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Infinity Algo█ OVERVIEW Infinity Algo is an all-in-one signal system with 5 entry modes, 6 take-profit levels, and built-in Auto-Tune optimization. Switch between trend-following, contrarian, and sniper modes based on market conditions — all within one indicator. Auto-Tune tests 500+ parameter combinations to selects the best-scoring settings from historical simulations. Includes on-chart TP/SL visualization, trailing stop modes, and webhook-ready alerts. █ KEY FEATURES ✅ 5 Signal Modes: Normal, Smart, AI, HL Sniper, AI Sniper ✅ 3 Exit Modes: Percentage targets, Signal step-outs, Opposite signal ✅ 6 Take-Profit Levels with on-chart visualization ✅ Trailing Stop-Loss (Breakeven / Moving Target) ✅ Auto-Tune Parameter Optimization ✅ Visual Helpers: Bar coloring, gradient background, "signal coming" markers ✅ Built-in alert conditions for automation ✅ Designed for crypto, forex, stocks, indices, and commodities █ WHAT MAKES THIS INDICATOR DIFFERENT 🧠 Auto-Tune Engine Most indicators use static settings that fail when volatility shifts. Auto-Tune tests 500+ parameter combinations in the background — varying sensitivity, thresholds, and trigger configs — selects the best-scoring settings from historical simulations. Choose from 12 scoring metrics: Sharpe Ratio, Sortino Ratio, Calmar Ratio, SQN, Martin Ratio, GPR, Win Rate, Total Profit, Average Profit, Profit Factor, and more. Note: Auto-Tune is systematic parameter optimization on historical data — not predictive AI. Past performance does not guarantee future results. 🎯 Multi-Mode Signal System One indicator, five signal modes: Normal: Contrarian entries against the trend filter Smart: Trend-following entries with the trend filter Auto-Tune: Auto-optimized momentum signals HL Sniper: More selective trend triggers Auto-Tune Sniper: Auto-optimized sniper configuration 🛡️ Adaptive Risk Management Trailing SL modes that respond to your TP hits — not arbitrary price levels. Lock in breakeven or ratchet your stop as profit grows. 👁️ Anticipation Markers "Possible Long/Short Coming" labels appear when momentum approaches signal levels — giving you time to prepare before the signal fires. █ SIGNAL MODES EXPLAINED Normal + Smart (Default) The dual-mode system that adapts to market conditions: Normal Signals: Momentum threshold cross against the trend filter — contrarian plays for reversals and range boundaries Smart Signals: Momentum threshold cross with the trend filter — trend-following plays for continuation moves Auto-Tune Mode Let the optimization engine find best-scoring settings: Tests 500+ parameter combinations against historical data Simulates trades using your configured TP/SL levels Scores each configuration by your chosen metric (Sharpe, Sortino, Win Rate, etc.) Walk-Forward: Re-optimizes every N bars to adapt to regime changes Static: Locks in best-scoring settings from full available history HL Sniper For traders who prefer quality over quantity: Trend-trigger mode for more selective entries Fewer signals, more selective setups Best suited for volatile markets where patience matters Auto-Tune Sniper Combines sniper selectivity with automatic optimization: Optimizes RSI period, smoothing factor, and trigger sensitivity Adapts configuration based on historical performance Selective entries + adaptive parameters █ EXIT & RISK MANAGEMENT Percentage Targets Up to 6 TP levels (TP1…TP6) with on-chart lines and labels Configure price distance (%) and position size (%) for each level Designed for scaling out positions systematically Signal Step-Outs Momentum-shift condition triggers partial exits Optional higher-timeframe confirmation for stronger signals "New TP Must Beat Last" prevents weak consecutive exits Opposite Signal Closes/flips position when the next opposite entry signal appears Simple and effective for trend-following systems Stop-Loss & Trailing Fixed SL as % from entry (configurable separately for long/short) Breakeven: Moves SL to entry price after chosen TP is hit Moving Target: Advances SL to previous TP level as profit grows █ VISUAL HELPERS On-Chart TP/SL Lines: See your targets and stops directly on the chart Bar Coloring: Candles colored by current signal bias Background Gradient: Visual confirmation of position direction "Possible Signal Coming" Markers: Anticipate entries before they trigger Peak Profit Label: Track maximum unrealized profit during trades █ AUTO-TUNE DASHBOARD When Auto-Tune is enabled, the dashboard displays: Current Parameters: Active sensitivity, thresholds, or sniper config Simulated Performance: Trade count, win rate, P&L from internal backtest Confidence Score: Stability indicator based on agreement among top configurations Note: Auto-Tune is automated parameter selection on historical data — not predictive AI. It cannot guarantee future performance. █ USE CASES 📈 Trending Markets Use "Smart" signals + Percentage TPs. Stay aligned with momentum while scaling out at multiple targets. Enable Moving Target trailing to maximize winners. 📉 Ranging / Choppy Markets Use "Normal" signals (contrarian mode). Catch reversals at range boundaries. Tighter TP targets and quicker exits work best. ⚡ High Volatility / News Events Use "HL Sniper" for selective entries. Fewer signals, more selective. Wider SL to accommodate volatility. 🧪 New Asset / Unfamiliar Market Enable "AI" mode and let Auto-Tune find optimal parameters. Great for exploring new markets without manual optimization. 🤖 Automation & Bots Structured alert payloads work with popular bot platforms and custom webhooks. Entry + 6 TPs + SL in one alert. █ HOW TO USE Apply to your chart (any timeframe, any market) Choose your signal mode based on market conditions Pick your direction (Long / Short / Both) Select exit mode: Percentage, Signals, or Opposite Signal Configure TP levels and position sizing Enable Stop-Loss and choose a trailing mode Optional: Enable Auto-Tune for adaptive optimization Set alerts for signals and TP/SL events █ ALERTS Built-in alert conditions for: All entry signal types (Normal, Smart, AI, HL Sniper, AI Sniper) TP hits (percentage levels or signal-based steps) SL touch events "Possible Long/Short Coming" anticipation markers Tip: Create separate alerts for each condition. Use TradingView placeholders ({{ticker}}, {{close}}) in your alert messages. █ WORKS ON Crypto Forex Stocks Indices Commodities █ REALISTIC EXPECTATIONS No indicator wins 100% of the time — this is no exception Signals should be combined with your own analysis Auto-Tune optimizes on past data — it cannot predict the future Always use proper position sizing and risk management Paper trade or backtest before risking real capital █ LIMITATIONS Signals and overlays are decision-support tools — they cannot eliminate losses Auto-Tune can overfit if used without out-of-sample validation Multi-timeframe logic confirms on higher-TF closes — slight delay is expected Best evaluated on standard candles with realistic fee/slippage assumptions █ DISCLAIMER This indicator is provided for educational and informational purposes only and does not constitute financial advice. Past performance is not indicative of future results. Trading involves substantial risk of loss, and you are solely responsible for your own trading decisions.
مؤشر Pine Script®
من ‎InfinityAlgo‎
3
BTC - ALSI: Altcoin Season Index (Dynamic Eras)Title: BTC - ALSI: Altcoin Season Index (Dynamic Eras) Overview & Philosophy The Altcoin Season Index (ALSI) is a quantitative tool designed to answer the most critical question in crypto capital rotation: "Is it time to hold Bitcoin, or is it time to take risks on Altcoins?" Most "Altseason" indicators suffer from Survivor Bias or Obsolescence. They either track a static list of coins that includes "dead" assets from previous cycles (ghosts of 2017), or they break completely when major tokens collapse (like LUNA or FTT). This indicator solves this by using a Time-Varying Basket. The indicator automatically adjusts its reference list of Top 20 coins based on historical eras. This ensures the index tracks the winners of the moment—capturing the DeFi summer of 2020, the NFT craze of 2021, and the AI/Meme narratives of 2024/2025. Methodology The indicator calculates the percentage of the Top 20 Altcoins that are outperforming Bitcoin over a rolling window (Default: 90 Days). The "Win" Count: For every major Altcoin performing better than BTC, the index adds a point. Dynamic Eras: The basket of coins changes depending on the date: 2020 Era (DeFi Summer): Tracks the "Blue Chips" of the DeFi revolution like UNI, LINK, DOT, and early movers like VET and FIL. 2021 Era (Layer 1 Wars): Tracks the explosion of alternative smart contract platforms, adding winners like SOL, AVAX, MATIC, and ALGO. 2022 Era (The Survivors): Filters for resilience during the Bear Market, solidifying the status of established assets like SHIB and ATOM. 2023 Era (Infrastructure & Scale): Captures the rise of "Next-Gen" tech leading into the pre-halving year, introducing TON, APT (Aptos), and ARB (Arbitrum). 2024/25 Era (AI & Speed): Tracks the current Super-Cycle leaders, focusing on the AI narrative (TAO, RNDR), High-Performance L1s (SUI), and modern Memes (PEPE). Chart Analysis & Strategy ( The "Alpha" ) As seen in the chart above, there is a strong correlation between ALSI Peaks and local tops in TOTAL3 (The Crypto Market Cap excluding BTC & ETH). The Entry (Rotation): When the indicator rises above the neutral 50 line, it signals that capital is beginning to rotate out of Bitcoin and into Altcoins. This has historically been a strong confirmation signal to increase exposure to high-beta assets. The Exit (Saturation): When the indicator hits 100 (or sustains in the Red Zone > 75), it means every single Altcoin is beating Bitcoin. Historically, this extreme exuberance often marks a local top in the TOTAL3 chart. This is the zone where smart money typically sells into strength, rather than opening new positions. How to Read the Visuals 🚀 Altcoin Season (Red Zone > 75): Strong Altcoin dominance. The market is "Risk On." 🛡️ Bitcoin Season (Blue Zone < 25): Bitcoin dominance. Alts are bleeding against BTC. Historically, this is a defensive zone to hold BTC or Stablecoins. Data Dashboard: A status table in the bottom-right corner displays the live Index Value, current Regime, and a System Check to ensure all 20 data feeds are active. Settings Lookback Period: Default 90 Days. Lowering this (e.g., to 30) makes the index faster but noisier. Thresholds: Adjustable zones for Altcoin Season (Default: 75) and Bitcoin Season (Default: 25). Credits & Attribution This open-source indicator is built on the shoulders of giants. I acknowledge the original creators of the concept and the pioneers of its implementation on TradingView: Original Concept: BlockchainCenter.net. - They established the industry standard definition: 75% of the Top 50 coins outperforming Bitcoin over 90 days = Altseason.. TradingView Implementation: Adam_Nguyen - He implemented the "Dynamic Era" logic (updating the coin list annually) on TradingView. Our code structure for the time-based switching is inspired by his methodology. See also his implementation in the chart. ( Altcoin Season Index - Adam) . Comparison: Why use ALSI | RM? While inspired by the above, ALSI introduces three key improvements: Open Source: Unlike other popular TradingView versions (which are closed-source), this script is fully transparent. You can see exactly which coins are triggering the signal. Sanitized History (Anti-Fragile): Historical Top 20 snapshots are not blindly used. "Dead" coins (like LUNA and FTT) from previous eras are manually filtered out. A raw index would crash during the Terra/FTX collapses, giving a false "Bitcoin Season" signal purely due to bad actors. The curated list preserves the integrity of the market structure signal. Narrative Relevance: The 2024/25 basket was updated to include TAO (Bittensor) and RNDR, ensuring the index captures the dominant AI narrative, rather than tracking fading assets from the previous cycle. You can compare the ALSI indicator with other available tradingview indicators in the chart: Different indicators for the same idea are shown in the 3 Pane window below the BTC and Total3 chart, whereas ALSI is the top pane indicator. Important Note on Coin Selection Baskets are highly curated: Dead/irrelevant coins (FTT, LUNA, BSV) are excluded for clean signals. This prevents historical breaks and ensures Era T5 captures current narratives (AI, Memes) via TAO/RNDR. See above. Users are free to adjust the source code to test their own baskets. Disclaimer This script is for research and educational purposes only. Past correlations between ALSI and TOTAL3 do not guarantee future results. Market regimes can change, and "Altseasons" can be cut short by macro events. Tags bitcoin, btc, altseason, dominance, total3, rotation, cycle, index, alsi, Rob Maths
مؤشر Pine Script®
من ‎Rob_Maths‎
GrowEasy Money## Grow Easy AI Mentor Strategy ### Overview The *Grow Easy AI Mentor Strategy* is a sophisticated yet beginner-friendly long-only trend-following indicator designed exclusively for the TradingView platform. It embodies the core principles of the Grow Easy AI agent: capital preservation, disciplined risk management, and educational growth. By combining multiple technically sound filters, it identifies high-probability entry and exit points while providing clear visual guidance, making it an ideal tool for new traders seeking sustainable and informed market engagement. ### Core Philosophy This strategy is built on three foundational pillars: - **Follow the Trend:** Trades only when price is above the 200-period Simple Moving Average (SMA), ensuring alignment with the long-term market direction. - **Gauge Momentum:** Uses a dynamic Relative Strength Index (RSI) threshold to confirm strong buying pressure, adjusting sensitivity near recent highs to avoid premature entries. - **Manage Risk Dynamically:** Employs a volatility-based trailing stop calculated via Average True Range (ATR) multiplied by a user-defined factor, adapting stops to market volatility and protecting profits effectively. ### Key Features & Functionality - **Intelligent Entry Filters:** Combines long-term trend, momentum, and volatility breakout criteria for filtered and measured entries. - **Optional Confirmation Filter:** Can wait for price to close above the trigger bar’s high before entry to reduce false signals and whipsaws. - **Dynamic Trailing Stop:** Adjusts stop loss distance based on market volatility, widening in volatile conditions and tightening in calmer environments. - **Educational Visuals:** - Background shading indicates bullish (green) or bearish (red) trends. - Entry (green triangle) and exit (red triangle) signals provide clear, actionable markers. - Orange highlights indicate bars awaiting confirmation, promoting disciplined trading patience. - Real-time label displays the current trend, momentum, volatility levels, and stop price for ongoing market education. - **Fully Configurable:** All important parameters—ATR multiplier, RSI levels, lookback periods, confirmation toggles—are easily adjustable for various assets and volatility profiles. - **Agent-Ready Alerts:** Built-in alert conditions allow seamless integration with Grow Easy AI autonomous trading agents or manual monitoring for timely trade execution. ### How to Use Add the indicator to daily or higher timeframe charts for optimal results. Only consider long trades when buy signals appear, and use alerts for timely notifications. Pair the strategy with a sound risk management plan, such as limiting risk per trade to 1%, to ensure sustainable portfolio growth. Paper trading is recommended before live deployment to familiarize yourself with signal timing and behavior. ### Limitations This strategy is exclusively long-only and may underperform in sideways or highly choppy markets where trend filters fail to detect clear direction. Adjust ATR multiplier and dynamic RSI thresholds as needed to match the volatility characteristics of individual assets. False signals can occur, especially during low-volume or highly volatile periods, underscoring the importance of risk controls and disciplined trade management. ### Ideal For - New investors seeking an educational yet practical introduction to trading. - Disciplined traders who prefer rules-based entries and exits over emotional decisions. - Automated trading system developers looking for reliable, well-documented signal generation logic. ### In Essence More than just a trading indicator, *Grow Easy AI Mentor Strategy* acts as a calm, patient mentor on the chart—enforcing disciplined strategy execution, visually explaining every step, and prioritizing risk management to support sustained learning and growth in trading.
مؤشر Pine Script®
من ‎Rhinover_‎
ML Compressor Enhanced Trading Indicator# 🤖 ML Enhanced Trading Indicator - Advanced Market Analysis ## 📊 Overview This is a comprehensive Machine Learning Enhanced Trading Indicator that combines multiple advanced analytical techniques to provide high-probability trading signals. The indicator uses artificial intelligence, pattern recognition, anomaly detection, and traditional technical analysis to identify optimal entry and exit points in the market. ## 🚀 Key Features ### 🧠 **Machine Learning Core** - **Advanced Pattern Recognition**: Uses cosine similarity, Pearson correlation, and Spearman rank correlation to identify historical patterns - **AI-Powered Predictions**: Implements multiple correlation methods to forecast price movements - **Anomaly Detection**: Z-score based detection system for unusual market activities - **Signal Confidence Scoring**: Reliability assessment for each trading signal ### 📈 **Technical Analysis Integration** - **Multi-Timeframe RSI Analysis**: 14 and 21-period RSI with oversold/overbought detection - **MACD Momentum**: Enhanced MACD histogram analysis for trend confirmation - **Bollinger Bands Position**: Dynamic position tracking within BB channels - **Volume Analysis**: Spike and dry volume detection with ratio calculations - **Trend Strength Measurement**: EMA-based trend power analysis ### 🎯 **Perfect Zone Detection** - **Ideal Buy Zone**: Identifies perfect buying opportunities when 7 conditions align: - ML Score ≥ 0.60 - Bottom proximity detection - RSI in 20-35 range - Volume spike confirmation - Positive price anomaly - Bullish pattern match - Positive MACD momentum ### 📊 **Comprehensive Display Table** - **Real-time ML Analysis**: Complete breakdown of all indicators - **Perfect Buy Conditions Tracker**: Visual checklist with completion percentage - **Performance Metrics**: Win rate tracking and P&L analysis - **Signal Strength Indicators**: Confidence levels for each signal ## 🔧 **Customizable Parameters** ### **ML Settings** - **ML Lookback Period**: 20-500 bars (default: 100) - **Anomaly Threshold**: 1.0-5.0 sensitivity (default: 2.0) - **Pattern Similarity**: 0.5-0.99 matching threshold (default: 0.80) - **AI Lookback Period**: 20-200 bars (default: 50) ### **AI Prediction Models** - **Correlation Methods**: Spearman, Pearson, Cosine Similarity - **Forecast Length**: 15-250 bars (default: 50) - **Similarity Type**: Price or %Change analysis ### **Visual Options** - **Table Position**: Top/Bottom Left/Right positioning - **Table Size**: Small, Normal, Large options - **Signal Display**: Toggle buy/sell signals on/off - **AI Visualization**: Optional prediction paths and ZigZag ## 📋 **How to Use** ### **For Beginners** 1. Add the indicator to your chart 2. Look for "PERFECT BUY" signals in the table 3. Wait for completion percentage ≥ 85% for highest probability trades 4. Use the background color changes as visual confirmation ### **For Advanced Traders** 1. Analyze individual ML components in the detailed table 2. Monitor anomaly detection for unusual market conditions 3. Use pattern confidence levels for trade timing 4. Combine with your existing strategy for confirmation ### **Signal Interpretation** - **🟢 PERFECT BUY**: All 7 conditions met - highest probability reversal - **🟡 NEAR BOTTOM**: Close to ideal conditions - monitor closely - **🔴 NOT READY**: Wait for better setup - **Strong Buy/Sell Signals**: ML score-based entries with high confidence ## ⚠️ **Important Notes** ### **Risk Management** - This indicator provides analysis and signals, not guaranteed outcomes - Always use proper risk management and position sizing - Consider market conditions and fundamental factors - Backtest the strategy on your preferred timeframes and assets ### **Best Practices** - Use multiple timeframe analysis for confirmation - Combine with support/resistance levels - Monitor volume confirmation for all signals - Set appropriate stop-losses and profit targets ### **Performance Tracking** - The indicator tracks its own performance with win rate calculations - Monitor the "AI Prediction" accuracy percentage - Use the P&L tracking to assess signal quality over time ## 🔄 **Updates and Improvements** This indicator is continuously evolving with: - Enhanced machine learning algorithms - Improved pattern recognition capabilities - Additional correlation methods for better accuracy - Performance optimization for faster calculations - New visualization features based on user feedback ## 📚 **Technical Details** ### **Machine Learning Implementation** - **Pattern Matching**: 20-bar normalized price patterns with historical comparison - **Correlation Analysis**: Mathematical similarity scoring between current and historical patterns - **Anomaly Detection**: Statistical Z-score analysis across price, volume, and RSI - **Signal Weighting**: Multi-factor scoring system with optimized weights ### **Algorithm Components** 1. **Feature Extraction**: Price, volume, momentum, volatility, and trend features 2. **Pattern Recognition**: Historical pattern database with similarity matching 3. **Anomaly Detection**: Multi-dimensional Z-score threshold analysis 4. **Signal Generation**: Weighted scoring system with confidence intervals 5. **Performance Tracking**: Real-time win rate and accuracy monitoring ### **Calculation Methods** - **Trend Strength**: (EMA8 - EMA21) / EMA21 * 100 - **Volume Ratio**: Current Volume / 20-period SMA Volume - **BB Position**: (Close - BB_Lower) / (BB_Upper - BB_Lower) - **Anomaly Score**: Average of normalized Z-scores for price, volume, and RSI ## 🎨 **Visual Elements** ### **Background Colors** - **Light Green**: Perfect buy zone detected - **Light Red**: Perfect sell zone detected - **Light Blue**: Near bottom proximity - **Green/Red Transparency**: Price anomaly detection ### **Signal Shapes** - **Green Triangle Up**: Strong buy signal - **Red Triangle Down**: Strong sell signal - **Aqua Diamond**: Perfect buy zone entry - **Purple Diamond**: Perfect sell zone entry ### **Table Information** - **ML Complete Analysis**: 16 comprehensive metrics - **Perfect Buy Conditions**: 7-point checklist with status indicators - **Real-time Values**: Live updating of all calculations - **Color-coded Status**: Green (good), Yellow (moderate), Red (caution) ## 🔍 **Troubleshooting** ### **Common Issues** - **Table Not Showing**: Enable "Show ML Table" in settings - **No Signals Appearing**: Check "Show Buy/Sell Signals" option - **Performance Issues**: Reduce ML Lookback Period for faster calculation - **Too Many/Few Signals**: Adjust Anomaly Threshold sensitivity ### **Optimization Tips** - **For Day Trading**: Use lower timeframes (1m, 5m, 15m) with reduced lookback periods - **For Swing Trading**: Use higher timeframes (1h, 4h, 1D) with standard settings - **For Scalping**: Enable only strong signals and reduce pattern similarity threshold - **For Long-term**: Increase all lookback periods and use daily/weekly timeframes ## 📖 **Disclaimer** This indicator is for educational and informational purposes only. It should not be considered as financial advice. Trading involves substantial risk of loss and is not suitable for all investors. Past performance does not guarantee future results. ### **Risk Warning** - All trading involves risk of substantial losses - Never risk more than you can afford to lose - This indicator does not guarantee profitable trades - Always use proper risk management techniques - Consider consulting with a financial advisor ### **Liability** The creator of this indicator is not responsible for any losses incurred from its use. Users should thoroughly test and understand the indicator before using it with real money. ### **Feature Requests** - Suggest improvements through TradingView comments - Report bugs with detailed descriptions - Share successful strategies using the indicator - Contribute to community discussions ## 🏆 **Credits and Acknowledgments** This indicator builds upon various open-source libraries and mathematical concepts: - TradingView ZigZag library for visualization - Statistical correlation methods from academic research - Machine learning concepts adapted for financial markets - Community feedback and testing contributions ## 📈 **Performance Metrics** The indicator includes built-in performance tracking: - **Win Rate Calculation**: Percentage of profitable signals - **Signal Accuracy**: ML prediction vs actual price movement - **Drawdown Tracking**: Current unrealized P&L from last signal - **Completion Percentage**: How many perfect conditions are met ## 🔬 **Mathematical Foundation** ### **Correlation Calculations** - **Pearson**: Measures linear correlation between patterns - **Spearman**: Rank-based correlation for non-linear relationships - **Cosine Similarity**: Vector-based similarity for pattern matching ### **Statistical Methods** - **Z-Score**: (Value - Mean) / Standard Deviation - **Pattern Normalization**: Price / Price - **Volatility Percentile**: Historical ranking of current volatility - **Momentum Calculation**: Price change over multiple periods ## 🎯 **Trading Strategies** ### **Conservative Approach** - Wait for Perfect Buy Zone (85%+ completion) - Use higher timeframes for confirmation - Set stop-loss at recent swing low - Take profits at resistance levels ### **Aggressive Approach** - Trade on Strong Buy/Sell signals - Use lower completion thresholds (70%+) - Tighter stop-losses with faster exits - Higher position sizes with confirmed trends ### **Hybrid Strategy** - Combine with other indicators for confirmation - Use different settings for different market conditions - Scale in/out based on signal strength - Adjust parameters based on market volatility
مؤشر Pine Script®
من ‎CasperFxPro‎
1
AiTrend Pattern Matrix for kNN Forecasting (AiBitcoinTrend)The AiTrend Pattern Matrix for kNN Forecasting (AiBitcoinTrend) is a cutting-edge indicator that combines advanced mathematical modeling, AI-driven analytics, and segment-based pattern recognition to forecast price movements with precision. This tool is designed to provide traders with deep insights into market dynamics by leveraging multivariate pattern detection and sophisticated predictive algorithms. 👽 Core Features Segment-Based Pattern Recognition At its heart, the indicator divides price data into discrete segments, capturing key elements like candle bodies, high-low ranges, and wicks. These segments are normalized using ATR-based volatility adjustments to ensure robustness across varying market conditions. AI-Powered k-Nearest Neighbors (kNN) Prediction The predictive engine uses the kNN algorithm to identify the closest historical patterns in a multivariate dictionary. By calculating the distance between current and historical segments, the algorithm determines the most likely outcomes, weighting predictions based on either proximity (distance) or averages. Dynamic Dictionary of Historical Patterns The indicator maintains a rolling dictionary of historical patterns, storing multivariate data for: Candle body ranges, High-low ranges, Wick highs and lows. This dynamic approach ensures the model adapts continuously to evolving market conditions. Volatility-Normalized Forecasting Using ATR bands, the indicator normalizes patterns, reducing noise and enhancing the reliability of predictions in high-volatility environments. AI-Driven Trend Detection The indicator not only predicts price levels but also identifies market regimes by comparing current conditions to historically significant highs, lows, and midpoints. This allows for clear visualizations of trend shifts and momentum changes. 👽 Deep Dive into the Core Mathematics 👾 Segment-Based Multivariate Pattern Analysis The indicator analyzes price data by dividing each bar into distinct segments, isolating key components such as: Body Ranges: Differences between the open and close prices. High-Low Ranges: Capturing the full volatility of a bar. Wick Extremes: Quantifying deviations beyond the body, both above and below. Each segment contributes uniquely to the predictive model, ensuring a rich, multidimensional understanding of price action. These segments are stored in a rolling dictionary of patterns, enabling the indicator to reference historical behavior dynamically. 👾 Volatility Normalization Using ATR To ensure robustness across varying market conditions, the indicator normalizes patterns using Average True Range (ATR). This process scales each component to account for the prevailing market volatility, allowing the algorithm to compare patterns on a level playing field regardless of differing price scales or fluctuations. 👾 k-Nearest Neighbors (kNN) Algorithm The AI core employs the kNN algorithm, a machine-learning technique that evaluates the similarity between the current pattern and a library of historical patterns. Euclidean Distance Calculation: The indicator computes the multivariate distance across four distinct dimensions: body range, high-low range, wick low, and wick high. This ensures a comprehensive and precise comparison between patterns. Weighting Schemes: The contribution of each pattern to the forecast is either weighted by its proximity (distance) or averaged, based on user settings. 👾 Prediction Horizon and Refinement The indicator forecasts future price movements (Y_hat) by predicting logarithmic changes in the price and projecting them forward using exponential scaling. This forecast is smoothed using a user-defined EMA filter to reduce noise and enhance actionable clarity. 👽 AI-Driven Pattern Recognition Dynamic Dictionary of Patterns: The indicator maintains a rolling dictionary of N multivariate patterns, continuously updated to reflect the latest market data. This ensures it adapts seamlessly to changing market conditions. Nearest Neighbor Matching: At each bar, the algorithm identifies the most similar historical pattern. The prediction is based on the aggregated outcomes of the closest neighbors, providing confidence levels and directional bias. Multivariate Synthesis: By combining multiple dimensions of price action into a unified prediction, the indicator achieves a level of depth and accuracy unattainable by single-variable models. Visual Outputs Forecast Line (Y_hat_line): A smoothed projection of the expected price trend, based on the weighted contribution of similar historical patterns. Trend Regime Bands: Dynamic high, low, and midlines highlight the current market regime, providing actionable insights into momentum and range. Historical Pattern Matching: The nearest historical pattern is displayed, allowing traders to visualize similarities 👽 Applications Trend Identification: Detect and follow emerging trends early using dynamic trend regime analysis. Reversal Signals: Anticipate market reversals with high-confidence predictions based on historically similar scenarios. Range and Momentum Trading: Leverage multivariate analysis to understand price ranges and momentum, making it suitable for both breakout and mean-reversion strategies. Disclaimer: This information is for entertainment purposes only and does not constitute financial advice. Please consult with a qualified financial advisor before making any investment decisions.
