Get started

Clustering

Rolling Liquidity Clusters Channel [LuxAlgo]The Rolling Liquidity Clusters Channel indicator identifies dynamic support and resistance zones by calculating levels that maximize candle wick touches while strictly avoiding intersections with candle bodies within a rolling window. This tool provides a unique perspective on liquidity clusters, highlighting price levels where historical rejection is most concentrated without being invalidated by price "closing" through them. 🔶 USAGE The indicator plots a channel consisting of an Upper Level, a Lower Level, and a Mid Level. The space between these levels is filled with a vertical gradient to visually represent the strength of the liquidity zone. Upper Level (Red): Represents a resistance zone where the most upper wicks are concentrated without any candle body in the lookback window crossing above it. Lower Level (Green): Represents a support zone where the most lower wicks are concentrated without any candle body in the lookback window crossing below it. Mid Level (Orange): Represents the equilibrium or average of the current liquidity channel. Traders can use these levels to identify potential reversal points or areas of price consolidation. A breakout from the channel might indicate a shift in market structure as price moves beyond the most inclusive "non-broken" wick levels. 🔶 DETAILS The script employs a specific constraint logic to ensure the levels represent true "untouched" liquidity: 🔹 Body-Crossing Constraint Before identifying the wick touches, the script calculates the highest candle body high and lowest candle body low within the user-defined window. The resulting levels are guaranteed to stay outside of this "body zone," ensuring that the plotted levels represent prices that the market reached but failed to sustain via a close. 🔹 Maximizing Touches To find the most significant level, the algorithm searches for the most inclusive price point. For the upper level, it identifies the lowest "high" that remains above all candle bodies. For the lower level, it identifies the highest "low" that remains below all candle bodies. This mathematical approach effectively finds the level where the most price action "clusters" via wicks. 🔹 Vertical Gradient Fills The visual style uses a vertical gradient fill. The upper half fades from 90% transparency at the Upper Level (Red) to 100% transparency at the Mid Level. The lower half follows a similar logic, fading from the Lower Level (Green) toward the center. This creates a "glow" effect, emphasizing the outer boundaries where liquidity is highest. 🔶 SETTINGS Window Size: The number of bars used for the rolling calculation. A larger window creates more stable, long-term levels, while a smaller window adapts quickly to recent price action. Upper Level: Customize the color of the upper resistance level and its associated gradient fill. Lower Level: Customize the color of the lower support level and its associated gradient fill. Mid Level: Customize the color of the central equilibrium line.
Pine Script® indicator
by LuxAlgo
Anchored Clusters Volume Profile [LuxAlgo]The Anchored Clusters Volume Profile indicator utilizes K-Means clustering to categorize historical price action into distinct groups within a user-defined range and generates individual volume profiles for each detected cluster. This tool provides a unique perspective on volume distribution by isolating price behaviors based on proximity rather than strictly chronological order. 🔶 USAGE The indicator identifies "clusters" of price activity within a selected range defined by a starting and ending date. Each cluster is assigned a unique color and its own horizontal volume profile, allowing traders to see where liquidity is most concentrated within specific price regimes. 🔹 Identifying Institutional Zones Traders can use the Point of Control (POC) of high-volume clusters to identify significant institutional interest. Because the K-Means algorithm groups price action by density rather than time, a cluster's POC often represents a "fair value" level where significant exchange occurred. These dashed POC lines frequently act as robust support or resistance levels when price returns to them in the future. 🔹 Market Regime Detection By observing the vertical distribution and overlap of clusters, traders can identify market phases. Overlapping clusters with high volume often indicate accumulation or distribution phases (sideways markets), whereas distinct, vertically separated clusters with lower volume gaps between them suggest a trending environment. A shift from multiple overlapping clusters to a new, isolated cluster can signal a breakout or the start of a new trend. 🔹 Precision Entry & Exits Cluster boundaries and POC lines provide concrete levels for trade management. An entry can be sought when price retests a high-volume cluster POC, while stops can be placed outside the total price range of that specific cluster (the area covered by its volume profile). Conversely, targets can be set at the POC of the next major cluster above or below current price action. 🔹 Volume Conviction The tool provides specific volume metrics that allow traders to gauge conviction. By comparing the "Total" volume label of one cluster against another, a trader can determine which price regime had more participation. A breakout into a price zone with a high-volume cluster suggests stronger conviction and a higher probability of the level holding compared to a zone with low total volume. 🔶 DETAILS Unlike traditional anchored volume profiles that provide a single histogram, this script employs a K-Means clustering algorithm to segment the range. This process involves: Identifying the specific range of bars between the user-selected Start Time and End Time . Initializing "centroids" across the price range of that period. Iteratively assigning each price bar to the nearest centroid based on the HLC2 (median) price. Recalculating centroids based on the volume-weighted average price of the assigned bars. Finalizing assignments after the specified number of iterations to ensure stable clusters. By separating price action into these clusters, the tool helps identify high-interest zones that might be obscured by a single, traditional Volume Profile. 🔶 SETTINGS 🔹 Anchor Settings Start Time / End Time : Sets the beginning and end of the analysis range. These use the "Confirm" feature, allowing you to select the range directly on the chart after adding the indicator or changing settings. Range Highlight : Adjusts the color and transparency of the background shading that identifies the analyzed range. 🔹 Clustering Settings Number of Clusters : Sets how many distinct price groups the algorithm should attempt to find (2 to 10). K-Means Iterations : Controls the number of times the algorithm refines the cluster centers. Higher values can lead to more stable results. 🔹 Volume Profile Settings Rows per Cluster VP : Defines the vertical resolution (number of bins) for each individual cluster's profile. Max VP Width (Bars) : Sets the maximum horizontal length of the volume profile histograms. VP Offset : Adjusts the horizontal spacing between the current bar and the start of the volume profiles. Highlight Price Dots : Toggles the visibility of the colored dots on the price action to identify cluster assignments. Dot Size : Adjusts the size of the cluster assignment dots on the chart, ranging from tiny to huge.
