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Expected Move BandsExpected move is the amount that an asset is predicted to increase or decrease from its current price, based on the current levels of volatility. In this model, we assume asset price follows a log-normal distribution and the log return follows a normal distribution. Note: Normal distribution is just an assumption, it's not the real distribution of return Settings: "Estimation Period Selection" is for selecting the period we want to construct the prediction interval. For "Current Bar", the interval is calculated based on the data of the previous bar close. Therefore changes in the current price will have little effect on the range. What current bar means is that the estimated range is for when this bar close. E.g., If the Timeframe on 4 hours and 1 hour has passed, the interval is for how much time this bar has left, in this case, 3 hours. For "Future Bars", the interval is calculated based on the current close. Therefore the range will be very much affected by the change in the current price. If the current price moves up, the range will also move up, vice versa. Future Bars is estimating the range for the period at least one bar ahead. There are also other source selections based on high low. Time setting is used when "Future Bars" is chosen for the period. The value in time means how many bars ahead of the current bar the range is estimating. When time = 1, it means the interval is constructing for 1 bar head. E.g., If the timeframe is on 4 hours, then it's estimating the next 4 hours range no matter how much time has passed in the current bar. Note: It's probably better to use "probability cone" for visual presentation when time > 1 Volatility Models : Sample SD: traditional sample standard deviation, most commonly used, use (n-1) period to adjust the bias Parkinson: Uses High/ Low to estimate volatility, assumes continuous no gap, zero mean no drift, 5 times more efficient than Close to Close Garman Klass: Uses OHLC volatility, zero drift, no jumps, about 7 times more efficient Yangzhang Garman Klass Extension: Added jump calculation in Garman Klass, has the same value as Garman Klass on markets with no gaps. about 8 x efficient Rogers: Uses OHLC, Assume non-zero mean volatility, handles drift, does not handle jump 8 x efficient EWMA: Exponentially Weighted Volatility. Weight recently volatility more, more reactive volatility better in taking account of volatility autocorrelation and cluster. YangZhang: Uses OHLC, combines Rogers and Garmand Klass, handles both drift and jump, 14 times efficient, alpha is the constant to weight rogers volatility to minimize variance. Median absolute deviation: It's a more direct way of measuring volatility. It measures volatility without using Standard deviation. The MAD used here is adjusted to be an unbiased estimator. Volatility Period is the sample size for variance estimation. A longer period makes the estimation range more stable less reactive to recent price. Distribution is more significant on a larger sample size. A short period makes the range more responsive to recent price. Might be better for high volatility clusters. Standard deviations: Standard Deviation One shows the estimated range where the closing price will be about 68% of the time. Standard Deviation two shows the estimated range where the closing price will be about 95% of the time. Standard Deviation three shows the estimated range where the closing price will be about 99.7% of the time. Note: All these probabilities are based on the normal distribution assumption for returns. It's the estimated probability, not the actual probability. Manually Entered Standard Deviation shows the range of any entered standard deviation. The probability of that range will be presented on the panel. People usually assume the mean of returns to be zero. To be more accurate, we can consider the drift in price from calculating the geometric mean of returns. Drift happens in the long run, so short lookback periods are not recommended. Assuming zero mean is recommended when time is not greater than 1. When we are estimating the future range for time > 1, we typically assume constant volatility and the returns to be independent and identically distributed. We scale the volatility in term of time to get future range. However, when there's autocorrelation in returns( when returns are not independent), the assumption fails to take account of this effect. Volatility scaled with autocorrelation is required when returns are not iid. We use an AR(1) model to scale the first-order autocorrelation to adjust the effect. Returns typically don't have significant autocorrelation. Adjustment for autocorrelation is not usually needed. A long length is recommended in Autocorrelation calculation. Note: The significance of autocorrelation can be checked on an ACF indicator. ACF The multimeframe option enables people to use higher period expected move on the lower time frame. People should only use time frame higher than the current time frame for the input. An error warning will appear when input Tf is lower. The input format is multiplier * time unit. E.g. : 1D Unit: M for months, W for Weeks, D for Days, integers with no unit for minutes (E.g. 240 = 240 minutes). S for Seconds. Smoothing option is using a filter to smooth out the range. The filter used here is John Ehler's supersmoother. It's an advance smoothing technique that gets rid of aliasing noise. It affects is similar to a simple moving average with half the lookback length but smoother and has less lag. Note: The range here after smooth no long represent the probability Panel positions can be adjusted in the settings. X position adjusts the horizontal position of the panel. Higher X moves panel to the right and lower X moves panel to the left. Y position adjusts the vertical position of the panel. Higher Y moves panel up and lower Y moves panel down. Step line display changes the style of the bands from line to step line. Step line is recommended because it gets rid of the directional bias of slope of expected move when displaying the bands. Warnings: People should not blindly trust the probability. They should be aware of the risk evolves by using the normal distribution assumption. The real return has skewness and high kurtosis. While skewness is not very significant, the high kurtosis should be noticed. The Real returns have much fatter tails than the normal distribution, which also makes the peak higher. This property makes the tail ranges such as range more than 2SD highly underestimate the actual range and the body such as 1 SD slightly overestimate the actual range. For ranges more than 2SD, people shouldn't trust them. They should beware of extreme events in the tails. Different volatility models provide different properties if people are interested in the accuracy and the fit of expected move, they can try expected move occurrence indicator. (The result also demonstrate the previous point about the drawback of using normal distribution assumption). Expected move Occurrence Test The prediction interval is only for the closing price, not wicks. It only estimates the probability of the price closing at this level, not in between. E.g., If 1 SD range is 100 - 200, the price can go to 80 or 230 intrabar, but if the bar close within 100 - 200 in the end. It's still considered a 68% one standard deviation move.