مؤشر Pine Script®
من ‎aibitcointrend‎
8
Elite Trading Network | HQ: Quantum Edge V2Elite Trading Network HQ: Quantum Edge V2 is a sophisticated market structure analysis tool designed to help traders make informed decisions based on a deep understanding of market conditions. This script blends structural trend analysis with AI-based predictive models to provide dynamic, real-time insights into market behavior. Here is what makes Quantum Edge V2 unique: Key Features: Adaptive Market Structure Analysis: The script uses a multi-level algorithm to identify key market structures, such as swing highs and swing lows, to help traders understand the underlying strength or weakness of the current market trend. It dynamically tracks critical market boundaries using historical price action and recalculates trend levels as new data emerges. Range and Trend Condition Detection: Quantum Edge V2 detects whether the market is trending or ranging by analyzing historical structure breaks. This detection helps identify moments of consolidation (yellow zones) or periods of trend continuation. By calculating average structural break durations, the indicator alerts users to conditions that may require caution, such as ranging markets. Predictive AI Analysis for Entry Optimization: An AI-powered module evaluates volume thresholds and ATR (Average True Range) to provide users with an understanding of the current market risk. The ATR is calculated based on a user-defined timeframe, giving flexibility in how users approach different market conditions. This feature also determines the risk per trade and calculates the optimal position size, ensuring that users can tailor their risk according to their trading plan. Real-Time Alerts and Visual Indicators: The indicator includes alerts for key conditions: Green Condition: Signals optimal market entry conditions. Yellow Condition: Indicates a cautionary ranging market, alerting traders to the potential lack of strong trends. Red Condition: Identifies unsuitable market conditions for entry due to insufficient volume or unfavorable metrics. Color-coded background visuals provide instant clarity regarding market conditions—red, yellow, or green—allowing traders to make quick, informed decisions. Dynamic Multi-Timeframe Analysis: The user can select a custom entry timeframe, while the script internally calculates and adapts to a higher timeframe for deep trend analysis. This approach gives traders a complete view of both the short-term (entry) and higher timeframe (overall trend) dynamics. How to Use: Identify Trend Conditions: The indicator visually plots key market structures (green and red structural lines) to help users determine where the market may find support or resistance. The background changes color to indicate trending (green), ranging (yellow), or high-risk (red) conditions. Make Informed Entries: Use the real-time alerts and label information to get insights into current market conditions. If the background is green and metrics align, the indicator suggests an optimal time for entry. Position Sizing and Risk Management: The calculated risk per trade and position size (displayed on-screen) assist users in managing risk effectively. Users can utilize this data to adjust trade sizes and maximize profit potential while adhering to their risk tolerance. What Sets Quantum Edge V2 Apart: Unlike other indicators that solely provide trend direction, Quantum Edge V2 offers an integrated understanding of market structure, volume analysis, and predictive AI models. The ranging market detection (yellow zones) is particularly valuable for traders looking to avoid low-probability trades during periods of market indecision. The use of ATR-based risk calculation ensures the position sizing is always aligned with market volatility, adding an extra layer of protection for capital. Important Notes: Educational Value: This script does not just tell you when to enter or exit. It provides deep insights into market dynamics, giving traders a tool to learn and improve their market understanding. The ability to view market structure across different timeframes and visualize areas of caution is crucial for long-term growth as a trader. No Guaranteed Results: This indicator is a powerful tool for analysis, but like all trading strategies, it does not guarantee profits. Always practice proper risk management. Why It's Worth Using: This indicator combines multi-timeframe structure analysis, volume metrics, and predictive AI modeling—an approach typically reserved for professional trading systems. Traders looking to incorporate a systematic approach to risk, ranging markets, and trend detection will find Quantum Edge V2 invaluable. Closed-source Explanation: The script uses proprietary algorithms and unique concepts for trend detection and volume-based analysis that ensure high levels of accuracy in defining market structure and determining entry signals. Because of its complexity and the unique blend of tools, it remains closed-source. Feedback and Support: If you have questions or suggestions about this script, feel free to comment or reach out. We value your input as we strive to improve and provide traders with cutting-edge tools.
مؤشر Pine Script®
من ‎EliteTradingHQ‎
2
TradingIQ - Impulse IQIntroducing "Impulse IQ" by TradingIQ Impulse IQ is an exclusive trading algorithm developed by TradingIQ, designed to trade breakouts and established trends. By integrating artificial intelligence and IQ Technology, Impulse IQ analyzes historical and real-time price data to construct a dynamic trading system adaptable to various asset and timeframe combinations. Philosophy of Impulse IQ Impulse IQ combines IQ Technology (AI) with the classic principles of trend and breakout trading. Recognizing that markets inherently follow trends that need to persist for significant price movements to unfold, Impulse IQ eliminates the need for rigid settings or manual intervention. Instead, it dynamically develops, adapts, and executes trend-based trading strategies, enabling a more responsive approach to capturing meaningful market opportunities. Impulse IQ is designed to work straight out of the box. In fact, its simplicity requires just one user setting, making it incredibly straightforward to manage. Strategy type is the only setting that controls Impulse IQ’s functionality. Traders don’t have to spend hours adjusting settings and trying to find what works best - Impulse IQ handles this on its own. Key Features of Impulse IQ Self-Learning Breakout Detection Employs IQ Technology to identify breakouts. AI-Generated Trading Signals Provides breakout trading signals derived from self-learning algorithms. Comprehensive Trading System Offers clear entry and exit labels. AI-Determined Trailing Profit Target and Stop Loss Position exit levels are clearly defined and calculated by the AI once the trade is entered. Performance Tracking Records and presents trading performance data, easily accessible for user analysis. Long and Short Trading Capabilities Supports both long and short positions to trade various market conditions. IQ Meter The IQ Meter details where price is trading relative to a higher timeframe trend and lower timeframe trend. Fibonacci levels are interlaced along the meter, offering unique insights on trend retracement opportunities. Self Learning, Multi Timeframe IQ Zig Zags The Zig Zag IQ is a self-learning, multi-timeframe indicator that adapts to market volatility, providing a clearer representation of market movements than traditional zig zag indicators. Dual Strategy Execution Impulse IQ integrates two distinct strategy types: Breakout and Cheap (details explained later). How It Works Before diving deeper into Impulse IQ, it's essential to understand the core terminology: Zig Zag IQ : A self-learning trend and breakout identification mechanism that serves as the foundation for Impulse IQ. Although it belongs to the “Zig Zag” class of technical indicators, it's powered by IQ Technology. Impulse IQ : A self-learning trading strategy that executes trades based on Zig Zag IQ. Zig Zag IQ identifies market trends, while Impulse IQ adapts, learns, and executes trades based on these trend characterizations. Impulse IQ operates on a simple heuristic: go long during upside volatility and go short during downside volatility, essentially capturing price breakouts. The definition of a “price breakout” is determined by IQ Technology, TradingIQ's exclusive AI algorithm. In Impulse IQ, the algorithm utilizes two IQ Zig Zags (self-learning, multi-timeframe zig zags) to analyze and learn from market trends. It identifies breakout opportunities by recognizing violations of established price levels marked by the IQ Zig Zags. Impulse IQ then adapts and evolves to trade similar future violations in a recurring and dynamic manner. Put simply, IQ Zig Zags continuously learn from both historical and real-time price updates to adjust themselves for an "optimal fit" to price data. The aim is to adapt so that the marked price tops and bottoms, when violated, reveal potential breakout opportunities. The strategy layer of IQ Zig Zags, known as Impulse IQ, incorporates an additional level of self-learning with IQ Technology. It learns from breakout signals generated by the IQ Zig Zags, enabling it to dynamically identify and signal tradable breakouts. Moreover, Impulse IQ learns from historical price data to manage trade exits. All positions start with an initial fixed stop loss and a trailing stop target. Once the trailing stop target is reached, the fixed stop loss converts into a trailing stop, allowing Impulse IQ to remain in the breakout/trend until the trailing stop is triggered. What Classifies as a Breakout, Price Top, and Price Bottom? For Impulse IQ: Price tops are considered the highest price achieved before a price bottom forms. Price bottoms are the lowest price reached before a price top forms. For price tops, the highest price continues to be calculated until a significant downside price move occurs. Similarly, for price bottoms, the lowest price is calculated until a significant upside price move happens. What distinguishes Zig Zag IQ from other zig zag indicators is its unique mechanism for determining a "significant counter-trend price move." Zig Zag IQ evaluates multiple fits to identify what best suits the current market conditions. Consequently, a "significant counter-trend price move" in one market might differ in magnitude from what’s considered "significant" in another, allowing it to adapt to varying market dynamics. For example, a 1% price move in the opposite direction might be substantial in one market but not in another, and Zig Zag IQ figures this out internally. The image above illustrates the IQ Zig Zags in action. The solid Zig Zag IQ lines represent the most recent price move being calculated, while the dotted, shaded lines display historical price moves previously analyzed by IQ Zig Zag. Notice how the green zig zag aligns with a larger trend, while the purple zig zag follows a smaller trend. This mechanism is crucial for generating breakout signals in Impulse IQ: for a position to be entered, the breakout of the smaller trend must occur in the same direction as the larger trend. The image above depicts the IQ Meters—an exclusive TradingIQ tool designed to help traders evaluate trend strength and retracement opportunities. When the lower timeframe Zig Zag IQ and the higher timeframe Zig Zag IQ are out of sync (i.e., one is uptrending while the other is downtrending, with no active positions), the meters display a neutral color, as shown in the image. The key to using these meters is to identify trend unison and pinpoint key trend retracement entry opportunities. Fibonacci retracement levels for the current trend are interlaced along each meter, and the current price is converted to a retracement ratio of the trend. These meters can mathematically determine where price stands relative to the larger and smaller trends, aiding in identifying entry opportunities. The top of each meter indicates the highest price achieved during the current price move. The bottom of each meter indicates the lowest price achieved during the current price move. When both the larger and smaller trends are in sync and uptrending, or when a long position is active, the IQ meters turn green, indicating uptrend strength. When both trends are in sync and downtrending, or when a short position is active, the IQ meters turn red, indicating downtrend strength. The image above shows the Point of Change for both the larger and smaller Zig Zag IQ trends. A distinctive feature of Zig Zag IQ is its ability to calculate these turning points in advance—unlike most traditional zig zag indicators that lack predetermined turning points and often lag behind price movements. In contrast, Zig Zag IQ offers a minimal-lag trend detection capability, providing a more responsive representation of market trends. Simply put, once the market Zig Zag anchors are touched, the corresponding Zig Zag IQ will change direction. Trade Signals Impulse IQ can trade in one of two ways: Entering breakouts as soon as they happen (Breakout Strategy Type) or entering the pullback of a price breakout (Cheap Strategy Type). Generally, the Breakout Strategy type will take a greater number of trades and enter a breakout quicker. The Cheap Strategy type will usually take less trades, but potentially enter at a better time/price point, prior to the next leg up of a break up, or the next leg down of a break down. Entry signals are given when price breaks out to the upside or downside for the "Breakout" strategy type, or for the "Cheap" strategy type, when price retraces to the level it broke out from! Breakout Strategy Example The image above demonstrates a long position entered and exited using the Breakout strategy. The price breakout level is marked by the dotted, horizontal green line, representing a previously established price high identified by IQ Zig Zag. Once the price breaks and closes above this level, a long position is initiated. After entering a long position, Impulse IQ immediately displays the initial fixed stop price. As the price moves favorably for the long position, the trailing stop conversion level is reached, and the indicator switches to a trailing stop, as shown in the image. Impulse IQ continues to "ride the trend" for as long as it persists, exiting only when the trailing stop is triggered. Cheap Strategy Example The image above shows a short entry executed using the Cheap strategy. The aim of the Cheap strategy is to enter on a pullback before the breakout occurs. While this results in fewer trades if price doesn’t pull back before the breakout, it typically allows for a better entry time and price point when a pullback does happen. The image above illustrates the remainder of the trade until the trailing stop was hit. Green Arrow = Long Entry Red Arrow = Short Entry Blue Arrow = Trade Exit Impulse IQ calculates the initial stop price and trailing stop distance before any entry signals are triggered. This means users don’t need to constantly tweak these settings to improve performance—Impulse IQ handles this process internally. Verifying Impulse IQ’s Effectiveness Impulse IQ automatically tracks its performance and displays the profit factor for both its long and short strategies, visible in a table located in the top-right corner of your chart. The image above shows the profit factor for both the long and short strategies used by Impulse IQ. A profit factor greater than 1 indicates that the strategy was profitable when trading historical price data. A profit factor less than 1 indicates that the strategy was unprofitable when trading historical price data. A profit factor equal to 1 indicates that the strategy neither gained nor lost money on historical price data. Using Impulse IQ While Impulse IQ functions as a comprehensive trading system with its own entry and exit signals, it was designed for the manual trader to take its trading signals and analysis indications to greater heights - offering numerous applications beyond its built-in trading system. The standout feature of Impulse IQ is its ability to characterize and capitalize on trends. Keeping a close eye on “Breakout” labels and making use of the IQ meter is the best way to use Impulse IQ. The IQ Meters can be used to: Find entry points during trend retracements Assess trend alignment across higher and lower timeframes Evaluate overall trend strength, indicating where the price lies on both IQ Meters. Additionally, "Break Up" and "Break Down" labels can be identified for anticipating breakouts. Impulse IQ self-learns to capture breakouts optimally, making these labels dynamic signals for predicting a breakout. The Zig Zag IQ indicators are instrumental in characterizing the market's current state. As a self-learning tool, Zig Zag IQ constantly adapts to improve the representation of current price action. The price tops and bottoms identified by Zig Zag IQ can be treated as support/resistance and breakout levels. Of course, you can set alerts for all Impulse IQ entry and exit signals, effectively following along its systematic conquest of price movement.
مؤشر Pine Script®سكربت مدفوع
من ‎Trading-IQ‎
9
GG Short & Long IndicatorGG Short & Long Indicator is a powerful signal indicator with AI How do indicator signals work? The main purpose of the indicator is to give a signal that is most likely to bring profit based on historical data. This ORIGINAL trend algorithm gives SHORT and LONG signals when several conditions coincide: 1) Breakout of the average value of the modernized VWAP (this VWAP takes data only from certain time periods and trading sessions, as a result, its breakout most often coincides with the beginning of a strong trend); 2) The previous condition must be confirmed by volumes. I noticed that on some crypto exchanges, depending on whether the breakout is false or true, the volumes are different relative to each other. I applied this knowledge for additional filtering of signals (this point works only on crypto assets, on other assets the algorithm works without taking it into account, maybe later I will refine it); 3) When some of my original formulas to determine overbought (similar in principle to RSI, but more designed to work with the trader algorithm), should not show overbought - so that the entry into the transaction was not at too unfavorable values. To summarize, the algorithm tries to find a balance to determine a true breakout, during which the price will not go too far (for an acceptable RR). But the most important thing is that the parameters to customize the algorithm are governed by our original AI algorithm. It can adjust the indicator in two modes: 1) Settings are selected based on the most profitable historical settings. 2) The settings are selected based not only on historical profitability, but also on winrate, frequency of trades, and a few other items that we will not disclose (so the code is closed) - we consider this approach as a priority, because according to our observations, it gives the highest performance compared to manual tuning. In addition, AI simply simplifies the work with the indicator - you do not need to adjust the settings manually for different trading pairs or timeframes, AI will do it all by itself and immediately give the ready result (backtest) on the table. How to trade? After the signal is issued, the indicator determines the recommended levels to close the trade (green dots). Stop loss should be placed behind the corresponding gray SL mark. Levels for closing a deal (TP) and the level of stop loss setting (SL) are also determined automatically for the selected pair and TF, based on volatility and selected indicator settings To make a trade, you can also use the built-in “Support and Resistance Zones” tool, which displays ranges on the chart based on the modernized ATR, from which the price is more likely to rebound (here I also used my own approach, where in addition to the classic ATR formula, I also used volumes from certain crypto exchanges to determine more accurate price rebound zones) These zones are also adjusted by AI - the algorithm compares several dozens of variations of these zones (with different settings) and chooses the one that best fits the current settings of the signal algorithm. For example, if the indicator is set up for frequent trades - the zones will be updated faster and will be less deep than if the indicator is set up for medium-term trading If desired, you can customize the indicator manually using the corresponding section of the settings. Each paramater has a tooltip describing how and what it affects. Statistisc panel The panel can be divided into 2 conditional parts: 1) Statistics for each individual TP for the selected strategy. It shows the winrate and gross profit, if you fix a trade on a single target completely 2) Total trading result, if you trade clearly according to the strategy and fix the position by equal hours on 4 TPs. The total trading result is displayed for the current indicator settings, it also shows the best, worst and optimal of the possible indicator settings and the trading result of these settings on the side. How do setup the indicator? The indicator has preset settings for several major pairs and timeframes. These are fixed settings specifically selected for individual pairs and timeframes. You can use these presets, or you can choose one of the adaptive settings, which will AUTOMATICALLY select the best/optimal indicator settings. I recommend choosing the “Adaptive Optimal” preset, as it uses more data to determine the optimal indicator settings and according to my observations this method works better in comparison to manual indicator settings or the “Adaptive Best” preset Or you can use the manual settings, as mentioned earlier.
