Liquidity Sentiment Profile (Auto-Anchored) [LuxAlgo]
The Liquidity Sentiment Profile (Auto-Anchored) is an advanced charting tool that measures by combining PRICE and VOLUME data over specified anchored periods and highlights the distribution of the liquidity and the market sentiment at specific price levels. This version is a variation of the previously published Liquidity Sentiment Profile , wherewith this version allows users to select a variety of different anchoring periods, such as 'Auto', 'Fixed Range', 'Swing High', 'Swing Low', 'Session', 'Day', 'Week', 'Month', 'Quarter', and 'Year'
Liquidity refers to the availability of orders at specific price levels in the market, allowing transactions to occur smoothly.
🔶 USAGE
A Liquidity Sentiment Profile (Auto-Anchored) is a combination of liquidity and a sentiment profile, where the right side of the profile highlights the distribution of the traded activity at different price levels, and the left side of the profile highlights the market sentiment at those price levels
The liquidity profile is categorized by assigning different colors based on the significance of the traded activity of the specific price levels, allowing traders to reveal significant price levels, such as support and resistance levels, supply and demand zones, liquidity gaps, consolidation zones, etc
The Liquidity Sentiment Profiles aim to present Value Areas based on the significance of price levels, thus allowing users to identify value areas that can be formed more than once within the range of a single profile
Level of Significance Line - displays the changes in the price levels with the highest traded activity (developing POC)
Buyside & Sellside Liquidity Zones - displays Liquidity Levels, also known as Supply and Demand Zones
🔶 SETTINGS
The script takes into account user-defined parameters and plots the profiles, where detailed usage for each user-defined input parameter in indicator settings is provided with the related input's tooltip.
🔹 Liquidity Sentiment Profile
Anchor Period: The indicator resolution is set by the input of the Anchor Period.
Fixed Period: Applicable if the Anchor Period is set to 'Fixed Range' then the period of the profile is defined with this option
Swing Detection Length: Applicable if the Anchor Period is set to 'Swing High' or 'Swing Low' then the length required to detect the Swing Levels is defined with this option which is then used to determine the period of the profile
🔹 Liquidity Profile
Liquidity Profile: Toggles the visibility of the Liquidity Profiles
High Traded Nodes: Threshold and Color option for High Traded Nodes
Average Traded Nodes: Color option for Average Traded Nodes
Low Traded Nodes: Threshold and Color option for Low Traded Nodes
🔹 Sentiment Profile
Sentiment Profile: Toggles the visibility of the Sentiment Profiles
Bullish Nodes: Color option for Bullish Nodes
Bearish Nodes: Color option for Bearish Nodes
🔹 Buyside & Sellside Liquidity Zones
Buyside & Sellside Liquidity Zones: Toggles the visibility of the Liquidity Levels
Buyside Liquidity Nodes: Color option for Buyside Liquidity Nodes
Sellside Liquidity Nodes: Color option for Sellside Liquidity Nodes
🔹 Other Settings
Level of Significance: Toggles the visibility of the Level of Significance Line
Price Levels, Color: Toggles the visibility of the Profile Price Levels
Number of Rows: Specify how many rows each profile histogram will have. Caution, having it set to high values will quickly hit Pine Script™ drawing objects limit and fewer historical profiles will be displayed
Profile Width %: Alters the width of the rows in the histogram, relative to the profile length
Profile Range Background Fill: Toggles the visibility of the Profiles Range
🔶 RELATED SCRIPTS
Liquidity-Sentiment-Profile
Buyside-Sellside-Liquidity
ICT-Concepts
Support and Resistance
Bollinger Bands Liquidity Cloud [ChartPrime]This indicator overlays a heatmap on the price chart, providing a detailed representation of Bollinger bands' profile. It offers insights into the price's behavior relative to these bands. There are two visualization styles to choose from: the Volume Profile and the Z-Score method.
Features
Volume Profile: This method illustrates how the price interacts with the Bollinger bands based on the traded volume.
Z-Score: In this mode, the indicator samples the real distribution of Z-Scores within a specified window and rescales this distribution to the desired sample size. It then maps the distribution as a heatmap by calculating the corresponding price for each Z-Score sample and representing its weight via color and transparency.
Parameters
Length: The period for the simple moving average that forms the base for the Bollinger bands.
Multiplier: The number of standard deviations from the moving average to plot the upper and lower Bollinger bands.
Main:
Style: Choose between "Volume" and "Z-Score" visual styles.
Sample Size: The size of the bin. Affects the granularity of the heatmap.
Window Size: The lookback window for calculating the heatmap. When set to Z-Score, a value of `0` implies using all available data. It's advisable to either use `0` or the highest practical value when using the Z-Score method.
Lookback: The amount of historical data you want the heatmap to represent on the chart.
Smoothing: Implements sinc smoothing to the distribution. It smoothens out the heatmap to provide a clearer visual representation.
Heat Map Alpha: Controls the transparency of the heatmap. A higher value makes it more opaque, while a lower value makes it more transparent.
Weight Score Overlay: A toggle that, when enabled, displays a letter score (`S`, `A`, `B`, `C`, `D`) inside the heatmap boxes, based on the weight of each data point. The scoring system categorizes each weight into one of these letters using the provided percentile ranks and the median.
Color
Color: Color for high values.
Standard Deviation Color: Color to represent the standard deviation on the Bollinger bands.
Text Color: Determines the color of the letter score inside the heatmap boxes. Adjusting this parameter ensures that the score is visible against the heatmap color.
Usage
Once this indicator is applied to your chart, the heatmap will be overlaid on the price chart, providing a visual representation of the price's behavior in relation to the Bollinger bands. The intensity of the heatmap is directly tied to the price action's intensity, defined by your chosen parameters.
When employing the Volume Profile style, a brighter and more intense area on the heatmap indicates a higher trading volume within that specific price range. On the other hand, if you opt for the Z-Score method, the intensity of the heatmap reflects the Z-Score distribution. Here, a stronger intensity is synonymous with a more frequent occurrence of a specific Z-Score.
For those seeking an added layer of granularity, there's the "Weight Score Overlay" feature. When activated, each box in your heatmap will sport a letter score, ranging from `S` to `D`. This score categorizes the weight of each data point, offering a concise breakdown:
- `S`: Data points with a weight of 1.
- `A`: Weights below 1 but greater than or equal to the 75th percentile rank.
