Uptrick: Volatility Adjusted TrailIntroduction
The "Uptrick: Volatility Adjusted Trail" is a dynamic trailing band indicator. It adapts in real time to changing market conditions by adjusting both to volatility and trend consistency. Inspired by Supertrend-style logic, it enhances traditional approaches by introducing adaptive mechanisms for more context-sensitive behavior in both trending and consolidating environments.
Overview
This indicator combines an exponential moving average (EMA) as its basis with an Average True Range (ATR)-derived multiplier that adjusts dynamically. Unlike fixed-multiplier tools, this indicator modifies its band distances in real time according to volatility expansion and trend persistence. The result is a trailing system that adapts to the prevailing market regime, providing traders with clearer signals for trend bias, stop placement, and potential momentum shifts.
Originality
The script’s originality lies in its multi-layered approach to trail calculation. It introduces a real-time ATR multiplier adjustment driven by two factors: a volatility expansion ratio and a trend persistence model. The expansion ratio compares the current ATR to its moving average, making the indicator more sensitive during volatile conditions and less sensitive during quieter periods. The trend persistence model assesses directional consistency to widen the bands during sustained trends. This dual adjustment method creates a system that evolves with market behavior, making it more responsive and adaptive than static-band or fixed-multiplier alternatives.
Components & Inspiration
This indicator was designed with specific components that work together:
Exponential Moving Average (EMA): Chosen as the central baseline because it responds faster to recent price changes than a simple moving average, providing a more current reference for trailing bands.
Average True Range (ATR): Used as the volatility measure because it accounts for both intraday and gap movement, making it a robust and widely accepted standard for market volatility.
Dynamic Multiplier: The multiplier is adjusted by both volatility expansion and trend persistence to produce bands that tighten during low volatility and widen during consistent trends. This combination was chosen to give the indicator the ability to self-regulate across different market regimes.
Trend Persistence Model: Integrated to assess directional consistency, ensuring the bands expand during strong trends, which can prevent premature stop-outs.
Flip Confirmation Logic: Added to filter out noise by requiring multiple bar closes beyond a band before confirming a state change, reducing false reversals.
For inspiration, the indicator draws on the core idea behind Supertrend—using a baseline and volatility-derived bands to define trailing stop levels. However, while Supertrend uses a fixed ATR multiplier, this indicator introduces a dynamic multiplier system and persistence weighting, making it more adaptive and suited for varying conditions.
Inputs and Parameters
Basis EMA Length
Defines the period for the EMA that serves as the core price reference.
ATR Length
Sets the lookback period for the Average True Range calculation used in band spacing.
Base ATR Mult
The base multiplier applied to ATR before adjustments. Forms the starting scale of the band offset.
Volatility Expansion Sensitivity
Controls how strongly the band spacing reacts to short-term volatility bursts. Higher values create more pronounced band expansions or contractions.
Trend Persistence Window
Determines how many bars are used to calculate directional trend consistency using a smoothed step function.
Persistence Impact
Scales how much influence the trend persistence has on band widening. Values range from 0 (no effect) to 1 (maximum effect).
Min Effective Mult
Sets the minimum value that the adjusted multiplier can reach. Prevents the bands from becoming too narrow.
Max Effective Mult
Sets the maximum value the adjusted multiplier can reach. Prevents the bands from over-expanding during high volatility.
Bars Above/Below to Confirm Flip
Number of consecutive bars required to close above or below the opposing trail before confirming a bullish or bearish flip. Helps reduce noise and false signals.
Show Flip Labels
Enables or disables the display of flip markers on the chart.
Label Size
Allows users to adjust the size of flip labels from Tiny to Huge.
Label ATR Offset
Adjusts the vertical placement of flip labels in relation to the trail using an ATR-based offset.
Features and Logic
EMA Basis: All calculations stem from an EMA that tracks the centerline of price action.
Dynamic ATR Multiplier: The ATR multiplier adjusts in real time based on volatility expansion and trend persistence.
Clamped Multiplier: The adjusted multiplier is limited between user-defined minimum and maximum values to keep the band scale practical.
Upper and Lower Bands: Bands are plotted above and below the EMA using the dynamic multiplier and ATR values.
Trailing Logic: The script uses Supertrend-style trailing logic, updating the active band in the current trend direction and resetting the opposite band.
Trend State Detection: A state variable tracks the current market regime (bullish, bearish, or neutral). Transitions are confirmed only after a user-specified number of bars close beyond the respective bands.
Visual Elements: Trail lines and fill zones are color-coded (bullish cyan, bearish magenta). Candlestick and bar colors match the trend state. Optional flip labels mark confirmed transitions.
Alerts: Built-in alert conditions allow users to receive real-time notifications for bullish or bearish flips.
Usage Guidelines
This indicator can be used for:
Defining context-aware dynamic stop levels that adjust with market behavior.
Identifying trend direction and reversal points based on adaptive logic.
Filtering entry or exit signals during trending vs. consolidating conditions.
Supplementing trade management strategies with responsive visual markers.
Entering long or short positions based on the appearance of flip labels and managing stop losses by following the adaptive trail.
