Dynamic Volume-Volatility Adjusted MomentumThis Indicator in a refinement of my earlier script PC*VC Moving average Old with easier to follow color codes, overbought and oversold zones. This script has converted the previous script into a standardized measure by converting it into Z-scores and also incorporated a volatility based dynamic length option. Below is a detailed Explanation.
The "Dynamic Volume-Volatility Adjusted Momentum" or "Nasan Momentum Oscillator" is designed to capture market momentum while accounting for volume and volatility fluctuations. It leverages the Typical Price (TP), calculated as the average of high, low, and close prices, and introduces the Price Coefficient (PC) based on deviations from the simple moving average (SMA) across various time frames. Additionally, the Volume Coefficient (VC) compares current volume to SMA, and calculates Intraday Volatility (IDV) which gauges the daily price range relative to the close. Then intraday volatility ratio is calculated ( IDV Ratio) as the ratio of current Intraday Volatility (IDV) to the average of IDV for three different length periods, which provides a relative measure of current intraday volatility compared to its recent historical average. An inter-day ATR based Relative Volatility (RV) is calculated to adjusts for changing market volatility based on which the dynamic length adjustment adapts the moving average (standard length is 14). The PC *VC/IDV Ratio integrates price, volume, and volatility information which provides a volume and volatility adjusted momentum. This volume and volatility adjusted momentum is converted into a standardized Z-Score. The Z-Score measures deviations from the mean. Color-coded plots visually represent momentum, and thresholds aid in identifying overbought or oversold conditions.
The indicator incorporates a nuanced approach to emphasize the joint impact of price and volume while considering the stabilizing effect of lower intraday volatility. Placing the volume ratio (VC) in the numerator means that higher volume positively contributes to the overall ratio, aligning with the observation that increased volumes often accompany robust price movements. Simultaneously, the decision to include the inverse of intraday volatility (1/IDV) in the denominator acts as a dampener, reducing the impact of extreme intraday volatility on the momentum indicator. This design choice aims to filter out noise, giving more weight to significant price changes supported by substantial trading activity. In essence, the indicator's design seeks to provide a more robust momentum measure that balances the influence of price, volume, and volatility in the analysis of market dynamics.
在腳本中搜尋"Volatility"
Change of VolatilityOVERVIEW
The Change of Volatility indicator is a technical indicator that gauges the amount of volatility currently present in the market. The purpose of this indicator is to filter out with-trend signals during ranging/non-trending/consolidating conditions.
CONCEPTS
This indicator assists traders in capitalizing on the assumption that trends are more likely to start during periods of high volatility compared to periods of low volatility . This is because high volatility indicates that there are bigger players currently in the market, which is necessary to begin a sustained trending move.
So, to determine whether the current volatility in the market is low, the indicator will grey out all the areas on the chart whose short term standard deviation of volatility is lower than the long term standard deviation of volatility.
If the short term standard deviation of volatility is above the long term standard deviation of volatility, the current volatility in the market is considered high. This would the ideal time to enter a trending trade due to the assumption that trends are more likely to start during these high-volatility periods.
HOW DO I READ THIS INDICATOR
When the histogram is grey, don't take any trend trades since the current volatility is less than the usual volatility experienced in the market.
When the histogram is green, take all valid with-trend trades since the current volatility is greater than the usual volatility experienced in the market.
Implied Volatility Estimator using Black Scholes [Loxx]Implied Volatility Estimator using Black Scholes derives a estimation of implied volatility using the Black Scholes options pricing model. The Bisection algorithm is used for our purposes here. This includes the ability to adjust for dividends.
Implied Volatility
The implied volatility (IV) of an option contract is that value of the volatility of the underlying instrument which, when input in an option pricing model (such as Black–Scholes), will return a theoretical value equal to the current market price of that option. The VIX , in contrast, is a model-free estimate of Implied Volatility. The latter is viewed as being important because it represents a measure of risk for the underlying asset. Elevated Implied Volatility suggests that risks to underlying are also elevated. Ordinarily, to estimate implied volatility we rely upon Black-Scholes (1973). This implies that we are prepared to accept the assumptions of Black Scholes (1973).
Inputs
Spot price: select from 33 different types of price inputs
Strike Price: the strike price of the option you're wishing to model
Market Price: this is the market price of the option; choose, last, bid, or ask to see different results
Historical Volatility Period: the input period for historical volatility ; historical volatility isn't used in the Bisection algo, this is to serve as a comparison, even though historical volatility is from price movement of the underlying asset where as implied volatility is the volatility of the option
Historical Volatility Type: choose from various types of implied volatility , search my indicators for details on each of these
Option Base Currency: this is to calculate the risk-free rate, this is used if you wish to automatically calculate the risk-free rate instead of using the manual input. this uses the 10 year bold yield of the corresponding country
% Manual Risk-free Rate: here you can manually enter the risk-free rate
Use manual input for Risk-free Rate? : choose manual or automatic for risk-free rate
% Manual Yearly Dividend Yield: here you can manually enter the yearly dividend yield
Adjust for Dividends?: choose if you even want to use use dividends
Automatically Calculate Yearly Dividend Yield? choose if you want to use automatic vs manual dividend yield calculation
Time Now Type: choose how you want to calculate time right now, see the tool tip
Days in Year: choose how many days in the year, 365 for all days, 252 for trading days, etc
Hours Per Day: how many hours per day? 24, 8 working hours, or 6.5 trading hours
Expiry date settings: here you can specify the exact time the option expires
*** the algorithm inputs for low and high aren't to be changed unless you're working through the mathematics of how Bisection works.