- `B`: Weights under the 75th percentile but at or above the median.
- `C`: Weights beneath the median but surpassing the 25th percentile rank.
- `D`: All that fall below the 25th percentile rank.
This scoring feature augments the heatmap's visual data, facilitating a quicker interpretation of the weight distribution across the dataset.
Further Explanations
Volume Profile
A volume profile is a tool used by traders to visualize the amount of trading volume occurring at specific price levels. This kind of profile provides a deep insight into the market's structure and helps traders identify key areas of support and resistance, based on where the most trading activity took place. The concept behind the volume profile is that the amount of volume at each price level can indicate the potential importance of that price.
In this indicator:
- The volume profile mode creates a visual representation by sampling trading volumes across price levels.
- The representation displays the balance between bullish and bearish volumes at each level, which is further differentiated using a color gradient from `low_color` to `high_color`.
- The volume profile becomes more refined with sinc smoothing, helping to produce a smoother distribution of volumes.
Z-Score and Distribution Resampling
Z-Score, in the context of trading, represents the number of standard deviations a data point (e.g., closing price) is from the mean (average). It’s a measure of how unusual or typical a particular data point is in relation to all the data. In simpler terms, a high Z-Score indicates that the data point is far away from the mean, while a low Z-Score suggests it's close to the mean.
The unique feature of this indicator is that it samples the real distribution of z-scores within a window and then resamples this distribution to fit the desired sample size. This process is termed as "resampling in the context of distribution sampling" . Resampling provides a way to reconstruct and potentially simplify the original distribution of z-scores, making it easier for traders to interpret.
In this indicator:
- Each Z-Score corresponds to a price value on the chart.
- The resampled distribution is then used to display the heatmap, with each Z-Score related price level getting a heatmap box. The weight (or importance) of each box is represented as a combination of color and transparency.
How to Interpret the Z-Score Distribution Visualization:
When interpreting the Z-Score distribution through color and alpha in the visualization, it's vital to understand that you're seeing a representation of how unusual or typical certain data points are without directly viewing the numerical Z-Score values. Here's how you can interpret it:
Intensity of Color: This often corresponds to the distance a particular data point is from the mean.
Lighter shades (closer to `low_color`) typically indicate data points that are more extreme, suggesting overbought or oversold conditions. These could signify potential reversals or significant deviations from the norm.
Darker shades (closer to `high_color`) represent data points closer to the mean, suggesting that the price is relatively typical compared to the historical data within the given window.
Alpha (Transparency): The degree of transparency can indicate the significance or confidence of the observed deviation. More opaque boxes might suggest a stronger or more reliable deviation from the mean, implying that the observed behavior is less likely to be a random occurrence.
More transparent boxes could denote less certainty or a weaker deviation, meaning that the observed price behavior might not be as noteworthy.
- Combining Color and Alpha: By observing both the intensity of color and the level of transparency, you get a richer understanding. For example:
- A light, opaque box could suggest a strong, significant deviation from the mean, potentially signaling an overbought or oversold scenario.
- A dark, transparent box might indicate a weak, insignificant deviation, suggesting the price is behaving typically and is close to its average.
Grid by Volatility (Expo)█ Overview
The Grid by Volatility is designed to provide a dynamic grid overlay on your price chart. This grid is calculated based on the volatility and adjusts in real-time as market conditions change. The indicator uses Standard Deviation to determine volatility and is useful for traders looking to understand price volatility patterns, determine potential support and resistance levels, or validate other trading signals.
█ How It Works
The indicator initiates its computations by assessing the market volatility through an established statistical model: the Standard Deviation. Following the volatility determination, the algorithm calculates a central equilibrium line—commonly referred to as the "mid-line"—on the chart to serve as a baseline for additional computations. Subsequently, upper and lower grid lines are algorithmically generated and plotted equidistantly from the central mid-line, with the distance being dictated by the previously calculated volatility metrics.
█ How to Use
Trend Analysis: The grid can be used to analyze the underlying trend of the asset. For example, if the price is above the Average Line and moves toward the Upper Range, it indicates a strong bullish trend.
Support and Resistance: The grid lines can act as dynamic support and resistance levels. Price tends to bounce off these levels or breakthrough, providing potential trade opportunities.
Volatility Gauge: The distance between the grid lines serves as a measure of market volatility. Wider lines indicate higher volatility, while narrower lines suggest low volatility.
█ Settings
Volatility Length: Number of bars to calculate the Standard Deviation (Default: 200)
Squeeze Adjustment: Multiplier for the Standard Deviation (Default: 6)
Grid Confirmation Length: Number of bars to calculate the weighted moving average for smoothing the grid lines (Default: 2)
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Linear Regression IndicatorThis tool can be used to determine the direction of the current trend.
The indicator changes the color of the candles based on the direction of the linear regression formula. This is made settings the length of the short-term linear regression in the settings, the longer one is also based on that parameter but significantly larger.
The indicator also plots the average between the two linear regression lines used in the candle coloring formula, and can be used both for support and resistance or as a trend line used to analyze breakouts.
Support and Resistance Signals MTF [LuxAlgo]The Support and Resistance Signals MTF indicator aims to identify undoubtedly one of the key concepts of technical analysis Support and Resistance Levels and more importantly, the script aims to capture and highlight major price action movements, such as Breakouts , Tests of the Zones , Retests of the Zones , and Rejections .
The script supports Multi-TimeFrame (MTF) functionality allowing users to analyze and observe the Support and Resistance Levels/Zones and their associated Signals from a higher timeframe perspective.
This script is an extended version of our previously published Support-and-Resistance-Levels-with-Breaks script from 2020.
Identification of key support and resistance levels/zones is an essential ingredient to successful technical analysis.
🔶 USAGE
Support and resistance are key concepts that help traders understand, analyze and act on chart patterns in the financial markets. Support describes a price level where a downtrend pauses due to demand for an asset increasing, while resistance refers to a level where an uptrend reverses as a sell-off happens.
The creation of support and resistance levels comes as a result of an initial imbalance of supply/demand, which forms what we know as a swing high or swing low. This script starts its processing using the swing highs/lows. Swing Highs/Lows are levels that many of the market participants use as a historical reference to place their trading orders (buy, sell, stop loss), as a result, those price levels potentially become and serve as key support and resistance levels.
