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 !
"Volatility" için komut dosyalarını ara
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
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).
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/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.
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.
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!
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.
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.
Implied Volatility PercentileThis script calculates the Implied Volatility (IV) based on the daily returns of price using a standard deviation. It then annualizes the 30 day average to create the historical Implied Volatility. This indicator is intended to measure the IV for options traders but could also provide information for equities traders to show how price is extended in the expected price range based on the historical volatility.
The IV Rank (Green line) is then calculated by looking at the high and low volatility over the number of days back specified in the input parameter, default is 252 (trading days in 1 year) and then calculating the rank of the current IV compared to the High and Low. This is not as reliable as the IV Percentile as the and extreme high or low could have a side effect on the ranking but it is included for those that want to use.
The IV Percentile is calculated by counting the number of days below the current IV, then returns this as a % of the days back in the input
You can adjust the number of days back to check the IV Rank & IV Percentile if you are not wanting to look back a whole year.
This will only work on Daily or higher timeframe charts.
JPMorgan G7 Volatility IndexThe JPMorgan G7 Volatility Index: Scientific Analysis and Professional Applications
Introduction
The JPMorgan G7 Volatility Index (G7VOL) represents a sophisticated metric for monitoring currency market volatility across major developed economies. This indicator functions as an approximation of JPMorgan's proprietary volatility indices, providing traders and investors with a normalized measurement of cross-currency volatility conditions (Clark, 2019).
Theoretical Foundation
Currency volatility is fundamentally defined as "the statistical measure of the dispersion of returns for a given security or market index" (Hull, 2018, p.127). In the context of G7 currencies, this volatility measurement becomes particularly significant due to the economic importance of these nations, which collectively represent more than 50% of global nominal GDP (IMF, 2022).
According to Menkhoff et al. (2012, p.685), "currency volatility serves as a global risk factor that affects expected returns across different asset classes." This finding underscores the importance of monitoring G7 currency volatility as a proxy for global financial conditions.
Methodology
The G7VOL indicator employs a multi-step calculation process:
Individual volatility calculation for seven major currency pairs using standard deviation normalized by price (Lo, 2002)
- Weighted-average combination of these volatilities to form a composite index
- Normalization against historical bands to create a standardized scale
- Visual representation through dynamic coloring that reflects current market conditions
The mathematical foundation follows the volatility calculation methodology proposed by Bollerslev et al. (2018):
Volatility = σ(returns) / price × 100
Where σ represents standard deviation calculated over a specified timeframe, typically 20 periods as recommended by the Bank for International Settlements (BIS, 2020).
Professional Applications
Professional traders and institutional investors employ the G7VOL indicator in several key ways:
1. Risk Management Signaling
According to research by Adrian and Brunnermeier (2016), elevated currency volatility often precedes broader market stress. When the G7VOL breaches its high volatility threshold (typically 1.5 times the 100-period average), portfolio managers frequently reduce risk exposure across asset classes. As noted by Borio (2019, p.17), "currency volatility spikes have historically preceded equity market corrections by 2-7 trading days."
2. Counter-Cyclical Investment Strategy
Low G7 volatility periods (readings below the lower band) tend to coincide with what Shin (2017) describes as "risk-on" environments. Professional investors often use these signals to increase allocations to higher-beta assets and emerging markets. Campbell et al. (2021) found that G7 volatility in the lowest quintile historically preceded emerging market outperformance by an average of 3.7% over subsequent quarters.
3. Regime Identification
The normalized volatility framework enables identification of distinct market regimes:
- Readings above 1.0: Crisis/high volatility regime
- Readings between -0.5 and 0.5: Normal volatility regime
- Readings below -1.0: Unusually calm markets
According to Rey (2015), these regimes have significant implications for global monetary policy transmission mechanisms and cross-border capital flows.
Interpretation and Trading Applications
G7 currency volatility serves as a barometer for global financial conditions due to these currencies' centrality in international trade and reserve status. As noted by Gagnon and Ihrig (2021, p.423), "G7 currency volatility captures both trade-related uncertainty and broader financial market risk appetites."
Professional traders apply this indicator in multiple contexts:
- Leading indicator: Research from the Federal Reserve Board (Powell, 2020) suggests G7 volatility often leads VIX movements by 1-3 days, providing advance warning of broader market volatility.