Traders may tune the parameters to suit different trading styles or timeframes. For example, lower ATR and EMA values may suit intraday setups, while longer settings may benefit swing or positional trading.
Summary
The "Uptrick: Volatility Adjusted Trail" provides a flexible, adaptive trailing band system that accounts for both volatility and directional consistency. By combining an EMA baseline with a dynamic ATR multiplier influenced by volatility expansion and trend persistence, it creates a context-sensitive trailing system that aligns with changing market conditions. Customizable confirmation, flip labels, alerts, and dynamic visual cues make it a versatile tool for trend-following, breakout filtering, and trailing stop logic.
Disclaimer
This indicator is provided for educational and research purposes only. It does not constitute financial advice. Trading involves risk, and past performance does not guarantee future results. Always conduct your own analysis and risk management before making trading decisions.
Pesquisar nos scripts por "Volatility"
Volatility Quality [Alpha Extract]The Alpha-Extract Volatility Quality (AVQ) Indicator provides traders with deep insights into market volatility by measuring the directional strength of price movements. This sophisticated momentum-based tool helps identify overbought and oversold conditions, offering actionable buy and sell signals based on volatility trends and standard deviation bands.
🔶 CALCULATION
The indicator processes volatility quality data through a series of analytical steps:
Bar Range Calculation: Measures true range (TR) to capture price volatility.
Directional Weighting: Applies directional bias (positive for bullish candles, negative for bearish) to the true range.
VQI Computation: Uses an exponential moving average (EMA) of weighted volatility to derive the Volatility Quality Index (VQI).
vqiRaw = ta.ema(weightedVol, vqiLen)
Smoothing: Applies an additional EMA to smooth the VQI for clearer signals.
Normalization: Optionally normalizes VQI to a -100/+100 scale based on historical highs and lows.
Standard Deviation Bands: Calculates three upper and lower bands using standard deviation multipliers for volatility thresholds.
vqiStdev = ta.stdev(vqiSmoothed, vqiLen)
upperBand1 = vqiSmoothed + (vqiStdev * stdevMultiplier1)
upperBand2 = vqiSmoothed + (vqiStdev * stdevMultiplier2)
upperBand3 = vqiSmoothed + (vqiStdev * stdevMultiplier3)
lowerBand1 = vqiSmoothed - (vqiStdev * stdevMultiplier1)
lowerBand2 = vqiSmoothed - (vqiStdev * stdevMultiplier2)
lowerBand3 = vqiSmoothed - (vqiStdev * stdevMultiplier3)
Signal Generation: Produces overbought/oversold signals when VQI reaches extreme levels (±200 in normalized mode).
Formula:
Bar Range = True Range (TR)
Weighted Volatility = Bar Range × (Close > Open ? 1 : Close < Open ? -1 : 0)
VQI Raw = EMA(Weighted Volatility, VQI Length)
VQI Smoothed = EMA(VQI Raw, Smoothing Length)
VQI Normalized = ((VQI Smoothed - Lowest VQI) / (Highest VQI - Lowest VQI) - 0.5) × 200
Upper Band N = VQI Smoothed + (StdDev(VQI Smoothed, VQI Length) × Multiplier N)
Lower Band N = VQI Smoothed - (StdDev(VQI Smoothed, VQI Length) × Multiplier N)
🔶 DETAILS
Visual Features:
VQI Plot: Displays VQI as a line or histogram (lime for positive, red for negative).
Standard Deviation Bands: Plots three upper and lower bands (teal for upper, grayscale for lower) to indicate volatility thresholds.
Reference Levels: Horizontal lines at 0 (neutral), +100, and -100 (in normalized mode) for context.
Zone Highlighting: Overbought (⋎ above bars) and oversold (⋏ below bars) signals for extreme VQI levels (±200 in normalized mode).
Candle Coloring: Optional candle overlay colored by VQI direction (lime for positive, red for negative).
Interpretation:
VQI ≥ 200 (Normalized): Overbought condition, strong sell signal.
VQI 100–200: High volatility, potential selling opportunity.
VQI 0–100: Neutral bullish momentum.
VQI 0 to -100: Neutral bearish momentum.
VQI -100 to -200: High volatility, strong bearish momentum.
VQI ≤ -200 (Normalized): Oversold condition, strong buy signal.
🔶 EXAMPLES
Overbought Signal Detection: When VQI exceeds 200 (normalized), the indicator flags potential market tops with a red ⋎ symbol.
Example: During strong uptrends, VQI reaching 200 has historically preceded corrections, allowing traders to secure profits.
Oversold Signal Detection: When VQI falls below -200 (normalized), a lime ⋏ symbol highlights potential buying opportunities.
Example: In bearish markets, VQI dropping below -200 has marked reversal points for profitable long entries.
Volatility Trend Tracking: The VQI plot and bands help traders visualize shifts in market momentum.
Example: A rising VQI crossing above zero with widening bands indicates strengthening bullish momentum, guiding traders to hold or enter long positions.
Dynamic Support/Resistance: Standard deviation bands act as dynamic volatility thresholds during price movements.
Example: Price reversals often occur near the third standard deviation bands, providing reliable entry/exit points during volatile periods.
