cankardesler stoploss v2This stoploss allows to filter high volatility fake trends;
But how we are made it; we are calculating the last spikes value average and calculating the standart deviation, after we added to the standart stoploss formula price+2atr and voila!!
Your stop loss is ready.
The idea behind this formula: what is explosing our stops? fake-out spikes.
We think if we get the last spikes average and calculate the standart deviation on it and after add it to the original stop formula, its gonna help for bypassing the spikes.
Thanks a lot @ocankardes for helping me to developing this formula
Cerca negli script per "Volatility"
Damiani Volatmeter [loxx]I wasn't going to publish this since it's one my go to private indicators, but I decided to push this out anyway. This is a variation on Damiani Volatmeter to make it easier to understand what's going on. Damiani Volatmeter uses ATR and Standard deviation to tease out ticker volatility so you can better understand when it's the ideal time to trade. The idea here is that you only take trades when volatility is high so this indicator is to be coupled with various other indicators to validate the other indicator's signals. This is also useful for detecting crabbing and chopping markets.
Shoutout to user @xinolia for the DV function used here.
Anything red means that volatility is low. Remember volatility doesn't have a direction. Anything green means volatility high despite the direction of price. The core signal line here is the green and red line that dips below two while threshold lines to "recharge". Maximum recharge happen when the core signal line shows a yellow ping. Soon after one or many yellow pings you should expect a massive upthrust of volatility. The idea here is you don't trade unless volatility is rising or green. This means that the Volatmeter has to dip into the recharge zone, recharge and then spike upward. You can also attempt to buy or sell reversals with confluence indicators when volatility is in the recharge zone, but I wouldn't recommend this. However, if you so choose to do this, then use the following indicator for confluence.
And last reminder, volatility doesn't have a direction ! Red doesn't mean short, and green doesn't mean long, Red means don't trade period regardless of direction long/short, and green means trade no matter the direction long/short. This means you'll have to add an indicator that does show direction such as a mean reversion indicator like Fisher Transform or a Gaussian Filter. You can search my public scripts for various Fisher Transform and Gaussian Filter indicators.
Price-Filtered Spearman Rank Correl. w/ Floating Levels is considered the Mercedes Benz of reversal indcators
How signals work
RV = Rising Volatility
VD = Volatility Dump
Plots
White line is signal
Thick red/green line is the Volatmeter line
The dotted lower lines are the zero line and minimum recharging line
Included
Bar coloring
Alerts
Signals
Related indicators
Variety Moving Average Waddah Attar Explosion (WAE)
vol_boxA simple script to draw a realized volatility forecast, in the form of a box. The script calculates realized volatility using the EWMA method, using a number of periods of your choosing. Using the "periods per year", you can adjust the script to work on any time frame. For example, if you are using an hourly chart with bitcoin, there are 24 periods * 365 = 8760 periods per year. This setting is essential for the realized volatility figure to be accurate as an annualized figure, like VIX.
By default, the settings are set to mimic CBOE volatility indices. That is, 252 days per year, and 20 period window on the daily timeframe (simulating a 30 trading day period).
Inside the box are three figures:
1. The current realized volatility.
2. The rank. E.g. "10%" means the current realized volatility is less than 90% of realized volatility measures.
3. The "accuracy": how often price has closed within the box, historically.
Inputs:
stdevs: the number of standard deviations for the box
periods to project: the number of periods to forecast
window: the number of periods for calculating realized volatility
periods per year: the number of periods in one year (e.g. 252 for the "D" timeframe)
pricing_tableThis script helps you evaluate the fair value of an option. It poses the question "if I bought or sold an option under these circumstances in the past, would it have expired in the money, or worthless? What would be its expected value, at expiration, if I opened a position at N standard deviations, given the volatility forecast, with M days to expiration at the close of every previous trading day?"
The default (and only) "hv" volatility forecast is based on the assumption that today's volatility will hold for the next M days.
To use this script, only one step is mandatory. You must first select days to expiration. The script will not do anything until this value is changed from the default (-1). These should be CALENDAR days. The script will convert to these to business days for forecasting and valuation, as trading in most contracts occurs over ~250 business days per year.
Adjust any other variables as desired:
model: the volatility forecasting model
window: the number of periods for a lagged model (e.g. hv)
filter: a filter to remove forecasts from the sample
filter type: "none" (do not use the filter), "less than" (keep forecasts when filter < volatility), "greater than" (keep forecasts when filter > volatility)
filter value: a whole number percentage. see example below
discount rate: to discount the expected value to present value
precision: number of decimals in output
trim outliers: omit upper N % of (generally itm) contracts
The theoretical values are based on history. For example, suppose days to expiration is 30. On every bar, the 30 days ago N deviation forecast value is compared to the present price. If the price is above the forecast value, the contract has expired in the money; otherwise, it has expired worthless. The theoretical value is the average of every such sample. The itm probabilities are calculated the same way.
The default (and only) volatility model is a 20 period EWMA derived historical (realized) volatility. Feel free to extend the script by adding your own.
The filter parameters can be used to remove some forecasts from the sample.
Example A:
filter:
filter type: none
filter value:
Default: the filter is not used; all forecasts are included in the the sample.
