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ORB [RAJ AI]Defines customizable opening range periods with flexible time settings Supports both single and multiple ORB sessions throughout the trading day Calculates dynamic high/low buffers with configurable points or percentage offsets Risk Management: Configurable take profit levels (up to 3 targets) for both long and short positions Adjustable stop loss settings with points or percentage-based calculations Advanced trade sequencing to prevent repeated signals Distance-based entry restrictions from previous trades
مؤشر Pine Script®
من ‎devriturajkumar‎
The Wave Levels (ORB Indicator)This Indicator is made for the ORB trader. It's purpose is to help make your charting faster by providing some basic key levels to reference at a glance. This was optimized to be used on the 5m timeframe. Key features: 1. Green and Red ORB rays to indicate the 15 minute Opening Range. These ranges will only extend as long as its respective session's length. 2. Previous sessions zones for historical easily identification of historical data (best used in UTC-4 timezone) 3. White Liquidity Rays. These rays are used to mark a previous session's high or low which hasn't been swept yet. This makes for a good TP area or identifying a potential reversal area. Once a previous session high or low has been taken, the ray will automatically remove. I am not a professional coder. This indicator was created by continuously prompting AI commands over the course of 3 days.
مؤشر Pine Script®
من ‎EscoTrades671‎
1
Chronos Reversal Labs🧬 Chronos Reversal Lab - Machine Learning Market Structure Analysis OVERVIEW Chronos Reversal Lab (CRL) is an advanced market structure analyzer that combines computational intelligence kernels with classical technical analysis to identify high-probability reversal opportunities. The system integrates Shannon Entropy analysis, Detrended Fluctuation Analysis (DFA), Kalman adaptive filtering, and harmonic pattern recognition into a unified confluence-based signal engine. WHAT MAKES IT ORIGINAL Unlike traditional reversal indicators that rely solely on oscillators or pattern recognition, CRL employs a multi-kernel machine learning approach that analyzes market behavior through information theory, statistical physics, and adaptive state-space estimation. The system combines these computational methods with geometric pattern analysis and market microstructure to create a comprehensive reversal detection framework. HOW IT WORKS (Technical Methodology) 1. COMPUTATIONAL KERNELS Shannon Entropy Analysis Measures market uncertainty using information theory: • Discretizes price returns into bins (user-configurable 5-20 bins) • Calculates probability distribution entropy over lookback window • Normalizes entropy to 0-1 scale (0 = perfectly predictable, 1 = random) • Low entropy states (< 0.3 default) indicate algorithmic clarity phases • When entropy drops, directional moves become statistically more probable Detrended Fluctuation Analysis (DFA) Statistical technique measuring long-range correlations: • Analyzes price series across multiple box sizes (4 to user-set maximum) • Calculates fluctuation scaling exponent (Alpha) • Alpha > 0.5: Trend persistence (momentum regime) • Alpha < 0.5: Mean reversion tendency (reversal regime) • Alpha range 0.3-1.5 mapped to trading strategies Kalman Adaptive Filter State-space estimation for lag-free trend tracking: • Maintains separate fast and slow Kalman filters • Process noise and measurement noise are user-configurable • Tracks price state with adaptive gain adjustments • Calculates acceleration (second derivative) for momentum detection • Provides cleaner trend signals than traditional moving averages 2. HARMONIC PATTERN DETECTION Identifies geometric reversal patterns: • Gartley: 0.618 AB/XA, 0.786 AD/XA retracement • Bat: 0.382-0.5 AB/XA, 0.886 AD/XA retracement • Butterfly: 0.786 AB/XA, 1.272-1.618 AD/XA extension • Cypher: 0.382-0.618 AB/XA, 0.786 AD/XA retracement Pattern Validation Process: • Requires alternating swing structure (XABCD points) • Fibonacci ratio tolerance: 0.02-0.20 (user-adjustable precision) • Minimum 50% ratio accuracy score required • PRZ (Potential Reversal Zone) calculated around D point • Zone size: ATR-based with pattern-specific multipliers • Active pattern tracking with 100-bar invalidation window 3. MARKET STRUCTURE ANALYSIS Swing Point Detection: • Pivot-based swing identification (3-21 bars configurable) • Minimum swing size: ATR multiples (0.5-5.0x) • Adaptive filtering: volatility regime adjustment (0.7-1.3x) • Swing confirmation tracking with RSI and volume context • Maintains structural history (up to 500 swings) Break of Structure (BOS): • Detects price crossing previous swing highs/lows • Used for trend continuation vs reversal classification • Optional requirement for signal validation Support/Resistance Detection: • Identifies horizontal levels from swing clusters • Touch counting algorithm (price within ATR×0.3 tolerance) • Weighted by recency and number of tests • Dynamic updating as structure evolves 4. CONFLUENCE SCORING SYSTEM Multi-factor analysis with regime-aware weighting: Hierarchical Kernel Logic: • Entropy gates advanced kernel activation • Only when entropy < threshold do DFA and Kalman accelerate scoring • Prevents false signals during chaotic (high entropy) conditions Scoring Components: ML Kernels (when entropy low): • Low entropy + trend alignment: +3.0 points × trend weight • DFA super-trend (α>1.5): +4.0 points × trend weight • DFA persistence (α>0.65): +2.5 points × trend weight • DFA mean-reversion (α<0.35): +2.0 points × mean-reversion weight • Kalman acceleration: up to +3.0 points (scaled by magnitude) Classical Technical Analysis: • RSI oversold (<30) / overbought (>70): +1.5 points • RSI divergence (bullish/bearish): +2.5 points • High relative volume (>1.5x): +0-2.0 points (scaled) • Volume impulse (>2.0x): +1.5 points • VWAP extremes: +1.0 point • Trend alignment (Kalman fast vs slow): +1.5 points • MACD crossover/momentum: +1.0 point Structural Factors: • Near support (within 0.5 ATR): +0-2.0 points (inverse distance) • Near resistance (within 0.5 ATR): +0-2.0 points (inverse distance) • Harmonic PRZ zone: +3.0 to +6.0 points (pattern score dependent) • Break of structure: +1.5 points Regime Adjustments: • Trend weight: 1.5× in trend regime, 0.5× in mean-reversion • Mean-reversion weight: 1.5× in MR regime, 0.5× in trend • Volatility multiplier: 0.7-1.3× based on ATR regime • Theory mode multiplier: 0.8× (Conservative) to 1.2× (APEX) Final Threshold: Base threshold (default 3.5) adjusted by: • Theory mode: -0.3 (APEX) to +0.8 (Conservative) • Regime: +0.5 (high vol) to -0.3 (low vol or strong trend) • Filter: +0.2 if regime filter enabled 5. SIGNAL GENERATION ARCHITECTURE Five-stage validation process: Stage 1 - ML Kernel Analysis: • Entropy threshold check • DFA regime classification • Kalman acceleration confirmation Stage 2 - Structural Confirmation: • Market structure supports directional bias • BOS alignment (if required) • Swing point validation Stage 3 - Trigger Validation: • Engulfing candle (if required) • HTF bias confirmation (if strict HTF enabled) • Harmonic PRZ alignment (if confirmation enabled) Stage 4 - Consistency Check: • Anticipation depth: checks N bars back (1-13 configurable) • Ensures Kalman acceleration direction persists • Filters whipsaw conditions Stage 5 - Structural Soundness (Critical Filter): • Verifies adequate room before next major swing level • Long signals: must have >0.25 ATR clearance to last swing high • Short signals: must have >0.25 ATR clearance to last swing low • Prevents trades directly into obvious structural barriers Dynamic Risk Management: • Stop-loss: Placed beyond last structural swing ± 2 ticks • Take-profit 1: Risk × configurable R1 multiplier (default 1.5R) • Take-profit 2: Risk × configurable R2 multiplier (default 3.0R) • Confidence score: Calibrated 0-99% based on confluence + kernel boost 6. ADAPTIVE REGIME SYSTEM Continuous market state monitoring: Trend Regime: • Kalman fast vs slow positioning • Multi-timeframe alignment (optional HTF) • Strength: ATR-normalized fast/slow spread Volatility Regime: • Current ATR vs 100-bar average • Regime ratio: 0.7-1.3 typical range • Affects swing size filtering and cooldown periods Signal Cooldown: • Base: User-set bars (1-300) • High volatility (>1.5): cooldown × 1.5 • Low volatility (<0.5): cooldown × 0.7 • Post-BOS: minimum 20-bar cooldown enforced FOUR OPERATIONAL MODES CONSERVATIVE MODE: • Threshold adjustment: +0.8 • Mode multiplier: 0.8× • Strictest filtering for highest quality • Recommended for: Beginners, large accounts, swing trading • Expected signals: 3-5 per week (typical volatile instrument) BALANCED MODE: • Threshold adjustment: +0.3 • Mode multiplier: 1.0× • Standard operational parameters • Recommended for: General trading, learning phase • Expected signals: 5-10 per week APEX MODE: • Threshold adjustment: -0.3 • Mode multiplier: 1.2× • Maximum sensitivity, reduced cooldowns • Recommended for: Scalping, high volatility, experienced traders • Expected signals: 10-20 per week INSTITUTIONAL MODE: • Threshold adjustment: +0.5 • Mode multiplier: 1.1× • Enhanced structural weighting, HTF emphasis • Recommended for: Professional traders, swing positions • Expected signals: 4-8 per week VISUAL COMPONENTS 1. Fibonacci Retracement Levels • Auto-calculated from most recent swing structure • Standard levels: 0%, 23.6%, 38.2%, 50%, 61.8%, 78.6%, 100%, 127.2%, 161.8%, 200%, 261.8% • Key levels emphasized (50%, 61.8%, 100%, 161.8%) • Color gradient from bullish to bearish based on level • Automatic cleanup when levels are crossed • Label intensity control (None/Fib only/All) 2. Support and Resistance Lines • Dynamic horizontal levels from swing clusters • Width: 2px solid lines • Colors: Green (support), Red (resistance) • Labels show price and level type • Touch-based validation (minimum 2 touches) • Real-time updates and invalidation 3. Harmonic PRZ Boxes • Displayed around pattern completion (D point) • Pattern-specific colors (Gartley: purple, Bat: orange, etc.) • Box height: ATR-based zone sizing • Score-dependent transparency • 100-bar active window before removal 4. Confluence Boxes • Appear when confluence ≥ threshold • Yellow/orange gradient based on score strength • Height: High to low of bar • Width: 1 bar on each side • Real-time score-based transparency 5. Kalman Filter Lines • Fast filter: Bullish color (green default) • Slow filter: Bearish color (red default) • Width: 2px • Transparency adjustable (0-90%) • Optional display toggle 6. Signal Markers • Long: Green triangle below bar (tiny size) • Short: Red triangle above bar (tiny size) • Appear only on confirmed signals • Includes alert generation 7. Premium Dashboard Features real-time metrics with visual gauges: Layout Options: • Position: 4 corners selectable • Size: Small (9 rows) / Normal (12 rows) / Large (14 rows) • Themes: Supreme, Cosmic, Vortex, Heritage Metrics Displayed: • Gamma (DFA - 0.5): Shows trend persistence vs mean-reversion • TCI (Trend Strength): ATR-normalized Kalman spread with gauge • v/c (Relative Volume): Current vs average with color coding • Entropy: Market predictability state with gauge • HFL (High-Frequency Line): Kalman fast/slow difference / ATR • HFL_acc (Acceleration): Second derivative momentum • Mem Bias: Net bullish-bearish confluence (-1 to +1) • Assurance: Confidence × (1-entropy) metric • Squeeze: Bollinger Band / Keltner Channel squeeze detection • Breakout P: Probability estimate from DFA + trend + acceleration • Score: Final confluence vs threshold (normalized) • Neighbors: Active harmonic patterns count • Signal Strength: Strong/Moderate/Weak classification • Signal Banner: Current directional bias with emoji indicators Gauge Visualization: • 10-bar horizontal gauges (█ filled, ░ empty) • Color-coded: Green (strong) / Gold (moderate) / Red (weak) • Real-time updates every bar HOW TO USE Step 1: Configure Mode and Resolution • Select Theory Mode based on trading style (Conservative/Balanced/APEX/Institutional) • Set Structural Resolution (Standard for fast markets, High for balanced, Ultra/Institutional for swing) • Enable Adaptive Filtering (recommended for all volatile assets) Step 2: Enable Desired Kernels • Shannon Entropy: Essential for predictability detection (recommended ON) • DFA Analysis: Critical for regime classification (recommended ON) • Kalman Filter: Provides lag-free trend tracking (recommended ON) • All three work synergistically; disabling reduces effectiveness Step 3: Configure Confluence Factors • Enable desired technical factors (RSI, MACD, Volume, Divergence) • Enable Liquidity Mapping for support/resistance proximity scoring • Enable Harmonic Detection if trading pattern-based setups • Adjust base confluence threshold (3.5 default; higher = fewer, cleaner signals) Step 4: Set Trigger Requirements • Require Engulfing: Adds precision, reduces frequency (recommended for Conservative) • Require BOS: Ensures structural alignment (recommended for trend-following) • Require Structural Soundness: Critical filter preventing traps (highly recommended) • Strict HTF Bias: For multi-timeframe traders only Step 5: Adjust Visual Preferences • Enable/disable Fibonacci levels, S/R lines, PRZ boxes, confluence boxes • Set label intensity (None/Fib/All) • Adjust transparency (0-90%) for overlay clarity • Configure dashboard position, size, and theme Step 6: Configure Alerts • Enable master alerts toggle • Select alert types: Anticipation, Confirmation, High Confluence, Low Entropy • Enable JSON details for automated trading integration Step 7: Interpret Signals • Wait for triangle markers (green up = long, red down = short) • Check dashboard for confluence score, entropy, DFA regime • Verify signal aligns with higher timeframe bias (if using HTF setting) • Confirm adequate space to take-profit levels (no nearby structural barriers) Step 8: Execute and Manage • Enter at close of signal candle (or next bar open) • Set stop-loss at calculated level (visible in alert if JSON enabled) • Scale out at TP1 (1.5R default), trail remaining to TP2 (3.0R default) • Exit early if entropy spikes >0.7 or DFA regime flips against position CUSTOMIZATION GUIDE Timeframe Optimization: Scalping (1-5 minutes): • Theory Mode: APEX • Anticipation Depth: 3-5 • Structural Resolution: STANDARD • Signal Cooldown: 8-12 bars • Enable fast kernels, disable HTF bias Day Trading (15m-1H): • Theory Mode: BALANCED • Anticipation Depth: 5-8 • Structural Resolution: HIGH • Signal Cooldown: 12-20 bars • Standard configuration Swing Trading (4H-Daily): • Theory Mode: INSTITUTIONAL • Anticipation Depth: 8-13 • Structural Resolution: ULTRA or INSTITUTIONAL • Signal Cooldown: 20-50 bars • Enable HTF bias, strict confirmations Market Type Optimization: Forex Majors: • All kernels enabled • Harmonic patterns effective • Balanced or Institutional mode • Standard settings work well Stock Indices: • Emphasis on volume analysis • DFA critical for regime detection • Conservative or Balanced mode • Enable liquidity mapping Cryptocurrencies: • Adaptive filtering essential • Higher volatility regime expected • APEX mode for active trading • Wider ATR multiples for swing sizing IMPORTANT DISCLAIMERS • This indicator does not predict future price movements • Computational kernels calculate probabilities, not certainties • Past confluence scores do not guarantee future signal performance • Always backtest on YOUR specific instruments and timeframes before live trading • Machine learning kernels require calibration period (minimum 100 bars of data) • Performance varies significantly across market conditions and regimes • Signals are suggestions for analysis, not automated trading instructions • Proper risk management (stops, position sizing) is mandatory • Complex calculations may impact performance on lower-end devices • Designed for liquid markets; avoid illiquid or gap-prone instruments PERFORMANCE CONSIDERATIONS Computational Intensity: • DFA analysis: Moderate (scales with length and box size parameters) • Entropy calculation: Moderate (scales with lookback and bins) • Kalman filtering: Low (efficient state-space updates) • Harmonic detection: Moderate to High (pattern matching across swing history) • Overall: Medium computational load Optimization Tips: • Reduce Structural Analysis Depth (144 default → 50-100 for faster performance) • Increase Calc Step (2 default → 3-4 for lighter load) • Reduce Pattern Analysis Depth (8 default → 3-5 if harmonics not primary focus) • Limit Draw Window (150 bars default prevents visual clutter on long charts) • Disable unused confluence factors to reduce calculations Best Suited For: • Liquid instruments: Major forex, stock indices, large-cap crypto • Active timeframes: 5-minute through daily (avoid tick/second charts) • Trending or ranging markets: Adapts to both via regime detection • Pattern traders: Harmonic integration adds geometric confluence • Multi-timeframe analysts: HTF bias and regime detection support this approach Not Recommended For: • Illiquid penny stocks or micro-cap altcoins • Markets with frequent gaps (stocks outside regular hours without gap adjustment) • Extremely fast timeframes (tick, second charts) due to calculation overhead • Pure mean-reversion systems (unless using CONSERVATIVE mode with DFA filters) METHODOLOGY NOTE The computational kernels (Shannon Entropy, DFA, Kalman Filter) are established statistical and signal processing techniques adapted for financial time series analysis. These are deterministic mathematical algorithms, not predictive AI models. The term "machine learning" refers to the adaptive, data-driven nature of the calculations, not neural networks or training processes. Confluence scoring is rule-based with regime-dependent weighting. The system does not "learn" from historical trades but adapts its sensitivity to current volatility and trend conditions through mathematical regime classification. SUPPORT & UPDATES • Questions about configuration or usage? Send me a message on TradingView • Feature requests are welcome for consideration in future updates • Bug reports appreciated and addressed promptly • I respond to messages within 24 hours • Regular updates included (improvements, optimizations, new features) FINAL REMINDERS • This is an analytical tool for confluence analysis, not a standalone trading system • Combine with your existing strategy, risk management, and market analysis • Start with paper trading to learn the system's behavior on your markets • Allow 50-100 signals minimum for performance evaluation • Adjust parameters based on YOUR timeframe, instrument, and trading style • No indicator guarantees profitable trades - proper risk management is essential — Dskyz, Trade with insight. Trade with anticipation.
مؤشر Pine Script®
من ‎DskyzInvestments‎
4
MACD AI Flux Pro Dashboard V. 2Acknowledgment This indicator is built upon the MACD-V (Volatility-Normalized MACD) methodology originally created by Alex Spiroglou, CMT, whose research (2015–2022) introduced the principle of normalizing MACD momentum by volatility (MACD/ATR). Full acknowledgment and credit are hereby given to Mr. Spiroglou as the original author of the MACD-V concept and framework. Indicator Overview — MACD-V Flux Pro Dashboard V.2 The MACD-V Flux Pro Dashboard advances Spiroglou’s volatility-normalized foundation into a comprehensive multi-system architecture that unifies momentum, trend, volatility, and compression analytics in one visual framework. It is engineered for precision decision-making in both intraday and swing-trading environments. Key Dashboard Features: Dynamic Probability Engine: Calculates real-time long and short probabilities by weighting momentum, slope, compression, and volume pressure components into a composite score. Multi-Timeframe Confirmation (HTF Tiles): Displays live directional agreement across fast, mid, and slow timeframes for confidence filtering and signal validation. Regime Detection System: Automatically classifies the market as Trend Up, Trend Down, Compression, or Transition, applying background color cues for instant context. Risk and News Filters: Integrates ATR-based risk gating and customizable “mute windows” to block trade signals during high-volatility or scheduled news events. VWAP and Adaptive Bands: Plots VWAP with configurable ATR or standard-deviation bands to highlight over-extension and pullback zones. Trend-Day and Opening-Range Logic: Monitors RTH (Regular Trading Hours) price behavior to identify potential trend-day conditions. Smart Entry Arrows: Generates visual long/short signals only when multiple subsystems confirm direction, slope strength, and proximity to VWAP within defined thresholds. On-Chart Dashboard Panel: Presents live metrics including probability bias, regime state, ATR level, risk status, and news filters with adaptive color-coding and optional emoji cues for intuitive interpretation. Chart Display Summary: All elements are presented directly on the main chart, combining price structure, VWAP bands, EMAs, and regime background shading with the real-time dashboard panel. The design eliminates the need for a secondary pane, offering a consolidated and context-rich view of market dynamics
مؤشر Pine Script®
من ‎inkadinka_ka‎
5
XqKtPvRwSd-StrategyTradingView Pine Script Suite (v4.0) — General Overview Purpose A modular indicator and strategy suite designed for crypto markets, focusing on trend detection, momentum, range visualization, and signal generation. The suite supports popular symbols (BTC, ETH, BNB, SOL, DOGE) and offers flexible visualization and alert options. Main Components - v4.0-indicator: Multi-signal display for monitoring market conditions and trend bias. - v4.0-watch-15m: Watchlist-style indicator with symbol-aware auto-tuning and optional chart timeframe adaptation. - v4.0-strategy: Backtestable strategy using a unified, SuperTrend-based trend determination for entries/exits. - v4.0-range: Range and structure visualization with dynamic lines, zones, and basic breakout cues. - Two Poles.pinescript and fengzi/Two Poles Trend: Low-lag trend and smoothing filters for stability. - supertrend.pinescript: SuperTrend variations used across the suite for direction and stops. - fengzi utilities: - Hann Window FIR Filter: Denoising and smoothing to reduce noise without excessive lag. - Heatmap + Hann Resonance: Visual heat/energy mapping for momentum and resonance cues. - Heatmap Trailing Stop with Breakouts: Trailing stop framework with breakout detection. - Additional modules: super Z, AI JX, v3.2, and 4.0.pinescript (combined or experimental entries). Key Features - Unified Trend Logic: Strategy and visual components rely on a consistent SuperTrend-based direction model, improving clarity and consistency. - Adaptive Timeframe: Option to use the chart’s timeframe; parameters scale from a 15m baseline to maintain relative behavior. - Symbol Auto-Tuning: Default parameter sets are tailored for common crypto symbols; manual overrides are available. - RSI Top/Bottom Hints: Optional dynamic cues for potential peaks in uptrends and troughs in downtrends; can contribute to scoring and alerts. - Visual Customization: Configurable markers (arrows, dots, crosses), colors, line widths, and subtle background highlighting. - Scoring and Clustering: Combines checks into a simplified score and clusters signals to reduce noise. - Alerts: Supports TradingView alerts for entries, exits, and RSI hints. - Compatibility: Designed to compile across common Pine versions and avoid local-scope plotting issues. Basic Usage - Add the indicator or strategy to your chart depending on your goal (monitoring vs. backtesting). - Choose to follow the chart’s timeframe or set a working timeframe manually. - Keep symbol auto-tuning enabled for a balanced default, or adjust parameters to match your preferences. - Toggle optional modules (RSI hints, scoring contribution, visualization) based on your needs. - For strategies, run backtests across your target period and review performance metrics (win rate, drawdown, and trade frequency). Configuration Guidelines - For earlier entries: lower SuperTrend period or multiplier; relax RSI thresholds. - For more conservative behavior: increase SuperTrend period or multiplier; tighten thresholds; use stricter RSI logic. - For cleaner signals: rely on the unified trend and consider limiting trades during extreme volatility or illiquid sessions. Outputs - Trend markers and lines indicating bullish/bearish bias and reversals. - Range drawings with dynamic line coordinates, zones, and visual cues for consolidations and breakouts. - Optional multi-timeframe dashboard-style summaries where applicable. Extensibility - Parameters and defaults can be extended for additional symbols and styles. - Optional weighting or neutrality rules can be added to trend logic if you prefer stricter confirmation. - Additional filters (volatility, session, volume) can be integrated to refine entries and exits. Notes - Not financial advice; backtest thoroughly before using any strategy live. - Performance and signal quality vary by symbol, timeframe, and market conditions; adjust parameters as needed. - Pine version differences may require minor adjustments; keep your environment up to date.