🔶 SETTINGS
Customization Options:
VQI Length: Adjust the EMA period for VQI calculation (default: 14, range: 1–50).
Smoothing Length: Set the EMA period for smoothing (default: 5, range: 1–50).
Standard Deviation Multipliers: Customize multipliers for bands (defaults: 1.0, 2.0, 3.0).
Normalization: Toggle normalization to -100/+100 scale and adjust lookback period (default: 200, min: 50).
Display Style: Switch between line or histogram plot for VQI.
Candle Overlay: Enable/disable VQI-colored candles (lime for positive, red for negative).
The Alpha-Extract Volatility Quality Indicator empowers traders with a robust tool to navigate market volatility. By combining directional price range analysis with smoothed volatility metrics, it identifies overbought and oversold conditions, offering clear buy and sell signals. The customizable standard deviation bands and optional normalization provide precise context for market conditions, enabling traders to make informed decisions across various market cycles.
Price Prediction With Rolling Volatility [TradeDots]The "Price Prediction With Rolling Volatility" is a trading indicator that estimates future price ranges based on the volatility of price movements within a user-defined rolling window.
HOW DOES IT WORK
This indicator utilizes 3 types of user-provided data to conduct its calculations: the length of the rolling window, the number of bars projecting into the future, and a maximum of three sets of standard deviations.
Firstly, the rolling window. The algorithm amasses close prices from the number of bars determined by the value in the rolling window, aggregating them into an array. It then calculates their standard deviations in order to forecast the prospective minimum and maximum price values.
Subsequently, a loop is initiated running into the number of bars into the future, as dictated by the second parameter, to calculate the maximum price change in both the positive and negative direction.
The third parameter introduces a series of standard deviation values into the forecasting model, enabling users to dictate the volatility or confidence level of the results. A larger standard deviation correlates with a wider predicted range, thereby enhancing the probability factor.
APPLICATION
The purpose of the indicator is to provide traders with an understanding of the potential future movement of the price, demarcating maximum and minimum expected outcomes. For instance, if an asset demonstrates a substantial spike beyond the forecasted range, there's a significantly high probability of that price being rejected and reversed.
However, this indicator should not be the sole basis for your trading decisions. The range merely reflects the volatility within the rolling window and may overlook significant historical price movements. As with any trading strategies, synergize this with other indicators for a more comprehensive and reliable analysis.
Note: In instances where the number of predicted bars is exceedingly high, the lines may become scattered, presumably due to inherent limitations on the TradingView platform. Consequently, when applying three SD in your indicator, it is advised to limit the predicted bars to fewer than 80.
RISK DISCLAIMER
Trading entails substantial risk, and most day traders incur losses. All content, tools, scripts, articles, and education provided by TradeDots serve purely informational and educational purposes. Past performances are not definitive predictors of future results.
Volatility Cloud (SAR)Inspired by the Volatility Index from Wilder
Apply the SAR point to highs, lows ans medians and create a cloud of volatility
Candle Percent Volatility by AllenlkThis indicator gives you the percentage movement of each candle. Measurements are taken between the candle High point and Low point, and also between the Open and Close and calculated in percent %. From there it smooths out the data with a moving average. This gives you an idea of how much volatility is within each candle given the time resolution of the chart.
I like to use this information as a way to turn off a strategy, or select a proper time resolution for a strategy. If each candle has less than 2.5% Volatility most strategies will typically buy and sell rapidly at prices that are too close together, potentially losing money. During those times it seems best to either temporarily turn off the strategy, change the time resolution or switch to another strategy.
Scott’s ATR volatility histogram with smoothingATR shows volatility. The sma of the ATR (default=14 period) shows the average volatility over the look-back period, (default=200 period.)
When volatility is higher than average, the histogram turns green. When volatility is less than average, the histogram turns red. This shows volatility expansion and contraction. Volatility expansion is a good confirmation for entering a trade position. Volatility contraction is a sign that a trend is not developing.
Now I have added an sma which acts as a smoothing of expanding or contracting volatility. When the histogram is higher than this smoothing (default=21) then volatility expansion momentum is creasing. WWhen the histogram is lower than the smoothing sma, volatility contraction momentum is increasing.
I introduce an idea that volatility momentum can be used as a substitute for volatility expansion and contraction.
Now we have volatility expansion momentum and volatility contraction momentum.
Multi Fib Volatility StopA 7-band overlapping Fibonacci volatility stop. Select the start and multiplier and 6 increasing fibonacci bands will be overlayed to suggest areas of high probability buy/ sell opportunities.
[RS]Function Volatility Stop V0Function for Volatility Stop:
added some tweeks so it can be used on any series as in example a rsi.
Volatility Regime NavigatorA guide to understanding VIX, VVIX, VIX9D, VVIX/VIX, and the Composite Risk Score
1. Purpose of the Indicator
This dashboard summarizes short-term market volatility conditions using four core volatility metrics.
It produces:
• Individual readings
• A combined Regime classification
• A Composite Risk Score (0–100)
• A simplified Risk Bucket (Bullish → Stress)
Use this to evaluate market fragility, drift potential, tail-risk, and overall risk-on/off conditions.
