Volatilityoscillator — 指標和策略

Dual-Phase Trend Regime Oscillator (Zeiierman)█ Overview Trend Regime: Dual-Phase Oscillator (Zeiierman) is a volatility-sensitive trend classification tool that dynamically switches between two oscillators, one optimized for low volatility, the other for high volatility. By analyzing standard deviation-based volatility states and applying correlation-derived oscillators, this indicator reveals not only whether the market is trending but also what kind of trend regime it is in —Bullish or Bearish —and how that regime reacts to market volatility. █ Its Uniqueness Most trend indicators assume a static market environment; they don't adjust their logic when the underlying volatility shifts. That often leads to false signals in choppy conditions or late entries in trending phases. Trend Regime: Dual-Phase Oscillator solves this by introducing volatility-aware adaptability. It switches between a slow, stable oscillator in calm markets and a fast, reactive oscillator in volatile ones, ensuring the right sensitivity at the right time. █ How It Works ⚪ Volatility State Engine Calculates returns-based volatility using standard deviation of price change Smooths the current volatility with a moving average Builds a volatility history window and performs median clustering to determine typical "Low" and "High" volatility zones Dynamically assigns the chart to one of two internal volatility regimes: Low or High ⚪ Dual Oscillators In Low Volatility, it uses a Slow Trend Oscillator (longer lookback, smoother) In High Volatility, it switches to a Fast Trend Oscillator (shorter lookback, responsive) Both oscillators use price-time correlation as a measure of directional strength The output is normalized between 0 and 1, allowing for consistent interpretation ⚪ Trend Regime Classification The active oscillator is compared to a neutral threshold (0.5) If above: Bullish Regime, if below: Bearish Regime, else: Neutral The background and markers update to reflect regime changes visually Triangle markers highlight bullish/bearish regime shifts █ How to Use ⚪ Identify Current Trend Regime Use the background color and chart table to immediately recognize whether the market is trending up or down. ⚪ Trade Regime Shifts Use triangle markers (▲ / ▼) to spot fresh regime entries, which are ideal for confirming breakouts within trends. ⚪ Pullback Trading Look for pullbacks when the trend is in a stable condition and the slow oscillator remains consistently near the upper or lower threshold. Watch for moments when the fast oscillator retraces back toward the midline, or slightly above/below it — this often signals a potential pullback entry in the direction of the prevailing trend. █ Settings Explained Length (Slow Trend Oscillator) – Used in calm conditions. Longer = smoother signals Length (Fast Trend Oscillator) – Used in volatile conditions. Shorter = more responsive Volatility Refit Interval – Controls how often the system recalculates Low/High volatility levels Current Volatility Period – Lookback used for immediate volatility measurement Volatility Smoothing Length – Applies an SMA to the raw volatility to reduce noise ----------------- Disclaimer The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information. All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.

Market Flow Volatility Oscillator (AiBitcoinTrend)The Market Flow Volatility Oscillator (AiBitcoinTrend) is a cutting-edge technical analysis tool designed to evaluate and classify market volatility regimes. By leveraging Gaussian filtering and clustering techniques, this indicator provides traders with clear insights into periods of high and low volatility, helping them adapt their strategies to evolving market conditions. Built for precision and clarity, it combines advanced mathematical models with intuitive visual feedback to identify trends and volatility shifts effectively. 👽 How the Indicator Works 👾 Volatility Classification with Gaussian Filtering The indicator detects volatility levels by applying Gaussian filters to the price series. Gaussian filters smooth out noise while preserving significant price movements. Traders can adjust the smoothing levels using sigma parameters, enabling greater flexibility: Low Sigma: Emphasizes short-term volatility. High Sigma: Captures broader trends with reduced sensitivity to small fluctuations. 👾 Clustering Algorithm for Regime Detection The core of this indicator is its clustering model, which classifies market conditions into two distinct regimes: Low Volatility Regime: Calm periods with reduced market activity. High Volatility Regime: Intense periods with heightened price movements. The clustering process works as follows: A rolling window of data is analyzed to calculate the standard deviation of price returns. Two cluster centers are initialized using the 25th and 75th percentiles of the data distribution. Each price volatility value is assigned to the nearest cluster based on its distance to the centers. The cluster centers are refined iteratively, providing an accurate and adaptive classification. 👾 Oscillator Generation with Slope R-Values The indicator computes Gaussian filter slopes to generate oscillators that visualize trends: Oscillator Low: Captures low-frequency market behavior. Oscillator High: Tracks high-frequency, faster-changing trends. The slope is measured using the R-value of the linear regression fit, scaled and adjusted for easier interpretation. 👽 Applications 👾 Trend Trading When the oscillator rises above 0.5, it signals potential bullish momentum, while dips below 0.5 suggest bearish sentiment. 👾 Pullback Detection When the oscillator peaks, especially in overbought or oversold zones, provide early warnings of potential reversals. 👽 Indicator Settings 👾 Oscillator Settings Sigma Low/High: Controls the smoothness of the oscillators. Smaller Values: React faster to price changes but introduce more noise. Larger Values: Provide smoother signals with longer-term insights. 👾 Window Size and Refit Interval Window Size: Defines the rolling period for cluster and volatility calculations. Shorter windows: adapt faster to market changes. Longer windows: produce stable, reliable classifications. Disclaimer: This information is for entertainment purposes only and does not constitute financial advice. Please consult with a qualified financial advisor before making any investment decisions.