استراتيجية Pine Script®
من ‎taozitong‎
Buy/Sell Signals [WynTrader]My name is WynTrader. I cumulate 24 years of experience. This Indicator produces Buy/Sell Signals using these features: - Fast and Slow Moving averages (modifiable) optimized at EMA-8 and SMA-35 - Bollinger Bands (modifiable) optimized at Basis-18 and Multiplier-1 Also, the Buy/Sell Signals are conditioned by three Filters (optionable, modifiable) : . Bollinger-Bands Lookback . High-Low vs Candle Range % . Distance from Fast and Slow Moving averages % The Results Calculation presented in a Table are based : - on the Current Chart Visible Range (optionable) or - on the specified TIme Frame Start and End Dates (modifiable) The Table shows Calculation Results of the Buy and Sell Signals that are activated on the chart, with the Number of Trades (Signals), the Winning Points and the Win Rate %. The Buy&Hold starts calculation at the first Buy encountered. So be surprised by my Buy/Sell Indicator. But always remember that the world is not perfect. The Graal Indicator, even with AI, doesn't already exist, maybe one day (all of us richier...), but not now. , depending on the chart product (stocks...), volatility, probabilities, unpredictable behaviour. , the moves, etc. Enjoy WynTrader P. s. : My name is WynTrader. I cumulate 24 years of experience. In 2001, I took an intensive technical analysis course taught by an exceptional friend, Cyril, who taught me everything I know. The foundation I gained through his teaching remains solid and relevant to this day, never failing me. Before i made this Indicator, I have used many Trading View Buy/Sell Indicators using alone or combined RSI, SMI, OBV, MACD ATR, ADX, Neural, Fractal, Geometry, etc., that are already available for the Trading View community. A great thanks to those who give their time that help me build this tool. Note that I'm not a programmer, so... ;-)
مؤشر Pine Script®
من ‎Wyn-Trader‎
4
Quarter Levels — Auto Recentering NQ onlyQuarter Levels — Auto Recentering (PERMANENT) + Big Offset Labels What it is This tool paints true horizontal key levels that traders naturally anchor to: the 00 / 25 / 50 / 75 quarter levels (black), the 35 / 65 / 90 reaction levels (red), and the 10 / 80 sweep/edge levels (purple). Lines are infinite horizontals and the grid auto-recenters ±200 points around current price each new bar. Labels on the right show the last two digits (e.g., 25, 35, 50, 65, 75, 80, 90), so you instantly know which level you’re at. Why it helps Markets often “snap” to simple numbers. These levels create a clean scaffold for intraday structure, pullbacks, and rotations—without clutter or lagging math. Color Legend Black — 00 / 25 / 50 / 75: Core quarter levels. Expect frequent pauses, re-tests, and rotations. Use: default S/R map; bias for mean-reversion inside ranges. Red — 35 / 65 / 90: “Continuation / reaction” levels. Price often accelerates through these once momentum takes. Use: breakout guides and precise take-profit targets. Purple — 10 / 80: Sweep / edge levels. Price often wicks into these and rejects. Use: fade the last push, or confirm a sweep before a reversal. How it works The script draws the levels as extend.both horizontals (not derived from candle points). Every new bar, it rebuilds the grid around close ± 200 pts (editable in code: RANGE_POINTS). Prices are snapped to tick (syminfo.mintick) so lines lock to the Y-axis. Labels show only the offset (two-digit number) to keep the chart clean. Setup & Customization No inputs required. If you want tweaks, open the code and edit at the top: RANGE_POINTS – widen/narrow the vertical coverage. LABEL_OFFSET – push labels further to the right. LABEL_SIZE – size.small / normal / large. Color & width constants (per group). Practical Use (playbook) Use this grid as a price map, not a signal generator. Combine it with your execution tools. 1) In Range Conditions Fade to Black: When price rotates inside a range, look for exhaustion into black levels (00/25/50/75). Plan: wait for rejection (wick + failed follow-through), enter back toward the mid/next quarter. Stop just beyond the level; first target the next red or black. Purple Sweeps: Watch quick spikes into 10/80 that immediately fail. Plan: fade the sweep with tight risk; scale out at 25/75; hold a runner to 50. 2) In Trend / Momentum Red Rails (35/65/90): When momentum is strong, price often steps through red levels cleanly. Plan: use them as continuation targets or trail anchors. If pullback holds above a prior red level, consider continuation with stop below that level. Quarter-to-Quarter Ladders: In clean trends, expect quarter-to-quarter traversals (00→25→50→75→00…). Plan: add on pullbacks to 25 or 50 with trend confirmation (e.g., 9/21 EMA stack or anchored VWAP hold). 3) Confluence (AI-logic suggestions) Pair the grid with any two of: VWAP / Anchored VWAP: Rejections at a quarter level + VWAP = higher quality entry. EMAs (9/21/50/200): Use as directional filter. Only take longs at quarters when fast EMAs > slow EMAs. Liquidity cues: Prior high/low, session O/H/L, or liquidity pools aligning with a quarter level. Orderflow / footprint: Aggressive delta through a red level? Expect follow-through to the next black or red. Volatility (ATR): If ATR expands, lean more on red levels (continuations). In compression, lean more on black and purple (fades). Risk & Management Tips Stops: Just beyond the level you’re trading against. Let the level “be wrong” to prove you wrong. Targets: Next red or black line. Scale at the first, hold a small runner to the next. Session awareness: Levels interact differently in Asia/EU/US. In US RTH, expect sharper responses at red and purple. Timeframes: Works across all. Intraday (1–15m) for entries; 1h/4h daily for context. Do not chase: If you miss the touch, wait for the next level; the map is dense by design. Limitations This indicator does not generate buy/sell signals; it supplies a stable structure. In runaway trends, price can cut through multiple lines—use trend filters and risk caps. Auto-recentering means the visible band travels with price; if you need static levels far away, increase RANGE_POINTS. Troubleshooting No labels? Make sure max_labels_count isn’t hit and SHOW_LABELS = true. Labels too close to price? Increase LABEL_OFFSET. Too many lines? Reduce RANGE_POINTS or hide a color group in code. Credits / License Created by: TRC — The Refuge Camp License: Free to use on TradingView with attribution. If you fork or embed, please credit “TRC — The Refuge Camp” and link back to the original post/profile. Quick Start (TL;DR) Add the script. Trade the map: Fade purple/black in ranges. Target red/black in trends. Combine with VWAP/EMAs or your orderflow tool for confirmation. Respect stops just beyond the level; scale at the next line. Happy trading, and welcome to the Quarter-Level grid.