Example B:
filter: model
filter type: less than
filter value: 50
If the model is "hv", this will remove all forecasts when the historical volatility is greater than fifty.
Example C:
filter: rank
filter type: greater than
filter value: 75
If the model volatility is in the top 25% of the previous year's range, the forecast will be included in the sample apart from "model" there are some common volatility indexes to choose from, such as Nasdaq (VXN), crude oil (OVX), emerging markets (VXFXI), S&P; (VIX) etc.
Refer to the middle-right table to see the current forecast value, its rank among the last 252 days, and the number of business days until
expiration.
NOTE: This script is meant for the daily chart only.
vstop5 (RA)Upgrade standart Volatility Stop with 5 fixed values for selected tickers.
When switching between tickers - VStop multiplier will be changed to desired fixed value for fixed tickers.
If nothing mached - will be used standart value
See the example of setting here
As You can see on screenshot 5 different VStops can be set up for different tickers.
and as a result:
Доработка стандартного индикатора VStop, но с возможностью зафиксировать для 5-ти разных инструментов свое значение мультипликатора.
Далее при переключении с одного инструмента на другой - значение Мультипликатора VStop будет меняться в соответствии с сохраненными привязанными настройками. для всех НЕ привязанных инструментов - будет использовано значение Мультипликатора по умолчанию, которое также задается в Настройках.
Пример настроек тут
VMA's (T=1h, 2h, 4h, 8h)Plots four VMA's (Variable/Volatility Moving Average) in multiple static resolutions (1h, 2h, 4h, 8h), ideal for support/resistance/stops on predictably trending symbols like BTCUSD.
Example:
Donchian Channel Width The Donchian Channel was developed by Richard Donchian and it could be compared
to the Bollinger Bands. When it comes to volatility analysis, the Donchian Channel
Width was created in the same way as the Bollinger Bandwidth technical indicator was.
As was mentioned above the Donchian Channel Width is used in technical analysis to measure
volatility. Volatility is one of the most important parameters in technical analysis.
A price trend is not just about a price change. It is also about volume traded during this
price change and volatility of a this price change. When a technical analyst focuses his/her
attention solely on price analysis by ignoring volume and volatility, he/she only sees a part
of a complete picture only. This could lead to a situation when a trader may miss something and
lose money. Lets take a look at a simple example how volatility may help a trader:
Most of the price based technical indicators are lagging indicators.
When price moves on low volatility, it takes time for a price trend to change its direction and
it could be ok to have some lag in an indicator.
When price moves on high volatility, a price trend changes its direction faster and stronger.
An indicator's lag acceptable under low volatility could be financially suicidal now - Buy/Sell signals could be generated when it is already too late.
Another use of volatility - very popular one - it is to adapt a stop loss strategy to it:
Smaller stop-loss recommended in low volatility periods. If it is not done, a stop-loss could
be generated when it is too late.
Bigger stop-loss recommended in high volatility periods. If it is not done, a stop-loss could
be triggered too often and you may miss good trades.
Momentum Regression [BackQuant]Momentum Regression
The Momentum Regression is an advanced statistical indicator built to empower quants, strategists, and technically inclined traders with a robust visual and quantitative framework for analyzing momentum effects in financial markets. Unlike traditional momentum indicators that rely on raw price movements or moving averages, this tool leverages a volatility-adjusted linear regression model (y ~ x) to uncover and validate momentum behavior over a user-defined lookback window.
Purpose & Design Philosophy
Momentum is a core anomaly in quantitative finance — an effect where assets that have performed well (or poorly) continue to do so over short to medium-term horizons. However, this effect can be noisy, regime-dependent, and sometimes spurious.
The Momentum Regression is designed as a pre-strategy analytical tool to help you filter and verify whether statistically meaningful and tradable momentum exists in a given asset. Its architecture includes:
Volatility normalization to account for differences in scale and distribution.
Regression analysis to model the relationship between past and present standardized returns.
Deviation bands to highlight overbought/oversold zones around the predicted trendline.
Statistical summary tables to assess the reliability of the detected momentum.
Core Concepts and Calculations
The model uses the following:
Independent variable (x): The volatility-adjusted return over the chosen momentum period.
Dependent variable (y): The 1-bar lagged log return, also adjusted for volatility.
A simple linear regression is performed over a large lookback window (default: 1000 bars), which reveals the slope and intercept of the momentum line. These values are then used to construct:
A predicted momentum trendline across time.
Upper and lower deviation bands , representing ±n standard deviations of the regression residuals (errors).
These visual elements help traders judge how far current returns deviate from the modeled momentum trend, similar to Bollinger Bands but derived from a regression model rather than a moving average.
Key Metrics Provided
On each update, the indicator dynamically displays:
Momentum Slope (β₁): Indicates trend direction and strength. A higher absolute value implies a stronger effect.
Intercept (β₀): The predicted return when x = 0.
Pearson’s R: Correlation coefficient between x and y.
R² (Coefficient of Determination): Indicates how well the regression line explains the variance in y.
Standard Error of Residuals: Measures dispersion around the trendline.
t-Statistic of β₁: Used to evaluate statistical significance of the momentum slope.
These statistics are presented in a top-right summary table for immediate interpretation. A bottom-right signal table also summarizes key takeaways with visual indicators.