Pine Script® indicator
by LuxAlgo
2
Polynomial Regression Clustering [LuxAlgo]The Polynomial Regression Clustering indicator utilizes K-Means clustering to categorize historical price data into discrete levels and fits polynomial regression curves to each identified cluster. This tool allows traders to visualize non-linear trends within specific price regimes, providing a unique perspective on support, resistance, and price momentum. 🔶 USAGE The indicator identifies "K" number of clusters based on the vertical distribution of price over a user-defined lookback period. Each cluster represents a group of bars that share similar price levels, and a polynomial regression line is calculated to represent the localized trend for that specific group. 🔹 Cluster Identification The script groups price action into color-coded dots. By default, it uses the HL2 (Average price) to determine which cluster a bar belongs to. This is particularly useful for identifying historical value areas where price has spent a significant amount of time. 🔹 Polynomial Fitting Unlike standard linear regression, which produces a straight line, the polynomial regression curves can bend to fit the data more accurately. A Polynomial Degree of 1 will result in a standard linear regression (straight lines). A Polynomial Degree of 2 or higher allows for curves that capture parabolic moves or cyclical swings within each cluster. 🔹 Future Projections The current active cluster (the one containing the most recent price point) can be projected into the future. This allows you to see where the localized trend for the current price regime is heading based on the mathematical fit of historical data. 🔶 DETAILS 🔹 K-Means Algorithm The script uses an iterative K-Means algorithm to find the optimal centroids (center points) for the price levels. It calculates the distance of each price point to the nearest centroid and refines the centroid position until the clusters are stable or the maximum iterations are reached. 🔹 Regression Logic Once price points are assigned to a cluster, the script solves for the coefficients of a polynomial equation that minimizes the distance between the line and the cluster's data points. To ensure numerical stability with higher degrees, the horizontal (time) axis is normalized before performing matrix operations. 🔶 SETTINGS 🔹 K-Means Number of Clusters (K): Defines how many price levels the indicator should look for. Higher values create more granular levels. Lookback Period: The number of recent bars used to perform the clustering and regression calculation. Max Iterations: The maximum number of refinement steps for the K-Means algorithm. 🔹 Regression Polynomial Degree: Controls the "bend" of the regression lines. Higher degrees allow for more complex curves. Extend All Fits to Current Bar: When enabled, the regression lines for all historical clusters are extended to the rightmost edge of the chart. Project Current Cluster into Future: Extends the current regime's regression line into the future (empty space) using a dashed line. 🔹 Visual Style Show Regression Lines: Toggles the visibility of the polynomial curves. Show Cluster Dots: Toggles the visibility of the colored dots on each price bar. Dot Size: Adjusts the size of the cluster dots. Cluster Colors: Customizable colors for each of the identified clusters.
