Architecting MarketPulse: A Deep Dive into a Enterprise-Grade Financial Sentiment Pipeline

 

Introduction: In modern finance, alpha isn't just in the numbers; it’s in the speed of interpreting text. While price data is structured, news is messy. MarketPulse is a Retrieval and Sentiment Analysis pipeline designed to automate the correlation between financial headlines and market volatility.

The Problem: The "Context Gap" in Market Data Standard stock trackers tell you what happened, but rarely why. To build a system that understands the "why," I needed to bridge two different worlds: the Python data-science ecosystem and the TypeScript full-stack environment.

Architectural Overview: The Ingestion-to-Insight Flow The system is built on a Producer-Consumer architecture, organized within a unified src/ directory to ensure maintainability:

  1. Automated Data Ingestion (The Producer): Using Python and GitHub Actions, I implemented hourly "Cron-Jobs" that fetch data from yfinance and various news feeds. This ensures the database stays fresh without manual intervention.

  2. Specialized Sentiment Analysis (The Brain): Decision: Why not use standard GPT? Because financial language is unique. "Stock plummeted" is bad; "Losses narrowed" is actually good. Implementation: I utilized FinBERT (a BERT model pre-trained on financial communication). This model classifies headlines into Bullish, Bearish, or Neutral with far higher accuracy than general-purpose LLMs.

  3. The Unified API Layer (The Bridge): Decision: Node.js with Drizzle ORM. Implementation: By using a serverless PostgreSQL provider (Neon), I achieved a "zero-cold-start" experience. Drizzle ORM provides a type-safe bridge, ensuring that the React frontend never requests a field that doesn't exist in the database.

  4. The Visualization Layer: Built with React 18 and ShadcnUI. I leveraged Plotly.js to handle the rendering of high-frequency data points, allowing users to see sentiment shifts overlaid directly on price candles.

Key Technical Challenges & Solutions:

  • Path Nesting & Deployment: Moving from a "flat" Replit structure to a professional src/api and src/client architecture required a total overhaul of the Vite and TypeScript configurations to handle absolute path aliases (@/).

  • Data Retention Logic: To keep the database under the 0.5GB free-tier limit, I engineered a "Retention Worker" inside the pipeline that automatically purges news and stock data older than 365 days.

Learnings & Future Enhancements:

  • Case-Sensitivity in Production: A major learning moment was the shift from Windows (case-insensitive) to Render’s Linux environment (case-sensitive). Correcting import paths from Toaster to toaster was a reminder that local success is only half the battle.

  • Scalability: The current architecture is ready for horizontal scaling; the ingestion logic can be moved to AWS Lambda or Google Cloud Functions without changing a single line of core logic.

Conclusion: MarketPulse isn't just a side project; it’s a blueprint for handling multi-language stacks (Python + TypeScript) in a production environment. It proves that with the right architecture, a single engineer can build a system that processes, analyzes, and visualizes complex market data in real-time.

Explore the code and contribute on GitHub: stock-sentiment-pipeline #2026 jan

Check it out: Explore the code and contribute on GitHub.

#FinTech #Python #TypeScript #NLP #DataEngineering #PostgreSQL #React #MarketPulse #SoftwareEngineering

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