MarketPulse adopts a dual-engine-driven architecture design, perfectly integrating the powerful natural language processing capability of Google Gemini AI and the massive financial data source of Finnhub API. In terms of technical implementation, the system actively collects global financial information from the Finnhub platform every 30 minutes through a timed task module developed in Python, which is processed by Google Gemini AI for in-depth semantic analysis and structured processing, and ultimately generates professional reports that contain investment recommendations, confidence indices (quantified to a percentage), and credibility ratings of sources.
This integration innovation is reflected in three dimensions:
- In terms of real-time data, the system is set up with a minimum update interval of 5 minutes to capture sudden market changes
- In terms of depth of analysis, the Gemini model identifies market sentiment and potential impacts implicit in the news
- In terms of system extensibility, the architecture supports the flexibility to add new data sources and analytical models through the config.py file
In real-world deployments, the solution has proven to be effective in reducing information latency by 431 TP3T, resulting in a 601 TP3T reduction in average investor decision-making response time, which is of key value in rapidly volatile securities markets
This answer comes from the articleMarketPulse: a service that pushes AI analytics on financial news in real timeThe
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