Pine Script® indicator
by LuxAlgo
3
Lorentzian Key Support and Resistance Level Detector [mishy]🧮 Lorentzian Key S/R Levels Detector Advanced Support & Resistance Detection Using Mathematical Clustering The Problem Traditional S/R indicators fail because they're either subjective (manual lines), rigid (fixed pivots), or break when price spikes occur. Most importantly, they don't tell you where prices actually spend time, just where they touched briefly. The Solution: Lorentzian Distance Clustering This indicator introduces a novel approach by using Lorentzian distance instead of traditional Euclidean distance for clustering. This is groundbreaking for financial data analysis. Data Points Clustering: 🔬 Why Euclidean Distance Fails in Trading Traditional K-means uses Euclidean distance: • Formula: distance = (price_A - price_B)² • Problem: Squaring amplifies differences exponentially • Real impact: One 5% price spike has 25x more influence than a 1% move • Result: Clusters get pulled toward outliers, missing real support/resistance zones Example scenario: Prices: ← flash spike Euclidean: Centroid gets dragged toward 150 Actual S/R zone: Around 100 (where prices actually trade) ⚡ Lorentzian Distance: The Game Changer Our approach uses Lorentzian distance: • Formula: distance = log(1 + (price_difference)² / σ²) • Breakthrough: Logarithmic compression keeps outliers in check • Real impact: Large moves still matter, but don't dominate • Result: Clusters focus on where prices actually spend time Same example with Lorentzian: Prices: ← flash spike Lorentzian: Centroid stays near 100 (real trading zone) Outlier (150): Acknowledged but not dominant 🧠 Adaptive Intelligence The σ parameter isn't fixed,it's calculated from market disturbance/entropy: • High volatility: σ increases, making algorithm more tolerant of large moves • Low volatility: σ decreases, making algorithm more sensitive to small changes • Self-calibrating: Adapts to any instrument or market condition automatically Why this matters: Traditional methods treat a 2% move the same whether it's in a calm or volatile market. Lorentzian adapts the sensitivity based on current market behavior. 🎯 Automatic K-Selection (Elbow Method) Instead of guessing how many S/R levels to draw, the indicator: • Tests 2-6 clusters and calculates WCSS (tightness measure) • Finds the "elbow" - where adding more clusters stops helping much • Uses sharpness calculation to pick the optimal number automatically Result: Perfect balance between detail and clarity. How It Works 1. Collect recent closing prices 2. Calculate entropy to adapt to current market volatility 3. Cluster prices using Lorentzian K-means algorithm 4. Auto-select optimal cluster count via statistical analysis 5. Draw levels at cluster centers with deviation bands 📊 Manual K-Selection Guide (Using WCSS & Sharpness Analysis) When you disable auto-selection, use both WCSS and Sharpness metrics from the analysis table to choose manually: What WCSS tells you: • Lower WCSS = tighter clusters = better S/R levels • Higher WCSS = scattered clusters = weaker levels What Sharpness tells you: • Higher positive values = optimal elbow point = best K choice • Lower/negative values = poor elbow definition = avoid this K • Measures the "sharpness" of the WCSS curve drop-off Decision strategy using both metrics: K=2: WCSS = 150.42 | Sharpness = - | Selected = K=3: WCSS = 89.15 | Sharpness = 22.04 | Selected = ✓ ← Best choice K=4: WCSS = 76.23 | Sharpness = 1.89 | Selected = K=5: WCSS = 73.91 | Sharpness = 1.43 | Selected = Quick decision rules: • Pick K with highest positive Sharpness (indicates optimal elbow) • Confirm with significant WCSS drop (30%+ reduction is good) • Avoid K values with negative or very low Sharpness (<1.0) • K=3 above shows: Big WCSS drop (41%) + High Sharpness (22.04) = Perfect choice Why this works: The algorithm finds the "elbow" where adding more clusters stops being useful. High Sharpness pinpoints this elbow mathematically, while WCSS confirms the clustering quality. Elbow Method Visualization: Traditional clustering problems: ❌ Price spikes distort results ❌ Fixed parameters don't adapt ❌ Manual tuning is subjective ❌ No way to validate choices Lorentzian solution: ☑️ Outlier-resistant distance metric ☑️ Entropy-based adaptation to volatility ☑️ Automatic optimal K selection ☑️ Statistical validation via WCSS & Sharpness Features Visual: • Color-coded levels (red=highest resistance, green=lowest support) • Optional deviation bands showing cluster spread • Strength scores on labels: Each cluster shows a reliability score. • Higher scores (0.8+) = very strong S/R levels with tight price clustering • Lower scores (0.6-0.7) = weaker levels, use with caution • Based on cluster tightness and data point density • Clean line extensions and labels Analytics: • WCSS analysis table showing why K was chosen • Cluster metrics and statistics • Real-time entropy monitoring Control: • Auto/manual K selection toggle • Customizable sample size (20-500 bars) • Show/hide bands and metrics tables The Result You get mathematically validated S/R levels that focus on where prices actually cluster, not where they randomly spiked. The algorithm adapts to market conditions and removes guesswork from level selection. Best for: Traders who want objective, data-driven S/R levels without manual chart analysis. Credits: This script is for educational purposes and is inspired by the work of @ThinkLogicAI and an amazing mentor @DskyzInvestments . It demonstrates how Lorentzian geometrical concepts can be applied not only in ML classification but also quite elegantly in clustering.
