Machine Learning: Perceptron-based Strategy is a Pine Script overlay indicator intended to implement a simple perceptron machine learning model for signal generation.
However, the author deleted the underlying script logic from the source file, leaving only the study declaration header. As a result, the script performs no calculations, produces no plots or signals, and includes no customizable settings. It remains in the repository as a historical placeholder without functional trading or analytical features.
Third-party code. ForexR did not write, test or endorse this script, and the summary above was generated automatically from its code and README. Indicators describe the past, and a strategy's backtest can look better than live trading, especially when a script repaints. Check it in the Strategy Tester and on a demo account before you rely on it.
This indicator implements a K-means clustering algorithm in Pine Script to partition two-dimensional data points into user-defined clusters. It features an internal pseudo-random number generator for centroid initialization and computes Euclidean distances acr
This indicator uses a Lorentzian distance k-nearest neighbors classification algorithm to predict market direction based on up to five technical features, such as RSI, WaveTrend, CCI, and ADX. It compares current feature values against historical bar patterns
This indicator calculates a trend direction and smoothed moving average using a k-nearest neighbours (k-NN) classification algorithm directly on the main price chart. Users can select input price types, target values, neighborhood size, and smoothing periods.
This indicator classifies the current bar by comparing its feature vector with historical bars using Lorentzian distance and a k-nearest-neighbors vote. Configurable features include RSI, WaveTrend, CCI, and ADX. Optional volatility, regime, ADX, and EMA filte
Calculates and plots a forward price prediction directly on the main chart using an on-chart multilayer perceptron neural network. The model processes technical input features including moving average ratios, RSI, normalized ATR, and Bollinger bandwidth throug
This indicator uses a Hidden Markov Model (HMM) framework combined with an adaptive dual moving average engine and Directional Movement Index (DMI) features to identify market regimes (bullish, bearish, or ranging/chop). It calculates regime probabilities on e