7.5 Enhancing Investment Trading Agents with Interaction Data

In the realm of finance and investment trading, AI Agents hold immense potential. Continuous user interaction with Agents, including trading records, market feedback, and strategy adjustment requests, plays a crucial role in enhancing the performance of investment trading Agents. This interaction data empowers Agents to achieve significant improvements in various aspects, such as domain knowledge, strategy optimization, market adaptation, risk control, and personalized services.

7.5.1 Domain Knowledge Enhancement

The investment trading domain is replete with specialized terminology and industry jargon. When users interact with an Agent, whether using standard financial terms (e.g., “head and shoulders pattern,” “RSI oversold”) or informal terms (e.g., “golden dog,” “car head” in Web3), these inputs provide the Agent with opportunities to learn and expand its financial knowledge base.

Technical Value: Through Knowledge Extraction techniques, particularly Named Entity Recognition (NER) and Relation Extraction, financial entities (e.g., stock codes, indicator names) and their relationships can be identified from unstructured text inputs provided by users. This extracted knowledge can be used to construct and dynamically update a Financial Knowledge Graph, thereby enhancing the Agent’s depth and breadth of market analysis. Knowledge graphs help the Agent understand complex financial concepts and the relationships between events, supporting more precise decision-making.

7.5.2 Strategy Optimization and Dynamic Adjustment

User-described trading strategy logic in natural language provides the Agent with the opportunity to convert unstructured instructions into executable strategies. For example, a user might express complex rules like “buy when MACD golden cross occurs and trading volume increases” or “add to position when a crypto token breaks its previous high.”

Technical Value: The Agent can utilize Natural Language Processing (NLP) techniques to convert these unstructured languages into structured strategy rules or algorithm parameters. This complements the limitations of traditional quantitative models in handling vague, empirical, or qualitative strategies. Through user feedback and actual trading results, the Agent can continuously optimize and dynamically adjust these strategies, for instance, by automatically adjusting strategy parameters or trigger conditions based on user evaluations or corrections of strategy performance.

7.5.3 Dynamic Market Adaptability

Financial markets are highly dynamic and uncertain. User interaction data can help investment trading Agents better adapt to market changes.

Online Learning: The Agent can leverage continuously input streaming trading data and market feedback from users to perform online learning, updating model parameters in real-time. For example, using Incremental Learning frameworks (such as Python’s River library) to process real-time trading data enables the Agent to quickly respond to new market trends and patterns, avoiding model lag.

Reinforcement Learning (RL): User trading outcomes (profit/loss) can serve as reward signals for the Agent’s learning. Through reinforcement learning algorithms (such as the PPO algorithm), the Agent can learn to optimize its trading strategies, such as adjusting position ratios, stop-loss/take-profit points, or trading frequency, to maximize long-term returns. For example, if a user frequently trades AI tokens and achieves good returns, the Agent can use reinforcement learning to increase the weight of AI-related factors in its future decisions, thus favoring AI tokens.

7.5.4 Strategy Diversity and Robustness

A single trading strategy often struggles to cope with volatile market environments. Interaction data helps the Agent build a more diverse and robust portfolio of strategies.

Multimodal Data Fusion: In addition to traditional market data, the Agent can integrate user behavior data (e.g., heatmaps, browsing history), textual data (e.g., news comments, social media sentiment), and time-series data (e.g., price sequences) to construct composite features. This Multimodal Learning can provide the Agent with a more comprehensive market perspective.

Genetic Algorithms (GA): By treating user-provided strategy snippets or preferences as “genes,” the Agent can use genetic algorithms to perform crossover and mutation on strategy parameters, generating diverse strategy combinations. This method helps explore a broader strategy space and discover effective strategies that are difficult to find with traditional methods, thereby improving the overall robustness of the strategy.

7.5.5 Error Correction and Robustness Improvement

User identification of logical errors in the Agent is crucial for improving the Agent’s reliability and reducing “hallucinations.”

Technical Value: When users point out logical errors in the Agent (e.g., “the stop-loss point is set too low given current market volatility”), this feedback can be used to correct model biases. Through Contrastive Learning, the Agent can learn to distinguish between correct and incorrect trading patterns or market judgments, thereby reducing the generation of erroneous trading advice or analysis reports. This mechanism helps the Agent learn from mistakes and continuously improve the accuracy and reliability of its decisions.