One of the important features of the script is the signals it provides. The script follows the major price movements and highlights them on the chart.
🔹 Breakouts (non-repaint)
A breakout is a price moving outside a defined support or resistance level, the significance of the breakout can be measured by examining the volume. This script is not filtering them based on volume but provides volume information for the bar where the breakout takes place.
🔹 Retests
Retest is a case where the price action breaches a zone and then revisits the level breached.
🔹 Tests
Test is a case where the price action touches the support or resistance zones.
🔹 Rejections
Rejections are pin bar patterns with high trading volume.
Finally, Multi TimeFrame (MTF) functionality allows users to analyze and observe the Support and Resistance Levels/Zones and their associated Signals from a higher timeframe perspective.
🔶 SETTINGS
The script takes into account user-defined parameters to detect and highlight the zones, levels, and signals.
🔹 Support & Resistance Settings
Detection Timeframe: Set the indicator resolution, the users may examine higher timeframe detection on their chart timeframe.
Detection Length: Swing levels detection length
Check Previous Historical S&R Level: enables the script to check the previous historical levels.
🔹 Signals
Breakouts: Toggles the visibility of the Breakouts, enables customization of the color and the size of the visuals
Tests: Toggles the visibility of the Tests, enables customization of the color and the size of the visuals
Retests: Toggles the visibility of the Retests, enables customization of the color and the size of the visuals
Rejections: Toggles the visibility of the Rejections, enables customization of the color and the size of the visuals
🔹 Others
Sentiment Profile: Toggles the visibility of the Sentiment Profiles
Bullish Nodes: Color option for Bullish Nodes
Bearish Nodes: Color option for Bearish Nodes
🔶 RELATED SCRIPTS
Support-and-Resistance-Levels-with-Breaks
Buyside-Sellside-Liquidity
Liquidity-Levels-Voids
Support and Resistance Levels and Zones [Quantigenics]Support and Resistance Levels and Zones Indicator is an enhanced support and resistance indicator in that typical support and resistance levels are crucial concepts in technical analysis representing price levels where selling or buying momentum tends to halt, typically leading to a price reversal.
The Support and Resistance Levels and Zones Indicator goes beyond static levels by identifying dynamic 'zones'. These zones, depicted as shaded areas, offer more nuanced insights, acknowledging that markets are not rigid but fluctuating entities. Traders can leverage these zones, alongside the standard levels that the indicator plots, to better time their entries and exits, maximizing potential profitability and minimizing risk.
This is a "must-see on your charts" indicator and while scrolling back looking at historical data shows the amazing power of this indicator, it's even better in realtime LIVE price action and the price can tend to hit the Support and Resistance Levels and Zones multiple times intrabar.
TVC:GOLD 1HR
NYMEX:CL1! 15MIN
Enjoy!
Support and Resistance Oscillator [CC]The Support and Resistance Oscillator is an experimental script I created to identify when the current price breaks a support or resistance line and reflect this value in an oscillator formula. This indicator uses a threshold to decide the dividing line between buying and selling points. Feel free to change the threshold or smoothing settings to see if you find anything better since this is so experimental. I'm double smoothing the difference between the indicator and its signal line to attempt to capture a combo of the price momentum combined with the general support and resistance levels. I have used dark colors for strong signals and lighter colors for normal signals and make sure to buy when the line turns green and sell when it turns red.
Let me know if there are any other scripts or indicators you would like to see me publish!
Machine Learning Regression Trend [LuxAlgo]The Machine Learning Regression Trend tool uses random sample consensus (RANSAC) to fit and extrapolate a linear model by discarding potential outliers, resulting in a more robust fit.
🔶 USAGE
The proposed tool can be used like a regular linear regression, providing support/resistance as well as forecasting an estimated underlying trend.
Using RANSAC allows filtering out outliers from the input data of our final fit, by outliers we are referring to values deviating from the underlying trend whose influence on a fitted model is undesired. For financial prices and under the assumptions of segmented linear trends, these outliers can be caused by volatile moves and/or periodic variations within an underlying trend.
Adjusting the "Allowed Error" numerical setting will determine how sensitive the model is to outliers, with higher values returning a more sensitive model. The blue margin displayed shows the allowed error area.
The number of outliers in the calculation window (represented by red dots) can also be indicative of the amount of noise added to an underlying linear trend in the price, with more outliers suggesting more noise.
Compared to a regular linear regression which does not discriminate against any point in the calculation window, we see that the model using RANSAC is more conservative, giving more importance to detecting a higher number of inliners.
🔶 DETAILS
RANSAC is a general approach to fitting more robust models in the presence of outliers in a dataset and as such does not limit itself to a linear regression model.
This iterative approach can be summarized as follow for the case of our script:
Step 1: Obtain a subset of our dataset by randomly selecting 2 unique samples
Step 2: Fit a linear regression to our subset
Step 3: Get the error between the value within our dataset and the fitted model at time t , if the absolute error is lower than our tolerance threshold then that value is an inlier
Step 4: If the amount of detected inliers is greater than a user-set amount save the model
Repeat steps 1 to 4 until the set number of iterations is reached and use the model that maximizes the number of inliers
🔶 SETTINGS
Length: Calculation window of the linear regression.
Width: Linear regression channel width.
Source: Input data for the linear regression calculation.
🔹 RANSAC
Minimum Inliers: Minimum number of inliers required to return an appropriate model.
Allowed Error: Determine the tolerance threshold used to detect potential inliers. "Auto" will automatically determine the tolerance threshold and will allow the user to multiply it through the numerical input setting at the side. "Fixed" will use the user-set value as the tolerance threshold.
Maximum Iterations Steps: Maximum number of allowed iterations.
Smart Money Range [ChartPrime]The Smart Money Range indicator is designed to provide traders with a holistic view of market structure, emphasizing potential key support and resistance levels within a predefined range. This indicator is not just a visually pleasing, but also a comprehensive guide to understanding the market’s dynamics at a given level.
Key Features:
Defined Range: The indicator demarcates a clear range, highlighting support and resistance levels within it. This aids in identifying potential areas of buying and selling pressure. These are derived from highly significant areas that have been touched many times before.
Touches Counter: Underneath the support and resistance lines, there are numerical values that show the number of times price has interacted with these levels. This can provide insights into the strength or weakness of a particular level.