Included
Option pricing panel
Loxx's Expanded Source Types
Related Indicators
Cox-Ross-Rubinstein Binomial Tree Options Pricing Model
Volatility Bands Reversal Strategy [Long Only]This strategy based on existng indicator available on TV
If finds the reversals for LONG entries ... I have modified the settings to back test it ...
BUY
====
When the price touches lower band , and tries to close above lower band
some signals are mixed up, you can research and look for a confirmation ...
if the middle band is above EMA50 , you can simply follow the strategy BUY signal
but if the middle band is EMA50 , wait for the price to close above middle band
Sell / Close
==========
wait for the sell signa OR close when price touches the upper band
How do you want to close , you can chose in settings. Chnage these values and see the performance
Please note , sell means just closing the existing LONG position , not short selling
Stop Loss
=========
Stop Loss is defaulted to 6%
This is tested in 1HR, 2HR and 4 HRs chart for SPY and QQQ ETFS ...
for long term investing style , 4 Hrs is the best time frme for this strategy
Warning
========
It is not a financial advise , it is for educational purposes only. Please do your own research before taking any trading decission
Low Volatility Breakout Detector)This indicator is designed to visually identify potential breakouts from consolidation during periods of low volatility. It is based on classic Bollinger Bands and relative volume. Its primary purpose is not to generate buy or sell signals but to assist in spotting moments when the market exits a stagnation phase.
Arrows appear only when the price breaks above the upper or below the lower Bollinger Band, the band width is below a specified threshold (expressed in percentage), and volume is above its moving average multiplied by a chosen multiplier (default is 1). This combination may indicate the start of a new impulse following a period of low activity.
The chart background during low volatility is colored based on volume strength—the lower the volume during stagnation, the less transparent the background. This helps quickly spot unusual market behavior under seemingly calm conditions. The background opacity is dynamically scaled relative to the range of volumes over a selected period, which can be set manually (default is 50 bars).
The indicator works best in classic horizontal consolidations, where price moves within a narrow range and volatility and volume clearly decline. It is not intended to detect breakouts from formations such as triangles or wedges, which may not always exhibit low volatility relative to Bollinger Bands.
Settings allow you to adjust:
Bollinger Band length and multiplier,
Volatility threshold (in %),
Background and arrow colors,
Volume moving average length and multiplier,
Bar range used for background opacity scaling.
Note: For reliable results, it’s advisable to tailor the volatility threshold and volume/background ranges to the specific market and timeframe, as different instruments have distinct dynamics. If you want the background color to closely match the color of breakout arrows, you should set the same volume analysis period as the volume moving average length.
Additional note: To achieve a cleaner chart and focus solely on breakout signals, you can disable the background and Bollinger Bands display in the settings. This will leave only the breakout arrows visible on the chart, providing a clearer and more readable market picture.
Interpolated Median Volatility LSMA | OttoThis indicator combines trend-following and volatility analysis by enhancing traditional LSMA with percentile-based linear interpolation applied to both the Least Squares Moving Average (LSMA) and standard deviation. Rather than relying on raw values, it uses the interpolated median (50th percentile) to smooth out noise while preserving sensitivity to significant price shifts. This approach produces a cleaner trend signal that remains responsive to real market changes, adapts to evolving volatility conditions, and improves the accuracy of breakout detection.
Core Concept
The indicator builds on these core components:
LSMA (Least Squares Moving Average): A linear regression-based moving average that fits line using user selected source over user defined period. It offers a smoother and more reactive trend signal compared to standard moving averages.
Standard Deviation shows how much price varies from the mean. In this indicator, it’s used to measure market volatility.
Volatility Bands: Instead of traditional Bollinger-style bands, this script calculates custom upper and lower bands using percentile-based linear interpolation on both the LSMA and standard deviation. This method produces smoother bands that filter out noise while remaining adaptive to meaningful price movements, making them more aligned with real market behavior and helping reduce false signals.
Percentile interpolation estimates a specific percentile (like the median — the 50th percentile) from a set of values — even when that percentile doesn't fall exactly on one data point. Instead of selecting a single nearest value, it calculates a smoothed value between nearby points. In this script, it’s used to find the median of past LSMA and standard deviation values, reducing the impact of outliers and smoothing the trend and volatility signals for more robust results.
Signal Logic: A long signal is identified when close price goes above the upper band, and a short signal when close price goes below the lower band.
⚙️ Inputs
Source: The price source used in calculations
LSMA Length: Period for calculating LSMA
Standard Deviation Length: Period for calculating volatility
Percentile Length: Period used for interpolating percentile values of LSMA and standard deviation
Multiplier: Controls the width of the bands by scaling the interpolated standard deviation
📈 Visual Output
Colored LSMA Line: Changes color based on signal (green for bullish, purple for bearish)
Upper & Lower Bands: Volatility bands calculated using interpolated values (green for bullish, purple for bearish)
Bar Coloring: Price bars are colored to reflect signal state (green for bullish, purple for bearish)
Optional Candlestick Overlay: Enhances visual context by coloring candles to match the signal state (green for bullish, purple for bearish)
How to Use
Add the indicator to your chart and look for signals when close price goes above or below the bands.