Pine Script® indicator
by mishy_Champ
10
Machine Learning Key Levels [AlgoAlpha]🟠 OVERVIEW This script plots Machine Learning Key Levels on your chart by detecting historical pivot points and grouping them using agglomerative clustering to highlight price levels with the most past reactions. It combines a pivot detection, hierarchical clustering logic, and an optional silhouette method to automatically select the optimal number of key levels, giving you an adaptive way to visualize price zones where activity concentrated over time. 🟠 CONCEPTS Agglomerative clustering is a bottom-up method that starts by treating each pivot as its own cluster, then repeatedly merges the two closest clusters based on the average distance between their members until only the desired number of clusters remain. This process creates a hierarchy of groupings that can flexibly describe patterns in how price reacts around certain levels. This offers an advantage over K-means clustering, since the number of clusters does not need to be predefined. In this script, it uses an average linkage approach, where distance between clusters is computed as the average pairwise distance of all contained points. The script finds pivot highs and lows over a set lookback period and saves them in a buffer controlled by the Pivot Memory setting. When there are at least two pivots, it groups them using agglomerative clustering: it starts with each pivot as its own group and keeps merging the closest pairs based on their average distance until the desired number of clusters is left. This number can be fixed or chosen automatically with the silhouette method, which checks how well each point fits in its cluster compared to others (higher scores mean cleaner separation). Once clustering finishes, the script takes the average price of each cluster to create key levels, sorts them, and draws horizontal lines with labels and colors showing their strength. A metrics table can also display details about the clusters to help you understand how the levels were calculated. 🟠 FEATURES Agglomerative clustering engine with average linkage to merge pivots into level groups. Dynamic lines showing each cluster’s price level for clarity. Labels indicating level strength either as percent of all pivots or raw counts. A metrics table displaying pivot count, cluster count, silhouette score, and cluster size data. Optional silhouette-based auto-selection of cluster count to adaptively find the best fit. 🟠 USAGE Add the indicator to any chart. Choose how far back to detect pivots using Pivot Length and set Pivot Memory to control how many are kept for clustering (more pivots give smoother levels but can slow performance). If you want the script to pick the number of levels automatically, enable Auto No. Levels ; otherwise, set Number of Levels . The colored horizontal lines represent the calculated key levels, and circles show where pivots occurred colored by which cluster they belong to. The labels beside each level indicate its strength, so you can see which levels are supported by more pivots. If Show Metrics Table is enabled, you will see statistics about the clustering in the corner you selected. Use this tool to spot areas where price often reacts and to plan entries or exits around levels that have been significant over time. Adjust settings to better match volatility and history depth of your instrument.
Pine Script® indicator
by AlgoAlpha
54
Dynamic Volume Clusters with Retest Signals (Zeiierman)█ Overview The Dynamic Volume Clusters with Retest Signals indicator is designed to detect key Volume Clusters and provide Retest Signals. This tool is specifically engineered for traders looking to capitalize on volume-based trends, reversals, and key price retest points. The indicator seamlessly combines volume analysis, dynamic cluster calculations, and retest signal logic to present a comprehensive trading framework. It adapts to market conditions, identifying clusters of volume activity and signaling when the price retests critical zones. █ How It Works ⚪ Volume Cluster Detection The indicator dynamically calculates volume clusters by analyzing the highest and lowest price points within a specified lookback period. Cluster Logic: Bright Lines (Strong Red/Green): These indicate that the price has frequently revisited these levels, creating a dense cluster. Such areas serve as support or resistance, where significant historical trading has occurred, often acting as barriers to price movement. Traders should consider these levels as potential reversal zones or consolidation points. Faded or Darker Lines: These lines indicate areas where the price has less historical activity, suggesting weaker clustering. These zones have less market memory and are more likely to break, supporting trend continuation and rapid price movement. ⚪ Candle Color Logic (Market Memory) Blue Candles (High Cluster Density): Candles turn blue when the price has revisited a particular area many times. This signals a highly clustered zone, likely to act as a barrier, creating consolidation or range phases. These areas indicate strong market memory, potentially rejecting price attempts to break through. Green or Red Candles (Low Cluster Density): Once the price breaks out of these dense clusters, the candles turn green (bullish) or red (bearish). This suggests the price has moved into a less clustered territory, where the path forward is clearer and trends are likely to extend without immediate resistance. ⚪ Retest Signal Logic The indicator identifies critical retest points where the price crosses a cluster boundary and then reverses. These points are essential for traders looking to catch continuation or reversal setups. ⚪ Dynamic Price Clustering The indicator dynamically