Introduction

Financial news, tweets, earnings transcripts arrive constantly. Waiting for batch processing (accumulate 1000 items, run inference) introduces latency. Streaming NLP processes items one-by-one with minimal latency, enabling real-time trading signals from text.

Streaming Sentiment Analysis

Deploy lightweight sentiment model (DistilBERT, mobile BERT) for low latency. Process each news item as it arrives. Latency: 50-100ms per item (versus 500-1000ms for large models).

Trade accuracy for speed: DistilBERT achieves 92% accuracy vs 96% for BERT, but 4ms latency vs 15ms. For trading, 92% accuracy with 4ms latency beats 96% with 15ms.

Model Optimization Techniques

1. Quantization: convert FP32 to INT8, reduce model size 4x, speed 4x. 2. Pruning: remove unimportant weights, speed 2-3x. 3. Knowledge distillation: train small model to mimic large model, speed 10x at minor accuracy cost.

Combined: quantized + pruned DistilBERT achieves 2ms latency, 20x faster than original BERT.

Batch Processing with Windowing

Instead of processing one-at-a-time (overhead), process in small batches (16-32 items). Process new batch every 10-20 seconds. Maintains low latency (batch processing adds minimal overhead) while improving throughput.

Real-Time Aggregation

Aggregate sentiment over moving windows: rolling 5-minute sentiment, 30-minute sentiment, daily sentiment. Each window updated continuously. When sentiment crosses threshold, trigger trading signal immediately.

Infrastructure Considerations

Deploy models on low-latency infrastructure: GPU or TPU for acceleration, edge computing (on-premises or nearby data centers) to minimize network latency. Cloud inference (AWS SageMaker, Google Cloud AI) adds 50-200ms network round-trip; edge computing keeps it local.

Latency Breakdown on Real News Feed

Typical latency for processing financial news at publication:

  • Network delay (news → system): 5-20ms
  • Preprocessing (tokenization, normalization): 2-5ms
  • Model inference: 5-20ms (depending on model)
  • Post-processing and storage: 2-5ms
  • Total: 14-50ms, acceptable for most trading strategies

Handling Variable Input Length

Headlines are short (5-15 words); articles are long (500+ words). Models have fixed max length. For streaming, process headlines at full length, articles at truncated length (first 256 tokens). This maintains latency.

Streaming Updates for Contextual Models

Transformer models need context: surrounding words to understand sentiment. For streaming, maintain context window: current item + previous 3 items. This provides sufficient context without large latency.

Deployment and Monitoring

Deploy model in production with continuous monitoring: track inference latency, accuracy (compare to human labels on sample), throughput. Alert if latency degrades or accuracy drops. Retrain periodically to maintain performance.