️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.
Wskaźniki i strategie
TRharmonic Ultimate
TRharmonic Ultimate - Professional Harmonic Pattern Detection System
Technical Overview
TRharmonic Ultimate is a real-time harmonic pattern recognition system built on Pine Script v5. The system analyzes 25+ harmonic formations across multiple ZigZag depths simultaneously, providing traders with instant pattern detection and pre-calculated trading levels.
Core Features
The indicator uses a zero-lag ZigZag algorithm with right offset set to 0, eliminating the typical 1-5 bar delay found in standard pivot-based systems.
Pattern detection operates across 10 simultaneous ZigZag depth calculations ranging from 15 to 150 bars, ensuring coverage of both short-term and long-term formations.
Each detected pattern includes automatically calculated entry price, stop loss, and three take-profit levels based on standard Fibonacci retracement principles.
The system validates patterns using adjustable tolerance bands between 7% and 10%, allowing traders to balance between detection sensitivity and accuracy.
MACD confirmation can be optionally enabled to filter signals, reducing false positives by requiring momentum alignment with pattern direction.
Dragon pattern detection uses proprietary ratio validation specifically designed for this rare formation's unique Fibonacci relationships.
Wolfe Wave recognition includes full 6-point structure analysis with automatic EPA (Estimated Price Arrival) line projection.
The algorithm performs geometric validation beyond simple ratio checking, including trendline mathematics and positional requirements.
Pattern drawings automatically adapt to chart theme (dark/light mode) with customizable color schemes for all 25+ formations.
A built-in deduplication system prevents multiple alerts for the same pattern within a specified bar range.
Technical Advantages
The ZigZag calculation method processes pivot points in real-time without requiring bar closure confirmation.
Memory management is optimized to handle 500+ bars of historical data while maintaining calculation speed.
Pattern-specific algorithms account for individual formation characteristics rather than using generic detection logic.
The system can detect rare patterns like Dragon and Wolfe Wave that most commercial indicators cannot identify reliably.
All Fibonacci calculations are performed automatically, eliminating manual measurement errors common in discretionary trading.
The indicator maintains clean chart visualization by automatically removing outdated pattern drawings after a configurable time period.
Multi-layer validation processes include ratio checks, geometric positioning, and optional momentum confirmation.
Pattern labels display Fibonacci ratios directly on formations, providing transparency in detection criteria.
EMA Crossover + Angle + Candle Pattern + Breakout (Clean) finalmayank raj startegy of 9 15 ema with angle more th5 and bullish croosover or bearish crooswoveran 3
Elliott Wave - Wave 3 Entry EngineThis indicator is a Wave 3 entry engine built on top of an Elliott Wave–style 1-2-3 structure. It automatically finds potential Wave 3 trades, manages a simple R-multiple target/stop model, and marks outcomes directly on the chart.
What the indicator does
At a high level, the script:
Detects swing points on three “degrees”
Small (S) – fast, local swings
Medium (M) – broader swings
Large (L) – higher-timeframe context only
Looks for a 3-pivot pattern (W0 → W1 → W2)
Bullish setup: Low → High → Higher Low (L-H-L)
Bearish setup: High → Low → Lower High (H-L-H)
Checks whether that pattern is a valid Wave 1–2 structure
Using multiple rules:
Wave 2 retraces Wave 1 by a configurable fraction
Wave 1 is strong enough (percentage move + slope)
Wave 2 doesn’t overshoot Wave 0 too far
Trend direction and swing “consensus” across S/M/L degrees line up
Scores the setup (Pre-W3 Score)
The script calculates a 0–1 score based on:
How “nice” the Wave 2 retracement is vs the ideal level
How much stronger Wave 1’s slope is vs Wave 2’s pullback
How much consensus there is across the swing engine (S/M/L)
Only setups above your chosen minimum Pre-W3 score and that pass alignment checks become Wave 3 candidates.
