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Neuralnetwork

Strong Gold NN Forecast | ProjectSyndicateStrong Gold NN Forecast turns the last five days of Gold into a single question, answered by a genuine neural network: where are today's HIGH and LOW most likely to land? Instead of a restyled oscillator, it runs a real multi-layer perceptron — trained offline on XAUUSD daily history, its weights baked into the script — and performs the forward pass live on your chart. Every new daily candle, it reads only completed bars, locks a forecast for that day's High, Low and Close at the open, and never
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by ProjectSyndicate
1
AI Trend Detector | Adaptive Signals [NeuraLib Machine Learning]🔷 AI Trend Detector | Adaptive Signals AI Trend Detector is a NeuraLib-powered Machine Learning indicator. It trains a compact supervised neural model on confirmed historical movement, then uses the current market state to estimate Bear , Neutral , and Bull pressure. The model output is converted into a clean visual system: Trend Oscillator : A 0-100 pressure gauge. Lower values suggest bullish pressure or oversold conditions. Higher values suggest bearish pressure or overbought cond
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by Alien_Algorithms
AI Source Switching Moving Average (Zeiierman)█ Overview The AI Source Switching Moving Average is an adaptive price-selection and trend intelligence system that combines historical analog recognition, machine learning classification, neural learning, feature optimization, dynamic source selection, and AI-driven trend management into a single framework. Rather than calculating a moving average from a fixed source such as Close, Open, High, or Low, the indicator continuously evaluates which price source currently contains the most usefu
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by Zeiierman
24
Neural Weight Oscillator (Zeiierman)█ Overview The Neural Weight Oscillator (Zeiierman) is an adaptive multi-factor oscillator that combines structured decision-making with dynamic market learning. The script analyzes three core market behaviors: Trend, Mean Reversion, and Momentum. Instead of treating these components equally, the oscillator uses the Best-Worst Method (BWM) to determine which market behavior should have the greatest influence under current market conditions. An adaptive training layer then studies histori
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by Zeiierman
13
NeuraLib Expansion: Advanced Model LayersNeuraLib_Models is the companion model expansion for NeuraLib . NeuraLib provides the runtime: tensors, graph execution, datasets, scalers, losses, optimizers, training, inference, and validation tools. NeuraLib_Models builds on that foundation with higher-level neural architectures that are difficult and repetitive to write by hand. The purpose of this expansion is to keep the main NeuraLib runtime clean, compact, and general, while giving researchers ready-to-use model families for sequenc
Library
by Alien_Algorithms
1
NeuraLib: A Native AI and Deep Learning RuntimeNeuraLib is a tensor-based, auto-differentiating Machine Learning runtime built natively for Pine Script™. It brings real Deep Learning mechanisms that power modern Artificial Intelligence systems into TradingView. Instead of relying on fixed formulas, static regressions, or rigid structures, NeuraLib gives Pine developers a different tool: a compact neural runtime that can learn from the features you feed it, using the architecture you define. This means users are no longer limited to classi
Library
by Alien_Algorithms
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Machine Learning: seMLP Q-Wavelet RL Engine [Jamallo]Author Note: I always get asked: "How can I build a Machine Learning or Artificial Intelligence trading system?" I created the study "Machine Learning: seMLP Q-Wavelet RL Engine" to showcase exactly how it can be done in a beginner-friendly manner. We will break down exactly how this AI thinks in plain English, and then show you exactly how the Pine Script code executes it step-by-step. Introduction: The Institutional Approach to Algorithmic Trading Most retail and algorithmic traders sp
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by Jamallo22
SNP420 - SAO - Ultima - Multi-Asset Momentum IndicatorMulti-timeframe trend-following indicator for H1 charts. Combines D1 + H4 trend alignment with H1 entry precision using EMA, RSI, MACD, and ADX filters. Designed for EURUSD, USDJPY, and GBPUSD during London/NY sessions. Entry: Requires D1 and H4 trend agreement, price above/below EMA21, RSI in momentum zone (50-75 long / 25-50 short), positive MACD histogram, and ADX above 25. Signals only fire during active sessions (London, Overlap, NY). Exit logic (8 layers, priority-ordered): Hard SL at 2.
