Artificialintelligence — Indicators and Strategies

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. The system combines these pattern detections using adaptive weighting based on market volatility and trend strength, creating a sophisticated composite score that provides both directional bias and signal confidence on a normalized -1 to +1 scale. 🚀 CONCEPTS • Built on Convolutional Neural Network pattern recognition methodology adapted for financial markets • Six specialized kernels detect distinct price patterns: upward/downward momentum, peak/trough formations, consolidation, and breakout setups • Activation functions create non-linear responses with tanh-like behavior, mimicking neural network layers • Adaptive weighting system adjusts pattern importance based on current market regime (volatility < 2% and trend strength) • Multi-confirmation signals require CNN threshold breach (±0.65), RSI boundaries, and volume confirmation above 120% of 20-period average 🔧 FEATURES Six-Kernel Pattern Detection: Simultaneous analysis of upward momentum, downward momentum, peak/resistance, trough/support, consolidation, and breakout patterns using mathematically optimized convolution kernels. Adaptive Neural Architecture: Dynamic weight adjustment based on market volatility (ATR/Price) and trend strength (EMA differential), ensuring optimal performance across different market conditions. Professional Visual Themes: Four sophisticated color palettes (Professional, Ocean, Sunset, Monochrome) with cohesive design language. Default Monochrome theme provides clean, distraction-free analysis. Confidence Band System: Upper and lower confidence zones at 150% of threshold values (±0.975) help identify high-probability signal areas and potential exhaustion zones. Real-Time Information Panel: Live display of CNN score, market state with emoji indicators, net momentum, confidence percentage, and RSI confirmation with dynamic color coding based on signal strength. Individual Feature Analysis: Optional display of all six kernel outputs with distinct visual styles (step lines, circles, crosses, area fills) for advanced pattern component analysis. User Guide • Monitor CNN Score crossing above +0.65 for long signals or below -0.65 for short signals with volume confirmation • Use confidence bands to identify optimal entry zones - signals within confidence bands carry higher probability • Background intensity reflects signal strength - darker backgrounds indicate stronger conviction • Enter long positions when blue circles appear above oscillator with RSI < 75 and volume > 120% average • Enter short positions when dark circles appear below oscillator with RSI > 25 and volume confirmation • Information panel provides real-time confidence percentage and momentum direction for position sizing decisions • Individual feature plots allow granular analysis of specific pattern components for strategy refinement 💡Conclusion CNN Statistical Trading System represents the evolution of technical analysis, combining institutional-grade pattern recognition with retail accessibility. The six-kernel architecture provides comprehensive market pattern coverage while adaptive weighting ensures relevance across all market conditions. Whether you’re seeking systematic entry signals or advanced pattern confirmation, this indicator delivers mathematically rigorous analysis with intuitive visual presentation.

Professional MSTI+ Trading Indicator "Professional MSTI+ Trading Indicator" is a comprehensive technical analysis tool that combines over 20 indicators to generate high-quality trading signals and assess market sentiment. The script integrates standard indicators (MACD, RSI, Bollinger Bands, Stochastic, Simple Moving Averages, and Volume Analysis) with advanced components (Squeeze Momentum, Fisher Transform, True Strength Index, Heikin-Ashi, Laguerre RSI, Hull MA) and further includes metrics such as ADX, Chaikin Money Flow, Williams %R, VWAP, and EMA for in-depth market analysis. Key Features: Multiple Presets for Different Trading Styles: Choose from optimal configurations like Professional, Swing Trading, Day Trading, Scalping, or Reversal Hunter. Note that the presets may not work perfectly on all pairs, and manual calibration might be required. This flexibility allows you to fine-tune the settings to align with your unique strategies and signals. Multi-Layered Signal Filtering: Filters based on trend, volume, and volatility help eliminate false signals, enhancing the accuracy of market entries. Comprehensive Fear & Greed Index: The indicator aggregates data from RSI, volatility, momentum, trend, and volume to gauge overall market sentiment, providing an additional layer of market context. Dynamic Information Panel: Displays detailed status updates for each component (e.g., MACD, RSI, Laguerre RSI, TSI, Fisher Transform, Squeeze, Hull MA, etc.) along with a visual strength bar that represents the intensity of the trading signal. Signal Generation: Buy and sell signals are generated when a predefined number of conditions are met and confirmed over multiple bars. These signals are clearly displayed on the chart with arrows, making it easier to spot potential entry and exit points. Alert Setup: Built-in alert conditions allow you to receive real-time notifications when trading signals are generated, helping you stay on top of market movements. "Professional MSTI+ Trading Indicator" is designed to enhance your trading strategy by providing a multi-faceted market analysis and an intuitive visual interface. While the presets offer a robust starting point, they may require manual calibration on certain pairs, giving you the flexibility to configure your own unique strategies and signals.