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Advanced S&D Engine | ZikZak-Trader30About This Script This is a fully custom-built Supply & Demand Zone detection engine for TradingView written by ZikZak-Trader30 (Kotdwar, UK). The script identifies potential key supply and demand zones based on market structure and pattern logic widely used by professional traders. Detected Patterns: RBR (Rally-Base-Rally, demand) DBD (Drop-Base-Drop, supply) RBD (Rally-Base-Drop, supply) DBR (Drop-Base-Rally, demand) Features Highlight Detailed configurable zone filtering (freshness, gap detection, time spent, width, Fibonacci confluence, etc.) Fair and adjustable scoring system for zone strength Automatic management/removal of old or retested/violated zones Optional Fibonacci level confluence and dynamic labeling Transparency Statement How It Works: This script uses well-known price action concepts and compares candles’ movement, consolidation, and breakout patterns to mark S&D zones. There are no repaints or future leaks: all logic is based entirely on historical and current bars. Parameters and variables are fully described in the script inputs. The zone scoring and removal logic is also visible in the code for transparency. IMPORTANT: Usage & Fair-Use Policy This script is provided for educational and informational purposes only. It should not be considered as financial advice or a trading signal. Trading/investing involves risk—always do your own research or consult a financial advisor before making trading decisions. Past performance or backtest results are not necessarily indicative of future results. License & Fair Use The code is original, written by ZikZak-Trader30. All logic and comments are visible for users to study, adapt, or improve for personal, non-commercial use within TradingView. You may NOT resell, repackage, or repost this script as your own. If you fork or publicly remix/adapt the script, please credit "ZikZak-Trader30" and do not remove this disclosure section. If you use ideas or snippets, kindly reference this script and author. Absolutely NO plagiarized or resold code is permitted. This script is not for re-sale. Acknowledgements This indicator was inspired by years of price action study and usage of public S&D scripts. While the pattern logic is classic in nature, the version and scoring are original. No proprietary datasets or paid logic from other sources are included. Minor ideas on zone freshness and Fibonacci blending are common in the TradingView S&D community and have been custom-implemented here.
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ES-VIX Expected Move - Open basedES-VIX Daily Price Bands This indicator plots dynamic intraday price bands for ES futures based on real-time volatility levels measured by the VIX (CBOE Volatility Index). The bands evolve throughout the trading day, providing volatility-adjusted price targets. Formulas: Upper Band = Daily Open + (ES Price × VIX ÷ √252 ÷ 100) Lower Band = Daily Open - (ES Price × VIX ÷ √252 ÷ 100) The calculation uses the square root of 252 (trading days per year) to convert annualized VIX volatility into an expected daily move, then scales it as a percentage adjustment from the current day's open. Features: Real-time band calculation that updates throughout the trading session Upper band (green) extends from the current day's open Lower band (red) contracts from the current day's open Inner upper band (green) at 50% of expected move Inner lower band (red) at 50% of expected move Middle Inner upper band (green) at 80% of expected move Middle Inner lower band (red) at 80% of expected move Information table displaying: Current ES price and VIX level Daily Open Expected move
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THF Scalp & Trend + FVG [English]This indicator is a comprehensive "All-In-One" trading suite designed for Scalpers and Day Traders who look for confluence between Trend Following indicators and Price Action (Fair Value Gaps). It combines two powerful concepts into a single chart overlay: 1. Moving Average Crossovers & Trend Filtering (THF Logic). 2. Fair Value Gaps (FVG) detection for entry/exit targets. ### 🛠️ Key Features: **1. Trend & Scalp Signals:** - **Scalp Signals:** Based on fast EMA crossovers (default 7/21). These signals can be filtered by a long-term SMA (200) to ensure you are trading with the major trend. - **Trend Signals:** Identifies stronger trend shifts using EMA 21 crossing SMA 50. - **Major Crosses:** Automatically highlights Golden Cross (SMA 50 > 200) and Death Cross events. **2. Price Action (FVG - Fair Value Gaps):** - Integrated **LuxAlgo's Fair Value Gap** logic to identify imbalances in the market. - Displays Bullish/Bearish zones which act as magnets for price or support/resistance levels. - Includes a Dashboard to track mitigated vs. unmitigated zones. **3. Momentum & Volume Confluence:** - **Visual Volume:** Candles are colored based on volume relative to the average (Volume SMA). - **RSI & MACD Signals:** Optional overlays to spot overbought/oversold conditions or momentum shifts directly on the chart. ### 🎯 How to Use: - **For Scalping:** Wait for a "SCALP BUY" signal while the price is above the SMA 200 (Trend Filter). Use the FVG boxes as potential Take Profit targets. - **For Trend Trading:** Look for the "Trend BUY" label and confirm with the Golden Cross. - **Stop Loss:** Can be placed below the recent swing low or below the EMA 50. ---------------------------------------------------------------- **CREDITS & ATTRIBUTION:** This script is a mashup of custom trend logic and open-source community codes. - **Fair Value Gap:** Full credit goes to **LuxAlgo** for the FVG detection algorithm and dashboard logic. This script utilizes their open-source calculation methods to enhance the trend strategy. - **Trend Logic:** Based on classic Moving Average crossover strategies tailored for scalping. *Disclaimer: This tool is for educational purposes only. Always manage your risk.*
