Kmeans — Indicators and Strategies

Adaptive SuperTrend Oscillator [AlgoAlpha]Adaptive SuperTrend Oscillator 🤖📈 Introducing the Adaptive SuperTrend Oscillator , an innovative blend of volatility clustering and SuperTrend logic designed to identify market trends with precision! 🚀 This indicator uses K-Means clustering to dynamically adjust volatility levels, helping traders spot bullish and bearish trends. The oscillator smoothly tracks price movements, adapting to market conditions for reliable signals. Whether you're scalping or riding long-term trends, this tool has got you covered! 💹✨ 🔑 Key Features: 📊 Volatility Clustering with K-Means: Segments volatility into three levels (high, medium, low) using a K-Means algorithm for precise trend detection. 📈 Normalized Oscillator : Allows for customizable smoothing and normalization, ensuring the oscillator remains within a fixed range for easy interpretation. 🔄 Heiken Ashi Candles : Optionally visualize smoothed trends with Heiken Ashi-style candlesticks to better capture market momentum. 🔔 Alert System : Get notified when key conditions like trend shifts or volatility changes occur. 🎨 Customizable Appearance : Fully customizable colors for bullish/bearish signals, along with adjustable smoothing methods and lengths. 📚 How to Use: ⭐ Add the indicator to favorites by pressing the star icon. Customize settings to your preference: 👀 Watch the chart for trend signals and reversals. The oscillator will change color when trends shift, offering visual confirmation. 🔔 Enable alerts to be notified of critical trend changes or volatility conditions ⚙️ How It Works: This script integrates SuperTrend with volatility clustering by analyzing ATR (Average True Range) to dynamically identify high, medium, and low volatility clusters using a K-Means algorithm . The SuperTrend logic adjusts based on the assigned volatility level, creating adaptive trend signals. These signals are then smoothed and optionally normalized for clearer visual interpretation. The Heiken Ashi transformation adds an additional layer of smoothing, helping traders better identify the market's true momentum. Alerts are set to notify users of key trend shifts and volatility changes, allowing traders to react promptly.

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.

Machine Learning Adaptive SuperTrend [AlgoAlpha]📈🤖 Machine Learning Adaptive SuperTrend - Take Your Trading to the Next Level! 🚀✨ Introducing the Machine Learning Adaptive SuperTrend , an advanced trading indicator designed to adapt to market volatility dynamically using machine learning techniques. This indicator employs k-means clustering to categorize market volatility into high, medium, and low levels, enhancing the traditional SuperTrend strategy. Perfect for traders who want an edge in identifying trend shifts and market conditions. What is K-Means Clustering and How It Works K-means clustering is a machine learning algorithm that partitions data into distinct groups based on similarity. In this indicator, the algorithm analyzes ATR (Average True Range) values to classify volatility into three clusters: high, medium, and low. The algorithm iterates to optimize the centroids of these clusters, ensuring accurate volatility classification. Key Features 🎨 Customizable Appearance: Adjust colors for bullish and bearish trends. 🔧 Flexible Settings: Configure ATR length, SuperTrend factor, and initial volatility guesses. 📊 Volatility Classification: Uses k-means clustering to adapt to market conditions. 📈 Dynamic SuperTrend Calculation: Applies the classified volatility level to the SuperTrend calculation. 🔔 Alerts: Set alerts for trend shifts and volatility changes. 📋 Data Table Display: View cluster details and current volatility on the chart. Quick Guide to Using the Machine Learning Adaptive SuperTrend Indicator 🛠 Add the Indicator: Add the indicator to favorites by pressing the star icon. Customize settings like ATR length, SuperTrend factor, and volatility percentiles to fit your trading style. 📊 Market Analysis: Observe the color changes and SuperTrend line for trend reversals. Use the data table to monitor volatility clusters. 🔔 Alerts: Enable notifications for trend shifts and volatility changes to seize trading opportunities without constant chart monitoring. How It Works The indicator begins by calculating the ATR values over a specified training period to assess market volatility. Initial guesses for high, medium, and low volatility percentiles are inputted. The k-means clustering algorithm then iterates to classify the ATR values into three clusters. This classification helps in determining the appropriate volatility level to apply to the SuperTrend calculation. As the market evolves, the indicator dynamically adjusts, providing real-time trend and volatility insights. The indicator also incorporates a data table displaying cluster centroids, sizes, and the current volatility level, aiding traders in making informed decisions. Add the Machine Learning Adaptive SuperTrend to your TradingView charts today and experience a smarter way to trade! 🌟📊

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.

Support/Resistance v2 (ML) Kmean Kmean with Standard Deviation Channel 1. Description of Kmean Kmean (or K-means) is a popular clustering algorithm used to divide data into K groups based on their similarity. In the context of financial markets, Kmean can be applied to find the average price values over a specific period, allowing the identification of major trends and levels of support and resistance. 2. Application in Trading In trading, Kmean is used to smooth out the price series and determine long-term trends. This helps traders make more informed decisions by avoiding noise and short-term fluctuations. Kmean can serve as a baseline around which other analytical tools, such as channels and bands, are constructed. 3. Description of Standard Deviation (stdev) Standard deviation (stdev) is a statistical measure that indicates how much the values of data deviate from their mean value. In finance, standard deviation is often used to assess price volatility. A high standard deviation indicates strong price fluctuations, while a low standard deviation indicates stable movements. 4. Combining Kmean and Standard Deviation to Predict Short-Term Price Behavior Combining Kmean and standard deviation creates a powerful tool for analyzing market conditions. Kmean shows the average price trend, while the standard deviation channels demonstrate the boundaries within which the price can fluctuate. This combination helps traders to: Identify support and resistance levels. Predict potential price reversals. Assess risks and set stop-losses and take-profits. Should you have any questions about code, please reach me at Tradingview directly. Hope you find this script helpful!

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).

SignalProcessingClusteringKMeans Library "SignalProcessingClusteringKMeans" K-Means Clustering Method. nearest(point_x, point_y, centers_x, centers_y) finds the nearest center to a point and returns its distance and center index. Parameters: point_x : float, x coordinate of point. point_y : float, y coordinate of point. centers_x : float array, x coordinates of cluster centers. centers_y : float array, y coordinates of cluster centers. @ returns tuple of int, float. bisection_search(samples, value) Bissection Search Parameters: samples : float array, weights to compare. value : float array, weights to compare. Returns: int. label_points(points_x, points_y, centers_x, centers_y) labels each point index with cluster index and distance. Parameters: points_x : float array, x coordinates of points. points_y : float array, y coordinates of points. centers_x : float array, x coordinates of points. centers_y : float array, y coordinates of points. Returns: tuple with int array, float array. kpp(points_x, points_y, n_clusters) K-Means++ Clustering adapted from Andy Allinger. Parameters: points_x : float array, x coordinates of the points. points_y : float array, y coordinates of the points. n_clusters : int, number of clusters. Returns: tuple with 2 arrays, float array, int array.

Function K-Means Clustering Description: A Function that returns cluster centers for given data (X,Y) vector points. Inputs: _X: Array containing x data points.¹ _Y: Array containing y data points.¹ _number_of_clusters: number of clusters. Note: ¹: _X and _Y size must match. Outputs: _centers_x: Array containing x data points. _centers_y: Array containing y data points. Resources: rosettacode.org en.wikipedia.org