Cari dalam skrip untuk "Volatility"
Volatility-Adjusted Momentum Signal (VAMS)🔹 VAMS — Volatility-Adjusted Momentum Signal | QuantumResearch
Purpose:
VAMS is a composite momentum oscillator that merges price momentum and volatility momentum using z-score transformations. It helps identify meaningful trend shifts by emphasizing both directional move strength and the volatility regime.
How It Works:
The system builds two independent z-scores:
Price Z-Score: Measures how far current price deviates from its EMA.
Volatility Z-Score: Applies the same logic to volatility (standard deviation of price).
Both z-scores are combined to produce a Volatility-Adjusted Momentum Signal (Z_total), which is then compared to dynamic thresholds based on rolling standard deviation.
Signal Classification:
Bullish: Z_total exceeds upper dynamic threshold.
Bearish: Z_total falls below lower dynamic threshold.
Neutral: Values in between.
Display Features:
Oscillator line color-coded by signal state (bullish/bearish/neutral).
Background and bar colors reflect momentum strength using a gradient scale.
Real-time info table showing:
Z-score and rate-of-change (ROC) for both price and volatility.
Final momentum classification.
Key Features:
Fuses price and volatility into a single adaptive oscillator.
Dynamic thresholds prevent false signals in low-volatility environments.
Suitable for trend identification, momentum stacking, or signal confirmation.
Performance Notes:
VAMS has been tested on BTC, ETH, and ETH/BTC and consistently aligns well with trend inflection points — particularly during volatility regime shifts.
Trading Application:
Confirm breakouts or breakdowns.
Spot early trend strength.
Avoid false signals during low-volatility noise.
Disclaimer: This script is provided for educational and informational purposes only. Trading cryptocurrencies involves substantial risk and is not appropriate for all investors. Past performance is not indicative of future results.
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!
EMA Cloud Matrix with Trend Tablethis script builds upon a standard exponential moving average (ema) by adding volatility-based dynamic bands and persistent trend detection. it also enhances decision-making by including visual indicators (labels and clouds), a multi-timeframe trend table, and optional retest signals. here's an in-depth explanation:
volatility-based bands:
instead of just plotting an ema line, this script creates an upper and lower band around the ema using the average volatility (calculated as the average range of high-low over 100 bars).
the bands represent areas where price is likely to deviate significantly from the ema, signaling potential trend shifts.
persistent trend detection:
a persistent trend variable updates when price crosses above the upper band (bullish trend) or below the lower band (bearish trend). this ensures that the trend state persists until a new cross event occurs.
normal emas don't store such states—they merely provide a lagging representation of price.
visual enhancements:
a color-coded cloud dynamically highlights the area between the ema and the current trend line (upper or lower band), making trend direction clearer.
labels mark significant crossover or crossunder events, serving as potential buy or sell signals.
multi-timeframe trend table:
the table shows the trend direction (buy/sell) for the 15-minute, 4-hour, and daily timeframes, giving a broader perspective for trading decisions.
optional retest signals:
when enabled, it identifies situations where price tests the ema after trending away, providing additional opportunities for entries or exits.
first time ever - why use this and how?
why use this?
this is ideal for traders who:
struggle with trend-following strategies that lack clear entry/exit rules.
want a hybrid system combining ema-based smoothness with volatility-based adaptability.
need to visualize trends in multiple timeframes without switching charts.
how to use this?
buy signal: when the price crosses above the upper band, the trend flips to bullish. you’ll see a green upward arrow (▲) on the chart, indicating a potential long entry.
sell signal: when the price crosses below the lower band, the trend flips to bearish. a blue downward arrow (▼) appears on the chart, signaling a potential short entry.
retest signals (optional): if the price comes back to test the ema during a trend, a retest label can guide you for a secondary entry.
exit based on risk-reward ratio (rr)
this script doesn't explicitly calculate risk-reward ratios (rr), but you can manage exits effectively using the following ideas:
set a defined stop-loss:
if entering on a buy signal (crossover above upper band), place a stop below the ema or the lower band. for short signals, use the upper band as a stop.
this ensures the stop-loss dynamically adjusts with volatility.
use rr to set targets:
decide on a risk-reward ratio like 1:2 or 1:3. for example:
if your stop-loss is 20 points below your entry, set your target 40 or 60 points above for a 1:2 or 1:3 rr.
you can use trailing stops to lock in profits as the trend continues.
exit on opposite signal:
if the trend changes (e.g., price crosses below the lower band in a bullish trade), close the position.
how it gives signals and when to buy or sell
signal logic:
buy signal (bullish crossover):
when the price crosses above the upper band, the script marks it as a bullish trend and plots a green arrow (▲).
sell signal (bearish crossunder):
when the price crosses below the lower band, the script identifies it as a bearish trend and plots a blue arrow (▼).
