Hybrid News Sentiment Engine: Real-Time Market Analysis via Adaptive Ensemble Learning on News–Price Pairs

Battle Count: 56

Highly relevant to quantitative trading as a real-time news sentiment signal generator. The system provides actionable sentiment scores calibrated against actual price moves, supports cross-asset analysis (equity indices, commodities, crypto), and detects market regimes. However, the accuracy (ρ≈0.30) is moderate compared to LLM-based approaches, the 3-hour polling cycle limits high-frequency applications, and the lack of precise forward-window calibration introduces noise. Best suited as one component in a broader trading signal stack rather than a standalone alpha generator. The zero-cost, CPU-only architecture makes it accessible for retail and small-fund quant traders.

Low implementation complexity. The system uses only Python standard library plus three packages (requests, numpy, mysql-connector-python). No ML frameworks, GPU libraries, or deep learning toolkits required. ~1,200 lines of Python across 9 source files. Runs as a simple cron pipeline (ingest → score → calibrate). MySQL database with 4 tables. The TF-IDF clustering, lexicon scoring, and ensemble weighting are all straightforward algorithms. Main complexity lies in the data pipeline orchestration and the calibration logic. No model training, no hyperparameter tuning beyond the empirically set threshold θ=0.35.

Full source code is available on GitHub under MIT license (~1,200 lines of Python). However, the system depends on the proprietary Tradeflags NewsFeed API for data ingestion, which limits full reproducibility. The algorithmic details (TF-IDF clustering, lexicon scoring, ensemble weighting, calibration) are well-documented with pseudocode and formulas. No external ML frameworks required (only requests, numpy, mysql-connector-python). The MySQL schema is described in detail. Reproducibility is limited by the proprietary data source and the specific server infrastructure (Intel Xeon E-2288G, local MySQL at 10.0.0.44).