This is especially useful for intraday ES/NQ trading, expected-move context, and understanding when breakouts or fades have edge.
2. The Four Core Volatility Inputs
(1) VIX — Baseline Equity Volatility
• < 16: Complacent (easy drift-up, but watch for fragility)
• 16–22: Healthy, normal volatility → ideal trading conditions
• > 22: Stress rising
• > 26: Tail-risk / risk-off environment
(2) VIX9D — Short-Term Event Vol
Measures 9-day implied volatility. Reacts to immediate news/events.
• < 14: Strongly bullish (drift regime)
• 14–17: Bullish to neutral
• 17–20: Event risk building
• > 20: Short-term stress / caution
(3) VVIX — Volatility of VIX (fragility index)
Tracks volatility of volatility.
• < 100: “Bullish, Bullish” — very low fragility
• 100–120: Normal
• 120–140: Fragile
• > 140: Stress, hedging pressure
(4) VVIX/VIX Ratio — Microstructure Risk-On/Risk-Off
One of the most sensitive indicators of market confidence.
• 5.0–6.5: Strongest “normal/bullish” zone
• < 5.0: Bottom-stalking / fear regime
• > 6.5: Complacency → vulnerable to reversals
• > 7.5: Fragile / top-risk
3. Composite Risk Score (0–100)
The dashboard converts all four inputs into a single score.
Score Interpretation
• 80–100 → Bullish - Drift regime. Shallow pullbacks. Upside favored.
• 60–79 → Normal - Healthy tape. Balanced two-way trading.
• 40–59 → Fragile - Choppy, failed breakouts, thinner liquidity.
• 20–39 → Risk-Off - Downside tails active. Favor fades and defensive behavior.
• < 20 → Stress - Crisis or event-driven tape. Avoid longs.
Score updates every bar.
4. Regime Label
Independent of the composite score, the script provides a Regime classification based on combinations of VIX + VVIX/VIX:
• Bullish+ → Buying is easy, tape lifts passively
• Normal → Cleanest and most tradable conditions
• Complacent → Top-risk; be careful chasing upside
• Mixed → Signals conflict; chop potential
• Bottom Stalk → High VIX, low VVIX/VIX (capitulation signatures)
A trailing “+” or “*” indicates additional bullish or caution overlays from VIX9D/VVIX.
5. How to Use the Dashboard in Trading
When Bullish (Score ≥ 80):
• Expect drift-up behavior
• Downside limited unless catalyst hits
• Structure favors breakouts and trend continuation
• Mean reversion trades have lower expectancy
When Normal (Score 60–79):
• The “playbook regime”
• Breakouts and mean reversion both valid
• Best overall trading environment
When Fragile (Score 40–59):
• Expect chop
• Breakouts fail
• Take quicker profits
• Avoid overleveraged directional bets
When Risk-Off (20–39):
• Favor fades of strength
• Downside tails activate
• Trend-following short setups gain edge
• Respect volatility bands
When Stress (<20):
• Avoid long exposure
• Do not chase dips
• Expect violent, news-sensitive behavior
• Position sizing becomes critical
6. Quick Summary
• VIX = weather
• VIX9D = short-term storm radar
• VVIX = foundation stability
• VVIX/VIX = confidence vs fragility
• Composite Score = overall regime health
• Risk Bucket = simple “what do I do?” label
This dashboard gives traders a high-confidence, low-noise view of equity volatility conditions in real time.
Historical Volatility with HV Average & High/Low Trendlines
### 📊 **Indicator Title**: Historical Volatility with HV Average & High/Low Trendlines
**Version**: Pine Script v5
**Purpose**:
This script visualizes market volatility using **Historical Volatility (HV)** and enhances analysis by:
* Showing a **moving average** of HV to identify volatility trends.
* Marking **high and low trendlines** to highlight extremes in volatility over a selected period.
---
### 🔧 **Inputs**:
1. **HV Length (`length`)**:
Controls how many bars are used to calculate Historical Volatility.
*(Default: 10)*
2. **Average Length (`avgLength`)**:
Number of bars used for calculating the moving average of HV.
*(Default: 20)*
3. **Trendline Lookback Period (`trendLookback`)**:
Number of bars to look back for calculating the highest and lowest values of HV.
*(Default: 100)*
---
### 📈 **Core Calculations**:
1. **Historical Volatility (`hv`)**:
$$
HV = 100 \times \text{stdev}\left(\ln\left(\frac{\text{close}}{\text{close} }\right), \text{length}\right) \times \sqrt{\frac{365}{\text{period}}}
$$
* Measures how much the stock price fluctuates.
* Adjusts annualization factor depending on whether it's intraday or daily.
2. **HV Moving Average (`hvAvg`)**:
A simple moving average (SMA) of HV over the selected `avgLength`.
3. **HV High & Low Trendlines**:
* `hvHigh`: Highest HV value over the last `trendLookback` bars.
* `hvLow`: Lowest HV value over the last `trendLookback` bars.
---
### 🖍️ **Visual Plots**:
* 🔵 **HV**: Blue line showing raw Historical Volatility.
* 🔴 **HV Average**: Red line (thicker) indicating smoothed HV trend.