Features and Inputs
✅ Volatility-Adjusted Momentum : Reduces distortions from noisy price spikes.
✅ Custom Lookback Control : Set the number of bars to analyze regression.
✅ Extendable Trendlines : For continuous visualization into the future.
✅ Deviation Bands : Optional ±σ multipliers to detect abnormal price action.
✅ Contextual Tables : Help determine strength, direction, and significance of momentum.
✅ Separate Pane Design : Cleanly isolates statistical momentum from price chart.
How It Helps Traders
📉 Quantitative Strategy Validation:
Use the regression results to confirm whether a momentum-based strategy is worth pursuing on a specific asset or timeframe.
🔍 Regime Detection:
Track when momentum breaks down or reverses. Slope changes, drops in R², or weak t-stats can signal regime shifts.
📊 Trade Filtering:
Avoid false positives by entering trades only when momentum is both statistically significant and directionally favorable.
📈 Backtest Preparation:
Before running costly simulations, use this tool to pre-screen assets for exploitable return structures.
When to Use It
Before building or deploying a momentum strategy : Test if momentum exists and is statistically reliable.
During market transitions : Detect early signs of fading strength or reversal.
As part of an edge-stacking framework : Combine with other filters such as volatility compression, volume surges, or macro filters.
Conclusion
The Momentum Regression indicator offers a powerful fusion of statistical analysis and visual interpretation. By combining volatility-adjusted returns with real-time linear regression modeling, it helps quantify and qualify one of the most studied and traded anomalies in finance: momentum.
Volatility-Adjusted Trend Deviation Statistics (C-Ratios)The Pine Script logic provided generates and displays a table with key information derived from VWMA, EMA, and ATR-based "C Ratios," alongside stochastic oscillators, correlation coefficients, Z-scores, and bias indicators. Here’s an explanation of the logic and what the output in the table informs:
Key Calculations and Their Purpose
VWMA and EMA (Smoothing Lengths):
Multiple EMAs are calculated using VWMA as the source, with lengths spanning short-term (13) to long-term (233).
These EMAs provide a hierarchy of smoothed price levels to assess trends over various time horizons.
ATR-Based "C Ratios":
The C Ratios measure deviations of smoothed prices (a_1 to a_7) from the source price relative to ATR at corresponding lengths.
These values normalize deviations, giving insight into the price's relative movement strength and direction over various periods.
Stochastic Oscillator for C Ratios:
Calculates normalized stochastic values for each C Ratio to assess overbought/oversold conditions dynamically over a rolling window.
Helps identify short-term momentum trends within the broader context of C Ratios.
Displays the average stochastic value derived from all C Ratios.
Text: Shows overbought/oversold conditions (Overbought, Oversold, or ---).
Color: Green for strong upward momentum, red for downward, and white for neutral.
Weighted and Mean C Ratio:
The script computes both an arithmetic mean (c_mean) and a weighted mean (c_mean_w) for all C Ratios.
Weighted mean emphasizes short-term values using predefined weights.
Trend Bias and Reversal Detection:
The script calculates Z-scores for c_mean to identify statistically significant deviations.
It combines Z-scores and weighted C Ratio values to determine:
Bias (Bullish/Bearish based on Z-score thresholds and mean values).
Reversals (Based on relative positioning and how the weighted c_mean and un-weighted C_mean move. ).
Correlation Coefficient:
Correlation of mean C Ratios (c_mean) with bar indices over the short-term length (sl) assesses the strength and direction of trend consistency.
Table Output and Its Meaning
Stochastic Strength:
Long-term Correlation:
List of Lengths: Define the list of lengths for EMA and ATR explicitly (e.g., ).
Calculate Mean C Ratios: For each length in the list, calculate the mean C Ratio
Average these values over the entire dataset.
Store Lengths and Mean C Ratios: Maintain arrays for lengths and their corresponding mean C Ratios.
Correlation: compute the Pearson correlation between the list of lengths and the mean C Ratios.
Text: Indicates Uptrend, Downtrend, or neutral (---).
Color: Green for positive (uptrend), red for negative (downtrend), and white for neutral.
Z-Score Bias:
Assesses the statistical deviation of C Ratios from their historical mean.
Text: Bullish Bias, Bearish Bias, or --- (neutral).
Color: Green or red based on the direction and significance of the Z-score.
C-Ratio Mean:
Displays the weighted average C Ratio (c_mean_w) or a reversal condition.
Text: If no reversal is detected, shows c_mean_w; otherwise, a reversal condition (Bullish Reversal, Bearish Reversal).
Color: Indicates the strength and direction of the bias or reversal.
Practical Insights
Trend Identification: Correlation coefficients, Z-scores, and stochastic values collectively highlight whether the market is trending and the trend's direction.
Momentum and Volatility: Stochastic and ATR-normalized C Ratios provide insights into the momentum and price movement consistency across different timeframes.
Bias and Reversal Detection: The script highlights potential shifts in market sentiment or direction (bias or reversal) using statistical measures.
Customization: Users can toggle plots and analyze specific EMA lengths or focus on combined metrics like the weighted C Ratio.
Elastic Buy-Sell Volume Wighted SupertrendCredits: This uses Trading View's buy and sell volume script and the Super trend script.
