In den Scripts nach "Volatility" suchen
UM VIX30-rolling/VIX Ratio oscillatorSUMMARY
A forward-looking volatility tool that often signals VIX spikes and market reversals before they happen. MA direction flips spotlight the moment volatility pressure shifts.
DESCRIPTION
This indicator compares spot VIX to a synthetic 30-day constant-maturity volatility estimate (“VIX30”) built from VX1 and VX2 futures. The VIX30/VIX Ratio reveals short-term volatility pressure and regime shifts that traditional VX1/VX2 roll-yield alone often misses.
VIX30 is constructed using true calendar-day interpolation between VX1 and VX2, with VX1% and VX2% showing the real-time weights behind the 30-day volatility anchor. The table displays the volatility regime, the VX1/VX2 weights, spot-term roll yield (VIX30/VIX), and futures-term roll yield (VX2/VX1), giving a complete, front-of-the-curve perspective on volatility dynamics.
Use this to spot early vol expansions, collapsing contango, and regime transitions that influence VXX, UVXY, SVIX, VX options, and VIX futures.
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HOW IT WORKS
The script calculates the exact calendar days to expiration for the front two VIX futures. It then applies linear interpolation to blend VX1 and VX2 into a 30-day constant-maturity synthetic volatility measure (“VIX30”). Comparing VIX30 to spot VIX produces the VIX30/VIX Ratio, which highlights short-term volatility pressure and regime direction. A full term-structure table summarizes regime, VX1%/VX2% weights, and both spot-term and futures-term roll yields.
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DEFAULT SETTINGS
VX1! and VX2! are used by default for front-month and second-month futures. These may be manually overridden if TradingView rolls contracts early. The default timeframe is 30 minutes, and the VIX30/VIX Ratio uses a 21-period EMA for regime smoothing. The historical threshold is set to 1.08, reflecting the long-run average relationship between VIX30 and VIX. All settings are user-configurable.
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SUGGESTED USES
• Identify early volatility expansions before they appear in VX1/VX2 roll yield.
• Confirm contango/backwardation shifts with front-of-curve context.
• Time long/short volatility trades in VXX, UVXY, SVIX, and VX options.
• Monitor regime transitions (Low → Cautionary → High) to anticipate trend inflections.
• Combine with price action, NW trends, or MA color-flip systems for higher-confidence entries.
• MA red → green flips may signal opportunities to short volatility or increase equity exposure.
• MA green → red flips may signal opportunities to go long volatility, reduce equity exposure, or even take short-equity positions.
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ALERTS
Alerts trigger when the ratio crosses above or below the historical threshold or when the moving-average slope flips direction. A green flip signals rising volatility pressure; a red flip signals fading or collapsing volatility. These can be used to automate long/short volatility bias shifts or trade-entry notifications.
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FURTHER HINTS
• Increasing orange/red in the table suggests an emerging higher-volatility environment.
• SVIX (inverse volatility ETF) can trend strongly when volatility decays; on a 6h chart, MA green flips often align with attractive short-volatility opportunities.
• For long-volatility trades, consider shrinking to a 30-minute chart and watching for MA green → red flips as early entry cues.
• Experiment with different timeframes and smoothing lengths to match your trading style.
• Higher VIX30/VIX and VX2/VX1 roll yields generally imply faster decay in VXX, UVXY, and UVIX — or stronger upside momentum in SVIX.
HV-SMA DeltaHistorical Volatility with SMA Multiplier
Concept
This indicator acts as a "volatility explosion meter" for the market. Its core principle is to compare the current volatility with its historical average to detect moments when the market begins to "swing" with significantly more force.
The main components are as follows:
① Historical Volatility (HV) This line is an indicator of the current price volatility.
If this line moves higher, it means the price is swinging wildly (high volatility).
If this line is low, it means the price is calm or moving within a narrow range (low volatility).
② SMA x Multiplier This line functions as a "threshold" or "volatility resistance" level. It is calculated from the moving average of past volatility and then multiplied by an adjustable number (smaMultiplier) to create an upper band. In simple terms, this line tells us: "Normally, volatility should not exceed this level."
③ Difference (Histogram) This is the result of subtracting the Threshold Line (②) from the HV value (①).
Appear when the HV breaks above the threshold line. This signals that "volatility has now spiked significantly above its historical average."
Appear when the HV is still below the threshold line. This indicates that volatility remains at a normal or below-average level.
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How to Use
This indicator does not tell you the direction of the price. Instead, it indicates the "power" or "momentum" of the movement. Therefore, it should always be used in conjunction with other tools to confirm the direction.
① Look for "Volatility Breakout" signals.