Zig-Zag Projections: Within the range, there's a zig-zag pattern indicating possible future touches, helping traders anticipate future price movements.
Double-Sided Profile: To the right of the range, a dual-profile is showcased. One side of the profile displays the volume traded at specific price levels, giving insights into where significant buying or selling has occurred. On the other side, it reflects the number of touches at that given price level, reinforcing the importance of particular price points.
Customizability: Users have the option to adjust the period setting, allowing them to cater the indicator to their specific trading style and configuration. Additionally, with volume levels settings, traders can adjust the number of bins in the profile for a tailored view.
Parabolic SAR ZoneThe Parabolic SAR Zone indicator is a tool designed to help traders identify the best zone to enter in a position revisiting the usage of the standard Parabolic SAR indicator.
In the settings you can choose all the parameters of the standard indicator, and in addition to that you can also change the multiplier for the zone width.
This indicator provides two different Parabolic SAR indicators, the first one has the settings that you chose and displays the zone, meanwhile, the second one has half the parameters you have chosen and can be used to determine the long-term trend direction.
SuperTrend ZoneThe SuperTrend Zone indicator is a tool designed to help traders identify the best zone to enter in a position revisiting the usage of the standard SuperTrend indicator.
In the settings you can chose the ATR length and the Factor of the indicator, and in addition to that you can also change the multiplier for the zone width.
This indicator provide two different SuperTrend indicator, the first one has the settings that you chose and display the zone, meanwhile the second one has double the parameters you have chosen and can be used to determine the long term trend direction.
Support & Resistance AI (K means/median) [ThinkLogicAI]█ OVERVIEW
K-means is a clustering algorithm commonly used in machine learning to group data points into distinct clusters based on their similarities. While K-means is not typically used directly for identifying support and resistance levels in financial markets, it can serve as a tool in a broader analysis approach.
Support and resistance levels are price levels in financial markets where the price tends to react or reverse. Support is a level where the price tends to stop falling and might start to rise, while resistance is a level where the price tends to stop rising and might start to fall. Traders and analysts often look for these levels as they can provide insights into potential price movements and trading opportunities.
█ BACKGROUND
The K-means algorithm has been around since the late 1950s, making it more than six decades old. The algorithm was introduced by Stuart Lloyd in his 1957 research paper "Least squares quantization in PCM" for telecommunications applications. However, it wasn't widely known or recognized until James MacQueen's 1967 paper "Some Methods for Classification and Analysis of Multivariate Observations," where he formalized the algorithm and referred to it as the "K-means" clustering method.
So, while K-means has been around for a considerable amount of time, it continues to be a widely used and influential algorithm in the fields of machine learning, data analysis, and pattern recognition due to its simplicity and effectiveness in clustering tasks.
█ COMPARE AND CONTRAST SUPPORT AND RESISTANCE METHODS
1) K-means Approach:
Cluster Formation: After applying the K-means algorithm to historical price change data and visualizing the resulting clusters, traders can identify distinct regions on the price chart where clusters are formed. Each cluster represents a group of similar price change patterns.
Cluster Analysis: Analyze the clusters to identify areas where clusters tend to form. These areas might correspond to regions of price behavior that repeat over time and could be indicative of support and resistance levels.
Potential Support and Resistance Levels: Based on the identified areas of cluster formation, traders can consider these regions as potential support and resistance levels. A cluster forming at a specific price level could suggest that this level has been historically significant, causing similar price behavior in the past.
Cluster Standard Deviation: In addition to looking at the means (centroids) of the clusters, traders can also calculate the standard deviation of price changes within each cluster. Standard deviation is a measure of the dispersion or volatility of data points around the mean. A higher standard deviation indicates greater price volatility within a cluster.
Low Standard Deviation: If a cluster has a low standard deviation, it suggests that prices within that cluster are relatively stable and less likely to exhibit sudden and large price movements. Traders might consider placing tighter stop-loss orders for trades within these clusters.
High Standard Deviation: Conversely, if a cluster has a high standard deviation, it indicates greater price volatility within that cluster. Traders might opt for wider stop-loss orders to allow for potential price fluctuations without getting stopped out prematurely.
Cluster Density: Each data point is assigned to a cluster so a cluster that is more dense will act more like gravity and
2) Traditional Approach:
Trendlines: Draw trendlines connecting significant highs or lows on a price chart to identify potential support and resistance levels.
Chart Patterns: Identify chart patterns like double tops, double bottoms, head and shoulders, and triangles that often indicate potential reversal points.
Moving Averages: Use moving averages to identify levels where the price might find support or resistance based on the average price over a specific period.
Psychological Levels: Identify round numbers or levels that traders often pay attention to, which can act as support and resistance.
Previous Highs and Lows: Identify significant previous price highs and lows that might act as support or resistance.
The key difference lies in the approach and the foundation of these methods. Traditional methods are based on well-established principles of technical analysis and market psychology, while the K-means approach involves clustering price behavior without necessarily incorporating market sentiment or specific price patterns.
It's important to note that while the K-means approach might provide an interesting way to analyze price data, it should be used cautiously and in conjunction with other traditional methods. Financial markets are influenced by a wide range of factors beyond just price behavior, and the effectiveness of any method for identifying support and resistance levels should be thoroughly tested and validated. Additionally, developments in trading strategies and analysis techniques could have occurred since my last update.
█ K MEANS ALGORITHM
The algorithm for K means is as follows:
Initialize cluster centers
assign data to clusters based on minimum distance
calculate cluster center by taking the average or median of the clusters
repeat steps 1-3 until cluster centers stop moving
█ LIMITATIONS OF K MEANS
There are 3 main limitations of this algorithm:
Sensitive to Initializations: K-means is sensitive to the initial placement of centroids. Different initializations can lead to different cluster assignments and final results.
Assumption of Equal Sizes and Variances: K-means assumes that clusters have roughly equal sizes and spherical shapes. This may not hold true for all types of data. It can struggle with identifying clusters with uneven densities, sizes, or shapes.
Impact of Outliers: K-means is sensitive to outliers, as a single outlier can significantly affect the position of cluster centroids. Outliers can lead to the creation of spurious clusters or distortion of the true cluster structure.