Adaptive Volatility-Scaled Oscillator [AVSO] (Zeiierman)█ Overview The Adaptive Volatility-Scaled Oscillator (AVSO) is a dynamic trading indicator that measures and visualizes volatility-adjusted market behavior. By scaling various metrics (such as volume, price changes, standard deviation, ATR, and Yang-Zhang volatility) and applying adaptive smoothing, AVSO helps traders identify market conditions where volatility deviates significantly from the norm. This indicator uses standardized scaling (Z-Score logic) to highlight periods of abnormally high or low volatility relative to recent history. With gradient coloring and clear volatility zones, AVSO provides a visually intuitive way to analyze market volatility and adapt trading strategies accordingly. █ How It Works ⚪ Scaling Metrics: The indicator scales user-selected metrics (e.g., volume, ATR, standard deviation) relative to the market and price, providing a standardized volatility measure. ⚪ Z-Score Standardization: The scaled metric is normalized using a Z-Score to measure how far current volatility deviates from its recent mean. Positive Z-Score: Above-average volatility. Negative Z-Score: Below-average volatility. ⚪ Adaptive Smoothing: An Adaptive EMA smooths the Z-Score, dynamically adjusting its length based on the strength of the volatility. Stronger deviations result in shorter smoothing, increasing responsiveness. █ Unique Feature: Yang-Zhang Volatility The Yang-Zhang volatility estimator sets this indicator apart by providing a more robust and accurate measure of volatility compared to traditional methods like ATR or standard deviation. ⚪ What Makes Yang-Zhang Volatility Unique? Comprehensive Calculation: It combines overnight price gaps (log returns from the previous close to the current open) and intraday price movements (high, low, and close). Accurate for Gapped Markets: Traditional volatility measures can misrepresent price movement when significant gaps occur between sessions. Yang-Zhang accounts for these gaps, making it highly reliable for assets prone to overnight price jumps, such as stocks, cryptocurrencies, and futures. Adaptable to Real Market Conditions : By including both close-to-open returns and intraday volatility, it provides a balanced and adaptive measure that captures the full volatility picture. ⚪ Why This Matters to Traders Better Volatility Insights: Yang-Zhang offers a clearer view of true market volatility, especially in markets with price gaps or uneven trading sessions. Improved Trade Timing: By identifying volatility spikes and calm periods more effectively, traders can time their entries and exits with greater confidence. █ How to Use Identify High and Low Volatility A high Z-Score (>2) indicates significant market volatility. This can signal momentum-driven moves, breakouts, or areas of increased risk. A low Z-Score (<-2) suggests low volatility or a calm market environment. This often occurs before a potential breakout or reversal. Trade Signals High Volatility Zones (background highlight): Monitor for potential breakouts, trend continuations, or reversals. Low Volatility Zones: Anticipate range-bound conditions or upcoming volatility spikes. █ Settings Source: Select the price source for scaling calculations (close, high, low, open). Metric Measure: Choose the volatility measure: Volume: Scales raw volume. Close: Uses closing price changes. Standard Deviation: Price dispersion. ATR: Average True Range. Yang: Yang-Zhang volatility estimate. Bars to Analyze: Number of historical bars used to calculate the mean and standard deviation of the scaled metric. ATR / Standard Deviation Period: Lookback period for ATR or Standard Deviation calculation. Yang Volatility Period: Period for the Yang-Zhang volatility estimator. Smoothing Period: Base smoothing length for the adaptive smoothing line. ----------------- Disclaimer The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information. All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs. My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!