② Use it to confirm the strength of a trend.
③ Use it for risk management.
You can try adjusting the smaLength and smaMultiplier values in the indicator's settings to fit the specific asset and timeframe you are trading. More volatile assets may require a higher Multiplier.
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หลักการทำงาน (Concept)
Indicator ตัวนี้เป็น "เครื่องวัดการระเบิดของความผันผวน" ในตลาด
โดยแกนหลักเป็นการเปรียบเทียบความผันผวนในปัจจุบันกับความผันผวนโดยเฉลี่ยในอดีต
เพื่อหาจังหวะที่ตลาดเริ่ม "เหวี่ยง" แรงขึ้นอย่างมีนัยสำคัญ
ส่วนประกอบหลักๆ มีดังนี้:
① Historical Volatility (HV)
เส้นนี้คือตัวชี้วัดความผันผวนของราคา ณ ปัจจุบัน
ถ้าเส้นนี้วิ่งขึ้นสูง แปลว่าราคากำลังแกว่งตัวรุนแรง (ผันผวนสูง)
ถ้าเส้นนี้อยู่ต่ำ แปลว่าราคานิ่งๆ หรือเคลื่อนไหวในกรอบแคบๆ (ผันผวนต่ำ)
② SMA x Multiplier
เส้นนี้ทำหน้าที่เป็น "เส้นเกณฑ์" หรือ "แนวต้านของความผันผวน"
ถูกคำนวณมาจากเส้นค่าเฉลี่ยของความผันผวนในอดีต
แล้วคูณด้วยตัวเลข Adjustable (sma-Multiplier) เพื่อสร้างเป็นกรอบบน
พูดง่ายๆ คือ เส้นนี้บอกเราว่า "โดยปกติแล้ว ความผันผวนไม่ควรจะเกินระดับนี้"
③ Difference (Histogram)
เป็นผลลัพธ์จากการนำค่า HV ข้อ ① มาลบกับ เส้นเกณฑ์ ข้อ ②
เกิดขึ้นเมื่อ HV ทะลุเส้นเกณฑ์ขึ้นไป
เป็นสัญญาณว่า ณ ตอนนี้ "ความผันผวนได้พุ่งสูงกว่าค่าเฉลี่ยในอดีตอย่างมีนัยสำคัญ"
เกิดขึ้นเมื่อ HV ยังอยู่ต่ำกว่าเส้นเกณฑ์
บอกว่าความผันผวนยังอยู่ในระดับปกติหรือต่ำกว่าค่าเฉลี่ย
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วิธีการนำไปใช้ (How to Use)
Indicator ตัวนี้ ไม่ได้บอกทิศทางของราคา
แต่จะบอก "พลัง" หรือ "โมเมนตัม" ของการเคลื่อนไหว
เราจึงควรใช้มันร่วมกับเครื่องมืออื่นเพื่อยืนยันทิศทางเสมอ
① มองหาสัญญาณ "การระเบิดของราคา" (Volatility Breakout)
② ใช้ยืนยันความแข็งแกร่งของเทรนด์
③ ใช้ในการบริหารความเสี่ยง
สามารถลองปรับค่า smaLength และ smaMultiplier ในการตั้งค่า Indicator
เพื่อให้เข้ากับสินทรัพย์และ Timeframe ที่เทรดได้นะ
สินทรัพย์ที่เหวี่ยงแรงๆ อาจต้องใช้ Multiplier ที่สูงขึ้น เป็นต้น
Candle Spread
Candle Spread is an indicator that helps traders measure the range of price movement within each candle over a specified time period. It calculates the range of the candle between the High and Low (High - Low) and displays it in a separate window below the chart as columns.
Key Features:
Colored Bars: The bars are colored based on the candle's direction:
Bullish Candle: Bars are Green.
Bearish Candle: Bars are Red.
Moving Average: The indicator includes a 30-period Simple Moving Average (SMA), which represents the overall average range of the candles.
Helps Identify Market Volatility: This indicator helps traders identify wide-range candles (signaling high volatility in the market), which could indicate a surge in momentum or potential trend reversals.
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
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)
trend_vol_stopThe description below is copied from the script's comments. Because TradingView does not allow me to edit this description, please refer to the script's comments section, as well as the release notes, for the most up-to-date information.
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Usage:
The inputs define the trend and the volatility stop.
Trend:
The trend is defined by a moving average crossover. When the short
(or fast) moving average is above the long (slow) moving average, the
trend is up. Otherwise, the trend is down. The inputs are:
long: the number of periods in the long/slow moving average.
short: the number of periods in the short/fast moving average.