AI Adaptive Oscillator [PhenLabs]📊 Algorithmic Adaptive Oscillator Version: PineScript™ v6 📌 Description The AI Adaptive Oscillator is a sophisticated technical indicator that employs ensemble learning and adaptive weighting techniques to analyze market conditions. This innovative oscillator combines multiple traditional technical indicators through an AI-driven approach that continuously evaluates and adjusts component weights based on historical performance. By integrating statistical modeling with machine learning principles, the indicator adapts to changing market dynamics, providing traders with a responsive and reliable tool for market analysis. 🚀 Points of Innovation: Ensemble learning framework with adaptive component weighting Performance-based scoring system using directional accuracy Dynamic volatility-adjusted smoothing mechanism Intelligent signal filtering with cooldown and magnitude requirements Signal confidence levels based on multi-factor analysis 🔧 Core Components Ensemble Framework : Combines up to five technical indicators with performance-weighted integration Adaptive Weighting : Continuous performance evaluation with automated weight adjustment Volatility-Based Smoothing : Adapts sensitivity based on current market volatility Pattern Recognition : Identifies potential reversal patterns with signal qualification criteria Dynamic Visualization : Professional color schemes with gradient intensity representation Signal Confidence : Three-tiered confidence assessment for trading signals 🔥 Key Features The indicator provides comprehensive market analysis through: Multi-Component Ensemble : Integrates RSI, CCI, Stochastic, MACD, and Volume-weighted momentum Performance Scoring : Evaluates each component based on directional prediction accuracy Adaptive Smoothing : Automatically adjusts based on market volatility Pattern Detection : Identifies potential reversal patterns in overbought/oversold conditions Signal Filtering : Prevents excessive signals through cooldown periods and minimum change requirements Confidence Assessment : Displays signal strength through intuitive confidence indicators (average, above average, excellent) 🎨 Visualization Gradient-Filled Oscillator : Color intensity reflects strength of market movement Clear Signal Markers : Distinct bullish and bearish pattern signals with confidence indicators Range Visualization : Clean representation of oscillator values from -6 to 6 Zero Line : Clear demarcation between bullish and bearish territory Customizable Colors : Color schemes that can be adjusted to match your chart style Confidence Symbols : Intuitive display of signal confidence (no symbol, +, or ++) alongside direction markers 📖 Usage Guidelines ⚙️ Settings Guide Color Settings Bullish Color Default: #2b62fa (Blue) This setting controls the color representation for bullish movements in the oscillator. The color appears when the oscillator value is positive (above zero), with intensity indicating the strength of the bullish momentum. A brighter shade indicates stronger bullish pressure. Bearish Color Default: #ce9851 (Amber) This setting determines the color representation for bearish movements in the oscillator. The color appears when the oscillator value is negative (below zero), with intensity reflecting the strength of the bearish momentum. A more saturated shade indicates stronger bearish pressure. Signal Settings Signal Cooldown (bars) Default: 10 Range: 1-50 This parameter sets the minimum number of bars that must pass before a new signal of the same type can be generated. Higher values reduce signal frequency and help prevent overtrading during choppy market conditions. Lower values increase signal sensitivity but may generate more false positives. Min Change For New Signal Default: 1.5 Range: 0.5-3.0 This setting defines the minimum required change in oscillator value between consecutive signals of the same type. It ensures that new signals represent meaningful changes in market conditions rather than minor fluctuations. Higher values produce fewer but potentially higher-quality signals, while lower values increase signal frequency. AI Core Settings Base Length Default: 14 Minimum: 2 This fundamental setting determines the primary calculation period for all technical components in the