مؤشر Pine Script®
من ‎The_Refuge_Camp‎
1
Bubble Chart LiteBubble Chart Lite - Visual Market Intelligence ⸻ ⚡ Quick Start - Here is how you get started in 30 seconds Default view (Y-axis: None) = market heatmap X-axis always = performance Bubble size = importance (your choice of metric) Hover any bubble for details Switch timeframes to change the measurement window Pick any stock ticker to see their friends Pick one of the 143 etfs listed below and see their top constituents That's it. Everything else is deeper cuts of data ⸻ Overview The Bubble Chart is a market-wide visual map designed to instantly reveal how thousands of stocks and ETFs are performing relative to their peers, all in a single glance. It dynamically builds relationships between ~3,400 stocks and 143 ETFs , each with its own “friends list” of most-connected tickers. It’s a bit unlike all the other indicators, which you’ll see shortly. It’s a very Tops Down, then Sideways view of the market. The 144 ETFs covered in the Bubble Chart indicator are listed here in this watchlist: www.tradingview.com Each bubble represents a security. X-axis → performance (% change) Y-axis → variable (you choose the insight) Bubble size → market cap, relative weight, or %volume Color → relative performance (green up, red down) Border → sector color Your current chart’s timeframe determines the measurement window: Intraday chart → today so far Daily chart → week-to-date (WTD) Weekly chart → month-to-date (MTD) Monthly chart → year-to-date (YTD) Everything is relative to that timeframe’s performance window — making it as useful for morning scans as for long-term sector rotations. I recommend starting with an intraday chart. The bubbles represent the day so far on this timeframe. ⸻ 📦 Version Differences Bubble Chart Lite (Free): ✓ All features and dimensions ✓ Up to 5 bubbles displayed ✓ Perfect for tracking top movers Bubble Chart (Invite-Only): ✓ All features and dimensions ✓ Up to 38 bubbles displayed ✓ See actual market breadth and structure ✓ Indicator name: “Bubble Chart” ✓ Available under the indicator “Bubble Chart” (Invite-Only) — details on my profile ⸻ 📊 Y-Axis Options 1. “None” - Heatmap Mode By default, the Y-axis is set to “None”. In this mode, the chart functions as a market heatmap, with: Left-to-right representing relative performance (% change) Bubble color indicating gain/loss Bubble size reflecting your chosen metric (Market Cap, Weight, or %Volume) Up-down randomized just for bubble separation Think of it as a fancy heatmap with extra context — sector coloring, bubble sizing, and tooltips that surface live data. Perfect for a quick snapshot of the day’s winners and losers across your selected universe. ⸻ 1. %Turnover This measures conviction behind each move. Turnover = current money flow vs. average money flow over your lookback window. A large % move with low turnover = a weak move with little backing. A moderate % move with high turnover = strong participation, higher conviction. This is my personal favorite morning setup — it instantly reveals where real buying and selling pressure is emerging as the session unfolds. A horizontal line across your selected ticker acts as a benchmark, so you can compare others’ conviction levels relative to it. Any %turnover score >100 means more money than average is flowing in and out of this name. In the example above based on the top 5 holdings of GDX, there was one standout performer, and that’s WPM. this should put WPM on a watchlist if you’re interested in gold. It shows, that at least for one day, the market was putting money behind it.And more so than the other top 5 miners. ⸻ 2. %ATR Measures range expansion or compression relative to average volatility. A stock can move big in price but stay inside a tight range → no expansion. A stock can move little but break its typical volatility boundary → range expansion. Expansion often signals momentum continuation; compression after large moves can precede turnarounds or consolidations. This view helps you spot early volatility inflection points. In the example above, in the food and beverage space, based on the top 5 holdings of the ETF PBJ, KR and DASH had moves that will expand their ATR. This means volatility is increasing for this ticker, in the direction of the color of the circle. So KR is expanding to the upside, while DASH is expanding to the downside. It should make a person cautious to go long DASH, as that’s not what the market is showing.The other moves by the other bobbles, show up as volatility contraction. Their price (CTVA, PBJ, KDP, and SYY) is stabilizing. (over the lookback period in settings). ⸻ 3. ROC(5) Z-Score Z-Score quantifies how far a data point deviates from its mean, measured in standard deviations. Here it’s applied to 5-period Rate of Change (ROC5). A high positive Z-Score = performance far above its historical average. A low (negative) Z-Score = deeply oversold vs. history. Use this view to identify stretched momentum or mean-reversion candidates: Stocks high on the Y-axis and green = extended upside momentum Stocks high but red = potential reversal zones Stocks low and red = extreme washouts that may soon rebound This makes it a powerful stock-picking lens for traders who look for reversions or contrarian entries. The following is the XLU and its 5 top holdings. Looked at on the daily timeframe, which means the ROC(5) score is for its weekly ROC (see timeframe discussion above). What you can see here is NEE is at a z-score of 205 (it’s 2.05 but we multiply by 100 for spacing). This means its weekly ROC(5) only happens 4-5 percent of the time. This is a perfect candidate to fade the move. As you can see in the graph below, that charts NEE, every time its ROC(5) z-score (the bottom pane) has reached this level on the weekly, the move ends (at least for a short period of time. The rubber band of momentum is too far extended. You can use this for long setups too ⸻ 4. RSI(15) Z-Score Similar to the ROC version, but based on RSI(15). It contextualizes RSI against its own historical distribution, not the fixed 0–100 scale. When RSI’s Z-Score is above +100 → historically overbought. Below -100 → historically oversold. A stock with a high RSI Z-Score but negative performance may be starting to roll over. A stock with a low RSI Z-Score but positive performance could be beginning a rebound. This lens is especially powerful for early spotting of turning points in swing and position trades. Above is an example of how to read it with the top 5 stocks in the IFRA ETF. ⸻ 5. %52-Week High / %52-Week Low These two let you visualize positioning within the broader yearly range. %52-Week High: Shows how close each ticker is to its highs. Stocks near the top may be in breakout mode. %52-Week Low: Shows distance from the lows. Watching these can highlight potential recovery trades — many reversals start when beaten-down stocks begin to cluster and climb from their lows. Are you really going to want to mess around with VZ? Other companies are winning the race ⸻ ⚙️ Bubble Size Options Market Cap- Larger companies = larger bubbles. Ideal for weighting visibility by overall size of influence in the market or sector. ETF/Friend Weight- Scales bubbles by their relationship weight to the target ETF or stock. This helps identify which peers or constituents exert the most pull within the current context. %Volume- This scales by relative volume to average volume. Big bubbles here mean unusual activity, perfect for spotting where participation is surging. ⸻ 👥 Friends — Relationship Mapping Every ticker on the chart has its own “friends list.” These aren’t arbitrary. They’re discovered through a multi-stage algorithm that analyzes co-occurrence of holdings across ETFs and sectors, roughly like social network analysis for stocks. This is what allows a chart of one stock to intelligently surface others that behave like it, whether through shared ETFs, sector overlap, or statistical co-presence. Why Friends Matter: When you load AAPL, the chart doesn't just show random stocks. It shows AAPL's "friends", the tickers most connected to it through: Shared ETF holdings Sector relationships Statistical co-movement This means you're seeing AAPL's context, not just AAPL. Example: AAPL up 2% might look strong, but if all its friends are up 3-4%, AAPL is actually lagging. The chart reveals this instantly. ⸻ Common Setups - do these today Morning Momentum Scan: - Y-axis: %Turnover - Bubble Size: %Volume - Look for: Top-right quadrant (high performance + high conviction) Reversal Hunting: - Y-axis: RSI(15) Z-Score - Look for: Red bubbles above +100 (overbought rolling over) Green bubbles below -100 (oversold bouncing) Sector Rotation: - Y-axis: None (heatmap mode) - Bubble Size: Market Cap - Look for: Color clustering by sector (border colors) ⸻ 🧩 Data Sources ETF Constituents: ETF holdings are derived from information filed with the SEC’s EDGAR database, specifically N-PORT-P filings. These filings are public records submitted by ETF issuers. Because EDGAR data can vary in structure and naming conventions, additional parsing, fuzzy matching, and ticker reconciliation logic were applied. Some inconsistencies may remain, and minor inaccuracies are possible. EDGAR filings can also lag slightly behind real-time changes to ETF portfolios; however, for this visualization tool, that level of latency does not materially affect its purpose or insights. Exchange & Share Count Data: Information on exchanges and outstanding shares primarily comes from the SEC Company Facts API. When unavailable, supplemental values are inferred from public SEC filings such as 8-K, 10-Q, and 10-K reports, and the SEC Company Submissions API for general company metadata. All such data is publicly accessible through the SEC’s online systems. I will update the SEC information on the ETFs once every 3 months to ensure etf constituent accuracy. Sector & Industry Classification: Sector and industry classifications were developed through a custom workflow that combines automated and human-reviewed methods. An internal AI system analyzed each company’s publicly available website information to summarize business activities and assign one of 144 custom-defined industry categories. Results were cross-checked by multiple independent classification models, and any uncertain outputs were manually reviewed for accuracy. To improve interpretive consistency, publicly available information from StockAnalysis.com was also referenced (not republished) to inform final classifications. Their content was used in accordance with their stated policy allowing limited reference with attribution — no full content or proprietary data was reproduced. ⸻ 🚀 How to Use It Load the Bubble Chart on any stock, ETF, or futures symbol. Choose your Y-axis insight — start with “None” for the heatmap. Adjust bubble size to highlight capital weight or activity. Switch timeframes to shift context (today, this week, month, or year). Hover bubbles for details: sector, turnover, z-scores, %volume, and more. ⸻ ❓ Frequently Asked Questions Q: Why do I only see 5 bubbles? A: You're using Bubble Chart Lite. The full version shows up to 39 bubbles simultaneously for complete market breadth.. To get access: Find the "Bubble Chart" (invite-only) indicator on TradingView Read the description for access instructions Or visit my TradingView profile for details Q: Can I customize which tickers appear? A: The indicator automatically selects the most relevant tickers based on the current chart's symbol and the friends algorithm. This ensures you're seeing context, not random stocks. Q: What timeframe should I use? A: Any timeframe works. The chart adapts: - Intraday = today's performance - Daily = week-to-date - Weekly = month-to-date - Monthly = year-to-date Q: How often does the friends list update? A: Friends relationships are recalculated periodically as ETF holdings change (once every 3 months). The relationships are stable enough that daily changes are minimal. Q: Does this work on crypto/forex? A: Currently optimized for US equities and ETFs. Other asset classes may show limited friends data. Q: The chart looks cluttered. Help? A: Start with Y-axis: None and Bubble Size: Market Cap. You can also choose to pick less number of bubbles which will clear up the chart ⸻ The Bubble Chart is a market topology engine that visualizes participation, conviction, volatility, and sentiment in real time. Whether you’re scanning morning momentum, identifying exhausted moves, or exploring ETF ecosystems, it gives you a spatial view of where the action really is.