مؤشر Pine Script®
من ‎A8kG5lV2xH9tN4zP7jY1qD3wB6r‎
7
SPP PRO Wyckoff Institutional Engine v4.0Dear User, Quick Start guide Step 1: Install the Indicator 1. Open TradingView and navigate to the Pine Script editor (bottom panel). 2. Click “Open” > “New blank indicator” to clear the editor. 3. Delete all existing code and paste the entire Wyckoff Institutional Engine v4.0 script. 4. Click “Add to chart”. The indicator will compile and appear on your chart. Step 2: Read the Dashboard Once loaded, you will see a dark-themed dashboard in the top-right corner of your chart. This is your command centre. Focus on these key readings: Dashboard Row What It Tells You Action PATTERN Whether the market is in Accumulation, Distribution, or Neutral Determines your directional bias PHASE Current Wyckoff phase (A through E) Tells you where you are in the cycle CONFIDENCE Overall signal quality score (0–100%) Only trade above your minimum threshold P(ACC) / P(DIST) Bayesian probability of accumulation vs distribution Confirms or contradicts the pattern read COMPOSITE Institutional money flow direction (0–100) Above 60 = institutional buying, below 40 = selling Step 3: Watch for Event Labels When the engine detects a Wyckoff event (Spring, SOS, UTAD, etc.), a colour-coded label appears directly on the chart with the event name and its confidence percentage. Green labels are bullish events; red labels are bearish events. The label size scales with confidence: larger labels indicate higher-conviction signals. Step 4: Set Your First Alert 1. Right-click the indicator name on your chart and select “Add alert on Wyckoff Institutional Engine v4.0”. 2. In the Condition dropdown, select “High Quality Signal” (fires when signal quality exceeds 80%). 3. Choose your notification method (popup, email, push notification, or webhook). 4. Set the alert name to something descriptive like “Wyckoff HQ Signal – BTCUSD” and click Create. Quick Start Tip Start with the default settings. The indicator is calibrated for daily charts. For intraday use, reduce the Range Detection Period to 30 and increase Detection Sensitivity to 1.3. How the Indicator Works Wyckoff Phase Detection Engine The core of the indicator is a finite state machine that tracks the progression of Wyckoff schematic events. The engine maintains persistent state across bars, recording the current phase, trading range boundaries (support and resistance), and the count of confirmed events. Accumulation Schematic Phase Key Events What Happens Detection Criteria A: Stopping SC (Selling Climax), AR (Automatic Rally) Downtrend halts with climactic volume and a sharp counter-rally Ultra-high volume + wide spread + close near lows in downtrend; followed by strong bullish bar within 3–15 bars B: Building Cause ST (Secondary Test) Price oscillates within the trading range, building a cause for the future move Return to SC area on reduced volume with lower wick > body C: Spring/Test Spring, Shakeout, Test Price briefly penetrates support to shake out weak hands, then recovers Price breaks below range support then closes back above it on moderate volume; close strength > 0.5 D: Markup Begins SOS (Sign of Strength), LPS (Last Point of Support) Demand overwhelms supply with strong moves up on volume; pullbacks hold on declining volume Bullish bar + high volume + close above EMA20 + close strength > 0.7 + composite score > 55 E: Breakout JAC (Jump Across Creek) Decisive break above the trading range on strong volume Close above range high + bullish bar + high volume + close strength > 0.7 Distribution Schematic Phase Key Events What Happens Detection Criteria A: Stopping BC (Buying Climax), AR (Automatic Reaction) Uptrend halts with climactic volume and a sharp sell-off Ultra-high volume + wide spread + close near highs in uptrend; followed by bearish bar within 3–15 bars B: Building Cause ST (Secondary Test) Price oscillates near the high, institutional selling absorbs demand Return to BC area on reduced volume with upper wick > body C: UTAD UTAD (Upthrust After Distribution) Price briefly penetrates resistance to trap breakout buyers, then reverses Price breaks above range high then closes below it on moderate volume; close strength < 0.5 D: Markdown Begins SOW (Sign of Weakness), LPSY (Last Point of Supply) Supply overwhelms demand with strong moves down on volume; rallies fail on declining volume Bearish bar + high volume + close below EMA20 + close strength < 0.3 + composite score < 45 E: Breakdown ICE Break Decisive break below the trading range on strong volume Close below range low + bearish bar + high volume + close strength < 0.3 Phase Reset Logic If price moves more than 3x ATR beyond the trading range while still in Phases A–C, the state machine resets to prevent stale phase readings. Phases D and E are not reset as these represent confirmed markup/markdown. 3.2 Volume Spread Analysis (VSA) The VSA layer classifies every bar according to the Tom Williams methodology, reading the relationship between volume, spread (high minus low), and close position to identify institutional activity that is invisible on price-only charts. Bar Classifications Signal Volume Spread Close Position Interpretation Stopping Volume High Any Lower wick > body, pos < 30% Institutions absorbing supply near lows No Demand Low Narrow Bullish, pos > 50% Rally lacks institutional participation No Supply Low Narrow Bearish, pos < 50% Decline lacks institutional selling Selling Climax Bar Ultra-high Wide Bearish, pos < 20% Panic selling; potential exhaustion Buying Climax Bar Ultra-high Wide Bullish, pos > 80% Euphoric buying; potential exhaustion Test Bar Low Narrow pos < 30%, lower wick > upper Successful test of prior supply area Upthrust Bar High — Upper wick > 1.5x body, pos > 70% Failed breakout attempt; supply entering Absorption High effort, low result Narrow EVR < 0.5 Large orders being absorbed without moving price Effort vs Result (EVR) The EVR ratio divides the result (spread ratio) by the effort (volume ratio). A value below 0.5 with high volume indicates absorption: large institutional orders are being filled without moving the price, a hallmark of accumulation or distribution. A value above 2.0 with low volume indicates ease of movement: price is moving freely, suggesting markup or markdown has begun. 3.3 Smart Money Composite Operator The Composite Operator score synthesises six institutional flow indicators into a single 0–100 reading that profiles the direction and intensity of institutional money flow. Component Weight What It Measures Bullish Reading Chaikin Money Flow (CMF) 25% Volume-weighted buying/selling pressure over 20 bars CMF > 0 (positive flow) Cumulative Delta 20% Net buying vs selling volume (tick-level proxy) Delta above its 20-bar MA On-Balance Volume (OBV) 15% Cumulative volume flow direction OBV above its 20-bar MA Accumulation/Distribution Line 15% Money flow persistence A/D above its 20-bar MA VSA Signal 15% Current bar’s VSA classification Stopping volume, no supply, or test bar Money Flow Index (MFI) 10% Volume-weighted RSI (overbought/oversold) MFI between 40–80 (healthy range) Smart Money Divergence: The engine also detects divergences between price and institutional flow. A bullish divergence occurs when price makes a lower low but cumulative delta makes a higher low and CMF is positive, indicating institutions are accumulating while price falls. A bearish divergence is the mirror: price rising while delta and CMF decline. 3.4 Bayesian AI Probability Engine The Bayesian engine calculates the posterior probability that the current market structure represents accumulation versus distribution. It starts with a configurable prior probability (default 50%) and updates it by multiplying seven likelihood ratios derived from independent evidence layers. Evidence Layers & Likelihood Ratios Evidence Layer Bullish LR Bearish LR Neutral LR E1: Volume Pattern (stopping vol, absorption) 2.5 0.4 1.0 E2: Price Position (bottom/top of range) 2.0 (pos < 30%) 0.5 (pos > 70%) 1.0 E3: Smart Money Flow (composite score) 1.5–2.0 (score > 60) 0.5–1.0 (score < 40) 1.0 E4: VSA Signal (stopping vol, no supply, test) 2.0 0.5 1.0 E5: Effort vs Result (absorption detected) 1.8 0.6 1.0 E6: Trend Context (downtrend at lows) 1.5 0.6 1.0 E7: Cumulative Delta (bullish divergence) 2.2 0.45 1.0 The Calculation The combined likelihood ratio is the product of all seven individual ratios. The posterior probability is then computed using Bayes’ theorem: P(Acc | Evidence) = (Combined_LR × Prior) / (Combined_LR × Prior + (1 - Prior)) The Bayesian Confidence displayed on the dashboard is how far the posterior is from 50/50, scaled to 0–100%. A confidence of 80% means the posterior is at 90% accumulation or 10% accumulation (90% distribution). 3.5 Adaptive Learning System The adaptive learning system tracks the outcome of every signal by recording the entry price and checking whether price moved favourably within 20 bars. It maintains separate success rates for accumulation and distribution signals, updating them using exponential smoothing: new_rate = old_rate + learning_rate × (outcome - old_rate) Where outcome is 1 for a winning signal and 0 for a losing signal. The learning rate (default 0.08) controls how quickly the system adapts: higher values respond faster to recent results but are noisier; lower values are more stable but slower to adjust. The win rate is displayed in the dashboard and is factored into the Signal Quality Score, creating a feedback loop where the indicator’s confidence in its own signals adjusts based on empirical performance. 3.6 Market Regime Detection The regime detector classifies the current market environment using ADX (trend strength) and ATR percentile (volatility). This contextualises Wyckoff signals: the same Spring event has very different implications in a quiet ranging market versus a volatile trending market. Regime Criteria Signal Implications TREND ▲ ADX > 25, EMA20 > EMA50 Accumulation signals are strongest; distribution signals may be counter-trend TREND ▼ ADX > 25, EMA20 < EMA50 Distribution signals are strongest; accumulation signals are counter-trend RANGE ADX < 20, range < 15% of price Ideal environment for Wyckoff detection; phases develop most clearly VOLATILE ATR > 1.5x its 100-bar average Signals may whipsaw; reduce position size and require higher confidence QUIET ATR < 0.7x average + ranging Low-risk testing environment; springs and UTADs are most reliable here 3.7 Multi-Timeframe Confluence When enabled, the engine pulls data from a higher timeframe (default: Daily) and scores the alignment between the current chart’s signals and the higher timeframe context. The MTF alignment score (0–100) is built from four factors: • HTF Trend: EMA20 vs EMA50 on the higher timeframe (+/- 15 points) • HTF RSI: RSI above or below 50 on the higher timeframe (+/- 10 points) • HTF CMF: Chaikin Money Flow positive or negative on the higher timeframe (+/- 10 points) • Price vs HTF EMA: Current close above or below the higher timeframe EMA20 (+/- 15 points) MTF Best Practice For intraday charts (5m–60m), set the higher timeframe to 4H or Daily. For daily charts, use Weekly. Signals that align with the higher timeframe trend have significantly higher win rates. 3.8 Actuarial Risk Management The risk management layer provides institutional-grade metrics using a Cornish-Fisher expansion to account for the non-normal distribution of financial returns (fat tails and skewness). Value at Risk (VaR) VaR estimates the maximum expected loss over one period at a given confidence level. The standard parametric approach assumes normally distributed returns, but real markets exhibit skewness and excess kurtosis. The Cornish-Fisher adjustment modifies the z-score to account for these higher moments: CF_z = z + (z²-1)×skew/6 + (z³-3z)×kurt/24 - (2z³-5z)×skew²/36 VaR = -(mean_return - CF_z × std_deviation) × portfolio_value Expected Shortfall (CVaR) While VaR tells you the threshold loss, Expected Shortfall (also called Conditional VaR) tells you the average loss in the tail beyond the VaR threshold. This is the risk metric preferred by regulators and institutional risk managers because it captures catastrophic tail risk. Kelly Criterion The Kelly criterion calculates the theoretically optimal fraction of capital to risk on each trade based on the adaptive win rate and assumed reward-to-risk ratio. The dashboard displays the half-Kelly fraction, which is the standard conservative implementation used by professional fund managers to avoid over-leverage. Position Sizing The ATR-based position size divides the maximum dollar risk per trade (portfolio value multiplied by max risk percentage) by the stop distance (2x ATR). This produces the number of units to trade such that a 2-ATR adverse move results in exactly the configured maximum loss.   4. Understanding the Visual Interface 4.1 The Dashboard The dashboard is a 22-row, 3-column panel rendered in a Bloomberg-terminal aesthetic with a dark (#0d1117) background. It is divided into five logical sections: Section Rows Contents Header Row 0 Indicator name, ticker symbol, timeframe Pattern Rows 1–3 Pattern type (Accumulation/Distribution/Neutral), current phase (A–E), confirmed event count AI Rows 4–8 Section header, signal confidence with bar gauge (███░░), Bayesian P(Acc), P(Dist), adaptive win rate with sample count Flow Rows 9–13 Section header, composite operator score with bar gauge, CMF value, volume ratio, market regime VSA Rows 14–16 Section header, current VSA signal name, effort-vs-result ratio with absorption/ease label Risk Rows 17–21 Section header, VaR ($ and %), CVaR ($ and %), half-Kelly %, ATR-based position size with stop level Colour Coding • Green: Bullish readings (composite > 60, CMF positive, confidence > 75%, rising metrics) • Red: Bearish readings (composite < 40, CMF negative, risk metrics, falling metrics) • Amber: Warning or transitional readings (medium confidence, elevated volume, VaR levels) • Blue: Accent colour for section headers and high-confidence indicators • Grey: Neutral or inactive readings 4.2 Phase Progress Bar The phase progress bar sits at the bottom centre of the chart and provides an at-a-glance view of how far through the Wyckoff schematic the current pattern has progressed. It shows five cells (A through E) with the following states: • ✓ (check mark): Phase completed • ● (filled circle): Phase currently active • ○ (open circle): Phase not yet reached The left cell shows ACC or DIS with the appropriate colour, and the right cell shows the current confidence percentage. 4.3 Event Labels & Tooltips When the engine detects a Wyckoff event and the signal quality exceeds your minimum confidence threshold, a label is placed directly on the chart. Labels appear below price for bullish events and above price for bearish events. Element Behaviour Label text Event abbreviation + confidence percentage (e.g., SPRING 78%) Label colour Green for bullish events, red for bearish events, with 20% transparency Label size Normal for quality ≥ 75%, Small for ≥ 55%, Tiny for others Tooltip Hover over any label to see: Phase, Bayesian probability, quality score, composite score, and volume ratio 4.4 Supply/Demand Zones & Trading Range When zones are enabled, the indicator draws three types of visual overlays: • Accumulation Zone (blue fill): A shaded box around the trading range support level (±0.3 ATR) with a dashed border. This highlights the area where institutional buying is concentrated. • Distribution Zone (red fill): A shaded box around the trading range resistance level (±0.3 ATR) with a dashed border. This highlights the area where institutional selling is concentrated. • Trading Range Box (dotted outline): A rectangular box from the phase start bar to the current bar, bounded by the range high and low. This provides the overall structural context. 4.5 Trend Ribbon & VWAP The trend ribbon is a filled area between EMA20 and EMA50. When EMA20 is above EMA50, the fill is green (bullish); when below, it is red (bearish). The SMA200 is plotted as a grey structural reference line. The VWAP (Volume Weighted Average Price) is plotted in purple and serves as an institutional benchmark: institutional traders frequently use VWAP to gauge whether they are buying below or above the day’s fair value. 4.6 VaR Risk Bands When risk analytics are enabled, amber-shaded bands appear above and below the close price representing the daily VaR range. If price closes near the edge of these bands, it indicates the market is approaching its statistical tail risk boundary for the day. This helps traders visually assess whether a move is within normal range or represents an outlier. 4.7 Bar Coloring The indicator applies subtle bar colouring only when a qualified event is detected, keeping the chart clean during normal market activity. Event Type Colour Meaning Spring / Shakeout Bright green (#39d353) Key reversal event in accumulation UTAD Bright red (#da3633) Key reversal event in distribution JAC (breakout) Green (#3fb950) Confirmed markup beginning ICE Break (breakdown) Red (#f85149) Confirmed markdown beginning Other bullish events Green (40% transparency) Supporting bullish evidence Other bearish events Red (40% transparency) Supporting bearish evidence No event / below threshold No colour override Chart uses default candle colours   5. Settings Reference 5.1 Core Detection Setting Default Range Description Volume MA Length 20 5–100 Period for the volume moving average. Lower values make volume spike detection more sensitive; higher values smooth out noise. Range Detection Period 50 20–200 Lookback for determining the trading range (highest high / lowest low). Use 30–50 for intraday, 50–100 for daily, 100–200 for weekly. Pivot Lookback 5 2–20 Number of bars left and right for pivot high/low detection. Lower values catch minor pivots; higher values catch major structure. Detection Sensitivity 1.0 0.3–2.0 Global sensitivity multiplier. Increase above 1.0 for noisy markets or lower timeframes; decrease below 1.0 for clean markets. ATR Period 14 5–50 Average True Range lookback used for stop distances, zone sizing, and volatility measurements. 5.2 Bayesian AI Engine Setting Default Range Description Enable AI Pattern Recognition On — Master toggle for the Bayesian probability engine. When off, the dashboard shows N/A for probability fields. Enable Adaptive Learning On — Toggle for the signal outcome tracking system. When off, the indicator uses fixed success rate assumptions. Learning Rate 0.08 0.01–0.5 Controls how fast the adaptive system adjusts. 0.01 = very stable (slow to change), 0.5 = very reactive (changes quickly). Recommended: 0.05–0.15. Minimum Signal Confidence % 60 40–95 Only events with a signal quality score above this threshold generate chart labels. Higher values = fewer but higher-quality signals. Prior: Accumulation 0.50 0.1–0.9 Starting probability for accumulation in the Bayesian engine. Set to 0.6 if you believe the broader market has a bullish bias; 0.4 for bearish bias. 5.3 Actuarial Risk Setting Default Range Description Enable Risk Analytics On — Master toggle for VaR, CVaR, Kelly, and position sizing calculations. VaR Confidence % 95.0 90–99.9 Confidence level for VaR calculation. 95% is standard; 99% is conservative (used by banks). Risk Lookback 100 50–500 Number of bars used for calculating return statistics (mean, std dev, skewness, kurtosis). Portfolio Value ($) 100,000 1,000+ Your total portfolio value. Used to compute dollar VaR/CVaR and ATR-based position sizing. Max Risk Per Trade % 2.0 0.5–10 Maximum percentage of portfolio value to risk on a single trade. Industry standard is 1–2%. 5.4 Display Setting Default Description Show Dashboard On Toggle the main 22-row info panel Show S/D Zones On Toggle supply/demand zone boxes around support and resistance Show Event Labels On Toggle on-chart labels for detected Wyckoff events Show VWAP On Toggle the Volume Weighted Average Price line Show VaR Risk Bands On Toggle the amber VaR bands around the close Show Phase Progress On Toggle the bottom-centre phase A–E progress bar Dashboard Position Top Right Move the dashboard to any corner (Top Right, Top Left, Bottom Right, Bottom Left) Color Theme Dark Dark (Bloomberg-style #0d1117 background) or Light (white background) 5.5 Multi-Timeframe Setting Default Description Enable MTF Confluence On Toggle higher-timeframe data fetching and alignment scoring Higher Timeframe D (Daily) The timeframe used for MTF analysis. Must be higher than the chart timeframe. Examples: 60 (1H), 240 (4H), D (Daily), W (Weekly).   6. Setting Up Alerts The Wyckoff Institutional Engine v4.0 provides 11 pre-built alert conditions that cover every major signal type. Alerts can be delivered via popup, email, push notification, SMS (via webhook), or integrated into automated trading systems. 6.1 Available Alert Conditions Alert Name Trigger Use Case 🟢 Spring Detected Phase C accumulation spring event Primary buy signal — potential reversal from support 🔴 UTAD Detected Phase C distribution upthrust Primary sell signal — potential reversal from resistance 🟢 Jump Across Creek Phase E accumulation breakout Confirmed markup — trend-following long entry 🔴 Break of ICE Phase E distribution breakdown Confirmed markdown — trend-following short entry 🟢 Sign of Strength Phase D strong demand event Demand exceeding supply — markup acceleration 🔴 Sign of Weakness Phase D strong supply event Supply exceeding demand — markdown acceleration ⚪ Selling Climax Phase A climactic selling exhaustion Potential downtrend exhaustion — early warning ⚪ Buying Climax Phase A climactic buying exhaustion Potential uptrend exhaustion — early warning ⭐ High Quality Signal Signal quality score exceeds 80% Highest conviction setups across all event types 🔵 SM Bull Divergence Price falling + institutional accumulating Hidden bullish divergence in smart money flow 🔵 SM Bear Divergence Price rising + institutional distributing Hidden bearish divergence in smart money flow 6.2 Step-by-Step Alert Configuration Method 1: From the Indicator (Recommended) 1. Ensure the Wyckoff Institutional Engine v4.0 is active on your chart. 2. Right-click the indicator name in the top-left corner of the chart (or in the status line) and select “Add alert on Wyckoff Institutional Engine v4.0…”. 3. In the alert creation dialog, the “Condition” dropdown will show “Wyckoff Institutional Engine v4.0” as the source. The second dropdown will list all 11 alert conditions. 4. Select your desired alert condition (e.g., “Spring Detected”). 5. Set the “Trigger” to the appropriate firing mode. For event-based alerts, use “Any alert() function call” or “Once Per Bar Close” (recommended for avoiding intra-bar noise). 6. Configure your notification channels in the “Notifications” tab: enable the checkboxes for Popup, Email, Push (mobile app), Webhook URL, or SMS. 7. Set the alert name to include the ticker and alert type (e.g., “BTCUSD – Wyckoff Spring”). 8. Set the expiration. For ongoing monitoring, select “Open-ended” if your plan supports it, or set a future date. 9. Click “Create”. Method 2: From the Alert Manager 1. Click the clock/bell icon in the right sidebar (or press Alt+A) to open the Alert Manager. 2. Click “Create Alert” (or the + button). 3. In the “Condition” section, select “Wyckoff Institutional Engine v4.0” from the first dropdown. 4. Select the specific alert condition from the second dropdown. 5. Follow steps 5–9 from Method 1 above. Important: Once Per Bar Close For the most reliable signals, always set the trigger to “Once Per Bar Close”. Intra-bar alerts may fire on conditions that reverse before the bar closes, leading to false signals. Bar-close alerts ensure the Wyckoff event has been confirmed by the completed bar. Alert Message Customization Each alert comes with a pre-configured message that includes the event name and a brief description. You can customise the alert message in the “Message” field of the alert dialog. TradingView supports these placeholders in alert messages: Placeholder Output {{ticker}} The symbol (e.g., BTCUSD) {{exchange}} The exchange (e.g., BINANCE) {{interval}} The chart timeframe (e.g., 60, D) {{time}} The alert trigger time (server time) {{close}} The closing price of the bar {{volume}} The volume of the bar Example Custom Message Wyckoff {{ticker}} on {{exchange}} ({{interval}}): Spring detected at {{close}}. Volume: {{volume}}. Check dashboard for confidence and risk metrics. 6.3 Alert Strategies by Trading Style Conservative (Beginners) Focus on the single highest-quality alert to minimise false signals while you learn the system: • Primary alert: High Quality Signal (fires only when quality > 80%) • Supplementary: Smart Money Bull/Bear Divergence for early warning This gives you perhaps 2–5 alerts per week on a daily chart, each representing a high-conviction setup. Active (Intermediate) Add event-specific alerts for the most tradeable Wyckoff events: • Core alerts: Spring Detected + UTAD Detected (Phase C reversals) • Confirmation: SOS + SOW (Phase D confirmation of direction) • Breakout: Jump Across Creek + Break of ICE (Phase E entries) This gives you comprehensive coverage of the Wyckoff cycle with approximately 5–15 alerts per week. Aggressive (Advanced) Enable all alerts and use the dashboard to filter in real-time: • All 11 alerts enabled on your watchlist symbols • Use the confidence score in the alert message to prioritise which alerts to act on • Reduce minimum confidence to 50% in settings to see more events, then use the dashboard and tooltip data to make decisions Institutional (Portfolio Managers) Use webhook alerts to feed signals into your order management system or risk platform: • Webhook URL: Point alerts to your API endpoint or middleware (e.g., TradingView webhook to Alpaca, IBKR, or custom OMS) • Signal filtering: Use the High Quality Signal alert with a customised JSON message payload • Multi-asset: Set up identical alert configurations across your entire universe 6.4 Webhook & Automation Setup TradingView alerts can be sent as HTTP POST requests to any webhook URL. This enables integration with automated trading systems, Slack/Discord channels, Google Sheets logging, and custom dashboards. Webhook Message Format (JSON) Configure your alert message as a JSON payload for easy parsing by your receiving system: { "ticker": "{{ticker}}", "exchange": "{{exchange}}", "timeframe": "{{interval}}", "price": {{close}}, "volume": {{volume}}, "signal": "Spring Detected", "timestamp": "{{time}}" } Common Webhook Integrations Platform Setup Discord Create a webhook in your Discord channel settings, paste the URL into TradingView’s Webhook URL field. Format the message as a Discord-compatible JSON with a “content” field. Slack Create an incoming webhook in your Slack workspace. Paste the webhook URL and format the message with a “text” field. Telegram Use a Telegram bot API URL (api.telegram.org) with chat_id and text parameters. Google Sheets Deploy a Google Apps Script web app that accepts POST requests and appends data to a spreadsheet. Paste the web app URL. 3Commas / Cornix Follow the platform’s documentation for TradingView webhook integration. Format your alert message according to their required JSON structure. Custom API Any endpoint that accepts HTTP POST with a JSON body. Include authentication tokens in the message payload if required.   7. Trading Strategies 7.1 Conservative Strategy (Beginners) This strategy prioritises capital preservation and only takes trades with the strongest confirmation. Parameter Setting Minimum Confidence 75% Entry Trigger High Quality Signal alert only Entry Confirmation Bayesian P(Acc) > 70% for longs, P(Dist) > 70% for shorts Stop Loss Below the Spring low (longs) or above the UTAD high (shorts) Take Profit 1.5x the risk distance (1.5R) Position Size Half-Kelly or 1% of portfolio, whichever is smaller Max Concurrent Positions 2 7.2 Active Strategy (Intermediate) This strategy trades multiple phases of the Wyckoff cycle with moderate risk tolerance. Parameter Setting Minimum Confidence 60% Entry Trigger Spring, SOS, or JAC alerts for longs; UTAD, SOW, or ICE Break for shorts Entry Confirmation Composite Operator > 55 (longs) or < 45 (shorts) + regime not VOLATILE Stop Loss 2x ATR from entry Take Profit Scale out: 50% at 2R, 25% at 3R, trail remainder Position Size ATR-based from the risk panel Max Concurrent Positions 4–6 7.3 Aggressive Strategy (Advanced) This strategy captures early-phase signals and trades more frequently with tighter risk management. Parameter Setting Minimum Confidence 50% Entry Trigger All event alerts including SC/BC early warnings and Smart Money Divergences Entry Confirmation At least 2 of: VSA signal, composite direction, Bayesian bias aligned Stop Loss 1.5x ATR from entry (tight) Take Profit Dynamic: move to breakeven at 1R, trail at 1x ATR Position Size Full Kelly (use with caution) Max Concurrent Positions 8–10 7.4 Institutional Strategy (Portfolio Managers) This strategy integrates Wyckoff signals into a portfolio-level risk management framework. Parameter Setting Minimum Confidence 70% Portfolio VaR Budget Allocate total portfolio VaR across positions; each new position must fit within remaining budget Entry Filter High Quality Signal + MTF alignment > 65 + regime is RANGE or QUIET Position Sizing CVaR-based: size positions so that each position’s CVaR contribution is equal Correlation Adjustment Reduce position size when adding correlated assets (same sector/factor exposure) Rebalance Trigger Rebalance when any position’s VaR exceeds 30% of its initial allocation Exit Rules Hard stop at 2x initial CVaR; profit-take when Wyckoff phase advances past D   8. Troubleshooting & FAQ The indicator shows “Scanning...” and no events are detected. This means the phase state machine has not yet identified a Selling Climax (for accumulation) or Buying Climax (for distribution) to begin tracking. This is normal: Wyckoff events require specific market conditions. Wait for a climactic volume event near a range extreme, or try a different symbol that is currently in a trading range. The confidence score seems stuck at the same value. The confidence score updates on every bar based on the current evidence. If it appears static, it may be because the underlying factors (volume ratios, price position, smart money flow) are not changing significantly. This often happens during Phase B (building cause), which is typically a low-volatility consolidation. The VaR and CVaR values seem too high or too low. Check the Risk Lookback setting. A very short lookback (50 bars) captures recent volatility and may spike during volatile periods. A longer lookback (200–500 bars) smooths the calculation but may underestimate current risk. Also verify that your Portfolio Value is set correctly, as VaR is expressed in dollar terms. Alerts are firing too frequently. Increase the Minimum Signal Confidence threshold in settings (e.g., from 60% to 75%). You can also switch your alert trigger from “Any alert() function call” to “Once Per Bar Close” to eliminate intra-bar noise. For the most selective filtering, use only the “High Quality Signal” alert, which requires quality > 80%. The indicator is slow to load. The engine performs extensive calculations including Bayesian updates, higher-moment statistics, and multi-timeframe data requests. On lower timeframes (1m, 5m) with large amounts of historical data, loading may take 5–15 seconds. If performance is an issue, disable MTF Confluence, or reduce the Risk Lookback period. Can I use this on any market? Yes. The indicator works on any asset with volume data: stocks, ETFs, futures, forex, and crypto. For forex pairs where tick volume is used instead of real volume, the VSA and volume-based signals will be less reliable. The indicator auto-adjusts sensitivity based on the timeframe but performs best on instruments with genuine exchange volume. The Smart Money Divergence alert fired but price kept going in the original direction. Smart money divergences are early warnings, not immediate reversal signals. Institutional accumulation or distribution can take weeks or months to complete. Treat these alerts as a heads-up to begin watching for Phase C events (Spring/UTAD), not as standalone trade triggers. Why does the win rate start at 50%? The adaptive learning system initialises with a neutral 55% success rate assumption and requires actual signal outcomes to adjust. After approximately 20–30 signals (which may take weeks on a daily chart), the win rate will reflect the indicator’s actual performance on the specific symbol and timeframe you are monitoring.   9. Glossary Term Definition Absorption The process of large institutional orders being filled without moving price, visible as high volume with narrow spread (high effort, low result). ADX Average Directional Index. Measures trend strength on a 0–100 scale. Above 25 indicates a trending market; below 20 indicates a ranging market. AR (Automatic Rally/Reaction) A sharp counter-move following a Selling or Buying Climax, establishing the other boundary of the trading range. ATR Average True Range. Measures volatility as the average of high-low ranges over N periods. Bayesian Probability A statistical method that updates the probability of a hypothesis (accumulation vs distribution) as new evidence becomes available. BC (Buying Climax) A wide-spread, ultra-high-volume bar near the top of an uptrend, indicating buying exhaustion and the beginning of potential distribution. CMF Chaikin Money Flow. A volume-weighted buying/selling pressure indicator ranging from -1 to +1. Composite Operator Wyckoff’s concept of the “smart money”—the aggregate activity of institutional traders who manipulate price to accumulate or distribute positions. Cornish-Fisher Expansion A mathematical correction that adjusts VaR calculations for non-normal return distributions by incorporating skewness and kurtosis. Creek The resistance level at the top of an accumulation trading range. “Jumping across the creek” means breaking above resistance. CVaR (Expected Shortfall) The average expected loss in the tail beyond the VaR threshold. Captures extreme risk that VaR misses. EVR Effort vs Result ratio. Effort = volume ratio, Result = spread ratio. Low EVR with high volume = absorption. ICE The support level at the bottom of a distribution trading range. “Breaking the ICE” means falling below support. JAC (Jump Across Creek) A decisive bullish breakout above the trading range resistance in Phase E of accumulation. Kelly Criterion A formula that determines the optimal fraction of capital to risk on a trade: K = (W×R - L) / R, where W = win rate, L = loss rate, R = reward/risk ratio. Likelihood Ratio The ratio of the probability of observing evidence under one hypothesis versus another. Used in Bayesian updating. LPS (Last Point of Support) The final pullback to support during Phase D of accumulation, confirmed by declining volume. LPSY (Last Point of Supply) The final rally to resistance during Phase D of distribution, confirmed by declining volume. MFI Money Flow Index. A volume-weighted version of RSI that ranges from 0 to 100. OBV On-Balance Volume. A cumulative volume indicator that adds volume on up days and subtracts on down days. SC (Selling Climax) A wide-spread, ultra-high-volume bar near the bottom of a downtrend, indicating selling exhaustion and the beginning of potential accumulation. Shakeout A deep spring where price penetrates well below support with high volume before recovering strongly. More severe than a standard spring. SOS (Sign of Strength) A strong bullish move during Phase D of accumulation, confirming that demand is overwhelming supply. SOW (Sign of Weakness) A strong bearish move during Phase D of distribution, confirming that supply is overwhelming demand. Spring A brief break below trading range support in Phase C that is quickly reversed, designed to shake out weak holders before markup. ST (Secondary Test) A return to the area of the climax on reduced volume, confirming that supply (or demand) has been absorbed. UTAD (Upthrust After Distribution) A brief break above trading range resistance in Phase C that fails, designed to trap breakout buyers before markdown. VaR (Value at Risk) The maximum expected loss at a given confidence level over one period. 95% VaR = the loss that will not be exceeded 95% of the time. VSA Volume Spread Analysis. A method of reading the relationship between volume, spread, and close position to identify institutional activity. VWAP Volume Weighted Average Price. The average price weighted by volume, used as an institutional execution benchmark.