Waits for breakout → creates a Wave 3 “entry”
For longs: price breaks above the Wave 1 high (plus an optional tick buffer)
For shorts: price breaks below the Wave 1 low (minus buffer)
When triggered, the indicator:
Stores entry price (close at breakout)
Sets a stop beyond Wave 2 (with optional extra ticks)
Calculates a target based on a fixed R multiple (e.g., 2R)
Tracks the trade until exit or timeout
For each open W3 trade, it monitors:
Target hit → marks “W3 ✅”
Stop hit → marks “W3 ❌”
Bar where both could have hit → conservative loss “W3 ?/❌”
Time-based expiry (too many bars in trade) → “W3 ⏰”
Candidates that never get a breakout within your chosen max bars from W2 can also be marked as timeout (⏰).
Visual elements on the chart
The script can plot several helpful visuals:
Swing connector lines (Small/Medium/Large)
Small = blue
Medium = purple
Large = orange
These show the detected swings at each degree
Pre-W3 labels at Wave 2 (optional)
Signals :
"Pre-W3 Long XX%" or"Pre-W3 Short XX%"
Placed at the Wave 2 pivot
Colored yellow, with the % score rounded to an integer
W3 Entry labels (optional)
"W3 Long Entry" below the bar for longs (green)
"W3 Short Entry" above the bar for shorts (red)
Outcome labels (optional)
W3 ✅ – target hit
W3 ❌ – stop hit
W3 ?/❌ – both hit on same bar, treated as loss
W3 ⏰ – candidate or trade timed out
All these can be toggled in the “Wave 3 Engine (Pre-W3 + Entries + Outcomes)” group.
Input groups & how to use them
Swing Detection (Small / Medium / Large)
These groups control how the script finds swing highs/lows using a multi-parameter pivot scan:
Left Min / Left Max / Right Min / Right Max
Define the pivot “strength” ranges (how many bars to the left/right the high/low must dominate).
Minimum swing % (post-aggregation)
Ensures that, once swings are merged and cleaned up, each swing is at least this % move from the prior opposite swing.
Loop Filters (Small/Medium/Large loop min % change)
Extra gating inside the pivot-search loop, so small noise pivots can be ignored even before final swing construction.
Practical use:
Tighten % thresholds or increase left/right bars to reduce noise.
Loosen them to get more swings and more potential W3 setups.
Wave 3 Logic
Wave 2 depth
W2 min / max retracement of W1 (fraction)
Example: 0.30–0.80 means W2 must retrace 30–80% of W1.
Ideal W2 retracement (for scoring)
Often set around 0.618 (classic fib). The closer W2 is to this, the higher the retracement part of the score.
Max W2 beyond W0 (%)
How far W2 may push past W0 (in %) before the setup is invalid. Set to 0 to disable this filter.
Wave 1 strength
Min W1 move (%)
Ensures Wave 1 itself is meaningful.
Min |W1 slope| / |W2 slope|
Wave 1 must be “steeper” than Wave 2’s correction.
Slope ratio for max score
Above this, extra slope advantage doesn’t improve the score further.
Scoring & Trend Alignment
Min Pre-W3 score (0..1)
Hard gate: anything below this won’t become a W3 candidate.
Trend alignment (S/M/L)
Options:
None – ignore swing directions, purely pattern/score based
Majority – at least 2 of S/M/L must point in the W3 direction
AllThree, S+M, S+L, M+L – stricter alignment variants
Alignment uses the latest swing direction (up or down) for each degree.
Max W3 candidates to track
Limits how many candidates + trades are stored. Old, already-closed items are pruned first; open trades are never pruned.
This is an indicator, not an order engine**:** it doesn’t place trades; it only marks hypothetical Wave 3 entries and outcomes based on your settings. Always validate on historical data and combine with your own analysis and risk management before using it in live trading.
EMA Crossover + Angle + Candle Pattern + Breakout (Clean) finalmayank raj 9 15 ema strategy which will give me 1 crore
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.
