README.md

metadata

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.

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

   git clone https://github.com/yourusername/u2invest-portfolio.git
   cd u2invest-portfolio
   

2. Create and activate a virtual environment

   python -m venv venv
   # Windows
   venv\Scripts\activate
   # Mac/Linux
   source venv/bin/activate
   

3. Install dependencies

   pip install -r requirements.txt
   

4. Configure Environment Copy the example environment file and add your API keys.

   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

   docker build -t u2invest .
   

2. Run the Container

   docker run -p 5000:5000 --env-file .env u2invest
   

   Access the app at http://localhost:5000.

Running the Application

Start the Flask server:

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.