- Correlation shifts: During periods of elevated G7 volatility, cross-asset correlations typically increase what Brunnermeier and Pedersen (2009) term "correlation breakdown during stress periods." This phenomenon informs portfolio diversification strategies.
- Carry trade timing: Currency carry strategies perform best during low volatility regimes as documented by Lustig et al. (2011). The G7VOL indicator provides objective thresholds for initiating or exiting such positions.
References
Adrian, T. and Brunnermeier, M.K. (2016) 'CoVaR', American Economic Review, 106(7), pp.1705-1741.
Bank for International Settlements (2020) Monitoring Volatility in Foreign Exchange Markets. BIS Quarterly Review, December 2020.
Bollerslev, T., Patton, A.J. and Quaedvlieg, R. (2018) 'Modeling and forecasting (un)reliable realized volatilities', Journal of Econometrics, 204(1), pp.112-130.
Borio, C. (2019) 'Monetary policy in the grip of a pincer movement', BIS Working Papers, No. 706.
Brunnermeier, M.K. and Pedersen, L.H. (2009) 'Market liquidity and funding liquidity', Review of Financial Studies, 22(6), pp.2201-2238.
Campbell, J.Y., Sunderam, A. and Viceira, L.M. (2021) 'Inflation Bets or Deflation Hedges? The Changing Risks of Nominal Bonds', Critical Finance Review, 10(2), pp.303-336.
Clark, J. (2019) 'Currency Volatility and Macro Fundamentals', JPMorgan Global FX Research Quarterly, Fall 2019.
Gagnon, J.E. and Ihrig, J. (2021) 'What drives foreign exchange markets?', International Finance, 24(3), pp.414-428.
Hull, J.C. (2018) Options, Futures, and Other Derivatives. 10th edn. London: Pearson.
International Monetary Fund (2022) World Economic Outlook Database. Washington, DC: IMF.
Lo, A.W. (2002) 'The statistics of Sharpe ratios', Financial Analysts Journal, 58(4), pp.36-52.
Lustig, H., Roussanov, N. and Verdelhan, A. (2011) 'Common risk factors in currency markets', Review of Financial Studies, 24(11), pp.3731-3777.
Menkhoff, L., Sarno, L., Schmeling, M. and Schrimpf, A. (2012) 'Carry trades and global foreign exchange volatility', Journal of Finance, 67(2), pp.681-718.
Powell, J. (2020) Monetary Policy and Price Stability. Speech at Jackson Hole Economic Symposium, August 27, 2020.
Rey, H. (2015) 'Dilemma not trilemma: The global financial cycle and monetary policy independence', NBER Working Paper No. 21162.
Shin, H.S. (2017) 'The bank/capital markets nexus goes global', Bank for International Settlements Speech, January 15, 2017.
Momentum-Adjusted Volatility Ratio (MAVR)The Momentum-Adjusted Volatility Ratio (MAVR) indicator is designed to help you understand the strength of price movements relative to the market's volatility. It combines the concepts of rate of change (ROC) and average true range (ATR) and then calculates their ratio, which is then smoothed using an exponential moving average (EMA). Here's a general guide on how to use the MAVR indicator:
Identify the trend: Look for the overall direction of the EMA of the MAVR. When the EMA is above the zero line, it indicates that the momentum is positive and the trend is generally bullish. Conversely, when the EMA is below the zero line, it indicates that the momentum is negative, and the trend is generally bearish.
Assess momentum strength: Pay attention to the distance between the EMA of the MAVR and the zero line. A larger distance indicates a stronger momentum, while a smaller distance suggests weaker momentum. If the EMA of the MAVR moves further away from the zero line, it indicates that the price movement is becoming more robust relative to the market's volatility.
Look for potential entry and exit signals: When the EMA of the MAVR crosses the zero line, it could provide a potential trading signal. For instance, a cross from below to above the zero line may indicate a potential buying opportunity, while a cross from above to below the zero line may signal a potential selling opportunity. Keep in mind that the MAVR indicator should not be used in isolation, and it's essential to combine it with other technical analysis tools and risk management techniques.