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.
Marumaroo's RSI + MFI (가격과 거래량의 이중 체크)매매할 때 RSI랑 MFI를 같이 보는데, 지표창 두 개 띄우기 귀찮아서 하나로 합쳤습니다.
RSI(가격)만 보면 가짜 반등에 속을 때가 많은데, MFI(거래량)랑 같이 보면 다이버전스나 휩소 걸러내기가 훨씬 수월합니다.
특징:
보기 편함: RSI는 빨강, MFI는 회색입니다.
배경색 알림: 과매수(80 이상) 구간은 빨간 배경, 과매도(20 이하) 구간은 초록 배경이 뜹니다. 한눈에 파악하기 좋습니다.
복잡한 기능 다 빼고 깔끔하게 만들었으니 필요하신 분 쓰세요.
I combined RSI and MFI into a single chart to save screen space and filter out fake signals.
Checking Money Flow (MFI) alongside Price Action (RSI) helps in spotting divergences and avoiding traps.
Features:
Clean Look: RSI is Red, MFI is Gray.
Background Colors: automatically highlights Overbought (>80) zones in Red and Oversold (<20) zones in Green.
Simple and lightweight script. Hope it helps!
Kabir – (Dist + Accu + Scoreboard)if you are going to use it with respect and care its utmost best and the only thing you need for predicting market tops and yes discretion is still needed
Trend Pullback S-MSNRThis Indicator Identify two Major Time Frames for Trend Selection and Pullback.
NY time 10:00 AM to 10:15 AM zone will decide for trend.
NY time 10:30 AM to 11:30 AM zone will Pullback and Follow the Previous Trend.
Use S-MSNR Strategy for these two time Zone.
dr ram's banknifty fad%banknifty fad% calculation as per dr ram sir. based on 4 quadrant analysis . one of the criteria is calculating future asset difference for predicting market direction and entry plan.
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.
Quantum Trend MatrixThe Quantum Trend Matrix (QTM) is a comprehensive technical analysis suite designed to solve the problem of market noise by combining Statistical Volatility Structure with Momentum Trend Filtration.
Many traders struggle because they trade momentum signals (like crossovers) without considering the daily structural limits of the market. This script integrates these two concepts into a single "Roadmap" to help traders align their entries with institutional price structure.
🎯 Concept & Methodology (How it Works)
This script is not merely a collection of indicators; it is a logic-based system where components effectively filter one another:
1. Structural Volatility Levels (The "Map")
Unlike standard Support/Resistance which is subjective, QTM calculates objective levels based on the internal logic.
Methodology: The script applies specific percentage-based volatility coefficients (tailored to the asset class, e.g., Indices ,Commodities,etc) to the Price.
* The Green Line (Breakout Level) : Represents the statistical upper volatility limit above which a "Bullish Expansion" is expected to occur.
* The Red Line (Breakdown Level): Represents the statistical lower volatility limit Below which a "Bearish Expansion" is expected to occur.
* Why this is useful: It prevents traders from chasing trends in the "chop zone" (between the lines) and highlights high-probability breakout areas.
2. The Value Zone (Trend Validation)
* Methodology: This utilizes a High-Timeframe moving average ribbon logic (calculated using Daily data).
* Function: It acts as a dynamic trend filter. A breakout signal (Green Line cross) is statistically significant if the Price is also supported by the Value Zone (Blue Ribbon). If the Ribbon is Orange, a bullish breakout is likely a "False Trap".
3. Momentum & Exhaustion Logic
* Crossovers (Circles): Validates short-term trend shifts using smoothed exponential average crossovers.
* Mean Reversion (Diamonds): Uses an integrated Oscillator Momentum logic to detect over-extended price action. A Diamond signal warns that the price has deviated too far from the mean (VWAP) and trend continuation is risky.
🛠️ Practical Application
This script is designed for a top-down decision process:
1. Wait for Structure: For Trending Moves do not trade inside the Pivot (Blue) to Breakout (Green/Red) range. This is the "Noise" zone.
