Artificialintelligence — Indicators and Strategies — TradingView

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.

ML: Lorentzian Classification Premium █ OVERVIEW Lorentzian Classification Premium represents the culmination of two years of collaborative development with over 1,000 beta testers from the TradingView community. Building upon the foundation of the open-source version, this premium edition introduces powerful enhancements that transform how machine-learning classification can be applied to market analysis. The premium version maintains the core Lorentzian distance-based classification algorithm while expanding its capabilities through triple the feature dimensionality (up to 15 features), sophisticated mean-reversion detection, first-pullback identification, and a comprehensive signal taxonomy that goes far beyond simple buy/sell signals. Whether you're building automated trading systems, conducting deep market research, or integrating proprietary indicators into ML workflows, this tool provides the advanced edge needed for professional-grade analysis. █ BACKGROUND Lorentzian Classification analyzes market structures, especially those exhibiting non-linear distortions under stress, by employing advanced distance metrics like the Lorentzian metric, prominent in fields such as relativity theory. Where traditional indicators assume flat space, we embrace the curve. The heart of this approach is the Lorentzian distance metric—a sophisticated mathematical tool. This framework adeptly navigates the complex curves and distortions of market space, aiming to provide insights that traditional analysis might miss, especially during moments of extreme volatility. It analyzes historical data from a multi-dimensional feature space consisting of various technical indicators of your choosing. Where traditional approaches fail, Lorentzian space reveals the true geometry of market dynamics. Neighborhoods in Different Geometries: In the above figure, the Lorentzian metric creates distinctive cross-patterns aligned with feature axes (RSI, CCI, ADX), capturing both local similarity and dimensional extremes. This unique geometry allows the algorithm to recognize similar market conditions that Euclidean spheres and Manhattan diamonds would miss entirely. In LC Premium, users can have up to 15 features -- you are not limited to 3-dimensions. Among the thousands of distance metrics discovered by mathematicians, each perceives data through its own geometric lens. The Lorentzian metric stands apart with its unique ability to capture market behavior during volatile events. █ COMMUNITY-DRIVEN EVOLUTION It has been profoundly humbling over the past 2 years to witness this indicator's evolution through the collaborative efforts of our incredible community. This journey has been shaped by thousands of user suggestions and validated through real-world application. A particularly amazing milestone was the development of a complete community-driven Python port, which meticulously matched even the most minute PineScript quirks. Building on this solid foundation, a new command-line interface (CLI) has opened up exciting possibilities for chart-specific parameter optimization: Early insights from parameter optimization research: Through grid-search testing across thousands of parameter combinations, the analysis identifies which parameters have the biggest effects on performance and maps regions of stability across different market regimes. This reveals that optimal neighbor counts vary significantly based on market conditions—opening up incredible potential for timeframe-specific optimization. This is just one of the insights gleaned so far from this ongoing investigation. The potential for chart-specific optimization for any given timeframe could transform how traders approach parameter selection. Demand from power users for extra capabilities—while keeping the open-source version simple—sparked this Premium release. The open-source branch remains maintained, but the premium tier adds unique features for those who need an analytical edge and to leverage their own custom indicators as feature series for the algorithm. █ KEY PREMIUM FEATURES 📈 First Pullback Detection System Automatically identifies high-probability trend-continuation entries after initial momentum moves. Detects when price retraces to optimal entry zones following breakouts or trend initiations. Green/red triangle signals often fire before main classification arrows. Dedicated alerts for both bullish and bearish pullback opportunities. Based on veryfid's extensive research into pullback mechanics and market structure. 