🚀 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...
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...
📈🤖 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,...
█ Introduction and How it is Different The AI SuperTrend x Pivot Percentile strategy is a sophisticated trading approach that integrates AI-driven analysis with traditional technical indicators. Combining the AI SuperTrend with the Pivot Percentile strategy highlights several key advantages: 1. Enhanced Accuracy in Trend Prediction: The AI SuperTrend utilizes...
## █ Introduction and How it is Different The trading strategy in question is an enhanced version of the SuperTrend indicator, combined with AI elements and an ADX filter. It's a multi-timeframe strategy that incorporates two SuperTrends from different timeframes and utilizes a k-nearest neighbors (KNN) algorithm for trend prediction. It's different from...
We provide an implementation of the Gaussian Process Regression (GPR), a popular machine-learning method capable of estimating underlying trends in prices as well as forecasting them. While this implementation is adapted to real-time usage, do remember that forecasting trends in the market is challenging, do not use this tool as a standalone for your trading...
█ Introduction and How It is Different The Double AI Super Trend Trading Strategy is a cutting-edge approach that leverages the power of not one, but two AI algorithms, in tandem with the SuperTrend technical indicator. The strategy aims to provide traders with enhanced precision in market entry and exit points. It is designed to adapt to market conditions...
█ Introduction and How it is Different The AI Supertrend Strategy is a unique hybrid approach that employs both traditional technical indicators and machine learning techniques. Unlike standard strategies that rely solely on traditional indicators or mathematical models, this strategy integrates the power of k-Nearest Neighbors (KNN), a machine learning...
Library "FunctionNNLayer" Generalized Neural Network Layer method. function(inputs, weights, n_nodes, activation_function, bias, alpha, scale) Generalized Layer. Parameters: inputs : float array, input values. weights : float array, weight values. n_nodes : int, number of nodes in layer. activation_function : string, default='sigmoid',...
Library "FunctionNNPerceptron" Perceptron Function for Neural networks. function(inputs, weights, bias, activation_function, alpha, scale) generalized perceptron node for Neural Networks. Parameters: inputs : float array, the inputs of the perceptron. weights : float array, the weights for inputs. bias : float, default=1.0, the default bias...
Library "MLActivationFunctions" Activation functions for Neural networks. binary_step(value) Basic threshold output classifier to activate/deactivate neuron. Parameters: value : float, value to process. Returns: float linear(value) Input is the same as output. Parameters: value : float, value to process. Returns: float sigmoid(value) ...
Library "MLLossFunctions" Methods for Loss functions. mse(expects, predicts) Mean Squared Error (MSE) " MSE = 1/N * sum ((y - y')^2) ". Parameters: expects : float array, expected values. predicts : float array, prediction values. Returns: float binary_cross_entropy(expects, predicts) Binary Cross-Entropy Loss (log). Parameters: ...
Hello Traders/Programmers, For long time I thought that if it's possible to make a script that has own memory and criterias in Pine. it would learn and find patterns as images according to given criterias. after we have arrays of strings, lines, labels I tried and made this experimental script. The script works only for Long positions. Now lets look at how it...
This script created by training WTI 4 hour data , 7 indicators and 12 Guppy Exponential Moving Averages. Details : Learning cycles: 1 AutoSave cycles: 100 Training error: 0.007593 ( Smaller than average target ! ) Input columns: 19 Output columns: 1 Excluded columns: 0 Training example rows: 300 Validating example rows: 0 Querying example rows: 0 Excluded...
This script is formed by training the S & P 500 Index with various indicators. Details : Learning cycles: 78089 AutoSave cycles: 100 Training error: 0.011650 (Far less than the target, but acceptable.) Input columns: 19 Output columns: 1 Excluded columns: 0 Training example rows: 300 Validating example rows: 0 Querying example rows: 0 Excluded...
This script is the 2nd version of the BTC Deep Learning (ANN) system. Created with the following indicators and tools: RSI MACD MOM Bollinger Bands Guppy Exponential Moving Averages: (3,5,8,10,12,15,30,35,40,45,50,60) Note: I was inspired by the CM Guppy Ema script. Thank you very much to dear wroclai for his great help. He has been a big help in the deep...