AI Trading Advisor: Technical Architecture and Practice of an Open-Source Intelligent Trading Analysis Platform

An open-source AI trading analysis platform based on React and Node.js, integrating TradingView charts, OpenAI decision engine, backtesting simulator, and risk management tools to provide investors with data-driven trading insights.

In today's rapidly developing fintech landscape, individual investors face challenges of information overload and decision-making difficulties. Traditional trading analysis tools are often expensive or have closed features. As an open-source project, AI Trading Advisor is committed to providing investors with a free, transparent, and fully functional intelligent trading analysis platform.

Built using a modern web technology stack, the project's core positioning is an educational and analytical tool that emphasizes data-driven decision support rather than direct investment advice. This design philosophy not only ensures the learnability of the technology but also clarifies the boundaries of tool usage.

The platform deeply integrates TradingView's industry-standard chart components, providing users with professional-level technical analysis capabilities. This includes multi-timeframe switching, a rich library of technical indicators, drawing tools, and support for custom indicators. For users accustomed to TradingView, this integration greatly reduces the learning curve while ensuring consistency in the analysis experience.

The core highlight of the project is its AI Decision Center, which adopts a unique dual-engine architecture design:

• OpenAI Integration Engine: When users configure an OpenAI API key, the system calls the GPT-4.1-mini model for in-depth market analysis and trading recommendation generation. This cloud-based AI capability provides strong natural language understanding and reasoning abilities.

• Local Expert Engine: When no API key is available, the system automatically downgrades to the local expert engine. This design reflects the developers' thoughtful consideration of user experience—even in offline scenarios or with limited budgets, users can still access basic analysis functions.

This dual-engine architecture not only improves system availability but also provides users with flexible choices.

The core of investment is risk control. AI Trading Advisor has a built-in Risk Desk to help users quantify and manage the risk exposure of their investment portfolios. Combined with the Scenario Lab function, users can simulate portfolio performance under different market conditions, conduct stress tests and scenario analysis.

These tools are of great value for cultivating risk awareness and testing the robustness of investment strategies, especially suitable for educational purposes and strategy validation.

The platform provides a complete backtesting simulator that allows users to validate the effectiveness of trading strategies based on historical data. This is an indispensable link in quantitative investment—any strategy should undergo sufficient historical backtesting before being put into live trading.

Meanwhile, the system's built-in Trading Log function uses the browser's localStorage to store users' trading records and supports export functionality. This design not only protects user privacy (data stored locally) but also provides a basis for trade review and performance analysis.

Users can track their focused stocks or assets through the Watchlist function, and the system automatically aggregates relevant market intelligence, including price changes, volume changes, technical indicator signals, etc. This information aggregation capability helps investors efficiently monitor multiple targets and not miss important market dynamics.

The project is built based on React and adopts modern frontend engineering practices. In terms of deployment, it is a standard Node.js application, using npm for dependency management and script execution.