trend continuation:
the trend state persists until the opposite condition occurs, helping you avoid noise or whipsaws.
multi-timeframe insights:
consult the trend table for confirmation across timeframes. for example:
if the 15-minute and 4-hour timeframes align with a buy trend, it strengthens the case for a long trade.
conflicting signals might suggest waiting for further confirmation.
using retest signals:
during strong trends, price often revisits the ema before resuming. if the optional retest signals are enabled, you’ll see labels at these points. they can be used to:
add to an existing position.
enter a trade if you missed the initial breakout.
key event: price crosses above the upper band
when the price closes above the upper band (ema + volatility buffer), the script identifies a bullish trend.
a green upward arrow (▲) is plotted on the chart, signaling the beginning of a long trend.
visual confirmation:
the cloud between the ema and the trend line (lower band) is filled with a light green color, representing a bullish phase.
the trend table will display "buy" with an upward arrow for the respective timeframe(s).
actionable insight:
entry: take a long position when the green ▲ appears, confirming a bullish crossover.
continuation trades: use the optional retest signals to identify pullbacks to the ema as opportunities to add to the long position.
exit: close the position when a bearish crossunder (sell signal) occurs.
identifying short trends (sell signal)
key event: price crosses below the lower band
when the price closes below the lower band (ema - volatility buffer), the script identifies a bearish trend.
a blue downward arrow (▼) is plotted on the chart, signaling the beginning of a short trend.
visual confirmation:
the cloud between the ema and the trend line (upper band) is filled with a light blue color, representing a bearish phase.
the trend table will display "sell" with a downward arrow for the respective timeframe(s).
actionable insight:
entry: take a short position when the blue ▼ appears, confirming a bearish crossunder.
continuation trades: use the optional retest signals to identify rallies back to the ema as opportunities to add to the short position.
exit: close the position when a bullish crossover (buy signal) occurs.
what makes it different from other ema indicators?
dynamic volatility adaptation:
standard ema indicators only track the average price over a given period, making them susceptible to market noise in highly volatile conditions.
this script uses a volatility buffer (average true range of high-low) to create upper and lower bands around the ema, filtering out insignificant movements and focusing on meaningful breakouts.
persistent trend logic:
unlike traditional emas that simply follow price direction, this script maintains a persistent trend state until a clear crossover or crossunder occurs:
bullish trends persist above the upper band.
bearish trends persist below the lower band.
this minimizes whipsaws in choppy markets.
visual enhancements:
the trend-colored cloud (green for long trends, blue for short trends) helps you quickly identify the market’s state.
labels (▲ and ▼) mark critical entry signals, making it easier to spot potential trades.
multi-timeframe trend confirmation:
the trend table integrates higher and lower timeframes, providing a multi-timeframe perspective:
short-term (15 minutes) for active trading.
medium-term (4 hours) for swing positions.
long-term (daily) for overall trend direction.
optional retest signals:
most ema-based strategies miss the retest phase after a breakout.
this script includes an optional feature to identify pullbacks to the ema during a trend, helping traders enter or add positions at better prices.
all-in-one system:
while traditional ema indicators only show a smoothed average line, this script integrates trend detection, volatility bands, visual aids, and multi-timeframe analysis in a single tool, reducing the need for additional indicators.
summary
this script goes beyond a simple ema by incorporating trend persistence, volatility bands, and multi-timeframe analysis. buy signals occur when price crosses above the upper band, initiating a long trend, while sell signals occur when price crosses below the lower band, initiating a short trend. it stands out due to its ability to adapt to market conditions, provide clear visual cues, and avoid the noise common in standard ema-based systems.
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.
Volatility Adjusted ADX (VADX)I sincerely wish to express my heartfelt gratitude to the vast community of coders on TradingView who have previously crafted various Average Directional Index (ADX) scripts. Their innovative approaches have laid a solid foundation, and I'm incredibly grateful for their inspiring work. In essence, their accomplishments have ignited the creative spark that led to the development of the Volatility Adjusted ADX (VADX) script.
VADX is not your run-of-the-mill script. It distinguishes itself from the myriad of ADX indicators on TradingView due to its unique volatility-adjustment mechanism. The primary purpose of this script is to augment the ADX's ability to quantify trend strength by introducing a layer of sensitivity to volatility shifts through the Average True Range (ATR). The interaction between these two crucial market measurements is where the novelty lies.
While the standard ADX does an excellent job of diagnosing the trend's vigor, its evaluation can sometimes be skewed when markets oscillate between periods of high and low volatility. Integrating the ATR – a reliable indicator of market volatility – into the ADX calculation mitigates this limitation, resulting in a more robust, volatility-adjusted trend strength measurement.
The specifics of the mathematical adjustment, our secret ingredient, will remain undisclosed for proprietary reasons. Nevertheless, I assure you that it creates a dynamic and balanced interplay between the trend strength and volatility, enabling a more nuanced understanding of the market.
