FinGenius

In the A-share market, “information overload, delayed decision-making, and difficult risk control” have always been the core pain points of investors and financial institutions – individual investors have no way to start in the face of massive financial report data and public opinion information, institutional analysts need to spend days integrating market dynamics to output reports, and traditional financial instruments are difficult to simulate complex market game behavior. The emergence of FinGenius, the world’s first A-share AI financial game agent, takes “multi-agent game architecture + real-time data processing + personalized decision support” as the core breakthrough, and forms an “AI analysis team” of 16 professional agents (public opinion, floating capital, risk control, etc.), which compresses the financial analysis that originally took several days to the minute level by simulating the interaction of market participants and game theory, and provides “accuracy” for A-share investment decision-making , efficient and safe”.

1. Core subversion: from “single analysis” to “game simulation”, redefining A-share financial analysis

The most revolutionary value of FinGenius lies in its ability to break away from the limitations of traditional financial tools and build a “multi-agent collaborative game” analysis model, transforming financial analysis from “static reporting” to “dynamic decision simulation” through three core advantages.

(1) Multi-Agent game system: 16 “super analysts” work together

FinGenius breaks the bottleneck of “single AI model analysis” and breaks down market analysis into 16 professional dimensions based on a multi-agent game architecture, each of which is responsible for a dedicated agent, forming an AI analysis team with a clear division of labor and efficient collaboration. For example, when analyzing a leading consumer stock:

• Public opinion agent: capture public opinion trends in social media, financial news, and brokerage research reports in real time, and identify key public opinion labels such as “performance exceeding expectations” and “supply chain risk”;
• Floating Capital Agent: Track the data of the Dragon and Tiger List, simulate the flow of floating funds, and predict short-term stock price fluctuation trends;
• Risk control agent: integrate company financial reports, industry policies, and macroeconomic data to assess potential risks such as “declining gross profit margin” and “tightening policy supervision”;
• Strategic Agents: Summarize the analysis results of 15 other agents, combine the game theory model to simulate the market interaction behavior of “retail investors, institutions, and floating capital”, and finally output a comprehensive report containing “target stock price range, risk warning points, and investment strategy suggestions”. This multi-agent collaboration model avoids the analysis bias of a single perspective and brings the conclusion closer to the real ecology of the “multi-participant game” in the A-share market. After using FinGenius, a brokerage reduced the production time of monthly analysis reports in the consumer industry from 3 days to 20 minutes, and increased the accuracy of identifying key risk points by 40%.

(2) Real-time data processing and risk warning: Millisecond-level response to lock risks in advance

The A-share market is volatile, and “data lag” often leads to missed opportunities or risks in decision-making. FinGenius has the ability to “process massive financial data in milliseconds” and can integrate three types of core data in real time: first, structured data such as listed companies’ financial reports, dragon and tiger lists, and capital flows; second, unstructured data such as news and public opinion, social media comments; The third is external data such as macroeconomic indicators and industry policies. The system will clean and analyze these data in real time, and monitor abnormalities through the risk warning system – for example, when a technology stock has a combination signal of “significant flight of main funds + surge in negative public opinion”, the risk control agent will trigger an early warning within 10 seconds, push a “short-term correction risk” prompt, and attach specific data support (such as “a net outflow of 500 million yuan within 30 minutes, and the negative proportion of public opinion rises to 60%”). With the help of FinGenius, a private equity team successfully received a risk warning 3 hours before the plunge of a new energy stock, avoiding nearly 20% of the net worth drawdown.

(3) Growth ring memory rule algorithm: “Exclusive AI advisor” for personalized decision-making

Different from the “one-size-fits-all” analysis output of traditional financial instruments, FinGenius deeply adapts to users’ investment habits and risk appetite through the “Growth Ring Memory Rule Algorithm” to provide personalized decision-making support. The system records the user’s historical actions (e.g., “preference for long-term value investment” and “good at capturing thematic stock opportunities”), risk tolerance (e.g., “acceptable maximum drawdown of 10%)”, and adjusts the analysis focus accordingly. For example:

• For conservative investors, the analysis report gives priority to highlighting value indicators such as “company cash flow stability” and “dividend rate”, and the risk warning threshold is lower;
• For short-term traders, the report focuses on “floating capital flows” and “technical support pressure levels”, and provides “intraday trading signals” suggestions;
• For institutional users, the system will automatically generate “sector rotation analysis” and “portfolio risk exposure assessment” based on their investment portfolios (such as “heavy consumption + technology sector”) to help optimize asset allocation. This personalization capability makes FinGenius an “AI advisor that understands the user” rather than a cold analysis tool.

2. Functional matrix: covering the whole process of “analysis-decision-risk” to create an “all-round tool” for A-share investment

FinGenius has designed a series of practical functions around the full cycle of A-share investment, allowing financial analysis to extend from “data integration” to “decision-making”, taking into account professionalism and ease of use.