مؤشر Pine Script®
من ‎Tr33man‎
2
Bubble ChartBubble Chart- Visual Market Intelligence ⸻ ⚡ Quick Start - Here is how you get started in 30 seconds Default view (Y-axis: None) = market heatmap X-axis always = performance Bubble size = importance (your choice of metric) Hover any bubble for details Switch timeframes to change the measurement window Pick any stock ticker to see their friends Pick one of the 143 etfs listed below and see their top constituents That's it. Everything else is deeper cuts of data ⸻ Overview The Bubble Chart is a market-wide visual map designed to instantly reveal how thousands of stocks and ETFs are performing relative to their peers, all in a single glance. It dynamically builds relationships between ~3,400 stocks and 143 ETFs , each with its own “friends list” of most-connected tickers. It’s a bit unlike all the other indicators, which you’ll see shortly. It’s a very Tops Down, then Sideways view of the market. The 144 ETFs covered in the Bubble Chart indicator are listed here in this watchlist: www.tradingview.com Each bubble represents a security. X-axis → performance (% change) Y-axis → variable (you choose the insight) Bubble size → market cap, relative weight, or %volume Color → relative performance (green up, red down) Border → sector color Your current chart’s timeframe determines the measurement window: Intraday chart → today so far Daily chart → week-to-date (WTD) Weekly chart → month-to-date (MTD) Monthly chart → year-to-date (YTD) Everything is relative to that timeframe’s performance window — making it as useful for morning scans as for long-term sector rotations. I recommend starting with an intraday chart. The bubbles represent the day so far on this timeframe. ⸻ 📦 Version Differences Bubble Chart Lite (Free): ✓ All features and dimensions ✓ Up to 5 bubbles displayed ✓ Perfect for tracking top movers Bubble Chart (Invite-Only): ✓ All features and dimensions ✓ Up to 38 bubbles displayed ✓ See actual market breadth and structure ✓ Indicator name: “Bubble Chart” ✓ Available under the indicator “Bubble Chart” (Invite-Only) — details on my profile ⸻ 📊 Y-Axis Options 1. “None” - Heatmap Mode By default, the Y-axis is set to “None”. In this mode, the chart functions as a market heatmap, with: Left-to-right representing relative performance (% change) Bubble color indicating gain/loss Bubble size reflecting your chosen metric (Market Cap, Weight, or %Volume) Up-down randomized just for bubble separation Think of it as a fancy heatmap with extra context — sector coloring, bubble sizing, and tooltips that surface live data. Perfect for a quick snapshot of the day’s winners and losers across your selected universe. ⸻ 1. %Turnover This measures conviction behind each move. Turnover = current money flow vs. average money flow over your lookback window. A large % move with low turnover = a weak move with little backing. A moderate % move with high turnover = strong participation, higher conviction. This is my personal favorite morning setup — it instantly reveals where real buying and selling pressure is emerging as the session unfolds. A horizontal line across your selected ticker acts as a benchmark, so you can compare others’ conviction levels relative to it. Any %turnover score >100 means more money than average is flowing in and out of this name. In the example above, ELS, AMT, SUI, and PSA were positive on the day and saw more than the average amount of money being transacted on these tickers today. Do the same for the negative (KIM, ESS, HST, etc), and you know where the money is going. Below 100, the move lacked conviction. ⸻ 2. %ATR Measures range expansion or compression relative to average volatility. A stock can move big in price but stay inside a tight range → no expansion. A stock can move little but break its typical volatility boundary → range expansion. Expansion often signals momentum continuation; compression after large moves can precede turnarounds or consolidations. This view helps you spot early volatility inflection points. In the example above, in XLRE, you can see there are a lot of companies that are experiencing a range expansion to the downside. These stocks are now short setup stocks, as the power is pretty overwhelming (number of top companies as well as magnitude over the 100 index). However, there are 3 Stocks that are doing something completely different than the rest. AMT, SBAC, and CCI are experiencing range expansion (volatility) to the upside. These may become the new leaders. You would have to inspect each ticker to see what’s going on. ⸻ 3. ROC(5) Z-Score Z-Score quantifies how far a data point deviates from its mean, measured in standard deviations. Here it’s applied to 5-period Rate of Change (ROC5). A high positive Z-Score = performance far above its historical average. A low (negative) Z-Score = deeply oversold vs. history. Use this view to identify stretched momentum or mean-reversion candidates: Stocks high on the Y-axis and green = extended upside momentum Stocks high but red = potential reversal zones Stocks low and red = extreme washouts that may soon rebound This makes it a powerful stock-picking lens for traders who look for reversions or contrarian entries. The following is the XLU and its 5 top holdings. Looked at on the daily timeframe, which means the ROC(5) score is for its weekly ROC (see timeframe discussion above). What you can see here is most stocks are within their normal acceleration band. However BIIB is very close to -200. This is uncommon.As you can see from the chart of BIIB with it’s ROC(5) graphed below it, this does indicate a short term turn, and is a high probability long setup. ⸻ 4. RSI(15) Z-Score Similar to the ROC version, but based on RSI(15). It contextualizes RSI against its own historical distribution, not the fixed 0–100 scale. When RSI’s Z-Score is above +100 → historically overbought. Below -100 → historically oversold. A stock with a high RSI Z-Score but negative performance may be starting to roll over. A stock with a low RSI Z-Score but positive performance could be beginning a rebound. This lens is especially powerful for early spotting of turning points in swing and position trades. In this view, we can see a bunch of stocks that are at or below their -200 Z-Score which suggests RSI is going to increase soon. Taking a look at KKR, we see that it is indeed an area where we might want to look for a short term bounce. . ⸻ 5. %52-Week High / %52-Week Low These two let you visualize positioning within the broader yearly range. %52-Week High: Shows how close each ticker is to its highs. Stocks near the top may be in breakout mode. %52-Week Low: Shows distance from the lows. Watching these can highlight potential recovery trades — many reversals start when beaten-down stocks begin to cluster and climb from their lows. Are you really going to want to mess around with VZ? Other companies are winning the race ⸻ ⚙️ Bubble Size Options Market Cap- Larger companies = larger bubbles. Ideal for weighting visibility by overall size of influence in the market or sector. ETF/Friend Weight- Scales bubbles by their relationship weight to the target ETF or stock. This helps identify which peers or constituents exert the most pull within the current context. %Volume- This scales by relative volume to average volume. Big bubbles here mean unusual activity, perfect for spotting where participation is surging. ⸻ 👥 Friends — Relationship Mapping Every ticker on the chart has its own “friends list.” These aren’t arbitrary. They’re discovered through a multi-stage algorithm that analyzes co-occurrence of holdings across ETFs and sectors, roughly like social network analysis for stocks. This is what allows a chart of one stock to intelligently surface others that behave like it, whether through shared ETFs, sector overlap, or statistical co-presence. Why Friends Matter: When you load AAPL, the chart doesn't just show random stocks. It shows AAPL's "friends", the tickers most connected to it through: Shared ETF holdings Sector relationships Statistical co-movement This means you're seeing AAPL's context, not just AAPL. Example: AAPL up 2% might look strong, but if all its friends are up 3-4%, AAPL is actually lagging. The chart reveals this instantly. In this friendship look, you can see companies that are in better (and worse) shape for the month (we are looking at it on the “W” timeframe). If I didn’t own ORCL, INTC, or MU (hidden use tooltip), I should start looking at them. ⸻ Common Setups - do these today Morning Momentum Scan: - Y-axis: %Turnover - Bubble Size: %Volume - Look for: Top-right quadrant (high performance + high conviction) Reversal Hunting: - Y-axis: RSI(15) Z-Score - Look for: Red bubbles above +100 (overbought rolling over) Green bubbles below -100 (oversold bouncing) Sector Rotation: - Y-axis: None (heatmap mode) - Bubble Size: Market Cap - Look for: Color clustering by sector (border colors) ⸻ 🧩 Data Sources ETF Constituents: ETF holdings are derived from information filed with the SEC’s EDGAR database, specifically N-PORT-P filings. These filings are public records submitted by ETF issuers. Because EDGAR data can vary in structure and naming conventions, additional parsing, fuzzy matching, and ticker reconciliation logic were applied. Some inconsistencies may remain, and minor inaccuracies are possible. EDGAR filings can also lag slightly behind real-time changes to ETF portfolios; however, for this visualization tool, that level of latency does not materially affect its purpose or insights. Exchange & Share Count Data: Information on exchanges and outstanding shares primarily comes from the SEC Company Facts API. When unavailable, supplemental values are inferred from public SEC filings such as 8-K, 10-Q, and 10-K reports, and the SEC Company Submissions API for general company metadata. All such data is publicly accessible through the SEC’s online systems. I will update the SEC information on the ETFs once every 3 months to ensure etf constituent accuracy. Sector & Industry Classification: Sector and industry classifications were developed through a custom workflow that combines automated and human-reviewed methods. An internal AI system analyzed each company’s publicly available website information to summarize business activities and assign one of 144 custom-defined industry categories. Results were cross-checked by multiple independent classification models, and any uncertain outputs were manually reviewed for accuracy. To improve interpretive consistency, publicly available information from StockAnalysis.com was also referenced (not republished) to inform final classifications. Their content was used in accordance with their stated policy allowing limited reference with attribution — no full content or proprietary data was reproduced. ⸻ 🚀 How to Use It Load the Bubble Chart on any stock, ETF, or futures symbol. Choose your Y-axis insight — start with “None” for the heatmap. Adjust bubble size to highlight capital weight or activity. Switch timeframes to shift context (today, this week, month, or year). Hover bubbles for details: sector, turnover, z-scores, %volume, and more. ⸻ ❓ Frequently Asked Questions Q: Why do I only see 5 bubbles? A: You're using Bubble Chart Lite. The full version shows up to 39 bubbles simultaneously for complete market breadth.. To get access: Find the "Bubble Chart" (invite-only) indicator on TradingView Read the description for access instructions Or visit my TradingView profile for details Q: Can I customize which tickers appear? A: The indicator automatically selects the most relevant tickers based on the current chart's symbol and the friends algorithm. This ensures you're seeing context, not random stocks. Q: What timeframe should I use? A: Any timeframe works. The chart adapts: - Intraday = today's performance - Daily = week-to-date - Weekly = month-to-date - Monthly = year-to-date Q: How often does the friends list update? A: Friends relationships are recalculated periodically as ETF holdings change (once every 3 months). The relationships are stable enough that daily changes are minimal. Q: Does this work on crypto/forex? A: Currently optimized for US equities and ETFs. Other asset classes may show limited friends data. Q: The chart looks cluttered. Help? A: Start with Y-axis: None and Bubble Size: Market Cap. You can also choose to pick less number of bubbles which will clear up the chart ⸻ The Bubble Chart is a market topology engine that visualizes participation, conviction, volatility, and sentiment in real time. Whether you’re scanning morning momentum, identifying exhausted moves, or exploring ETF ecosystems, it gives you a spatial view of where the action really is.