🔄 Dynamic Kernel Regression Envelope Powerful, zero-setup confluence layer that immediately communicates trend shifts. Dual-kernel system creates a visual envelope between trend estimates. Color gradient dynamically represents prediction strength and market conviction. Crossovers provide additional confirmation without cluttering your chart. Professional visualization that rivals institutional-grade analysis tools. ✨ Massively Expanded Dimensionality: 10 Custom Sources, 5 Built-In Sources Transform the indicator from 5 built-in standard to 15 total total features—triple the analytical power. Integrate ANY TradingView indicator as a machine learning feature. Built-in normalization ensures all indicators contribute equally regardless of scale. Create theme-based systems: pure volume analysis, multi-timeframe momentum, or hybrid approaches. 📊 Tiered Mean Reversion Signals with Scalping Alerts Regular (🔄) and Strong (⬇️/⬆️) mean reversion signals based on statistical extremes. Opportunities often arise before candle close—perfect for scalping entries. Visual markers appear at high-probability reversal zones. Four specialized alert types: upward/downward for both regular and strong reversals. Pre-optimized probability thresholds, no fine-tuning required. 📅 Daily Kernel Trend Filter Instantly cleans up noisy intraday charts by aligning with higher timeframe trends. Swing traders report immediate signal quality improvement. Automatically deactivates on daily+ timeframes (intelligent context awareness). Reduces counter-trend signals by up to 60% on lower timeframes. Simple toggle—no complex multi-timeframe setup required. 📋 Professional Backtesting Stream (-6 to +6) Multiple distinct signal types (including pullbacks, mean reversions, and kernel deviations) vs. basic binary (buy/sell) output for nuanced analysis. Enables detailed walk-forward analysis and ML model training. Compatible with external backtesting frameworks via numeric stream. Rare precision for TradingView indicators—usually only found in institutional tools. Perfect for quants building sophisticated strategy layers. ⚡ Performance Optimizations Faster distance calculations through algorithmic improvements. Reduced indicator load time (measured via Pine Profiler). Handles 15 active features without timeouts—critical for multi-chart setups. Optimized for live auto-trading bots requiring minimal latency. 🎨 Full Visual Customization & Accessibility Complete color control for all visual elements. Colorblind-safe default palette with customization options. Dark mode optimization for extended trading sessions. Professional appearance matching your trading workspace. Accessibility features meeting modern UI standards. 🛠️ Advanced Training Modes Downsampling mode for training on diverse market conditions; Down-sampling and remote-fractals for exotic pattern discovery. Remote fractals option extends analysis to deep historical patterns. Reset factor control for fine-tuning neighbor diversity; Reset-factor tuning to control neighbor diversity. Appeals to systematic traders exploring exotic data approaches. Prevents temporal clustering bias in model training. █ HOW TO USE Understanding the Approach (Core Concept): Lorentzian Classification uses a k-Nearest Neighbors (k-NN) algorithm. It searches for historical price action "neighborhoods" similar to the current market state. Instead of a simple straight-line (Euclidean) distance, it primarily uses a Lorentzian distance metric, which can account for market "warping" or distortions often seen during high volatility or significant events. Each historical neighbor "votes" on what happened next in its context, and these votes aggregate into a classification score for the current bar. Interpreting Bar Scores & Signals (Interpreting the Chart): Bar Prediction Values: Numbers over each candle (e.g., ranging from -8 to +8 if Neighbors Count is 8) represent the aggregated vote from the nearest neighbors. Strong positive scores (e.g., +7, +8) indicate a strong bullish consensus among historical analogs. Strong negative scores (e.g., -7, -8) indicate a strong bearish consensus. Scores near zero suggest neutrality or conflicting signals from neighbors. The intensity of bar colors (if Use Confidence Gradient is on) often reflects these scores. Main Arrows (Main Buy/Sell Labels): Large ▲/▼ labels are the primary entry signals generated when the overall classification (after filters) is bullish or bearish. Pullback Triangles: Small green/red ▲/▼ identify potential trend continuation entries. These signals often appear after an initial price move and a subsequent minor retracement, suggesting the trend might resume. This is based on recognizing patterns where a brief counter-movement is followed by a continued advance in the initial trend direction. Mean-Reversion Symbols: 🔄 (Regular Reversion) appears when price has crossed the average band of the Dynamic Kernel Regression Envelope. ⬇️/⬆️ (Strong Reversion) means price has crossed the far band of the envelope, indicating a more extreme deviation and potentially a stronger reversion opportunity. Custom Mean Reversion Deviation Markers (Deviation Dots): If Enable Custom Mean