(1) In-depth data integration and multimodal reporting: from “data stacking” to “insight refinement”

While traditional financial instruments often fall into the misconception of “data piling”, FinGenius can extract core insights from massive information to generate structured multimodal reports:

• Breadth of data integration: covering the financial report data of all A-share listed companies, the dragon and tiger list in the past 5 years, real-time public opinion (processing more than 1 billion pieces of information per day), macroeconomic indicators (GDP, CPI, etc.), and industry policy documents to ensure that there are no information dead ends in analysis;
• Report presentation forms: support text reports, visual charts (such as “stock price trend forecast line charts” and “risk point heat maps”), and voice interpretation, individual investors can quickly understand the core conclusions through voice, and institutional analysts can export detailed text reports for internal reporting;
• Key information annotation: The report will be marked with “core positive” (green), “potential risk” (red), and “signal to be verified” (yellow) in different colors, for example, in a new energy stock report, “cost pressure caused by rising upstream lithium prices” is marked in red, and “overseas orders exceed expectations” in green to help users quickly grasp the key points.

(2) Dual-environment architecture: a closed loop from “research and analysis” to “decision-making debate”

FinGenius innovatively designed the “Research + Battle” dual-environment architecture to bring the analysis process closer to the real decision-making process of the institution:

• Research environment: 16 agents complete data integration and preliminary analysis here, and output conclusions in their respective fields, such as the public opinion agent proposes that “public opinion is favorable”, and the speculative agent believes that “short-term funds will flow out”;
• Battle environment: Based on the results of the research environment, a “structured debate” is launched between agents – the risk control agent proposes that “policy regulation may inhibit short-term capital flows” in response to the conclusion of the floating capital agent, while the strategic agent combines the views of both sides to optimize the decision-making model through cumulative debate (integrating all historical discussions) to finally form a more comprehensive analysis result. This dual-environment model avoids the “black box of the analysis process” and allows users to trace the generation logic of conclusions and enhance decision-making confidence.

(3) MCP intelligent calling: plug-and-play financial instrument ecosystem

FinGenius uses Model Context Protocol (MCP) technology to seamlessly integrate with existing financial instruments and create an “open and scalable” analysis ecosystem. Users can call external tools directly in FinGenius as needed:

• Call the quantitative trading platform: Convert FinGenius’ analysis results into trading signals and automatically execute “buy/sell” operations;
• Access to the company’s internal database: Institutional users can connect their own financial report analysis systems and customer risk assessment models to make FinGenius’ analysis more suitable for internal business needs.
• Integrated visualization tools: Export analysis data to formats that are recognizable by Tableau and Power BI for easy secondary processing and reporting. This “plug and play” feature allows FinGenius to become a “financial instrument hub” instead of replacing existing tools, improving overall analysis efficiency.

3. Technical support: dual guarantee of real-time and security

FinGenius’ smooth experience stems from the underlying support of “real-time data processing technology + continuous state management”, ensuring that analysis is both efficient and reliable in the dynamic environment of the A-share market.

(1) Millisecond-level data processing: Keep up with the rhythm of the A-share market

The A-share market fluctuates frequently during the day, and the speed of data processing directly affects the timeliness of decision-making. FinGenius uses a distributed computing architecture that can perform three core operations in milliseconds:

• Data capture: Real-time docking with exchange market interfaces, financial information platforms, and social media APIs to ensure that the information delay does not exceed 1 second.
• Data cleaning: Automatically filter out “fake news” and “duplicate data”, such as identifying and eliminating “unsourced performance forecasts” to ensure the accuracy of the analysis basis;
• Data analysis: Through GPU-accelerated computing, financial report data comparison, public opinion sentiment analysis, and game model calculation can be quickly completed, so that the average generation time of a complete individual stock analysis report is only 3 minutes, which is much faster than the hours or even days of traditional tools.

(2) Continuous state management: ensure the coherence and accuracy of decision-making

• Historical data memory: Record the user’s past analysis preferences, focus targets, and risk thresholds, for example, if the user has focused on the “new energy track”, the system will prioritize pushing the latest analysis in this field;
• Analysis process traceability: save the generation logic of each report, including “which agents were called, key data sources, and model parameter settings”, and users can check back at any time to avoid “no basis” for decision-making;
• Dynamic Result Update: When there is a new change in the market (such as sudden policies or company announcements), the system will automatically update the analysis results of relevant agents and adjust the final conclusion simultaneously.

4. Usage process: Four steps to get started, from registration to obtaining analysis reports only 10 minutes

1. Registration and Environment Selection: Visit the FinGenius official website (http://fingenius.cn/) or download the app from the Honor, Xiaomi, and Vivo app marketplaces, and register and log in with your mobile phone number or corporate email. Individual users enter “Simplified Mode” by default, and institutional users can switch to “Professional Mode” to unlock more advanced features (such as viewing the multi-agent debate process and customizing agent parameters).
2. Input analysis requirements: Enter the target target (such as “Kweichow Moutai” or “New Energy Industry”) or analyze the demand (such as “2025 Q2 Consumer Sector Investment Strategy”) in the search box, and the system will automatically identify the demand type and call the corresponding agent.
3. Customized settings (optional): Individual users can adjust risk tolerance (conservative/balanced/aggressive) and investment horizon (short/medium/long-term) in “Preferences”, and institutional users can add API keys through the profile to connect to their own databases or tools;
4. View reports and risk monitoring: The system generates analysis reports within 3-5 minutes, and users can view the three modules of “Core Conclusion, Data Support, and Risk Warning”, and click “In-depth Analysis” to view the collaboration process of each agent. If risk warning is enabled, the system will monitor market changes in real time and push reminders through the APP when abnormalities occur.