Monitor for divergences: Sometimes, the price and the EMA of the MAVR can show divergences. For example, if the price makes a higher high while the EMA of the MAVR makes a lower high, it could signal a bearish divergence, suggesting a potential trend reversal. Similarly, if the price makes a lower low while the EMA of the MAVR makes a higher low, it could indicate a bullish divergence, suggesting a possible trend reversal.
Remember that no indicator is perfect, and the MAVR should be used in conjunction with other technical analysis tools and a solid trading strategy to increase the chances of success. Always use proper risk management techniques to protect your capital.
Normalized VolatilityOVERVIEW
The Normalized Volatility indicator is a technical indicator that gauges the amount of volatility currently present in the market, relative to the average volatility in the market. The purpose of this indicator is to filter out with-trend signals during ranging/non-trending/consolidating conditions.
CONCEPTS
This indicator assists traders in capitalizing on the assumption that trends are more likely to start during periods of high volatility compared to periods of low volatility. This is because high volatility indicates that there are bigger players currently in the market, which is necessary to begin a sustained trending move.
So, to determine whether the current volatility is "high", it is compared to an average volatility for however number of candles back the user specifies.
If the current volatility is greater than the average volatility, it is reasonable to assume we are in a high-volatility period. Thus, this is the ideal time to enter a trending trade due to the assumption that trends are more likely to start during these high-volatility periods.
HOW DO I READ THIS INDICATOR
When the column's color is red, don't take any trend trades since the current volatility is less than the average volatility experienced in the market.
When the column's color is green, take all valid with-trend trades since the current volatility is greater than the average volatility experienced in the market.
Relative Historical Volatility MCMRelative Historical Volatility
Historical Volatility is relative to it's doubled lookback period of the historical volatility to calculate relative historical volatility.
Including a standard deviation to calculate the volatility value itself is useless. It filters out 32% of the most volatile movements of the asset that you are observing.
Example of RHV:
Period of Volatility Value (POVV) : 10
Relative Historical Volatility : POVV / POVV*2
Historical Volatility of past 10 Bars is compared to the historical volatility of the bast 20 bars to show real growth/decrease of volatility relative to the time of the performing asset.
Comparing historical volatility to the current bar includes much more noise, the relative historical volatility can be perceived as a smoothed historical volatility ind.
Marginal notes:
Added standard deviations adjusted to the relative volatility value to predict probable future volatility of the stock.
Volatility Adjusted Moving Average - JD@version=3
This indicator gives an adjusted moving average, based on the volatility of the past x amount of bars, measured against the ema of a certain length.
The idea came out of my VA adjusted Bands indicator where the VAMA is actually the center line.
I scripted the moving average as a function so it is easy to inport into other scripts,
Feel free to use it in your scripts and experiment with it,
of cousre, if you want to publish your script, a little mention in the notes is always appreciated.
At first view I might add some smoothing otions and
a couple of different ma options as a base anchor in future releases.
If you have any other ideas for further development,... let me know!!
JD.
#NotTradingAdvice #DYOR
I build these indicators for myself and provide them open source, to use for free to use and improve upon,
as I believe the best way to learn is toghether.
volatility-adjusted breakout envelopethis indicator is designed to help traders visually identify potential entry and exit points based on volatility-adjusted price thresholds. it works by calculating a dynamic expected price move around the previous close using historical volatility data smoothed by exponential moving averages to reduce noise and present a clear range boundary on the chart.
the indicator first computes the logarithmic returns over a user-defined lookback period and calculates the standard deviation of these returns, which represents raw volatility. it annualizes this volatility according to the chart timeframe selected, then uses it to estimate an expected price movement for the current timeframe. this expected move is smoothed to avoid sudden spikes or drops that could cause confusing signals.
using this expected move, the indicator generates two key threshold lines: an upper threshold and a lower threshold. these lines create a volatility-based range around the smoothed previous close price. the thresholds themselves are further smoothed with exponential moving averages to produce smooth, easy-to-interpret lines that adapt to changing market conditions without being choppy.
the core trading signals are generated when the price closes outside of these smoothed threshold ranges. specifically, a long entry signal is indicated when the price closes above the upper threshold for the first time, signaling potential upward momentum beyond normal volatility expectations. a short entry signal occurs when the price closes below the lower threshold for the first time, indicating potential downward momentum.