ensemble (RSI, CCI, Stochastic, etc.). It represents the lookback window for each component’s base calculation. Shorter periods create a more responsive but potentially noisier oscillator, while longer periods produce smoother signals with potential lag. Adaptive Speed Default: 0.1 Range: 0.01-0.3 Controls how quickly the oscillator adapts to new market conditions through its volatility-adjusted smoothing mechanism. Higher values make the oscillator more responsive to recent price action but potentially more erratic. Lower values create smoother transitions but may lag during rapid market changes. This parameter directly influences the indicator’s adaptiveness to market volatility. Learning Lookback Period Default: 150 Minimum: 10 Determines the historical data range used to evaluate each ensemble component’s performance and calculate adaptive weights. This setting controls how far back the AI “learns” from past performance to optimize current signals. Longer periods provide more stable weight distribution but may be slower to adapt to regime changes. Shorter periods adapt more quickly but may overreact to recent anomalies. Ensemble Size Default: 5 Range: 2-5 Specifies how many technical components to include in the ensemble calculation. Understanding The Interaction Between Settings Base Length and Learning Lookback : The base length determines the reactivity of individual components, while the lookback period determines how their weights are adjusted. These should be balanced according to your timeframe - shorter timeframes benefit from shorter base lengths, while the lookback should generally be 10-15 times the base length for optimal learning. Adaptive Speed and Signal Cooldown : These settings control sensitivity from different angles. Increasing adaptive speed makes the oscillator more responsive, while reducing signal cooldown increases signal frequency. For conservative trading, keep adaptive speed low and cooldown high; for aggressive trading, do the opposite. Ensemble Size and Min Change : Larger ensembles provide more stable signals, allowing for a lower minimum change threshold. Smaller ensembles might benefit from a higher threshold to filter out noise. Understanding Signal Confidence Levels The indicator provides three distinct confidence levels for both bullish and bearish signals: Average Confidence (▲ or ▼) : Basic signal that meets the minimum pattern and filtering criteria. These signals indicate potential reversals but with moderate confidence in the prediction. Consider using these as initial alerts that may require additional confirmation. Above Average Confidence (▲+ or ▼+) : Higher reliability signal with stronger underlying metrics. These signals demonstrate greater consensus among the ensemble components and/or stronger historical performance. They offer increased probability of successful reversals and can be traded with less additional confirmation. Excellent Confidence (▲++ or ▼++) : Highest quality signals with exceptional underlying metrics. These signals show strong agreement across oscillator components, excellent historical performance, and optimal signal strength. These represent the indicator’s highest conviction trade opportunities and can be prioritized in your trading decisions. Confidence assessment is calculated through a multi-factor analysis including: Historical performance of ensemble components Degree of agreement between different oscillator components Relative strength of the signal compared to historical thresholds ✅ Best Use Cases: Identify potential market reversals through oscillator extremes Filter trade signals based on AI-evaluated component weights Monitor changing market conditions through oscillator direction and intensity Confirm trade signals from other indicators with adaptive ensemble validation Detect early momentum shifts through pattern recognition Prioritize trading opportunities based on signal confidence levels Adjust position sizing according to signal confidence (larger for ++ signals, smaller for standard signals) ⚠️ Limitations Requires sufficient historical data for accurate performance scoring Ensemble weights may lag during dramatic market condition changes Higher ensemble sizes require more computational resources Performance evaluation quality depends on