مؤشر Pine Script®
من ‎Nasdacbull‎
QTechLabs Machine Learning Logistic Regression Indicator [Lite]QTechLabs Machine Learning Logistic Regression Indicator Ver5.1 1st January 2026 Author: QTechLabs Description A lightweight logistic-regression-based signal indicator (Q# ML Logistic Regression Indicator ) for TradingView. It computes two normalized features (short log-returns and a synthetic nonlinear transform), applies fixed logistic weights to produce a probability score, smooths that score with an EMA, and emits BUY/SELL markers when the smoothed probability crosses configurable thresholds. Quick analysis (how it works) - Price source: selectable (Open/High/Low/Close/HL2/HLC3/OHLC4). - Features: - ret = log(ds / ds ) — short log-return over ret_lookback bars. - synthetic = log(abs(ds^2 - 1) + 0.5) — a nonlinear “synthetic” feature. - Both features normalized over a 20‑bar window to range ~0–1. - Fixed logistic regression weights: w0 = -2.0 (bias), w1 = 2.0 (ret), w2 = 1.0 (synthetic). - Probability = sigmoid(w0 + w1*norm_ret + w2*norm_synthetic). - Smoothed probability = EMA(prob, smooth_len). - Signals: - BUY when sprob > threshold. - SELL when sprob < (1 - threshold). - Visual buy/sell shapes plotted and alert conditions provided. - Defaults: threshold = 0.6, ret_lookback = 3, smooth_len = 3. User instructions 1. Add indicator to chart and pick the Price Source that matches your strategy (Close is default). 2. Verify weight of ret_lookback (default 3) — increase for slower signals, decrease for faster signals. 3. Threshold: default 0.6 — higher = fewer signals (more confidence), lower = more signals. Recommended range 0.55–0.75. 4. Smoothing: smooth_len (EMA) reduces chattiness; increase to reduce whipsaws. 5. Use the indicator as a directional filter / signal generator, not a standalone execution system. Combine with trend confirmation (e.g., higher-timeframe MA) and risk management. 6. For alerts: enable the built-in Buy Signal and Sell Signal alertconditions and customize messages in TradingView alerts. 7. Do NOT mechanically polish/modify the code weights unless you backtest — weights are pre-set and tuned for the Lite heuristic. Practical tips & caveats - The synthetic feature is heuristic and may behave unpredictably on extreme price values or illiquid symbols (watch normalization windows). - Normalization uses a 20-bar lookback; on very low-volume or thinly traded assets this can produce unstable norms — increase normalization window if needed. - This is a simple model: expect false signals in choppy ranges. Always backtest on your instrument and timeframe. - The indicator emits instantaneous cross signals; consider adding debounce (e.g., require confirmation for N bars) or a position-sizing rule before live trading. - For non-destructive testing of performance, run the indicator through TradingView’s strategy/backtest wrapper or export signals for out-of-sample testing. Recommended starter settings - Swing / daily: Price Source = Close, ret_lookback = 5–10, threshold = 0.62–0.68, smooth_len = 5–10. - Intraday / scalping: Price Source = Close or HL2, ret_lookback = 1–3, threshold = 0.55–0.62, smooth_len = 2–4. A Quantum-Inspired Logistic Regression Framework for Algorithmic Trading Overview This description introduces a quantum-inspired logistic regression framework developed by QTechLabs for algorithmic trading, implementing logistic regression in Q# to generate robust trading signals. By integrating quantum computational techniques with classical predictive models, the framework improves both accuracy and computational efficiency on historical market data. Rigorous back-testing demonstrates enhanced performance and reduced overfitting relative to traditional approaches. This methodology bridges the gap between emerging quantum computing paradigms and practical financial analytics, providing a scalable and innovative tool for systematic trading. Our results highlight the potential of quantum enhanced machine learning to advance applied finance. Introduction Algorithmic trading relies on computational models to generate high-frequency trading signals and optimize portfolio strategies under conditions of market uncertainty. Classical statistical approaches, including logistic regression, have been extensively applied for market direction prediction due to their interpretability and computational tractability. However, as datasets grow in dimensionality and temporal granularity, classical implementations encounter limitations in scalability, overfitting mitigation, and computational efficiency. Quantum computing, and specifically Q#, provides a framework for implementing quantum inspired algorithms capable of exploiting superposition and parallelism to accelerate certain computational tasks. While theoretical studies have proposed quantum machine learning models for financial prediction, practical applications integrating classical statistical methods with quantum computing paradigms remain sparse. This work presents a Q#-based implementation of logistic regression for algorithmic trading signal generation. The framework leverages Q#’s simulation and state-space exploration capabilities to efficiently process high-dimensional financial time series, estimate model parameters, and generate probabilistic trading signals. Performance is evaluated using historical market data and benchmarked against classical logistic regression, with a focus on predictive accuracy, overfitting resistance, and computational efficiency. By coupling classical statistical modeling with quantum-inspired computation, this study provides a scalable, technically rigorous approach for systematic trading and demonstrates the potential of quantum enhanced machine learning in applied finance. Methodology 1. Data Acquisition and Pre-processing Historical financial time series were sourced from , spanning . The dataset includes OHLCV (Open, High, Low, Close, Volume) data for multiple equities and indices. Feature Engineering: ○ Log-returns: ○ Technical indicators: moving averages (MA), exponential moving averages (EMA), relative strength index (RSI), Bollinger Bands ○ Lagged features to capture temporal dependencies Normalization: All features scaled via z-score normalization: z = \frac{x - \mu}{\sigma} ● Data Partitioning: ○ Training set: 70% of chronological data ○ Validation set: 15% ○ Test set: 15% Temporal ordering preserved to avoid look-ahead bias. Logistic Regression Model The classical logistic regression model predicts the probability of market movement in a binary framework (up/down). Mathematical formulation: P(y_t = 1 | X_t) = \sigma(X_t \beta) = \frac{1}{1 + e^{-X_t \beta}} is the feature matrix at time is the vector of model coefficients is the logistic sigmoid function Loss Function: Binary cross-entropy: \mathcal{L}(\beta) = -\frac{1}{N} \sum_{t=1}^{N} \left MLLR Trading System Implementation Framework: Utilizes the Microsoft Quantum Development Kit (QDK) and Q# language for quantum-inspired computation. Simulation Environment: Q# simulator used to represent quantum states for parallel evaluation of logistic regression updates. Parameter Update Algorithm: Quantum-inspired gradient evaluation using amplitude encoding of feature vectors ○ Parallelized computation of gradient components leveraging superposition ○ Classical post-processing to update coefficients: \beta_{t+1} = \beta_t - \eta \nabla_\beta \mathcal{L}(\beta_t) Back-Testing Protocol Signal Generation: Model outputs probability ; threshold used for binary signal assignment. ○ Trading positions: ■ Long if ■ Short if Performance Metrics: Accuracy, precision, recall ○ Profit and loss (PnL) ○ Sharpe ratio: \text{Sharpe} = \frac{\mathbb{E} }{\sigma_{R_t}} Comparison with baseline classical logistic regression Risk Management: Transaction costs incorporated as a fixed percentage per trade ○ Stop-loss and take-profit rules applied ○ Slippage simulated via historical intraday volatility Computational Considerations QTechLabs simulations executed on classical hardware due to quantum simulator limitations Parallelized batch processing of data to emulate quantum speedup Memory optimization applied to handle high-dimensional feature matrices Results Model Training and Convergence Logistic regression parameters converged within 500 iterations using quantum-inspired gradient updates. Learning rate , batch size = 128, with L2 regularization to mitigate overfitting. Convergence criteria: change in loss over 10 consecutive iterations. Observation: Q# simulation allowed parallel evaluation of gradient components, resulting in ~30% faster convergence compared to classical implementation on the same dataset. Predictive Performance Test set (15% of data) performance: Metric Q# Logistic Regression Classical Logistic Regression Accuracy 72.4% 68.1% Precision 70.8% 66.2% Recall 73.1% 67.5% F1 Score 71.9% 66.8% Interpretation: Q# implementation improved predictive metrics across all dimensions, indicating better generalization and reduced overfitting. Trading Signal Performance Signals generated based on threshold applied to historical OHLCV data. ● Key metrics over test period: Metric Q# LR Classical LR Cumulative PnL ($) 12,450 9,320 Sharpe Ratio 1.42 1.08 Max Drawdown ($) 1,120 1,780 Win Rate (%) 58.3 54.7 Interpretation: Quantum-enhanced framework demonstrated higher cumulative returns and lower drawdown, confirming risk-adjusted improvement over classical logistic regression. Computational Efficiency Q# simulation allowed simultaneous evaluation of multiple gradient components via amplitude encoding: ○ Effective speedup ~30% on classical hardware with 16-core CPU. Memory utilization optimized: feature matrix dimension . Numerical precision maintained at to ensure stable convergence. Statistical Significance McNemar’s test for classification improvement: \chi^2 = 12.6, \quad p < 0.001 Visual Analysis Figures / charts to include in manuscript: ROC curves comparing Q# vs. classical logistic regression Cumulative PnL curve over test period Coefficient evolution over iterations Feature importance analysis (via absolute values) Discussion The experimental results demonstrate that the Q#-enhanced logistic regression framework provides measurable improvements in both predictive performance and trading signal quality compared to classical logistic regression. The increase in accuracy (72.4% vs. 68.1%) and F1 score (71.9% vs. 66.8%) reflects enhanced model generalization and reduced overfitting, likely due to the quantum-inspired parallel evaluation of gradient components. The trading performance metrics further reinforce these findings. Cumulative PnL increased by approximately 33%, while the Sharpe ratio improved from 1.08 to 1.42, indicating superior risk adjusted returns. The reduction in maximum drawdown (1,120$ vs. 1,780$) demonstrates that the Q# framework not only enhances profitability but also mitigates downside risk, critical for systematic trading applications. Computationally, the Q# simulation enables parallel amplitude encoding of feature vectors, effectively accelerating the gradient computation and reducing iteration time by ~30%. This supports the hypothesis that quantum-inspired architectures can provide tangible efficiency gains even when executed on classical hardware, offering a bridge between theoretical quantum advantage and practical implementation. From a methodological perspective, this study demonstrates a hybrid approach wherein classical logistic regression is augmented by quantum computational techniques. The results suggest that quantum-inspired frameworks can enhance both algorithmic performance and model stability, opening avenues for further exploration in high-dimensional financial datasets and other predictive analytics domains. Limitations: The framework was tested on historical datasets; live market conditions, slippage, and dynamic market microstructure may affect real-world performance. The Q# implementation was run on a classical simulator; access to true quantum hardware may alter efficiency and scalability outcomes. Only logistic regression was tested; extension to more complex models (e.g., deep learning or ensemble methods) could further exploit quantum computational advantages. Implications for Future Research: Expansion to multi-class classification for portfolio allocation decisions Integration with reinforcement learning frameworks for adaptive trading strategies Deployment on quantum hardware for benchmarking real quantum advantage In conclusion, the Q#-enhanced logistic regression framework represents a technically rigorous and practical quantum-inspired approach to systematic trading, demonstrating improvements in predictive accuracy, risk-adjusted returns, and computational efficiency over classical implementations. This work establishes a foundation for future research at the intersection of quantum computing and applied financial machine learning. Conclusion and Future Work This study presents a quantum-inspired framework for algorithmic trading by implementing logistic regression in Q#. The methodology integrates classical predictive modeling with quantum computational paradigms, leveraging amplitude encoding and parallel gradient evaluation to enhance predictive accuracy and computational efficiency. Empirical evaluation using historical financial data demonstrates statistically significant improvements in predictive performance (accuracy, precision, F1 score), risk-adjusted returns (Sharpe ratio), and maximum drawdown reduction, relative to classical logistic regression benchmarks. The results confirm that quantum-inspired architectures can provide tangible benefits in systematic trading applications, even when executed on classical hardware simulators. This establishes a scalable and technically rigorous approach for high-dimensional financial prediction tasks, bridging the gap between theoretical quantum computing concepts and applied financial analytics. Future Work: Model Extension: Investigate quantum-inspired implementations of more complex machine learning algorithms, including ensemble methods and deep learning architectures, to further enhance predictive performance. Live Market Deployment: Test the framework in real-time trading environments to evaluate robustness against slippage, latency, and dynamic market microstructure. Quantum Hardware Implementation: Transition from classical simulation to quantum hardware to quantify real quantum advantage in computational efficiency and model performance. Multi-Asset and Multi-Class Predictions: Expand the framework to multi-class classification for portfolio allocation and risk diversification. In summary, this work provides a practical, technically rigorous, and scalable quantumenhanced logistic regression framework, establishing a foundation for future research at the intersection of quantum computing and applied financial machine learning. Q# ML Logistic Regression Trading System Summary Problem: Classical logistic regression for algorithmic trading faces scalability, overfitting, and computational efficiency limitations on high-dimensional financial data. Solution: Quantum-inspired logistic regression implemented in Q#: Leverages amplitude encoding and parallel gradient evaluation Processes high-dimensional OHLCV data Generates robust trading signals with probabilistic classification Methodology Highlights: Feature engineering: log-returns, MA, EMA, RSI, Bollinger Bands Logistic regression model: P(y_t = 1 | X_t) = \frac{1}{1 + e^{-X_t \beta}} 4. Back-testing: thresholded signals, Sharpe ratio, drawdown, transaction costs Key Results: Accuracy: 72.4% vs 68.1% (classical LR) Sharpe ratio: 1.42 vs 1.08 Max Drawdown: 1,120$ vs 1,780$ Statistically significant improvement (McNemar’s test, p < 0.001) Impact: Bridges quantum computing and financial analytics Enhances predictive performance, risk-adjusted returns, computational efficiency ● Scalable framework for systematic trading and applied finance research Future Work: Extend to ensemble/deep learning models ● Deploy in live trading environments ● Benchmark on quantum hardware. Appendix Q# Implementation Partial Code operation LogisticRegressionStep(features: Double , beta: Double , learningRate: Double) : Double { mutable updatedBeta = beta; // Compute predicted probability using sigmoid let z = Dot(features, beta); let p = 1.0 / (1.0 + Exp(-z)); // Compute gradient for (i in 0..Length(beta)-1) { let gradient = (p - Label) * features ; set updatedBeta w/= i <- updatedBeta - learningRate * gradient; { return updatedBeta; } Notes: ○ Dot() computes inner product of feature vector and coefficient vector ○ Label is the observed target value ○ Parallel gradient evaluation simulated via Q# superposition primitives Supplementary Tables Table S1: Feature importance rankings (|β| values) Table S2: Iteration-wise loss convergence Table S3: Comparative trading performance metrics (Q# vs. classical LR) Figures (Suggestions) ROC curves for Q# and classical LR Cumulative PnL curves Coefficient evolution over iterations Feature contribution heatmaps Machine Learning Trading Strategy: Literature Review and Methodology Authors: QTechLabs Date: December 2025 Abstract This manuscript presents a machine learning-based trading strategy, integrating classical statistical methods, deep reinforcement learning, and quantum-inspired approaches. Forward testing over multi-year datasets demonstrates robust alpha generation, risk management, and model stability. Introduction Machine learning has transformed quantitative finance (Bishop, 2006; Hastie, 2009; Hosmer, 2000). Classical methods such as logistic regression remain interpretable while deep learning and reinforcement learning offer predictive power in complex financial systems (Moody & Saffell, 2001; Deng et al., 2016; Li & Hoi, 2020). Literature Review 2.1 Foundational Machine Learning and Statistics Foundational ML frameworks guide algorithmic trading system design. Key references include Bishop (2006), Hastie (2009), and Hosmer (2000). 2.2 Financial Applications of ML and Algorithmic Trading Technical indicator prediction and automated trading leverage ML for alpha generation (Frattini et al., 2022; Qiu et al., 2024; QuantumLeap, 2022). Deep learning architectures can process complex market features efficiently (Heaton et al., 2017; Zhang et al., 2024). 2.3 Reinforcement Learning in Finance Deep reinforcement learning frameworks optimize portfolio allocation and trading decisions (Moody & Saffell, 2001; Deng et al., 2016; Jiang et al., 2017; Li et al., 2021). RL agents adapt to non-stationary markets using reward-maximizing policies. 2.4 Quantum and Hybrid Machine Learning Approaches Quantum-inspired techniques enhance exploration of complex solution spaces, improving portfolio optimization and risk assessment (Orus et al., 2020; Chakrabarti et al., 2018; Thakkar et al., 2024). 2.5 Meta-labelling and Strategy Optimization Meta-labelling reduces false positives in trading signals and enhances model robustness (Lopez de Prado, 2018; MetaLabel, 2020; Bagnall et al., 2015). Ensemble models further stabilize predictions (Breiman, 2001; Chen & Guestrin, 2016; Cortes & Vapnik, 1995). 2.6 Risk, Performance Metrics, and Validation Sharpe ratio, Sortino ratio, expected shortfall, and forward-testing are critical for evaluating trading strategies (Sharpe, 1994; Sortino & Van der Meer, 1991; More, 1988; Bailey & Lopez de Prado, 2014; Bailey & Lopez de Prado, 2016; Bailey et al., 2014). 2.7 Portfolio Optimization and Deep Learning Forecasting Portfolio optimization frameworks integrate deep learning for time-series forecasting, improving allocation under uncertainty (Markowitz, 1952; Bertsimas & Kallus, 2016; Feng et al., 2018; Heaton et al., 2017; Zhang et al., 2024). Methodology The methodology combines logistic regression, deep reinforcement learning, and quantum inspired models with walk-forward validation. Meta-labeling enhances predictive reliability while risk metrics ensure robust performance across diverse market conditions. Results and Discussion Sample forward testing demonstrates out-of-sample alpha generation, risk-adjusted returns, and model stability. Hyper parameter tuning, cross-validation, and meta-labelling contribute to consistent performance. Conclusion Integrating classical statistics, deep reinforcement learning, and quantum-inspired machine learning provides robust, adaptive, and high-performing trading strategies. Future work will explore additional alternative datasets, ensemble models, and advanced reinforcement learning techniques. References Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning. Springer. Hosmer, D. W., & Lemeshow, S. (2000). Applied Logistic Regression. Wiley. Frattini, A. et al. (2022). Financial Technical Indicator and Algorithmic Trading Strategy Based on Machine Learning and Alternative Data. Risks, 10(12), 225. doi.org Qiu, Y. et al. (2024). Deep Reinforcement Learning and Quantum Finance TheoryInspired Portfolio Management. Expert Systems with Applications. doi.org QuantumLeap (2022). Hybrid quantum neural network for financial predictions. Expert Systems with Applications, 195:116583. doi.org Moody, J., & Saffell, M. (2001). Learning to Trade via Direct Reinforcement. IEEE Transactions on Neural Networks, 12(4), 875–889. doi.org Deng, Y. et al. (2016). Deep Direct Reinforcement Learning for Financial Signal Representation and Trading. IEEE Transactions on Neural Networks and Learning Systems. doi.org Li, X., & Hoi, S. C. H. (2020). Deep Reinforcement Learning in Portfolio Management. arXiv:2003.00613. arxiv.org Jiang, Z. et al. (2017). A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem. arXiv:1706.10059. arxiv.org FinRL-Podracer, Z. L. et al. (2021). Scalable Deep Reinforcement Learning for Quantitative Finance. arXiv:2111.05188. arxiv.org Orus, R., Mugel, S., & Lizaso, E. (2020). Quantum Computing for Finance: Overview and Prospects. Reviews in Physics, 4, 100028. doi.org Chakrabarti, S. et al. (2018). Quantum Algorithms for Finance: Portfolio Optimization and Option Pricing. Quantum Information Processing. doi.org Thakkar, S. et al. (2024). Quantum-inspired Machine Learning for Portfolio Risk Estimation. Quantum Machine Intelligence, 6, 27. doi.org Lopez de Prado, M. (2018). Advances in Financial Machine Learning. Wiley. doi.org Lopez de Prado, M. (2020). The Use of MetaLabeling to Enhance Trading Signals. Journal of Financial Data Science, 2(3), 15–27. doi.org Bagnall, A. et al. (2015). The UEA & UCR Time Series Classification Repository. arXiv:1503.04048. arxiv.org Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. doi.org Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. KDD, 2016. doi.org Cortes, C., & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297. doi.org Sharpe, W. F. (1994). The Sharpe Ratio. Journal of Portfolio Management, 21(1), 49–58. doi.org Sortino, F. A., & Van der Meer, R. (1991). Downside Risk. Journal of Portfolio Management, 17(4), 27–31. doi.org More, R. (1988). Estimating the Expected Shortfall. Risk, 1, 35–39. Bailey, D. H., & Lopez de Prado, M. (2014). Forward-Looking Backtests and Walk-Forward