The slow moving average is shown in various colors (see explanation
below. The fast moving average is a faint blue.
Volatility stop:
The volatility stop has two modes, percentage and rank. The percentage
stop is given in terms of annualized volatility. The rank stop is given
in terms of percentile.
stop_pct and stop_rank are initialized with "-1". You need to set one of
these to the values you want after adding the indicator to your chart.
This is the only setting that requires your input.
mode: choose "rank" for a rank stop, "percentage" for a percentage stop.
vol_window: the number of periods in the historical volatility
calculation. e.g. "30" means the volatility will be a weighted
average of the previous 30 periods. applies to both types of stop.
stop_pct: the volatility limit, annualized. for example, "50" means
that the trend will not be followed when historical volatility rises
above 50%.
stop_rank: the trend will not be followed when the volatility is in the
N-th percentile. for example, "75" means the trend will not be
followed when the current historical volatility is greater than 75%
of previous volatilities.
rank_window: the number of periods in the rank percentile calculation.
for example, if rank_window is "252" and "stop_rank" is "80", the
trend will not be followed when current historical volatility is
greater than 80% of the previous 252 historical volatilities.
Outputs:
The outputs include moving averages, to visually identify the trend,
a volatility table, and a performance table.
Moving averages:
The slow moving average is colored green in an uptrend, red in a
downtrend, and black when the volatility stop is in place.
Volatility table:
The volatility table gives the current historical volatility, annualized
and expressed as a whole number percentage. E.g. "65" means the
instrument's one standard deviation annual move is 65% of its price.
The current rank is expressed, also as a whole number percentage. E.g.
"15" means the current volatility is greater than 15% of previous
volatilities. For convenience, the volatilities corresponding to the
0, 25, 50, 75, and 100th percentiles are also shown.
Performance table:
The performance table shows the current strategy's performance versus
buy-and-hold. If the trend is up, the instrument's return for that
period is added to the strategy's return, because the strategy is long.
If the trend is down, the negative return is added, because the strategy
is short. If the volatility stop is in (the slow moving average is
black), that period's return is excluded from the strategy returns.
Every period's return is added to the buy-and-hold returns.
The table shows the average return, the standard deviation of returns,
and the sharpe ratio (average return / standard deviation of returns).
All figures are expressed as per-period, whole number percentages.
For exmaple, "0.1" in the mean column on a daily chart means a
0.1% daily return.
The number of periods (samples) for each strategy is also shown.
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.
Liquidity Trend & Squeeze RadarThe Liquidity Trend & Squeeze Radar is a comprehensive trading system designed to visualize the three most critical components of price action: Trend, Volatility, and Momentum. The core philosophy of this tool is to identify periods of market "compression" (low volatility), where energy builds up, and then signal when that energy is released (expansion) for a potential breakout trade. It combines an EMA Cloud for trend direction with a TTM-style Squeeze indicator and a linear regression momentum filter.
Key Components
Trend Cloud (Structure) This component identifies the overall market bias. It uses a Fast EMA and a Slow EMA to create a shaded "Cloud."
Uptrend: The Fast EMA is above the Slow EMA. The Cloud is shaded green (default).
Downtrend: The Fast EMA is below the Slow EMA. The Cloud is shaded red (default).
Usage: Generally, traders should look to take Long signals only when the Trend Cloud is bullish and Short signals when the Trend Cloud is bearish.
Volatility Radar (The Squeeze) This logic detects when the market enters a period of low volatility. It calculates this by comparing Bollinger Bands (Expansion) against Keltner Channels (Average Range).
Squeeze Active: When the Bollinger Bands narrow and go inside the Keltner Channels, a "Squeeze" is active. This is represented by gray dots plotted along the Fast EMA and gray-colored price candles.
Usage: Do not trade during a Squeeze. This indicates indecision and chop. Treat this as a "Wait" signal while potential energy builds.
Momentum Filter (Hidden Logic) While the Squeeze is active, the script calculates the underlying momentum using Linear Regression. This predicts the likely direction of the breakout before it happens. This data is displayed in the Dashboard.
Breakout Signals (Fire) When the Squeeze condition ends (volatility expands), the script checks the Momentum filter.
Bullish Breakout: If the Squeeze ends and Momentum is positive, a triangle pointing up is plotted below the bar.
Bearish Breakout: If the Squeeze ends and Momentum is negative, a triangle pointing down is plotted above the bar.
Status Dashboard A table located in the top-right corner provides a real-time summary of the market state without needing to interpret the chart visuals manually. It lists the current Trend direction, Volatility state (Squeeze vs. Expansion), and Momentum value (Positive vs. Negative).
