Authors:
(1) Xiao-Yang Liu, Hongyang Yang, Columbia University (xl2427,hy2500@columbia.edu);
(2) Jiechao Gao, University of Virginia (jg5ycn@virginia.edu);
(3) Christina Dan Wang (Corresponding Author), New York University Shanghai (christina.wang@nyu.edu). 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 THE PROPOSED FINRL FRAMEWORK In this section, we first present an overview of the FinRL framework and describe its layers. Then, we propose a training-testing-trading pipeline as a standard evaluation of the trading performance. 3.1 Overview of FinRL Framework The FinRL framework has three layers, application layer, agent layer, and environment layer, as shown in Fig. 2. • On the application layer, FinRL aims to provide hundreds of demonstrative trading tasks, serving as stepping stones for users to develop their strategies. • On the agent layer, FinRL supports fine-tuned DRL algorithms from DRL libraries in a plug-and-play manner, following the unified workflow in Fig. 1. • On the environment layer, FinRL aims to wrap historical data and live trading APIs of hundreds of markets into training environments, following the defacto standard Gym [5]. Upper-layer trading tasks can directly call DRL algorithms in the agent layer and market environments in the environment layer. The FinRL framework has the following features: • Layered architecture: The lower layer provides APIs for the upper layer, ensuring transparency. The agent layer interacts with the environment layer in an exploration-exploitation manner. Updates in each layer is independent, as long as keeping the APIs in Table 2 unchanged. • Modularity and extensibility: Each layer has modules that define self-contained functions. A user can select certain modules to implement her trading task. We reserve interfaces for users to develop new modules, e.g., adding new DRL algorithms. • Simplicity and applicability: FinRL provides benchmark trading tasks that are reproducible for users, and also enables users to customize trading tasks via simple configurations. In addition, hands-on tutorials are provided in a beginner-friendly fashion. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Xiao-Yang Liu, Hongyang Yang, Columbia University (xl2427,hy2500@columbia.edu); (2) Jiechao Gao, University of Virginia (jg5ycn@virginia.edu); (3) Christina Dan Wang (Corresponding Author), New York University Shanghai (christina.wang@nyu.edu). Authors: Authors: (1) Xiao-Yang Liu, Hongyang Yang, Columbia University (xl2427,hy2500@columbia.edu); (2) Jiechao Gao, University of Virginia (jg5ycn@virginia.edu); (3) Christina Dan Wang ( Corresponding Author ), New York University Shanghai (christina.wang@nyu.edu). Corresponding Author Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Related Works and 2.1 Deep Reinforcement Learning Algorithms 2 Related Works and 2.1 Deep Reinforcement Learning Algorithms 2.2 Deep Reinforcement Learning Libraries and 2.3 Deep Reinforcement Learning in Finance 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 The Proposed FinRL Framework and 3.1 Overview of FinRL Framework 3.2 Application Layer 3.2 Application Layer 3.3 Agent Layer 3.3 Agent Layer 3.4 Environment Layer 3.4 Environment Layer 3.5 Training-Testing-Trading Pipeline 3.5 Training-Testing-Trading Pipeline 4 Hands-on Tutorials and Benchmark Performance and 4.1 Backtesting Module 4 Hands-on Tutorials and Benchmark Performance and 4.1 Backtesting Module 4.2 Baseline Strategies and Trading Metrics 4.2 Baseline Strategies and Trading Metrics 4.3 Hands-on Tutorials 4.3 Hands-on Tutorials 4.4 Use Case I: Stock Trading 4.4 Use Case I: Stock Trading 4.5 Use Case II: Portfolio Allocation and 4.6 Use Case III: Cryptocurrencies Trading 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, and References 3 THE PROPOSED FINRL FRAMEWORK In this section, we first present an overview of the FinRL framework and describe its layers. Then, we propose a training-testing-trading pipeline as a standard evaluation of the trading performance. 3.1 Overview of FinRL Framework The FinRL framework has three layers, application layer, agent layer, and environment layer, as shown in Fig. 2. • On the application layer, FinRL aims to provide hundreds of demonstrative trading tasks, serving as stepping stones for users to develop their strategies. • On the agent layer, FinRL supports fine-tuned DRL algorithms from DRL libraries in a plug-and-play manner, following the unified workflow in Fig. 1. • On the environment layer, FinRL aims to wrap historical data and live trading APIs of hundreds of markets into training environments, following the defacto standard Gym [5]. Upper-layer trading tasks can directly call DRL algorithms in the agent layer and market environments in the environment layer. The FinRL framework has the following features: • Layered architecture: The lower layer provides APIs for the upper layer, ensuring transparency. The agent layer interacts with the environment layer in an exploration-exploitation manner. Updates in each layer is independent, as long as keeping the APIs in Table 2 unchanged. • Layered architecture: • Modularity and extensibility: Each layer has modules that define self-contained functions. A user can select certain modules to implement her trading task. We reserve interfaces for users to develop new modules, e.g., adding new DRL algorithms. • Modularity and extensibility: • Simplicity and applicability: FinRL provides benchmark trading tasks that are reproducible for users, and also enables users to customize trading tasks via simple configurations. In addition, hands-on tutorials are provided in a beginner-friendly fashion. • Simplicity and applicability: This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

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FinRL: The Blueprint for Automated Trading Strategies

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