Intraday Volatility Jump Mean-Reversion Strategy for BTC-USD in Python

Cryptocurrency markets exhibit frequent volatility spikes — sudden price movements that deviate significantly from normal trading behavior. These "jumps" often trigger algorithmic stop-losses and momentum trades, but empirical evidence suggests prices frequently revert after such extreme moves. This creates a statistical edge for mean-reversion strategies. This article implements a Jump Mean-Reversion (JMR) strategy for Bitcoin. We'll build a complete system that detects intraday volatility jumps using rolling standard deviation thresholds, generates contrarian trading signals, and evaluates performance with proper metrics. The approach is parameter-driven and adaptable to other assets. Most algo trading content gives you theory. This gives you the code. 3 Python strategies. Fully backtested. Colab notebook included. Plus a free ebook with 5 more strategies the moment you subscribe. 5,000 quant traders already run these: Subscribe | AlgoEdge Insights This article covers: Section 1: The