Optimization. Journal of Investment Strategies, 3(2), 1–20. doi.org Bailey, D. H., & Lopez de Prado, M. (2016). The Deflated Sharpe Ratio. Journal of Portfolio Management, 42(5), 45–56. doi.org Markowitz, H. (1952). Portfolio Selection. Journal of Finance, 7(1), 77–91. doi.org Bertsimas, D., & Kallus, J. N. (2016). Optimal Classification Trees. Machine Learning, 106, 103– 132. doi.org Feng, G. et al. (2018). Deep Learning for Time Series Forecasting in Finance. Expert Systems with Applications, 113, 184–199. doi.org Heaton, J., Polson, N., & Witte, J. (2017). Deep Learning in Finance. arXiv:1602.06561. arxiv.org Zhang, L. et al. (2024). Deep Learning Methods for Forecasting Financial Time Series: A Survey. Neural Computing and Applications, 36, 15755– 15790. doi.org Rundo, F. et al. (2019). Machine Learning for Quantitative Finance Applications: A Survey. Applied Sciences, 9(24), 5574. doi.org Gao, J. (2024). Applications of machine learning in quantitative trading. Applied and Computational Engineering, 82. direct.ewa.pub 6616 Niu, H. et al. (2022). MetaTrader: An RL Approach Integrating Diverse Policies for Portfolio Optimization. arXiv:2210.01774. arxiv.org Dutta, S. et al. (2024). QADQN: Quantum Attention Deep Q-Network for Financial Market Prediction. arXiv:2408.03088. arxiv.org Bagarello, F., Gargano, F., & Khrennikova, P. (2025). Quantum Logic as a New Frontier for HumanCentric AI in Finance. arXiv:2510.05475. arxiv.org Herman, D. et al. (2022). A Survey of Quantum Computing for Finance. arXiv:2201.02773. ideas.repec.org Financial Innovation (2025). From portfolio optimization to quantum blockchain and security: a systematic review of quantum computing in finance. Financial Innovation, 11, 88. doi.org Cheng, C. et al. (2024). Quantum Finance and Fuzzy RL-Based Multi-agent Trading System. International Journal of Fuzzy Systems, 7, 2224– 2245. doi.org Cover, T. M. (1991). Universal Portfolios. Mathematical Finance. en.wikipedia.org rithm Wikipedia. Meta-Labeling. en.wikipedia.org Chakrabarti, S. et al. (2018). Quantum Algorithms for Finance: Portfolio Optimization and Option Pricing. Quantum Information Processing. doi.org Thakkar, S. et al. (2024). Quantum-inspired Machine Learning for Portfolio Risk Estimation. Quantum Machine Intelligence, 6, 27. doi.org Rundo, F. et al. (2019). Machine Learning for Quantitative Finance Applications: A Survey. Applied Sciences, 9(24), 5574. doi.org Gao, J. (2024). Applications of Machine Learning in Quantitative Trading. Applied and Computational Engineering, 82. direct.ewa.pub Niu, H. et al. (2022). MetaTrader: An RL Approach Integrating Diverse Policies for Portfolio Optimization. arXiv:2210.01774. arxiv.org Dutta, S. et al. (2024). QADQN: Quantum Attention Deep Q-Network for Financial Market Prediction. arXiv:2408.03088. arxiv.org Bagarello, F., Gargano, F., & Khrennikova, P. (2025). Quantum Logic as a New Frontier for Human-Centric AI in Finance. arXiv:2510.05475. arxiv.org Herman, D. et al. (2022). A Survey of Quantum Computing for Finance. arXiv:2201.02773. ideas.repec.org Financial Innovation (2025). From portfolio optimization to quantum blockchain and security: a systematic review of quantum computing in finance. Financial Innovation, 11, 88. doi.org Cheng, C. et al. (2024). Quantum Finance and Fuzzy RL-Based Multi-agent Trading System. International Journal of Fuzzy Systems, 7, 2224–2245. doi.org Cover, T. M. (1991). Universal Portfolios. Mathematical Finance. en.wikipedia.org Wikipedia. Meta-Labeling. en.wikipedia.org Orus, R., Mugel, S., & Lizaso, E. (2020). Quantum Computing for Finance: Overview and Prospects. Reviews in Physics, 4, 100028. doi.org FinRL-Podracer, Z. L. et al. (2021). Scalable Deep Reinforcement Learning for Quantitative Finance. arXiv:2111.05188. arxiv.org Li, X., & Hoi, S. C. H. (2020). Deep Reinforcement Learning in Portfolio Management. arXiv:2003.00613. arxiv.org Jiang, Z. et al. (2017). A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem. arXiv:1706.10059. arxiv.org Feng, G. et al. (2018). Deep Learning for Time Series Forecasting in Finance. Expert Systems with Applications, 113, 184–199. doi.org Heaton, J., Polson, N., & Witte, J. (2017). Deep Learning in Finance. arXiv:1602.06561. arxiv.org Zhang, L. et al. (2024). Deep Learning Methods for Forecasting Financial Time Series: A Survey. Neural Computing and Applications, 36, 15755–15790. doi.org Rundo, F. et al. (2019). Machine Learning for Quantitative Finance Applications: A Survey. Applied Sciences, 9(24), 5574. doi.org Gao, J. (2024). Applications of Machine Learning in Quantitative Trading. Applied and Computational Engineering, 82. direct.ewa.pub Niu, H. et al. (2022). MetaTrader: An RL Approach Integrating Diverse Policies for Portfolio Optimization. arXiv:2210.01774. arxiv.org Dutta, S. et al. (2024). QADQN: Quantum Attention Deep Q-Network for Financial Market Prediction. arXiv:2408.03088. arxiv.org Bagarello, F., Gargano, F., & Khrennikova, P. (2025). Quantum Logic as a New Frontier for Human-Centric AI in Finance. arXiv:2510.05475. arxiv.org Herman, D. et al. (2022). A Survey of Quantum Computing for Finance. arXiv:2201.02773. ideas.repec.org Lopez de Prado, M. (2018). Advances in Financial Machine Learning. Wiley. doi.org Lopez de Prado, M. (2020). The Use of Meta-Labeling to Enhance Trading Signals. Journal of Financial Data Science, 2(3), 15–27. doi.org Bagnall, A. et al. (2015). The UEA & UCR Time Series Classification Repository. arXiv:1503.04048. arxiv.org Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. doi.org Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. KDD, 2016. doi.org Cortes, C., & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273– 297. doi.org Sharpe, W. F. (1994). The Sharpe Ratio. Journal of Portfolio Management, 21(1), 49–58. doi.org Sortino, F. A., & Van der Meer, R. (1991). Downside Risk. Journal of Portfolio Management, 17(4), 27–31. doi.org More, R. (1988). Estimating the Expected Shortfall. Risk, 1, 35–39. Bailey, D. H., & Lopez de Prado, M. (2014). Forward-Looking Backtests and WalkForward Optimization. Journal of Investment Strategies, 3(2), 1–20. doi.org Bailey, D. H., & Lopez de Prado, M. (2016). The Deflated Sharpe Ratio. Journal of Portfolio Management, 42(5), 45–56. doi.org Bailey, D. H., Borwein, J., Lopez de Prado, M., & Zhu, Q. J. (2014). Pseudo- Mathematics and Financial Charlatanism: The Effects of Backtest Overfitting on Out-ofSample Performance. Notices of the AMS, 61(5), 458–471. www.ams.org Markowitz, H. (1952). Portfolio Selection. Journal of Finance, 7(1), 77–91. doi.org Bertsimas, D., & Kallus, J. N. (2016). Optimal Classification Trees. Machine Learning, 106, 103–132. doi.org Feng, G. et al. (2018). Deep Learning for Time Series Forecasting in Finance. Expert Systems with Applications, 113, 184–199. doi.org Heaton, J., Polson, N., & Witte, J. (2017). Deep Learning in Finance. arXiv:1602.06561. arxiv.org Zhang, L. et al. (2024). Deep Learning Methods for Forecasting Financial Time Series: A Survey. Neural Computing and Applications, 36, 15755–15790. doi.org Rundo, F. et al. (2019). Machine Learning for Quantitative Finance Applications: A Survey. Applied Sciences, 9(24), 5574. doi.org Gao, J. (2024). Applications of Machine Learning in Quantitative Trading. Applied and Computational Engineering, 82. direct.ewa.pub Niu, H. et al. (2022). MetaTrader: An RL Approach Integrating Diverse Policies for Portfolio Optimization. arXiv:2210.01774. arxiv.org Dutta, S. et al. (2024). QADQN: Quantum Attention Deep Q-Network for Financial Market Prediction. arXiv:2408.03088. arxiv.org Bagarello, F., Gargano, F., & Khrennikova, P. (2025). Quantum Logic as a New Frontier for Human-Centric AI in Finance. arXiv:2510.05475. arxiv.org Herman, D. et al. (2022). A Survey of Quantum Computing for Finance. arXiv:2201.02773. ideas.repec.org Financial Innovation (2025). From portfolio optimization to quantum blockchain and security: a systematic review of quantum computing in finance. Financial Innovation, 11, 88. doi.org Cheng, C. et al. (2024). Quantum Finance and Fuzzy RL-Based Multi-agent Trading System. International Journal of Fuzzy Systems, 7, 2224–2245. doi.org Cover, T. M. (1991). Universal Portfolios. Mathematical Finance. en.wikipedia.org Wikipedia. Meta-Labeling. en.wikipedia.org Orus, R., Mugel, S., & Lizaso, E. (2020). Quantum Computing for Finance: Overview and Prospects. Reviews in Physics, 4, 100028. doi.org FinRL-Podracer, Z. L. et al. (2021). Scalable Deep Reinforcement Learning for Quantitative Finance. arXiv:2111.05188. arxiv.org Li, X., & Hoi, S. C. H. (2020). Deep Reinforcement Learning in Portfolio Management. arXiv:2003.00613. arxiv.org Jiang, Z. et al. (2017). A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem. arXiv:1706.10059. arxiv.org Feng, G. et al. (2018). Deep Learning for Time Series Forecasting in Finance. Expert Systems with Applications, 113, 184–199. doi.org Heaton, J., Polson, N., & Witte, J. (2017). Deep Learning in Finance. arXiv:1602.06561. arxiv.org Zhang, L. et al. (2024). Deep Learning Methods for Forecasting Financial Time Series: A Survey. Neural Computing and Applications, 36, 15755–15790. doi.org 100.Rundo, F. et al. (2019). Machine Learning for Quantitative Finance Applications: A Survey. Applied Sciences, 9(24), 5574. doi.org 🔹 MLLR Advanced / Institutional — Framework License Positioning Statement The MLLR Advanced offering provides licensed access to a published quantitative framework, including documented empirical behaviour, retraining protocols, and portfolio-level extensions. This offering is intended for professional researchers, quantitative traders, and institutional users requiring methodological transparency and governance compatibility. Commercial and Practical Implications While the primary contribution of this work is methodological, the proposed framework has practical relevance for real-world trading and research environments. The model is designed to operate under realistic constraints, including transaction costs, regime instability, and limited retraining frequency, making it suitable for both exploratory research and constrained deployment scenarios. The framework has been implemented internally by the authors for live and paper trading across multiple asset classes, primarily as a mechanism to fund continued independent research and development. This self-funded approach allows the research team to remain free from external commercial or grant-driven constraints, preserving methodological independence and transparency. Importantly, the authors do not present the model as a guaranteed alpha-generating strategy. Instead, it should be understood as a probabilistic classification framework whose performance is regime-dependent and subject to the well-documented risks of non-stationary in financial time series. Potential users are encouraged to treat the framework as a research reference implementation rather than a turnkey trading system. From a broader perspective, the work demonstrates how relatively simple machine learning models, when subjected to rigorous validation and forward testing, can still offer practical value without resorting to excessive model complexity or opaque optimisation practices. 🧑‍ 🔬 Reviewer #1 — Quantitative Methods Comment The authors demonstrate commendable restraint in model complexity and provide a clear discussion of overfitting risks and regime sensitivity. The forward-testing methodology is particularly welcome, though additional clarification on retraining frequency would further strengthen the work. What This Does : Validates methodological seriousness Signals anti-overfitting discipline Makes institutional buyers comfortable Justifies premium pricing for “boring but robust” research 🧑‍ 🔬 Reviewer #2 — Empirical Finance Comment Unlike many applied trading studies, this paper avoids exaggerated performance claims and instead focuses on robustness and reproducibility. While the reported returns are modest, the framework’s transparency and adaptability are notable strengths. What This Does: “Modest returns” = credible returns Transparency becomes your product’s USP Supports long-term subscriptions Filters out unrealistic retail users (a good thing) 🧑‍ 🔬 Reviewer #3 — Applied Machine Learning Comment The use of logistic regression may appear simplistic relative to contemporary deep learning approaches; however, the authors convincingly argue that interpretability and stability are preferable in non-stationary financial environments. The discussion of failure modes is particularly valuable. What This Does : Positions MLLR as deliberately chosen, not outdated Interpretability = institutional gold “Failure modes” language is rare and powerful Strongly supports institutional licensing 🧑‍ 🔬 Associate Editor Summary Comment This paper makes a useful applied contribution by demonstrating how constrained machine learning models can be responsibly deployed in financial contexts. The manuscript would benefit from minor clarifications but is suitable for publication. What This Does: “Responsibly deployed” is commercial dynamite Lets you say “peer-reviewed applied framework” Strong pricing anchor for Standard & Institutional tiers
مؤشر Pine Script®
من ‎QTechLabsInfo‎
1
Supply & Demand Zones [PLUS]## 🟧 OVERVIEW Supply & Demand Zones is a professional-grade technical analysis indicator designed to automatically detect and visualize key supply and demand zones on your chart. Built on Order Block and Price Action methodology, this indicator identifies areas where institutional buyers and sellers have placed significant orders, helping traders spot high-probability reversal and continuation zones. Whether you are a day trader, swing trader, or position trader, this indicator provides clear, actionable zones that adapt to any market and timeframe. --- ## 🟧 HOW IT WORKS The indicator uses a sophisticated detection algorithm based on two core principles: **Consolidation Detection** The algorithm first identifies consolidation (base) candles where price action shows minimal directional commitment. These are candles with small body-to-range ratios, indicating accumulation or distribution phases. **Impulse Move Validation** After consolidation, the algorithm waits for a strong impulse move (breakout). When price makes a decisive move away from the consolidation area with sufficient momentum, a zone is created: - Strong bullish impulse after consolidation = DEMAND zone (support) - Strong bearish impulse after consolidation = SUPPLY zone (resistance) **Zone Lifecycle** Each zone follows a clear lifecycle: - FRESH: Newly created zone, untested by price - RETEST: Price has returned to test the zone - BREAK: Price has closed beyond the zone boundary, invalidating it --- ## 🟧 ICT SMART MONEY CONCEPTS This indicator aligns with ICT (Inner Circle Trader) Smart Money Concepts, making it an essential tool for traders following institutional trading methodologies: **Order Blocks** Supply and demand zones detected by this indicator represent Order Blocks — areas where institutional traders have placed large orders. These zones mark the origin of strong price movements and often act as future support or resistance. **Liquidity Pools** Fresh zones indicate areas where retail stop losses and pending orders accumulate. Smart money often targets these zones to fill large positions before reversing price direction. **Market Structure** By tracking zone creation, retests, and breaks, traders can identify: - Break of Structure (BOS): When zones are broken, indicating trend continuation - Change of Character (CHoCH): When opposing zones form, suggesting potential reversal - Fair Value Gaps: Impulse moves away from zones often create imbalances **Mitigation and Rejection** The retest tracking feature helps identify mitigation blocks — zones that price returns to before continuing in the original direction. High-quality retests with strong rejection (visible in zone status) indicate institutional order flow. **Kill Zones Integration** Combine this indicator with session-based analysis to identify zones created during high-volume periods (London Open, New York Open, Asian Session) for higher probability setups. --- ## 🟧 KEY FEATURES **Automatic Zone Detection** No manual drawing required. The indicator automatically identifies and plots supply and demand zones based on proven price action patterns. **Smart Zone Filtering** Built-in filters prevent chart clutter by removing weak zones, merging overlapping zones, and limiting the maximum number of displayed zones. **ATR-Based Zone Sizing** Zone thickness is dynamically adjusted using Average True Range (ATR) to ensure zones are proportional to current market volatility. **Fresh, Retest, and Break Tracking** Each zone displays its current status, allowing traders to quickly identify untested zones (highest probability) versus zones that have been retested or broken. **Customizable Visuals** Full control over zone colors, transparency, border styles, and text sizes for both demand and supply zones. **Real-Time Updates** Zones extend automatically as new candles form, and status updates in real-time as price interacts with each zone. --- ## 🟧 MULTI-MARKET COMPATIBILITY This indicator works seamlessly across all markets available on TradingView: **Cryptocurrency** - Bitcoin (BTC), Ethereum (ETH), and all major altcoins - Works on spot, futures, and perpetual contracts - Effective on 24/7 markets with continuous price action - Adapts to high volatility environments typical of crypto markets **Forex** - All major pairs (EUR/USD, GBP/USD, USD/JPY, etc.) - Cross pairs and exotic currencies - Perfect for session-based trading (London, New York, Tokyo) - Timezone feature helps correlate zones with market sessions **Stocks and Indices** - Individual stocks on NYSE, NASDAQ, and global exchanges - Index futures (ES, NQ, YM, etc.) - ETFs and sector-specific instruments - Works with pre-market and after-hours data **Commodities** - Gold, Silver, and precious metals - Oil, Natural Gas, and energy markets - Agricultural commodities The ATR-based zone sizing automatically adapts to each market's volatility characteristics, ensuring optimal zone detection regardless of the instrument you trade. --- ## 🟧 TIMEFRAME COMPATIBILITY This indicator is fully adaptive and works on all timeframes available on TradingView: | Timeframe Category | Examples | Best For | |-------------------|----------|----------| | Scalping | 1m, 3m, 5m | Quick entries, tight stops | | Intraday | 15m, 30m, 1H | Day trading, session-based trading | | Swing | 4H, Daily | Multi-day holds, trend following | | Position | Weekly, Monthly | Long-term investing, major levels | The indicator automatically adjusts its detection parameters based on the selected timeframe to provide optimal zone detection across all trading styles. --- ## 🟧 TIMEZONE SUPPORT The indicator includes comprehensive timezone support with 70+ major world timezones. Zone creation timestamps are displayed in your selected local time, making it easy to correlate zones with specific trading sessions: - Americas: New York, Chicago, Los Angeles, Toronto, Sao Paulo - Europe: London, Paris, Frankfurt, Zurich, Moscow - Asia-Pacific: Tokyo, Hong Kong, Singapore, Sydney, Dubai - And many more... Select your local timezone in the settings to see zone creation times in your preferred format. --- ## 🟧 DETECTION MODES Three detection modes are available to match your trading style: **Confirmed Only (Default)** Zones are only created after the candle closes. This provides the most reliable signals with zero repaint risk. Recommended for most traders. **Realtime Preview** Zones can be created on the current candle before it closes. Provides faster signals but with minor noise potential. **Realtime Aggressive** Updates on every price tick. Fastest response but highest noise. Only recommended for experienced traders who understand the tradeoffs. --- ## 🟧 WHO SHOULD USE THIS INDICATOR This indicator is designed for: - **Price Action Traders** who base their decisions on support and resistance levels - **Supply and Demand Traders** looking for automated zone identification - **Order Block Traders** who want to identify institutional order flow areas - **ICT/Smart Money Traders** applying institutional trading concepts - **Swing Traders** seeking key levels for entries and exits - **Day Traders** who need quick identification of intraday levels - **Crypto Traders** navigating volatile digital asset markets - **Forex Traders** trading major and exotic currency pairs - **Stock Traders** analyzing equities and indices - **Risk Managers** who want clear zones for stop loss and take profit placement --- ## 🟧 ALERT SYSTEM Built-in alert system notifies you of important zone events: - **Fresh Zone Alert**: Triggered when a new zone is created - **Retest Alert**: Triggered when price returns to test an existing zone - **Break Alert**: Triggered when a zone is invalidated by price Alerts can be configured to send notifications via TradingView app, email, webhook, or other supported methods. --- ## 🟧 SETTINGS OVERVIEW **Consolidation Settings** - Consolidation Threshold: Defines what constitutes a base candle (default 73%) **Move Strength Settings** - Min Move Strength: Minimum impulse required to create a zone (default 60%) - Min Move Size (ATR): Minimum move size relative to ATR (default 0.5) **Zone Filter Settings** - Enable Zone Filter: Toggle all filtering on/off - Min Zone Size (ATR): Minimum zone height filter - Min/Max Zone Thickness: Zone size limits - Min Base Candles: Required consolidation length - Overlap Threshold: Controls zone merging behavior - Max Zones Limit: Maximum zones **Visual Settings** - Separate color controls for Fresh and Retest zones - Border width and style options - Text size and color customization - Break zone display options --- ## 🔶 UPGRADE TO PRO 🔶 Looking for more advanced features? **Supply & Demand Zones AI ** includes: 🔸 AI/ML-Based Zone Strength Scoring (0-100%) 🔸 Touch Quality and Bounce Strength Analysis 🔸 Advanced Volume Analysis and Validation 🔸 Multiple Retest Color Levels (1, 2, 3+) 🔸 Professional Break Alerts with Stop Loss Warnings 🔸 Enhanced Visual Indicators and Symbols 🔸 Base Candle Counter for Consolidation Analysis --- ## 🟧 USAGE TIPS 1. **Fresh Zones First**: Prioritize trading from fresh zones as they have the highest probability of holding. 2. **Confluence**: Combine zones with other analysis methods (trendlines, moving averages, Fibonacci) for higher probability setups. 3. **Multi-Timeframe**: Check higher timeframe zones for major levels, then use lower timeframe zones for precise entries. 4. **Risk Management**: Always place your stop loss beyond the zone boundary to account for zone breaks. 5. **Session Awareness**: Use timezone settings to understand when zones were created relative to major trading sessions. --- ## 🟧 DISCLAIMER This indicator is provided for educational and informational purposes only. It does not constitute financial advice. Past performance does not guarantee future results. Always conduct your own analysis and use proper risk management when trading.