How to Trade This Indicator
Step 1: Identify the Trend Observe the background Cloud. Ensure you are trading in the direction of the dominant flow. If the Cloud is green, favor Longs. If red, favor Shorts.
Step 2: Wait for the Squeeze Look for the gray dots to appear on the moving average line and for the candles to turn gray. This indicates the market is resting and building energy. During this phase, you are stalking the trade. Avoid entering positions while the gray dots remain visible.
Step 3: The Breakout (The Trigger) Wait for the gray dots to disappear. This means the Squeeze has "Fired."
Long Entry: Look for a Triangle Up signal. Ideally, this should occur when the Trend Cloud is green.
Short Entry: Look for a Triangle Down signal. Ideally, this should occur when the Trend Cloud is red.
Step 4: Confirmation Check the Dashboard table. High-probability trades occur when all three metrics align (e.g., Trend is BULL, Volatility is EXPANSION, and Momentum is POSITIVE).
Settings Guide
Trend Structure:
Fast/Slow EMA Length: Adjusts the sensitivity of the Trend Cloud. Higher numbers effectively smooth out noise but react slower to trend changes.
Show Trend Cloud: Toggles the shaded area between EMAs on or off.
Volatility Radar:
Bollinger/Keltner Settings: These define the Squeeze sensitivity.
Keltner Mult: The most important setting. The default is 1.5. Lowering this to 1.0 will make the Squeeze harder to trigger (requiring extreme compression), leading to fewer but potentially more explosive signals.
Momentum:
Momentum Length: The lookback period for the linear regression calculation used to determine breakout direction.
Visuals:
Colorize Candles: Paints the price bars based on the current state (Gray for Squeeze, Green/Red for Trend).
Show Dashboard: Toggles the visibility of the data table.
Disclaimer This indicator and guide are for educational and informational purposes only. They do not constitute financial, investment, or trading advice. Trading in financial markets involves a significant risk of loss and is not suitable for every investor. Past performance of any trading system or methodology is not necessarily indicative of future results. The user assumes all responsibility for any trades made using this tool. Always use proper risk management.
ATR Daily (Classic vs Robust, NY-Fix, Spike Control)📘 What this indicator does
This tool provides an advanced view of daily market volatility by comparing two versions of the Average True Range (ATR):
• Classic ATR — standard Wilder smoothing
• Robust ATR — uses median-based filtering and spike-control logic to reduce distortion from abnormal candles
Both values are calculated using daily data aligned to the New York trading session, so volatility resets at the same moment each institutional trading day begins. This keeps readings consistent across crypto, forex and stocks, even on intraday charts.
⚙️ How it works (in simple terms)
The script evaluates each True Range (TR) value relative to a median-based threshold:
• Abnormally large ranges are either clamped to a limit or excluded from updating ATR
• A hard cap prevents single spikes from inflating the entire indicator
• The result is a smoother and more realistic representation of daily volatility
This allows ATR to reflect typical market behaviour instead of rare one-off events.
📊 What appears on the chart
• Two daily ATR lines (Classic and Robust)
• Histogram showing the percentage of daily range already completed
• Red bars when price exceeds 100% of daily ATR
• A data table with volatility metrics
• Background highlights on days with extreme values
💡 How traders can use it
• Identify when a market has already completed most of its typical daily move
• Compare Classic vs Robust ATR to spot news-driven distortion
• Use Robust ATR for more stable stop-loss and take-profit logic
• Track volatility expansion or contraction across sessions
⚙️ Key settings
Setting Purpose
ATR period Standard smoothing length (default 14)
Robust mode Clamp, Freeze or Off
MAD multiplier Sensitivity to outliers
Cap × median(TR) Maximum allowed spike size
Base for passed ATR Which ATR is used to measure daily %
Freeze weekends Keeps ATR unchanged on Sat/Sun
🧩 Unique concept
Unlike typical ATR indicators, this one combines robust statistics (median + MAD) with session-based fixation. ATR values update only once per New York session, creating stable volatility measurements that match institutional timing.
🔒 Source code
The script is published with protected source code to preserve its statistical structure and prevent unauthorized modification.
🧭 Summary
ATR Daily (Classic vs Robust, NY-Fix) provides a clearer and more reliable view of daily volatility.
It helps determine whether the market is still in the early phase of its daily range or already exhausted.
MACD Scaled Overlay█ OVERVIEW
The "MACD Scaled Overlay" indicator is an advanced version of the classic MACD (Moving Average Convergence Divergence) oscillator that displays signals directly on the price chart. Instead of a traditional separate panel, the MACD line, signal line, and histogram are scaled and overlaid on the price chart, making it easier to identify key price levels and potential reversal points. The indicator also supports the detection of divergences (regular and hidden) and offers extensive customization options, such as adjusting colors, line thickness, and enabling/disabling visual elements.