Elastic Buy-Sell Volume Wighted Supertrend can be used like a traditional supertrend indicator however we do not have to arbitrarily choose a multiplier depending on the stock and time frame the code dynamically adjust the multiplier and this is described below.
The buy and sell ATR (Average True Range) play a crucial role in determining the levels for potential buy and sell signals in the market. These ATR values are calculated based on volume-weighted averages, providing insights into the strength of buying and selling pressures. By incorporating volume into the ATR calculation, the indicator can better adapt to market dynamics, as volume often reflects the intensity of price movements. Instead of using Volume as whole this uses up and down volume derived from lower time frames which is used to calculate buy and sell ATR.
The multiplier is a key factor in the Supertrend calculation, which adjusts the width of the trend bands. The multiplier in this indicator dynamically adjusts itself based on two key components: the ratio of the asset's Average True Range (ATR) to that of a broader market benchmark and the coefficient of variation (CV) of the True Range (TR). The ratio comparison provides a historical context of the asset's volatility relative to the wider market over a longer time frame, while the CV accounts for short-term fluctuations in volatility.
By comparing the asset's ATR to that of the benchmark, traders gain insights into the asset's historical volatility behavior. A higher multiplier suggests that the asset's volatility has historically exceeded that of the benchmark, indicating potentially larger price movements compared to the broader market. Conversely, a lower multiplier suggests the opposite.
The CV component measures short-term variability in the asset's volatility, ensuring that the multiplier adapts to both long-term trends and short-term fluctuations. This combined approach enables traders to make informed decisions, considering both historical volatility relative to the broader market and short-term variability. Ultimately, the dynamic multiplier enhances traders' ability to adjust their strategies effectively across various market conditions.
Overall, the use of buy and sell ATR, along with a dynamically adjusted multiplier, enhances the indicator's ability to identify trend directions and to use a dynamic stop loss level.
NASDAQ 100 Peak Hours StrategyNASDAQ 100 Peak Hours Trading Strategy
Description
Our NASDAQ 100 Peak Hours Trading Strategy leverages a carefully designed algorithm to trade within specific hours of high market activity, particularly focusing on the first two hours of the trading session from 09:30 AM to 11:30 AM GMT-5. This period is identified for its increased volatility and liquidity, offering numerous trading opportunities.
The strategy incorporates a blend of technical indicators to identify entry and exit points for both long and short positions. These indicators include:
Exponential Moving Averages (EMAs) : A short-term 9-period EMA and a longer-term 21-period EMA to determine the market trend and momentum.
Relative Strength Index (RSI) : A 14-period RSI to gauge the market's momentum.
Average True Range (ATR) : A 14-period ATR to assess market volatility and to set dynamic stop losses and trailing stops.
Volume Weighted Average Price (VWAP) : To identify the market's average price weighted by volume, serving as a benchmark for the trading day.
Our strategy uniquely applies a volatility filter using the ATR, ensuring trades are only executed in conditions that favor our setup. Additionally, we consider the direction of the EMAs to confirm the market's trend before entering trades.
Originality and Usefulness
This strategy stands out by combining these indicators within the NASDAQ 100's peak hours, exploiting the specific market conditions that prevail during these times. The inclusion of a volatility filter and dynamic stop-loss mechanisms based on the ATR provides a robust method for managing risk.
By focusing on the early trading hours, the strategy aims to capture the initial market movements driven by overnight news and the opening rush, often characterized by higher volatility. This approach is particularly useful for traders looking to maximize gains from short-term fluctuations while limiting exposure to longer-term market uncertainty.
Strategy Results
To ensure the strategy's effectiveness and reliability, it has undergone rigorous backtesting over a significant dataset to produce a sample size of more than 100 trades. This testing phase helps in identifying the strategy's potential in various market conditions, its consistency, and its risk-to-reward ratio.
Our backtesting adheres to realistic trading conditions, accounting for slippage and commission to reflect actual trading scenarios accurately. The strategy is designed with a conservative approach to risk management, advising not to risk more than 5-10% of equity on a single trade. The default settings in the script align with these principles, ensuring that users can replicate our tested conditions.
Using the Strategy
The strategy is designed for simplicity and ease of use:
Trade Hours : Focuses on 09:30 AM to 11:30 AM GMT-5, during the NASDAQ 100's peak activity hours.
Entry Conditions : Trades are initiated based on the alignment of EMAs, RSI, VWAP, and the ATR's volatility filter within the designated time frame.
Exit Conditions : Includes dynamic trailing stops based on ATR, a predefined time exit strategy, and a trend reversal exit condition for risk management.
This script is a powerful tool for traders looking to leverage the NASDAQ 100's peak hours, providing a structured approach to navigating the early market hours with a robust set of criteria for making informed trading decisions.
VIX Dashboard [NariCapitalTrading]Overview
This VIX Dashboard is designed to provide traders with a quick visual reference into the current volatility and trend direction of the market as measured by CBOE VIX. It uses statistical measures and indicators including Rate of Change (ROC), Average True Range (ATR), and simple moving averages (SMA) to analyze the VIX.
Components
ATR Period : The ATR Period is used to calculate the Average True Range. The default period set is 24.
Trend Period : This period is used for the Simple Moving Average (SMA) to determine the trend direction. The default is set to 48.