█ LIMITATIONS IN APPLICATION OF K MEANS IN TRADING
Trading data often exhibits characteristics that can pose challenges when applying indicators and analysis techniques. Here's how the limitations of outliers, varying scales, and unequal variance can impact the use of indicators in trading:
Outliers are data points that significantly deviate from the rest of the dataset. In trading, outliers can represent extreme price movements caused by rare events, news, or market anomalies. Outliers can have a significant impact on trading indicators and analyses:
Indicator Distortion: Outliers can skew the calculations of indicators, leading to misleading signals. For instance, a single extreme price spike could cause indicators like moving averages or RSI (Relative Strength Index) to give false signals.
Risk Management: Outliers can lead to overly aggressive trading decisions if not properly accounted for. Ignoring outliers might result in unexpected losses or missed opportunities to adjust trading strategies.
Different Scales: Trading data often includes multiple indicators with varying units and scales. For example, prices are typically in dollars, volume in units traded, and oscillators have their own scale. Mixing indicators with different scales can complicate analysis:
Normalization: Indicators on different scales need to be normalized or standardized to ensure they contribute equally to the analysis. Failure to do so can lead to one indicator dominating the analysis due to its larger magnitude.
Comparability: Without normalization, it's challenging to directly compare the significance of indicators. Some indicators might have a larger numerical range and could overshadow others.
Unequal Variance: Unequal variance in trading data refers to the fact that some indicators might exhibit higher volatility than others. This can impact the interpretation of signals and the performance of trading strategies:
Volatility Adjustment: When combining indicators with varying volatility, it's essential to adjust for their relative volatilities. Failure to do so might lead to overemphasizing or underestimating the importance of certain indicators in the trading strategy.
Risk Assessment: Unequal variance can impact risk assessment. Indicators with higher volatility might lead to riskier trading decisions if not properly taken into account.
█ APPLICATION OF THIS INDICATOR
This indicator can be used in 2 ways:
1) Make a directional trade:
If a trader thinks price will go higher or lower and price is within a cluster zone, The trader can take a position and place a stop on the 1 sd band around the cluster. As one can see below, the trader can go long the green arrow and place a stop on the one standard deviation mark for that cluster below it at the red arrow. using this we can calculate a risk to reward ratio.
Calculating risk to reward: targeting a risk reward ratio of 2:1, the trader could clearly make that given that the next resistance area above that in the orange cluster exceeds this risk reward ratio.
2) Take a reversal Trade:
We can use cluster centers (support and resistance levels) to go in the opposite direction that price is currently moving in hopes of price forming a pivot and reversing off this level.
Similar to the directional trade, we can use the standard deviation of the cluster to place a stop just in case we are wrong.
In this example below we can see that shorting on the red arrow and placing a stop at the one standard deviation above this cluster would give us a profitable trade with minimal risk.
Using the cluster density table in the upper right informs the trader just how dense the cluster is. Higher density clusters will give a higher likelihood of a pivot forming at these levels and price being rejected and switching direction with a larger move.
█ FEATURES & SETTINGS
General Settings:
Number of clusters: The user can select from 3 to five clusters. A good rule of thumb is that if you are trading intraday, less is more (Think 3 rather than 5). For daily 4 to 5 clusters is good.
Cluster Method: To get around the outlier limitation of k means clustering, The median was added. This gives the user the ability to choose either k means or k median clustering. K means is the preferred method if the user things there are no large outliers, and if there appears to be large outliers or it is assumed there are then K medians is preferred.
Bars back To train on: This will be the amount of bars to include in the clustering. This number is important so that the user includes bars that are recent but not so far back that they are out of the scope of where price can be. For example the last 2 years we have been in a range on the sp500 so 505 days in this setting would be more relevant than say looking back 5 years ago because price would have to move far to get there.
Show SD Bands: Select this to show the 1 standard deviation bands around the support and resistance level or unselect this to just show the support and resistance level by itself.
Features:
Besides the support and resistance levels and standard deviation bands, this indicator gives a table in the upper right hand corner to show the density of each cluster (support and resistance level) and is color coded to the cluster line on the chart. Higher density clusters mean price has been there previously more than lower density clusters and could mean a higher likelihood of a reversal when price reaches these areas.
█ WORKS CITED
Victor Sim, "Using K-means Clustering to Create Support and Resistance", 2020, towardsdatascience.com
Chris Piech, "K means", stanford.edu
█ ACKNOLWEDGMENTS
@jdehorty- Thanks for the publish template. It made organizing my thoughts and work alot easier.
Liquidity Levels/Voids (VP) [LuxAlgo]The Liquidity Levels/Voids (VP) is a script designed to detect liquidity voids & levels by measuring traded volume at all price levels on the market between two swing points and highlighting the distribution of the liquidity voids & levels at specific price levels.
🔶 USAGE
Liquidity is a fundamental market force that shapes the trajectory of assets.
The creation of a liquidity level comes as a result of an initial imbalance of supply/demand, which forms what we know as a swing high or swing low. As more players take positions in the market, these are levels that market participants will use as a historical reference to place their stops. When the levels are then re-tested, a decision will be made. The binary outcome here can be a breakout of the level or a reversal back to the mean.
Liquidity voids are sudden price changes that occur in the market when the price jumps from one level to another with little trading activity (low volume), creating an imbalance in price. The price tends to fill or retest the liquidity voids area, and traders understand at which price level institutional players have been active.
Liquidity voids are a valuable concept in trading, as they provide insights about where many orders were injected, creating this inefficiency in the market. The price tends to restore the balance.
🔶 SETTINGS
The script takes into account user-defined parameters and detects the liquidity voids based on them, where detailed usage for each user-defined input parameter in indicator settings is provided with the related input's tooltip.
🔹 Liquidity Levels / Voids
Liquidity Levels/Voids: Color customization option for Unfilled Liquidity Levels/Voids.
Detection Length: Lookback period used for the calculation of Swing Levels.
Threshold %: Threshold used for the calculation of the Liquidity Levels & Voids.
Sensitivity: Adjusts the number of levels between two swing points, as a result, the height of a level is determined, and then based on the above-given threshold the level is checked if it matches the liquidity level/void conditions.
Filled Liquidity Levels/Voids: Toggles the visibility of the Filled Liquidity Levels/Voids and color customization option for Filled Liquidity Levels/Voids.
🔹 Other Features
Swing Highs/Lows: Toggles the visibility of the Swing Levels, where tooltips present statistical information, such as price, price change, and cumulative volume between the two swing levels detected based on the detection length specified above, Coloring options to customize swing low and swing high label colors, and Size option to adjust the size of the labels.
