Hurstexponent — Göstergeler ve Stratejiler

Hurst Exponent Adaptive Filter (HEAF) [PhenLabs]📊 PhenLabs - Hurst Exponent Adaptive Filter (HEAF) Version: PineScript™ v6 📌 Description The Hurst Exponent Adaptive Filter (HEAF) is an advanced Pine Script indicator designed to dynamically adjust moving average calculations based on real time market regimes detected through the Hurst Exponent. The intention behind the creation of this indicator was not a buy/sell indicator but rather a tool to help sharpen traders ability to distinguish regimes in the market mathematically rather than guessing. By analyzing price persistence, it identifies whether the market is trending, mean-reverting, or exhibiting random walk behavior, automatically adapting the MA length to provide more responsive alerts in volatile conditions and smoother outputs in stable ones. This helps traders avoid false signals in choppy markets and capitalize on strong trends, making it ideal for adaptive trading strategies across various timeframes and assets. Unlike traditional moving averages, HEAF incorporates fractal dimension analysis via the Hurst Exponent to create a self-tuning filter that evolves with market conditions. Traders benefit from visual cues like color coded regimes, adaptive bands for volatility channels, and an information panel that suggests appropriate strategies, enhancing decision making without constant manual adjustments by the user. 🚀 Points of Innovation Dynamic MA length adjustment using Hurst Exponent for regime-aware filtering, reducing lag in trends and noise in ranges. Integrated market regime classification (trending, mean-reverting, random) with visual and alert-based notifications. Customizable color themes and adaptive bands that incorporate ATR for volatility-adjusted channels. Built-in information panel providing real-time strategy recommendations based on detected regimes. Power sensitivity parameter to fine-tune adaptation aggressiveness, allowing personalization for different trading styles. Support for multiple MA types (EMA, SMA, WMA) within an adaptive framework. 🔧 Core Components Hurst Exponent Calculation: Computes the fractal dimension of price series over a user-defined lookback to detect market persistence or anti-persistence. Adaptive Length Mechanism: Maps Hurst values to MA lengths between minimum and maximum bounds, using a power function for sensitivity control. Moving Average Engine: Applies the chosen MA type (EMA, SMA, or WMA) to the adaptive length for the core filter line. Adaptive Bands: Creates upper and lower channels using ATR multiplied by a band factor, scaled to the current adaptive length. Regime Detection: Classifies market state with thresholds (e.g., >0.55 for trending) and triggers alerts on regime changes. Visualization System: Includes gradient fills, regime-colored MA lines, and an info panel for at-a-glance insights. 🔥 Key Features Regime-Adaptive Filtering: Automatically shortens MA in mean-reverting markets for quick responses and lengthens it in trends for smoother signals, helping traders stay aligned with market dynamics. Custom Alerts: Notifies on regime shifts and band breakouts, enabling timely strategy adjustments like switching to trend-following in bullish regimes. Visual Enhancements: Color-coded MA lines, gradient band fills, and an optional info panel that displays market state and trading tips, improving chart readability. Flexible Settings: Adjustable lookback, min/max lengths, sensitivity power, MA type, and themes to suit various assets and timeframes. Band Breakout Signals: Highlights potential overbought/oversold conditions via ATR-based channels, useful for entry/exit timing. 🎨 Visualization Main Adaptive MA Line: Plotted with regime-based colors (e.g., green for trending) to visually indicate market state and filter position relative to price. Adaptive Bands: Upper and lower lines with gradient fills between them, showing volatility channels that widen in random regimes and tighten in trends. Price vs. MA Fills: Color-coded areas between price and MA (e.g., bullish green above MA in trending modes) for quick trend strength assessment. Information Panel: Top-right table displaying current regime (e.g., "Trending Market") and strategy suggestions like "Follow trends" or "Trade ranges." 📖 Usage Guidelines Core Settings Hurst Lookback Period Default: 100 Range: 20-500 Description: Sets the period for Hurst Exponent calculation; longer values provide more stable regime detection but may lag, while shorter ones are more responsive to recent changes. Minimum MA Length Default: 10 Range: 5-50 Description: Defines the shortest possible adaptive MA length, ideal for fast responses in mean-reverting conditions. Maximum MA Length Default: 200 Range: 50-500 Description: Sets the longest adaptive MA length for smoothing in strong trends; adjust based on asset volatility. Sensitivity Power Default: 2.0 Range: 1.0-5.0 Description: Controls how aggressively the length adapts to Hurst changes; higher values make it more sensitive to regime shifts. MA Type Default: EMA Options: EMA, SMA, WMA Description: Chooses the moving average calculation method; EMA is more responsive, while SMA/WMA offer different weighting. 🖼️ Visual Settings Show Adaptive Bands Default: True Description: Toggles visibility of upper/lower bands for volatility channels. Band Multiplier Default: 1.5 Range: 0.5-3.0 Description: Scales band width using ATR; higher values create wider channels for conservative signals. Show Information Panel Default: True Description: Displays regime info and strategy tips in a top-right panel. MA Line Width Default: 2 Range: 1-5 Description: Adjusts thickness of the main MA line for better visibility. Color Theme Default: Blue Options: Blue, Classic, Dark Purple, Vibrant Description: Selects color scheme for MA, bands, and fills to match user preferences. 🚨 Alert Settings Enable Alerts Default: True Description: Activates notifications for regime changes and band breakouts. ✅ Best Use Cases Trend-Following Strategies: In detected trending regimes, use the adaptive MA as a trailing stop or entry filter for momentum trades. Range Trading: During mean-reverting periods, monitor band breakouts for buying dips or selling rallies within channels. Risk Management in Random Markets: Reduce exposure when random walk is detected, using tight stops suggested in the info panel. Multi-Timeframe Analysis: Apply on higher timeframes for regime confirmation, then drill down to lower ones for entries. Volatility-Based Entries: Use upper/lower band crossovers as signals in adaptive channels for overbought/oversold trades. ⚠️ Limitations Lagging in Transitions: Regime detection may delay during rapid market shifts, requiring confirmation from other tools. Not a Standalone System: Best used in conjunction with other indicators; random regimes can lead to whipsaws if traded aggressively. Parameter Sensitivity: Optimal settings vary by asset and timeframe, necessitating backtesting. 💡 What Makes This Unique Hurst-Driven Adaptation: Unlike static MAs, it uses fractal analysis to self-tune, providing regime-specific filtering that's rare in standard indicators. Integrated Strategy Guidance: The info panel offers actionable tips tied to regimes, bridging analysis and execution. Multi-Regime Visualization: Combines adaptive bands, colored fills, and alerts in one tool for comprehensive market state awareness. 🔬 How It Works Hurst Exponent Computation: Calculates log returns over the lookback period to derive the rescaled range (R/S) ratio. Normalizes to a 0-1 value, where >0.55 indicates trending, <0.45 mean-reverting, and in-between random. Length Adaptation: Maps normalized Hurst to an MA length via a power function, clamping between min and max. Applies the selected MA type to close prices using this dynamic length. Visualization and Signals: Plots the MA with regime colors, adds ATR-based bands, and fills areas for trend strength. Triggers alerts on regime changes or band crosses, with the info panel suggesting strategies like momentum riding in trends. 💡 Note: For optimal results, backtest settings on your preferred assets and combine with volume or momentum indicators. Remember, no indicator guarantees profits—use with proper risk management. 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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.

