A Data-Driven Model for Early Detection of Market Volatility in Cryptocurrency Trading Using Cloud Computing Infrastructure

Abstract

This research develops a comprehensive early warning system for cryptocurrency volatility detection using Google Cloud Platform's managed services architecture. The system integrates three primary data sources: real-time price data from exchange APIs, large on-chain transaction alerts from Whale Alert API, and social media sentiment analysis from Twitter and Reddit. Implementation utilizes Cloud Pub/Sub for data ingestion, Dataflow for real-time processing, BigQuery for storage, and Vertex AI for machine learning. Key technical indicators include Average True Range, Bollinger Bands, whale volume metrics, and sentiment scores derived from AutoML text classification. The serverless streaming architecture continuously processes multi-source data streams and dispatches notifications via email, and SMS when volatility thresholds are breached. The evaluation demonstrates superior performance with precision of 0.80, recall of 0.70, and F1 score of 0.75, representing a 23 percent and 21 percent improvement in precision and recall over price-only baseline models. Findings validate that integrating diverse data sources significantly enhances cryptocurrency volatility prediction accuracy.