Speed Up/Down Thresholds : These thresholds are used to determine significant increases or decreases in the VIX’s rate of change, signaling potential market volatility spikes or drops. These are customizable in the input section.
VIX Data : The script fetches the closing price of the VIX from a specified source (CBOE:VIX) with a 60-minute interval.
Rate of Change (ROC) : The ROC measures the percentage change in price from one period to the next. The script uses a default period of 20. The period can be customized in the input section.
VIX ATR : This is the Average True Range of the VIX, indicating the daily volatility level.
Trend Direction : Determined by comparing the VIX data with its SMA, indicating if the trend is up, down, or neutral. The trend direction can be customized in the input section.
Dashboard Display : The script creates a table on the chart that dynamically updates with the VIX ROC, ATR, trend direction, and speed.
Calculations
VIX ROC : Calculated as * 100
VIX ATR : ATR is calculated using the 'atrPeriod' and is a measure of volatility.
Trend Direction : Compared against the SMA over 'trendPeriod'.
Trader Interpretation
High ROC Value : Indicates increasing volatility, which could signal a market turn or increased uncertainty.
High ATR Value : Suggests high volatility, often seen in turbulent market conditions.
Trend Direction : Helps in understanding the overall market sentiment and trend.
Speed Indicators : “Mooning” suggests rapid increase in volatility, whereas “Cratering” indicates a rapid decrease.
The interpretation of these indicators should be combined with other market analysis tools for best results.
Average True Range Level█ Overview
The indicator uses color-coded columns to represent different levels of normalized ATR, helping traders identify periods of high or low volatility.
█ Calculations
The normalization process involves dividing the current True Range by the Average True Range. The formula for normalized ATR in the code is:
nAtr = nz(barRange/atr)
█ How To Use
Level < 1
During periods when the normalized ATR is less than 1, suggesting a lower level of volatility, traders may explore inside bar strategies. These strategies focus on trading within the range of the previous bar, aiming to capitalize on potential breakout opportunities.
Level between 1 and 3
In instances where the normalized ATR falls between 1 and 3, indicating moderate volatility, a pullback strategy may be considered. Traders look for temporary corrections against the prevailing trend, entering positions in anticipation of the trend's resumption
Level between 2 and 3
Within the range of normalized ATR between 2 and 3, signifying a balanced level of volatility, traders might explore breakout strategies. These strategies involve identifying potential breakout levels using support and resistance or other indicators and entering trades in the direction of the breakout.
Level > 3
When the normalized ATR exceeds 3, signaling high volatility, traders should approach with caution. While not ideal for typical mean reversion strategies, this condition may indicate that the price has become overextended. Traders might wait for subsequent candles, observing a normalized ATR between 2 and 3, to consider mean reversion opportunities after potential overpricing during the high volatility period.
* Note: These strategies are suggestions and may not be suitable for all trading scenarios. Traders should exercise discretion, conduct their own analysis, and adapt strategies based on individual preferences and risk tolerance.
Bull / Bear Market RegimeBull / Bear Market Regime
Instructions:
- A simple risk on or risk off indicator based on CBOE's Implied Correlation and VIX to highlight and indicate Bull / Bear Markets. To be used with the S&P500 index as that's the source from where the CBOE calculates and measures implied volatility & implied correlation. Can also be used with the other indices such as: Dow Jones, S&P 500, Nasdaq, & Nasdaq100, & Index ETF's such as DIA, SPY, QQQ, etc.
- Know the active regime, see the larger picture using the Daily or Weekly view, and visualize the current "Risk On (Bull) or Risk Off (Bear)" environment.
Description:
- Risk On and Risk Off simplified & visualized. Know if we are in a RISK ON or RISK OFF environment (Bull or Bear Market). (Absolute bottoms and tops will occur BEFORE a Risk On (Bull Market) or Risk Off (Bear Market) environment is confirmed!) This indicator is not meant to bottom tick or uptick market price action, but to show the active regime.
- Green: Bull Market, Risk On, low volatility, and low risk.
- Red: Bear Market, Risk Off, high volatility, and higher risk.
Buy & Sell Indicators (DAILY time frame)
- Nothing is 100% guaranteed! Can be used for short to medium term trades at the users discretion in BEAR MARKETS!!
- These signals are meant to be used during a RISK OFF / BEAR MARKET environment that tends to be accompanied with high volatility. A Risk on / Bull Market environment tends to have low volatility and endless rallies, so the signals will differ and in most instances not apply for Bull market / Risk on regime.
- The SELL signal will more often than not signal that a pullback is near in a BULL market and that a BMR-Bear Market Rally is almost over in a BEAR market.
- The BUY signal will have far more accuracy in a BEAR market-high volatility environment and can Identify short-term and major bottoms.
Always use proper sizing and risk management!
vol_premiaThis script shows the volatility risk premium for several instruments. The premium is simply "IV30 - RV20". Although Tradingview doesn't provide options prices, CBOE publishes 30-day implied volatilities for many instruments (most of which are VIX variations). CBOE calculates these in a standard way, weighting at- and out-of-the-money IVs for options that expire in 30 days, on average. For realized volatility, I used the standard deviation of log returns. Since there are twenty trading periods in 30 calendar days, IV30 can be compared to RV20. The "premium" is the difference, which reflects market participants' expectation for how much upcoming volatility will over- or under-shoot recent volatility.