مؤشر Pine Script®
من ‎Tr33man‎
3
aEMA Cross - Long EditionaEMA Cross – Long Edition Smart, Automated, and Rule-Based Trading Framework Overview: The aEMA Cross – Long Edition is an advanced automated trading system that intelligently identifies trends, filters weak signals, and manages trades with precision. It integrates EMA crossover logic, breakout candle confirmation, and time-based exits to help traders capture consistent opportunities while minimizing risk and manual intervention. Designed and developed with algorithmic trading platforms in mind, the indicator can be seamlessly integrated with most Algo platforms through TradingView alerts for automated execution. Note: The default setup is optimized for the ETHUSD chart. Core Concept: The strategy is built around two Exponential Moving Averages (EMAs): - Short EMA – Responds quickly to short-term market changes. - Long EMA (default 200) – Represents the overall market trend. When the Short EMA crosses specific buffer zones around the Long EMA, it confirms genuine momentum before generating Buy or Sell signals. This ensures cleaner and more reliable trade entries. Key Features: 1. Signal Generation • Dual logic modes: Candle-based or EMA-based signal detection. • Breakout Candle System to confirm strong price movements before entries. • Integrated RSI and ADX filters to ensure trades occur only in favorable market conditions. 2. Smart Trade Management • Automated Target and Stoploss management. • Trailing Stop Loss (TSL) dynamically locks in profits as prices move favorably. • Sequential Signal Logic ensures no repeated or conflicting trade signals. 3. Universal Exit (Time-Based Auto Exit) • Automatically exits all positions at a specified time (e.g., 23:40). • Works consistently across all timeframes (1m, 3m, 5m, etc.). • Can be configured for selected weekdays or every trading day. • Prevents overnight exposure and resets trading cleanly for the next session. 4. Safety and Control • EMA buffer zones help avoid false breakouts and choppy market signals. • Blocks new entries after a Universal Exit until a fresh crossover occurs. • Automatically resets breakout levels and internal logic daily for consistency. 5. Visualization and Alerts • Plots EMAs, buffer zones, breakout levels, and entry/exit markers directly on the chart. • Highlights the Universal Exit visually with background shading. • Sends real-time alerts for Buy, Sell, Exit, and Universal Exit events. Why It Stands Out: • Works reliably across multiple timeframes. • Fully rule-based with no emotional bias. • Highly customizable – adjust filters, targets, buffers, and exit rules as needed. • Complete framework – handles entry, management, and exit automatically. • Engineered for compatibility – can be integrated with most Algo trading platforms. How It Works: 1. The Short EMA and Long EMA define the primary market direction. 2. A breakout or EMA crossover triggers a potential signal. 3. RSI and ADX filters confirm market strength before allowing entry. 4. Target, Stoploss, and TSL manage trades automatically. 5. Universal Exit closes all trades at a defined time, resetting the logic for the next session. How to Use: 1. Apply the aEMA Cross – Long Edition indicator to your chart. 2. Choose your primary logic: Candle-based or Short EMA-based. 3. Adjust RSI, ADX, Buffer, and Target/SL settings according to your trading style. 4. Configure Universal Exit time and alert options. 5. Use the “Once Per Bar Close” alert type for confirmed signals. 6. Always backtest your configuration before enabling automation or live execution. Important Note on Alert Setup: - When using the RSI filter, signals may fluctuate if RSI hovers near the trigger level. To avoid this, use “Once Per Bar Close” for stable and confirmed alerts. - If RSI is disabled, “Once Per Bar” alerts can be safely used, even when the Breakout Candle High/Low Crossover option is enabled. Disclaimer: • This strategy is intended for educational and research purposes only. • It does not guarantee profits. Always perform proper backtesting and apply sound risk management before live trading. • The author is not responsible for any financial losses resulting from its use. Developer Information: Developer: ikunalsingh Built using AI + the best of human logic.
مؤشر Pine Script®
من ‎ikunalsingh‎
BTC 5-MA Multi Cross Strategy By Hardik Prajapati Ai TradelabThis strategy is built around the five most powerful and commonly used moving averages in crypto trading — 5, 20, 50, 100, and 200-period SMAs (Simple Moving Averages) — applied on a 1-hour Bitcoin chart. Core Idea: The strategy aims to identify strong bullish trends by confirming when the price action crosses above all key moving averages. This alignment of multiple MAs indicates momentum shift and helps filter out false breakouts. ⸻ ⚙️ How It Works: 1. Calculates 5 Moving Averages: • 5 MA → Short-term momentum (fastest signal) • 20 MA → Near-term trend confirmation • 50 MA → Mid-term trend filter • 100 MA → Long-term trend foundation • 200 MA → Macro-trend direction (strongest support/resistance) 2. Buy Condition (Entry): • A Buy is triggered when: • The price crosses above the 5 MA, and • The closing price remains above all other MAs (20, 50, 100, 200) This signals that momentum is aligned across all time horizons — a strong uptrend confirmation. 3. Sell Condition (Exit): • The position is closed when price crosses below the 20 MA, showing weakness in short-term momentum. 4. Visual Signals: • 🟢 BUY triangle below candles → Entry signal • 🔴 SELL triangle above candles → Exit signal • Colored MAs plotted for trend clarity. ⸻ 📈 Recommended Usage: • Chart: BTC/USDT • Timeframe: 1 Hour • Type: Trend-following crossover strategy • Ideal for: Identifying major breakout moves and confirming trend reversals. ⸻ ⚠️ Notes: • This script is meant for educational and backtesting purposes only. • Always apply additional confirmation tools (like RSI, Volume, or VIX-style filters) before live trading. • Works best during trending markets; may produce whipsaws in sideways zones.
استراتيجية Pine Script®
من ‎hpstudio3d‎
Round Number Analyzer v3Round Number Analyzer v3 is an indicator designed to analyze how price interacts with round number levels (levels spaced at fixed intervals in points or pips). The indicator does not generate entry/exit signals, but provides detailed statistics to better understand market dynamics around these key levels. ✨ Key Features Cross Counting: detects every time the price crosses a round number level (up = Long, down = Short). Continuations & Reversals: classifies each cross as: Continuation: the move continues in the same direction as the previous sequence. Reversal: the move changes direction compared to the previous sequence. Sequence Classification (L1…L5+): each level is labelled based on its position within the consecutive cross sequence: L1 = first level of the sequence, L2 = second consecutive, … L5+ = fifth or higher. Comprehensive Stats Table (top right corner): Total crosses (Long, Short, Totals). Total continuations + breakdown by L1…L5+. Total reversals + breakdown by L1…L5+. Percentages calculated against the proper denominator, displayed directly inside the cells next to the absolute values. Date range of analysis (user-defined). Customizable Step: Works in both points and pips, making the indicator suitable for indices and forex. ⚙️ Main Inputs Start date / End date → sets the analysis period. Step mode → Points or Pips. Step value → distance between round levels. Pip size → pip size (default = 0.0001, typical for forex). 📈 How to Interpret A high continuation percentage after L1–L2 suggests the market tends to extend multiple times beyond the first breakout levels. Higher reversal percentages at advanced levels (L4–L5+) may signal trend exhaustion. The analysis helps estimate the probability of continuation or reversal depending on how many consecutive levels have already been crossed. 🔎 Practical Applications Support for breakout or mean-reversion strategies. Comparative analysis across different markets (e.g. indices vs forex) or different time periods. 📝 Notes The indicator is timeframe-robust, as it accounts for multiple steps within the same candle, ensuring results do not depend on the selected timeframe (except for TradingView’s historical data limits). It does not provide automatic trading signals, but serves as a quantitative analysis tool to refine your strategies. --- Round Number Analyzer v3 è un indicatore pensato per analizzare come il prezzo interagisce con i livelli di round number (livelli a distanza fissa in punti o pips). L’indicatore non genera segnali di ingresso/uscita, ma fornisce statistiche dettagliate utili per comprendere la dinamica del mercato attorno a questi livelli. ✨ Funzionalità principali Conteggio dei Cross: rileva ogni volta che il prezzo attraversa un livello round (verso l’alto = Long, verso il basso = Short). Continuations & Reversals: classifica ogni attraversamento come: Continuation: il movimento prosegue nella stessa direzione della sequenza precedente. Reversal: il movimento inverte la direzione rispetto alla sequenza precedente. Classificazione per sequenza (L1…L5+): ogni livello è etichettato in base alla sua posizione nella sequenza di cross consecutivi: L1 = primo livello della sequenza, L2 = secondo consecutivo, … L5+ = quinto o superiore. Statistiche complete in tabella (in alto a destra): Cross totali (Long, Short, Totals). Continuations totali + breakdown per L1…L5+. Reversals totali + breakdown per L1…L5+. Percentuali calcolate sul denominatore corretto, mostrate direttamente dentro le celle accanto ai valori assoluti. Date range di analisi (impostabile dall’utente). Step personalizzabile: puoi lavorare sia in punti che in pips, così l’indicatore è adatto sia per indici che per forex. ⚙️ Input principali Start date / End date → imposta l’intervallo temporale di analisi. Step mode → punti o pips. Step value → ampiezza tra i livelli round. Pip size → dimensione del pip (default = 0.0001, tipico per il forex). 📈 Come interpretarlo Una percentuale di continuation molto alta dopo L1–L2 indica che il mercato tende a proseguire più volte oltre i primi livelli di breakout. Percentuali di reversal più elevate nei livelli avanzati (L4–L5+) possono suggerire esaurimento della spinta. L’analisi permette di stimare la probabilità che un movimento in corso continui o si inverta in base a quanti livelli sono già stati attraversati consecutivamente. 🔎 Applicazioni pratiche Supporto per strategie di breakout o mean reversion. Analisi comparativa tra mercati (es. indici vs forex) o tra periodi temporali diversi. 📝 Note L’indicatore è timeframe-robust: il conteggio tiene conto di multipli step dentro la stessa candela, così i risultati non dipendono dal timeframe scelto (salvo i limiti di caricamento storico di TradingView). Non fornisce segnali operativi automatici, ma è un tool di analisi quantitativa per affinare le proprie strategie.
مؤشر Pine Script®
من ‎steviatrades‎
BayesCore AI Golden BarsThis indicator analyzes the market candle by candle, using the overall trend as a reference to highlight entry opportunities (buy or sell) by coloring candles in gold. These golden bars signal probabilistic opportunities for entries and re-entries throughout the trade. We’ve added three classic moving averages so traders can have a visual reference of zones and trends to decide whether to act on the opportunities suggested by the indicator. Tip: The best entries usually occur near the regions of the moving averages (blue and green lines). That’s why we included these classic moving averages, to give traders a clear trend reference. Additionally, the indicator provides alerts such as “Only Buy,” “Only Sell,” or “No Action” to help traders maintain psychological discipline during the trading process and seek the best entries aligned with the market trend. Giant candles, which can signal the ignition force of a market trend, are also colored gold and marked with an elephant icon, symbolizing the “leader of the herd,” suggesting that the movement may be worth following. On the chart, the indicator plots two lines above and two lines below the candles near the active candle. These lines suggest possible stop-loss placements based on previous swing highs and lows, ensuring they are not overly costly stops. They serve only as indicative stop-loss positions, leaving it up to the trader to assess—based on their risk management—whether the suggested placement is appropriate. Always consider whether the golden candle coloring aligns with the overall market trend. Past results do not guarantee future returns.