Long Signal: close Price goes above the upper band
Short Signal: close Price goes below the lower band
🔔 Alerts:
This script supports alert conditions for long and short signals. You can set alerts based on band crossovers to be notified of potential entries/exits.
⚠️ Disclaimer:
This indicator is intended for educational and informational purposes only. Trading/investing involves risk, and past performance does not guarantee future results. Always test and evaluate strategies before applying them in live markets. Use at your own risk.
Trend Volatility Index (TVI)Trend Volatility Index (TVI)
A robust nonparametric oscillator for structural trend volatility detection
⸻
What is this?
TVI is a volatility oscillator designed to measure the strength and emergence of price trends using nonparametric statistics.
It calculates a U-statistic based on the Gini mean difference across multiple simple moving averages.
This allows for objective, robust, and unbiased quantification of trend volatility in tick-scale values.
⸻
What can it do?
• Quantify trend strength as a continuous value aligned with tick price scale
• Detect trend breakouts and volatility expansions
• Identify range-bound market states
• Detect early signs of new trends with minimal lag
⸻
What can’t it do?
• Predict future price levels
• Predict trend direction before confirmation
⸻
How it works
TVI computes a nonparametric dispersion metric (Gini mean difference) from multiple SMAs of different lengths.
As this metric shares the same dimension as price ticks, it can be directly interpreted on the chart as a volatility gauge.
The output is plotted using candlestick-style charts to enhance visibility of change rate and trend behavior.
⸻
Disclaimer
TVI does not predict price. It is a structural indicator designed to support discretionary judgment.
Trading carries inherent risk, and this tool does not guarantee profitability. Use at your own discretion.
⸻
Innovation
This indicator introduces a novel approach to trend volatility by applying U-statistics over time series
to produce a nonparametric, unbiased, and robust estimate of structural volatility.
日本語要約
Trend Volatility Index (TVI) は、ノンパラメトリックなU統計量（Gini平均差）を使ってトレンドの強度を客観的に測定することを目的に開発されたボラティリティ・オシレーターです。
ティック単位で連続的に変化し、トレンドのブレイク・レンジ・初動の予兆を定量的に検出します。
未来の価格や方向は予測せず、現在の構造的ばらつきだけをロバストに評価します。
Adaptive Fibonacci Volatility Bands (AFVB)
**Adaptive Fibonacci Volatility Bands (AFVB)**
### **Overview**
The **Adaptive Fibonacci Volatility Bands (AFVB)** indicator enhances standard **Fibonacci retracement levels** by dynamically adjusting them based on market **volatility**. By incorporating **ATR (Average True Range) adjustments**, this indicator refines key **support and resistance zones**, helping traders identify **more reliable entry and exit points**.
**Key Features:**
- **ATR-based adaptive Fibonacci levels** that adjust to changing market volatility.
- **Buy and Sell signals** based on price interactions with dynamic support/resistance.
- **Toggleable confirmation filter** for refining trade signals.
- **Customizable color schemes** and alerts.
---
## **How This Indicator Works**
The **AFVB** operates in three main steps:
### **1️⃣ Detecting Key Fibonacci Levels**
The script calculates **swing highs and swing lows** using a user-defined lookback period. From this, it derives **Fibonacci retracement levels**:
- **0% (High)**
- **23.6%**
- **38.2%**
- **50% (Mid-Level)**
- **61.8%**
- **78.6%**
- **100% (Low)**
### **2️⃣ Adjusting for Market Volatility**
Instead of using **fixed retracement levels**, this indicator incorporates an **ATR-based adjustment**:
- **Resistance levels** shift **upward** based on ATR.
- **Support levels** shift **downward** based on ATR.
- This makes levels more **responsive** to price action.
### **3️⃣ Generating Buy & Sell Signals**
AFVB provides **two types of signals** based on price interactions with key levels:
✔ **Buy Signal**:
Occurs when price **dips below** a support level (78.6% or 100%) and **then closes back above it**.
- **Optionally**, a confirmation buffer can be enabled to require price to close **above an additional threshold** (based on ATR).
✔ **Sell Signal**:
Triggered when price **breaks above a resistance level** (0% or 23.6%) and **then closes below it**.
📌 **Important:**
- The **buy threshold setting** allows traders to **fine-tune** entry conditions.
- Turning this setting **off** generates **more frequent** buy signals.
- Keeping it **on** reduces false signals but may result in **fewer trade opportunities**.
---
## **How to Use This Indicator in Trading**
### 🔹 **Entry Strategy (Buying)**
1️⃣ Look for **buy signals** at the **78.6% or 100% Fibonacci levels**.
2️⃣ Ensure price **closes above** the support level before entering a long trade.
3️⃣ **Enable or disable** the buy threshold filter depending on desired trade strictness.
### 🔹 **Exit Strategy (Selling)**
1️⃣ Watch for **sell signals** at the **0% or 23.6% Fibonacci levels**.
2️⃣ If price **breaks above resistance and then closes below**, consider exiting long positions.
3️⃣ Can be used **alone** or **combined with trend confirmation tools** (e.g., moving averages, RSI).
### 🔹 **Using the Toggleable Buy Threshold**
- **ON**: Buy signal requires **extra confirmation** (reduces false signals but fewer trades).