once an entry signal is triggered, the indicator waits for the price to close back inside the threshold range before signaling an exit. when this occurs, an exit marker is displayed to indicate that the price has returned within normal volatility bounds, which may suggest that the previous trend is losing strength or the breakout has ended.
these signals are visually represented on the chart using small shapes: triangles pointing upwards mark the initial long entries, triangles pointing downwards mark short entries, and x shapes mark the exits for both long and short positions. the colors of these shapes are customizable to suit user preferences.
to use this indicator effectively, traders should watch for the first close outside the smoothed volatility range to consider entering a position in the breakout direction. the exit signals help identify when price action reverts back into the expected range, which can be used to close or reduce the position. this method emphasizes trading breakouts supported by statistically significant moves relative to recent volatility while providing a clear exit discipline.
this indicator is best applied to intraday or daily charts with consistent volatility and volume characteristics. users should adjust the volatility lookback period, smoothing factor, and trading session times to match their specific market and trading style. because it relies on price volatility rather than fixed price levels, it can adapt to changing market conditions but should be combined with other analysis tools and proper risk management.
overall, this indicator provides a smoothed, dynamic volatility envelope with clear visual entry and exit cues based on first closes outside and back inside these envelopes, making it a helpful assistant for manual traders seeking to capture statistically significant breakouts while maintaining disciplined exits.
Volatility Gaussian Bands [BigBeluga]The Volatility Gaussian Bands indicator is a cutting-edge tool designed to analyze market trends and volatility with high precision. By applying a Gaussian filter to smooth price data and implementing dynamic bands based on market volatility, this indicator provides clear signals for trend direction, strength, and potential reversals. With updated volatility calculations, it enhances the accuracy of trend detection, making it a powerful addition to any trader's toolkit.
⮁ KEY FEATURES & USAGE
● Gaussian Filter Trend Bands:
The Gaussian Filter forms the foundation of this indicator by smoothing price data to reveal the underlying trend. The trend is visualized through upper and lower bands that adjust dynamically based on market volatility. These bands provide clear visual cues for traders: a crossover above the upper band indicates a potential uptrend, while a cross below the lower band signals a potential downtrend. This feature allows traders to identify trends with greater accuracy and act accordingly.
● Dynamic Trend Strength Gauges:
The indicator includes trend strength gauges positioned at the top and bottom of the chart. These gauges dynamically measure the strength of the uptrend and downtrend, based on the middle Gaussian line. Even if the trend is downward, a rising midline will cause the upward trend strength gauge to show an increase, offering a nuanced view of the market’s momentum.
Weakening of the trend:
● Fast Trend Change Indicators:
Triangles with a "+" symbol appear on the chart to signal rapid changes in trend direction. These indicators are particularly useful when the trend changes swiftly while the midline continues to grow in its previous direction. For instance, during a downtrend, if the trend suddenly shifts upward while the midline is still declining, a triangle with a "+" will indicate this quick reversal. This feature is crucial for traders looking to capitalize on rapid market movements.
● Retest Signals:
Retest signals, displayed as triangles, highlight potential areas where the price may retest the Gaussian line during a trend. These signals provide an additional layer of analysis, helping traders confirm trend continuations or identify possible reversals. The retest signals can be customized based on the trader’s preferences.
⮁ CUSTOMIZATION
● Length Adjustment:
The length of the Gaussian filter can be customized to control the sensitivity of trend detection. Shorter lengths make the indicator more responsive, while longer lengths offer a smoother, more stable trend line.
● Volatility Calculation Mode:
Traders can select from different modes (AVG, MEDIAN, MODE) to calculate the Gaussian filter, allowing for flexibility in how trends are detected and analyzed.
● Retest Signals Toggle:
Enable or disable the retest signals based on your trading strategy. This toggle allows traders to choose whether they want these additional signals to appear on the chart, providing more control over the information displayed during their analysis.
⮁ CONCLUSION
The Volatility Gaussian Bands indicator is a versatile and powerful tool for traders focused on trend and volatility analysis. By combining Gaussian-filtered trend lines with dynamic volatility bands, trend strength gauges, and rapid trend change indicators, this tool provides a comprehensive view of market conditions. Whether you are following established trends or looking to catch early reversals, the Volatility Gaussian Bands offers the precision and adaptability needed to enhance your trading strategy.
