📈🤖 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,...
🚀 Elevate Your Trading with Machine Learning Dynamic Support and Resistance! The Machine Learning Dynamic Support and Resistance by AlgoAlpha leverages advanced machine learning techniques to identify dynamic support and resistance levels on your chart. This tool is designed to help traders spot key price levels where the market might reverse or stall, enhancing...
Introducing the "Machine Learning Signal Filter," an innovative trading indicator designed to leverage the power of machine learning to enhance trading strategies. This tool combines advanced data processing capabilities with user-friendly customization options, offering traders a sophisticated yet accessible means to optimize their market analysis and...
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...
🌟🚀 Dive into the future of trading with our latest innovation: the AI Adaptive Money Flow Index by AlgoAlpha Indicator! 🚀🌟 Developed with the cutting-edge power of Machine Learning, this indicator is designed to revolutionize the way you view market dynamics. 🤖💹 With its unique blend of traditional Money Flow Index (MFI) analysis and advanced k-means clustering,...
Multiple Logistic Regression Indicator The Logistic Regression Indicator for TradingView is a versatile tool that employs multiple logistic regression based on various technical indicators to generate potential buy and sell signals. By utilizing key indicators such as RSI, CCI, DMI, Aroon, EMA, and SuperTrend, the indicator aims to provide a systematic approach...
This indicator is designed for traders and analysts who employ Machine Learning (ML) techniques for cross-validation in financial markets. The script visually segments a selected range of historical price data into splits and batches, helping in the assessment of model performance over different market conditions. User Theory In ML, cross-validation is a...
Building upon the innovative foundations laid by Zeiierman's Machine Learning Momentum Index (MLMI), this variation introduces a series of refinements and new features aimed at bolstering the model's predictive accuracy and responsiveness. Licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0), my...
My native language is Chinese. The following introduction is translated using ChatGPT, and I hope the translation is fluent. Introduction This indicator is based on the machine learning model, Radius Neighbors Regressor, which predicts the target based on the similarity of past 500 input data. The provided indicator itself is merely a tool, requiring users to...
This Indicator aims to fill a gap within traditional Standard Deviation Analysis. Rather than its usual applications, this Indicator focuses on applying Standard Deviation within an Oscillator and likewise applying a Machine Learning approach to it. By doing so, we may hope to achieve an Adaptive Oscillator which can help display when the price is deviating from...
This Indicator allows for a ‘Optimal Length’ to be inputted within the Settings as a Source. Unlike most Indicators and/or Strategies that rely on either Static Lengths or Internal calculations for the length, this Indicator relies on the Length being derived from an external Indicator in the form of a Source Input. This may not sound like much, but this...
This Indicator aims to solve an issue that most others face; static lengths. This Indicator will scan lengths from the Min to Max setting (1 - 400 by default) to calculate which is the most Optimal Length in the current market condition. Almost every Indicator uses a length in some part of their calculation, and this length is usually adjustable via the Settings;...
Machine Learning: Anchored Gaussian Process Regression is an anchored version of Machine Learning: Gaussian Process Regression . It implements Gaussian Process Regression (GPR), a popular machine-learning method capable of estimating underlying trends in prices as well as forecasting them. Users can set a Training Window by choosing 2 points. GPR will be...
Machine Learning: VWAP aims to use Machine Learning to Identify the best location to Anchor the VWAP at. Rather than using a traditional fixed length or simply adjusting based on a Date / Time; by applying Machine Learning we may hope to identify crucial areas which make sense to reset the VWAP and start anew. VWAP’s may act similar to a Bollinger Band in the...
This Indicator, will rate multiple different lengths of RSIs to determine which RSI to RSI MA cross produced the highest profit within the lookback span. This ‘Optimal RSI’ is then passed back, and if toggled will then be thrown into a Machine Learning calculation. You have the option to Filter RSI and RSI MA’s within the Machine Learning calculation. What this...
The SuperTrend is a very useful Indicator to display when trends have shifted based on the Average True Range (ATR). Its underlying ideology is to calculate the ATR using a fixed length and then multiply it by a factor to calculate the SuperTrend +/-. When the close crosses the SuperTrend it changes direction. This Strategy features the Traditional SuperTrend...
The script provided is a comprehensive illustration of how to implement and execute a simplistic Neural Network (NN) on TradingView using PineScript. It encompasses the entire workflow from data input, weight initialization, implicit neuron calculation, feedforward computation, backpropagation for weight adjustments, generating predictions, to visualizing the...
This strategy uses a Machine Learning approach on the Donchian Channels with a DCA and Grid purchase/sell Strategy. Not only that, but it uses a custom Bollinger calculation to determine its Basis which is used as a mild sell location. This strategy is a pure DCA strategy in the sense that no shorts are used and theoretically it can be used in webhooks on most...