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A Deep Reinforcement Learning Algorithms of Soft Actor-Critic for Optimizing Stock Portfolio Allocation
Deep Reinforcement Learning
Soft Actor-Critic Algorithm
|Issue Date:||2020-09-02 13:14:56 (UTC+8)|
|Abstract:||透過人工智慧演算法進行自動化交易是當前股市投資管理研究的發展趨勢。本研究結合深度強化學習與金融科技，探討運用 Soft Actor-Critic(SAC)演算法於股市資產配置之效益，並驗證演算法是否能有效應用於金融交易市場及藉配置資產提高投資總體價值。本研究自 Datastream 數據資料庫選定我國股票市場中 5 支股票為實驗標的，利用演算法在 OpenAI Gym 環境中訓練、運算並驗證該演算法在股市資產投資分配上之成效。實驗結果顯示，該 演算法能根據歷史數據學習預測目標股票未來績效表現，發揮自動調控風險及配置資產權 重之能力，產生最佳投資組合模型。另外本實驗結果與泛化投資組合策略(Universal Portfolio)相比，展現更為優異而穩定之收益，亦初步驗證深度強化學習能有效應用於金融交易市場。|
The applications of artificial intelligence algorithms to automated trading have become one of the prominent domains of portfolio management studies. This study combines the key concepts of both deep reinforcement learning and financial technology, exploring the performance of applying soft actor-critic (SAC) algorithm for the optimal stock portfolio allocation.
In this thesis, we select five stocks via Taiwan stock market from the Datastream database as our experimental target. Then, with the operation of Docker containerization technology, we apply the SAC algorithm to train, calculate and come up with the most optimal stock portfolio allocation. A comparative analysis of the deep reinforcement learning based portfolio optimization versus the more traditional “Universal Portfolio”, “Best so Far”, and “Buy and Hold” is conducted to verify the effectiveness and stability of the overall performance of our SAC model.
The preliminary results show that through its off-policy updates with a stable stochastic actor- critic formulation, the SAC approach is capable of predicting future stock performance from the input training of historical data. Furthermore, with its automated learning process, the risk and asset allocation weight are under dynamic management, thus generating the optimal stock portfolio with a better and more stable performance, comparing with other traditional quantitative strategies.
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