The script loads pretty slow since there are lots of symbols, so feel free to delete the ones you don't care about. Hopefully the code is straightforward enough. I won't list the meaning of every symbols here, since I might change them later, but you can type them into tradingview for data, and read about their volatility index on CBOE's website. Some of the more well-known ones are:
ES: S&P futures, which I prefer to the SPX index). Its implied volatility is VIX.
USO: the oil ETF representing WTI future prices. Its IV is OVX.
GDX: the gold miner's ETF, which is usually more volatile than gold. Its IV is VXGDX.
FXI: a china ETF, whose volatility is VXFXI.
And so on. In addition to the premium, the "percentile" column shows where this premium ranks among the previous 252 trading days. 100 = the highest premium, 0 = the lowest premium.
strangle_pricerUsage:
1. Set the put and call strike inputs to values of your choosing.
2. Select "days to expiration".
3. Set the put and call standard deviations using the output table.
The indicator is meant help price a strangle using historical data and a volatility model. By default, the model is an ewma-method historical volatility. After selecting strikes and standard their corresponding standard deviation, theoretical values and probabilities will be shown in the table. The script is initialized with -1 for several inputs, and won't show any data until these are adjusted.
The theoretical values shown assume a strangle was bought or sold on every historical bar, and averaging their value at expiration.
For example, if you choose the $50 call and $40 put when the underlying is at $45 and there are 30 days until expiration, suppose the volatility is N and
these strikes correspond to M standard deviations. Input those and the resulting theoretial values shown will be based on opening a 30 dte call and put at M standard deviations with respect to the volatility at each bar.
- Past volatility forecasts are plotted in blue, and hidden by default.
- The current volatility forecast is drawn as a blue line.
- The put and call strikes are drawn as red lines.
This indicator is only meant for the daily chart!
Since I won't be able to edit this description later, also check the release notes and script comments for important changes.
3D Wave-PMThe Wave-PM (Whistler Active Volatility Energy - Price Mass) indicator is an oscillator described in Mark Whistler's book 'Volatility Illuminated'.
The Wave-PM was specifically designed to help read cycles of volatility. When visualizing volatility cycles as a heatmap we can get a clear overview of market volatility phases on multiple timeframes, and more importantly as traders give us insight into 'potential' volatility from to pent up energy signaled by the blue and green plumes which invariably give way to big moves signaled by the orange and red plumes.
This indicator can be quite GPU intensive, so simple and also line based visualization methods are included. Also, its free and open source so go ahead and hack it to your hearts content. Enjoy!
Trend MagicTrend Magic is originally a MT platform (MetaTrader) indicator and it can be used with any forex trading systems / strategies for additional confirmation of trading entries or exits. Converted the MT platform code to TradingView Pine version 4. Also you can use Multiple Time Frame.
It also works well with Crypto and Stock Markets.
Trend Magic consists of two main calculation parts as momentum and volatility:
First part is ATR based (like ATR Trailing Stop) logic, second part is all about CCI which also determines the color of Trend Magic.
Blue: when CCI is positive
Red: when CCI is negative
Also added alert condition regarding price crosses :
when LOW CrossesAbove TM
and HIGH CrossesBelow TM
Enjoy
Kıvanç Özbilgiç
PLAIN VAMSThe PLAIN VAMS (Volatility-Adjusted Momentum Score) is a visual tool designed to help traders identify momentum shifts relative to prevailing volatility conditions. Unlike traditional momentum indicators, VAMS adapts dynamically to price fluctuations by comparing current price levels to volatility-based boundaries derived from customizable moving averages.
Key Features:
- Volatility-Adjusted Zones: Prices are evaluated against upper and lower dynamic boundaries, signaling potential overbought or oversold momentum conditions.
Two Modes:
- PLAIN VAMS (default): Uses a longer lookback period for smoother, trend-following behavior.
- RAW VAMS: A shorter lookback for high-sensitivity, intraday or scalping setups.
Customizable Moving Averages:
Choose from multiple MA types (EMA, SMA, WMA, etc.) to match your strategy preferences.
Visual Clarity:
- Color-coded candles for quick signal recognition.
- Optional background shading for immediate context.
- Boundary lines to define momentum thresholds.
How It Works:
The script calculates a moving average (based on user-selected type and period) and applies an upper and lower multiplier to create dynamic price boundaries. When price closes beyond these bands, it suggests a strong directional momentum move. The indicator is fully customizable to adapt to your trading style and timeframe.
Use Cases:
- Identify potential breakouts or trend continuations.
- Filter entries/exits based on momentum strength.
- Combine with other tools for confirmation in your strategy.
This indicator does not repaint or use future-looking data. It’s designed for discretionary and systematic traders looking for an adaptive way to visualize momentum relative to market volatility.
LULD Bands & Trading Halt Detector [Volume Vigilante]📖 LULD Bands & Trading Halt Detector
This advanced tool visualizes official Limit Up / Limit Down (LULD) price bands and detects regulatory trading halts and resumptions based on SEC and NASDAQ rules. It is engineered for high accuracy by anchoring all calculations to the 1-minute timeframe, ensuring reliable signals across any chart resolution.
