands-on Tutorials
4.4 Use Case I: Stock Trading
4.5 Use Case II: Portfolio Allocation and 4.6 Use Case III: Cryptocurrencies Trading
5 Ecosystem of FinRL and Conclusions, and References
3.3 Agent Layer
FinRL allows users to plug in and play with standard DRL algorithms, following the unified workflow in Fig. 1. As a backbone, we fine-tune three representative open-source DRL libraries, namely Stable Baseline 3 [37], RLlib [25] and ElegantRL [28]. User can also design new DRL algorithms by adapting existing ones.
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3.3.1 Agent APIs. FinRL uses unified Python APIs for training a trading agent. The Python APIs are flexible so that a DRL algorithm can be easily plugged in. To train a DRL trading agent, as in Fig. 2, a user chooses an environment (i,e., StockTradingEnv, StockPortfolioEnv) built on historical data or live trading APIs with default parameters (envkwargs), and picks a DRL algorithm (e.g., PPO [42]). Then, FinRL initializes the agent class with the environment, sets a DRL algorithm with its default hyperparameters (modelkwargs), then launches a training process and returns a trained model.
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The main APIs are given in Table 2, while the details of building an environments, importing an algorithm, and constructing an agents are hidden in the API calls.
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3.3.2 Plug-and-Play DRL Libraries. Fig. 3 compares the three DRL libraries. The details of each library are summarised as follows.
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Stable Baselines 3 [37] is a set of improved implementations of DRL algorithms over the OpenAI Baselines [10]. FinRL chooses to support Stable baselines 3 due to its advantages: 1). User-friendly, 2). Easy to replicate, refine, and identify new ideas, and 3). Good documentation. Stable Baselines 3 is used as a base around which new ideas can be added, and as a tool for comparing a new approach against existing ones. The purpose is that the simplicity of these tools will allow beginners to experiment with a more advanced tool set, without being buried in implementation details.
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RLlib [25] is an open-source high performance library for a variety of general applications. FinRL chooses to support RLlib due to its advantages: 1). High performance and parallel DRL training framework; 2). Scale training onto large-scale distributed servers; and 3). Allowing the multi-processing technique to efficiently train on laptops. RLlib natively supports TensorFlow, TensorFlow Eager, and PyTorch, but most of its internals are framework agnostic.
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ElegantRL [28] is designed for researchers and practitioners with finance-oriented optimizations. FinRL chooses to support ElegantRL due to its advantages: 1). Lightweight: core codes have less than 1,000 lines, less dependable packages, only using PyTorch (train), OpenAI Gym [5] (env), NumPy, Matplotlib (plot); 2). Customization: Due to the completeness and simplicity of the code structure, users can easily customize their own agents; 3). Efficient: Performance is comparable with RLlib [25]; and 4). Stable: As stable as Stable baseline 3 [37].
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ElegantRL supports state-of-the-art DRL algorithms, including both discrete and continuous ones, and provides user-friendly tutorials in Jupyter Notebooks. ElegantRL implements DRL algorithms under the Actor-Critic framework, where an agent consists of an actor network and a critic network. The ElegantRL library enables researchers and practitioners to pipeline the disruptive “design, development and deployment” of DRL technology.
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Customizing trading strategies. Due to the uniqueness of different financial markets, customization becomes a vital character to design trading strategies. Users are able to select a DRL algorithm and easily customize it for their trading tasks by specifying the state-action-reward tuple in Table 1. We believe that among
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the three state-of-the-art DRL libraries, ElegentRL is a practically useful option for financial tasks because of its completeness and simplicity along with its comparable performance with RLlib [25] and stability with Stable Baselines 3 [37].
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This paper is available on arxiv under CC BY 4.0 DEED license.
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