paint-brush
FinRL's Implementation of DRL Algorithms for Stock Tradingby@reinforcement
106 reads

FinRL's Implementation of DRL Algorithms for Stock Trading

tldt arrow

Too Long; Didn't Read

FinRL showcases its implementation of an ensemble strategy for stock trading, achieving impressive performance metrics like a Sharpe ratio of 2.81 and an annual return of 52.61%. The backtesting results validate FinRL's ability to reproduce successful trading strategies.
featured image - FinRL's Implementation of DRL Algorithms for Stock Trading
Reinforcement Technology Advancements HackerNoon profile picture

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]).

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

4.4 Use Case I: Stock Trading

We use FinRL to reproduce both [50] and [51] for stock trading. The ensemble strategy [51] combines three DRL algorithms (PPO [42], A2C [32] and DDPG [26]) to improve the robustness.


The implementation is easy with FinRL. We choose three algorithms (PPO, A2C, DDPG) in the agent layer, and an environment with start and end dates in the environment layer. The implementations of DRL algorithms and data preprocessing are transparent to users, alleviating the programming and debugging workloads. Thus, FinRL greatly facilitates the strategy design, allowing users to focus on improving the trading performance.


Fig. 5 and Table 3 show the backtesting performance on Dow 30 constituent stocks, accessed at 2020/07/01. The training period


Figure 5: Performance of stock trading [51] using the FinRL framework.


Figure 6: Performance of portfolio allocation [21] using the FinRL framework.


Table 3: Performance of stock trading and portfolio allocation over the DJIA constituents stocks using FinRL. The Sharpe ratios of the ensemble strategy and the individual DRL agents excceed those of the DJIA index, and of the min-variance strategy.


is from 2009/01/01 to 2020/06/30 on a daily basis, and the testing period is from 2020/07/01 to 2021/06/30. The performance in terms of multiple metrics is consistent with the results reported in [51] and [50], and here we show results in a recent trading period. We can see from the DJIA index that the trading period is a bullish market with an annual return of 32.84%. The ensemble strategy achieves a Sharpe ratio of 2.81 and an annual return of 52.61%. It beats A2C with a Sharpe ratio of 2.24, PPO with a Sharpe ratio of 2.23, DDPG with a Sharpe ratio of 2.05, DJIA with a Sharpe ratio of 2.02, and min-variance portfolio allocation with a Sharpe ratio of 1.98, respectively. Therefore, the backtesting performance demonstrates that FinRL successfully reproduces the ensemble strategy [51].


Figure 7: Cumulative returns (5-minute) of top 10 market cap cryptocurrencies trading using FinRL.


This paper is available on arxiv under CC BY 4.0 DEED license.