مؤشر Pine Script®
من ‎ricciardi‎
PongExperience PONG! The classic arcade game, now on your charts! With this indicator, you can finally achieve your lifelong dream of beating the Markets. . . at PONG! Pong is jam-packed with features! Such as: 2 Paddles A moving dot Floating numbers The idea of a net This indicator is solely a visualization, it serves simply as an exercise to depict what is capable through PineScript. It can be used to re-skin other indicators or data, but on its own, is not intended as a market indicator. With that out of the way... > PONG The Pong indicator is a recreation of the classic arcade game Pong developed to pit the markets against the cold hard logic of a CPU player. Given the lack of interaction that is capable, the game is not played in the typical sense, by a player and computer or 2 players. This version of Pong uses the chart price movements to control the "Market" Paddle, and it is contrasted by a (not AI) "CPU" Paddle, which is controlled by its own set of logic. > Market Paddle The Market Paddle is controlled by a data source which can be input by the user. By default (Auto Mode), the Market Paddle is controlled through a fixed length Donchian channel range, pinning the range high to 100 and range low to 0. As seen below. This can be altered to use data from different symbols or indicators, and can optionally be smoothed using multiple types of Moving Averages. In the chart below, you can see how the RSI indicator is imported and smoothed to control the Market Paddle. Note: The Market Paddle follows the moving average. If not desired, simply set the "Smoothing" input to "NONE". > CPU Paddle In simple terms, the CPU Paddle is a handicapped Aimbot. Its logic is, more or less, "move directly towards the ball's vertical location". If it were allowed to have full range of the screen, it would be impossible for it to lose a point. Due to this, we must slow it down to "play fair"... as fair as that may be. The CPU Paddle is allowed to move at a rate specified by a certain Percent of its vertical width. By default, this is set to 2%. Each update, the CPU Paddle can advance up or down 2% of its vertical width. The directional movement is determined based on the angle of the ball, and it's current position relative to the CPU Paddle's position. Given that it is not a direct follow, it may at times seem more... "human". When a point is scored, the CPU paddle maintains its position, similar to the original Pong game, the paddles were controlled solely by the raw output of the controllers and did not reset. > Ball At the start of each point, the ball begins at the center of the screen and moves in a randomly determined angle at its base speed. The direction is determined by the player who scored the last point. The loser of the last point "serves" the ball. Given the circumstances, serving is a gigantic advantage. So the loser serving is just another place where the Market is given an advantage. The ball's base speed is 1, it will move 1 (horizontal) bar on each update of the script. This speed can "technically" increase to infinity over time, if given the perfect rally. This is due to the hit logic as described below. Note: The minimum ball speed is also 1. > Bonk Math When the ball hits a paddle, essentially 3 outcomes can occur, each resulting in the ball's direction being changed from positive to negative. Action A: Its angle is doubled, and its speed is doubled. Action B: Its angle is reversed, and its speed is decreased if it is going faster than base speed. Action C: Its angle is preserved, and its speed is preserved. "Basic Bounce" Each paddle is segmented into 3 zones, with the higher and lower tips (20%) of the paddles producing special actions. The central 60% of each paddle produces a basic bounce. The special actions are determined by the trajectory of the ball and location on the paddle. > Custom Mode As stated above, the script loads in "Auto Mode" by default. While this works fine to simply watch the gameplay, the Custom Mode unlocks the ability to visualize countless possibilities of indicators and analyses playing Pong! In the chart below, we have set up the game to use the NYSE TICK Index as our Market Player. The NYSE TICK Index shows the number of NYSE stocks trading on an uptick minus those on a downtick. Its values fluctuate throughout the day, typically ranging between +1000 and -1000. Therefore, we have set up Pong to use Ticker USI:TICK and set the Upper Boundary to 1000 and Lower Boundary to -1000. With this method, the paddle is directly controlled by the overall (NYSE) market behaviors. As seen in a chart earlier, you can also take advantage of the Custom Mode to overlay Pong onto traditional oscillators for use anywhere! > Styles This version of Pong comes stocked with 5 colorways to suit your chart vibes! > Pro Tips & Additional Information - This game has sound! For the full experience, set alerts for this indicator and a notification sound will play on each hit!* *Due to server processing, the notification sounds are not precisely played at each hit. :( - In auto mode, decreasing the length used will give an advantage to the market, as its actions become more sporadic over this window. - The CPU logic system actually allows the market to have a "technical" edge, since the Market Paddle is not bound to any speed, and is solely controlled by the raw market movements/data input. - This type of visualization only works on live charts, charts without updates will not see any movement. - Indicator sources can only be imported from other indicators on the same chart. - The base screen resolution is 159 bars wide, with the height determined by the boundaries. - When using a symbol and an outside source, be mindful that the script is attempting to pull the source from the input symbol. Data can appear wonky when not considering the interactions of these inputs. There are many small interesting details that can't be seen through the description. For example, the mid-line is made from a box. This is because a line object would not appear on top of the box used for the screen. For those keen eye'd coders, feel free to poke around in the source code to make the game truly custom. Just remember: The market may never be fair, but now at least it can play Pong! Enjoy!
مؤشر Pine Script®
من ‎SamRecio‎
2
Small Business Economic Conditions - Statistical Analysis ModelThe Small Business Economic Conditions Statistical Analysis Model (SBO-SAM) represents an econometric approach to measuring and analyzing the economic health of small business enterprises through multi-dimensional factor analysis and statistical methodologies. This indicator synthesizes eight fundamental economic components into a composite index that provides real-time assessment of small business operating conditions with statistical rigor. The model employs Z-score standardization, variance-weighted aggregation, higher-order moment analysis, and regime-switching detection to deliver comprehensive insights into small business economic conditions with statistical confidence intervals and multi-language accessibility. 1. Introduction and Theoretical Foundation The development of quantitative models for assessing small business economic conditions has gained significant importance in contemporary financial analysis, particularly given the critical role small enterprises play in economic development and employment generation. Small businesses, typically defined as enterprises with fewer than 500 employees according to the U.S. Small Business Administration, constitute approximately 99.9% of all businesses in the United States and employ nearly half of the private workforce (U.S. Small Business Administration, 2024). The theoretical framework underlying the SBO-SAM model draws extensively from established academic research in small business economics and quantitative finance. The foundational understanding of key drivers affecting small business performance builds upon the seminal work of Dunkelberg and Wade (2023) in their analysis of small business economic trends through the National Federation of Independent Business (NFIB) Small Business Economic Trends survey. Their research established the critical importance of optimism, hiring plans, capital expenditure intentions, and credit availability as primary determinants of small business performance. The model incorporates insights from Federal Reserve Board research, particularly the Senior Loan Officer Opinion Survey (Federal Reserve Board, 2024), which demonstrates the critical importance of credit market conditions in small business operations. This research consistently shows that small businesses face disproportionate challenges during periods of credit tightening, as they typically lack access to capital markets and rely heavily on bank financing. The statistical methodology employed in this model follows the econometric principles established by Hamilton (1989) in his work on regime-switching models and time series analysis. Hamilton's framework provides the theoretical foundation for identifying different economic regimes and understanding how economic relationships may vary across different market conditions. The variance-weighted aggregation technique draws from modern portfolio theory as developed by Markowitz (1952) and later refined by Sharpe (1964), applying these concepts to economic indicator construction rather than traditional asset allocation. Additional theoretical support comes from the work of Engle and Granger (1987) on cointegration analysis, which provides the statistical framework for combining multiple time series while maintaining long-term equilibrium relationships. The model also incorporates insights from behavioral economics research by Kahneman and Tversky (1979) on prospect theory, recognizing that small business decision-making may exhibit systematic biases that affect economic outcomes. 2. Model Architecture and Component Structure The SBO-SAM model employs eight orthogonalized economic factors that collectively capture the multifaceted nature of small business operating conditions. Each component is normalized using Z-score standardization with a rolling 252-day window, representing approximately one business year of trading data. This approach ensures statistical consistency across different market regimes and economic cycles, following the methodology established by Tsay (2010) in his treatment of financial time series analysis. 2.1 Small Cap Relative Performance Component The first component measures the performance of the Russell 2000 index relative to the S&P 500, capturing the market-based assessment of small business equity valuations. This component reflects investor sentiment toward smaller enterprises and provides a forward-looking perspective on small business prospects. The theoretical justification for this component stems from the efficient market hypothesis as formulated by Fama (1970), which suggests that stock prices incorporate all available information about future prospects. The calculation employs a 20-day rate of change with exponential smoothing to reduce noise while preserving signal integrity. The mathematical formulation is: Small_Cap_Performance = (Russell_2000_t / S&P_500_t) / (Russell_2000_{t-20} / S&P_500_{t-20}) - 1 This relative performance measure eliminates market-wide effects and isolates the specific performance differential between small and large capitalization stocks, providing a pure measure of small business market sentiment. 2.2 Credit Market Conditions Component Credit Market Conditions constitute the second component, incorporating commercial lending volumes and credit spread dynamics. This factor recognizes that small businesses are particularly sensitive to credit availability and borrowing costs, as established in numerous Federal Reserve studies (Bernanke and Gertler, 1995). Small businesses typically face higher borrowing costs and more stringent lending standards compared to larger enterprises, making credit conditions a critical determinant of their operating environment. The model calculates credit spreads using high-yield bond ETFs relative to Treasury securities, providing a market-based measure of credit risk premiums that directly affect small business borrowing costs. The component also incorporates commercial and industrial loan growth data from the Federal Reserve's H.8 statistical release, which provides direct evidence of lending activity to businesses. The mathematical specification combines these elements as: Credit_Conditions = α₁ × (HYG_t / TLT_t) + α₂ × C&I_Loan_Growth_t where HYG represents high-yield corporate bond ETF prices, TLT represents long-term Treasury ETF prices, and C&I_Loan_Growth represents the rate of change in commercial and industrial loans outstanding. 2.3 Labor Market Dynamics Component The Labor Market Dynamics component captures employment cost pressures and labor availability metrics through the relationship between job openings and unemployment claims. This factor acknowledges that labor market tightness significantly impacts small business operations, as these enterprises typically have less flexibility in wage negotiations and face greater challenges in attracting and retaining talent during periods of low unemployment. The theoretical foundation for this component draws from search and matching theory as developed by Mortensen and Pissarides (1994), which explains how labor market frictions affect employment dynamics. Small businesses often face higher search costs and longer hiring processes, making them particularly sensitive to labor market conditions. The component is calculated as: Labor_Tightness = Job_Openings_t / (Unemployment_Claims_t × 52) This ratio provides a measure of labor market tightness, with higher values indicating greater difficulty in finding workers and potential wage pressures. 