Advanced Fractal and Hurst Indicator Advanced Fractal and Hurst Indicator (AFHI) Description: The Advanced Fractal and Hurst Indicator (AFHI) is a custom technical analysis tool designed to identify market trends and potential reversals by leveraging the concepts of Fractal Dimension and the Hurst Exponent . These advanced mathematical concepts provide insights into the complexity and persistence of price movements, making this indicator a powerful addition to any trader's toolkit. How It Works: Fractal Dimension (FD) : The Fractal Dimension measures the complexity of price movements. A higher Fractal Dimension indicates a more complex, choppy market, while a lower value suggests smoother trends. The FD is calculated using the log difference of price movements over a specified length. Hurst Exponent (HE) : The Hurst Exponent indicates the tendency of a time series to either regress to the mean or cluster in a direction. Values below 0.5 indicate a tendency to revert to the mean (mean-reverting), while values above 0.5 suggest a trending market. The HE is calculated using the rescaled range method, comparing the range of price movements to the standard deviation. Composite Indicator : The Composite Indicator combines the smoothed Fractal Dimension and Hurst Exponent to provide a single value indicating market conditions. This is done by normalizing the FD and HE values and combining them into one metric. A positive Composite Indicator suggests an uptrend, while a negative value indicates a downtrend. Smoothing : Both FD and HE values are smoothed using a simple moving average to reduce noise and provide clearer signals. Trend Confirmation : A 50-period moving average (MA) is used to confirm the trend direction. The price being above the MA indicates an uptrend, while below the MA indicates a downtrend. Background Shading : The indicator pane is shaded green during uptrend conditions (positive Composite Indicator and price above MA) and red during downtrend conditions (negative Composite Indicator and price below MA). How Traders Can Use It: Identifying Trends : Traders can use the AFHI to identify current market trends. The background shading in the indicator pane provides a visual cue for trend direction, with green indicating an uptrend and red indicating a downtrend. Trend Confirmation : The Composite Indicator line, plotted in purple, helps confirm the trend. Positive values suggest a strong uptrend, while negative values indicate a strong downtrend. Entry and Exit Signals : Traders can use the transitions of the Composite Indicator and the background shading to time their entry and exit points. For instance, a shift from red to green shading suggests a potential buy opportunity, while a shift from green to red suggests a potential sell opportunity. Alerts : The script includes alert conditions that can notify traders when the Composite Indicator signals a new trend direction. Alerts can be set up for both uptrends and downtrends, helping traders stay informed of key market changes. Strategy Development : By integrating AFHI into their trading strategies, traders can develop more robust systems that account for market complexity and persistence. The indicator can be used alongside other technical tools to enhance decision-making and improve trade accuracy.