- **OFF**: Buy triggers as soon as price **closes back above support** (more signals, but may include weaker setups).
---
## **User Inputs**
### **🔧 Customization Options**
- **ATR Length**: Defines the period for **ATR calculation**.
- **Swing Lookback**: Determines how far back to find **swing highs and lows**.
- **ATR Multiplier**: Adjusts the size of **volatility-based modifications**.
- **Buy/Sell Threshold Factor**: Fine-tunes the **entry signal strictness**.
- **Show Level Labels**: Enables/disables **Fibonacci level annotations**.
- **Color Settings**: Customize **support/resistance colors**.
### **📢 Alerts**
AFVB includes built-in **alert conditions** for:
- **Buy Signals** ("AFVB BUY SIGNAL - Possible reversal at support")
- **Sell Signals** ("AFVB SELL SIGNAL - Possible reversal at resistance")
- **Any Signal Triggered** (Useful for automated alerts)
---
## **Who Is This Indicator For?**
✅ **Scalpers & Day Traders** – Helps identify **short-term reversals**.
✅ **Swing Traders** – Useful for **buying dips** and **selling rallies**.
✅ **Trend Traders** – Can be combined with **momentum indicators** for confirmation.
**Best Timeframes:**
⏳ **15-minute, 1-hour, 4-hour, Daily charts** (works across multiple assets).
---
## **Limitations & Considerations**
🚨 **Important Notes**:
- **No indicator guarantees profits**. Always **combine** it with **risk management strategies**.
- Works best **in trending & mean-reverting markets**—may generate false signals in **choppy conditions**.
- Performance may vary across **different assets & timeframes**.
📢 **Backtesting is recommended** before using it for live trading.
Hyperbolic Tangent Volatility Stop [InvestorUnknown]The Hyperbolic Tangent Volatility Stop (HTVS) is an advanced technical analysis tool that combines the smoothing capabilities of the Hyperbolic Tangent Moving Average (HTMA) with a volatility-based stop mechanism. This indicator is designed to identify trends and reversals while accounting for market volatility.
Hyperbolic Tangent Moving Average (HTMA):
The HTMA is at the heart of the HTVS. This custom moving average uses a hyperbolic tangent transformation to smooth out price fluctuations, focusing on significant trends while ignoring minor noise. The transformation reduces the sensitivity to sharp price movements, providing a clearer view of the underlying market direction.
The hyperbolic tangent function (tanh) is commonly used in mathematical fields like calculus, machine learning and signal processing due to its properties of “squashing” inputs into a range between -1 and 1. The function provides a non-linear transformation that can reduce the impact of extreme values while retaining a certain level of smoothness.
tanh(x) =>
e_x = math.exp(x)
e_neg_x = math.exp(-x)
(e_x - e_neg_x) / (e_x + e_neg_x)
The HTMA is calculated by applying a non-linear transformation to the difference between the source price and its simple moving average, then adjusting it using the standard deviation of the price data. The result is a moving average that better tracks the real market direction.
htma(src, len, mul) =>
tanh_src = tanh((src - ta.sma(src, len)) * mul) * ta.stdev(src, len) + ta.sma(src, len)
htma = ta.sma(tanh_src, len)
Important Note: The Hyperbolic Tangent function becomes less accurate with very high prices. For assets priced above 100,000, the results may deteriorate, and for prices exceeding 1 million, the function may stop functioning properly. Therefore, this indicator is better suited for assets with lower prices or lower price ratios.
Volatility Stop (VolStop):
HTVS employs a Volatility Stop mechanism based on the Average True Range (ATR). This stop dynamically adjusts based on market volatility, ensuring that the indicator adapts to changing conditions and avoids false signals in choppy markets.
The VolStop follows the price, with a higher ATR pushing the stop farther away to avoid premature exits during volatile periods. Conversely, when volatility is low, the stop tightens to lock in profits as the trend progresses.
The ATR Length and ATR Multiplier are customizable, allowing traders to control how tightly or loosely the stop follows the price.
pine_volStop(src, atrlen, atrfactor) =>
if not na(src)
var max = src
var min = src
var uptrend = true
var float stop = na
atrM = nz(ta.atr(atrlen) * atrfactor, ta.tr)
max := math.max(max, src)
min := math.min(min, src)
stop := nz(uptrend ? math.max(stop, max - atrM) : math.min(stop, min + atrM), src)
uptrend := src - stop >= 0.0
if uptrend != nz(uptrend , true)
max := src
min := src
stop := uptrend ? max - atrM : min + atrM
Backtest Mode:
HTVS includes a built-in backtest mode, allowing traders to evaluate the indicator's performance on historical data. In backtest mode, it calculates the cumulative equity curve and compares it to a simple buy and hold strategy.
Backtesting features can be adjusted to focus on specific signal types, such as Long Only, Short Only, or Long & Short.
An optional Buy and Hold Equity plot provides insight into how the indicator performs relative to simply holding the asset over time.
The indicator includes a Hints Table, which provides useful recommendations on how to best display the indicator for different use cases. For example, when using the overlay mode, it suggests displaying the indicator in the same pane as price action, while backtest mode is recommended to be used in a separate pane for better clarity.