Dynamic Price Oscillator (Zeiierman)█ Overview The Dynamic Price Oscillator (DPO) by Zeiierman is designed to gauge the momentum and volatility of asset prices in trading markets. By integrating elements of traditional oscillators with volatility adjustments and Bollinger Bands, the DPO offers a unique approach to understanding market dynamics. This indicator is particularly useful for identifying overbought and oversold conditions, capturing price trends, and detecting potential reversal points. █ How It Works The DPO operates by calculating the difference between the current closing price and a moving average of the closing price, adjusted for volatility using the True Range method. This difference is then smoothed over a user-defined period to create the oscillator. Additionally, Bollinger Bands are applied to the oscillator itself, providing visual cues for volatility and potential breakout signals. █ How to Use ⚪ Trend Confirmation The DPO can serve as a confirmation tool for existing trends. Traders might look for the oscillator to maintain above or below its mean line to confirm bullish or bearish trends, respectively. A consistent direction in the oscillator's movement alongside price trend can provide additional confidence in the strength and sustainability of the trend. ⚪ Overbought/Oversold Conditions With the application of Bollinger Bands directly on the oscillator, the DPO can highlight overbought or oversold conditions in a unique manner. When the oscillator moves outside the Bollinger Bands, it signifies an extreme condition. ⚪ Volatility Breakouts The width of the Bollinger Bands on the oscillator reflects market volatility. Sudden expansions in the bands can indicate a breakout from a consolidation phase, which traders can use to enter trades in the direction of the breakout. Conversely, a contraction suggests a quieter market, which might be a signal for traders to wait or to look for range-bound strategies. ⚪ Momentum Trading Momentum traders can use the DPO to spot moments when the market momentum is picking up. A sharp move of the oscillator towards either direction, especially when crossing the Bollinger Bands, can indicate the start of a strong price movement. ⚪ Mean Reversion The DPO is also useful for mean reversion strategies, especially considering its volatility adjustment feature. When the oscillator touches or breaches the Bollinger Bands, it indicates a deviation from the normal price range. Traders might look for opportunities to enter trades anticipating a reversion to the mean. ⚪ Divergence Trading Divergences between the oscillator and price action can be a powerful signal for reversals. For instance, if the price makes a new high but the oscillator fails to make a corresponding high, it may indicate weakening momentum and a potential reversal. Traders can use these divergence signals to initiate counter-trend moves. █ Settings Length: Determines the lookback period for the oscillator and Bollinger Bands calculation. Increasing this value smooths the oscillator and widens the Bollinger Bands, leading to fewer, more significant signals. Decreasing this value makes the oscillator more sensitive to recent price changes, offering more frequent signals but with increased noise. Smoothing Factor: Adjusts the degree of smoothing applied to the oscillator's calculation. A higher smoothing factor reduces noise, offering clearer trend identification at the cost of signal timeliness. Conversely, a lower smoothing factor increases the oscillator's responsiveness to price movements, which may be useful for short-term trading but at the risk of false signals. ----------------- Disclaimer The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information. All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs. My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!