2. Confirm the Breakout: Wait for a candle to CLOSE outside the Green or Red volatility levels or to take Support/Resistance from Red/Green Levels respectively.
3. Check the "Value Zone": Ensure the background ribbon color matches the breakout direction (Blue for Long, Orange for Short).
4. Monitor Health: Use the bottom-right panel (displaying RSI, ADX, and DI metrics) to ensure trend strength is sufficient to sustain the move.
⚠️ Disclaimer & Risk Disclosure
* Logic Disclosure: While the specific volatility coefficients and smoothing lengths are proprietary, this script relies on standard technical analysis concepts including Moving Averages, RSI, ADX, and Percentage-based levels relative to the Price.
* No Guarantee: Technical analysis is probabilistic, not predictive. Past performance does not guarantee future results.
* Risk Management: Always use Stop Losses. This tool is an aid for analysis, not a replacement for risk management.
🔒 Access Information
This is a proprietary Invite-Only script.
*(Note: Do not ask for access in the comments below. Please refer to the author's signature or profile for more information).*
MTF RSI + MACD Bullish Confluencethis based on rsi more then 50 and macd line bullish crossover or above '0' and time frame 15 min, 1 hour, 4 hour , 1 day and 1 week
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.
BankNifty Aggregate Weighted OBVDescription-
This indicator calculates the aggregate On Balance Volume (OBV) of the entire Bank Nifty Index by analyzing its 12 individual constituents rather than the index futures volume.
Why is this different?
Standard OBV on the Bank Nifty Index usually analyzes the volume of the Index Futures or the raw index volume (which can be inaccurate or derivative-heavy). This script queries the real-time volume and price action of the 12 specific banks that make up the index (HDFC, ICICI, SBI, Axis, Kotak, etc.).
How it works-
Weighted Calculation:- It calculates the Net Flow (Volume * Weightage) for every single bank for the current bar.
Aggregation:- It sums the Net Flow of all 12 banks to create a "Total Sector Flow."
Accumulation:- It generates the OBV line based on this aggregated sector flow.
Normalization:- Unlike simple summation scripts, this calculates flow per bar before accumulating, ensuring that stocks with longer trading histories do not skew the data.
Features:
Customizable Weights:- Users can adjust the weightage of each bank if NSE rebalances the index.
Toggle Constituents:- You can turn specific banks on/off to see their impact.
Signal Line:- Includes an SMA/EMA signal line to help identify volume trend reversals.
Trend Coloring:- The fill color changes (Green/Red) based on the OBV's position relative to the signal line.
How to use:
Trend Confirmation: If Bank Nifty price is rising but this Weighted OBV is falling, it indicates a divergence and potential weakness in the move (lack of institutional participation).
Breakouts: Use the Signal Line crossover to validate breakout moves.
Aggregated Liquidations by ktp. GonzoAggregated Liquidations combines real-time liquidation data from multiple major futures exchanges into a single, unified view. This tool helps traders identify liquidation clusters, squeezes, and high-impact forced-exit events that often mark key reversal or continuation points.
This script delivers a clean, aggregated perspective on one of the most impactful forms of market data—providing clarity during volatile, liquidation-driven moves.
Supported Exchanges
Binance: USDT, USDC, USD
BitMEX: USDT, USD
Bybit: USDT, USDC, USD
Deribit: USDC, USD
HTX: USDT, USD (optional, tick-volume based)
OKX: USDT, USDC, USD (partially reported liquidations)
Toggle each feed individually for complete control over your data sources.