🔹 Display Options
Mode: Controls the lookback length of detection and visualization.
# Bars: Lookback length customization, in case Mode is set to Present.
🔶 RELATED SCRIPTS
Liquidity-Voids-FVG
Buyside-Sellside-Liquidity
Swing-Volume-Profiles
Doji TrenderDoji Trender searches multiple timeframes for candles where open and close are less than dojiPercent apart (default 0.025%), and plots the trends between them.
Experiment with dojiPercent to change the number of "dojis" detected. I will add doji sub-type indication if it appears to be meaningful.
By default, it plots the 5m (red), 15m (orange), 1h (yellow), 4h (green), and chart (cyan). If the chart timeframe is any of the configurable ones, the chart copy won't be drawn. (I might reverse that, so that cyan is always drawn.)
Since doji points are somewhat sparse, and the lookback is short (default 10), the EMA's make drastic corrections toward new indecision. (I'm not convinced the EMA's are useful and/or relevant.)
This works on any timeframe, but seems to work best on the 1D. (5m is somewhat irrelevant on the 1D, so there are tweaks to be made.)
Dojis from a timeframe are corrections to a doji trend from a higher timeframe.
Red corrects to orange, corrects to yellow, corrects to green.
If the chart timeframe is > 4h, the others will correct to cyan.
Otherwise, cyan will fit in-between the adjacent timeframes.
Multiple indecision candles within a short timespan forming sharp peaks indicate retests, backtests, rejections, and bounces off of support/resistance.
With a correct larger-timeframe channel, one would expect lower-timeframe indecision at/along typical levels.
Although the doji's have unpredictable wicks, the dots printed by this indicator do not. Matched with volume, they reveal the prices where the most violent battles between bulls and bears took place, and are likely to take place, again.
One could:
1) Put trends on the longest segments, then look for confluence along them, and/or near the intersections.
2) Use lower-timeframe doji trends to estimate the direction of the higher-timeframe doji trends, before they become detectable to Doji Trender. Confirm by looking for confluence where those trends intersect with horizontal support/resistance, this indicator, and/or others.
3) Notice that multiple legs on the same trend line are close to parallel, if not colinear.
4) Notice that many of the doji segments point toward (very-distant) future dojis.
5) Drop horizontal lines on the dots where we previously reversed, and find confluence in VRVP when we revisit them.
6) Create parallel (fib/whatever) channels that more-closely match MM's intent. The segments one uses to set the angle of the channel, and those used to align the channel, vertically, are not always the same:
a) Match the channel slope to as many doji slopes as possible, considering every trend.
b) Figure out where the channel actually belongs, re-considering every trend.
Fibo Levels with Volume Profile and Targets [ChartPrime]The Fib Levels With Volume Profile and Targets (FIVP) is a trading tool designed to provide traders with a unique understanding of price movement and trading volume through the lens of Fibonacci levels. This dynamic indicator merges the concepts of Fibonacci retracement levels with trading volume analytics to offer predictive insights into potential price trajectories.
Features:
1. Fibonacci Levels: The FPI showcases three prominent Fibonacci levels on both sides of the current price, offering an intricate picture of potential support and resistance levels.
2. Support and Resistance Recognition: Harnessing the power of Fibonacci levels, the FPI provides traders with potential areas of support and resistance, aiding in informed decision-making for entries, exits, and stop placements.
3. Customizable Timeframe Settings: In order to cater to different trading strategies and styles, users can manually select their preferred timeframe for the Fibonacci calculations, ensuring optimal relevance and accuracy for their trading approach.
4. Volume Analytics: One of the standout features of the FIVP is its ability to calculate trading volume for every bar that is sandwiched between the top and lower Fibonacci levels. This ensures traders have a clear vision of where the majority of trading activity is occurring, lending weight to the credibility of the displayed support and resistance zones.
5. Volume-Derived Price Targeting: The Possible Target Arrow function is an innovative feature. By analyzing and comparing the trading volume in the bearish and bullish zones, it provides an arrow indicating the potential direction the market might take. If the bull volume surpasses the bear volume, the market is likely skewing bullish and vice versa.
Usage
Ideal for both novice and seasoned traders, the FPI offers a rich tapestry of information. It allows for refined technical analysis, more precise entries and exits, and a holistic view of the interplay between price and trading volume. Whether you're scalping, day trading, or swing trading, the Fibonacci Profile Indicator is designed to enhance your trading strategy, providing a comprehensive perspective of the market's potential movements.
Monday Session High/LowThis script tracks and draw the High and the Low for Monday session across the whole week period.
Many crypto traders use the high and low extremes for Monday session as an confluence on how the price action during the week reacts against it.
Filtered Volume Profile [ChartPrime]The "Filtered Volume Profile" is a powerful tool that offers insights into market activity. It's a technical analysis tool used to understand the behavior of financial markets. It uses a fixed range volume profile to provide a histogram representing how much volume occurred at distinct price levels.
Profile in action with various significant levels displayed
How to Use
The script is designed to analyze cumulative trading volumes in different price bins over a certain period, also known as `'lookback'`. This lookback period can be defined by the user and it represents the number of bars to look back for calculating levels of support and resistance.
The `'Smoothing'` input determines the degree to which the output is smoothed. Higher values lead to smoother results but may impede the responsiveness of the indicator to rapid changes in volatility.
The `'Peak Sensitivity'` input is used to adjust the sensitivity of the script's peak detection algorithm. Setting this to a lower value makes the algorithm more sensitive to local changes in trading volume and may result in "noisier" outputs.
The `'Peak Threshold'` input specifies the number of bins that the peak detection mechanism should account for. Larger numbers imply that more volume bins are taken into account, and the resultant peaks are based on wider intervals.
The `'Mean Score Length'` input is used for scaling the mean score range. This is particularly important in defining the length of lookback bars that will be used to calculate the average close price.
Sinc Filter
The application of the sinc-filter to the Filtered Volume Profile reduces the risk of viewing artefacts that may misrepresent the underlying market behavior. Sinc filtering is a high-quality and sharp filter that doesn't manifest any ringing effects, making it an optimal choice for such volume profiling.
Histogram
On the histogram, the volume profile is colored based on the balance of bullish to bearish volume. If a particular bar is more intense in color, it represents a larger than usual volume during a single price bar. This is a clear signal of a strong buying or selling pressure at a particular price level.
