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 4.2 Baseline Strategies and Trading Metrics Baseline trading strategies are provided to compare with DRL strategies. Investors usually have two mutually conflicting objectives: the highest possible profits and the lowest possible risks [43]. We include three conventional strategies as baselines. Passive trading strategy [31] is an easy and popular strategy that has the minimal trading activities. Investors simply buy and hold index ETFs [46] to replicate a broad market index or indices such as Dow Jones Industrial Average (DJIA) index and Standard & Poor’s 500 (S&P 500) index. Mean-variance and min-variance strategy [2] both aim to achieve an optimal balance between the risks and profits. It selects a diversified portfolio with risky assets, and the risk is diversified when traded together. Equally weighted strategy is a type of portfolio allocation method. It gives the same importance to each asset in a portfolio. FinRL includes common metrics to evaluate trading performance: Final portfolio value: the amount of money at the end of the trading period. Cumulative return: subtracting the initial value from the final portfolio value, then dividing by the initial value. Annualized return and standard deviation: geometric average return in a yearly sense, and the corresponding deviation. Maximum drawdown ratio: the maximum observed loss from a historical peak to a trough of a portfolio, before a new peak is achieved. Maximum drawdown is an indicator of downside risk over a time period. Sharpe ratio in (1) is the average return earned in excess of the risk-free rate per unit of volatility. 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 4.2 Baseline Strategies and Trading Metrics Baseline trading strategies are provided to compare with DRL strategies. Investors usually have two mutually conflicting objectives: the highest possible profits and the lowest possible risks [43]. We include three conventional strategies as baselines. Passive trading strategy [31] is an easy and popular strategy that has the minimal trading activities. Investors simply buy and hold index ETFs [46] to replicate a broad market index or indices such as Dow Jones Industrial Average (DJIA) index and Standard & Poor’s 500 (S&P 500) index. Passive trading strategy Mean-variance and min-variance strategy [2] both aim to achieve an optimal balance between the risks and profits. It selects a diversified portfolio with risky assets, and the risk is diversified when traded together. Mean-variance and min-variance strategy Equally weighted strategy is a type of portfolio allocation method. It gives the same importance to each asset in a portfolio. Equally weighted strategy FinRL includes common metrics to evaluate trading performance: Final portfolio value : the amount of money at the end of the trading period. Final portfolio value Cumulative return : subtracting the initial value from the final portfolio value, then dividing by the initial value. Cumulative return Annualized return and standard deviation : geometric average return in a yearly sense, and the corresponding deviation. Annualized return and standard deviation Maximum drawdown ratio : the maximum observed loss from a historical peak to a trough of a portfolio, before a new peak is achieved. Maximum drawdown is an indicator of downside risk over a time period. Maximum drawdown ratio Sharpe ratio in (1) is the average return earned in excess of the risk-free rate per unit of volatility. Sharpe ratio 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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Unveiling FinRL's Baseline Strategies and Key Trading Metrics for Portfolio Evaluation

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