█ CONCEPTS
The "MACD Scaled Overlay" indicator is designed to simplify trend and reversal analysis by integrating MACD signals with the price chart. The MACD Scaled Overlay is scaled relative to the average candle range, allowing the lines and histogram to dynamically adjust to market volatility. Additionally, the indicator enables the detection of divergences (bullish and bearish, both regular and hidden) based on the traditional MACD histogram (before scaling), ensuring consistency with classic divergence analysis. The indicator is most effective when combined with other technical analysis tools, such as Fibonacci levels, pivot points, or trend lines.
█ MACD Calculations and Scaling
The indicator is based on the classic MACD formula, which includes:
-MACD Line: The difference between the fast EMA (default: 12) and the slow EMA (default: 26).
-Signal Line: The EMA of the MACD line (default: 9).
-Histogram: The difference between the MACD line and the signal line.
Scaling is achieved by normalizing the MACD values relative to the standard deviation and the average candle range. This makes the lines and histogram dynamically adjust to market volatility, improving their readability and utility on the price chart. The scaling formulas are:
-MACD Scaled: macdNorm * avgRangeLines * scaleFactor
-Signal Scaled: signalNorm * avgRangeLines * scaleFactor
-Histogram Scaled: histNorm * avgRangeHist * scaleFactor
Where:
-macdNorm and signalNorm are the normalized MACD and signal line values.
-avgRangeLines and avgRangeHist are the average candle ranges.
-scaleFactor is the scaling multiplier (default: 2).
The positioning of the lines and histogram is relative to the candle midpoint (candleMid = (high + low) / 2), ensuring proper display on the price chart. Divergences are calculated based on the traditional MACD histogram (before scaling), maintaining consistency with standard divergence detection methodology.
█INDICATOR FEATURES
-Dynamic MACD and Signal Lines: Scaled and overlaid on the price chart, facilitating the identification of reversal points.
-Histogram: Displays the difference between the MACD and signal lines, dynamically adjusted to market volatility.
-Divergence Detection: Ability to detect regular and hidden divergences (bullish and bearish) based on the traditional MACD histogram, with options to enable/disable their display.
-Visual Customization: Options to adjust colors, line thickness, transparency, and enable/disable elements such as the zero line, MACD line, signal line, or histogram.
-Smoothing: Smoothing length for lines (default: 1) and histogram (default: 3). Smoothing may delay crossover signals, which should be considered during analysis.
-Alerts: Alert conditions for MACD and signal line crossovers, enabling notifications for potential buy/sell signals.
█ HOW TO SET UP THE INDICATOR
-Add the "MACD Scaled Overlay" indicator to your TradingView chart.
-Configure parameters in the settings, such as EMA lengths, scaling multiplier, or smoothing periods, to match your trading style.
-Enable or disable the display of the zero line, MACD line, signal line, or histogram based on your needs.
-Adjust colors and line thickness in the "Style" section and transparency settings in the input section to optimize visualization.
█ HOW TO USE
Add the indicator to your chart, configure the parameters, and observe the interactions of the price with the MACD line, signal line, and histogram to identify potential entry and exit points. Key signals include:
-MACD and Signal Line Crossovers: A crossover of the MACD line above the signal line may indicate a buy signal (bullish cross), while a crossover below the signal line may indicate a sell signal (bearish cross).
-Crossings Through the Price Line (Zero): The MACD line or histogram crossing the price line (candle midpoint) may indicate a change in momentum. For example, the histogram moving from negative to positive values near the price line may signal increasing bullish trend strength.
-Divergences: Detection of regular and hidden divergences (bullish and bearish) based on the traditional MACD histogram can help predict trend reversals. Divergences are not standalone signals, as they are delayed by the specified pivot length (default: 3). However, they help strengthen the significance of other signals, such as crossovers or support/resistance levels.
The indicator is most effective when combined with other tools, such as Fibonacci levels, pivot points, or support/resistance lines, to confirm signals.
Range TableThe Range Table indicator calculates and displays the Daily Average True Range (ATR), the current day's True Range (TR), and two customizable ATR percentage values in a clean table format. It provides values in ticks, points, and USD, helping traders set stop-loss buffers based on market volatility.
**Features:**
- Displays the Daily ATR (14-period) and current day's True Range (TR) with its percentage of the Daily ATR.
- Includes two customizable ATR percentages (default: 75% and 10%, with the second disabled by default).
- Shows values in ticks, points, and USD based on the symbol's tick size and point value.
