Real-time sentiment & volatility forecasting system for fintech

Overview

This project involved building a proprietary predictive intelligence platform for a Belgian fintech firm focused on cryptocurrency risk mitigation. The system was engineered as a loss-prevention agent designed to forecast negative market shocks by triangulating over 400 heterogeneous data streams. LLMs were integrated into the sentiment pipeline using the most advanced models available during the project timeline: BERT (2018) for initial embedding work, followed by GPT-2 (staged release February–November 2019) and SentenceBERT (August 2019) for semantic similarity and panic classification

Achievements

The system flagged 89% of monitored rug pull events with a 4–6 hour lead time. During the 2020 "Black Thursday" volatility spike, the sentiment velocity algorithm provided a 12-minute early warning, reducing client exposure by an estimated 22% relative to market benchmarks. The Sentence-BERT-enhanced sentiment layer improved F1 score on panic detection by 27% compared to prior lexicon-based models

Responsibilities

• Architected an asynchronous streaming framework to normalize real-time feeds from WebSocket APIs, blockchain nodes, and social platforms. Deployed Redis Streams (introduced with Redis 5.0 in November 2018) for highthroughput backpressure management.
• Deployed Sentence-BERT following its August 2019 release to analyze Telegram and Twitter corpora. The model was prompted via few-shot classification to distinguish between "coordinated FUD campaign" and "legitimate technical concern," calculating Jensen-Shannon Divergence between rolling language distributions to detect narrative hijacking events.
• Developed a multi-factor anomaly detection model using XGBoost (first public version 2014, Python package released January 2018) and Isolation Forests (incorporated into Scikit-learn circa 2016). The system monitored liquidity pool constant product changes and "dev wallet velocity."
• Built a dashboard displaying SHAP value contributions (first Python package released May 2017) for each alert and GPT-2-generated natural language summaries of alert rationales for portfolio manager audit, following the full 1.5B parameter release in November 2019
• Utilized TimescaleDB (first stable 1.0.0 release September 2018) as a PostgreSQL extension for efficient storage and querying of market data with subsecond latency requirements.

Technologies Used