* 🟢 **HV High**: Green horizontal line marking volatility peaks.
* 🟠 **HV Low**: Orange horizontal line marking volatility lows.
---
### ✅ **Usage**:
* **High HV**: Indicates increased risk or potential breakout conditions.
* **Low HV**: Suggests consolidation or calm markets.
* **Cross of HV above Average**: May signal rising volatility (e.g., before breakout).
* **Touching High/Low Levels**: Helps identify volatility extremes and possible reversal zones.
Volatility-Enhanced Williams %R [AIBitcoinTrend]👽 Volatility-Enhanced Williams %R (AIBitcoinTrend)
The Volatility-Enhanced Williams %R takes the classic Williams %R oscillator to the next level by incorporating volatility-adaptive smoothing, making it significantly more responsive to market dynamics. Unlike the traditional version, which uses a fixed calculation method, this indicator dynamically adjusts its smoothing factor based on market volatility, helping traders capture trends more effectively while filtering out noise.
Additionally, the indicator includes real-time divergence detection and an ATR-based trailing stop system, providing traders with enhanced risk management tools and early reversal signals.
👽 What Makes the Volatility-Enhanced Williams %R Unique?
Unlike the standard Williams %R, which applies a simple lookback-based formula, this version integrates adaptive smoothing and volatility-based filtering to refine its signals and reduce false breakouts.
✅ Volatility-Adaptive Smoothing – Adjusts dynamically based on standard deviation, enhancing signal accuracy.
✅ Real-Time Divergence Detection – Identifies bullish and bearish divergences for early trend reversal signals.
✅ Crossovers & Trailing Stops – Implements Williams %R crossovers with ATR-based trailing stops for intelligent trade management.
👽 The Math Behind the Indicator
👾 Volatility-Adaptive Smoothing
The indicator smooths the Williams %R calculation by applying an adaptive filtering mechanism, which adjusts its responsiveness based on market conditions. This helps to eliminate whipsaws and makes trend-following strategies more reliable.
The smoothing function is defined as:
clamp(x, lo, hi) => math.min(math.max(x, lo), hi)
adaptive(src, prev, len, divisor, minAlpha, maxAlpha) =>
vol = ta.stdev(src, len)
alpha = clamp(vol / divisor, minAlpha, maxAlpha)
prev + alpha * (src - prev)
Where:
Volatility Factor (vol) measures price dispersion using standard deviation.
Adaptive Alpha (alpha) dynamically adjusts smoothing strength.
Clamped Output ensures that the smoothing factor remains within a stable range.
👽 How Traders Can Use This Indicator
👾 Divergence Trading Strategy
Bullish Divergence Setup:
Price makes a lower low, while Williams %R forms a higher low.
Buy signal is confirmed when Williams %R reverses upward.
Bearish Divergence Setup:
Price makes a higher high, while Williams %R forms a lower high.
Sell signal is confirmed when Williams %R reverses downward.
👾 Trailing Stop & Signal-Based Trading
Bullish Setup:
✅ Williams %R crosses above trigger level → Buy signal.
✅ A bullish trailing stop is placed at Low - (ATR × Multiplier).
✅ Exit if price crosses below the stop.
Bearish Setup:
✅ Williams %R crosses below trigger level → Sell signal.
✅ A bearish trailing stop is placed at High + (ATR × Multiplier).
✅ Exit if price crosses above the stop.
👽 Why It’s Useful for Traders
Adaptive Filtering Mechanism – Avoids excessive noise while maintaining responsiveness.
Real-Time Divergence Alerts – Helps traders anticipate market reversals before they occur.
ATR-Based Risk Management – Stops dynamically adjust based on market volatility.
Multi-Market Compatibility – Works effectively across stocks, forex, crypto, and futures.
👽 Indicator Settings
Smoothing Factor – Controls how aggressively the indicator adapts to volatility.
Enable Divergence Analysis – Activates real-time divergence detection.
Lookback Period – Defines the number of bars for detecting pivot points.
Enable Crosses Signals – Turns on Williams %R crossover-based trade signals.
ATR Multiplier – Adjusts trailing stop sensitivity.
Disclaimer: This indicator is designed for educational purposes and does not constitute financial advice. Please consult a qualified financial advisor before making investment decisions.
EWMA Implied Volatility based on Historical VolatilityVolatility is the most common measure of risk.
Volatility in this sense can either be historical volatility (one observed from past data), or it could implied volatility (observed from market prices of financial instruments.)
The main objective of EWMA is to estimate the next-day (or period) volatility of a time series and closely track the volatility as it changes.
The EWMA model allows one to calculate a value for a given time on the basis of the previous day's value.
The EWMA model has an advantage in comparison with SMA, because the EWMA has a memory.
The EWMA remembers a fraction of its past by a factor A, that makes the EWMA a good indicator of the history of the price movement if a wise choice of the term is made.
Full details regarding the formula :
www.investopedia.com
In this scenario, we are looking at the historical volatility using the anual length of 252 trading days and a monthly length of 21.
Once we apply all of that we are going to get the yearly volatility.
After that we just have to divide that by the square root of number of days in a year, or weeks in a year or months in a year in order to get the daily/weekly/monthly expected volatility.