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SNP420/RSI_GOD_KOMPLEXRSI_GOD_KOMPLEX is a multi–timeframe RSI scanner for TradingView that displays a compact table in the top-right corner of the chart. For each timeframe (1m, 5m, 15m, 30m, 1h, 4h, 1d) it tracks the fast RSI line (not the smoothed/main one) and marks BUY in green when RSI crosses up through 30 (leaving oversold territory) and SELL in red when RSI crosses down through 70 (leaving overbought territory), always using only closed candles for reliable, non-repainting signals. The indicator remembers the last valid signal per timeframe, so the table always shows the most recent directional impulse from RSI across all selected timeframes on the same instrument. author: SNP420 + Jarvis project: FNXS ps: piece and love
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AlphaRank MA Lens – Multi-Timeframe Moving Average MapAlphaRank MA Lens – Multi-Timeframe Moving Average Map AlphaRank MA Lens is a clean, open-source moving-average overlay that turns price action into an easy-to-read trend map. It focuses on structure and context only — no signals, no backtest, no hype — just a clear view of where price sits relative to key moving averages. The script plots the 10 / 20 / 50 / 100 / 150 / 200 / 730 moving averages with full color control and a single “MA Type” switch, so you can flip the whole stack between SMA and EMA in one click. Instead of loading multiple separate MA indicators, this puts the full trend stack in one tool. An optional background highlight lets you choose a reference MA (for example the 200 MA) and softly shade the chart: Green when price is above that MA Red when price is below it This makes trend regime changes easy to see at a glance. How traders typically use it (education only): 10/20/50 MAs → short-term trend and momentum. 100/150/200/730 MAs → bigger structural trend and “where price lives” in the long-term range. Many traders consider conditions healthier when price and the short MAs are stacked above the longer MAs, and weaker when price trades below them. Follow my work: AlphaRank This script is for educational and analytical purposes only and does not provide trading advice or performance promises. Always combine it with your own judgment, testing, and risk management.
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️Omega RatioThe Omega Ratio is a risk-return performance measure of an investment asset, portfolio, or strategy. It is defined as the probability-weighted ratio, of gains versus losses for some threshold return target. The ratio is an alternative for the widely used Sharpe ratio and is based on information the Sharpe ratio discards. █ OVERVIEW As we have mentioned many times, stock market returns are usually not normally distributed. Therefore the models that assume a normal distribution of returns may provide us with misleading information. The Omega Ratio improves upon the common normality assumption among other risk-return ratios by taking into account the distribution as a whole. █ CONCEPTS Two distributions with the same mean and variance, would according to the most commonly used Sharpe Ratio suggest that the underlying assets of the distribution offer the same risk-return ratio. But as we have mentioned in our Moments indicator, variance and standard deviation are not a sufficient measure of risk in the stock market since other shape features of a distribution like skewness and excess kurtosis come into play. Omega Ratio tackles this problem by employing all four Moments of the distribution and therefore taking into account the differences in the shape features of the distributions. Another important feature of the Omega Ratio is that it does not require any estimation but is rather calculated directly from the observed data. This gives it an advantage over standard statistical estimators that require estimation of parameters and are therefore sampling uncertainty in its calculations. █ WAYS TO USE THIS INDICATOR Omega calculates a probability-adjusted ratio of gains to losses, relative to the Minimum Acceptable Return (MAR). This means that at a given MAR using the simple rule of preferring more to less, an asset with a higher value of Omega is preferable to one with a lower value. The indicator displays the values of Omega at increasing levels of MARs and creating the so-called Omega Curve. Knowing this one can compare Omega Curves of different assets and decide which is preferable given the MAR of your strategy. The indicator plots two Omega Curves. One for the on chart symbol and another for the off chart symbol that u can use for comparison. When comparing curves of different assets make sure their trading days are the same in order to ensure the same period for the Omega calculations. Value interpretation: Omega<1 will indicate that the risk outweighs the reward and therefore there are more excess negative returns than positive. Omega>1 will indicate that the reward outweighs the risk and that there are more excess positive returns than negative. Omega=1 will indicate that the minimum acceptable return equals the mean return of an asset. And that the probability of gain is equal to the probability of loss. █ FEATURES • "Low-Risk security" lets you select the security that you want to use as a benchmark for Omega calculations. • "Omega Period" is the size of the sample that is used for the calculations. • “Increments” is the number of Minimal Acceptable Return levels the calculation is carried on. • “Other Symbol” lets you select the source of the second curve. • “Color Settings” you can set the color for each curve.
