# A Stock Market Decision-Making Framework Based on CMR-DQN
## 1 Introduction  
In the current global financial market, the ultimate goal pursued by investors is not only the appreciation of capital but also determining how to effectively manage and control investment risks during this process. The core of this goal lies in finding the optimal balance between risk and reward to ensure that investments can bring sustainable and long-term returns. These methods are not only time-consuming and labor-intensive but are also prone to the influence of human emotions and biases when processing and analyzing large-scale financial data, thus affecting the objectivity and effectiveness of decision-making. Especially when faced with rapidly changing market information, human traders find it difficult to react accurately in real-time, which poses significant challenges to traditional trading methods in the modern financial market [1]. In this context, algorithmic trading, as an innovative trading method, automates trading decision-making and execution through computer programs and brings new possibilities to modern financial transactions with its efficient and objective characteristics. Financial decision-making problems have traditionally been modeled using stochastic processes and techniques from stochastic control. The selection of models often aims to balance simplicity with practical applicability. Simple models yield tractable and implementable strategies that can be expressed in closed-form or resolved through traditional numerical methods. However, these models may oversimplify market behaviors, potentially resulting in suboptimal strategies that could lead to financial losses. Conversely, models that aim to capture more realistic market features tend to be complex and often prove to be mathematically and computationally intractable using classical stochastic control tools. In recent years, the surge of financial data on transactions, quotes, and orderflows in electronic order-driven markets has revolutionized data processing and statistical modeling techniques in finance, posing new theoretical and computational challenges [2]. The core driving force for the exploration and application of algorithmic trading lies in the use of advanced computing technology to solve the limitations of traditional trading methods. In the practice of algorithmic trading, rule-based methods and machine-learning-based strategies are the two main directions. Rule-based algorithmic trading relies on an in-depth understanding of market rules through the setting of a fixed set of trading rules to automate buying and selling operations. The advantage of this approach is that it is straightforward and easy to implement, but at the same time, it is also inflexible and may not be able to adapt in time in the face of rapid changes in the market [3]. In contrast, machine-learning-based algorithmic trading is more capable of data processing and adaptability by training models to identify and learn complex patterns in the market. Deep learning models, in particular, are able to extract valuable features from large amounts of historical data automatically to support more accurate trading decisions [4]. However, the ultimate goal of both rule-based and machine learning methods is to ensure the high complexity and uncertainty of the financial markets for strategy execution, and developing trading strategies that can effectively capture market opportunities while adapting to their dynamic changes remains a major challenge in the current algorithmic trading field [5]. In the past decade, deep learning and reinforcement learning (RL) technology have shown great potential and application value in the optimization of financial trading systems. Deep learning provides a new perspective for the analysis and prediction of financial markets through its ability to identify complex patterns and features in massive amounts of data automatically.