Once we have the expected volatility, we can estimate with a high chance where the market top and bottom is going to be and continue our analysis on that premise.
If you have any questions, please let me know !
Relative Normalized VolatilityThere are plenty of indicators that aim to measure the volatility (degree of variation) in the price of an instrument, the most well known being the average true range and the rolling standard deviation. Volatility indicators form the key components of most bands and trailing stops indicators, but can also be used to normalize oscillators, they are therefore extremely versatile.
Today proposed indicator aim to compare the estimated volatility of two instruments in order to provide various informations to the user, especially about risk and profitability.
CALCULATION
The relative normalized volatility (RNV) indicator is the ratio between the moving average of the absolute normalized price changes value of two securities, that is:
SMA(|Δ(a)/σ(a)|)
―――――――――――
SMA(|Δ(b)/σ(b)|)
Where a and b are two different securities (note that notation "Δ(x)" refer to the 1st difference of x, and the "||" notation is used to indicate absolute value, for example "|x|" means absolute value of x) .
INTERPRETATION
The indicator aim tell us which security is more volatile between a and b , with a value of the indicator greater than 1 indicating that a is on average more volatile than b over the last length period, while a value lower than 1 indicating that the security b is more on average volatile than a .
The indicator use the current symbol as a , while the second security b must be defined in the setting window (by default the S&P500). Risk and profitability are closely related to volatility, as larger price variations could potentially mean larger losses (but also larger gains), therefore a value of the indicator greater than 1 can indicate that it could be more risked (and profitable) to trade security a .
RNV using AMD (top) volatility against Intel (bottom) volatility.
RNV using EURUSD (top) volatility against USDJPY (bottom) volatility.
Larger values of length will make the indicator fluctuate less often around 1. You can also plot the logarithm of the ratio instead in order to have the indicator centered around 0, it will also help make values originally below 1 have more importance in the scale.
POSSIBLE ERRORS
If you compare different types of markets the indicator might return NaN values, this is because one market might be closed, for example if you compare AMD against BTCUSD with the indicator you will get NaN values. If you really need to compare two markets then increase your time frame, else use an histogram or area plot in order to have a cleaner plot.
CONCLUSION
An original indicator comparing the volatility between two securities has been presented. The choice of posting a volatility indicator has been made by my twitter followers, so if you want to decide which type of indicator i should do next make sure to check my twitter to see if there are polls available (i should do one after every posted indicator).
Dynamic Volatility Channel (DVC) - Smooth
The indicator's adaptability comes from a unique blend of well-known concepts:
The Adaptive Engine (ADX): The indicator uses the Average Directional Index (ADX) in the background to analyze the strength of the trend. This acts as the "brain", telling the channel whether the market is trending strongly or moving sideways.
Hybrid Volatility: This is the core of the indicator. The width of the channel is determined by a weighted mix of two volatility measures:
In trending markets (high ADX), the channel gives more weight to the Average True Range (ATR).
In ranging markets (low ADX), the channel gives more weight to Standard Deviation.
Smooth Centerline (HMA): The channel is centered around a Hull Moving Average (HMA), which is known for its smoothness and reduced lag compared to other moving averages.
Advanced Smoothing Layers: This version includes dedicated smoothing for both the volatility components (ATR and StDev) and the logic that switches between regimes. This ensures the channel expands, contracts, and adapts in a very fluid manner, eliminating sudden jumps and reducing market noise.
Mean Reversion: In ranging markets (indicated by a flatter channel), the outer bands can act as dynamic support and resistance levels. Look for opportunities to sell near the upper band and buy near the lower band, always waiting for price action confirmation like reversal candles.
Trend Following: In strong trends (indicated by a steeply sloped channel), the centerline (HMA) often serves as a dynamic level of support (in an uptrend) or resistance (in a downtrend). Pullbacks to the centerline can present opportunities to join the trend. A "band ride," where price action consistently pushes against the upper or lower band, signals a very strong trend.
Volatility Analysis: A "squeeze," where the bands come very close together, indicates low volatility and can foreshadow a significant price breakout. A sudden expansion of the bands signals an increase in volatility and the potential start of a new, powerful move.
All core parameters are fully customizable to suit your trading style and preferred assets:
You can adjust the lengths for the HMA, ATR, StDev, and the ADX filter.
You can change the multipliers for the ATR and Standard Deviation components.
Crucially, you can control the Volatility Smoothing Length and Logic Smoothing Length to find the perfect balance between responsiveness and smoothness.
Disclaimer: This indicator is provided for educational and analytical purposes only. It is not financial advice, and past performance is not indicative of future results. Always conduct your own research and backtesting before risking capital in a live market.
Position Sizer by VolatilityDescription :
The **Position Sizer by Volatility (PSV)** is an indicator that helps traders determine what percentage of their deposit a position will occupy, taking into account the current market volatility. PSV calculates the range of price movements over recent periods and shows how large this movement is compared to historical data. The lower the value, the lower the volatility, and the smaller the stop-loss required relative to the current price.