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Linear Moments█ OVERVIEW The Linear Moments indicator, also known as L-moments, is a statistical tool used to estimate the properties of a probability distribution. It is an alternative to conventional moments and is more robust to outliers and extreme values. █ CONCEPTS █ Four moments of a distribution We have mentioned the concept of the Moments of a distribution in one of our previous posts. The method of Linear Moments allows us to calculate more robust measures that describe the shape features of a distribution and are anallougous to those of conventional moments. L-moments therefore provide estimates of the location, scale, skewness, and kurtosis of a probability distribution. The first L-moment, λ₁, is equivalent to the sample mean and represents the location of the distribution. The second L-moment, λ₂, is a measure of the dispersion of the distribution, similar to the sample standard deviation. The third and fourth L-moments, λ₃ and λ₄, respectively, are the measures of skewness and kurtosis of the distribution. Higher order L-moments can also be calculated to provide more detailed information about the shape of the distribution. One advantage of using L-moments over conventional moments is that they are less affected by outliers and extreme values. This is because L-moments are based on order statistics, which are more resistant to the influence of outliers. By contrast, conventional moments are based on the deviations of each data point from the sample mean, and outliers can have a disproportionate effect on these deviations, leading to skewed or biased estimates of the distribution parameters. █ Order Statistics L-moments are statistical measures that are based on linear combinations of order statistics, which are the sorted values in a dataset. This approach makes L-moments more resistant to the influence of outliers and extreme values. However, the computation of L-moments requires sorting the order statistics, which can lead to a higher computational complexity. To address this issue, we have implemented an Online Sorting Algorithm that efficiently obtains the sorted dataset of order statistics, reducing the time complexity of the indicator. The Online Sorting Algorithm is an efficient method for sorting large datasets that can be updated incrementally, making it well-suited for use in trading applications where data is often streamed in real-time. By using this algorithm to compute L-moments, we can obtain robust estimates of distribution parameters while minimizing the computational resources required. █ Bias and efficiency of an estimator One of the key advantages of L-moments over conventional moments is that they approach their asymptotic normal closer than conventional moments. This means that as the sample size increases, the L-moments provide more accurate estimates of the distribution parameters. Asymptotic normality is a statistical property that describes the behavior of an estimator as the sample size increases. As the sample size gets larger, the distribution of the estimator approaches a normal distribution, which is a bell-shaped curve. The mean and variance of the estimator are also related to the true mean and variance of the population, and these relationships become more accurate as the sample size increases. The concept of asymptotic normality is important because it allows us to make inferences about the population based on the properties of the sample. If an estimator is asymptotically normal, we can use the properties of the normal distribution to calculate the probability of observing a particular value of the estimator, given the sample size and other relevant parameters. In the case of L-moments, the fact that they approach their asymptotic normal more closely than conventional moments means that they provide more accurate estimates of the distribution parameters as the sample size increases. This is especially useful in situations where the sample size is small, such as when working with financial data. By using L-moments to estimate the properties of a distribution, traders can make more informed decisions about their investments and manage their risk more effectively. Below we can see the empirical dsitributions of the Variance and L-scale estimators. We ran 10000 simulations with a sample size of 100. Here we can clearly see how the L-moment estimator approaches the normal distribution more closely and how such an estimator can be more representative of the underlying population. █ WAYS TO USE THIS INDICATOR The Linear Moments indicator can be used to estimate the L-moments of a dataset and provide insights into the underlying probability distribution. By analyzing the L-moments, traders can make inferences about the shape of the distribution, such as whether it is symmetric or skewed, and the degree of its spread and peakedness. This information can be useful in predicting future market movements and developing trading strategies. One can also compare the L-moments of the dataset at hand with the L-moments of certain commonly used probability distributions. Finance is especially known for the use of certain fat tailed distributions such as Laplace or Student-t. We have built in the theoretical values of L-kurtosis for certain common distributions. In this way a person can compare our observed L-kurtosis with the one of the selected theoretical distribution. █ FEATURES Source Settings Source - Select the source you wish the indicator to calculate on Source Selection - Selec whether you wish to calculate on the source value or its log return Moments Settings Moments Selection - Select the L-moment you wish to be displayed Lookback - Determine the sample size you wish the L-moments to be calculated with Theoretical Distribution - This setting is only for investingating the kurtosis of our dataset. One can compare our observed kurtosis with the kurtosis of a selected theoretical distribution.