The Hyperbolic Tangent Volatility Stop offers traders a balanced approach to trend-following, using the robustness of the HTMA for smoothing and the adaptability of the Volatility Stop to avoid whipsaw trades during volatile periods. With its backtesting features and alert system, this indicator provides a comprehensive toolkit for active traders.
Volatility ATR Support and Resistance Bands [Quantigenics]Volatility ATR Support and Resistance Bands
The “Volatility ATR Support and Resistance Bands” is a trend visualization tool that uses Average True Range (ATR) to create a dynamic channel around price action, adapting to changes in volatility and offering clear trend indicators. The band direction can indicate trend and the lines can indicate support and resistance levels.
The script works by calculating a series of moving averages from the highest and lowest prices, then applies an ATR-based multiplier to generate a set of bands. These bands expand and contract with the market’s volatility, providing a visual guide to the strength and potential direction of price movements.
How to Trade with Volatility ATR Band:
Identify Trend Direction: When the bands slope upwards, the market is trending upwards, which may be a good opportunity to consider a long position. When the bands slope downward, the market is trending downwards, which could be a sign to sell or short.
Volatility Awareness: The wider the bands, the higher the market volatility. Narrow bands suggest a quieter market, which might indicate consolidation or a potential breakout/breakdown.
Confirm Entries and Exits: Use the bands as dynamic support and resistance; entering trades as the price bounces off the bands and considering exits as it reaches the opposite side or breaches the bands.
Hope you enjoy this script!
Happy trading!
Parkinson's Volatility EstimatorThe Parkinson's Volatility Estimator (PVE) provides an alternative method for assessing market volatility using the highest and lowest prices within a given period. Unlike traditional models that predominantly rely on closing prices, the PVE considers the full range of intra-candle price movements, thereby potentially offering a more comprehensive gauge of market volatility. The estimator is derived from the logarithm of the ratio of the high to low prices, squared and then averaged over the period of interest. This calculation is rooted in the assumption that the logarithmic high-to-low ratio represents a normalized measure of price movements, capturing both upward and downward volatility in a symmetric manner (Parkinson, 1980).
In this specific implementation, the estimator is calculated as follows:
Parkinson’s Volatility = (1/4 log(2)) * (1/n) * Σ from i=1 to n of (log(High_i/Low_i))^2
where n is the lookback period defined by the user, and High_i and Low_i are the highest and lowest prices at each interval i within that period. This formulation takes advantage of the logarithmic properties to scale the volatility measure appropriately, utilizing a factor of 1/4 log(2) to normalize the variance estimate (Parkinson, 1980).
This implementation includes options for output normalization between 0 and 1 and for plotting horizontal lines at specified levels, allowing the estimator to function like an oscillator to evaluate volatility relative to recent market regimes. Users can customize these features through script inputs, enhancing flexibility for various trading scenarios and improving its utility for real-time volatility assessments on the TradingView platform.
Reference:
Parkinson, M. (1980). The extreme value method for estimating the variance of the rate of return. The Journal of Business, 53(1), 61-65.
Percent Volatility MomentumThis pine script calculates percent volatility momentum, negative percent volatility and positive percent volatility. The blue line is the overall momentum of the current percent volatility trend. The red line only includes negative movements in the percent volatility of the source. The green line includes only positive movements of the percent volatility of the source. The script also includes an angle and a normalized angle setting that allows one to determine the angle of the source curve. Note, the angle was transformed from -90 to 90 to 0 to 100. Such that an angle of -90 is transformed to 0. An angle of 0 is transformed to 50 and an angle of 90 is transformed to 100. This is the first draft of this script and my first pine script published. Any feedback is welcome. I borrowed code from TradingView's Linear Regression Channel and Relative Strength Index pine scripts.
Garman-Klass-Yang-Zhang Historical Volatility Bands [Loxx]Garman-Klass-Yang-Zhang Historical Volatility Bands are constructed using:
Average as the middle line.
Upper and lower bands using the Garman-Klass-Yang-Zhang Historical Volatility Bands for bands calculation.
What is Garman-Klass-Yang-Zhang Historical Volatility?
Yang and Zhang derived an extension to the Garman Klass historical volatility estimator that allows for opening jumps. It assumes Brownian motion with zero drift. This is currently the preferred version of open-high-low-close volatility estimator for zero drift and has an efficiency of 8 times the classic close-to-close estimator. Note that when the drift is nonzero, but instead relative large to the volatility, this estimator will tend to overestimate the volatility. The Garman-Klass-Yang-Zhang Historical Volatility calculation is as follows:
GKYZHV = sqrt((Z/n) * sum((log(open(k)/close(k-1)))^2 + (0.5*(log(high(k)/low(k)))^2) - (2*log(2) - 1)*(log(close(k)/open(2:end)))^2))
The color of the middle line, unlike the bands colors, has 3 colors. When colors of the bands are the same, then the middle line has the same color, otherwise it's white.
Included
Alerts
Signals
Loxx's Expanded Source Types
Bar coloring
Related Indicators
Garman & Klass Estimator Historical Volatility Bands
Big Snapper Alerts R3.0 + Chaiking Volatility condition + TP RSI//@version=5
//
// Bannos
// #NotTradingAdvice #DYOR
// Disclaimer.
// I AM NOT A FINANCIAL ADVISOR.
// THESE IDEAS ARE NOT ADVICE AND ARE FOR EDUCATION PURPOSES ONLY.