📌 What Does This Script Do?
- Draws real-time LULD price band estimations and optional buffer (caution) zones directly on the chart.
- Detects trading halt resumptions by monitoring time gaps between candles and other regulatory criteria. (Note: Due to Pine Script limitations, halts cannot be detected in real-time, only resumptions after they occur.)
- Triggers real-time alerts for:
- Trading Resumptions (Limit Up & Limit Down)
- LULD Zone Entries (Caution Zone)
- Band Breaches (Limit Up and Limit Down)
- Plots historical halt resumption markers to analyse past events.
📐 How It Works:
- Implements official SEC/NASDAQ LULD rules for Tier 1 and Tier 2 securities.
- Applies special band adjustments for the final 25 minutes of trading (after 3:35 PM ET).
- Anchors all logic to the 1-minute timeframe for precise calculations, even on higher timeframe charts.
- Includes adjustable volume and volatility filters to eliminate false signals (ghost halts) on low-- liquidity assets, especially Tier 2 securities when TradingView fails to print candles.
⚙️ How to Use It:
1.) Apply the script to any asset or timeframe.
2.) Adjust Volume and Volatility Filters to reduce noise. (Recommended: 500,000+ volume, 10%+ volatility.)
3.) Enable or disable visual components like bands, buffer zones, and halt resumption labels.
4.) Configure alerts directly from the script settings panel.
5.) Apply alerts to individual assets via "Add Alert On..." or to entire watchlists using "Add Alert on the List."
🧩 What Makes This Script Unique?
- True 1-Minute Anchored Calculations: Ensures alerts and visuals match official trading halt criteria regardless of chart timeframe.
- Customisable Buffered Zones: Visualise proximity to regulatory price limits and avoid volatility traps.
- Combines halt resumption detection, limit up/down band visualisation, and real-time alerts into one clean, modular tool.
📚 Disclaimer:
This script is for educational purposes only and does not constitute financial advice. Use at your own discretion and consult a licensed financial advisor before making trading decisions based on it.
Official Resources:
- NASDAQ LULD Regulations (FAQ):
www.nasdaqtrader.com
Current Nasdaq Trading Halts:
www.nasdaqtrader.com
VoluTility🌊 VoluTility forecasts trend exhaustion, breakout pressure, and structural inflection by measuring volatility within the effort stream. Built on the concept of ATR applied to volume, it doesn’t read raw volume — it reveals whether that volume is stable, chaotic, or compressing ahead of a move. The goal is to detect structural setups before they resolve. The lower the timeframe, the greater the alpha.
🧠 Core Logic
A zero-centered histogram shows the deviation of smoothed volume from its own volatility baseline. Positive bars indicate expansion; negative bars signal compression. Color reflects rate-of-change in volume volatility. Opacity tracks effort/result strength — showing when moves are real or hollow.
The overlaid ribbon (EMA vs HMA) highlights rhythm shifts. Orange fill signals real expansion; yellow shows decay or absorption. Together, they expose pre-breakout compression and exhaustion tails before price reacts.
🏗️ Structural Read
On the 1H BTC chart shown, price coils into a shallow pullback, compressing within a narrow range marked by shrinking candle bodies and muted wick aggression. A sudden expansion candle breaks the coil cleanly, with no immediate rejection or wick reversion. Price holds above the breakout pivot, establishing a baseline for structural acceptance and shifting bias toward continuation.
🔰 Zone Descriptions
🔴 Volatile blowout
🟠 Clean expansion
🟡 Passive or absorbed effort
🟢 Steady-state rhythm
🔵 Compression coil
🧐 Suggested Use
VoluTility is expressly designed as an overlay for sub-pane indicators, where it acts as a second-order rhythm map — exposing hidden structural pressure within volume or volatility streams. When paired with volume (like ZVOL or OBVX), it highlights when flow is expanding with intent versus fading into noise. When layered over volatility signals (like ATR Turbulence or WIRE), it reveals whether expansion has real effort behind it — or is just structural slack.
It pairs especially well with the Relative Directional Index (RDI), where its histogram and ribbon offer early exhaustion signals before traditional trend or momentum fades appear. On raw momentum tools, it acts as a filter: softening false breaks and confirming pressure-backed continuation.
Run on 15m or lower charts for early entry cues or breakout anticipation. On 1H charts, use it to validate compression resolution or detect fatigue before structure turns. It doesn’t react to price — it forecasts readiness.
Volatility Price FlowCapitalize on market volatility with our new volatility price flow indicator. We have designed this indicator to process historical price movements and indicate when price may have reached exhaustion in the context of current volatility.
This is achieved by taking the price deviation from a user defined moving average, and applying a weighting to the deviations from the candle body and candle wick on both buy side and sell side, over a user defined period. The period of the base moving average, type of moving average and the period of the historical price deviations can all be modified. This creates a typical 'band' style indicator, though with a unique characteristic that the buy and sell side vary independently as well as the band expansion being based on weighted variables tied to the actual price changes, rather than just a standard deviation the moves uniformly.
Additionally, these bands can be merged with an anchored vwap - we do this so that the deviations of price from the moving average can include a more volume based approach to identifying potential pivots.