- Customizable table position, background color, text color, and font size.
- Toggle visibility for the table and percentage rows via input settings.
**How to Use:**
1. Add the indicator to your chart.
2. Adjust the table position, colors, and font size in the input settings.
3. Enable or disable the 75% and 10% ATR rows or customize their percentages.
4. Use the displayed values to set stop-loss or take-profit levels based on volatility.
**Ideal For:**
- Day traders and swing traders looking to set volatility-based stop-losses.
- Users analyzing tick, point, and USD-based risk metrics.
**Notes:**
- Ensure your chart is set to a timeframe that aligns with the daily ATR calculations.
- USD values are approximate if `syminfo.pointvalue` is unavailable.
Developed by FlyingSeaHorse.
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.
Recency-Weighted Market Memory w/ Quantile-Based DriftRecency-Weighted Market Memory w/ Quantile-Based Drift
This indicator combines market memory, recency-weighted drift, quantile-based volatility analysis, momentum (RoC) filtering, and historical correlation checks to generate dynamic forecasts of possible future price levels. It calculates bullish and bearish forecast lines at each horizon, reflecting how the price might behave based on historical similarities.
Trading Concepts & Mathematical Foundations Explained
1) Market Memory
Concept:
Markets tend to repeat past behaviors under similar conditions. By identifying historical market states that closely match current conditions, we predict future price movements based on what happened historically.
Calculation Steps:
We select a historical lookback window (for example, 210 bars).
Each historical bar within this window is evaluated to see if its conditions match the current market. Conditions include:
Correlation between price change and bullish/bearish volume changes (over a user-defined correlation lookback period).
Momentum (Rate of Change, RoC) measured over a separate lookback period.
Only bars closely matching current conditions (within user-defined tolerance percentages) are included.
2) Recency-Weighted Drift
Concept:
Recent market movements often influence future direction. We assign more importance to recent bars to capture the current market bias effectively.
Calculation Steps:
Consider recent price changes between opens and closes for a user-defined drift lookback (for example, last 20 bars).
Give higher weight to recent bars (the most recent bar gets the highest weight, and weights decrease progressively for older bars).
Average these weighted changes separately for upward and downward movements, then combine these averages to calculate a final drift percentage relative to the current price.
3) Correlation Filtering
Concept:
Price changes often correlate strongly with bullish or bearish volume activity. By using historical correlation comparisons, we focus only on past market states with similar volume-price dynamics.
Calculation Steps:
Compute current correlations between price changes and bullish/bearish volume over the user-defined correlation lookback.
Evaluate each historical bar to see if its correlation closely matches the current correlation (within a user-specified percentage tolerance).
Only historical bars meeting this correlation criterion are selected.
4) Momentum (RoC) Filtering
Concept:
Two market periods may exhibit similar correlation structures but differ in how fast prices move (momentum). To ensure true similarity, momentum is checked as an additional filter.
Calculation Steps:
Compute the current Rate of Change (RoC) over the specified RoC lookback.
For each candidate historical bar, calculate its historical RoC.
Only include historical bars whose RoC closely matches the current RoC (within the RoC percentage tolerance).
5) Quantile-Based Volatility and Drift Amplification
Concept:
Quantiles (such as the 95th, 50th, and 5th percentiles) help gauge if current prices are near historical extremes or the median. Quantile bands measure volatility expansions and contractions.
Calculation Steps:
Calculate the 95%, 50%, and 5% quantiles of price over the quantile lookback period.
Add and subtract multiples of the standard deviation to these quantiles, creating upper and lower bands.
Measure the bands' widths relative to the current price as volatility indicators.
Determine the active quantile (95%, 50%, or 5%) based on proximity to the current price (within a percentage tolerance).
Compute the rate of change (RoC) of the active quantile to detect directional bias.
Combine volatility and quantile RoC into a scaling factor that amplifies or dampens expected price moves.
6) Expected Value (EV) Computation & Forecast Lines
Concept:
We forecast future prices based on how similarly-conditioned historical periods performed. We average historical moves to estimate the expected future price.
Calculation Steps:
For each forecast horizon (e.g., 1 to 27 bars ahead), collect all historical price moves that passed correlation and RoC filters.
Calculate average historical moves for bullish and bearish cases separately.
Adjust these averages by applying recency-weighted drift and quantile-based scaling.
Translate adjusted percentages into absolute future price forecasts.
Draw bullish and bearish forecast lines accordingly.
Indicator Inputs & Their Roles
Correlation Tolerance (%)
Adjusts how strictly the indicator matches historical correlation. Higher tolerance includes more matches, lower tolerance selects fewer but closer matches.