Threshold for Peaks
The `peak_thresh` input determines the number of bins the algorithm takes in account for the peak detection feature. The 'peak' represents the level where a significant amount of volume trading has occurred, and usually is of interest as an indicative of support or resistance level.
By increasing the `peak_thresh`, you're raising the bar for what the algorithm perceives as a peak. This could result in fewer, but more significant peaks being identified.
History of Volume Profiles and Evolution into Sinc Filtering
Volume profiling has a rich history in market analysis, dating back to the 1950s when Richard D. Wyckoff, a legendary trader, introduced the concept of volume studies. He understood the critical significance of volume and its relationship with market price movement. The core of Wyckoff's technical analysis suite was the relationship between prices and volume, often termed as "Effort vs Results".
Moving forward, in the early 1800s, the esteemed mathematician J. R. Carson made key improvements to the sinc function, which formed the basis for sinc filtering application in time series data. Following these contributions, trading studies continued to create and integrate more advanced statistical measures into market analysis.
This culminated in the 1980s with J. Peter Steidlmayer’s introduction of Market Profile. He suggested that markets were a function of continuous two-way auction processes thus introducing the concept of viewing markets in price/time continuum and price distribution forms. Steidlmayer's Market Profile was the first wide-scale operation of organized volume and price data.
However, despite the introduction of such features, challenges in the analysis persisted, especially due to noise that could misinform trading decisions. This gap has given rise to the need for smoothing functions to help eliminate the noise and better interpret the data. Among such techniques, the sinc filter has become widely recognized within the trading community.
The sinc filter, because of its properties of constructing a smooth passing through all data points precisely and its ability to eliminate high-frequency noise, has been considered a natural transition in the evolution of volume profile strategies. The superior ability of the sinc filter to reduce noise and shield against over-fitting makes it an ideal choice for smoothing purposes in trading scripts, particularly where volume profiling forms the crux of the market analysis strategy, such as in Filtered Volume Profile.
Moving ahead, the use of volume-based studies seems likely to remain a core part of technical analysis. As long as markets operate based on supply and demand principles, understanding volume will remain key to discerning the intent behind price movements. And with the incorporation of advanced methods like sinc filtering, the accuracy and insight provided by these methodologies will only improve.
Mean Score
The mean score in the Filtered Volume Profile script plays an important role in probabilistic inferences regarding future price direction. This score essentially characterizes the statistical likelihood of price trends based on historical data.
The mean score is calculated over a configurable `'Mean Score Length'`. This variable sets the window or the timeframe for calculation of the mean score of the closing prices.
Statistically, this score takes advantage of the concept of z-scores and probabilities associated with the t-distribution (a type of probability distribution that is symmetric and bell-shaped, just like the standard normal distribution, but has heavier tails).
The z-score represents how many standard deviations an element is from the mean. In this case, the "element" is the price level (Point of Control).
The mean score section of the script calculates standard errors for the root mean squared error (RMSE) and addresses the uncertainty in the prediction of the future value of a random variable.
The RMSE of a model prediction concerning observed values is used to measure the differences between values predicted by a model and the values observed.
The lower the RMSE, the better the model is able to predict. A zero RMSE means a perfect fit to the data. In essence, it's a measure of how concentrated the data is around the line of best fit.
Through the mean score, the script effectively predicts the likelihood of the future close price being above or below our identified price level.
Summary
Filtered Volume Profile is a comprehensive trading view indicator which utilizes volume profiling, peak detection, mean score computations, and sinc-filter smoothing, altogether providing the finer details of market behavior.
It offers a customizable look back period, smoothing options, and peak sensitivity setting along with a uniquely set peak threshold. The application of the Sinc Filter ensures a high level of accuracy and noise reduction in volume profiling, making this script a reliable tool for gaining market insights.
Furthermore, the use of mean score calculations provides probabilistic insights into price movements, thus providing traders with a statistically sound foundation for their trading decisions. As trading markets advance, the use of such methodologies plays a pivotal role in formulating effective trading strategies and the Filtered Volume Profile is a successful embodiment of such advancements in the field of market analysis.
HTF Support & Resistance [QuantVue]The "HTF Support / Resistance" indicator highlights critical price levels across multiple timeframes helping you recognize major support/resistance areas.
Whether you're a day trader needing to understand the current day's price action, or a long-term investor looking to analyze yearly trends, this indicator can be a valuable addition to your trading toolkit.
The daily levels, which can be enabled or disabled according to your preference, will give you insights into the open, high, and low levels for the current and previous day.
Similarly, weekly levels provide information for the current and previous weeks, while monthly levels cover the current and previous months.
In addition, the indicator offers more extended views through its quarterly and yearly levels. These will help you understand long-term trends and major support/resistance areas, and they can be particularly beneficial for major support/resistance levels.
Features:
🔹Visualization: View support and resistance levels from Daily, Weekly, Monthly, Quarterly, and Yearly timeframes.
🔹Customizable Appearance: Tailor the display colors and line styles for each level according to your preferences.
🔹Clear Labeling: Each level is clearly labeled for quick identification.
🔹Extension Option: Opt to extend the support and resistance lines across the chart for better visualization.
Give this indicator a BOOST and COMMENT your thoughts!
We hope you enjoy.
Cheers.
Bitcoin as Heikin Ashi Candles with Pivot PointsI use this Indicator to show me where Bitcoin is heading.
Most pine programmers are not aware of the possibility to combine Heikin Ashi Candles with Pivot Points that easy.
You can switch between Traditional, Fibonacci, Woodie, Classic, DM, and Camarilla as usual.
When on a Intraday Chart it will automaticly calculate daily Pivots for Haikin Ashi candles.
On the daily Chart it will calculate weekly Pivots, and when switching to monthly candles it will calculate Pivots for one year.
There is also an option where you can deactivate all support and resistance lines, except for today. Meaning you will see the Central Pivot Point on all previous days only.
This is a lagging indicator!
When you can see Altcoin correlation with BTC then both might get pretty strong leading indicators.
I am pleased to hear some advice/wishes to improve this script.
It is still in the beginning and some updates will follow, I promise.
Happy Trading!
Dynamic Levels Breakouts [Angel Algo]INTRODUCTION
The Dynamic Levels Breakouts indicator is a powerful tool designed to identify dynamic support and resistance levels in the price action. It plots these levels on the chart and provides visual signals for bullish and bearish breakouts.