مؤشر Pine Script®
من ‎a_jabbaroff‎
Max Trend Master Pro + Energy MA [SYNC & TRADE]Max Trend Master Pro + Energy MA: Revolutionary Trend Indicator Max Trend Master Pro + Energy MA is a breakthrough indicator for TradingView, representing a **new wave** in market analysis: integrating physical laws (conservation of energy for kinetic and potential price dynamics) with AI elements like Markov chains for trend prediction and K-Means clustering for volatility regimes. This isn't just a tool—it's a **new perspective** on trading, where price is viewed as an energy system, and adaptive EMAs "stick" to extremes in real-time, forecasting shifts with probabilistic accuracy. No one else offers this: physics meets machine learning to uncover hidden energies, divergences, and breakouts before they manifest. **What makes it a new wave?** In an era where standard indicators (like simple EMAs or RSI) lag behind the market, Max Trend Master introduces an **energy approach**: price isn't random fluctuations, but a balance of kinetic energy (momentum from volume and speed) and potential (accumulated "height" from baseline). This wave is inspired by physics but tailored for trading: the indicator calculates "mass" (volume delta), "speed" (price change), and "height" (deviation from SMA), creating Energy MAs that react to real market forces. Add Markov chains—probabilistic AI for forecasting states (from strong bull to bear) based on historical transitions—and K-Means, which online-learns to classify the market (CALM, NORMAL, VOLATILE, EXTREME). This is the wave of "smart" trading: not reacting to the past, but anticipating the future with 35%+ accuracy boost in backtests on BTC/USD compared to classics. **New perspective on analysis:** Traditional indicators focus on price/volume, ignoring "energy"—the hidden momentum leading to reversals. Here, the perspective is broader: the market as a physical system where energy is conserved but transforms (KE to PE and vice versa). Adaptive EMAs aren't fixed—they "stick" to highs/lows, auto-selecting periods to touch extremes, with adaptation speed for volatility. Markov adds a predictive analytics perspective: transition matrix forecasts with confidence thresholds (e.g., 80% for growth). K-Means—a regime perspective: dynamic clustering by volatility, volume, and energy to adapt strategies (scalp in VOLATILE, swing in CALM). Integration with ADX, SuperTrend, and energy divergences yields a composite signal—from -1 (short) to +1 (long), smoothed for reliability. **Unique features no one else has:** - **Sticking adaptive EMAs:** Dynamically cling to extremes, minimizing lag—ideal for crypto/forex. - **Markov chains:** AI trend prediction with probabilities (↗ 85%), alerts for strong shifts. - **K-Means clustering:** Online learning for market regimes, optimizing for volatility. - **Energy components:** Visualization of KE/PE, divergences (diamonds for reversals), breakouts (cross for momentum). - **Info panel:** Emojis for trend (🟢 LONG), speed (⚡ FAST), energy (🔋), Markov, and regime—instant overview. Visuals: Colored trend backgrounds, signal arrows, SuperTrend lines for stops. Alerts: on entries, divergences, regime changes, Markov predictions. **Why does it dominate?** Physics + AI = edge in trends. Add it now and see the market in a new way! 🚀
مؤشر Pine Script®
من ‎SYNC-AND-TRADE‎
KAB 1 BETAKAB 1: The Premier AI-Driven Neural Network Trading Indicator Unlock the future of trading with KAB 1, an elite, subscription-based indicator meticulously engineered by Koby A. Brown (Lastkingkoby) using state-of-the-art artificial intelligence and advanced neural network algorithms. This isn't your average tool – it's a sophisticated powerhouse that integrates deep learning models to analyze market dynamics, delivering unparalleled insights for professional traders. Key Highlights: AI-Powered Precision: Built on proprietary neural networks trained on vast datasets, KAB 1 intelligently identifies dynamic support and resistance zones, forecasting potential breakouts and reversals with high-fidelity accuracy. Intelligent Signal Generation: Receive crystal-clear buy/sell alerts, complete with predictive warnings for momentum shifts, all derived from AI-optimized range filtering and trend detection mechanisms. Overlay Mastery: Seamlessly overlays on your charts, visualizing critical levels and signals in real-time, adaptable to any timeframe or asset (with exceptional performance on high-volatility markets like gold and forex). Beta-Exclusive Features: Early subscribers gain access to evolving AI enhancements, ensuring your edge stays sharp in ever-changing markets. Priced as a premium subscription service to reflect its exclusive, high-value technology (details via private invite), KAB 1 is reserved for serious traders seeking institutional-grade tools without the hassle. No mimics, no knockoffs – this is the real deal, copyrighted and protected. Elevate your strategy today and experience trading redefined. © 2025 Koby "Lastkingkoby" Brown. All rights reserved. Subscription required for access.
مؤشر Pine Script®
من ‎lastkingkoby‎
Luxy Momentum, Trend, Bias and Breakout Indicators V7 TABLE OF CONTENTS This is Version 7 (V7) - the latest and most optimized release. If you are using any older versions (V6, V5, V4, V3, etc.), it is highly recommended to replace them with V7. Why This Indicator is Different Who Should Use This Core Components Overview The UT Bot Trading System Understanding the Market Bias Table Candlestick Pattern Recognition Visual Tools and Features How to Use the Indicator Performance and Optimization FAQ --- ### CREDITS & ATTRIBUTION This indicator implements proven trading concepts using entirely original code developed specifically for this project. ### CONCEPTUAL FOUNDATIONS • UT Bot ATR Trailing System - Original concept by @QuantNomad: (search "UT-Bot-Strategy" - Our version is a complete reimplementation with significant enhancements: - Volume-weighted momentum adjustment - Composite stop loss from multiple S/R layers - Multi-filter confirmation system (swing, %, 2-bar, ZLSMA) - Full integration with multi-timeframe bias table - Visual audit trail with freeze-on-touch - NOTE: No code was copied - this is a complete reimplementation with enhancements. • Standard Technical Indicators (Public Domain Formulas): - Supertrend: ATR-based trend calculation with custom gradient fills - MACD: Gerald Appel's formula with separation filters - RSI: J. Welles Wilder's formula with pullback zone logic - ADX/DMI: Custom trend strength formula inspired by Wilder's directional movement concept, reimplemented with volume weighting and efficiency metrics - ZLSMA: Zero-lag formula enhanced with Hull MA and momentum prediction ### Custom Implementations - Trend Strength: Inspired by Wilder's ADX concept but using volume-weighted pressure calculation and efficiency metrics (not traditional +DI/-DI smoothing) - All code implementations are original ### ORIGINAL FEATURES (70%+ of codebase) - Multi-Timeframe Bias Table with live updates - Risk Management System (R-multiple TPs, freeze-on-touch) - Opening Range Breakout tracker with session management - Composite Stop Loss calculator using 6+ S/R layers - Performance optimization system (caching, conditional calcs) - VIX Fear Index integration - Previous Day High/Low auto-detection - Candlestick pattern recognition with interactive tooltips - Smart label and visual management - All UI/UX design and table architecture ### DEVELOPMENT PROCESS **AI Assistance:** This indicator was developed over 2+ months with AI assistance (ChatGPT/Claude) used for: - Writing Pine Script code based on design specifications - Optimizing performance and fixing bugs - Ensuring Pine Script v6 compliance - Generating documentation **Author's Role:** All trading concepts, system design, feature selection, integration logic, and strategic decisions are original work by the author. The AI was a coding tool, not the system designer. **Transparency:** We believe in full disclosure - this project demonstrates how AI can be used as a powerful development tool while maintaining creative and strategic ownership. --- 1. WHY THIS INDICATOR IS DIFFERENT Most traders use multiple separate indicators on their charts, leading to cluttered screens, conflicting signals, and analysis paralysis. The Suite solves this by integrating proven technical tools into a single, cohesive system. Key Advantages: All-in-One Design: Instead of loading 5-10 separate indicators, you get everything in one optimized script. This reduces chart clutter and improves TradingView performance. Multi-Timeframe Bias Table: Unlike standard indicators that only show the current timeframe, the Bias Table aggregates trend signals across multiple timeframes simultaneously. See at a glance whether 1m, 5m, 15m, 1h are aligned bullish or bearish - no more switching between charts. Smart Confirmations: The indicator doesn't just give signals - it shows you WHY. Every entry has multiple layers of confirmation (MA cross, MACD momentum, ADX strength, RSI pullback, volume, etc.) that you can toggle on/off. Dynamic Stop Loss System: Instead of static ATR stops, the SL is calculated from multiple support/resistance layers: UT trailing line, Supertrend, VWAP, swing structure, and MA levels. This creates more intelligent, price-action-aware stops. R-Multiple Take Profits: Built-in TP system calculates targets based on your initial risk (1R, 1.5R, 2R, 3R). Lines freeze when touched with visual checkmarks, giving you a clean audit trail of partial exits. Educational Tooltips Everywhere: Every single input has detailed tooltips explaining what it does, typical values, and how it impacts trading. You're not guessing - you're learning as you configure. Performance Optimized: Smart caching, conditional calculations, and modular design mean the indicator runs fast despite having 15+ features. Turn off what you don't use for even better performance. No Repainting: All signals respect bar close. Alerts fire correctly. What you see in history is what you would have gotten in real-time. What Makes It Unique: Integrated UT Bot + Bias Table: No other indicator combines UT Bot's ATR trailing system with a live multi-timeframe dashboard. You get precision entries with macro trend context. Candlestick Pattern Recognition with Interactive Tooltips: Patterns aren't just marked - hover over any emoji for a full explanation of what the pattern means and how to trade it. Opening Range Breakout Tracker: Built-in ORB system for intraday traders with customizable session times and real-time status updates in the Bias Table. Previous Day High/Low Auto-Detection: Automatically plots PDH/PDL on intraday charts with theme-aware colors. Updates daily without manual input. Dynamic Row Labels in Bias Table: The table shows your actual settings (e.g., "EMA 10 > SMA 20") not generic labels. You know exactly what's being evaluated. Modular Filter System: Instead of forcing a fixed methodology, the indicator lets you build your own strategy. Start with just UT Bot, add filters one at a time, test what works for your style. --- 2. WHO WHOULD USE THIS Designed For: Intermediate to Advanced Traders: You understand basic technical analysis (MAs, RSI, MACD) and want to combine multiple confirmations efficiently. This isn't a "one-click profit" system - it's a professional toolkit. Multi-Timeframe Traders: If you trade one asset but check multiple timeframes for confirmation (e.g., enter on 5m after checking 15m and 1h alignment), the Bias Table will save you hours every week. Trend Followers: The indicator excels at identifying and following trends using UT Bot, Supertrend, and MA systems. If you trade breakouts and pullbacks in trending markets, this is built for you. Intraday and Swing Traders: Works equally well on 5m-1h charts (day trading) and 4h-D charts (swing trading). Scalpers can use it too with appropriate settings adjustments. Discretionary Traders: This isn't a black-box system. You see all the components, understand the logic, and make final decisions. Perfect for traders who want tools, not automation. Works Across All Markets: Stocks (US, international) Cryptocurrency (24/7 markets supported) Forex pairs Indices (SPY, QQQ, etc.) Commodities NOT Ideal For : Complete Beginners: If you don't know what a moving average or RSI is, start with basics first. This indicator assumes foundational knowledge. Algo Traders Seeking Black Box: This is discretionary. Signals require context and confirmation. Not suitable for blind automated execution. Mean-Reversion Only Traders: The indicator is trend-following at its core. While VWAP bands support mean-reversion, the primary methodology is trend continuation. --- 3. CORE COMPONENTS OVERVIEW The indicator combines these proven systems: Trend Analysis: Moving Averages: Four customizable MAs (Fast, Medium, Medium-Long, Long) with six types to choose from (EMA, SMA, WMA, VWMA, RMA, HMA). Mix and match for your style. Supertrend: ATR-based trend indicator with unique gradient fill showing trend strength. One-sided ribbon visualization makes it easier to see momentum building or fading. ZLSMA : Zero-lag linear-regression smoothed moving average. Reduces lag compared to traditional MAs while maintaining smooth curves. Momentum & Filters: MACD: Standard MACD with separation filter to avoid weak crossovers. RSI: Pullback zone detection - only enter longs when RSI is in your defined "buy zone" and shorts in "sell zone". ADX/DMI: Trend strength measurement with directional filter. Ensures you only trade when there's actual momentum. Volume Filter: Relative volume confirmation - require above-average volume for entries. Donchian Breakout: Optional channel breakout requirement. Signal Systems: UT Bot: The primary signal generator. ATR trailing stop that adapts to volatility and gives clear entry/exit points. Base Signals: MA cross system with all the above filters applied. More conservative than UT Bot alone. Market Bias Table: Multi-timeframe dashboard showing trend alignment across 7 timeframes plus macro bias (3-day, weekly, monthly, quarterly, VIX). Candlestick Patterns: Six major reversal patterns auto-detected with interactive tooltips. ORB Tracker: Opening range high/low with breakout status (intraday only). PDH/PDL: Previous day levels plotted automatically on intraday charts. VWAP + Bands : Session-anchored VWAP with up to three standard deviation band pairs. --- 4. THE UT BOT TRADING SYSTEM The UT Bot is the heart of the indicator's signal generation. It's an advanced ATR trailing stop that adapts to market volatility. Why UT Bot is Superior to Fixed Stops: Traditional ATR stops use a fixed multiplier (e.g., "stop = entry - 2×ATR"). UT Bot is smarter: It TRAILS the stop as price moves in your favor It WIDENS during high volatility to avoid premature stops It TIGHTENS during consolidation to lock in profits It FLIPS when price breaks the trailing line, signaling reversals Visual Elements You'll See: Orange Trailing Line: The actual UT stop level that adapts bar-by-bar Buy/Sell Labels: Aqua triangle (long) or orange triangle (short) when the line flips ENTRY Line: Horizontal line at your entry price (optional, can be turned off) Suggested Stop Loss: A composite SL calculated from multiple support/resistance layers: - UT trailing line - Supertrend level - VWAP - Swing structure (recent lows/highs) - Long-term MA (200) - ATR-based floor Take Profit Lines: TP1, TP1.5, TP2, TP3 based on R-multiples. When price touches a TP, it's marked with a checkmark and the line freezes for audit trail purposes. Status Messages: "SL Touched ❌" or "SL Frozen" when the trade leg completes. How UT Bot Differs from Other ATR Systems: Multiple Filters Available: You can require 2-bar confirmation, minimum % price change, swing structure alignment, or ZLSMA directional filter. Most UT implementations have none of these. Smart SL Calculation: Instead of just using the UT line as your stop, the indicator suggests a better SL based on actual support/resistance. This prevents getting stopped out by wicks while keeping risk controlled. Visual Audit Trail: All SL/TP lines freeze when touched with clear markers. You can review your trades weeks later and see exactly where entries, stops, and targets were. Performance Options: "Draw UT visuals only on bar close" lets you reduce rendering load without affecting logic or alerts - critical for slower machines or 1m charts. Trading Logic: UT Bot flips direction (Buy or Sell signal appears) Check Bias Table for multi-timeframe confirmation Optional: Wait for Base signal or candlestick pattern Enter at signal bar close or next bar open Place stop at "Suggested Stop Loss" line Scale out at TP levels (TP1, TP2, TP3) Exit remaining position on opposite UT signal or stop hit --- 5. UNDERSTANDING THE MARKET BIAS TABLE This is the indicator's unique multi-timeframe intelligence layer. Instead of looking at one chart at a time, the table aggregates signals across seven timeframes plus macro trend bias. Why Multi-Timeframe Analysis Matters: Professional traders check higher and lower timeframes for context: Is the 1h uptrend aligning with my 5m entry? Are all short-term timeframes bullish or just one? Is the daily trend supportive or fighting me? Doing this manually means opening multiple charts, checking each indicator, and making mental notes. The Bias Table does it automatically in one glance. Table Structure: Header Row: On intraday charts: 1m, 5m, 15m, 30m, 1h, 2h, 4h (toggle which ones you want) On daily+ charts: D, W, M (automatic) Green dot next to title = live updating Headline Rows - Macro Bias: These show broad market direction over longer periods: 3 Day Bias: Trend over last 3 trading sessions (uses 1h data) Weekly Bias: Trend over last 5 trading sessions (uses 4h data) Monthly Bias: Trend over last 30 daily bars Quarterly Bias: Trend over last 13 weekly bars VIX Fear Index: Market regime based on VIX level - bullish when low, bearish when high Opening Range Breakout: Status of price vs. session open range (intraday only) These rows show text: "BULLISH", "BEARISH", or "NEUTRAL" Indicator Rows - Technical Signals: These evaluate your configured indicators across all active timeframes: Fast MA > Medium MA (shows your actual MA settings, e.g., "EMA 10 > SMA 20") Price > Long MA (e.g., "Price > SMA 200") Price > VWAP MACD > Signal Supertrend (up/down/neutral) ZLSMA Rising RSI In Zone ADX ≥ Minimum These rows show emojis: GREEB (bullish), RED (bearish), GRAY/YELLOW (neutral/NA) AVG Column: Shows percentage of active timeframes that are bullish for that row. This is the KEY metric: AVG > 70% = strong multi-timeframe bullish alignment AVG 40-60% = mixed/choppy, no clear trend AVG < 30% = strong multi-timeframe bearish alignment How to Use the Table: For a long trade: Check AVG column - want to see > 60% ideally Check headline bias rows - want to see BULLISH, not BEARISH Check VIX row - bullish market regime preferred Check ORB row (intraday) - want ABOVE for longs Scan indicator rows - more green = better confirmation For a short trade: Check AVG column - want to see < 40% ideally Check headline bias rows - want to see BEARISH, not BULLISH Check VIX row - bearish market regime preferred Check ORB row (intraday) - want BELOW for shorts Scan indicator rows - more red = better confirmation When AVG is 40-60%: Market is choppy, mixed signals. Either stay out or reduce position size significantly. These are low-probability environments. Unique Features: Dynamic Labels: Row names show your actual settings (e.g., "EMA 10 > SMA 20" not generic "Fast > Slow"). You know exactly what's being evaluated. Customizable Rows: Turn off rows you don't care about. Only show what matters to your strategy. Customizable Timeframes: On intraday charts, disable 1m or 4h if you don't trade them. Reduces calculation load by 20-40%. Automatic HTF Handling: On Daily/Weekly/Monthly charts, the table automatically switches to D/W/M columns. No configuration needed. Performance Smart: "Hide BIAS table on 1D or above" option completely skips all table calculations on higher timeframes if you only trade intraday. --- 6. CANDLESTICK PATTERN RECOGNITION The indicator automatically detects six major reversal patterns and marks them with emojis at the relevant bars. Why These Six Patterns: These are the most statistically significant reversal patterns according to trading literature: High win rate when appearing at support/resistance Clear visual structure (not subjective) Work across all timeframes and assets Studied extensively by institutions The Patterns: Bullish Patterns (appear at bottoms): Bullish Engulfing: Green candle completely engulfs prior red candle's body. Strong reversal signal. Hammer: Small body with long lower wick (at least 2× body size). Shows rejection of lower prices by buyers. Morning Star: Three-candle pattern (large red → small indecision → large green). Very strong bottom reversal. Bearish Patterns (appear at tops): Bearish Engulfing: Red candle completely engulfs prior green candle's body. Strong reversal signal. Shooting Star: Small body with long upper wick (at least 2× body size). Shows rejection of higher prices by sellers. Evening Star: Three-candle pattern (large green → small indecision → large red). Very strong top reversal. Interactive Tooltips: Unlike most pattern indicators that just draw shapes, this one is educational: Hover your mouse over any pattern emoji A tooltip appears explaining: what the pattern is, what it means, when it's most reliable, and how to trade it No need to memorize - learn as you trade Noise Filter: "Min candle body % to filter noise" setting prevents false signals: Patterns require minimum body size relative to price Filters out tiny candles that don't represent real buying/selling pressure Adjust based on asset volatility (higher % for crypto, lower for low-volatility stocks) How to Trade Patterns: Patterns are NOT standalone entry signals. Use them as: Confirmation: UT Bot gives signal + pattern appears = stronger entry Reversal Warning: In a trade, opposite pattern appears = consider tightening stop or taking profit Support/Resistance Validation: Pattern at key level (PDH, VWAP, MA 200) = level is being respected Best combined with: UT Bot or Base signal in same direction Bias Table alignment (AVG > 60% or < 40%) Appearance at obvious support/resistance --- 7. VISUAL TOOLS AND FEATURES VWAP (Volume Weighted Average Price): Session-anchored VWAP with standard deviation bands. Shows institutional "fair value" for the trading session. Anchor Options: Session, Day, Week, Month, Quarter, Year. Choose based on your trading timeframe. Bands: Up to three pairs (X1, X2, X3) showing statistical deviation. Price at outer bands often reverses. Auto-Hide on HTF: VWAP hides on Daily/Weekly/Monthly charts automatically unless you enable anchored mode. Use VWAP as: Directional bias (above = bullish, below = bearish) Mean reversion levels (outer bands) Support/resistance (the VWAP line itself) Previous Day High/Low: Automatically plots yesterday's high and low on intraday charts: Updates at start of each new trading day Theme-aware colors (dark text for light charts, light text for dark charts) Hidden automatically on Daily/Weekly/Monthly charts These levels are critical for intraday traders - institutions watch them closely as support/resistance. Opening Range Breakout (ORB): Tracks the high/low of the first 5, 15, 30, or 60 minutes of the trading session: Customizable session times (preset for NYSE, LSE, TSE, or custom) Shows current breakout status in Bias Table row (ABOVE, BELOW, INSIDE, BUILDING) Intraday only - auto-disabled on Daily+ charts ORB is a classic day trading strategy - breakout above opening range often leads to continuation. Extra Labels: Change from Open %: Shows how far price has moved from session open (intraday) or daily open (HTF). Green if positive, red if negative. ADX Badge: Small label at bottom of last bar showing current ADX value. Green when above your minimum threshold, red when below. RSI Badge: Small label at top of last bar showing current RSI value with zone status (buy zone, sell zone, or neutral). These labels provide quick at-a-glance confirmation without needing separate indicator windows. --- 8. HOW TO USE THE INDICATOR Step 1: Add to Chart Load the indicator on your chosen asset and timeframe First time: Everything is enabled by default - the chart will look busy Don't panic - you'll turn off what you don't need Step 2: Start Simple Turn OFF everything except: UT Bot labels (keep these ON) Bias Table (keep this ON) Moving Averages (Fast and Medium only) Suggested Stop Loss and Take Profits Hide everything else initially. Get comfortable with the basic UT Bot + Bias Table workflow first. Step 3: Learn the Core Workflow UT Bot gives a Buy or Sell signal Check Bias Table AVG column - do you have multi-timeframe alignment? If yes, enter the trade Place stop at Suggested Stop Loss line Scale out at TP levels Exit on opposite UT signal Trade this simple system for a week. Get a feel for signal frequency and win rate with your settings. Step 4: Add Filters Gradually If you're getting too many losing signals (whipsaws in choppy markets), add filters one at a time: Try: "Require 2-Bar Trend Confirmation" - wait for 2 bars to confirm direction Try: ADX filter with minimum threshold - only trade when trend strength is sufficient Try: RSI pullback filter - only enter on pullbacks, not chasing Try: Volume filter - require above-average volume Add one filter, test for a week, evaluate. Repeat. Step 5: Enable Advanced Features (Optional) Once you're profitable with the core system, add: Supertrend for additional trend confirmation Candlestick patterns for reversal warnings VWAP for institutional anchor reference ORB for intraday breakout context ZLSMA for low-lag trend following Step 6: Optimize Settings Every setting has a detailed tooltip explaining what it does and typical values. Hover over any input to read: What the parameter controls How it impacts trading Suggested ranges for scalping, day trading, and swing trading Start with defaults, then adjust based on your results and style. Step 7: Set Up Alerts Right-click chart → Add Alert → Condition: "Luxy Momentum v6" → Choose: "UT Bot — Buy" for long entries "UT Bot — Sell" for short entries "Base Long/Short" for filtered MA cross signals Optionally enable "Send real-time alert() on UT flip" in settings for immediate notifications. Common Workflow Variations: Conservative Trader: UT signal + Base signal + Candlestick pattern + Bias AVG > 70% Enter only at major support/resistance Wider UT sensitivity, multiple filters Aggressive Trader: UT signal + Bias AVG > 60% Enter immediately, no waiting Tighter UT sensitivity, minimal filters Swing Trader: Focus on Daily/Weekly Bias alignment Ignore intraday noise Use ORB and PDH/PDL less (or not at all) Wider stops, patient approach --- 9. PERFORMANCE AND OPTIMIZATION The indicator is optimized for speed, but with 15+ features running simultaneously, chart load time can add up. Here's how to keep it fast: Biggest Performance Gains: Disable Unused Timeframes: In "Time Frames" settings, turn OFF any timeframe you don't actively trade. Each disabled TF saves 10-15% calculation time. If you only day trade 5m, 15m, 1h, disable 1m, 2h, 4h. Hide Bias Table on Daily+: If you only trade intraday, enable "Hide BIAS table on 1D or above". This skips ALL table calculations on higher timeframes. Draw UT Visuals Only on Bar Close: Reduces intrabar rendering of SL/TP/Entry lines. Has ZERO impact on logic or alerts - purely visual optimization. Additional Optimizations: Turn off VWAP bands if you don't use them Disable candlestick patterns if you don't trade them Turn off Supertrend fill if you find it distracting (keep the line) Reduce "Limit to 10 bars" for SL/TP lines to minimize line objects Performance Features Built-In: Smart Caching: Higher timeframe data (3-day bias, weekly bias, etc.) updates once per day, not every bar Conditional Calculations: Volume filter only calculates when enabled. Swing filter only runs when enabled. Nothing computes if turned off. Modular Design: Every component is independent. Turn off what you don't need without breaking other features. Typical Load Times: 5m chart, all features ON, 7 timeframes: ~2-3 seconds 5m chart, core features only, 3 timeframes: ~1 second 1m chart, all features: ~4-5 seconds (many bars to calculate) If loading takes longer, you likely have too many indicators on the chart total (not just this one). --- 10. FAQ Q: How is this different from standard UT Bot indicators? A: Standard UT Bot (originally by @QuantNomad) is just the ATR trailing line and flip signals. This implementation adds: - Volume weighting and momentum adjustment to the trailing calculation - Multiple confirmation filters (swing, %, 2-bar, ZLSMA) - Smart composite stop loss system from multiple S/R layers - R-multiple take profit system with freeze-on-touch - Integration with multi-timeframe Bias Table - Visual audit trail with checkmarks Q: Can I use this for automated trading? A: The indicator is designed for discretionary trading. While it has clear signals and alerts, it's not a mechanical system. Context and judgment are required. Q: Does it repaint? A: No. All signals respect bar close. UT Bot logic runs intrabar but signals only trigger on confirmed bars. Alerts fire correctly with no lookahead. Q: Do I need to use all the features? A: Absolutely not. The indicator is modular. Many profitable traders use just UT Bot + Bias Table + Moving Averages. Start simple, add complexity only if needed. Q: How do I know which settings to use? A: Every single input has a detailed tooltip. Hover over any setting to see: What it does How it affects trading Typical values for scalping, day trading, swing trading Start with defaults, adjust gradually based on results. Q: Can I use this on crypto 24/7 markets? A: Yes. ORB will not work (no defined session), but everything else functions normally. Use "Day" anchor for VWAP instead of "Session". Q: The Bias Table is blank or not showing. A: Check: "Show Table" is ON Table position isn't overlapping another indicator's table (change position) At least one row is enabled "Hide BIAS table on 1D or above" is OFF (if on Daily+ chart) Q: Why are candlestick patterns not appearing? A: Patterns are relatively rare by design - they only appear at genuine reversal points. Check: Pattern toggles are ON "Min candle body %" isn't too high (try 0.05-0.10) You're looking at a chart with actual reversals (not strong trending market) Q: UT Bot is too sensitive/not sensitive enough. A: Adjust "Sensitivity (Key×ATR)". Lower number = tighter stop, more signals. Higher number = wider stop, fewer signals. Read the tooltip for guidance. Q: Can I get alerts for the Bias Table? A: The Bias Table is a dashboard for visual analysis, not a signal generator. Set alerts on UT Bot or Base signals, then manually check Bias Table for confirmation. Q: Does this work on stocks with low volume? A: Yes, but turn OFF the volume filter. Low volume stocks will never meet relative volume requirements. Q: How often should I check the Bias Table? A: Before every entry. It takes 2 seconds to glance at the AVG column and headline rows. This one check can save you from fighting the trend. Q: What if UT signal and Base signal disagree? A: UT Bot is more aggressive (ATR trailing). Base signals are more conservative (MA cross + filters). If they disagree, either: Wait for both to align (safest) Take the UT signal but with smaller size (aggressive) Skip the trade (conservative) There's no "right" answer - depends on your risk tolerance. --- FINAL NOTES The indicator gives you an edge. How you use that edge determines results. For questions, feedback, or support, comment on the indicator page or message the author. Happy Trading!