Explanation of PSV Parameters:
- ` len ` (Period Length):** This parameter sets the number of candles (bars) on the chart that will be used to calculate volatility. For example, if `len` is set to 250, the indicator will analyze price movements over the last 250 bars. The larger the value, the longer the period used for volatility assessment.
- ` percent ` (Percentile):** This parameter determines how strong price fluctuations you want to account for. For instance, if you set `percent` to 95, the indicator will focus on the 5% of instances where the price range was the largest over the specified period. This helps evaluate volatility during periods of sharp price movements, which may require a larger stop-loss. A higher percentile accounts for rarer but stronger movements, and vice versa.
Advanced Volatility Oscillator with SignalsTitle: Advanced Volatility Oscillator with Signals (AVO-S)
In-Depth Description:
Introduction:
The Advanced Volatility Oscillator with Signals (AVO-S) is designed to offer traders a nuanced understanding of market volatility, combining traditional concepts with innovative visual aids and signal interpretation. This indicator is tailored for diverse financial markets, helping to identify potential trend reversals and momentum shifts.
Calculation and Methodology:
Spike Calculation: The core of AVO-S is the 'spike', calculated as the difference between the closing and opening prices (spike = close - open). This measure provides a straightforward gauge of intra-period volatility.
Standard Deviation: The indicator employs standard deviation to assess the variability of the 'spike', offering a dynamic threshold for understanding market extremities (stdDev = stdev(spike, length)).
Colored Columns: These columns visually represent the 'spike'. Their color changes based on the spike’s value relative to the zero line and the standard deviation threshold, providing an immediate visual cue of market state.
Blue Columns: Indicate moderate positive movement when the spike is above zero but below the standard deviation.
Green and Red Columns: Suggest stronger bullish (above standard deviation) and bearish (below negative standard deviation) movements, respectively.
Bullish and Bearish Signals:
The indicator generates signals based on the relationship between the 'spike' and its standard deviation.
Bullish Signals: Shown as upward triangles, these are formed when the 'spike' crosses above the standard deviation, indicating potential upward momentum.
Bearish Signals: Represented by downward triangles, these signals are generated when the 'spike' falls below the negative standard deviation, hinting at potential downward trends.
Usage and Application:
Traders can use the colored columns to quickly assess market sentiment and volatility.
The bullish and bearish signals serve as potential indicators for market entry or exit points, or for further analysis in conjunction with other technical tools.
Inspiration and Credits:
Inspired by Veryfid's original Volatility Oscillator, the AVO-S refines and builds upon these ideas to provide a comprehensive and user-friendly tool for market analysis. This indicator is a testament to the continuous evolution of technical analysis tools in the trading community.
Natenberg's VolatilityThis indicator is historical volatility indicator created by Sheldon Natenberg , as the standard deviation of the logarithmic price changes measured at regular intervals of time.
In Mr. Natenberg's book, Option Volatility & Pricing, he covers volatility in detail and gives the formula for computing historical volatility.
My changes :
I didn't changed formula, i just added smooth version of volatility it can be used as trigger when cross(over/under) non-smoothed volatility.
Note:
There is two formulas for daily and weekly. Indicator showing only daily formula !
Who wants to display the weekly formula change line 17, namely remove "//"
Enjoy!
Volatility/Volume ImpactWe often hear statements such as follow the big volume to project possible price movements. Or low volatility is good for trend. How much of it is statistically right for different markets. I wrote this small script to study the impact of Volatility and Volume on price movements.
Concept is as below:
Compare volume with a reference median value. You can also use moving average or other types for this comparison.
If volume is higher than median, increment positive value impact with change in close price. If volume is less than median, then increment negative value impact with change in close price.
With this we derive pvd and nvd which are measure of price change when volume is higher and lower respectively. pvd measures the price change when volume is higher than median whereas nvd measures price change when volume is lower than median.
Calculate correlation of pvd and nvd with close price to see what is impacting the price by higher extent.
Colors are applied to plots which have higher correlation to price movement. For example, if pvd has higher correlation to price movement, then pvd is coloured green whereas nvd is coloured silver. Similarly if nvd has higher correlation to price then nvd is coloured in red whereas pvd is coloured in silver.
Similar calculation also applied for volatility.
With this, you can observe how price change is correlated to high/low volume and volatility.
Let us see some examples on different markets.
Example 1: AMEX:SPY
From the chart snapshot below, it looks evident that SPY always thrive when there is low volatility and LOW VOLUME!!
Example 2: NASDAQ:TSLA
The picture will be different if you look at individual stocks. For Tesla, the price movement is more correlated to high volume (unlike SPY where low volume days define the trend)
Example 3: KUCOIN:BTCUSDT
Unlike stocks and indices, high volatility defined the trend for BTC for long time. It thrived when volatility is more. We can see that high volume is still major influencer in BTC price movements.
Settings are very simple and self explanatory.
Hint: You can also move the indicator to chart overlay for better visualisation of comparison with close price.
Wilder's Volatility Trailing Stop Strategy with various MA'sFor Educational Purposes. Results can differ on different markets and can fail at any time. Profit is not guaranteed.