Williams Vix Fix ultra complete indicator (Tartigradia)Williams VixFix is a realized volatility indicator developed by Larry Williams, and can help in finding market bottoms. Indeed, as Williams describe in his paper, markets tend to find the lowest prices during times of highest volatility, which usually accompany times of highest fear. The VixFix is calculated as how much the current low price statistically deviates from the maximum within a given look-back period. Although the VixFix originally only indicates market bottoms, its inverse may indicate market tops. As masa_crypto writes : "The inverse can be formulated by considering "how much the current high value statistically deviates from the minimum within a given look-back period." This transformation equates Vix_Fix_inverse. This indicator can be used for finding market tops, and therefore, is a good signal for a timing for taking a short position." However, in practice, the Inverse VixFix is much less reliable than the classical VixFix, but is nevertheless a good addition to get some additional context. For more information on the Vix Fix, which is a strategy published under public domain: * The VIX Fix, Larry Williams, Active Trader magazine, December 2007, web.archive.org * Fixing the VIX: An Indicator to Beat Fear, Amber Hestla-Barnhart, Journal of Technical Analysis, March 13, 2015, ssrn.com * Replicating the CBOE VIX using a synthetic volatility index trading algorithm, Dayne Cary and Gary van Vuuren, Cogent Economics & Finance, Volume 7, 2019, Issue 1, doi.org Created By ChrisMoody on 12-26-2014... V3 MAJOR Update on 1-05-2014 tista merged LazyBear's Black Dots filter in 2020: Extended by Tartigradia in 10-2022: * Can select a symbol different from current to calculate vixfix, allows to select SP:SPX to mimic the original VIX index. * Inverse VixFix (from masa_crypto and web.archive.org) * VixFix OHLC Bars plot * Price / VixFix Candles plot (Pro Tip: draw trend lines to find good entry/exit points) * Add ADX filtering, Minimaxis signals, Minimaxis filtering (from samgozman ) * Convert to pinescript v5 * Allow timeframe selection (MTF) * Skip off days (more accurate reproduction of original VIX) * Reorganized, cleaned up code, commented out parts, commented out or removed unused code (eg, some of the KC calculations) * Changed default Bollinger Band settings to reduce false positives in crypto markets. Set Index symbol to SPX, and index_current = false, and timeframe Weekly, to reproduce the original VIX as close as possible by the VIXFIX (use the Add Symbol option, because you want to plot CBOE:VIX on the same timeframe as the current chart, which may include extended session / weekends). With the Weekly timeframe, off days / extended session days should not change much, but with lower timeframes this is important, because nights and weekends can change how the graph appears and seemingly make them different because of timing misalignment when in reality they are not when properly aligned.

Adaptive Jurik Filter Volatility Oscillator [Loxx]Adaptive Jurik Filter Volatility Oscillator uses Jurik Volty and Adaptive Double Jurik Filter Moving Average (AJFMA) to derive Jurik Filter smoothed volatility. What is Jurik Volty? One of the lesser known qualities of Juirk smoothing is that the Jurik smoothing process is adaptive. "Jurik Volty" (a sort of market volatility ) is what makes Jurik smoothing adaptive. The Jurik Volty calculation can be used as both a standalone indicator and to smooth other indicators that you wish to make adaptive. What is the Jurik Moving Average? Have you noticed how moving averages add some lag (delay) to your signals? ... especially when price gaps up or down in a big move, and you are waiting for your moving average to catch up? Wait no more! JMA eliminates this problem forever and gives you the best of both worlds: low lag and smooth lines. Ideally, you would like a filtered signal to be both smooth and lag-free. Lag causes delays in your trades, and increasing lag in your indicators typically result in lower profits. In other words, late comers get what's left on the table after the feast has already begun. That's why investors, banks and institutions worldwide ask for the Jurik Research Moving Average ( JMA ). You may apply it just as you would any other popular moving average. However, JMA's improved timing and smoothness will astound you. What is adaptive Jurik volatility? One of the lesser known qualities of Juirk smoothing is that the Jurik smoothing process is adaptive. "Jurik Volty" (a sort of market volatility ) is what makes Jurik smoothing adaptive. The Jurik Volty calculation can be used as both a standalone indicator and to smooth other indicators that you wish to make adaptive. What is an adaptive cycle, and what is Ehlers Autocorrelation Periodogram Algorithm? From his Ehlers' book Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers , 2013, page 135: "Adaptive filters can have several different meanings. For example, Perry Kaufman’s adaptive moving average ( KAMA ) and Tushar Chande’s variable index dynamic average ( VIDYA ) adapt to changes in volatility . By definition, these filters are reactive to price changes, and therefore they close the barn door after the horse is gone.The adaptive filters discussed in this chapter are the familiar Stochastic , relative strength index ( RSI ), commodity channel index ( CCI ), and band-pass filter.The key parameter in each case is the look-back period used to calculate the indicator. This look-back period is commonly a fixed value. However, since the measured cycle period is changing, it makes sense to adapt these indicators to the measured cycle period. When tradable market cycles are observed, they tend to persist for a short while.Therefore, by tuning the indicators to the measure cycle period they are optimized for current conditions and can even have predictive characteristics. The dominant cycle period is measured using the Autocorrelation Periodogram Algorithm. That dominant cycle dynamically sets the look-back period for the indicators. I employ my own streamlined computation for the indicators that provide smoother and easier to interpret outputs than traditional methods. Further, the indicator codes have been modified to remove the effects of spectral dilation.This basically creates a whole new set of indicators for your trading arsenal." Included - UI options to color bars