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Historical Volatility EstimatorsHistorical volatility is a statistical measure of the dispersion of returns for a given security or market index over a given period. This indicator provides different historical volatility model estimators with percentile gradient coloring and volatility stats panel. █ OVERVIEW There are multiple ways to estimate historical volatility. Other than the traditional close-to-close estimator. This indicator provides different range-based volatility estimators that take high low open into account for volatility calculation and volatility estimators that use other statistics measurements instead of standard deviation. The gradient coloring and stats panel provides an overview of how high or low the current volatility is compared to its historical values. █ CONCEPTS We have mentioned the concepts of historical volatility in our previous indicators, Historical Volatility, Historical  Volatility  Rank, and Historical  Volatility  Percentile. You can check the definition of these scripts. The basic calculation is just the sample standard deviation of log return scaled with the square root of time. The main focus of this script is the difference between volatility models. Close-to-Close HV Estimator: Close-to-Close is the traditional historical volatility calculation. It uses sample standard deviation. Note: the TradingView build in historical volatility value is a bit off because it uses population standard deviation instead of sample deviation. N – 1 should be used here to get rid of the sampling bias. Pros:   • Close-to-Close HV estimators are the most commonly used estimators in finance. The calculation is straightforward and easy to understand. When people reference historical volatility, most of the time they are talking about the close to close estimator. Cons:   • The Close-to-close estimator only calculates volatility based on the closing price. It does not take account into intraday volatility drift such as high, low. It also does not take account into the jump when open and close prices are not the same.   • Close-to-Close weights past volatility equally during the lookback period, while there are other ways to weight the historical data.   • Close-to-Close is calculated based on standard deviation so it is vulnerable to returns that are not normally distributed and have fat tails. Mean and Median absolute   deviation makes the historical volatility more stable with extreme values. Parkinson Hv Estimator:   • Parkinson was one of the first to come up with improvements to historical volatility calculation. • Parkinson suggests using the High and Low of each bar can represent volatility better as it takes into account intraday volatility. So Parkinson HV is also known as Parkinson High Low HV. • It is about 5.2 times more efficient than Close-to-Close estimator. But it does not take account into jumps and drift. Therefore, it underestimates volatility. Note: By Dividing the Parkinson Volatility by Close-to-Close volatility you can get a similar result to Variance Ratio Test. It is called the Parkinson number. It can be used to test if the market follows a random walk. (It is mentioned in Nassim Taleb's Dynamic Hedging book but it seems like he made a mistake and wrote the ratio wrongly.) Garman-Klass Estimator:   • Garman Klass expanded on Parkinson’s Estimator. Instead of Parkinson’s estimator using high and low, Garman Klass’s method uses open, close, high, and low to find the minimum variance method.   • The estimator is about 7.4 more efficient than the traditional estimator. But like Parkinson HV, it ignores jumps and drifts. Therefore, it underestimates volatility. Rogers-Satchell Estimator:   • Rogers and Satchell found some drawbacks in Garman-Klass’s estimator. The Garman-Klass assumes price as Brownian motion with zero drift.   • The Rogers Satchell Estimator calculates based on open, close, high, and low. And it can also handle drift in the financial series.   • Rogers-Satchell HV is more efficient than Garman-Klass HV when there’s drift in the data. However, it is a little bit less efficient when drift is zero. The estimator doesn’t handle jumps, therefore it still underestimates volatility. Garman-Klass Yang-Zhang extension:   • Yang Zhang expanded Garman Klass HV so that it can handle jumps. However, unlike the Rogers-Satchell estimator, this estimator cannot handle drift. It is about 8 times more efficient than the traditional estimator.   • The Garman-Klass Yang-Zhang extension HV has the same value as Garman-Klass when there’s no gap in the data such as in cryptocurrencies. Yang-Zhang Estimator:   • The Yang Zhang Estimator combines Garman-Klass and Rogers-Satchell Estimator so that it is based on Open, close, high, and low and it can also handle non-zero drift. It also expands the calculation so that the estimator can also handle overnight jumps in the data.   • This estimator is the most powerful estimator among the range-based estimators. It has the minimum variance error among them, and it is 14 times more efficient than the close-to-close estimator. When the overnight and daily volatility are correlated, it might underestimate volatility a little.   • 1.34 is the optimal value for alpha according to their paper. The alpha constant in the calculation can be adjusted in the settings. Note: There are already some volatility estimators coded on TradingView. Some of them are right, some of them are wrong. But for Yang Zhang Estimator I have not seen a correct version on TV. EWMA Estimator: • EWMA stands for Exponentially Weighted Moving Average. The Close-to-Close and all other estimators here are all equally weighted.   • EWMA weighs more recent volatility more and older volatility less. The benefit of this is that volatility is usually autocorrelated. The autocorrelation has close to exponential decay as you can see using an Autocorrelation Function indicator on absolute or squared returns. The autocorrelation causes volatility clustering which values the recent volatility more. Therefore, exponentially weighted volatility can suit the property of volatility well.   • RiskMetrics uses 0.94 for lambda which equals 30 lookback period. In this indicator Lambda is coded to adjust with the lookback. It's also easy for EWMA to forecast one period volatility ahead.   • However, EWMA volatility is not often used because there are better options to weight volatility such as ARCH and GARCH. Adjusted Mean Absolute Deviation Estimator:   • This estimator does not use standard deviation to calculate volatility. It uses the distance log return is from its moving average as volatility.   • It’s a simple way to calculate volatility and it’s effective. The difference is the estimator does not have to square the log returns to get the volatility. The paper suggests this estimator has more predictive power.   • The mean absolute deviation here is adjusted to get rid of the bias. It scales the value so that it can be comparable to the other historical volatility estimators.   • In Nassim Taleb’s paper, he mentions people sometimes confuse MAD with standard deviation for volatility measurements. And he suggests people use mean absolute deviation instead of standard deviation when we talk about volatility. Adjusted Median Absolute Deviation Estimator:   • This is another estimator that does not use standard deviation to measure volatility.   • Using the median gives a more robust estimator when there are extreme values in the returns. It works better in fat-tailed distribution.   • The median absolute deviation is adjusted by maximum likelihood estimation so that its value is scaled to be comparable to other volatility estimators. █ FEATURES   • You can select the volatility estimator models in the Volatility Model input   • Historical Volatility is annualized. You can type in the numbers of trading days in a year in the Annual input based on the asset you are trading.   • Alpha is used to adjust the Yang Zhang volatility estimator value.   • Percentile Length is used to Adjust Percentile coloring lookbacks.   • The gradient coloring will be based on the percentile value (0- 100). The higher the percentile value, the warmer the color will be, which indicates high volatility. The lower the percentile value, the colder the color will be, which indicates low volatility.   • When percentile coloring is off, it won’t show the gradient color.   • You can also use invert color to make the high volatility a cold color and a low volatility high color. Volatility has some mean reversion properties. Therefore when volatility is very low, and color is close to aqua, you would expect it to expand soon. When volatility is very high, and close to red, you would it expect it to contract and cool down.   • When the background signal is on, it gives a signal when HVP is very low. Warning there might be a volatility expansion soon. • You can choose the plot style, such as lines, columns, areas in the plotstyle input.   • When the show information panel is on, a small panel will display on the right.   • The information panel displays the historical volatility model name, the 50th percentile of HV, and HV percentile. 50 the percentile of HV also means the median of HV. You can compare the value with the current HV value to see how much it is above or below so that you can get an idea of how high or low HV is. HV Percentile value is from 0 to 100. It tells us the percentage of periods over the entire lookback that historical volatility traded below the current level. Higher HVP, higher HV compared to its historical data. The gradient color is also based on this value. █ HOW TO USE If you haven’t used the hvp indicator, we suggest you use the HVP indicator first. This indicator is more like historical volatility with HVP coloring. So it displays HVP values in the color and panel, but it’s not range bound like the HVP and it displays HV values. The user can have a quick understanding of how high or low the current volatility is compared to its historical value based on the gradient color. They can also time the market better based on volatility mean reversion. High volatility means volatility contracts soon (Move about to End, Market will cooldown), low volatility means volatility expansion soon (Market About to Move). █ FINAL THOUGHTS HV vs ATR The above volatility estimator concepts are a display of history in the quantitative finance realm of the research of historical volatility estimations. It's a timeline of range based from the Parkinson Volatility to Yang Zhang volatility. We hope these descriptions make more people know that even though ATR is the most popular volatility indicator in technical analysis, it's not the best estimator. Almost no one in quant finance uses ATR to measure volatility (otherwise these papers will be based on how to improve ATR measurements instead of HV). As you can see, there are much more advanced volatility estimators that also take account into open, close, high, and low. HV values are based on log returns with some calculation adjustment. It can also be scaled in terms of price just like ATR. And for profit-taking ranges, ATR is not based on probabilities. Historical volatility can be used in a probability distribution function to calculated the probability of the ranges such as the Expected Move indicator. Other Estimators There are also other more advanced historical volatility estimators. There are high frequency sampled HV that uses intraday data to calculate volatility. We will publish the high frequency volatility estimator in the future. There's also ARCH and GARCH models that takes volatility clustering into account. GARCH models require maximum likelihood estimation which needs a solver to find the best weights for each component. This is currently not possible on TV due to large computational power requirements. All the other indicators claims to be GARCH are all wrong.