// ALWAYS DO YOUR OWN RESEARCH
//
// Author: Adaptation from JustUncleL Big Snapper by Bannos
// Date: May-2022
// Version: R1.0
//Description of this addon - Script using several new conditions to give Long/short and SL levels which was not proposed in the Big Snapper strategy "Big Snapper Alerts R3.0"
//"
//This strategy is based on the use of the Big Snapper outputs from the JustUncleL script and the addition of several conditions to define filtered conditions selecting signal synchrones with a trend and a rise of the volatility.
//Also the strategy proposes to define proportional stop losses and dynamic Take profit using an RSI strategy.
// After delivering the temporary ong/short signal and ploting a green or purple signal, several conditions are defined to consider a Signal is Long or short.
//Let s take the long signal as example(this is the same process with the opposite values for a short).
//step 1 - Long Definition:
// Snapper long signal stored in the buffer variable Longbuffer to say that in a close future, we could have all conditions for a long
// Now we need some conditions to combine with it:
//the second one is to be over the Ma_medium(55)
//and because this is not selective enough, the third one is a Volatility indicator "Chaikin Volatility" indicator giving an indication about the volatility of the price compared to the 10 last values
// -> Using the volatility indicator gives the possibility to increase the potential rise if the volatility is higher compared to the last periods.
//With these 3 signals, we get a robust indication about a potential long signal which is then stored in the variable "Longe"
//Now we have a long signal and can give a long signal with its Stop Loss
// The Long Signal is automatically given as the 3 conditions above are satisfied.
// The Stop loss is a function of the last Candle sizes giving a stop below the 70% of the overall candle which can be assimilated to a Fibonacci level. Below this level it makes sense to stop the trade as the chance to recover the complete Candle is more than 60%
//Now we are in an open Long and can use all the mentioned Stop loss condition but still need a Take Profit condition
//The take profit condition is based on a RSI strategy consisting in taking profit as soon as the RSI come back from the overbought area (which is here defined as a rsi over 70) and reaching the 63.5 level to trigger the Take Profit
//This TP condition is only active when Long is active and when an entry value as been defined.
//Entry and SL level appreas as soon as a Long or short arrow signal does appears. The Take profit will be conidtioned to the RSI.
//The final step in the cycle is a reinitialization of all the values giving the possibility to detect and treat any long new signal coming from the Big Snapper signal.
S&P500 VIX Volatility Warning IndicatorToday I am sharing with the community a volatility indicator that can help you or your algorithms avoid black swan events. Variance is most commonly used in statistics to derive standard deviation (with its square root). It does have another practical application, and that is to identify outliers in a sample of data. Variance in statistics is defined as the squared difference between a value and its mean. Calculating that squared difference means that the farther away the value is from the mean, the more the variance will grow (exponentially). This exponential difference makes outliers in the variance data more apparent.
Why does this matter?
There are assets or indices that exist in the stock market that might make us adjust our trading strategy if they are behaving in an unusual way. In some instances, we can use variance to identify that behavior and inform our strategy.
Is that really possible?
Let’s look at the relationship between VIX and the S&P500 as an example. If you trade an S&P500 index with a mean reversion strategy or algorithm, you know that they typically do best in times of volatility. These strategies essentially attempt to “call bottom” on a pullback. Their downside is that sometimes a pullback turns into a regime change, or a black swan event. The other downside is that there is no logical tight stop that actually increases their performance, so when they lose they tend to lose big.
So that begs the question, how might one quantitatively identify if this dip could turn into a regime change or black swan event?
The CBOE Volatility Index (VIX) uses options data to identify, on a large scale, what investors overall expect the market to do in the near future. The Volatility Index spikes in times of uncertainty and when investors expect the market to go down. However, during a black swan event, the VIX spikes a lot harder. We can use variance here to identify if a spike in the VIX exceeds our threshold for a normal market pullback, and potentially avoid entering trades for a period of time (I.e. maybe we don’t buy that dip).
Does this actually work?
In backtesting, this cut the drawdown of my index reversion strategies in half. It also cuts out some good trades (because high investor fear isn’t always indicative of a regime change or black swan event). But, I’ll happily lose out on some good trades in exchange for half the drawdown. Lets look at some examples of periods of time that trades could have been avoided using this strategy/indicator:
Example 1 – With the Volatility Warning Indicator, the mean reversion strategy could have avoided repeatedly buying this pullback that led to SPXL losing over 75% of its value:
Example 2 - June 2018 to June 2019 - With the Volatility Warning Indicator, the drawdown during this period reduces from 22% to 11%, and the overall returns increase from -8% to +3%
How do you use this indicator?
This indicator determines the variance of the VIX against a long term mean. If the variance of the VIX spikes over an input threshold, the indicator goes up. The indicator will remain up for a defined period of bars/time after the variance returns below the threshold. I have included default values I’ve found to be significant for a short-term mean-reversion strategy, but your inputs might depend on your risk tolerance and strategy time-horizon. The default values are for 1hr VIX data. It will pull in variance data for the VIX regardless of which chart the indicator is applied to.
Disclaimer : Open-source scripts I publish in the community are largely meant to spark ideas or be used as building blocks for part of a more robust trade management strategy. If you would like to implement a version of any script, I would recommend making significant additions/modifications to the strategy & risk management functions. If you don’t know how to program in Pine, then hire a Pine-coder. We can help!