Reversion Alerts is on, these dots appear when price deviates from the main kernel regression line by a user-defined ATR multiple, signaling a custom-defined reversion opportunity. Kernel Regression Lines & Envelope: The Main Kernel Estimate (thicker line) is an adaptive moving average that smooths price and helps identify trend direction. Its color indicates the current trend bias. The Envelope (outer bands and a midline) creates a channel around price, and its interaction with price generates mean reversion signals. Key Input Groups & Their Purpose: 🔧 GENERAL SETTINGS: Reduce Price-Time Warping : Toggles the distance metric. When enabled, it reduces the characteristic "warping" effect of the default Lorentzian metric, making the distance calculation more Euclidean in nature. This may be suited for periods exhibiting less pronounced price-time distortions. Source : Price data for calculations (default: close ). Neighbors Count : The 'k' in k-NN – number of historical analogs considered. Max Bars Back : How far back the indicator looks for historical patterns. Show Exits / Use Dynamic Exits : Controls visibility and logic for exit signals. Include Full History (Use Remote Fractals) : Allows model to pick "exotic" fractals from deep chart history. Use Downsampling / Reset Factor : Advanced training parameters affecting neighbor selection. Show Trade Stats / Use Worst Case Estimates : Displays a real-time performance table (for calibration only). 🎛️ DEFINE CUSTOM SOURCES (OPTIONAL): Integrate up to 10 external data series (e.g., from other indicators) as features. Each can be optionally normalized. Load the external indicator on your chart first for it to appear in the dropdown. 🧠 FEATURE ENGINEERING: Configure up to 15 features for the k-NN algorithm. Select type (RSI, WT, CCI, ADX, Custom Sources), parameters, and enable/disable. Start simple (3-5 features) and add complexity gradually. Normalize features with vastly different scales. 🖥️ DISPLAY SETTINGS: Controls visibility of chart elements: bar colors, prediction values/labels, envelope, etc. Align Signal with Current Bar : If true, pullback signals appear on the current bar (calculated on closed data). If false (default), they appear on the next bar. Use ATR Offset : Positions bar prediction values using ATR for visibility. 🧮 FILTERS SETTINGS: Refine raw classification signals: Volatility, Regime, ADX, EMA/SMA, and Daily Kernel filters. 🌀 KERNEL SETTINGS (Main Kernel): Adjust parameters for the primary Nadaraya-Watson Kernel Regression line. Lookback Window , Relative Weighting , Regression Level , Lag control sensitivity and smoothness. ✉️ ENVELOPE SETTINGS (for Mean Reversion): Configure the dynamic Kernel Regression Envelope. ATR Length , Near/Far ATR Factor define band width. 🎨 COLOR SETTINGS (Colors): Customize colors for all visual elements; override every palette element. General Approach to Using the Indicator (Suggested Workflow): Load defaults and observe behavior: Familiarize yourself with the indicator's behavior. Feature Engineering: Experiment with features, considering momentum, trend, and volatility. Add/replace features gradually. Apply Filters: Refine signals according to your trading style. Contextualize: Use kernels and envelope to understand broader trend and potential overbought/oversold areas. Observe Signals: Pay attention to the interplay of main signals, pullbacks, and mean reversions. Watch interplay of main, pullback & mean-reversion signals. Calibrate (Not Backtest): Use the "Trade Stats" table for real-time feedback on current settings. This is for calibration, *not a substitute for rigorous backtesting.* Iterate & refine: Adjust settings, observe outcomes, and refine your approach. █ ACKNOWLEDGMENTS This premium version wouldn't exist without the invaluable contributions of: veryfid for his groundbreaking ideas on unifying pullback detection with Lorentzian Classification, but most of all for always believing in and encouraging me and so many others. For being a mentor and, most importantly, a friend. We all miss you. RikkiTavi for his help in creating the settings optimization framework and for other invaluable theoretical discussions. The 1,000+ beta testers worldwide who provided continuous feedback over two years. The Python porting team who created the foundation for advanced optimization; for the cross-language clone. The broader TradingView community for making this one of the platform's most popular indicators. █ FUTURE DEVELOPMENT The Premium version will continue to evolve based on community feedback. Planned enhancements include: Specialized exit model trained independently from entry signals (ML-based exit model). Feature hub with pre-normalized, commonly requested indicators (Pre-normalized feature hub). Better risk-management options (Enhanced risk-management options). Fully automated settings optimization (Auto-settings optimization tool).