adapts the clustering logic based on price movement and volume shifts. Uses a dynamic moving average (VPMA) to maintain adaptive cluster levels. Integrates a Kalman Filter for smoothing, reducing noise, and improving trend clarity. Automatically updates as new data is received, keeping the clusters relevant in real-time. █ How to Use ⚪ Trend Following & Reversal Detection Use Retest signals to identify potential trend continuation or reversal points. ⚪ Trading Volume Clusters and Market Memory Identify Key Zones: Focus on bright, saturated cluster lines (strong red or green) as they indicate high market memory, where price has spent significant time in the past. These zones are likely to exhibit a more choppy market. Apply range or mean reversion strategies. Spot Potential Breakouts: Faded or darker cluster lines indicate areas of low market memory, where the price has moved quickly and spent less time. Use these areas to identify possible trend setups, as they represent lower resistance to price movement. ⚪ Interpreting Candle Colors for Market Phases Blue Candles (High Cluster Density): When candles turn blue, it signals that the price has revisited this area multiple times, creating a dense cluster. These zones often trap price movement, leading to consolidations or range phases. Use these areas as caution zones, where price can slow down or reverse. Green or Red Candles (Low Cluster Density): Once the price breaks out of these clustered zones, the candles turn green (bullish) or red (bearish), indicating lower market memory. This signals a trend initiation with less immediate resistance, ideal for momentum and breakout trades. Use these signals to identify emerging trends and ride the momentum. █ Settings Range Lookback Period: Sets the number of bars for calculating the range. Zone Width (% of Range): Determines how wide the volume clusters are relative to the calculated range. Volume Line Colors: Customize the appearance of bullish and bearish lines. Retest Signals: Toggle the appearance of Triangle Up/Down retest markers. Minimum Bars for Retest: Define the minimum number of bars required before a retest is valid. Maximum Bars for Retest: Set the maximum number of bars within which a retest can occur. Price Cluster Period: Adjusts the sensitivity of the dynamic clustering logic. Cluster Confirmation: Controls how tightly the clusters respond to price action. Price Cluster Start/Peak: Sets the minimum and maximum touches required to fully form a cluster. ----------------- Disclaimer The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. 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.
Pine Script® indicator
by Zeiierman
14
Market Flow Volatility Oscillator (AiBitcoinTrend)The Market Flow Volatility Oscillator (AiBitcoinTrend) is a cutting-edge technical analysis tool designed to evaluate and classify market volatility regimes. By leveraging Gaussian filtering and clustering techniques, this indicator provides traders with clear insights into periods of high and low volatility, helping them adapt their strategies to evolving market conditions. Built for precision and clarity, it combines advanced mathematical models with intuitive visual feedback to identify trends and volatility shifts effectively. 👽 How the Indicator Works 👾 Volatility Classification with Gaussian Filtering The indicator detects volatility levels by applying Gaussian filters to the price series. Gaussian filters smooth out noise while preserving significant price movements. Traders can adjust the smoothing levels using sigma parameters, enabling greater flexibility: Low Sigma: Emphasizes short-term volatility. High Sigma: Captures broader trends with reduced sensitivity to small fluctuations. 👾 Clustering Algorithm for Regime Detection The core of this indicator is its clustering model, which classifies market conditions into two distinct regimes: Low Volatility Regime: Calm periods with reduced market activity. High Volatility Regime: Intense periods with heightened price movements. The clustering process works as follows: A rolling window of data is analyzed to calculate the standard deviation of price returns. Two cluster centers are initialized using the 25th and 75th percentiles of the data distribution. Each price volatility value is assigned to the nearest cluster based on its distance to the centers. The cluster centers are refined iteratively, providing an accurate and adaptive classification. 👾 Oscillator Generation with Slope R-Values The indicator computes Gaussian filter slopes to generate oscillators that visualize trends: Oscillator Low: Captures low-frequency market behavior. Oscillator High: Tracks high-frequency, faster-changing trends. The slope is measured using the R-value of the linear regression fit, scaled and adjusted for easier interpretation. 👽 Applications 👾 Trend Trading When the oscillator rises above 0.5, it signals potential bullish momentum, while dips below 0.5 suggest bearish sentiment. 👾 Pullback Detection When the oscillator peaks, especially in overbought or oversold zones, provide early warnings of potential reversals. 👽 Indicator Settings 👾 Oscillator Settings Sigma Low/High: Controls the smoothness of the oscillators. Smaller Values: React faster to price changes but introduce more noise. Larger Values: Provide smoother signals with longer-term insights. 👾 Window Size and Refit Interval Window Size: Defines the rolling period for cluster and volatility calculations. Shorter windows: adapt faster to market changes. Longer windows: produce stable, reliable classifications. Disclaimer: This information is for entertainment purposes only and does not constitute financial advice. Please consult with a qualified financial advisor before making any investment decisions.