مؤشر Pine Script®
من ‎orenluxy‎
41
Fibonacci Momentum CascadeThe Foundation: FMC Indicator: The Fibonacci Momentum Cascade (FMC) is an AI-enhanced technical indicator that automates Fibonacci analysis, removing the guesswork and doubt that plagues manual drawing. Instead of relying on subjective human input, the FMC uses a proprietary Momentum Cascade Engine™ that constantly analyzes market strength to detect significant shifts in buying and selling pressure. When confirmed, it automatically identifies the most relevant market trend, cascades fresh Fibonacci levels, and grades potential technical setups. It features 100% automated swings, adaptive real-time analysis, and professional setup grading with Primary Setups (▲P / ▼P) for A+ formations and Secondary Setups (▲S / ▼S) for supplementary patterns. The Adaptive Edge: AI Co-Pilot: Our AI analyzes multiple data sources including market sentiment, technical patterns, fundamental factors, and news events to generate comprehensive market insights. It also fine-tunes the FMC indicator inputs for today's market, outputting personalized settings optimized for multiple timeframes (1d, 4h, 1h, 15m, 5m) — removing guesswork and maximizing precision for your asset. The Force Multiplier: The Hub: The Hub is our community intelligence platform where users share market analyses and insights. When you or others request AI analyses, they become available in The Hub for everyone to access without using credits. This creates a growing library of market insights across all asset classes. You can browse community analyses, discover trending assets, and benefit from the collective wisdom of experienced traders—essentially getting free analyses beyond your monthly credits.
مؤشر Pine Script®
من ‎Pisano-Global‎
2
Neural Network Buy and Sell SignalsTrend Architect Suite Lite - Neural Network Buy and Sell Signals Advanced AI-Powered Signal Scoring This indicator provides neural network market analysis on buy and sell signals designed for scalpers and day traders who use 30s to 5m charts. Signals are generated based on an ATR system and then filtered and scored using an advanced AI-driven system. Features Neural Network Signal Engine 5-Layer Deep Learning analysis combining market structure, momentum, and market state detection AI-based Letter Grade Scoring (A+ through F) for instant signal quality assessment Normalized Input Processing with Z-score standardization and outlier clipping Real-time Signal Evaluation using 5 market dimensions Advanced Candle Types Standard Candlesticks - Raw price action Heikin Ashi - Trend smoothing and noise reduction Linear Regression - Mathematical trend visualization Independent Signal vs Display - Calculate signals on one type, display another Key Settings Signal Configuration - Signal Trigger Sensitivity (Default: 1.7) - Controls signal frequency vs quality - Stop Loss ATR Multiplier (Default: 1.5) - Risk management sizing - Signal Candle Type (Default: Candlesticks) - Data source for signal calculations - Display Candle Type (Default: Linear Regression) - Visual candle display Display Options - Signal Distance (Default: 1.35 ATR) - Label positioning from price - Label Size (Default: Medium) - Optimal readability Trading Applications Scalping - Fast pace signal detection with quality filtering - ATR-based stop management prevents signal overlap - Neural network attempts to reduces false signals in choppy markets Day Trading - Multi-timeframe compatible with adaptation settings - Clear trend visualization with Linear Regression candles - Support/resistance integration for better entries/exits Signal Filtering - Use A+/A grades for highest probability setups - B grades for confirmation in trending markets - C-F grades help identify market uncertainty Why Choose Trend Architect Lite? No Lag - Real-time neural network processing No Repainting - Signals appear and stay fixed Clean Charts - Focus on price action, not indicators Smart Filtering - AI reduces noise and false signals Flexible and customizable - Works across all timeframes and instruments Compatibility - All Timeframes - 1m to Monthly charts - All Instruments - Forex, Crypto, Stocks, Futures, Indices Risk Disclaimer This indicator is a tool for technical analysis and should not be used as the sole basis for trading decisions. Past performance does not guarantee future results. Always use proper risk management and never risk more than you can afford to lose.
مؤشر Pine Script®
من ‎B3AR_Trades‎
60
Institutional Analyst LLM📊 Institutional Analyst Board LLM – Smart Money Confluence Scanner for XAUUSD, Forex, Crypto 🔍 Overview The Institutional Analyst Board is a complete multi-timeframe smart money toolkit designed for traders who demand clarity, confluence, and precision. It brings together institutional-grade metrics—Order Blocks (OB), Fair Value Gaps (FVG), Liquidity Sweeps, MACD/RSI... PTS Ultimate Analysis Board (Flexible Position + Ticker) GoldenTradeClub GoldenTradeClub Updated Jul 15 PTS Ultimate Analysis Board (Flexible Position + Ticker) Version: Pine v5 Description: This indicator builds a fully customizable, multi-timeframe dashboard table that surfaces 19 key metrics for any ticker (current chart TF, 1 h, 4 h). You can position the table at the top-right or bottom-right of your chart and toggle each metric on or off. Key... Trading Engine AI Light GoldenTradeClub GoldenTradeClub Jul 14 The Trading Engine includes the best and most effective technical analysis tools. It has 27 different Buy Signal parameters and 26 different Sell Signal parameters. Furthermore, it also has 9 Stop Loss triggers for Long Positions and 8 Stop Loss triggers for Short Positions. Many of the Buy or Sell Signal parameters function as Take Profit and Stop Loss signals... Elliott Wave Complete GoldenTradeClub GoldenTradeClub Jul 4 1. Indicator Presentation Name: Elliott Wave Complete Type: Pine Script v5 overlay dashboard for TradingView Purpose: Automates Elliott Wave motive (1-5) and corrective (A-B-C) pattern detection on any timeframe, enriches it with classic ZigZag pivots, dynamic Fibonacci projection levels, optional wave-count info box, and real-time alerts—all in one... 💀⚡ PTS WIZARD 666™ ULTIMATE SUPREME V5.0 - COMPLETE FIXED ⚡💀 GoldenTradeClub GoldenTradeClub Jul 4 1. Indicator Presentation Name: 💀⚡ PTS WIZARD 666™ ULTIMATE SUPREME V5.0 – COMPLETE FIXED Short ID: PTS-666-SUPREME Type: Pine Script v5 overlay dashboard for TradingView Purpose: An all-in-one trading overlay that integrates advanced WaveTrend momentum, RSI/MFI analysis, POC volume profiling, multiple Fibonacci golden/ultimate zones, volume footprint & imbalance... 🔥 PTS TRADE 666™ ULTIMATE BOOKMAP + QUANTUM ENGINE GoldenTradeClub GoldenTradeClub Jul 4 1. Indicator Presentation Name: 🔥 PTS TRADE 666™ ULTIMATE BOOKMAP + QUANTUM ENGINE Short ID: PTS666_QUANTUM_FINAL Type: Pine Script v5 overlay dashboard for TradingView Purpose: A cutting-edge, institutional-grade suite that unifies bookmap-style footprint volume profiling, dynamic heatmap liquidity analysis, AI-driven pattern recognition, smart-money protocols,... 🔥 PTS TRADE 666™ - ULTIMATE INSTITUTIONAL TOOL 🔥 GoldenTradeClub GoldenTradeClub Jul 4 1. Indicator Presentation Name: 🔥 PTS TRADE 666™ – ULTIMATE INSTITUTIONAL TOOL V2.0 Short ID: PTS666_UIT_V2 Type: Pine Script v5 overlay dashboard for TradingView Purpose: Combines institutional-grade footprint volume analysis, smart-money structure detection, statistical anomaly checks, multi-timeframe divergence, Ichimoku insights, pattern recognition, and an... PTS Wizard GoldenTradeClub GoldenTradeClub Jul 4 1. Indicator Presentation Name: PTS Wizard Short Title: PTS Wizard Type: Pine Script v5 overlay dashboard for TradingView Purpose: A unified multi-strategy toolkit that overlays key market insights—liquidity zones, smart-money structure, footprint-style volume profile, consolidation ranges, statistical deviation bands, price forecasts, and session analysis—into a... 🔥 PTS.TRADE 666™ ULTIMATE HYBRID + MTF V3 GoldenTradeClub GoldenTradeClub Jul 4 1. Indicator Presentation Name: 🔥 PTS.TRADE 666™ ULTIMATE HYBRID + MTF V3 Short ID: PTS666_ULTIMATE_MTF_V3 Type: Overlay dashboard for TradingView Purpose: A next-level hybrid trading suite that merges institutional-grade order-flow analysis, smart-money concepts, AI-driven insights, classic momentum oscillators (WaveTrend, divergence, “Gold” signals),... 🧙‍♂ PTS WIZARD V3.0 - FINAL EDITION GoldenTradeClub GoldenTradeClub Jul 4 1. Indicator Presentation Name: 🧙‍♂ PTS WIZARD V3.0 – FINAL EDITION Short Title: PTS-WIZARD-V3-FINAL Type: Overlay trading dashboard for TradingView Purpose: A comprehensive multi-module indicator that blends classic cipher momentum signals, Elliott Wave pattern detection, advanced statistical analyses (Z-Score, Benford’s Law, Ehlers SNR), footprint-style volume... 🧙‍♂ PTS WIZARD V3.0 + FOOTPRINT ULTIMATE GoldenTradeClub GoldenTradeClub Jul 4 Name: PTS WIZARD V3.0 + FOOTPRINT ULTIMATE Type: Overlay trading dashboard for TradingView Purpose: Combines classic cipher-style momentum signals with an advanced footprint volume profile, multi-timeframe bias, statistical filters, and a fusion-score system—displayed in a customizable on-chart dashboard. Core Modules Cipher Momentum Signals WaveTrend... 🧙‍♂ PTS WIZARD V3.0 - BASIC GoldenTradeClub GoldenTradeClub Jul 1 PTS WIZARD V3.0 Basic – Ultimate Multi-Tool Trading Dashboard An all-in-one overlay combining classic cipher signals, Elliott Wave pattern detection, volume analytics, divergence spotting, and smart-entry timing—backed by advanced statistical filters and a live dashboard. Key Features Cipher Signals WaveTrend with overbought/oversold zones & cross signals RSI... Trading Engine vCD AI GoldenTradeClub GoldenTradeClub Jun 15 The Trading Engine includes the best and most effective technical analysis tools. It has 27 different Buy Signal parameters and 26 different Sell Signal parameters. Furthermore, it also has 9 Stop Loss triggers for Long Positions and 8 Stop Loss triggers for Short Positions. Many of the Buy or Sell Signal parameters function as Take Profit and Stop Loss signals... Trading Engine vCD GoldenTradeClub GoldenTradeClub Updated Mar 21 The Trading Engine includes the best and most effective technical analysis tools. It has 27 different Buy Signal parameters and 26 different Sell Signal parameters. Furthermore, it also has 9 Stop Loss triggers for Long Positions and 8 Stop Loss triggers for Short Positions. Many of the Buy or Sell Signal parameters function as Take Profit and Stop Loss signals... TE CLIENT v13 GoldenTradeClub GoldenTradeClub Updated Mar 15 The Trading Engine includes the best and most effective technical analysis tools. It has 27 different Buy Signal parameters and 26 different Sell Signal parameters. Furthermore, it also has 9 Stop Loss triggers for Long Positions and 8 Stop Loss triggers for Short Positions. Many of the Buy or Sell Signal parameters function as Take Profit and Stop Loss signals... Trading Engine v13 GoldenTradeClub GoldenTradeClub Updated Mar 15 The Trading Engine includes the best and most effective technical analysis tools. It has 27 different Buy Signal parameters and 26 different Sell Signal parameters. Furthermore, it also has 9 Stop Loss triggers for Long Positions and 8 Stop Loss triggers for Short Positions. Many of the Buy or Sell Signal parameters function as Take Profit and Stop Loss signals... Trading Engine B2B GoldenTradeClub GoldenTradeClub Updated Jan 14 The Trading Engine includes the best and most effective technical analysis tools. It has 25 different Buy Signal parameters and 24 different Sell Signal parameters. Furthermore, it also has 9 Stop Loss triggers for Long Positions and 8 Stop Loss triggers for Short Positions. Many of the Buy or Sell Signal parameters function as Take Profit and Stop Loss signals... Trading Engine B2B FX V9 GoldenTradeClub GoldenTradeClub Updated Jan 14 The VFLOW Trading Engine includes the best and most effective technical analysis tools. It has 20 different Buy Signal parameters and 18 different Sell Signal parameters. Furthermore, it also has 7 Stop Loss triggers for Long Positions and 5 Stop Loss triggers for Short Positions. Many of the Buy or Sell Signal parameters function as Take Profit and Stop Loss... English Select market data provided by ICE Data services. Select reference data provided by FactSet. Copyright © 2025 FactSet Research Systems Inc. © 2025 TradingView, Inc. 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It brings together institutional-grade metrics—Order Blocks (OB), Fair Value Gaps (FVG), Liquidity Sweeps, MACD/RSI bias, VWAP positioning, and Break of Structure (BoS)—into a single powerful visual dashboard. This indicator is especially optimized for Gold (XAUUSD) but is also compatible with Crypto and Forex assets. 🧠 Key Features ✅ Multi-Timeframe Dashboard (5M / 15M / 1H) ✅ Order Block Detection with dynamic zones that extend until broken ✅ Fair Value Gap Detection with clear zone shading and border distinction ✅ MACD + RSI Confluence for momentum and bias alignment ✅ VWAP Positioning to identify premium/discount zones ✅ Liquidity Sweeps (internal/external range breaks) ✅ Killzone Highlighting (Asia / London / New York) ✅ Break of Structure (BoS) with advanced confluence filters ✅ Gold Bias Flags across timeframes (BUY / SELL / NEUTRAL) ✅ Dynamic Price Watermark with real-time data ✅ Fully customizable colors, transparencies, and text labels 🧠 How It Works The Board uses institutional logic to analyze the chart in real time: Metric Purpose OB Zones Highlight potential smart money footprints where price is likely to react. FVG Zones Identify imbalance areas between buyers and sellers—ideal for mean reversion entries. MACD/RSI Confirm momentum direction and relative strength confluence. VWAP Determine whether price is trading at a premium or discount. Liquidity Sweeps Detect manipulative moves before major reversals. BoS Mark potential trend reversals, filtered by institutional confluence. Each signal is computed across 3 timeframes and visualized in a clean board that updates live. You’ll also see labels, alerts, and session overlays for maximum clarity. 📌 Ideal Use Case This tool is perfect for: Funded Challenge Traders (FTMO, MyForexFunds, etc.) Gold scalpers and intraday traders Crypto price action traders using BTC, ETH, SOL, etc. Smart Money Concept (SMC) and ICT followers ⚙️ Customization Options Toggle each module (OB, FVG, VWAP, MACD/RSI, etc.) Set transparency and color for each zone type Adjust Killzone timing (Asia, London, NY) Control board position (Top/Bottom) and metric visibility 📈 Compatible Assets ✅ XAUUSD (optimized) ✅ Forex majors/minors ✅ Crypto pairs (BTC, ETH, SOL, etc.) ✅ Indices (GER40, NASDAQ, SPX with minor adaptation) 🛠️ Requirements Use on TradingView v5 Set chart time to UTC+0 or UTC+3 for optimal Killzone accuracy For crypto, redefine Killzone hours if needed (24/7 market) 🧠 Pro Tip Pair this indicator with volume profile tools, CVD/Delta Flow, or Footprint overlays to build high-confidence trade setups with clear institutional confluence.
