Stock Agent AI: An Autonomous Stock Research and Analysis Platform Based on Multi-Agent Architecture

Stock Agent AI is an open-source stock research platform using an Agentic AI architecture. It completes market analysis, fundamental evaluation, news sentiment analysis, and risk scoring through collaboration among multiple agents, ultimately generating transparent investment recommendation reports. Its core innovation lies in abandoning traditional rigid workflows, enabling autonomous analysis with dynamically adjusted strategies and independent decision-making, providing users with professional-level stock research support.

Artificial intelligence in the financial sector is shifting from simple automation to autonomy. Traditional stock analysis tools follow fixed linear processes and lack flexibility in the face of complex markets; Agentic AI (autonomous agents), however, can dynamically adjust strategies, make independent decisions, and learn from mistakes. Stock Agent AI is a practice of this concept, demonstrating how multi-agent collaboration can revolutionize financial analysis workflows.

Stock Agent AI adopts a multi-agent collaboration architecture, inspired by the division of labor in professional investment teams:

• Supervisor Agent: Central coordinator, responsible for task decomposition, resource scheduling, quality control, and result integration;
• Market Agent: Technical analysis expert, analyzing price trends, momentum indicators, and volatility;
• Fundamentals Agent: Fundamental expert, evaluating financial health (valuation, growth, profitability, etc.);
• News Agent: Sentiment analysis expert, crawling news and quantifying sentiment polarity;
• Risk Agent: Risk assessment expert, integrating multi-dimensional signals to generate weighted risk scores;
• Memo Agent: Report writing expert, generating structured and transparent investment recommendation reports.

Stock Agent AI's Agentic features go beyond traditional automation:

1. Dynamic Planning: Adjust task plans based on user input and real-time data;
2. Reflection and Self-Correction: Monitor output confidence and automatically supplement analysis;
3. Self-Healing Toolchain: Automatically switch to backup sources and handle rate limits when APIs fail;
4. Memory System: Share context and store historical analysis results;
5. Confidence Scoring: Score the output quality of each agent and aggregate into an overall confidence level;
6. Transparent Reasoning: Investment recommendations come with clear bullish/bearish arguments and decision logic.

Technology Stack: Python (core language), Streamlit (web interface), Pandas/NumPy (data processing), Alpha Vantage/Finnhub API (data sources), TextBlob (sentiment analysis), SQLite (local storage). Application Scenarios:

• Retail investors: Obtain professional-level analysis at low cost;
• Quick screening: Efficient batch analysis of stocks;
• Financial education: Teaching tool for learning Agentic AI and financial analysis;
• AI research: Reference implementation of Agentic AI architecture.

Limitations:

• Data depends on the quality and availability of external APIs;
• Mainly supports the U.S. stock market;
• Data update frequency is difficult to meet high-frequency trading needs;
• Focuses on analysis rather than price prediction. Future Outlook:
• Expand support for multiple markets (A-shares, Hong Kong stocks, cryptocurrencies, etc.);
• Integrate deep learning models to improve news analysis;
• Add strategy backtesting functionality;
• Optimize recommendation algorithms based on user feedback.