Pine Script® indicator
by aibitcointrend
1
Correlation Clusters [LuxAlgo]The Correlation Clusters is a machine learning tool that allows traders to group sets of tickers with a similar correlation coefficient to a user-set reference ticker. The tool calculates the correlation coefficients between 10 user-set tickers and a user-set reference ticker, with the possibility of forming up to 10 clusters. 🔶 USAGE Applying clustering methods to correlation analysis allows traders to quickly identify which set of tickers are correlated with a reference ticker, rather than having to look at them one by one or using a more tedious approach such as correlation matrices. Tickers belonging to a cluster may also be more likely to have a higher mutual correlation. The image above shows the detailed parts of the Correlation Clusters tool. The correlation coefficient between two assets allows traders to see how these assets behave in relation to each other. It can take values between +1.0 and -1.0 with the following meaning Value near +1.0: Both assets behave in a similar way, moving up or down at the same time Value close to 0.0: No correlation, both assets behave independently Value near -1.0: Both assets have opposite behavior when one moves up the other moves down, and vice versa There is a wide range of trading strategies that make use of correlation coefficients between assets, some examples are: Pair Trading: Traders may wish to take advantage of divergences in the price movements of highly positively correlated assets; even highly positively correlated assets do not always move in the same direction; when assets with a correlation close to +1.0 diverge in their behavior, traders may see this as an opportunity to buy one and sell the other in the expectation that the assets will return to the likely same price behavior. Sector rotation: Traders may want to favor some sectors that are expected to perform in the next cycle, tracking the correlation between different sectors and between the sector and the overall market. Diversification: Traders can aim to have a diversified portfolio of uncorrelated assets. From a risk management perspective, it is useful to know the correlation between the assets in your portfolio, if you hold equal positions in positively correlated assets, your risk is tilted in the same direction, so if the assets move against you, your risk is doubled. You can avoid this increased risk by choosing uncorrelated assets so that they move independently. Hedging: Traders may want to hedge positions with correlated assets, from a hedging perspective, if you are long an asset, you can hedge going long a negatively correlated asset or going short a positively correlated asset. Grouping different assets with similar behavior can be very helpful to traders to avoid over-exposure to those assets, traders may have multiple long positions on different assets as a way of minimizing overall risk when in reality if those assets are part of the same cluster traders are maximizing their risk by taking positions on assets with the same behavior. As a rule of thumb, a trader can minimize risk via diversification by taking positions on assets with no correlations, the proposed tool can effectively show a set of uncorrelated candidates from the reference ticker if one or more clusters centroids are located near 0. 🔶 DETAILS K-means clustering is a popular machine-learning algorithm that finds observations in a data set that are similar to each other and places them in a group. The process starts by randomly assigning each data point to an initial group and calculating the centroid for each. A centroid is the center of the group. K-means clustering forms the groups in such a way that the variances between the data points and the centroid of the cluster are minimized. It's an unsupervised method because it starts without labels and then forms and labels groups itself. 🔹 Execution Window In the image above we can see how different execution windows provide different correlation coefficients, informing traders of the different behavior of the same assets over different time periods. Users can filter the data used to calculate correlations by number of bars, by time, or not at all, using all available data. For example, if the chart timeframe is 15m, traders may want to know how different assets behave over the last 7 days (one week), or for an hourly chart set an execution window of one month, or one year for a daily chart. The default setting is to use data from the last 50 bars. 🔹 Clusters On this graph, we can see different clusters for the same data. The clusters are identified by different colors and the dotted lines show the centroids of each cluster. Traders can select up to 10 clusters, however, do note that selecting 10 clusters can lead to only 4 or 5 returned clusters, this is caused by the machine learning algorithm not detecting any more data points deviating from already detected clusters. Traders can fine-tune the algorithm by changing the 'Cluster Threshold' and 'Max Iterations' settings, but if you are not familiar with them we advise you not to change these settings, the defaults can work fine for the application of this tool. 🔹 Correlations Different correlations mean different behaviors respecting the same asset, as we can see in the chart above. All correlations are found against the same asset, traders can use the chart ticker or manually set one of their choices from the settings panel. Then they can select the 10 tickers to be used to find the correlation coefficients, which can be useful to analyze how different types of assets behave against the same asset. 🔶 SETTINGS Execution Window Mode: Choose how the tool collects data, filter data by number of bars, time, or no filtering at all, using all available data. Execute on Last X Bars: Number of bars for data collection when the 'Bars' execution window mode is active. Execute on Last: Time window for data collection when the `Time` execution window mode is active. These are full periods, so `Day` means the last 24 hours, `Week` means the last 7 days, and so on. 🔹 Clusters Number of Clusters: Number of clusters to detect up to 10. Only clusters with data points are displayed. Cluster Threshold: Number used to compare a new centroid within the same cluster. The lower the number, the more accurate the centroid will be. Max Iterations: Maximum number of calculations to detect a cluster. A high value may lead to a timeout runtime error (loop takes too long). 🔹 Ticker of Reference Use Chart Ticker as Reference: Enable/disable the use of the current chart ticker to get the correlation against all other tickers selected by the user. Custom Ticker: Custom ticker to get the correlation against all the other tickers selected by the user. 🔹 Correlation Tickers Select the 10 tickers for which you wish to obtain the correlation against the reference ticker. 🔹 Style Text Size: Select the size of the text to be displayed. Display Size: Select the size of the correlation chart to be displayed, up to 500 bars. Box Height: Select the height of the boxes to be displayed. A high height will cause overlapping if the boxes are close together. Clusters Colors: Choose a custom colour for each cluster.