The end result is an indicator that reflects the current market price movements, identifies and capitalizes on impulsive or beginning moves to indicate potential tops / bottoms / reversals.
The signals are simple - anytime price closes within a band, having been outside the band, a signal is displayed. As a basic guide to setting the indicator up for the first time, we suggest reducing all of the multipliers to a value less than 1. Then gradually increase each one, until the signals reduce in quantity and improve in quality, starting with the price deviation multiplier, then the volatility multiplier and finally the expansion multiplier.
Last of all, alerts can be created based on the current chart timeframe and indicator settings, simply by adding an alert that uses the built in buy or sell signal.
Note: We cannot guarantee the accuracy of the signals provided, since the user creates the signals by modifying the settings, and as such we can take no responsibility for any trading losses incurred using the indicator and highly encourage all users to manage their risk and only risk what you can afford to lose.
Trading Ranges + ZScoreOverview
The "Trading Ranges + ZScore" script is a versatile technical indicator developed for TradingView. This tool combines two powerful concepts—price ranges and Z-Score analysis—to help traders identify potential trend reversals, overbought/oversold conditions, and trend strength. The script dynamically calculates price ranges based on recent price action and utilizes Z-Score to detect deviations from a statistical norm, providing valuable insights for decision-making in both ranging and trending markets.
Features
Price Ranges: Calculates dynamic upper and lower price boundaries based on volatility and market structure.
Z-Score Oscillator: A statistical measure that highlights overbought/oversold conditions based on the deviation from a moving average.
Trend Detection: Identifies trend continuation or reversal points by comparing current price action against historical levels.
Customizable Alerts: Generates visual signals (diamonds and X crosses) for potential long/short entries and exits.
Visual Representation: Colors the bars based on Z-Score and trend direction, enhancing the chart’s readability and signal clarity.
Customizable Parameters: The script allows users to fine-tune perception length, analysis period, factor multiplier, and oscillator thresholds to fit different market conditions.
Key Input Parameters
Perception: The length used for calculating highest/lowest price points (default: 20).
Analysis: The length used for calculating the moving average and volatility (default: 100).
Factor: A multiplier to adjust the width of the price ranges (default: 2.0).
Oscillator Threshold: The overbought/oversold threshold for the Z-Score oscillator (default: 70).
Trend Filter: A boolean switch that filters signals based on trend direction.
Fill Zones: Option to color-fill between price levels when certain conditions are met.
Bullish/Bearish/Neutral Colors: Customizable colors for bullish, bearish, and neutral signals.
How It Works
Price Ranges Calculation:
The script calculates five levels: two upper boundaries, the average price level, and two lower boundaries. These levels are based on the highest/lowest prices over a user-defined period and adjusted by volatility (Average True Range).
When the price crosses either of these levels, it suggests a significant change in market direction, potentially indicating a trend reversal.
Z-Score Oscillator:
The Z-Score is a statistical measurement of a price's position relative to its moving average. The indicator calculates two variations:
Z-Score based on the absolute difference between the price and the moving average.
Z-Score based on standard deviation.
These oscillators help detect extreme conditions where the price is likely to revert (overbought/oversold zones).
Trend Detection and Signals:
The indicator generates potential buy/sell signals when the price crosses the predefined levels or based on the fast Z-Score crossing the overbought/oversold thresholds.
Weak long/short signals are shown when the faster Z-Score oscillator reaches extreme levels but trend filters are applied to avoid noise.
Bar Colors and Signal Shapes:
Bar colors change dynamically to reflect the trend direction and Z-Score conditions. Signals for potential trades are displayed using diamonds and X crosses, making it easy to spot opportunities visually.
Visuals and Plots
Bar Colors: Changes the bar color based on Z-Score and trend direction.
Z-Score Plot: Displays two Z-Score oscillators, the standard and a faster one for detecting quicker price deviations.
Overbought/Oversold Zones: Highlighted by upper and lower thresholds of the Z-Score.
Long/Short Signals: Uses diamond-shaped markers for strong long/short signals and X-shaped markers for weaker signals.
Dynamic Range Lines: Plots lines for key price levels (upper/lower boundaries, mid-range) based on the dynamic range calculations.
Usage Guide
Identify Overbought/Oversold Conditions: Look for the Z-Score reaching extreme positive or negative values. When combined with trend signals, these conditions often point to a potential reversal.
Follow the Trend: Use the trend filter option to focus only on trades in the direction of the prevailing trend, reducing false signals in ranging markets.
Watch for Range Breakouts: Pay attention to the upper and lower boundaries. Price crossing these levels often signals the start of a new trend or a major price movement.
Adjust Parameters: Tailor the perception length, analysis length, and multiplier to suit different asset classes or timeframes.
Customization
You can adjust the key parameters to adapt the indicator to different markets or personal trading preferences:
- Perception & Analysis Lengths: Control the sensitivity of the price range calculations.
- Factor Multiplier: Adjusts the width of the ranges, with higher values indicating larger zones.
- Oscillator Threshold: Modify the overbought/oversold levels to suit different market volatility.
- Trend Filter: Toggle on/off to focus on trend-following strategies or range-bound conditions.
- Visual Options: Customize colors for bullish, bearish, and neutral signals, as well as enable/disable the zone fills.