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

Adaptive AI Predictor (v2) by Oberlunar Adaptive AI Predictor by Oberlunar This script is designed to dynamically adapt to market changes, leveraging a neural network-inspired model to identify reliable trading signals. It analyzes price variations, processes patterns in the market, and provides clear buy and sell signals based on dynamic force calculations. The script goes beyond simple indicators by incorporating adaptive learning principles. It tracks the success of its signals over time, calculating both the average and median forces behind winning trades. These insights allow the script to continuously refine its performance, ensuring it remains responsive to evolving market conditions. Clear signals are displayed on the chart, showing the strength of the signal and its median historical success. Configuration Parameters Number of Nodes: : This parameter controls the number of nodes through which the data is processed. A higher number of nodes can improve the model’s ability to represent complex dynamics, but may also increase bias and a low capacity of generalization. Input Scaling: Determines how much the input signal (percentage price change) is amplified before being processed. If the value is too low, the system may not react sufficiently to price changes; if too high, it might become too sensitive to market noise. Scaling: Controls the strength of interactions between internal nodes. A higher value makes interactions between the neurons (nodes) stronger, but might also lead to instability in the signals. Leak Rate: This parameter determines how fast information is "forgotten" within the system. A higher value means the model "forgets" previous information more quickly, making it more responsive to recent changes. Sparsity: Controls the density of connections between internal nodes. A higher value increases the likelihood that a connection between nodes is "active." This affects the system’s ability to model complex dynamics and can also influence computational speed. Signal Threshold: Sets the limit beyond which the predicted signal is considered significant. A value too low could generate too frequent and noisy signals, while a value too high might reduce useful signals. History Length: Determines how much historical data is considered for training the system. A higher value uses more historical data but could slow down computations. Learning Rate: Controls the speed at which the system updates its internal weights. A value too high might cause oscillations in the results, while one too low might slow down the adaptation process. Exponential Decay Factor: Defines how quickly the weights adapt based on errors. A higher value reduces the impact of older weights, allowing the model to adapt faster to recent changes. How It Works Input Signal: The system observes the percentage price change between two consecutive bars (current close vs. previous close). State Update: The states of the nodes are updated based on the input signal and internal interactions between the neurons. The update is influenced by the leak rate, which determines how fast nodes "forget" previous information. Weight Training: Weights are trained to minimize the error between the system’s prediction and the observed price change. The system uses exponential regression to update the weights efficiently. Signal Generation: Buy (BUY) and sell (SELL) signals are generated based on an analysis of the overall values of the nodes' states. If the overall strength (average of the nodes' states) exceeds a certain threshold, a buy signal is generated. If it's lower than a negative threshold, a sell signal is triggered. Visualization and Signals Signals on the Chart: Buy and sell signals are displayed on the chart with specific labels, indicating the signal's strength and median successful strength previously adopted . The strength is based on the distance from the threshold. The stronger the signal, the more intense the label color. Debug Table: A debug table shows details about the input weights, node states, and the success of buy/sell signals, allowing you to monitor the system's behavior in real-time. Simple Capital Management: The system calculates the position size based on available capital and updates the current capital after each trade. The profit or loss is displayed as a percentage of the initial capital. How to Use It Initial Configuration: Customize the configuration parameters based on your trading strategy and style. If you’re a more conservative trader, you might prefer higher thresholds and lower scaling. Monitor Signals: Follow the buy and sell signals generated on the chart. Each signal is accompanied by its strength (percentage), which will help you decide how aggressively to position. Very simple Position Management: When a buy signal is emitted, you can open a buy position, and when a sell signal is emitted, you can close the position. The system automatically calculates the profit or loss for each trade. Adapting to Market Conditions: Adjust the parameters based on market volatility and your risk tolerance. If the market is highly volatile, you might want to increase sensitivity to signals or reduce the number of nodes for faster responsiveness. With this system, you can leverage dynamic predictive signals based on a combination of historical data and continuous adaptation, improving your trading decisions. To obtain good results remember to fine-tune by a model reparametrization.

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.

NEURAL TREND AI - MULTI SCRIPT (With Alerts)This study is based on several Price Action parameters of :- • Candle Pattern, • Supply Demands, • Support and Resistance , • Breakouts, • Trend Series Forecasting, • Average true Range, • Neural Smoothing With Alpha, Beta Calculations for Filtering wrong trend breakouts. ► How To Use This Study ? • This Study is for positional trading. • Buy Whenever a GREEN Up Arrow Appears on Chart with text "BUY ACTIVATED". • Sell Whenever a RED Down Arrow Appears on Chart with text "SELL ACTIVATED". • Exit Buy Whenever a RED Down Arrow Appears with text "SELL ACTIVATED" After A Buy call and Exit Sell Whenever a up Arrow Appears with text "BUY ACTIVATED" After A Sell Call. • Trade every call and do positional trading • Alerts are inbuilt for both LONG and SHORT signals. Test Yourself and give feedback. PM us to obtain access.

KBL PLAY-ZONE PLOTTER - MCX CRUDE OIL ► How To Use This Indicator ? • New Intraday Trading Levels Will Be Generated At 09:30 AM (UTC +05:30) • Buy If 5 Minutes Candle Close Above '' BreakOut Buy Here '' Level. • Sell If 5 Minutes Candle Close Below '' BreakOut Sell Here '' Level. • Book Profits At Breakout Buy or BreakOut Sell Targets. • If 1st Call Target Hit , Then Do Not Trade More On That Day. • If 1st Call StopLoss Hit , Then Only Trade On 2nd Call. PM us to obtain access.