Features
Combined Long & Short Liquidations from all enabled exchanges
Configurable Currency Mode to show liquidation volume in base or quote currency
Adjustable Accumulation Window in bars, minutes, hours, or days
Threshold Lines to quickly spot abnormal liquidation spikes
How to Use
Track liquidation cascades across multiple venues
Spot potential long/short squeezes before price reacts
Identify exhaustion zones where forced liquidations dominate
Combine with order flow, volume, or momentum tools for confirmation
DTR Volume OBDTR Volume OB indicator identifies bullish and bearish order blocks and visualizes volume within each block for easy recognition of high-volume areas. It helps traders spot key supply and demand zones and anticipate market reactions.
Key Features:
- Detects bullish and bearish order blocks.
- Divides blocks into grids, highlighting high-volume regions.
- Configurable tuning period and number of grids.
- Flexible mitigation methods to track order block breaks.
- Customizable colors for high/low volume grids, borders, and background fill.
Usage:
- Identify important support and resistance zones.
- Spot high-probability areas for entries and exits.
- Combine with trend analysis or price action for improved strategies.
Ideal for swing traders, day traders, and scalpers looking for a visual, volume-informed approach to order block trading.
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.
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🎯 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!
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📁 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!
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**✅ 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!
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⚙️ 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
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💡 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
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🔧 COMPATIBLE WITH
- All major forex brokers
- Prop firms (FTMO, Alpha Capital, etc.)
- Works on any timeframe
- Futures symbols supported (MGC, M6E, etc.)
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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}}")
Volume Profile S/R + OB/OS + BreaksAs a support resistance trader I have created this indicator that shows SR lines. RSI over bought and over sold. I also added momentum candle.
It's easy to use. The arrows show over bought and over sold, that's where I start to be interested. Confirmation is if we are near a support/resistance area. shown as a red/green line.
Don't just trade the RSI, Be patient and only take the perfekt setups.
I't clean, it's simple it works.
DTR Volume FVGDTR Volume FVG detects bullish and bearish Fair Value Gaps and shows how much volume occurred inside each gap. Instead of only drawing the imbalance, the indicator analyzes a lower timeframe and builds a small volume profile inside every FVG. This helps you understand which gaps are strong, weak, likely to hold, or likely to fill.
How It Works:
- The indicator finds FVGs using a lower timeframe (Auto mode or manual selection).
- Each FVG is drawn as a colored zone: green for bullish, purple for bearish.
- Inside the gap, the script shows volume distribution using horizontal boxes.
- The FVG extends forward in time until the gap is fully filled or invalidated.
- Once price closes through the gap, the zone is removed automatically.
How to Use:
- High volume inside the FVG suggests strong interest and possible support or resistance.
- Low volume suggests the gap may fill more easily.
- Bullish FVGs are used as retracement zones in uptrends.
- Bearish FVGs are used as retracement zones in downtrends.
- Use the Display option to hide the volume boxes if you want a cleaner chart.
Best For:
- Finding strong retracement zones
- Identifying which gaps matter
- Understanding how price and volume behaved during displacement
- Improving entries and stop placement with volume levels inside FVGs
This indicator gives a clearer view of which imbalances are important by combining FVG structure with real volume data.
SPY → ES 11 Levels (Hybrid RTH/Globex) [Tick Fixed]📌 Description for SPY → ES 11-Level Converter (with Labels)
This script converts important SPY options-based levels into their equivalent ES futures prices and plots them directly on the ES chart.
Because SPY trades at a different price scale than ES, each SPY level is multiplied by a customizable ES/SPY ratio to project accurate ES levels.
It is designed for traders who use SpotGamma, GEXBot, MenthorQ, Vol-trigger levels, or their own gamma/oi/volume models.
🔍 Features
✅ Converts SPY → ES using custom or automatic ratio
Option to manually enter a ratio (recommended for accuracy)
Or automatically compute ES/SPY from live prices
✅ Plots 11 major levels on the ES chart
Each level can be individually turned ON/OFF:
Call Wall
Put Wall
Volume Trigger
Spot Price
+Gamma Level
–Gamma Level
Zero Gamma
Positive OI
Negative OI
Positive Volume
Negative Volume
All levels are drawn as clean horizontal lines using the converted ES value.
