Quantifying Stock Trends: The Rolling Hurst Exponent in Python

Ever tried catching the rhythm of the stock market? If you have, you’d know trends aren’t just about rising and falling arrows on a graph. They’re the pulse of market sentiment, the stories of gains and losses. In such environment, navigating the stock market can be likened to sailing turbulent waters, where understanding currents — or in this case, trends — can be a game changer. But how can we transition from a qualitative understanding to a quantitative measure of these trends? Here’s where the Hurst exponent enters the spotlight. A concept that’s often reserved for scholarly articles and niche financial discussions, the Hurst exponent offers a robust measure of a stock’s propensity to trend or mean-revert. While typically estimated as a point-in-time metric, what if we could gauge the Hurst exponent on a rolling basis?Using Python, we can implement the Rolling Hurst Exponent to analyze, visualize, and eventually leverage market trends in our decision-making process. <a href="https://medium.com/@crisvelasquez/quantifying-stock-trends-the-rolling-hurst-exponent-in-python-d7108fc0e837">Website</a>