the learning lookback period length Even high confidence signals should be considered within broader market context 💡 What Makes This Unique Adaptive Intelligence : Continuously adjusts component weights based on actual performance Ensemble Methodology : Combines strength of multiple indicators while minimizing individual weaknesses Volatility-Adjusted Smoothing : Provides appropriate sensitivity across different market conditions Performance-Based Learning : Utilizes historical accuracy to improve future predictions Intelligent Signal Filtering : Reduces noise and false signals through sophisticated filtering criteria Multi-Level Confidence Assessment : Delivers nuanced signal quality information for optimized trading decisions 🔬 How It Works The indicator processes market data through five main components: Ensemble Component Calculation : Normalizes traditional indicators to consistent scale Includes RSI, CCI, Stochastic, MACD, and volume components Adapts based on the selected ensemble size Performance Evaluation : Analyzes directional accuracy of each component Calculates continuous performance scores Determines adaptive component weights Oscillator Integration : Combines weighted components into unified oscillator Applies volatility-based adaptive smoothing Scales final values to -6 to 6 range Signal Generation : Detects potential reversal patterns Applies cooldown and magnitude filters Generates clear visual markers for qualified signals Confidence Assessment : Evaluates component agreement, historical accuracy, and signal strength Classifies signals into three confidence tiers (average, above average, excellent) Displays intuitive confidence indicators (no symbol, +, ++) alongside direction markers 💡 Note: The AI Adaptive Oscillator performs optimally when used with appropriate timeframe selection and complementary indicators. Its adaptive nature makes it particularly valuable during changing market conditions, where traditional fixed-weight indicators often lose effectiveness. The ensemble approach provides a more robust analysis by leveraging the collective intelligence of multiple technical methodologies. Pay special attention to the signal confidence indicators to optimize your trading decisions - excellent (++) signals often represent the most reliable trade opportunities.

QT RSI [ W.ARITAS ]The QT RSI is an innovative technical analysis indicator designed to enhance precision in market trend identification and decision-making. Developed using advanced concepts in quantum mechanics, machine learning (LSTM), and signal processing, this indicator provides actionable insights for traders across multiple asset classes, including stocks, crypto, and forex. Key Features: Dynamic Color Gradient: Visualizes market conditions for intuitive interpretation: Green: Strong buy signal indicating bullish momentum. Blue: Neutral or observation zone, suggesting caution or lack of a clear trend. Red: Strong sell signal indicating bearish momentum. Quantum-Enhanced RSI: Integrates adaptive energy levels, dynamic smoothing, and quantum oscillators for precise trend detection. Hybrid Machine Learning Model: Combines LSTM neural networks and wavelet transforms for accurate prediction and signal refinement. Customizable Settings: Includes advanced parameters for dynamic thresholds, sensitivity adjustment, and noise reduction using Kalman and Jurik filters. How to Use: Interpret the Color Gradient: Green Zone: Indicates bullish conditions and potential buy opportunities. Look for upward momentum in the RSI plot. Blue Zone: Represents a neutral or consolidation phase. Monitor the market for trend confirmation. Red Zone: Indicates bearish conditions and potential sell opportunities. Look for downward momentum in the RSI plot. Follow Overbought/Oversold Boundaries: Use the upper and lower RSI boundaries to identify overbought and oversold conditions. Leverage Advanced Filtering: The smoothed signals and quantum oscillator provide a robust framework for filtering false signals, making it suitable for volatile markets. Application: Ideal for traders and analysts seeking high-precision tools for: Identifying entry and exit points. Detecting market reversals and momentum shifts. Enhancing algorithmic trading strategies with cutting-edge analytics.