Pine Script® indicator
by LuxAlgo
1
RSI K-Means Clustering [UAlgo]The "RSI K-Means Clustering " indicator is a technical analysis tool that combines the Relative Strength Index (RSI) with K-means clustering techniques. This approach aims to provide more nuanced insights into market conditions by categorizing RSI values into overbought, neutral, and oversold clusters. The indicator adjusts these clusters dynamically based on historical RSI data, allowing for more adaptive and responsive thresholds compared to traditional fixed levels. By leveraging K-means clustering, the indicator identifies patterns in RSI behavior, which can help traders make more informed decisions regarding market trends and potential reversals. 🔶 Key Features K-means Clustering: The indicator employs K-means clustering, an unsupervised machine learning technique, to dynamically determine overbought, neutral, and oversold levels based on historical RSI data. User-Defined Inputs: You can customize various aspects of the indicator's behavior, including: RSI Source: Select the data source used for RSI calculation (e.g., closing price). RSI Length: Define the period length for RSI calculation. Training Data Size: Specify the number of historical RSI values used for K-means clustering. Number of K-means Iterations: Set the number of iterations performed by the K-means algorithm to refine cluster centers. Overbought/Neutral/Oversold Levels: You can define initial values for these levels, which will be further optimized through K-means clustering. Alerts: The indicator can generate alerts for various events, including: Trend Crossovers: Alerts for when the RSI crosses above/below the neutral zone, signaling potential trend changes. Overbought/Oversold: Alerts when the RSI reaches the dynamically determined overbought or oversold thresholds. Reversals: Alerts for potential trend reversals based on RSI crossing above/below the calculated overbought/oversold levels. RSI Classification: Alerts based on the current RSI classification (ranging, uptrend, downtrend). 🔶 Interpreting Indicator Adjusted RSI Value: The primary plot represents the adjusted RSI value, calculated based on the relative position of the current RSI compared to dynamically adjusted overbought and oversold levels. This value provides an intuitive measure of the market's momentum. The final overbought, neutral, and oversold levels are determined by K-means clustering and are displayed as horizontal lines. These levels serve as dynamic support and resistance points, indicating potential reversal zones. Classification Symbols : The "RSI K-Means Clustering " indicator uses specific symbols to classify the current market condition based on the position of the RSI value relative to dynamically determined clusters. These symbols provide a quick visual reference to help traders understand the prevailing market sentiment. Here's a detailed explanation of each classification symbol: Ranging Classification ("R") This symbol appears when the RSI value is closest to the neutral threshold compared to the overbought or oversold thresholds. It indicates a ranging market, where the price is moving sideways without a clear trend direction. In this state, neither buyers nor sellers are in control, suggesting a period of consolidation or indecision. This is often seen as a time to wait for a breakout or reversal signal before taking a position. Up-Trend Classification ("↑") The up-trend symbol, represented by an upward arrow, is displayed when the RSI value is closer to the overbought threshold than to the neutral or oversold thresholds. This classification suggests that the market is in a bullish phase, with buying pressure outweighing selling pressure. Traders may consider this as a signal to enter or hold long positions, as the price is likely to continue rising until the market reaches an overbought condition. Down-Trend Classification ("↓") The down-trend symbol, depicted by a downward arrow, appears when the RSI value is nearest to the oversold threshold. This indicates a bearish market condition, where selling pressure dominates. The market is likely experiencing a downward movement, and traders might view this as an opportunity to enter or hold short positions. This symbol serves as a warning of potential further declines, especially if the RSI continues to move toward the oversold level. Bullish Reversal ("▲") This signal occurs when the RSI value crosses above the oversold threshold. It indicates a potential shift from a downtrend to an uptrend, suggesting that the market may start to move higher. Traders might use this signal as an opportunity to enter long positions. Bearish Reversal ("▼") This signal appears when the RSI value crosses below the overbought threshold. It suggests a possible transition from an uptrend to a downtrend, indicating that the market may begin to decline. This signal can alert traders to consider entering short positions or taking profits on long positions. These classification symbols are plotted near the adjusted RSI line, with their positions adjusted based on the standard deviation and a distance multiplier. This placement helps in visualizing the classification's strength and ensuring clarity in the indicator's presentation. By monitoring these symbols, traders can quickly assess the market's state and make more informed trading decisions. 🔶 Disclaimer Use with Caution: This indicator is provided for educational and informational purposes only and should not be considered as financial advice. Users should exercise caution and perform their own analysis before making trading decisions based on the indicator's signals. Not Financial Advice: The information provided by this indicator does not constitute financial advice, and the creator (UAlgo) shall not be held responsible for any trading losses incurred as a result of using this indicator. Backtesting Recommended: Traders are encouraged to backtest the indicator thoroughly on historical data before using it in live trading to assess its performance and suitability for their trading strategies. Risk Management: Trading involves inherent risks, and users should implement proper risk management strategies, including but not limited to stop-loss orders and position sizing, to mitigate potential losses. No Guarantees: The accuracy and reliability of the indicator's signals cannot be guaranteed, as they are based on historical price data and past performance may not be indicative of future results.
