Real-time Financial Sentiment Engine
Developed a low-latency sentiment analysis pipeline that ingests real-time data from financial news APIs and Twitter firehose. Fine-tuned a RoBERTa model specifically on financial texts to accurately classify sentiment (bullish, bearish, neutral). The system processes thousands of messages per second and exposes a WebSocket API for downstream algorithmic trading systems to consume the aggregated sentiment scores.
01 / The Challenge
Generic sentiment models (like VADER or standard BERT) struggled with financial jargon (e.g., 'short' or 'bear' have specific negative contexts in finance).
- 01Minimizing inference latency to ensure trading signals remained actionable.
- 02Handling bursty traffic during major market events or earnings calls.
- 03Curating a high-quality, balanced dataset for fine-tuning.
02 / System Architecture
Fine-tuned a domain-specific transformer model (FinBERT/RoBERTa) and deployed it via a horizontally scalable inference architecture.
Python, PyTorch, HuggingFace, Kafka, FastAPI, Kubernetes, WebSockets.
TECHNOLOGY STACK
03 / Measurable Outcomes
Achieved 92% F1-score on financial text classification and processed 5000+ messages/sec with sub-50ms inference latency.