TradeWise-AI：结合机器学习与大语言模型的智能纸面交易平台

TradeWise-AI is an AI-driven paper trading platform that combines machine learning (ML) and large language models (LLM) to help users learn investment skills in a zero-risk environment. It addresses the gap between theoretical knowledge and practical experience for new investors by not only generating trading signals but also providing natural language explanations for decision logic.

Traditional paper trading platforms only offer basic price data and execution functions, lacking in-depth analysis of decision logic. TradeWise-AI breaks this limitation by integrating ML's technical analysis capabilities with LLM's natural language understanding. Its core concept is "know not just what to do, but why"—each trading signal comes with detailed, easy-to-understand explanations of market trends, technical indicators, and potential risks.

The platform uses a modern front-end and back-end separation architecture:

• Frontend: Built with Next.js (React-based full-stack framework) for user authentication (Firebase Auth), dashboard display (real-time market data, positions, trade history), and proxy routing to backend ML services.
• Backend: Powered by Python FastAPI (high-performance async framework) with dependencies like numpy, pandas, matplotlib, and QuantLib (for option pricing and risk analysis). FastAPI supports concurrent requests and auto-generates OpenAPI docs for easy integration.

TradeWise-AI's core value lies in its dual-engine decision system:

1. Intelligent Signal Generation: ML models analyze massive market data using classic (moving averages, RSI) and modern strategies to identify trading opportunities.
2. Natural Language Explanation: LLMs convert technical analysis results into human-readable text. For example: "Apple stock's RSI indicates an oversold state, and the 50-day moving average just crossed the 200-day (golden cross), suggesting rising momentum. Recent earnings exceeded expectations, boosting market sentiment." This helps users understand decisions and build analytical skills.

The project prioritizes developer experience:

• A single npm run dev command starts both frontend (localhost:3000) and backend (127.0.0.1:8000) services, lowering entry barriers for rapid prototyping.
• Environment configurations are layered: frontend Firebase settings in frontend/.env.example, backend settings in backend/.env.example, and root .env for local development—ensuring flexibility and security.

TradeWise-AI caters to multiple user groups:

• Investment Novices: Learn market mechanics via simulated trading without financial risk; AI explanations act as a tutor.
• Quant Strategy Developers: Validate new algorithms with an intuitive interface and interactive experience.
• Financial Educators: Use the platform as a teaching tool—AI-generated explanations serve as case studies for abstract technical concepts.

Key technical highlights:

1. Multi-modal AI Fusion: Combines ML (numerical prediction) and LLM (text generation) to deliver composite value beyond single models.
2. EdTech + FinTech Cross: Blends financial tools with educational elements, reducing the cognitive barrier to quantitative investing.
3. Modern Web Stack: Next.js and FastAPI demonstrate synergy between JavaScript/TypeScript and Python data science ecosystems, setting a template for similar projects.

TradeWise-AI represents a new AI application paradigm—focusing on explainable decisions rather than just answers. In FinTech, "explainable AI" is increasingly critical amid stricter regulations and demand for transparency. For learners, strategy developers, and system builders, it offers an excellent open-source reference with clear architecture, modern tech choices, and innovative features, pointing to future potential in the field.