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by SNP_420
SNP420_claudos v1.0Indicator Overview (Work in Progress) A technical analysis indicator enhanced with a machine learning model. Feedback is welcome. Chart Elements & Signal Logic BUY (green label up) – Green arrow below the candle: Long entry signal based on EMA bullish crossover + confirmed trend + RSI confirmation. ★ BUY (strong green) – Brighter green: High-confidence long signal, additionally near support → improved risk-to-reward ratio. SELL (red label down) – Red arrow above the candle: Short entry
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by SNP_420
Ghost Candles👻 Ghost Candles - Predictive Market Projection Ghost Candles is an advanced forward-projection indicator that visualizes potential future price action directly on your chart using dynamically generated “ghost” candles. Unlike traditional indicators that lag or react, Ghost Candles attempts to anticipate short-term market structure by learning from real-time order-flow proxies, volatility, and price behavior. 🔍 What It Does: Ghost Candles projects up to 32 future candles ahead of the current p
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by Transcenduality
9
Neural RibbonNeural Ribbon: 6 independent neural networks running live on your chart. Five predict price direction at different horizons (T+2 through T+32), forming the ribbon. A sixth "trader" network reads all five predictions, their gradients, and multi-timeframe order flow to generate entry and exit signals. When a trade closes, the actual P&L retrains the trader via reinforcement learning - wins reinforce the pattern, losses suppress it. All trade parameters (entry threshold, stop loss, take profit, h
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by Transcenduality
Neural Network Ensemble (6 Independent Nets)Six neural networks each predict where price will be at a different point in the future - from 1 bar ahead (T+1) to 32 bars ahead (T+32). The networks learn in real-time from raw price action and volume, adapting as the market evolves. Solid lines (white to grey): Where each network predicts price will be. White = nearest prediction, grey = furthest out. Thin dotted lines: Echo lines - each prediction replayed forward in time so you can scroll back and see how accurate past predictions were aga
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by Transcenduality
6
Institutional Levels (CNN) - [PhenLabs]📊Institutional Levels (Convolutional Neural Network-inspired) Version : PineScript™v6 📌Description The CNN-IL Institutional Levels indicator represents a breakthrough in automated zone detection technology, combining convolutional neural network principles with advanced statistical modeling. This sophisticated tool identifies high-probability institutional trading zones by analyzing pivot patterns, volume dynamics, and price behavior using machine learning algorithms. The indicator employ
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by PhenLabs
4
CNN Statistical Trading System [PhenLabs]📌 DESCRIPTION An advanced pattern recognition system utilizing Convolutional Neural Network (CNN) principles to identify statistically significant market patterns and generate high-probability trading signals. CNN Statistical Trading System transforms traditional technical analysis by applying machine learning concepts directly to price action. Through six specialized convolution kernels, it detects momentum shifts, reversal patterns, consolidation phases, and breakout setups simultaneously
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by PhenLabs
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Neural Adaptive VWAPNeural Adaptive VWAP with ML Features is an advanced trading indicator that enhances traditional Volume Weighted Average Price (VWAP) calculations through machine learning-inspired adaptive algorithms and predictive volume modeling. 🌟 Key Features: 🧠 Machine Learning-Inspired Adaptation Dynamic weight adjustment system that learns from prediction errors Multi-feature volume prediction using time-of-day patterns, price momentum, and volatility Adaptive learning mechanism that improves accurac
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by shui2967
1
Simple Neural Network Transformed RSI [QuantraSystems]Simple Neural Network Transformed RSI Introduction The Simple Neural Network Transformed RSI (ɴɴᴛ ʀsɪ) stands out as a formidable tool for traders who specialize in lower timeframe trading. It is an innovative enhancement of the traditional RSI readings with simple neural network smoothing techniques. This unique blend results in fairly accurate signals, tailored for swift market movements. The ɴɴᴛ ʀsɪ is particularly resistant to the usual market noise found in lower timeframes, ensurin
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by Cryptosystems
23
Esqvair's Neural Reversal Probability IndicatorIntroduction Esqvair's Neural Reversal Probability Indicator is the indicator that shows probability of reversal. Warning: This script should only be used on 1 minute chart. How to use When a signal appears (by default it is a green bar), a reversal should be expected. The signal appears when the indicator value >= Threshold. If you want more signals, you must lower the threshold, if less, you must increase the threshold. For some assets, like Forex pairs, you have to optimize the threshold
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by Esqvair_1
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FunctionNNLayerLibrary "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', name of the activation function used.      bias : float, default=1.0, bias to pass into activation function.      alpha : float, defau
Library
by RicardoSantos
FunctionNNPerceptronLibrary "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 of the perceptron.      activation_function : string, default='sigmoid', activation function applied to the output.      alpha : float
Library
by RicardoSantos
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MLActivationFunctionsLibrary "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) Sigmoid or logistic function.   Parameters:      value : float, value to process.   Returns: float sigmoid_derivative(value) Deriva
Library
by RicardoSantos
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MLLossFunctionsLibrary "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:      expects : float array, expected values.      predicts : float array, prediction values.   Returns: float
Library
by RicardoSantos
Candlesticks ANN for Stock Markets TF : 1WHello, this script consists of training candlesticks with Artificial Neural Networks (ANN). In addition to the first series, candlesticks' bodies and wicks were also introduced as training inputs. The inputs are individually trained to find the relationship between the subsequent historical value of all candlestick values ​​1.(High,Low,Close,Open) The outputs are adapted to the current values ​​with a simple forecast code. Once the OHLC value is found, the exponential moving averages of 5 and
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by Noldo
2