For technical users, you can also localize it through GitHub repositories (https://github.com/HuaYaoAI/FinGenius): use conda or uv to install dependencies, edit configuration files to add API keys, set output formats (such as PDF/Excel) through command-line parameters, and enable text-to-speech functions to meet personalized development needs.

5. Application scenarios: From personal investment to institutional services, covering all types of financial needs of A-shares

With its core capabilities of “multi-agent game analysis + real-time risk warning”, FinGenius has been applied in personal investment, institutional services, quantitative trading and other fields, becoming an “all-round financial assistant” in the A-share market.

(1) Individual investors: say goodbye to “blindly following the trend” and accurately seize opportunities

For individual investors, FinGenius is an “equalizing tool for professional analytical capabilities.” Novice investors can quickly understand the core logic of individual stocks through the “simplified version of the report” generated by the system (such as “a technology stock: AI business growth exceeds expectations, short-term target price of 50 yuan, and the risk point lies in chip supply”); Experienced traders can check out the “Multi-Agent Debate Process” to deeply analyze key issues such as “whether the inflow of floating capital is sustainable” and “whether favorable public opinion can translate into stock price increases”. An individual investor used FinGenius to reduce the time spent screening stocks from 2 hours a day to 10 minutes and increase the win rate of investment decisions by 25%.

(2) Financial institutions: reduce costs and increase efficiency, and improve research and risk control capabilities

• Brokerage Research Institute: Use FinGenius to quickly generate industry weekly reports and individual stock reviews, allowing analysts to focus on “deep logic mining” rather than data integration, increasing research efficiency by 3 times;
• Fund company: Through real-time monitoring of position portfolio risk, when a heavy stock shows a signal of “negative surge in public opinion + net outflow of funds”, the risk control agent immediately warns to help fund managers adjust their positions in a timely manner;
• Private equity institutions: Use multi-agent game models to simulate market behavior and optimize quantitative trading strategies, such as adjusting the entry timing of algorithms based on the capital flow data of floating agents to increase trading returns.

(3) Quantitative trading: Provide accurate signals to capture short-term opportunities in A-shares

FinGenius’ real-time data processing and multi-agent analysis capabilities provide quantitative traders with “timely and accurate” trading signals. The system can output quantitative indicators such as “short-term fluctuation range of individual stocks, inflection points of capital flow, and public opinion heat thresholds”, and traders can automate the whole process of “signal triggering, automatic trading, and risk control” after connecting these indicators to the quantitative model. A quantitative team developed the “Dragon and Tiger List Strategy” based on FinGenius’ floating capital data signals, achieving an excess return of 15% in the A-share volatile market in 2025.

(4) Risk management: identify risks in advance to ensure asset security

In the A-share market, “black swan events” are frequent, and FinGenius’ risk control agents have become “asset security barriers”. For example, when a listed company suddenly announces the “resignation of executives”, the system triggers an early warning within 10 seconds, and the risk control agent quickly integrates “executive responsibilities, corporate governance structure, and the impact of historical similar cases”, assesses the risk of “short-term stock price correction of 5%-8%”, and pushes “position reduction suggestions”; For financial institutions, the system can monitor “concentration risk” and “industry policy risk” in real time, for example, when the policy of the consumer sector is tightened, it automatically prompts that “the proportion of consumer stocks in the position is too high, and it is recommended to diversify the allocation” to help institutions avoid systemic risks.

6. Summary: The “AI game expert” of A-share financial decision-making opens a new era of intelligent investment

The emergence of FinGenius is not only an innovation in financial tools, but also a reconstruction of the A-share analysis logic – it uses a multi-agent game architecture to simulate the real market ecology, uses real-time data processing to keep up with the market rhythm, and uses personalized algorithms to adapt to different user needs, so that financial analysis shifts from “relying on experience” to “data-driven + game simulation”, providing A-share investors and institutions with “understandable, well-used, and trustworthy” intelligent decision-making support.

In the future, FinGenius is expected to further deepen the multi-agent game model (such as adding “foreign agents” and “industrial chain agents”), expand more vertical scenarios (such as IPO pricing analysis, M&A and restructuring evaluation), and strengthen in-depth cooperation with financial institutions to create a full-process closed loop of “analysis-decision-trading-risk control”. For the A-share market, the popularity of FinGenius will promote the transformation of investment decision-making from “personal experience-driven” to “AI collaborative game”, allowing more participants to accurately grasp opportunities and effectively control risks in a complex market environment, injecting intelligent momentum into the mature development of the A-share market.