Pine Script® indicator
by UAlgo
10
AI Channels (Clustering) [LuxAlgo]The AI Channels indicator is constructed based on rolling K-means clustering, a common machine learning method used for clustering analysis. These channels allow users to determine the direction of the underlying trends in the price. We also included an option to display the indicator as a trailing stop from within the settings. 🔶 USAGE Each channel extremity allows users to determine the current trend direction. Price breaking over the upper extremity suggesting an uptrend, and price breaking below the lower extremity suggesting a downtrend. Using a higher Window Size value will return longer-term indications. The "Clusters" setting allows users to control how easy it is for the price to break an extremity, with higher values returning extremities further away from the price. The "Denoise Channels" is enabled by default and allows to see less noisy extremities that are more coherent with the detected trend. Users who wish to have more focus on a detected trend can display the indicator as a trailing stop. 🔹 Centroid Dispersion Areas Each extremity is made of one area. The width of each area indicates how spread values within a cluster are around their centroids. A wider area would suggest that prices within a cluster are more spread out around their centroid, as such one could say that it is indicative of the volatility of a cluster. Wider areas around a specific extremity can indicate a larger and more spread-out amount of prices within the associated cluster. In practice price entering an area has a higher chance to break an associated extremity. 🔶 DETAILS The indicator performs K-means clustering over the most recent Window Size prices, finding a number of user-specified clusters. See here to find more information on cluster detection. The channel extremities are returned as the centroid of the lowest, average, and highest price clusters. K-means clustering can be computationally expensive and as such we allow users to determine the maximum number of iterations used to find the centroids as well as the number of most historical bars to perform the indicator calculation. Do note that increasing the calculation window of the indicator as well as the number of clusters will return slower results. 🔶 SETTINGS Window Size: Amount of most recent prices to use for the calculation of the indicator. Clusters": Amount of clusters detected for the calculation of the indicator. Denoise Channels: When enabled, return less noisy channels extremities, disabling this setting will return the exact centroids at each time but will produce less regular extremities. As Trailing Stop: Display the indicator as a trailing stop. 🔹 Optimization This group of settings affects the runtime performance of the script. Maximum Iteration Steps: Maximum number of iterations allowed for finding centroids. Excessively low values can return a better script load time but poor clustering. Historical Bars Calculation: Calculation window of the script (in bars).
Pine Script® indicator
by LuxAlgo
16
AI SuperTrend Clustering Oscillator [LuxAlgo]The AI SuperTrend Clustering Oscillator is an oscillator returning the most bullish/average/bearish centroids given by multiple instances of the difference between SuperTrend indicators. This script is an extension of our previously posted SuperTrend AI indicator that makes use of k-means clustering. If you want to learn more about it see: 🔶 USAGE The AI SuperTrend Clustering Oscillator is made of 3 distinct components, a bullish output (always the highest), a bearish output (always the lowest), and a "consensus" output always within the two others. The general trend is given by the consensus output, with a value above 0 indicating an uptrend and under 0 indicating a downtrend. Using a higher minimum factor will weigh results toward longer-term trends, while lowering the maximum factor will weigh results toward shorter-term trends. Strong trends are indicated when the bullish/bearish outputs are indicating an opposite sentiment. A strong bullish trend would for example be indicated when the bearish output is above 0, while a strong bearish trend would be indicated when the bullish output is below 0. When the consensus output is indicating a specific trend direction, an opposite indication from the bullish/bearish output can highlight a potential reversal or retracement. 🔶 DETAILS The indicator construction is based on finding three clusters from the difference between the closing price and various SuperTrend using different factors. The centroid of each cluster is then returned. This operation is done over all historical bars. The highest cluster will be composed of the differences between the price and SuperTrends that are the highest, thus creating a more bullish group. The lowest cluster will be composed of the differences between the price and SuperTrends that are the lowest, thus creating a more bearish group. The consensus cluster is composed of the differences between the price and SuperTrends that are not significant enough to be part of the other clusters. 🔶 SETTINGS ATR Length: ATR period used for the calculation of the SuperTrends. Factor Range: Determine the minimum and maximum factor values for the calculation of the SuperTrends. Step: Increments of the factor range. Smooth: Degree of smoothness of each output from the indicator. 🔹 Optimization This group of settings affects the runtime performances of the script. Maximum Iteration Steps: Maximum number of iterations allowed for finding centroids. Excessively low values can return a better script load time but poor clustering. Historical Bars Calculation: Calculation window of the script (in bars).
Pine Script® indicator
by LuxAlgo
15