The VADX script is user-friendly and includes three main inputs: ADX Smoothing, DI Length, and ATR Length. The ADX Smoothing parameter refines the ADX calculation, DI Length determines the period for the Directional Movement System calculation, and the ATR Length sets the period for the Average True Range.
Using this indicator is as easy as pie. After adding it to your chart, VADX will manifest itself as a separate panel beneath your price chart. When the VADX is escalating, it indicates that the strength of the trend is intensifying. Conversely, a declining VADX suggests diminishing trend strength. Two horizontal lines at the 25 and 75 levels provide a simple interpretation guide – they denote weak and strong trend phases, respectively.
This robust indicator is adaptable and can be effectively applied across multiple markets - from stocks, forex, and futures to cryptocurrencies. It also delivers valuable insights on any timeframe. However, as with any new indicator, I highly recommend initial testing and optimization to match your unique trading style and objectives.
To wrap up, the VADX indicator sets itself apart with its novel volatility adjustment, a feature not commonly found in existing TradingView scripts. This distinctive capability affords traders a more comprehensive view of the trend's strength by accounting for market volatility, adding an extra layer of depth to traditional ADX interpretation. I sincerely hope that this script enriches your trading arsenal and assists you in navigating the market with enhanced precision. As always, happy trading!
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)
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
Improved Multi-Timeframe (MTF) TRL - plots same as live dataThese multi-timeframe True Range Levels use an improved calculation to accurately calculate the indicator's value with every new bar on the time frame your chart is set to. Previously the indicator only recalculated with every new update on the timeframe used in its security function. This means that this improved script plots the real, current value of your indicator across your chosen timeframes on your chart's resolution and no longer only plots only the indicator's monthly/weekly/daily/4 hour/ect closing value on the your chart.
This indicator was previously published as "True Range Bands" and uses a similar calculation the "SuperTrend" and "Volatility Stop" indicators.
Input values are fixed to their default (close,14,3) configuration to make this indicator's improved calculation possible.
When using "Plot Higher Timeframe?" the script will set the indicator to only plot its value in closest larger timeframe. This option overrides the two following options. For example, when using the daily resolution , only the weekly value will plot, or when using the one hour (60m) resolution, only the 4 hour (240m) value will plot.
The "Omit Higher Timeframes?" option will set the indicator to only plot starting from the 1/2/3/4/5/6/7th closest larger timeframe. For example, when using the daily resolution and this option set to 0, all values from the weekly resolution and up will plot, but if set to 1, all values from the monthly resolution and up will plot instead.
The "Plot Yearly/Quarterly/Monthly/Weekly/Daily/4 Hour/1 Hour/15 Minute/5 Minute?" options allow enabling/disabling a specific timeframe. All are enabled by default. For example, if you do not want the yearly value of the indicator to ever plot, you can disable the "Plot Yearly?" option.
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.
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.
Nifty Trend vs Range (Final)This indicator is designed to help you quickly identify whether the Nifty market is trending, ranging, or preparing for a breakout by combining three volatility and trend-strength measures:
India VIX (Volatility Index)
ADX (Average Directional Index)
ATR (Average True Range)
It creates a Trend vs Range Decision Matrix that categorizes the market into actionable states such as Range – Quiet, Breakout Watch, Trend – Smooth, Trend – Confirmed, Trend – Volatile, or Choppy / Noisy.
🔑 How it Works
India VIX (Market Volatility)
Pulled directly from NSE:INDIAVIX (or your chosen symbol).
VIX thresholds are defined:
Below VIX Low → Calm market (often ranges).
Between VIX Low & High → Neutral/moderate volatility.
Above VIX High → High volatility (potential big moves or choppiness).
VIX can be scaled and plotted in the same pane with ADX/ATR, or shown separately with a companion script.
ADX (Trend Strength)
Custom calculation (Wilder’s smoothing, not built-in ta.adx), to ensure more consistent results.
Thresholds (auto-tuned by timeframe if enabled):
Low ADX → Weak/no trend, sideways.
High ADX → Strong directional trend.
ATR (Volatility Expansion)
ATR compared to a moving average of ATR detects whether volatility is rising or flat.
Used as confirmation for breakouts or fading moves.
🧠 Market State Logic
The script combines the three signals into an interpretable market state:
Range – Quiet → VIX low, ADX low, ATR flat
Trend – Smooth → VIX low, ADX high
Breakout Watch → VIX neutral, ADX low, ATR rising
Trend – Confirmed → VIX neutral, ADX high, ATR rising
Choppy / Noisy → VIX high, ADX low, ATR rising
Trend – Volatile → VIX high, ADX high, ATR rising
Neutral → fallback if conditions don’t match
Each state is color-coded with background shading and displayed as a persistent label with key metrics (VIX, ADX, ATR).