FEATURES
1. Dynamic Support and Resistance Levels.
The indicator calculates the maximum (resistance) and minimum (support) price levels within the defined rolling window. The highest high and lowest low are used to identify dynamic resistance and support levels, respectively.
2. Coloring Conditions
The indicator uses conditional coloring to highlight potential support and resistance levels. When a significant level is detected, it will be colored with a transparent overlay. Red color indicates potential resistance (max_level), and green color indicates potential support (min_level).
3. Bullish and Bearish Breakouts
The indicator also identifies potential bullish and bearish breakouts from the dynamic levels. A bullish breakout occurs when the closing price crosses above the dynamic resistance level (max_level). A bearish breakout occurs when the closing price crosses below the dynamic support level (min_level). The breakout signals are marked with arrow symbols (▲ for bullish and ▼ for bearish) below and above the respective bars.
4. Market Regime
To prevent multiple signals within a short period, the indicator considers the current market regime. If a bullish breakout has occurred recently, it will not generate a new bullish signal until a bearish breakout occurs, and vice versa.
HOW TO USE
The Dynamic Levels Breakouts indicator can be used to identify potential breakout trading opportunities. A bullish breakout signal (▲) suggests a potential long entry or an increase in buying pressure. A bearish breakout signal (▼) indicates a potential short entry or an increase in selling pressure. Traders can use these signals as a reference and combine them with other technical analysis tools and strategies for confirmation.
SETTINGS
Period (length): The user can adjust the "Period" input to define the rolling window for calculating the maximum and minimum price levels. The default value is 20, but it can be set anywhere between 2 and 30. A higher value may yield more significant levels but can also result in delayed signals
MTF Key Levels [Mxwll]Mxwll MTF S/R:
The Mxwll MTF Support & Resistance indicator is designed to identify crucial support and resistance levels across multiple timeframes. By considering various timeframes, this indicator provides a more comprehensive view of the market's underlying structure. It allows traders to extend lines in various configurations and covers timeframes ranging from 5 minutes to weekly. By considering price action across multiple timeframes, the indicator provides a more comprehensive understanding of the market's supply and demand dynamics. Traders can use the Mxwll MTF Support & Resistance Indicator to refine their trade entries and exits, manage risk, and establish potential price targets.
FEATURES
5 Minute to Weekly Key Levels
Accurate Multi-Timeframe Support and Resistance
Customize To Extend The Lines - Left, Right and Right Across The Chart
Interplay Between Support and Resistance Levels
Change Colours Of S&R
Change Colours Of S&R Lines
INSTRUCTIONS
Select Your Timeframe -> Unselect the S&R Levels That Are Less Than The Timeframe - Trade
Liquidity PoolsThis indicator displays liquidity based on swing highs and swing lows.
Since large liquidity rests above swing highs and below swing lows, the indicator can show swing highs and lows as well so that you can easily find where liquidity stays.
To determine swing high/low, left bars and right bars can be specified on inputs.
How to use this indicator?
You can use this indicator to;
observe how liquidity is used as the market moves.
watch which liquidity will be taken before the market moves and which liquidity will be targeted as the market progresses.
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リクイディティ(Liquidity)を表示するインジケーターです。
リクイディティとはマネー、注文を指します。
わかりやすくするために、敢えて損切り注文と書いてみましょう。
大きなリクイディティ(損切り注文)はスイングハイの直上(買いのストップオーダー)、スイングローの直下(売りのストップオーダー)に控えています。インジケーターはスイングハイ・ローも同時に表示することができるため、リクイディティ(損切り注文)を容易に特定することが可能です。
使い方
・リクイディティ(損切り注文)が相場が動く中でどのように使われているのかを観察
・マーケットが動く前にどのリクイディ(損切り注文)を取り、トレンドが進展するにつれどのリクイディティ(損切り注文)をターゲットとするかを観察
Swing Ranges [ChartPrime]Swing Ranges is an indicator designed to provide traders with valuable insights into swing movements and real-time support and resistance (SR) levels. This tool detects price swings and plots boxes around them, allowing traders to visualize the market dynamics efficiently. The indicator's primary focus is on real-time support and resistance levels, empowering traders to make well-informed decisions in dynamic market conditions.
Key Features:
Swing Box Visualization:
Swing Ranges excels at detecting swings in the price data and visually representing them with boxes on the price chart. This enables traders to quickly identify swing ranges, essential for understanding market trends and potential reversal points. VWAP POCs are also provided giving areas of high activity in each block.
Real-Time Support and Resistance Levels:
The core feature of Swing Ranges is its real-time support and resistance levels. These levels are dynamically calculated based on the volume-weighted data for each specific range. The indicator displays the strength of support and resistance zones with percentage bars, indicating the ratio between bullish and bearish volume. This real-time information empowers traders to assess the strength and significance of each SR level, enhancing their ability to execute well-timed trades.
ATR (Average True Range) Value:
Swing Ranges also includes an ATR value label, which shows the Average True Range for the selected period. ATR aids traders in understanding market volatility, enabling them to set appropriate stop-loss and take-profit levels for their trades.
VWAP (Volume Weighted Average Price) Information:
Traders c an readily access the VWAP value through the indicator's label. VWAP provides insights into the average price at which an asset has been traded, helping traders identify potential fair value areas and market trends.
Price Difference Percentage:
Swing Ranges displays the percentage difference between the high and low of each swing. This information allows traders to gauge the magnitude of price movements and assess potential profit targets more effectively.
The indicator also has a NV value. If the NV is high e.g. 10% or more there is indecision in the market and the market is trying to remain in a given range.
Settings Inputs:
1. Length Control:
The Length setting input in Swing Ranges allows traders to adjust the sensitivity of the indicator to detect swings. Traders can customize the length based on their trading strategies and timeframes.
2. ATR Period Adjustment:
The ATR Period input allows traders to fine-tune the calculation period for the Average True Range. This feature enables traders to adapt the indicator to different market conditions and asset classes.
Swing Ranges: Real-Time Support and Resistance Indicator is a comprehensive tool that combines swing visualization with dynamic support and resistance levels. By focusing on real-time SR levels, this indicator equips traders with the essential information needed to make confident trading decisions in ever-changing market conditions.