Hurst Exponent (Dubuc's variation method)Library "Hurst" hurst(length, samples, hi, lo) Estimate the Hurst Exponent using Dubuc's variation method Parameters: length : The length of the history window to use. Large values do not cause lag. samples : The number of scale samples to take within the window. These samples are then used for regression. The minimum value is 2 but 3+ is recommended. Large values give more accurate results but suffer from a performance penalty. hi : The high value of the series to analyze. lo : The low value of the series to analyze. The Hurst Exponent is a measure of fractal dimension, and in the context of time series it may be interpreted as indicating a mean-reverting market if the value is below 0.5 or a trending market if the value is above 0.5. A value of exactly 0.5 corresponds to a random walk. There are many definitions of fractal dimension and many methods for its estimation. Approaches relying on calculation of an area, such as the Box Counting Method, are inappropriate for time series data, because the units of the x-axis (time) do match the units of the y-axis (price). Other approaches such as Detrended Fluctuation Analysis are useful for nonstationary time series but are not exactly equivalent to the Hurst Exponent. This library implements Dubuc's variation method for estimating the Hurst Exponent. The technique is insensitive to x-axis units and is therefore useful for time series. It will give slightly different results to DFA, and the two methods should be compared to see which estimator fits your trading objectives best. Original Paper: Dubuc B, Quiniou JF, Roques-Carmes C, Tricot C. Evaluating the fractal dimension of profiles. Physical Review A. 1989;39(3):1500-1512. DOI: 10.1103/PhysRevA.39.1500 Review of various Hurst Exponent estimators for time-series data, including Dubuc's method: www.intechopen.com

HurstExponent Library "HurstExponent" Library to calculate Hurst Exponent refactored from Hurst Exponent - Detrended Fluctuation Analysis demean(src) Calculates a series subtracted from the series mean. Parameters: src : The series used to calculate the difference from the mean (e.g. log returns). Returns: The series subtracted from the series mean cumsum(src, length) Calculates a cumulated sum from the series. Parameters: src : The series used to calculate the cumulative sum (e.g. demeaned log returns). length : The length used to calculate the cumulative sum (e.g. 100). Returns: The cumulative sum of the series as an array aproximateLogScale(scale, length) Calculates an aproximated log scale. Used to save sample size Parameters: scale : The scale to aproximate. length : The length used to aproximate the expected scale. Returns: The aproximated log scale of the value rootMeanSum(cumulativeSum, barId, numberOfSegments) Calculates linear trend to determine error between linear trend and cumulative sum Parameters: cumulativeSum : The cumulative sum array to regress. barId : The barId for the slice numberOfSegments : The total number of segments used for the regression calculation Returns: The error between linear trend and cumulative sum averageRootMeanSum(cumulativeSum, barId, length) Calculates the Root Mean Sum Measured for each block (e.g the aproximated log scale) Parameters: cumulativeSum : The cumulative sum array to regress and determine the average of. barId : The barId for the slice length : The length used for finding the average Returns: The average root mean sum error of the cumulativeSum criticalValues(length) Calculates the critical values for a hurst exponent for a given length Parameters: length : The length used for finding the average Returns: The critical value, upper critical value and lower critical value for a hurst exponent slope(cumulativeSum, length) Calculates the hurst exponent slope measured from root mean sum, scaled to log log plot using linear regression Parameters: cumulativeSum : The cumulative sum array to regress and determine the average of. length : The length used for the hurst exponent sample size Returns: The slope of the hurst exponent smooth(src, length) Smooths input using advanced linear regression Parameters: src : The series to smooth (e.g. hurst exponent slope) length : The length used to smooth Returns: The src smoothed according to the given length exponent(src, hurstLength) Wrapper function to calculate the hurst exponent slope Parameters: src : The series used for returns calculation (e.g. close) hurstLength : The length used to calculate the hurst exponent (should be greater than 50) Returns: The src smoothed according to the given length

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.

[NLX-L2] Hurst Exponent Signal Filter - Hurst Exponent Signal Filter - The Hurst Exponent Signal Filter is meant to be used with an external signal source, this can be any indicator with a signal plot output (-1 Sell / 1 Buy) It filters out a lot of noisy signals and improves the performance of many indicators. - Example: How to Use - 1. Add a trend Indicator like Trend Index MTF to your chart 2. Add an indicator with a signal plot like Fishers Stochastic Center of Gravity to your Chart and select the Trend Index MTF with Type L1 in the Settings as Signal Source 3. Add this Hurst Signal Filter to your Chart and select the Fishers Stochastic Center of Gravity with Type L2 in the Settings as Signal Source 4. Add the Backtest Module to your Chart and select the Hurst Signal Filter with Type L2 as Source - Alerts for Automated Trading - See my signature below. Contact me for the Alert module.