Price RoC Lookback and Price RoC Tolerance (%)
Controls how momentum (speed of price moves) is matched historically. Increasing tolerance broadens historical matches.
Drift Lookback (bars)
Determines the number of recent bars influencing current drift estimation.
Quantile Lookback Period and Std Dev Multipliers
Defines quantile calculation and the size of the volatility bands.
Quantile Contact Tolerance (%)
Sets how close the current price must be to a quantile for it to be considered "active."
Forecast Horizons
Specifies how many future bars to forecast.
Continuous Forecast Lines
Toggles between drawing continuous lines or separate horizontal segments for each forecast horizon.
Practical Trading Applications
Bullish & Bearish EV Lines
These forecast lines indicate expected price levels based on historical similarity. Green indicates positive expectations; red indicates negative.
Momentum vs. Mean Reversion
Wide quantile bands and high drift suggest momentum, while extremes may signal possible reversals.
Volatility Sensitivity
Forecasts adapt dynamically to market volatility. Broader bands increase forecasted price movements.
Filtering Non-Relevant Historical Data
By using both correlation and RoC filtering, irrelevant past periods are excluded, enhancing forecast reliability.
Multi-Timeframe Suitability
Adaptable parameters make this indicator suitable for different trading styles and timeframes.
Complementary Tool
This indicator provides probabilistic projections rather than direct buy or sell signals. Combine it with other trading signals and analyses for optimal results.
Important Considerations
While historically-informed forecasts are valuable, market behavior can evolve unpredictably. Always manage risks and use supplementary analysis.
Experiment extensively with input settings for your specific market and timeframe to optimize forecasting performance.
Summary
The Recency-Weighted Market Memory w/ Quantile-Based Drift indicator uniquely merges multiple sophisticated concepts, delivering dynamic, historically-informed price forecasts. By combining historical similarity, adaptive drift, momentum filtering, and quantile-driven volatility scaling, traders gain an insightful perspective on future price possibilities.
Feel free to experiment, explore, and enjoy this powerful addition to your trading toolkit!
G-VIDYA | QuantEdgeBIntroducing G-VIDYA by QuantEdgeB
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🔹 Overview
The G-VIDYA | QuantEdgeB is a dynamic trend-following indicator that enhances market trend detection using Gaussian smoothing and an adaptive Variable Index Dynamic Average (VIDYA). It is designed to reduce noise, improve responsiveness, and adapt to volatility, making it a powerful tool for traders looking to capture long-term trends efficiently.
By integrating ATR-based filtering, the indicator creates a dynamic support and resistance band around VIDYA, allowing for more accurate trend confirmations. Additionally, traders have the option to enable trade labels for clearer visual signals.
This indicator is well-suited for medium to long-term trend traders, combining mathematical precision with market adaptability for robust trading strategies.
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🚀 Key Features
1. Gaussian Smoothing → Reduces market noise and smoothens price action.
2. VIDYA Adaptive Calculation → Adjusts dynamically based on market volatility.
3. ATR-Based Filtering → Creates a volatility-driven range around VIDYA.
4. Dynamic Trend Confirmation → Identifies bullish and bearish momentum shifts.
5. Trade Labels (Optional) → Can display Long/Cash labels on chart for better clarity.
6. Customizable Color Modes → Offers multiple visual themes for personalized experience.
7. Automated Alerts → Sends buy/sell alerts for crossover trend changes.
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📊 How It Works
1. Gaussian Smoothing is applied to the closing price to remove noise and improve signal clarity.
2. VIDYA Calculation dynamically adjusts to price movements, making it more reactive during high-volatility periods and stable in low-volatility environments.
3. ATR-Based Filtering establishes a dynamic range (Upper & Lower ATR Bands) around VIDYA:
- If price breaks above the upper ATR band, it signals a potential long trend.
- If price breaks below the lower ATR band, it signals a potential short trend.
4. The indicator assigns color-coded candles based on trend direction:
- Bullish Trend → Blue/Green (Uptrend)
- Bearish Trend → Red/Maroon (Downtrend)
5. Labels & Alerts (Optional)
- Users can activate Long/Cash labels to mark buy/sell opportunities.
- Built-in alerts trigger automatic notifications when trend direction changes.
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🎨 Visual Representation
- VIDYA Line → A smooth, trend-following line that dynamically adjusts to market conditions.
- Upper & Lower ATR Bands → Establishes a volatility-based corridor around VIDYA.
- Bar Coloring → Candles change color according to the detected trend.
- Long/Short Labels (Optional) → Displays trade entry/exit signals (can be enabled/disabled).
