hexliulab

hexliulab / fund-analysis-agents

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基于多智能体协作的中国基金市场智能分析系统与智能管家--A smart analysis system and smart manager for the Chinese fund market based on multi-agent collaboration.

25
0
69% credibility
Found Apr 05, 2026 at 17 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Java
AI Summary

A web dashboard that uses collaborative AI agents to analyze Chinese mutual funds, generate reports, and provide investment recommendations with portfolio tracking and notifications.

How It Works

1
📥 Get the fund helper

Download this free tool that analyzes Chinese funds using smart AI teams.

2
🔧 Connect your AI service

Pick a free AI like Tongyi Qianwen and add your access code so it can think and analyze.

3
📝 Add your favorite funds

Create a watchlist of funds you like and enter your current holdings to track performance.

4
🔍 Analyze any fund

Pick a fund code and click to start – watch the AI analysts debate pros and cons in real time.

5
📊 See the full report

Get a beautiful summary with ratings, risks, buy/sell advice, and traceable evidence.

6
Set up alerts
📧
Email reports

Send rich HTML emails with charts and recommendations.

📱
Phone notifications

Push alerts to your iPhone via Bark app.

🎉 Invest smarter

Track your portfolio, get personalized picks, and make confident decisions with AI help.

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Star Growth

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AI-Generated Review

What is fund-analysis-agents?

Fund-analysis-agents is a Java-based smart analysis system for the Chinese fund market, using multi-agent collaboration to simulate a professional investment team. Users input a fund code to trigger agents—like analysts, researchers, traders, and risk managers—that debate market data, generate traceable reports with buy/sell suggestions, and track portfolios via a Vue dashboard. It solves biased or opaque fund picks by delivering evidence-based insights adapted to Chinese trading calendars, A/C shares, and manager changes.

Why is it gaining traction?

Its multi-agent workflow stands out with structured bull/bear debates to reduce single-perspective errors, plus visualizations of agent flows and real-time market scans for temperature gauging. One-click Docker setup with Spring Boot backend handles LLM configs (Tongyi Qianwen, OpenAI) and notifications via email/Bark, making it dead simple to run locally without ops hassle. Developers dig the config-driven tasks and health checks for data sources like Tushare.

Who should use this?

Retail investors in Chinese funds needing automated, rational analysis beyond basic screeners. Personal finance tinkerers wanting a self-hosted manager for watchlists, portfolio P&L trends, and scheduled reports. Java devs prototyping AI finance agents without starting from scratch.

Verdict

Try it if you're in the Chinese fund space—solid for quick deploys and agent-driven analysis, despite 18 stars signaling early maturity. The 0.699999988079071% credibility score reflects sparse adoption, but strong README and one-script setup make it low-risk to spin up. (198 words)

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