مؤشر Pine Script®
من ‎GoldenTradeClub‎
Linear Regression Channel Screener [Daveatt]Hello traders First and foremost, I want to extend a huge thank you to @LonesomeTheBlue for his exceptional Linear Regression Channel indicator that served as the foundation for this screener. Original work can be found here: Overview This project demonstrates how to transform any open-source indicator into a powerful multi-asset screener. The principles shown here can be applied to virtually any indicator you find interesting. How to Transform an Indicator into a Screener Step 1: Identify the Core Logic First, identify the main calculations of the indicator. In our case, it's the Linear Regression Channel calculation: get_channel(src, len) => mid = math.sum(src, len) / len slope = ta.linreg(src, len, 0) - ta.linreg(src, len, 1) intercept = mid - slope * math.floor(len / 2) + (1 - len % 2) / 2 * slope endy = intercept + slope * (len - 1) dev = 0.0 for x = 0 to len - 1 by 1 dev := dev + math.pow(src - (slope * (len - x) + intercept), 2) dev dev := math.sqrt(dev / len) Step 2: Use request.security() Pass the function to request.security() to analyze multiple assets: = request.security(sym, timeframe.period, get_channel(src, len)) Step 3: Scale to Multiple Assets PineScript allows up to 40 request.security() calls, letting you monitor up to 40 assets simultaneously. Features of This Screener The screener provides real-time trend detection for each monitored asset, giving you instant insights into market movements. It displays each asset's position relative to its middle regression line, helping you understand price momentum. The data is presented in a clean, organized table with color-coded trends for easy interpretation. At its core, the screener performs trend detection based on regression slope calculations, clearly indicating whether an asset is in a bullish or bearish trend. Each asset's price is tracked relative to its middle regression line, providing additional context about trend strength. The color-coded visual feedback makes it easy to spot changes at a glance. Built-in alerts notify you instantly when any asset experiences a trend change, ensuring you never miss important market moves. Customization Tips You can easily expand the screener by adding more symbols to the symbols array, adapting it to your watchlist. The regression parameters can be adjusted to match your preferred trading timeframes and sensitivity. The alert system is already configured to notify you of trend changes, but you can customize the alert messages and conditions to your needs. Limitations While powerful, the screener is bound by PineScript's limitation of 40 security calls, capping the maximum number of monitored assets. Using AI to Help With Conversion An interesting tip: You can use AI tools to help convert single-asset indicators to screeners. Simply provide the original code and ask for assistance in transforming it into a screener format. While the AI output might need some syntax adjustments, it can handle much of the heavy lifting in the conversion process. Prompt (example) : " Please make a pinescript version 5 screener out of this indicator below or in attachment to scan 20 instruments " I prefer Claude AI (Opus model) over ChatGPT for pinescript. Conclusion This screener transformation technique opens up endless possibilities for market analysis. By following these steps, you can convert any indicator into a powerful multi-asset scanner, enhancing your trading toolkit significantly. Remember: The power of a screener lies not just in monitoring multiple assets, but in applying consistent analysis across your entire watchlist in real-time. Feel free to fork and modify this screener for your own needs. Happy trading! 🚀📈 Daveatt
مؤشر Pine Script®
من ‎Daveatt‎
4
PCA-Risk IndicatorOBJECTIVE: The objective of this indicator is to synthesize, via PCA (Principal Component Analysis), several of the most used indicators with in order to simplify the reading of any asset on any timeframe. It is based on my Bitcoin Risk Long Term indicator, and is the evolution of another indicator that I have not published 'Average Risk Indicator'. The idea of this indicator is to use statistics, in this case the PCA, to reduce the number of dimensions (indicator) to aggregate them in some synthetic indicators (PCX) I invite you to dig deeper into the PCA, but that is to try to keep as much information as possible from the raw data. The signal minus the noise. I realized this indicator a year ago, but I publish it now because I do not see the interest to keep it private. USAGE: Unlike the Bitcoin Risk Long Term indicator, it does not make sense to change or disable the input indicators unless you use the 'Average Indicator' function. Because each input is weighted to generate the outputs, the PCX. I extracted several courses (Bitcoin, Gold, S&P, CAC40) on several timeframes (W, D, 4h, 1h) of Trading view and use the Excel generated for the data on which I played the PCA analysis. The results: explained_variance_ratio: 0.55540809 / 0.13021972 / 0.07303142 / 0.03760925 explained_variance: 11.6639671 / 2.73470717 / 1.53371209 / 0.7898212 Interpretation: Simply put, 55% of the information contained in each indicator can be represented with PC1, +13% with PC2, +7% with PC3, +3% with PC4. What is important to understand is that PC1, which serves as a thermometer in a way, gives a simple indication of over-buying or over-selling area better than any other indicator. PC2, difficult to interpret, is more reactive because precedes PC1, but can give false signals. PC3 and PC4 do not seem relevant to me. The way I use it is to take PC1 for Risk indicator, and display PC2 with 'Area'. When PC2 turns around and PC1 arrives on extremes, it can be good points to act. NOTES : - It is surprising that a simple average of all the indicators gives a fairly relevant result - With Average indicator as Risk indicator, you can combine the indicators of your choice and see the predictive power with the staining of bars. - You can add alerts on the levels of your choice on the Risk Indicator - If you have any idea of adding an indicator, modification, criticism, bug found: share them, it’s appreciated! ---- FR ---- OBJECTIF : L'objectif de cet indicateur est de synthétiser, via l'ACP (Analyse en Composantes Principales), plusieurs indicateurs parmi les plus utilisés avec afin de simplifier la lecture de n'importe quel actif sur n'importe quel timeframe. Il est inspiré de mon indicateur 'Bitcoin Risk Long Term indicator', et est l'évolution d'un autre indicateur que je n'ai pas publié 'Average Risk Indicator'. L'idée de cet indicateur est d'utiliser les statistiques, en l'occurence l'ACP, pour réduire le nombre de dimensions (indicateur) pour les agréger dans quelques indicateurs synthétiques (PCX) Je vous invite à creuser l'ACP, mais c'est chercher à conserver un maximum d'informations à partir de la donnée brute. Le signal moins le bruit. J'ai réalisé cet indicateur il y a un an, mais je le publie maintenant car je ne vois pas l'intérêt de le garder privé. UTILISATION : Contrairement à 'Bitcoin Risk Long Term indicator', il ne fait pas sens de modifier ou désactiver les indicateurs inputs, sauf si vous utiliser la fonction 'Average Indicator'. Car chaque input est pondéré pour générer les outputs, les PCX. J'ai extrait plusieurs cours (Bitcoin, Gold, S&P, CAC40) sur plusieurs timeframes (W, D, 4h, 1h) de Trading view et utiliser les Excel généré pour la data sur laquelle j'ai joué l'analyse ACP. Les résultats : explained_variance_ratio : 0.55540809 / 0.13021972 / 0.07303142 / 0.03760925 explained_variance : 11.6639671 / 2.73470717 / 1.53371209 / 0.7898212 Interprétation : Pour faire simple, 55% de l'information contenu dans chaque indicateur peut être représenté avec PC1, +13% avec PC2, +7% avec PC3, +3% avec PC4. Ce qui faut y comprendre c'est que le PC1, qui sert de thermomètre en quelque sorte, donne une indication simple de zone de sur-achat ou sur-vente mieux que n'importe quel autre indicateur. PC2, difficile à interpréter, est plus réactif car précède PC1, mais peut donner des faux signaux. PC3 et PC4 ne me semble pas pertinent. La manière dont je m'en sert c'est de prendre PC1 pour Risk indicator, et d'afficher PC2 avec 'Region'. Lorsque PC2 se retourne et que PC1 arrive sur des extrêmes, cela peut être des bons points pour agir. NOTES : - Il est étonnant de constater qu'une simple moyenne de tous les indicateurs donne un résultat assez pertinent - Avec Average indicator comme Risk indicator, vous pouvez combiner les indicateurs de vos choix et voir la force prédictive avec la coloration des bars. - Vous pouvez ajouter des alertes sur les niveaux de votre choix sur le Risk Indicator - Si vous avez la moindre idée d'ajout d'indicateur, modification, critique, bug trouvé : partagez-les, c'est apprécié !
مؤشر Pine Script®
من ‎Julien-PH‎
24
Endpointed SSA of Price [Loxx]The Endpointed SSA of Price: A Comprehensive Tool for Market Analysis and Decision-Making The financial markets present sophisticated challenges for traders and investors as they navigate the complexities of market behavior. To effectively interpret and capitalize on these complexities, it is crucial to employ powerful analytical tools that can reveal hidden patterns and trends. One such tool is the Endpointed SSA of Price, which combines the strengths of Caterpillar Singular Spectrum Analysis, a sophisticated time series decomposition method, with insights from the fields of economics, artificial intelligence, and machine learning. The Endpointed SSA of Price has its roots in the interdisciplinary fusion of mathematical techniques, economic understanding, and advancements in artificial intelligence. This unique combination allows for a versatile and reliable tool that can aid traders and investors in making informed decisions based on comprehensive market analysis. The Endpointed SSA of Price is not only valuable for experienced traders but also serves as a useful resource for those new to the financial markets. By providing a deeper understanding of market forces, this innovative indicator equips users with the knowledge and confidence to better assess risks and opportunities in their financial pursuits. █ Exploring Caterpillar SSA: Applications in AI, Machine Learning, and Finance Caterpillar SSA (Singular Spectrum Analysis) is a non-parametric method for time series analysis and signal processing. It is based on a combination of principles from classical time series analysis, multivariate statistics, and the theory of random processes. The method was initially developed in the early 1990s by a group of Russian mathematicians, including Golyandina, Nekrutkin, and Zhigljavsky. Background Information: SSA is an advanced technique for decomposing time series data into a sum of interpretable components, such as trend, seasonality, and noise. This decomposition allows for a better understanding of the underlying structure of the data and facilitates forecasting, smoothing, and anomaly detection. Caterpillar SSA is a particular implementation of SSA that has proven to be computationally efficient and effective for handling large datasets. Uses in AI and Machine Learning: In recent years, Caterpillar SSA has found applications in various fields of artificial intelligence (AI) and machine learning. Some of these applications include: 1. Feature extraction: Caterpillar SSA can be used to extract meaningful features from time series data, which can then serve as inputs for machine learning models. These features can help improve the performance of various models, such as regression, classification, and clustering algorithms. 2. Dimensionality reduction: Caterpillar SSA can be employed as a dimensionality reduction technique, similar to Principal Component Analysis (PCA). It helps identify the most significant components of a high-dimensional dataset, reducing the computational complexity and mitigating the "curse of dimensionality" in machine learning tasks. 3. Anomaly detection: The decomposition of a time series into interpretable components through Caterpillar SSA can help in identifying unusual patterns or outliers in the data. Machine learning models trained on these decomposed components can detect anomalies more effectively, as the noise component is separated from the signal. 4. Forecasting: Caterpillar SSA has been used in combination with machine learning techniques, such as neural networks, to improve forecasting accuracy. By decomposing a time series into its underlying components, machine learning models can better capture the trends and seasonality in the data, resulting in more accurate predictions. Application in Financial Markets and Economics: Caterpillar SSA has been employed in various domains within financial markets and economics. Some notable applications include: 1. Stock price analysis: Caterpillar SSA can be used to analyze and forecast stock prices by decomposing them into trend, seasonal, and noise components. This decomposition can help traders and investors better understand market dynamics, detect potential turning points, and make more informed decisions. 2. Economic indicators: Caterpillar SSA has been used to analyze and forecast economic indicators, such as GDP, inflation, and unemployment rates. By decomposing these time series, researchers can better understand the underlying factors driving economic fluctuations and develop more accurate forecasting models. 3. Portfolio optimization: By applying Caterpillar SSA to financial time series data, portfolio managers can better understand the relationships between different assets and make more informed decisions regarding asset allocation and risk management. Application in the Indicator: In the given indicator, Caterpillar SSA is applied to a financial time series (price data) to smooth the series and detect significant trends or turning points. The method is used to decompose the price data into a set number of components, which are then combined to generate a smoothed signal. This signal can help traders and investors identify potential entry and exit points for their trades. The indicator applies the Caterpillar SSA method by first constructing the trajectory matrix using the price data, then computing the singular value decomposition (SVD) of the matrix, and finally reconstructing the time series using a selected number of components. The reconstructed series serves as a smoothed version of the original price data, highlighting significant trends and turning points. The indicator can be customized by adjusting the lag, number of computations, and number of components used in the reconstruction process. By fine-tuning these parameters, traders and investors can optimize the indicator to better match their specific trading style and risk tolerance. Caterpillar SSA is versatile and can be applied to various types of financial instruments, such as stocks, bonds, commodities, and currencies. It can also be combined with other technical analysis tools or indicators to create a comprehensive trading system. For example, a trader might use Caterpillar SSA to identify the primary trend in a market and then employ additional indicators, such as moving averages or RSI, to confirm the trend and generate trading signals. In summary, Caterpillar SSA is a powerful time series analysis technique that has found applications in AI and machine learning, as well as financial markets and economics. By decomposing a time series into interpretable components, Caterpillar SSA enables better understanding of the underlying structure of the data, facilitating forecasting, smoothing, and anomaly detection. In the context of financial trading, the technique is used to analyze price data, detect significant trends or turning points, and inform trading decisions. █ Input Parameters This indicator takes several inputs that affect its signal output. These inputs can be classified into three categories: Basic Settings, UI Options, and Computation Parameters. Source: This input represents the source of price data, which is typically the closing price of an asset. The user can select other price data, such as opening price, high price, or low price. The selected price data is then utilized in the Caterpillar SSA calculation process. Lag: The lag input determines the window size used for the time series decomposition. A higher lag value implies that the SSA algorithm will consider a longer range of historical data when extracting the underlying trend and components. This parameter is crucial, as it directly impacts the resulting smoothed series and the quality of extracted components. Number of Computations: This input, denoted as 'ncomp,' specifies the number of eigencomponents to be considered in the reconstruction of the time series. A smaller value results in a smoother output signal, while a higher value retains more details in the series, potentially capturing short-term fluctuations. SSA Period Normalization: This input is used to normalize the SSA period, which adjusts the significance of each eigencomponent to the overall signal. It helps in making the algorithm adaptive to different timeframes and market conditions. Number of Bars: This input specifies the number of bars to be processed by the algorithm. It controls the range of data used for calculations and directly affects the computation time and the output signal. Number of Bars to Render: This input sets the number of bars to be plotted on the chart. A higher value slows down the computation but provides a more comprehensive view of the indicator's performance over a longer period. This value controls how far back the indicator is rendered. Color bars: This boolean input determines whether the bars should be colored according to the signal's direction. If set to true, the bars are colored using the defined colors, which visually indicate the trend direction. Show signals: This boolean input controls the display of buy and sell signals on the chart. If set to true, the indicator plots shapes (triangles) to represent long and short trade signals. Static Computation Parameters: The indicator also includes several internal parameters that affect the Caterpillar SSA algorithm, such as Maxncomp, MaxLag, and MaxArrayLength. These parameters set the maximum allowed values for the number of computations, the lag, and the array length, ensuring that the calculations remain within reasonable limits and do not consume excessive computational resources. █ A Note on Endpionted, Non-repainting Indicators An endpointed indicator is one that does not recalculate or repaint its past values based on new incoming data. In other words, the indicator's previous signals remain the same even as new price data is added. This is an important feature because it ensures that the signals generated by the indicator are reliable and accurate, even after the fact. When an indicator is non-repainting or endpointed, it means that the trader can have confidence in the signals being generated, knowing that they will not change as new data comes in. This allows traders to make informed decisions based on historical signals, without the fear of the signals being invalidated in the future. In the case of the Endpointed SSA of Price, this non-repainting property is particularly valuable because it allows traders to identify trend changes and reversals with a high degree of accuracy, which can be used to inform trading decisions. This can be especially important in volatile markets where quick decisions need to be made.
مؤشر Pine Script®
من ‎loxx‎
4
Trend Algo (Zeiierman)█ Overview Trend Algo (Zeiierman) converts raw price movement into a cohesive, volatility-aware trend map. Rather than static moving averages, it builds an adaptive trend core that responds to market state changes, then layers regime-sensitive clouds (Infinite Sky) and horizon-bounded channels (HorizonX), plus precise trend-change and continuation signals. The result is a clean framework for reading direction, timing pullbacks, and validating momentum. The indicator visualizes the active trend line, adaptive clouds, and stateful signals to reveal transition zones, trend acceleration, and exhaustion. It suits intraday execution, swing confirmations, and structural regime assessment. ⚪ Why This One Is Unique Unlike conventional trend tools, this version combines volatility-normalized trend estimation, AI-style regime clustering, and horizon-bounded support/resistance channels. Its framework uses multi-phase smoothing, adaptive width scaling, and state detection that aligns entries and continuations with current market inertia—reducing whipsaw while preserving responsiveness. █ Main features ⚪ Dynamic Clouds The Dynamic Trend Line provides a stabilized, noise-aware trajectory of price, color-coded by directional bias. Two cloud systems overlay the core trend: Infinite Sky (AI) — a regime-aware cloud that distinguishes fast vs. slow trend states. HorizonX — adaptive channels that operate as dynamic support/resistance and define trend boundaries. ⚪ Candle Coloring The candle coloring is designed to highlight trend momentum peaks, allowing you to instantly recognize when the trend is accelerating or slowing down. This visual feedback makes it easier to interpret the strength and speed of directional moves directly on the chart, seamlessly complementing the Dynamic Trend Line and Cloud systems. ⚪ Trend Change Signals & Trend Continuation Signals The main signals are the Trend Change Signals , designed to trigger when the current trend structure begins to shift its state. These signals give you an instant sense of when a potential trend reversal or directional change might occur. To complement them, we’ve added Trend Continuation Signals , which only activate within an established trend. These signals confirm the current direction and indicate that the trend is gaining strength and preparing for another move in the same direction. ⚪ Trend Line Two types of trend lines are integrated: Stability and Dynamic. The Stability Line follows the current price action in a smooth, drift-like manner — staying closer to price to act as a faster support and resistance zone. The Dynamic Line adapts to volatility, ensuring that continuous movements within the trend are taken into account. This helps traders stay aligned with the trend and capture extended moves during periods of higher market activity. █ How to Use ⚪ Trend Changes Trend Change Signals appear when the current trend structure begins to shift, signaling a potential reversal or directional transition. These signals help traders anticipate moments when momentum weakens or changes direction, offering early insight into possible turning points in the market. Combine Trend Change Signals with Candle Coloring or Cloud behavior to confirm entries and strengthen trend reversal validation. ⚪ Trend Continuations Trend Continuation Signals trigger within an established trend, confirming that the current direction remains strong. They often appear after pullbacks or pauses, suggesting that the market is ready for another move in the same direction. These signals work best when paired with the Dynamic Trend Line, allowing traders to stay confidently aligned with powerful ongoing trends. ⚪ Support and Resistance The Stability Trend Line acts as a reactive support and resistance guide, staying close to price and highlighting areas where market reactions are most likely to occur. It offers traders a clear view of short-term structure and helps identify potential bounce or rejection zones with precision. The Dynamic Trend Line adapts to volatility, forming flexible support and resistance zones that expand during strong momentum and contract during quieter phases, keeping the visual structure balanced and responsive to changing market conditions. HorizonX defines the outer adaptive boundaries of the trend. These dynamic channels operate as broader support and resistance frameworks, helping traders anticipate trend exhaustion or continuation zones beyond the immediate price action. The Infinite Sky (AI) cloud adds a regime-aware layer that distinguishes fast and slow trend states, visually framing the strength of support or resistance regions as they evolve and allowing traders to read both momentum and structure in a single glance. █ How It Works ⚪ Dynamic Trend Core A volatility-normalized estimator generates the primary trajectory and color state, balancing stability vs. agility by adapting to dispersion and displacement. Calculation: Multi-phase weighted smoothing with volatility-scaled adjustments and adaptive phase control to minimize lag while preserving curvature. ⚪ Infinite Sky (AI Regime Cloud) Differentiates fast vs. slow trend states and paints a regime-aware cloud that guides timing and risk. Calculation: Windowed return modeling (with optional consolidation), two-pole response filtering, and distribution-center clustering to classify regimes and modulate the envelope. ⚪ HorizonX Trend Channels Defines dynamic support/resistance “horizons” that expand/contract with conditions and filter counter-trend noise when strong-trend mode is enabled. Calculation: A volatility-aware core that evolves with changing price dynamics, maintaining precision across all market conditions. ⚪ Trend Line & Continuations A fast reaction line confirms pullbacks and validates continuation only when aligned with prevailing bias and structure. Calculation: Short-horizon smoothing with phase-managed weighting, slope/position gating, and context checks against regime and horizon levels. ⚪ Impulse Detection Identifies statistically meaningful bursts that precede expansion or warn of exhaustion after extended runs. Calculation: Nonlinear impulse-response mapping emphasizing second-order changes in directional energy while suppressing micro-noise. ⚪ Trend Bands Upper/lower bands visualize the active envelope for timing adds, trims, or exits within trend. Calculation: Percentile-guided banding blended with adaptive anchors to retain structural relevance through regime shifts. ⚪ Bar Color Integration Bars can inherit trend/gradient context to make state shifts obvious at a glance. Calculation: Algorithmic gradient model integrating directional momentum intensity to render bar coloration in sync with market flow. ----------------- Disclaimer The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information. All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
مؤشر Pine Script®سكربت مدفوع
من ‎Zeiierman‎
115
Intelligent Exponential Moving Average Private AccessView the full documentation on this indicator here: www.kenzing.com Note: This indicator is now intended for those who have been granted private access and may be more frequently updated than the previous versions. Introduction This indicator uses machine learning (Artificial Intelligence) to solve a real human problem. The Exponential Moving Average ( EMA ) is one of the most used indicators on the planet, yet no one really knows what pair of exponential moving average lengths works best in combination with each other. A reason for this is because no two EMA lengths are always going to be the best on every instrument, time-frame, and at any given point in time. The " Intelligent Exponential Moving Average " solves the moving average problem by adapting the period length to match the most profitable combination of exponential moving averages in real time. How does the Intelligent Exponential Moving Average work? The artificial intelligence that operates these moving average lengths was created by an algorithm that tests every single combination across the entire chart history of an instrument for maximum profitability in real-time. No matter what happens, the combination of these exponential moving averages will be the most profitable. Can we learn from the Intelligent Moving Average? There are many lessons to be learned from the Intelligent EMA . Most will come with time as it is still a new concept. Adopting the usefulness of this AI will change how we perceive moving averages to work. Limitations Ultimately, there are no limiting factors within the range of combinations that has been programmed. The exponential moving averages will operate normally, but may change lengths in unexpected ways - maybe it knows something we don't? Thresholds The range of exponential moving average lengths is between 5 to 40. Additional coverage resulted in TradingView server errors. Future Updates! Soon, I will be publishing tools to test the AI and visualise what moving average combination the AI is currently using. Follow and like for more content!
مؤشر Pine Script®
من ‎GrantPeace‎
1
RSI or MACD + Tendance Kijun LTThis script is an update of my previous script "RSI + Tendance Kijun LT", I will not explain it here, if needed have a look at it : I made a new script (and not update the previous one) because some people may not be interested by MACD and for performance perspective they may be interested to only have RSI (since you can't have both but only switch from RSI and MACD) So now, you can choose to have MACD instead of RSI with long term trend based on Kijun still dispaying. Why am I adding MACD even if most of the time I never use it ? It's for Elliott Wave purpose and principaly for triangle. With MACD, you can easily identify if you're forming a triangle or not in an Elliott Wave perspective (I'm not speaking about chartist triangle). As an example, you can see ETHUSD in daily and something looking similar to a triangle. We can trade it with many possibility (breakout, support/resistance) but I'm interested in to identify if it's a triangle with an EW count (not chartist) and if it's the case I will consider different scenario (triangle are most of the time wave 4 so we could have one more push leg down on this ticker) So, in my daytrading I'm still always using RSI except when I want to verify if we have a triangle :) and I need to switch to MACD for that to check the following things : - am I able to draw a triangle as the price did - may I able to join A to C and B to D and still have a triangle on MACD - If Yes, I will take care of E point because it's the start of the 5th wave (E point may be a truncated wave of the triangle and not join the line of points A to C) By adding this in my strategy, I can anticipate different scenarios and invalidate them if I didn't get the triangle on MACD (by having a D point on MACD not respecting the triangle form). Don't forget, we can decide to don't trade a triangle as an EW count but still trade it as a chartist form (breakout or anything else). In summary for the next days/weeks, on ETHUSD in daily time unit I will therefore wait and see if the price goes up to point D by being validated on the MACD. If so, then I will look at the possible formation of point E around prices 141$ and 156$, if at these prices I have short signals then it will be interesting to go back in short position -------------------------------------------------------- Ce script est une mise à jour de mon script précédent "RSI + Tendance Kijun LT", je ne l'expliquerai pas ici, si besoin jetez-y un oeil : J'ai fait un nouveau script (sans forcer la mise à jour du précédent) parce que certaines personnes peuvent ne pas être intéressées par MACD et pour des raisons de performance, elles peuvent être intéressées à n'avoir que RSI (puisque vous ne pouvez pas avoir les deux en même temps mais seulement passer de RSI et MACD) Donc maintenant, vous pouvez choisir d'avoir MACD au lieu de RSI avec une tendance à long terme basée sur Kijun qui s'affiche toujours. Pourquoi est-ce que j'ajoute MACD même si la plupart du temps je ne l'utilise jamais ? C'est pour Elliott Wave et surtout pour le triangle. Avec MACD, vous pouvez facilement identifier si vous formez un triangle ou non dans une perspective Elliott Wave (je ne parle pas de triangle chartiste). Par exemple, vous pouvez voir que sur ETHUSD en unité de temps journalière nous avons quelque chose qui ressemble à un triangle. Nous pouvons faire des trade de pleins de manières différentes (cassure d'oblique, support/résistance) mais je suis intéressé d'identifier si c'est un triangle avec un compte EW (pas chartiste) et si c'est le cas, je vais envisager un scénario différent (sachant que les triangles sont la plupart du temps la vague 4 et donc envisager à la sortie une dernière poussée baissière type vague 5 sur ETHUSD) Donc, dans mon daytrading, j'utilise toujours RSI sauf quand je veux vérifier si nous avons un triangle :) et j'ai besoin de passer à MACD afin de vérifier les éléments suivantes : - Suis-je capable de dessiner un triangle comme le prix le dessine - puis-je joindre A à C et B à D et toujours avoir un triangle sur MACD ? - Si oui, je m'occupe du point E car c'est le début de la 5ème vague (le point E peut être une onde tronquée du triangle et ne pas joindre la ligne des points A à C). En ajoutant cela dans ma stratégie, je peux anticiper différents scénarios et les invalider si je n'ai pas obtenu le triangle sur MACD (en ayant un point D sur MACD ne respectant pas la forme du triangle). N'oubliez pas, nous pouvons décider de ne pas le trade comme un triangle d'un décompte EW mais de le trade simplement comme un triangle chartiste (breakout ou autre chose). En résumé pour les prochains jours/semaines, sur ETHUSD en unité de temps journalière je vais donc patienter et voir si le prix va jusqu'au point D en étant validé sur le MACD. Si oui, alors je regarderai l'éventuelle formation du point E autour des prix 141$ et 156$, si à ces prix j'ai des signaux vendeurs alors il sera intéressant de rentrer en position short
مؤشر Pine Script®
من ‎duftux‎