- Alerts → Generates trade notifications based on trend reversals.
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⚙️ Default Settings
- Gaussian Smoothing
- Length: 4
- Sigma: 2.0
- VIDYA Settings
- VIDYA Length: 46
- Standard Deviation Length: 28
- ATR Settings
- ATR Length: 14
- ATR Multiplier: 1.3
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💡 Who Should Use It?
✅ Trend Traders → Those who rely on medium-to-long-term trends for trading decisions.
✅ Swing Traders → Ideal for traders who want to capture trend reversals and ride momentum.
✅ Quantitative Analysts → Provides statistically driven smoothing and adaptive trend detection.
✅ Risk-Averse Traders → ATR filtering helps manage market volatility effectively.
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Conclusion
The G-VIDYA | QuantEdgeB is an advanced trend-following indicator that combines Gaussian smoothing, adaptive VIDYA filtering, and ATR-based dynamic trend analysis to deliver robust and reliable trade signals.
✅ Key Takeaways
📌 Adaptive & Dynamic: Adjusts to market conditions, making it effective for trend-following strategies.
📌 Noise Reduction: Gaussian smoothing helps filter out short-term fluctuations, improving signal clarity.
📌 Volatility Awareness: ATR-based filtering ensures better handling of market swings and trend reversals.
By blending mathematical precision and quantitative market analysis, G-VIDYA | QuantEdgeB offers a powerful edge in trend trading strategies.
🔹 Disclaimer: Past performance is not indicative of future results. No trading strategy can guarantee success in financial markets.
🔹 Strategic Advice: Always backtest, optimize, and align parameters with your trading objectives and risk tolerance before live trading.
Volatility Adaptive Signal Tracker (VAST)The Adaptive Trend Following Buy/Sell Signals Pine Script is designed to help traders identify and capitalize on market trends using an adaptive trend-following strategy. This script focuses on generating reliable buy and sell signals by analyzing market trends and volatility. It simplifies the trading process by providing clear signals without plotting additional lines, making it easy to use and interpret.
Key Features:
Adaptive Trend Following:
The script employs an adaptive trend-following approach that leverages market volatility to generate buy and sell signals. This method is effective in both trending and volatile markets.
Inputs and Customization:
The script includes customizable parameters for the Simple Moving Average (SMA) length, the Average True Range (ATR) length, and the ATR multiplier. These inputs allow traders to adjust the sensitivity of the signals to match their trading style and market conditions.
Signal Generation:
Buy Signal: Generated when the closing price crosses above the upper adaptive band, indicating a potential upward trend.
Sell Signal: Generated when the closing price crosses below the lower adaptive band, indicating a potential downward trend.
Visual Signals:
The script uses plotshape to mark buy signals with green labels below the bars and sell signals with red labels above the bars. This clear visual representation helps traders quickly identify trading opportunities.
Alert Conditions:
The script sets up alert conditions for both buy and sell signals. Traders can use these alerts to receive notifications when a signal is generated, ensuring they do not miss any trading opportunities.
How It Works:
SMA Calculation: The script calculates the Simple Moving Average (SMA) over a specified period, which helps in identifying the general trend direction.
ATR Calculation: The Average True Range (ATR) is calculated to measure market volatility.
Adaptive Bands: Upper and lower adaptive bands are created by adding and subtracting a multiple of the ATR to the SMA, respectively.
Signal Logic: Buy signals are generated when the closing price crosses above the upper band, while sell signals are generated when the closing price crosses below the lower band.
Example Use Case:
A trader looking to capitalize on medium-term trends in the Nifty futures market can use this script to receive timely buy and sell signals. By customizing the SMA length and ATR parameters, the trader can fine-tune the script to match their trading strategy, ensuring they enter and exit trades at optimal points.
Benefits:
Simplicity: The script provides clear buy and sell signals without cluttering the chart with additional lines or indicators.
Adaptability: Customizable parameters allow traders to adapt the script to various market conditions and trading styles.
Alerts: Built-in alert conditions ensure traders receive timely notifications, helping them to act quickly on trading signals.
How to Use:
Open TradingView: Go to the TradingView website and log in.
Create a New Chart: Click on the “Chart” button to open a new chart.
Open the Pine Script Editor: Click on the “Pine Editor” tab at the bottom of the chart.
Create a New Script: Delete any default code in the Pine Script editor and paste the provided script.
Add to Chart: Click on the “Add to Chart” button to compile and add the script to your chart.
Save the Script: Click “Save” and name the script.
Set Alerts: Right-click on the chart, select “Add Alert,” and choose the appropriate condition to set alerts for buy and sell signals.
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.
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)
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.
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.