⚙️ Features
✅ Intraday Auto-Tuning
ADX/ATR thresholds automatically adjust depending on chart timeframe (5m, 15m, etc.).
✅ Scalable VIX Plotting
Option to overlay a scaled VIX line in the same pane or hide it if you use a separate VIX pane.
✅ Persistent State Label
Shows the current regime, timeframe, and key values. Updates every bar without stacking multiple labels.
✅ Alerts Ready
Alerts for each market regime can be set directly in TradingView.
✅ Background Coloring
Quick at-a-glance identification of current state.
🎯 How to Use
Ranging markets (low VIX, low ADX, flat ATR): Favor mean-reversion strategies like option selling, iron condors, or scalping.
Smooth trends (low VIX, high ADX): Favor directional trades with futures/options spreads.
Breakout Watch: Stay alert for possible trend initiation.
Confirmed trends (neutral VIX, high ADX, rising ATR): Ideal for momentum trading.
Volatile trends (high VIX, high ADX): Use caution, hedge positions, or trade with wider stops.
Choppy/Noisy (high VIX, low ADX): Avoid overtrading, expect false 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.
VIX Term Structure Tracker [VX1!/VX2!]1. Data Preparation
The script starts by fetching four key data series on a daily ("D") timeframe:
VIX (CBOE:VIX): The spot VIX index, a real-time measure of market expectations of future volatility.
VX1! (CBOE:VX1!): The price of the front-month (nearest to expiration) VIX futures contract.
VX2! (CBOE:VX2!): The price of the next-month VIX futures contract.
SPX (SP:SPX): The S&P 500 Index, which serves as a benchmark for the overall market.
2. Term Structure and Market Psychology
The core of the indicator lies in analyzing the relationship between the futures prices.
term_slope = vx2 - vx1: This calculates the difference between the next-month and front-month VIX futures prices.
Contango: If vx2 > vx1, the slope is positive, and the market is in contango. This is the "normal" state, where traders expect volatility to be lower in the future. The script colors this background a light green.
Backwardation: If vx2 < vx1, the slope is negative, and the market is in backwardation. This is a rare state indicating elevated fear. It means traders are willing to pay a premium for short-term protection against volatility, implying that an increase in fear is imminent. The script colors this background a light red.
3. Roll Yield Calculation
roll_yield = ((vx1 - vix) / days_to_expiry) * 100: This calculates the estimated return (or loss) from rolling a futures position from the spot VIX to the front-month future. This is a key metric for understanding the cost of holding VIX futures. A negative roll yield means it is expensive to hold a long VIX position.
4. Equilibrium and Z-Score
This section of the code provides a statistical measure to determine how extreme the current term structure is compared to its historical average.
avg_slope = ta.sma(term_slope, 252): This calculates the one-year simple moving average of the term structure slope.
z_score: This is the most powerful part of the indicator. It measures how many standard deviations the current term_slope is from its one-year average.
A Z-score of +2 or higher indicates the market is in an extreme state of contango (complacency), where volatility is abnormally low.
A Z-score of -2 or lower indicates an extreme state of backwardation (fear), where there's an abnormal surge in short-term volatility expectations.
5. Visualizations & Signals
The script presents a comprehensive view of these metrics on the chart.
plot(term_slope): Shows a blue line representing the VIX term structure slope.
bgcolor(...): Visually highlights periods of contango (light green) and backwardation (light red).
plot(roll_yield): Displays a column chart showing the roll yield, indicating the cost or benefit of holding VIX futures.
plot(z_score): Shows an orange line representing the Z-score, with horizontal lines at +2 and -2 to highlight extreme conditions.
plotshape(...): These are your trading signals. A red arrow pointing up (shape.labelup) appears at the bottom of the chart when the Z-score drops below -2 (extreme backwardation), and a green arrow pointing down (shape.labeldown) appears at the top when the Z-score goes above +2 (extreme contango).
6. Interpretation and Trading Signals
The script provides a clear framework for interpreting market sentiment:
Extreme Backwardation Signal (Red Arrow Up): When the Z-score falls to -2 or below, it signals a period of extreme market fear. This is often an excellent contrarian signal for buying the VIX (or related products) and/or for caution in long equity positions.
Extreme Contango Signal (Green Arrow Down): When the Z-score rises to +2 or above, it signals a period of extreme market complacency. This can be a contrarian signal for selling the VIX (or related products) and/or for potential short-term weakness in the S&P 500.
The VIX/SPX Risk Ratio also provides a good visual of volatility relative to the S&P 500, with an increasing ratio signaling a rising risk environment.
Ultimately, this indicator provides a powerful way to visualize the VIX futures term structure and use statistical analysis (Z-score) to find high-probability signals of extreme market sentiment.
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.
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-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.
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.
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.
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.