(JS)S&P 500 Volatility Oscillator For Options 2.0I am going to start taking requests to open source my indicators and they will also be updated to Version 4 of Pinescript.
I added some features to the original code such the ability to smooth the oscillator and select the look back periods for the historical volatility.
Link to original:
Original post:
"The idea for this started here: www.tradingview.com with the user @dime
This should only be used on SPX or SPY (though you could use it on other things for correlation I suppose) given that the instrument used to create this calculation is derived from the S&P 500 (thank you VIX ). There's a lot of moving parts here though, so allow me to explain...
First: The main signal is when Implied Volatility (from VIX ) drops beneath Historical Volatility - which is what you want to see so you aren't purchasing a ton of premium on long options. Green and above 0 means that IV% has dropped lower than Historical Volatility . (this signal, for example, would suggest using a Long Call or Put depending on your sentiment)
Second: The green line running underneath zero is the bottom portion of the "Average True Range" derived from the values used to create the oscillator. the closer the bottom histogram is to the green line, the more "normal" IV% is. Obviously, if this gets far away from the line then it could be setting up nicely to short options and sell the IV premium to someone else. (this signal, for example, would suggest using something like a Bull Put Spread)
Third: The red background along with the white line that drops down below zero signals when (and how far) the IV% from 3 months out (from VIX3M ) is less than the current IV%. This would signal the current environment has IV way too high, a signal to short options once again (and don't take any long option positions!).
Tried to make this simple, yet effective. If you trade options on SPX , SPY , even ES1! futures - this is a tool tailored specifically for you! As I said before, if you want you can use it for correlation on other securities. Any other ideas or suggestions surrounding this, please let me know! Enjoy!
Feb 17, 2019
Release Notes: Cosmetic update for a much cleaner look:
-Replaced the "HIGH IV" with a simlple "H"
-Now the white line is constantly showing you the relationship between VIX and VIX3M - when VIX is greater than VIX3M the background still goes red
-However, now when VIX drops below Historical Volatility, the background is bright green
-When both above are true - it's dark green
-The Average True Range on the bottom is now a series of crosses"
Volume Volatility SpectrumThis indicator estimates price volatility and it is based on Volume only (presumably Tick Volume in Forex).
Tick volume is supposed to be a good proxy to actual volume in spot forex (study of Caspar Marney, 2011)
The advantage of this indicator is that it can be used with any pair, any timeframe.
The only parameters are the periods of the reference Volume Moving Average and the fast Volume MA.
The fluctuations of a short period Volume MA with respect to a gently MA with high period
are calculated.
RED areas depict low volatility
GREEN areas depict high volatility.
When the clouds are outside the region delimited by the aqua lines we have extreme conditions:
Extremely low volatility = red cloud outside the aqua bands
Extremely high volatility = green cloud outside the aqua bands
Vitelot/yanez/Vts September 2019.
Compare this indicator with the ATR Volatility Spectrum of myself
Volatility Bias ModelVolatility Bias Model
Overview
Volatility Bias Model is a purely mathematical, non-indicator-based trading system that detects directional probability shifts during high volatility market phases. Rather than relying on classic tools like RSI or moving averages, this strategy uses raw price behavior and clustering logic to determine potential breakout direction based on recent market bias.
How It Works
Over a defined lookback window (default 10 bars), the strategy counts how many candles closed in the same direction (i.e., bullish or bearish).
Simultaneously, it calculates the price range during that window.
If volatility is above a minimum threshold and a clear directional bias is detected (e.g., >60% of closes are bullish), a trade is opened in the direction of that bias.
This approach assumes that when high volatility is coupled with directional closing consistency, the market is probabilistically more likely to continue in that direction.
ATR-based stop-loss and take-profit levels are applied, and trades auto-exit after 20 bars if targets are not hit.
Key Features
- 100% non-indicator-based logic
- Statistically-driven directional bias detection
- Works across all timeframes (1H, 4H, 1D)
- ATR-based risk management
- No pyramiding, slippage and commissions included
- Compatible with real-world backtesting conditions
Realism & Assumptions
To make this strategy more aligned with actual trading environments, it includes 0.05% commission per trade and a 1-point slippage on every entry and exit.
Additionally, position sizing is set at 10% of a $10,000 starting capital, and no pyramiding is allowed.
These assumptions help avoid unrealistic backtest results and make the performance metrics more representative of live conditions.
Parameter Explanation
Bias Window (10 bars): Number of past candles used to evaluate directional closings
Bias Threshold (0.60): Required ratio of same-direction candles to consider a bias valid
Minimum Range (1.5%): Ensures the market is volatile enough to avoid noise
ATR Length (14): Used to dynamically define stop-loss and target zones
Risk-Reward Ratio (2.0): Take-profit is set at twice the stop-loss distance
Max Holding Bars (20): Trades are closed automatically after 20 bars to prevent stagnation
Originality Note
Unlike common strategies based on oscillators or moving averages, this script is built on pure statistical inference. It models the market as a probabilistic process and identifies directional intent based on historical closing behavior, filtered by volatility. This makes it a non-linear, adaptive model grounded in real-world price structure — not traditional technical indicators.
Disclaimer
This strategy is for educational and experimental purposes only. It does not constitute financial advice. Always perform your own analysis and test thoroughly before applying with real capital.
