\n In this course, we will deeply explore the basics of algorithmic trading using machine learning and deep learning, as well as policy-based reinforcement learning.
\n Analyzing historical data is essential for making informed decisions when developing investment strategies.
\n Machine learning algorithms provide insights for these decisions, while deep learning expands their scope.\n<\/p>\n
\n Machine learning is a technique that learns patterns from given data to predict future data.
\n Deep learning, a field of machine learning that uses multi-layered neural networks, enables more complex pattern recognition and predictions, primarily excelling with large datasets.\n<\/p>\n
\n In algorithmic trading, “state” represents the current situation of the market, including information like stock prices, trading volumes, and volatility.
\n “Action” refers to strategic decisions including buying, selling, or holding.
\n A policy refers to the method of deciding which action to take in a given state.\n<\/p>\n
\n States consist of various elements. Efficiently defining the state significantly impacts the model’s performance.
\n Generally, the following variables can be considered as the state:\n<\/p>\n
\n Actions must also be clearly defined. Representative types of actions include:\n<\/p>\n
\n A policy refers to the mapping from state to action. Policies can be designed in various ways, one of which is using reinforcement learning algorithms such as Q-learning.
\n Q-learning learns the value of state-action pairs and helps choose the optimal action.\n<\/p>\n
\n Reinforcement learning is a technique where an agent interacts with the environment to learn the optimal policy. The key components include:\n<\/p>\n
\n Q-learning is one of the most widely used reinforcement learning algorithms, learning the Q-value for state-action pairs.
\n The agent selects an action in a given state, receives a reward as a result, and updates the Q-value.
\n The update formula for Q-learning is as follows:\n<\/p>\n
\n Here, \u03b1<\/strong> is the learning rate, \u03b3<\/strong> is the discount factor, r<\/strong> is the reward, \n To overcome the limitations of Q-learning, deep Q-learning was developed, combining deep learning techniques. \n In algorithmic trading, data collection and preprocessing are crucial processes. \n This is the stage where models are trained based on collected data and evaluated for performance. \n Once machine learning and deep learning models have been successfully trained, the next step is to integrate them into a real trading system. \n Algorithmic trading based on machine learning and deep learning is gaining increasing attention in modern financial markets. \n Additionally, it is important to continuously improve your strategies through research and experimentation. <\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
\nQ(s, a) <- Q(s, a) + \u03b1[r + \u03b3 max(Q(s', a')) - Q(s, a)]
\n<\/code><\/p>\n
\n s<\/strong> is the current state, a<\/strong> is the action, and s’<\/strong> is the next state.\n<\/p>\n3.2. Deep Q-Learning<\/h4>\n
\n In deep Q-learning, neural networks are used to approximate the Q-values, allowing for effective handling of complex state spaces.\n<\/p>\n4. Market Data Collection and Preprocessing<\/h3>\n
\n Key considerations in this stage include:\n<\/p>\n\n
5. Model Training and Evaluation<\/h3>\n
\n Typically, data is divided into training and testing sets.
\n Key evaluation metrics used in this process include:\n<\/p>\n\n
6. Building an Actual Trading System<\/h3>\n
\n Considerations for system construction include:\n<\/p>\n\n
7. Conclusion<\/h3>\n
\n The process of transitioning from state to action through policy is crucial for making investment decisions.
\n Based on the content introduced in this course, we hope you can enhance your trading strategies and lay the groundwork for successful investing.\n<\/p>\n
\n We look forward to seeing what changes machine learning technology will bring to future financial markets.\n<\/p>\n