7.5.6 Risk Control Precision Optimization

User interaction data provides the Agent with the ability to identify and manage risks with greater precision. Anomaly Detection Models: Based on user historical trading patterns (e.g., high-frequency short-term trading, unusually large transactions), the Agent can build Anomaly Detection Models to identify abnormal trading behaviors in real-time (e.g., suspected market manipulation, money laundering, or compromised user accounts) and issue timely warnings.

Causal Inference: By analyzing the causal relationship between user trading decisions and market fluctuations, the Agent can more accurately predict potential risks. For example, analyzing the trading behavior of specific user groups after certain market events can reveal potential risk exposures or changes in market sentiment, providing deeper insights for risk management.

7.5.7 Personalized Service Capability Upgrade

The personalized service of an investment trading Agent is one of its core competencies. User feedback and behavioral data can dynamically update user profiles, leading to highly customized investment advice.

Dynamic User Profile Updates: Utilizing user trading frequency, risk preferences (obtained through questionnaires and behavioral analysis), and other data, the Agent can construct and dynamically update user tags. For instance, user feedback like “reduce AI token allocation” directly reflects a change in their risk preference, and the Agent can adjust its recommended portfolio weights accordingly.

Personalized Strategy Generation: Based on Collaborative Filtering or Knowledge Graph technologies, the Agent can recommend investment portfolios tailored to the user’s style (e.g., prioritizing low-volatility ETFs for conservative users). By constructing preference vectors from user historical interactions, the Agent can drive the generation of personalized strategies, ensuring investment advice highly matches the user’s risk tolerance and investment goals.

7.6 User Actions: Building a Human-AI Collaborative Optimization Loop

To maximize the utilization of user-AI Agent interaction data and accelerate the Agent’s iterative optimization, it is crucial to design a comprehensive and easy-to-operate user participation mechanism. This not only provides high-quality feedback signals but also enhances user engagement and trust in the Agent. The following are specific actions users can take, aimed at building a human-AI collaborative optimization loop.

7.6.1 Explicit Feedback Mechanisms: Directly Expressing User Intent and Evaluation

Explicit feedback is how users directly express their evaluation and intent regarding the Agent’s output or behavior to the Agent or system. It is key to obtaining high-quality supervisory signals.

• Like/Dislike: This is the most direct and rapid binary feedback form. Users can quickly evaluate each of the Agent’s responses or completed tasks as “good” or “bad.” This feedback signal can serve as direct input for training a Reward Model, used in the Reinforcement Learning from Human Feedback (RLHF) phase to guide the Agent in learning human preferences.
• Text Comments/Suggestions: After liking/disliking, the system should allow users to input specific text explanations, such as “This answer is not detailed enough,” “The information is incorrect, the correct information is…,” or “I wish you could be more concise.” These qualitative feedbacks are valuable clues for identifying specific Agent problems (e.g., knowledge errors, logical flaws, inappropriate expression style) and directions for improvement. By analyzing these comments using natural language processing techniques, actionable improvement suggestions can be extracted.
• Error Tagging/Highlighting: Provide a dedicated interface or tool that allows users to highlight erroneous parts of the Agent’s response and select from a predefined list of error types (e.g., “knowledge error,” “logical error,” “format error,” “unclear expression,” “redundant information”). This fine-grained annotation is crucial for diagnosing model issues and performing targeted data cleaning and model fine-tuning, especially in complex tasks or specialized domains .
• Satisfaction Rating: After a conversation ends or a task is completed, provide an overall satisfaction rating (e.g., 1-5 stars). This can serve as a macroscopic indicator for measuring user experience, used to evaluate the Agent’s overall performance and user loyalty. Long-term tracking of these ratings helps identify trends and potential problems in Agent performance.
• Surveys: Periodically or occasionally invite users to participate in more in-depth surveys to collect qualitative feedback and requirements regarding the Agent’s functionality, usability, reliability, personalization level, etc. Surveys can obtain deeper user insights, complement automated metrics, and provide strategic guidance for the Agent’s future development.

7.6.2 Task Collaboration and Correction: Guiding Agent Learning and Adaptation

Users are not only consumers of Agent services but also “teachers” and “collaborators” in the Agent’s learning process. By directly participating in task correction and guidance, users can effectively guide the Agent’s behavior.

