Quantifying Stock Trends: The Rolling Hurst Exponent in Python
<h1>1. Introduction</h1>
<p>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.</p>
<p>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.</p>
<h1>2. Basic of the Hurst Exponent</h1>
<p>The Hurst Exponent, denoted as <strong><em>H</em></strong>, is a statistical measure that gives us a window into the behavior of time series data. Whether it’s the price of a stock, the flow rate of a river, or even internet traffic, <strong><em>H</em></strong> seeks to determine the nature of its behavior. Put simply, the interpretation of the Hurst Exponent is as follows</p>
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