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| title: U2INVEST | |
| emoji: 📈 | |
| colorFrom: blue | |
| colorTo: indigo | |
| sdk: docker | |
| app_port: 7860 | |
| pinned: false | |
| short_description: Full-stack Flask + React stock education and agent demo | |
| # U2INVEST | |
| **Your path, Your Choice, Your Future, You to Invest.** | |
| **Financial intelligence platform featuring a RAG-enabled AI Agent (DeepSeek-V3 + LangGraph), interactive Trading Lab, and Knowledge Academy. Orchestrated with Flask, LangChain 1.1, and AkShare.** | |
| [](./USER_GUIDE.md) | |
| <img src="static/images/LOGO_final.png" width="280" alt="U2INVEST Logo"> | |
| ## Key Features | |
| ### U2CHAT (AI Agent) | |
| * **Powered by DeepSeek-V3:** Utilizes state-of-the-art LLM reasoning for financial queries. | |
| * **LangGraph & RAG Architecture:** Orchestrates complex workflows and retrieves knowledge from local investment guides (PDFs). | |
| * **Real-time Data:** Integrated with **AkShare** to fetch live market data. | |
| * **Visual Analysis:** Generates interactive ECharts for price trends and K-line data. | |
| * **Session Management:** Supports multiple chat sessions with persistent history (SQLite). | |
| ### Trading Lab | |
| * **Real-time Simulation:** Trade popular stocks (Moutai, CATL, BYD) with virtual cash ($100k starting balance). | |
| * **Professional Dashboard:** Includes K-line charts (60/120/250 days), portfolio tracking, and trade history. | |
| * **Beginner Guide:** A step-by-step interactive tutorial on ownership and risk. | |
| ### Knowledge Academy | |
| * **50+ Modules:** Covers everything from "Time Value of Money" to "Options Trading". | |
| * **Interactive Learning:** Video lessons, key takeaways, and outcomes. | |
| * **Learning Roadmap:** Visual d3.js roadmap to track progress (Foundation → Advanced → Professional). | |
| ## Tech Stack | |
| * **Backend:** Python 3.13+, Flask | |
| * **AI & Logic:** LangChain 1.1, LangGraph, ChromaDB (Vector Store) | |
| * **Data:** AkShare (Financial Data), SQLite (Persistence) | |
| * **Frontend:** HTML5, TailwindCSS, ECharts, D3.js | |
| ## Architecture | |
| The system uses a **LangGraph** workflow to manage state and tool execution. | |
| * **State Management:** `AgentState` tracks conversation history and tool outputs. | |
| * **Persistence:** SQLite checkpoints ensure chat sessions persist across restarts. | |
| * **RAG Pipeline:** ChromaDB indexes financial PDFs for semantic retrieval. | |
|  | |
| ## Getting Started | |
| ### Prerequisites | |
| * Python 3.10+ | |
| * An API Key for DeepSeek (or compatible OpenAI-format provider). | |
| ### Installation | |
| 1. **Clone the repository** | |
| ```bash | |
| git clone https://github.com/yourusername/u2invest-portfolio.git | |
| cd u2invest-portfolio | |
| ``` | |
| 2. **Create and activate a virtual environment** | |
| ```bash | |
| python -m venv venv | |
| # Windows | |
| venv\Scripts\activate | |
| # Mac/Linux | |
| source venv/bin/activate | |
| ``` | |
| 3. **Install dependencies** | |
| ```bash | |
| pip install -r requirements.txt | |
| ``` | |
| 4. **Configure Environment** | |
| Copy the example environment file and add your API keys. | |
| ```bash | |
| cp .env.example .env | |
| ``` | |
| Open `.env` and set your `DEEPSEEK_API_KEY`. | |
| 5. **Initialize Knowledge Base (Optional)** | |
| Place your financial PDF documents in the `knowledge/` folder. The system will automatically vectorize them on the first run. | |
| ### Docker Deployment (Recommended) | |
| To run the application in a containerized environment: | |
| 1. **Build the Image** | |
| ```bash | |
| docker build -t u2invest . | |
| ``` | |
| 2. **Run the Container** | |
| ```bash | |
| docker run -p 5000:5000 --env-file .env u2invest | |
| ``` | |
| Access the app at `http://localhost:5000`. | |
| ### Running the Application | |
| Start the Flask server: | |
| ```bash | |
| python web_app.py | |
| ``` | |
| Visit `http://localhost:5000` in your browser. | |
| ## Project Structure | |
| * `web_app.py`: Main Flask application entry point & API routes. | |
| * `agent_graph.py`: LangGraph definition for the AI agent's logic. | |
| * `tools.py`: Custom tools for stock data (AkShare) and RAG. | |
| * `vector_store.py`: Logic for parsing PDFs and building the ChromaDB index. | |
| * `templates/`: HTML frontend files. | |
| * `static/`: CSS, Images, and JS assets. | |
| ## Introduction & Acknowledgements | |
| This platform was **independently developed over the course of one month** as a comprehensive full-stack engineering project. It represents a deep dive into modern AI agent architectures and financial data visualization. | |
| **Development Highlights:** | |
| * **Solo Full-Stack Engineering:** Handled the entire lifecycle from backend Flask logic and LangGraph orchestration to the frontend D3.js visualization and UI design. | |
| * **AI-Augmented Workflow:** Leveraged **Gemini CLI** (integrated directly into VSCode) and **Claude** to accelerate coding, debug complex logic, and refine architectural decisions. | |
| * **APIs & Data:** Integrated multiple financial data sources, including **AkShare** for real-time market data. | |
| **Future Outlook:** | |
| I am actively looking forward to further cooperation to refine this project, optimize the architecture, and evolve it into a robust, enterprise-ready solution suitable for production purposes. | |
| **Special Thanks:** | |
| To the open-source communities behind LangChain, DeepSeek, and AkShare for providing the robust tools that made this agentic workflow possible. | |
| ## Portfolio & License | |
| **Copyright © 2026 U2INVEST. All Rights Reserved.** | |
| This project is a **Portfolio Showcase** designed to demonstrate full-stack engineering, AI agent architecture, and financial data analysis capabilities. | |
| * **For Recruiters:** You are welcome to review the code structure, architecture patterns, and implementation details. | |
| * **For Others:** This code is proprietary. Copying, distributing, or using this codebase for commercial purposes is strictly prohibited without explicit permission. | |