• Task Correction: When the Agent’s task execution deviates or fails to fully meet user requirements, users can directly correct the Agent’s actions or outputs. For example, if the Agent generates code with a bug, the user can directly modify the code and tell the Agent, “No, it should be changed this way.” This direct correction provides strong supervisory signals, helping the Agent learn correct task execution paths and output formats.
• Step-by-Step Guidance: When the Agent encounters difficulties, gets stuck in a loop, or cannot understand complex instructions, users can provide step-by-step guidance or hints to help the Agent overcome obstacles and complete the task. For example, “You should first search for this information, and then analyze it.” This interaction mode is similar to a human mentor guiding a student, helping the Agent learn more effective task decomposition and planning strategies.
• Preference Setting: Users can actively set their preferences, such as “I prefer concise answers,” “Please use more professional terminology,” “Please avoid slang,” or “Please prioritize the lowest-cost solution.” The Agent should incorporate these explicitly set preferences into its decision-making and generation processes to provide services that better meet users’ personalized needs. These preferences can be stored in the user’s long-term memory module for continuous personalization across sessions.
• Memory Management: Allow users to view, edit, or delete the Agent’s memory content (e.g., historical preferences, key information, personal data). This not only enhances user control over personal data but also ensures the accuracy and privacy of the Agent’s personalized services. Users can correct erroneous information in the Agent’s memory or delete outdated information that is no longer relevant, thereby maintaining the cleanliness and effectiveness of the Agent’s memory.

7.6.3 Interaction Mode Selection: Customizing Agent Behavior Style

Users can choose the Agent’s interaction mode and behavior style based on different scenarios and personal habits, thereby enhancing the flexibility and comfort of the user experience.

• Dialogue Style Selection: Users can choose the Agent’s dialogue style, such as “formal,” “informal,” “humorous,” “professional,” “concise,” or “detailed.” This allows the Agent to better adapt to different communication scenarios and user personalities, improving the naturalness and friendliness of the conversation.
• Information Granularity Selection: Users can specify the granularity of information they want the Agent to provide. For example, “Give me a summary,” “Please explain each step in detail,” or “Just tell me the key points.” This helps the Agent adjust the level of detail in its output according to users’ immediate needs, avoiding information overload or insufficiency.
• Tool Usage Preference: Users can express their preference for the Agent to use specific tools. For example, “Please prioritize displaying data with charts,” “Please give me the API call code directly, without explanation,” or “Please only use the internal knowledge base, do not search online.” This preference setting can guide the Agent in selecting the tools and output formats that users prefer when performing tasks, improving efficiency and satisfaction.

7.6.4 Knowledge Contribution: Jointly Building and Maintaining the Knowledge System

Users, as experts in specific domains or owners of information, can directly participate in the construction and maintenance of the Agent’s knowledge system, forming a crowdsourced knowledge optimization model.

• Knowledge Contribution: When the Agent lacks knowledge or cannot answer a question, users can actively supplement relevant knowledge or provide reference materials (e.g., document links, professional articles, personal experience). This user-contributed knowledge can be incorporated into the Agent’s knowledge base (e.g., RAG’s retrieval base) after verification, thereby expanding the Agent’s knowledge boundaries.
• Knowledge Correction: Users can correct erroneous or outdated information in the Agent’s knowledge base. For example, pointing out that certain data has been updated or that the explanation of a concept is incorrect. This error correction mechanism is crucial for maintaining the accuracy and timeliness of the Agent’s knowledge base, especially in rapidly changing information domains.

7.6.5 Privacy and Data Management: Building Trust and Transparency

While collecting and utilizing user interaction data, ensuring user privacy and data security is fundamental to building trust. Transparent data management strategies can encourage users to participate more actively in providing feedback.

• Data Usage Consent: Clearly inform users how their interaction data will be collected, stored, and used (e.g., whether data can be used for model training, personalized service improvement, etc.), and provide clear data usage consent options. Users should have the right to choose whether to consent to their data being used for Agent optimization.
• Data Deletion Request: Users should have the right to request the deletion of their historical interaction data and related personal information. Providing a convenient data deletion mechanism complies with data privacy regulations and enhances user confidence in data control.

By implementing the user actions described above, AI Agent systems can transform from passive responders into active learners, collaboratively building a continuously optimized, highly personalized, and trustworthy intelligent interaction experience with users. This human-AI collaborative model is an important direction for the future development of AI Agents.