Ultimate Volatility Scanner by NHBprod - Requested by Client!Hey Everyone!
I created another script to add to my growing library of strategies and indicators that I use for automated crypto and stock trading! This strategy is for BITCOIN but can be used on any stock or crypto. This was requested by a client so I thought I should create it and hopefully build off of it and build variants!
This script gets and compares the 14-day volatility using the ATR percentage for a list of cryptocurrencies and stocks. Cryptocurrencies are preloaded into the script, and the script will show you the TOP 5 coins in terms of volatility, and then compares it to the Bitcoin volatility as a reference. It updates these values once per day using daily timeframe data from TradingView. The coins are then sorted in descending order by their volatility.
If you don't want to use the preloaded set of coins, you have the option of inputting your own coins AND/OR stocks!
Let me know your thoughts.
Roger & Satchell Estimator Historical Volatility Bands [Loxx]Roger & Satchell Estimator Historical Volatility Bands are constructed using:
Average as the middle line.
Upper and lower bands using theRoger & Satchell Estimator Historical Volatility Bands for bands calculation.
What is Roger & Satchell Estimator Historical Volatility?
The Rogers–Satchell estimator does not handle opening jumps; therefore, it underestimates the volatility. It accurately explains the volatility portion that can be attributed entirely to a trend in the price evolution. Rogers and Satchell try to embody the frequency of price observations in the model in order to overcome the drawback. They claim that the corrected estimator outperforms the uncorrected one in a study based on simulated data.
RSEHV = sqrt((Z/n) * sum((log(high/close)*log(high/open)) + (log(low/close)*log(low/open))))
The color of the middle line, unlike the bands colors, has 3 colors. When colors of the bands are the same, then the middle line has the same color, otherwise it's white.
Included
Alerts
Signals
Loxx's Expanded Source Types
Bar coloring
ATR Volatility Spectrum
This indicator estimates price volatility and it is based on ATR only.
The advantage of this indicator is that it can be used with any pair, any time frame.
The fluctuations of a short period ATR with respect to a gently ATR with high period
are calculated.
The only parameters are the periods of the reference ATR and fast ATR, which could be
safely let untouched and modified by experts.
RED areas depict low volatility
GREEN areas depict high volatility.
When the clouds are outside the region delimited by the aqua lines we have
extreme conditions:
Extremely low volatility = red cloud outside the aqua bands
Extremely high volatility = green cloud outside the aqua bands
Vitelot/yanez/Vts December 2018.
Hitting the like button is free act of gratitude
Multiple Frequency Volatility CorrelationThis is a complex indicator that looks to provide some insight into the correlation between volume and price volatility.
Rising volatility is depicted with the color green while falling volatility is depicted with purple.
Lightness of the color is used to depict the length of the window used, darker == shorter in the 2 -> 512 window range.
Inverse MACD + DMI Scalping with Volatility Stop (By Coinrule)This script is focused on shorting during downtrends and utilises two strength based indicators to provide confluence that the start of a short-term downtrend has occurred - catching the opportunity as soon as possible.
This script can work well on coins you are planning to hodl for long-term and works especially well whilst using an automated bot that can execute your trades for you. It allows you to hedge your investment by allocating a % of your coins to trade with, whilst not risking your entire holding. This mitigates unrealised losses from hodling as it provides additional cash from the profits made. You can then choose to hodl this cash, or use it to reinvest when the market reaches attractive buying levels.
Alternatively, you can use this when trading contracts on futures markets where there is no need to already own the underlying asset prior to shorting it.
ENTRY
The trading system uses the Momentum Average Convergence Divergence (MACD) indicator and the Directional Movement Index (DMI) indicator to confirm when the best time is for selling. Combining these two indicators prevents trading during uptrends and reduces the likelihood of getting stuck in a market with low volatility.
The MACD is a trend following momentum indicator and provides identification of short-term trend direction. In this variation it utilises the 12-period as the fast and 26-period as the slow length EMAs, with signal smoothing set at 9.
The DMI indicates what way price is trending and compares prior lows and highs with two lines drawn between each - the positive directional movement line (+DI) and the negative directional movement line (-DI). The trend can be interpreted by comparing the two lines and what line is greater. When the negative DMI is greater than the positive DMI, there are more chances that the asset is trading in a sustained downtrend, and vice versa.
The system will enter trades when two conditions are met:
1) The MACD histogram turns bearish.
2) When the negative DMI is greater than the positive DMI.
EXIT
The strategy comes with a fixed take profit combined with a volatility stop, which acts as a trailing stop to adapt to the trend's strength. Depending on your long-term confidence in the asset, you can edit the fixed take profit to be more conservative or aggressive.
The position is closed when:
Take-Profit Exit: +8% price decrease from entry price.
OR
Stop-Loss Exit: Price crosses above the volatility stop.
In general, this approach suits medium to long term strategies. The backtesting for this strategy begins on 1 April 2022 to 18 July 2022 in order to demonstrate its results in a bear market. Back testing it further from the beginning of 2022 onwards further also produces good returns.
Pairs that produce very strong results include SOLUSDT on the 45m timeframe, MATICUSDT on the 2h timeframe, and AVAUSDT on the 1h timeframe. Generally, the back testing suggests that it works best on the 45m/1h timeframe across most pairs.
A trading fee of 0.1% is also taken into account and is aligned to the base fee applied on Binance.