Machine Learning Moving Average [LuxAlgo]The Machine Learning Moving Average (MLMA) is a responsive moving average making use of the weighting function obtained Gaussian Process Regression method. Characteristic such as responsiveness and smoothness can be adjusted by the user from the settings. The moving average also includes bands, used to highlight possible reversals. 🔶 USAGE The Machine Learning Moving Average smooths out noisy variations from the price, directly estimating the underlying trend in the price. A higher "Window" setting will return a longer-term moving average while increasing the "Forecast" setting will affect the responsiveness and smoothness of the moving average, with higher positive values returning a more responsive moving average and negative values returning a smoother but less responsive moving average. Do note that an excessively high "Forecast" setting will result in overshoots, with the moving average having a poor fit with the price. The moving average color is determined according to the estimated trend direction based on the bands described below, shifting to blue (default) in an uptrend and fushia (default) in downtrends. The upper and lower extremities represent the range within which price movements likely fluctuate. Signals are generated when the price crosses above or below the band extremities, with turning points being highlighted by colored circles on the chart. 🔶 SETTINGS Window: Calculation period of the moving average. Higher values yield a smoother average, emphasizing long-term trends and filtering out short-term fluctuations. Forecast: Sets the projection horizon for Gaussian Process Regression. Higher values create a more responsive moving average but will result in more overshoots, potentially worsening the fit with the price. Negative values will result in a smoother moving average. Sigma: Controls the standard deviation of the Gaussian kernel, influencing weight distribution. Higher Sigma values return a longer-term moving average. Multiplicative Factor: Adjusts the upper and lower extremity bounds, with higher values widening the bands and lowering the amount of returned turning points. 🔶 RELATED SCRIPTS Machine-Learning-Gaussian-Process-Regression SuperTrend-AI-Clustering

Machine Learning using Neural Networks | Educational 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 Mean Squared Error (MSE) Loss Curve for monitoring the training phase. In the visual example above, you can see that the prediction is not aligned with the actual value. This is intentional for demonstrative purposes, and by incrementing the Epochs or Learning Rate, you will see these two values converge as the accuracy increases. Hyperparameters: Learning Rate, Epochs, and the choice between Simple Backpropagation and a verbose version are declared as script inputs, allowing users to tailor the training process. Initialization: Random initialization of weight matrices (w1, w2) is performed to ensure asymmetry, promoting effective gradient updates. A seed is added for reproducibility. Utility Functions: Functions for matrix randomization, sigmoid activation, MSE loss calculation, data normalization, and standardization are defined to streamline the computation process. Neural Network Computation: The feedforward function computes the hidden and output layer values given the input. Two variants of the backpropagation function are provided for weight adjustment, with one offering a more verbose step-by-step computation of gradients. A wrapper train_nn function iterates through epochs, performing feedforward, loss computation, and backpropagation in each epoch while logging and collecting loss values. Training Invocation: The input data is prepared by normalizing it to a value between 0 and 1 using the maximum standardized value, and the training process is invoked only on the last confirmed bar to preserve computational resources. Output Forecasting and Visualization: Post training, the NN's output (predicted price) is computed, standardized and visualized alongside the actual price on the chart. The MSE loss between the predicted and actual prices is visualized, providing insight into the prediction accuracy. Optionally, the MSE Loss Curve is plotted on the chart, illustrating the loss trajectory through epochs, assisting in understanding the training performance. Customizable Visualization: Various inputs control visualization aspects like Chart Scaling, Chart Horizontal Offset, and Chart Vertical Offset, allowing users to adapt the visualization to their preference. ------------------------------------------------------- The following is this Neural Network structure, consisting of one hidden layer, with two hidden neurons. Through understanding the steps outlined in my code, one should be able to scale the NN in any way they like, such as changing the input / output data and layers to fit their strategy ideas. Additionally, one could forgo the backpropagation function, and load their own trained weights into the w1 and w2 matrices, to have this code run purely for inference. ------------------------------------------------------- While this demonstration does create a “prediction”, it is on historical data. The purpose here is educational, rather than providing a ready tool for non-programmer consumers. Normally in Machine Learning projects, the training process would be split into two segments, the Training and the Validation parts. For the purpose of conveying the core concept in a concise and non-repetitive way, I have foregone the Validation part. However, it is merely the application of your trained network on new data (feedforward), and monitoring the loss curve. Essentially, checking the accuracy on “unseen” data, while training it on “seen” data. ------------------------------------------------------- I hope that this code will help developers create interesting machine learning applications within the Tradingview ecosystem.