Pine Script® göstergesi
Motgench tarafından
FVG + Bollinger + Toggles + Swing H&L (Taken/Close modes)This indicator combines multiple advanced market-structure tools into one unified system. It detects A–C Fair Value Gaps (FVG) and plots them as dynamic boxes projected a fixed number of bars forward. Each bullish or bearish FVG updates in real time and “closes” once price breaks through the opposite boundary. The indicator also includes Bollinger Bands based on EMA-50 with adjustable deviation settings for volatility context. Swing Highs and Swing Lows are identified using pivot logic and are drawn as dynamic lines that change color once taken out. You can choose whether swings end on a close break or on any touch/violation of the level. All visual elements—FVGs, Bollinger Bands, and Swing Lines—can be individually toggled on or off from the settings panel. A time-window session box is included, allowing you to highlight a custom intraday window based on your selected timezone. The session box automatically tracks the high and low of the window and locks the final range once the window closes. Overall, the tool is designed for traders who want a structured, multi-layered view of liquidity, volatility, and intraday timing.
Pine Script® göstergesi
Menash100 tarafından
HTF FVG + SessionsThis indicator combines multi-timeframe FVG A–C detection with intraday session boxes on a single chart. It automatically finds bullish and bearish Fair Value Gaps on 15m, 30m, 1H, 4H, 1D and 1W timeframes. Fresh FVGs are drawn in a transparent gold color, then dynamically shrink as price trades back into the gap. Once price fully fills the gap, the FVG box and its label are automatically removed from the chart. After the first touch, each FVG changes to a per-timeframe gray shade, making overlapping HTF gaps easy to see. You can toggle each timeframe on/off and also globally enable/disable all FVGs from the settings panel. Session boxes highlight Asia, London, NY AM, NY Lunch and NY PM using soft colored rectangles. Each session box is plotted from the high to the low of that session and labeled with its name in white text. A global “Show all session boxes” switch allows you to quickly hide or display the session structure. This tool is designed for traders who want to combine FVG liquidity maps with clear intraday session context.
Pine Script® göstergesi
Menash100 tarafından
1
SYMBOL NOTES - UNCORRELATED TRADING GROUPSWrite symbol-specific notes that only appear on that chart. Organized into 6 uncorrelated groups for safe multi-pair trading. 📝 SYMBOL NOTES - UNCORRELATED TRADING GROUPS This indicator solves two problems every serious trader faces: 1. Keeping Track of Your Analysis Write notes for each trading pair and they'll only appear when you view that specific chart. No more forgetting your key levels, trade ideas, or analysis! 2. Avoiding Correlated Risk The symbols are organized into 6 groups where ALL pairs within each group are completely UNCORRELATED. Trade any combination from the same group without worrying about double exposure. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🎯 THE PROBLEM THIS SOLVES Have you ever: - Opened XAUUSD and EURUSD at the same time, then Fed news hit and BOTH positions went against you? - Traded GBPUSD and GBPJPY together, then BOE announcement stopped out both trades? - Forgotten what levels you were watching on a pair? This indicator helps you avoid these costly mistakes! ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📁 THE 6 UNCORRELATED GROUPS Each group contains pairs that share NO common currency: ``` GRUP 1: XAUUSD • EURGBP • NZDJPY • AUDCHF • NATGAS GRUP 2: EURUSD • GBPJPY • AUDNZD • CADCHF GRUP 3: GBPUSD • EURJPY • AUDCAD • NZDCHF GRUP 4: USDJPY • EURCHF • GBPAUD • NZDCAD GRUP 5: USDCAD • EURAUD • GBPCHF GRUP 6: NAS100 • DAX40 • UK100 • JPN225 ``` **Example - GRUP 1:** - XAUUSD → Uses USD + Gold - EURGBP → Uses EUR + GBP - NZDJPY → Uses NZD + JPY - AUDCHF → Uses AUD + CHF - NATGAS → Commodity (independent) = 7 different currencies, ZERO overlap! ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ **✅ HOW TO USE** 1. Add indicator to any chart 2. Open Settings (gear icon ⚙️) 3. Find your symbol's group and input field 4. Write your note (support levels, trade ideas, etc.) 5. Switch charts - your note appears only on that symbol! ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ⚙️ SETTINGS - Note Position: Choose where the note box appears (6 positions) - Text Size: Tiny, Small, Normal, or Large - Show Group Name: Display which correlation group - Show Symbol Name: Display current symbol - Colors: Customize background, text, group label, and border colors ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 💡 TRADING STRATEGY TIPS Safe Multi-Pair Trading: 1. Pick ONE group for the day 2. Look for setups on ANY symbol in that group 3. Open positions freely - they won't correlate! 4. Even if major news hits, only ONE position is affected ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🔧 COMPATIBLE WITH - All major forex brokers - Prop firms (FTMO, Alpha Capital, etc.) - Works on any timeframe - Futures symbols supported (MGC, M6E, etc.) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Pine Script® göstergesi
Saeid21 tarafından
Abu Basel IQOption 2m Signals//@version=5 indicator("Abu Basel IQOption 2m Signals", overlay = true, timeframe = "", timeframe_gaps = true) //======================== // الإعدادات //======================== emaFastLen = input.int(9, "EMA سريع (9)") emaSlowLen = input.int(21, "EMA بطيء (21)") rsiLen = input.int(14, "RSI Length", minval = 2) rsiBuyLevel = input.float(50.0, "RSI حد الشراء (أعلى من)", minval = 0, maxval = 100) rsiSellLevel= input.float(50.0, "RSI حد البيع (أقل من)", minval = 0, maxval = 100) bbLen = input.int(20, "Bollinger Length") bbMult = input.float(2.0, "Bollinger Deviation") showSignals = input.bool(true, "إظهار الأسهم (CALL / PUT)") showBg = input.bool(true, "تلوين الخلفية عند الإشارات") //======================== // المؤشرات الأساسية //======================== emaFast = ta.ema(close, emaFastLen) emaSlow = ta.ema(close, emaSlowLen) basis = ta.sma(close, bbLen) dev = bbMult * ta.stdev(close, bbLen) bbUpper = basis + dev bbLower = basis - dev rsi = ta.rsi(close, rsiLen) // رسم المتوسطات والبولينجر plot(emaFast, title = "EMA 9", linewidth = 2) plot(emaSlow, title = "EMA 21", linewidth = 2) plot(basis, title = "BB Basis", linewidth = 1) plot(bbUpper, title = "BB Upper", linewidth = 1, style = plot.style_line) plot(bbLower, title = "BB Lower", linewidth = 1, style = plot.style_line) //======================== // دوال أشكال الشموع الانعكاسية //======================== bodySize = math.abs(close - open) fullRange = high - low upperWick = high - math.max(open, close) lowerWick = math.min(open, close) - low isSmallBody = bodySize <= fullRange * 0.3 // Hammer صاعدة (ذيل سفلي طويل) bullHammer() => lowerWick > bodySize * 2 and upperWick <= bodySize and close > open // Shooting Star هابطة (ذيل علوي طويل) bearShootingStar() => upperWick > bodySize * 2 and lowerWick <= bodySize and close < open // Bullish Engulfing bullEngulfing() => close > open and close < open and close > open and open < close // Bearish Engulfing bearEngulfing() => close < open and close > open and close < open and open > close // تجميع أنماط صعود/هبوط bullPattern = bullHammer() or bullEngulfing() bearPattern = bearShootingStar() or bearEngulfing() //======================== // شروط الدخول //======================== // تقاطع المتوسطات bullCross = ta.crossover(emaFast, emaSlow) // صعود bearCross = ta.crossunder(emaFast, emaSlow) // هبوط // شروط شراء CALL: // 1) تقاطع EMA9 فوق EMA21 // 2) السعر فوق خط وسط البولنجر // 3) RSI أعلى من 50 // 4) شمعة انعكاسية صاعدة (Hammer أو Engulfing) callCond = bullCross and close > basis and rsi > rsiBuyLevel and bullPattern // شروط بيع PUT: // 1) تقاطع EMA9 تحت EMA21 // 2) السعر تحت خط وسط البولنجر // 3) RSI أقل من 50 // 4) شمعة انعكاسية هابطة (Shooting Star أو Bearish Engulfing) putCond = bearCross and close < basis and rsi < rsiSellLevel and bearPattern //======================== // رسم الإشارات على الشارت //======================== plotshape(showSignals and callCond, title="CALL 2m", style=shape.labelup, location=location.belowbar, text="CALL 2m", size=size.tiny) plotshape(showSignals and putCond, title="PUT 2m", style=shape.labeldown, location=location.abovebar, text="PUT 2m", size=size.tiny) // تلوين الخلفية عند الإشارات bgcolor(showBg and callCond ? color.new(color.green, 85) : showBg and putCond ? color.new(color.red, 85) : na) //======================== // شروط التنبيه (Alerts) //======================== alertcondition(callCond, title="CALL 2m Signal", message="Abu Basel Signal: CALL 2m on {{ticker}} at {{close}}") alertcondition(putCond, title="PUT 2m Signal", message="Abu Basel Signal: PUT 2m on {{ticker}} at {{close}}")
Pine Script® göstergesi
abobasel5544 tarafından