Hurstoscillator — Indicators and Strategies

Hurst Exponent Oscillator [PhenLabs]📊 Hurst Exponent Oscillator - Version: PineScript™ v5 📌 Description The Hurst Exponent Oscillator (HEO) by PhenLabs is a powerful tool developed for traders who want to distinguish between trending, mean-reverting, and random market behaviors with clarity and precision. By estimating the Hurst Exponent—a statistical measure of long-term memory in financial time series—this indicator helps users make sense of underlying market dynamics that are often not visible through traditional moving averages or oscillators. Traders can quickly know if the market is likely to continue its current direction (trending), revert to the mean, or behave randomly, allowing for more strategic timing of entries and exits. With customizable smoothing and clear visual cues, the HEO enhances decision-making in a wide range of trading environments. 🚀 Points of Innovation Integrates advanced Hurst Exponent calculation via Rescaled Range (R/S) analysis, providing unique market character insights. Offers real-time visual cues for trending, mean-reverting, or random price action zones. User-controllable EMA smoothing reduces noise for clearer interpretation. Dynamic coloring and fill for immediate visual categorization of market regime. Configurable visual thresholds for critical Hurst levels (e.g., 0.4, 0.5, 0.6). Fully customizable appearance settings to fit different charting preferences. 🔧 Core Components Log Returns Calculation: Computes log returns of the selected price source to feed into the Hurst calculation, ensuring robust and scale-independent analysis. Rescaled Range (R/S) Analysis: Assesses the dispersion and cumulative deviation over a rolling window, forming the core statistical basis for the Hurst exponent estimate. Smoothing Engine: Applies Exponential Moving Average (EMA) smoothing to the raw Hurst value for enhanced clarity. Dynamic Rolling Windows: Utilizes arrays to maintain efficient, real-time calculations over user-defined lengths. Adaptive Color Logic: Assigns different highlight and fill colors based on the current Hurst value zone. 🔥 Key Features Visually differentiates between trending, mean-reverting, and random market modes. User-adjustable lookback and smoothing periods for tailored sensitivity. Distinct fill and line styles for each regime to avoid ambiguity. On-chart reference lines for strong trending and mean-reverting thresholds. Works with any price series (close, open, HL2, etc.) for versatile application. 🎨 Visualization Hurst Exponent Curve: Primary plotted line (smoothed if EMA is used) reflects the ongoing estimate of the Hurst exponent. Colored Zone Filling: The area between the Hurst line and the 0.5 reference line is filled, with color and opacity dynamically indicating the current market regime. Reference Lines: Dash/dot lines mark standard Hurst thresholds (0.4, 0.5, 0.6) to contextualize the current regime. All visual elements can be customized for thickness, color intensity, and opacity for user preference. 📖 Usage Guidelines Data Settings Hurst Calculation Length Default: 100 Range: 10-300 Description: Number of bars used in Hurst calculation; higher values mean longer-term analysis, lower values for quicker reaction. Data Source Default: close Description: Select which data series to analyze (e.g., Close, Open, HL2). Smoothing Length (EMA) Default: 5 Range: 1-50 Description: Length for smoothing the Hurst value; higher settings yield smoother but less responsive results. Style Settings Trending Color (Hurst > 0.5) Default: Blue tone Description: Color used when trending regime is detected. Mean-Reverting Color (Hurst < 0.5) Default: Orange tone Description: Color used when mean-reverting regime is detected. Neutral/Random Color Default: Soft blue Description: Color when market behavior is indeterminate or shifting. Fill Opacity Default: 70-80 Range: 0-100 Description: Transparency of area fills—higher opacity for stronger visual effect. Line Width Default: 2 Range: 1-5 Description: Thickness of the main indicator curve. ✅ Best Use Cases Identifying if a market is regime-shifting from trending to mean-reverting (or vice versa). Filtering signals in automated or systematic trading strategies. Spotting periods of randomness where trading signals should be deprioritized. Enhancing mean-reversion or trend-following models with regime-awareness. ⚠️ Limitations Not predictive: Reflects current and recent market state, not future direction. Sensitive to input parameters—overfitting may occur if settings are changed too frequently. Smoothing can introduce lag in regime recognition. May not work optimally in markets with structural breaks or extreme volatility. 💡 What Makes This Unique Employs advanced statistical market analysis (Hurst exponent) rarely found in standard toolkits. Offers immediate regime visualization through smart dynamic coloring and zone fills. 🔬 How It Works Rolling Log Return Calculation: Each new price creates a log return, forming the basis for robust, non-linear analysis. This ensures all price differences are treated proportionally. Rescaled Range Analysis: A rolling window maintains cumulative deviations and computes the statistical “range” (max-min of deviations). This is compared against the standard deviation to estimate “memory”. Exponent Calculation & Smoothing: The raw Hurst value is translated from the log of the rescaled range ratio, and then optionally smoothed via EMA to dampen noise and false signals. Regime Detection Logic: The smoothed value is checked against 0.5. Values above = trending; below = mean-reverting; near 0.5 = random. These control plot/fill color and zone display. 💡 Note: Use longer calculation lengths for major market character study, and shorter ones for tactical, short-term adaptation. Smoothing balances noise vs. lag—find a best fit for your trading style. Always combine regime awareness with broader technical/fundamental context for best results.

Hurst Exponent My first try to implement Full Hurst Exponent. The Hurst exponent is used as a measure of long-term memory of time series. It relates to the autocorrelations of the time series and the rate at which these decrease as the lag between pairs of values increases The Hurst exponent is referred to as the "index of dependence" or "index of long-range dependence". It quantifies the relative tendency of a time series either to regress strongly to the mean or to cluster in a direction. In short, depending on the value you can spot the trending / reversing market. Values 0.5 to 1 - market trending Values 0 to 0.5 - market tend to mean revert Hurst Exponent is computed using Rescaled range (R/S) analysis. I split the lookback period (N) in the number of shorter samples (for ex. N/2, N/4, N/8, etc.). Then I calculate rescaled range for each sample size. The Hurst exponent is estimated by fitting the power law. Basically finding the slope of log(samples_size) to log(RS). You can choose lookback and sample sizes yourself. Max 8 possible at the moment, if you want to use less use 0 in inputs. It's pretty computational intensive, so I added an input so you can limit from what date you want it to be calculated. If you hit the time limit in PineScript - limit the history you're using for calculations. #################### Disclaimer Please remember that past performance may not be indicative of future results. Due to various factors, including changing market conditions, the strategy may no longer perform as good as in historical backtesting. This post and the script don’t provide any financial advice.

Simple Hurst Exponent [QuantNomad]This is a simplified version of the Hurst Exponent indicator. In the meantime, I'm working on the full version. It's computationally intensive, so it's a challenge to squeeze it to PineScript limits. It will require some time to optimize it, so I decided to publish a simplified version for now. The Hurst exponent is used as a measure of long-term memory of time series. It relates to the autocorrelations of the time series, and the rate at which these decrease as the lag between pairs of values increases The Hurst exponent is referred to as the "index of dependence" or "index of long-range dependence". It quantifies the relative tendency of a time series either to regress strongly to the mean or to cluster in a direction. In short depend on value you can spot trending / reversing market. Values 0.5 to 1 - market trending Values 0 to 0.5 - market tend to mean revert #################### Disclaimer Please remember that past performance may not be indicative of future results. Due to various factors, including changing market conditions, the strategy may no longer perform as good as in historical backtesting. This post and the script don’t provide any financial advice.