This only works in a few markets and in certain situations. Changing the settings can give better or worse results for other markets. This strategy is based on Wilder's Volatility System. It is an ATR trailing stop that is used for long term trends. This strategy focuses on the trailing stop alone and goes long and short only when it goes above or below the trailing line. It is similar to Donchian channels except it does not include the certain period channel breakout, only the trailing signal. This is only the trailing stop and an attempt to show how well it works standalone as Wilder described.
In his book, Wilder recommends a multiplier of 2.8-3.1 and an ATR lookback of 7 periods along with a running moving average or otherwise known as Wilder's moving average. The calculation and programming part for the trailing stop varies everywhere. I opted to keep it as simple and accurate as I could think of and interpret from the book. The variations to these types of indicators are numerous unfortunately, but Wilder seems to be the original author of ATR and this ATR-based trailing stop. In his book he says to use the significant closing price or highest/lowest closing price for the calculation part but I also included the option of choosing the highest high and lowest low, and the option to choose various moving averages in case anyone wants to experiment.
Comparing this and Donchian channels, it seems that a 2.5 multiplier is somewhat similar to the middle band of DCs and a 3.0 multiplier is somewhat similar to a double length middle band of DCs. It's hard to say which is the better trailing stop for a long term strategy. It's hard to beat the simplicity of DCs but maybe some might find a need for more inputs in a trailing stop or maybe an ATR based one like Wilder's can work better depending on what setting or strategy it's used in.
Volatility Stop Flow [AR]The indicator is designed to scan cross multiple timeframes and display the Volatility Stop Value.
Realized Volatility IIR Filters with BandsDISCLAIMER:
The Following indicator/code IS NOT intended to be a formal investment advice or recommendation by the author, nor should be construed as such. Users will be fully responsible by their use regarding their own trading vehicles/assets.
The following indicator was made for NON LUCRATIVE ACTIVITIES and must remain as is following TradingView's regulations. Use of indicator and their code are published by Invitation Only for work and knowledge sharing. All access granted over it, their use, copy or re-use should mention authorship(s) and origin(s).
WARNING NOTICE!
THE INCLUDED FUNCTION MUST BE CONSIDERED AS TESTING. The models included in the indicator have been taken from open sources on the web and some of them has been modified by the author, problems could occur at diverse data sceneries.
WHAT'S THIS...?
Work derived by previous own research for study:
This is mainly an INFINITE IMPULSE RESPONSE FILTERING INDICATOR , it's purpose is to catch trend given by the nature of lag given by a VOLATILITY ESTIMATION ALGORITHM as it's coefficient. It provides as well an INFINITE IMPULSE RESPONSE DEVIATION FILTER that uses the same coefficients of the main filter to plot deviation bands as an auxiliary tool.
The given Filter based indicator provides my own Multi Volatility-Estimators Function with only 3 models:
ELASTIC VOLUME WEIGHTED VOLATILITY : This is a Modified Daigler & Padungsaksawasdi "Volume Weighted Volatility" as on DOI: 10.1504/IJBAAF.2018.089423 but with Elastic Volume Weighted Moving Average instead of VWAP (intraday) for faster (but inaccurate) calculation. A future version is planned on the way using intra-bar inspection for intraday timeframe as described in original paper.
GARMAN & KLASS / YANG-ZANG EXTENSION : As one of the best range based (OHLC) with open gaps inclusion in a single bar.
PETER MARTIN'S ULCER INDEX : This is a better approach to measure realized volatility than standard deviation of log returns given it's proven convex risk metric for DrawDowns as shown in Chekhlov et al. (2005) . Regarding this particular model, I take a different approach to use it as coefficient feed: Given that the UI only takes in consideration DrawDawns, I code myself the inverse of this to compute Draw-Ups as well and use both of them to filter minimums volatility levels in order to create a SLOW version of the IIR filter, and maximums of both to calculate as FAST variation. This approach can be used as a better proxy instead of any other common moving average given that with NO COMPOUND IN TIME AT ALL (N=1) or only using as long as N=3 bars of compund, the filter can catch a trend easily, making the indicator nearly a NON PARAMETRIC FILTER.
NOTES:
This version DO NOT INCLUDE ALERTS.
This version DO NOT INCLUDE STRATEGY: ALL Feedback welcome.
DERIVED WORK:
Incremental calculation of weighted mean and variance by Tony Finch (fanf2@cam. ac .uk) (dot@dotat.at), 2009.
Volume weighted volatility: empirical evidence for a new realised volatility measure by Chaiyuth Padungsaksawasdi & Robert T. Daigler, 2018.
Basic DSP Tips & Trics by TradingView user @alexgrover
CHEERS!
@XeL_Arjona 2020.
Volatility Adjusted Profit Target
In my 'Volatility Adjusted Profit Target' indicator, I've crafted a dynamic tool for calculating target profit percentages suitable for both long and short trading strategies. It evaluates the highest and lowest prices over the anticipated duration of your trade, establishing a profit target that shifts with market volatility. As volatility increases, the potential for profit follows, with the target percentage rising accordingly; conversely, it declines with decreasing volatility. As a trader, setting an optimal Take Profit level has always been a challenge. This indicator not only helps in determining that level but also dynamically adjusts it throughout the trade's duration, providing a strategic edge in volatile markets.