2.4 Consumer Demand Strength Component Consumer Demand Strength represents the fourth component, combining consumer sentiment data with retail sales growth rates. Small businesses are disproportionately affected by consumer spending patterns, making this component crucial for assessing their operating environment. The theoretical justification comes from the permanent income hypothesis developed by Friedman (1957), which explains how consumer spending responds to both current conditions and future expectations. The model weights consumer confidence and actual spending data to provide both forward-looking sentiment and contemporaneous demand indicators. The specification is: Demand_Strength = β₁ × Consumer_Sentiment_t + β₂ × Retail_Sales_Growth_t where β₁ and β₂ are determined through principal component analysis to maximize the explanatory power of the combined measure. 2.5 Input Cost Pressures Component Input Cost Pressures form the fifth component, utilizing producer price index data to capture inflationary pressures on small business operations. This component is inversely weighted, recognizing that rising input costs negatively impact small business profitability and operating conditions. Small businesses typically have limited pricing power and face challenges in passing through cost increases to customers, making them particularly vulnerable to input cost inflation. The theoretical foundation draws from cost-push inflation theory as described by Gordon (1988), which explains how supply-side price pressures affect business operations. The model employs a 90-day rate of change to capture medium-term cost trends while filtering out short-term volatility: Cost_Pressure = -1 × (PPI_t / PPI_{t-90} - 1) The negative weighting reflects the inverse relationship between input costs and business conditions. 2.6 Monetary Policy Impact Component Monetary Policy Impact represents the sixth component, incorporating federal funds rates and yield curve dynamics. Small businesses are particularly sensitive to interest rate changes due to their higher reliance on variable-rate financing and limited access to capital markets. The theoretical foundation comes from monetary transmission mechanism theory as developed by Bernanke and Blinder (1992), which explains how monetary policy affects different segments of the economy. The model calculates the absolute deviation of federal funds rates from a neutral 2% level, recognizing that both extremely low and high rates can create operational challenges for small enterprises. The yield curve component captures the shape of the term structure, which affects both borrowing costs and economic expectations: Monetary_Impact = γ₁ × |Fed_Funds_Rate_t - 2.0| + γ₂ × (10Y_Yield_t - 2Y_Yield_t) 2.7 Currency Valuation Effects Component Currency Valuation Effects constitute the seventh component, measuring the impact of US Dollar strength on small business competitiveness. A stronger dollar can benefit businesses with significant import components while disadvantaging exporters. The model employs Dollar Index volatility as a proxy for currency-related uncertainty that affects small business planning and operations. The theoretical foundation draws from international trade theory and the work of Krugman (1987) on exchange rate effects on different business segments. Small businesses often lack hedging capabilities, making them more vulnerable to currency fluctuations: Currency_Impact = -1 × DXY_Volatility_t 2.8 Regional Banking Health Component The eighth and final component, Regional Banking Health, assesses the relative performance of regional banks compared to large financial institutions. Regional banks traditionally serve as primary lenders to small businesses, making their health a critical factor in small business credit availability and overall operating conditions. This component draws from the literature on relationship banking as developed by Boot (2000), which demonstrates the importance of bank-borrower relationships, particularly for small enterprises. The calculation compares regional bank performance to large financial institutions: Banking_Health = (Regional_Banks_Index_t / Large_Banks_Index_t) - 1 3. Statistical Methodology and Advanced Analytics The model employs statistical techniques to ensure robustness and reliability. Z-score normalization is applied to each component using rolling 252-day windows, providing standardized measures that remain consistent across different time periods and market conditions. This approach follows the methodology established by Engle and Granger (1987) in their cointegration analysis framework. 3.1 Variance-Weighted Aggregation The composite index calculation utilizes variance-weighted aggregation, where component weights are determined by the inverse of their historical variance. This approach, derived from modern portfolio theory, ensures that more stable components receive higher weights while reducing the impact of highly volatile factors. The mathematical formulation follows the principle that optimal weights are inversely proportional to variance, maximizing the signal-to-noise ratio of the composite indicator. The weight for component i is calculated as: w_i = (1/σᵢ²) / Σⱼ(1/σⱼ²) where σᵢ² represents the variance of component i over the lookback period. 3.2 Higher-Order Moment Analysis Higher-order moment analysis extends beyond traditional mean and variance calculations to include skewness and kurtosis measurements. Skewness provides insight into the asymmetry of the sentiment distribution, while kurtosis measures the tail behavior and potential for extreme events. These metrics offer valuable information about the underlying distribution characteristics and potential regime changes. Skewness is calculated as: Skewness = E / σ³ Kurtosis is calculated as: Kurtosis = E / σ⁴ - 3 where μ represents the mean and σ represents the standard deviation of the distribution. 3.3 Regime-Switching Detection The model incorporates regime-switching detection capabilities based on the Hamilton (1989) framework. This allows for identification of different economic regimes characterized by distinct statistical properties. The regime classification employs percentile-based thresholds: - Regime 3 (Very High): Percentile rank > 80 - Regime 2 (High): Percentile rank 60-80 - Regime 1 (Moderate High): Percentile rank 50-60 - Regime 0 (Neutral): Percentile rank 40-50 - Regime -1 (Moderate Low): Percentile rank 30-40 - Regime -2 (Low): Percentile rank 20-30 - Regime -3 (Very Low): Percentile rank < 20 3.4 Information Theory Applications The model incorporates information theory concepts, specifically Shannon entropy measurement, to assess the information content of the sentiment distribution. Shannon entropy, as developed by Shannon (1948), provides a measure of the uncertainty or information content in a probability distribution: H(X) = -Σᵢ p(xᵢ) log₂ p(xᵢ) Higher entropy values indicate greater unpredictability and information content in the sentiment series. 3.5 Long-Term Memory Analysis The Hurst exponent calculation provides insight into the long-term memory characteristics of the sentiment series. Originally developed by Hurst (1951) for analyzing Nile River flow patterns, this measure has found extensive application in financial time series analysis. The Hurst exponent H is calculated using the rescaled range statistic: H = log(R/S) / log(T) where R/S represents the rescaled range and T represents the time period. Values of H > 0.5 indicate long-term positive autocorrelation (persistence), while H < 0.5 indicates mean-reverting behavior. 3.6 Structural Break Detection The model employs Chow test approximation for structural break detection, based on the methodology developed by Chow (1960). This technique identifies potential structural changes in the underlying relationships by comparing the stability of regression parameters across different time periods: Chow_Statistic = (RSS_restricted - RSS_unrestricted) / RSS_unrestricted × (n-2k)/k where RSS represents residual sum of squares, n represents sample size, and k represents the number of parameters. 4. Implementation Parameters and Configuration 4.1 Language Selection Parameters The model provides comprehensive multi-language support across five languages: English, German (Deutsch), Spanish (Español), French (Français), and Japanese (日本語). This feature enhances accessibility for international users and ensures cultural appropriateness in terminology usage. The language selection affects all internal displays, statistical classifications, and alert messages while maintaining consistency in underlying calculations. 4.2 Model Configuration Parameters Calculation Method: Users can select from four aggregation methodologies: - Equal-Weighted: All components receive identical weights - Variance-Weighted: Components weighted inversely to their historical variance - Principal Component: Weights determined through principal component analysis - Dynamic: Adaptive weighting based on recent performance Sector Specification: The model allows for sector-specific calibration: - General: Broad-based small business assessment - Retail: Emphasis on consumer demand and seasonal factors - Manufacturing: Enhanced weighting of input costs and currency effects - Services: Focus on labor market dynamics and consumer demand - Construction: Emphasis on credit conditions and monetary policy Lookback Period: Statistical analysis window ranging from 126 to 504 trading days, with 252 days (one business year) as the optimal default based on academic research. Smoothing Period: Exponential moving average period from 1 to 21 days, with 5 days providing optimal noise reduction while preserving signal integrity. 4.3 Statistical Threshold Parameters Upper Statistical Boundary: Configurable threshold between 60-80 (default 70) representing the upper significance level for regime classification. Lower Statistical Boundary: Configurable threshold between 20-40 (default 30) representing the lower significance level for regime classification. Statistical Significance Level (α): Alpha level for statistical tests, configurable between 0.01-0.10 with 0.05 as the standard academic default. 4.4 Display and Visualization Parameters Color Theme Selection: Eight professional color schemes optimized for different user preferences and accessibility requirements: - Gold: Traditional financial industry colors - EdgeTools: Professional blue-gray scheme - Behavioral: Psychology-based color mapping - Quant: Value-based quantitative color scheme - Ocean: Blue-green maritime theme - Fire: Warm red-orange theme - Matrix: Green-black technology theme - Arctic: Cool blue-white theme Dark Mode Optimization: Automatic color adjustment for dark chart backgrounds, ensuring optimal readability across different viewing conditions. Line Width Configuration: Main index line thickness adjustable from 1-5 pixels for optimal visibility. Background Intensity: Transparency control for statistical regime backgrounds, adjustable from 90-99% for subtle visual enhancement without distraction. 4.5 Alert System Configuration Alert Frequency Options: Three frequency settings to match different trading styles: - Once Per Bar: Single alert per bar formation - Once Per Bar Close: Alert only on confirmed bar close - All: Continuous alerts for real-time monitoring Statistical Extreme Alerts: Notifications when the index reaches 99% confidence levels (Z-score > 2.576 or < -2.576). Regime Transition Alerts: Notifications when statistical boundaries are crossed, indicating potential regime changes. 5. Practical Application and Interpretation Guidelines 5.1 Index Interpretation Framework The SBO-SAM index operates on a 0-100 scale with statistical normalization ensuring consistent interpretation across different time periods and market conditions. Values above 70 indicate statistically elevated small business conditions, suggesting favorable operating environment with potential for expansion and growth. Values below 30 indicate statistically reduced conditions, suggesting challenging operating environment with potential constraints on business activity. The median reference line at 50 represents the long-term equilibrium level, with deviations providing insight into cyclical conditions relative to historical norms. The statistical confidence bands at 95% levels (approximately ±2 standard deviations) help identify when conditions reach statistically significant extremes. 5.2 Regime Classification System The model employs a seven-level regime classification system based on percentile rankings: Very High Regime (P80+): Exceptional small business conditions, typically associated with strong economic growth, easy credit availability, and favorable regulatory environment. Historical analysis suggests these periods often precede economic peaks and may warrant caution regarding sustainability. High Regime (P60-80): Above-average conditions supporting business expansion and investment. These periods typically feature moderate growth, stable credit conditions, and positive consumer sentiment. Moderate High Regime (P50-60): Slightly above-normal conditions with mixed signals. Careful monitoring of individual components helps identify emerging trends. Neutral Regime (P40-50): Balanced conditions near long-term equilibrium. These periods often represent transition phases between different economic cycles. Moderate Low Regime (P30-40): Slightly below-normal conditions with emerging headwinds. Early warning signals may appear in credit conditions or consumer demand. Low Regime (P20-30): Below-average conditions suggesting challenging operating environment. Businesses may face constraints on growth and expansion. Very Low Regime (P0-20): Severely constrained conditions, typically associated with economic recessions or financial crises. These periods often present opportunities for contrarian positioning. 5.3 Component Analysis and Diagnostics Individual component analysis provides valuable diagnostic information about the underlying drivers of overall conditions. Divergences between components can signal emerging trends or structural changes in the economy. Credit-Labor Divergence: When credit conditions improve while labor markets tighten, this may indicate early-stage economic acceleration with potential wage pressures. Demand-Cost Divergence: Strong consumer demand coupled with rising input costs suggests inflationary pressures that may constrain small business margins. Market-Fundamental Divergence: Disconnection between small-cap equity performance and fundamental conditions may indicate market inefficiencies or changing investor sentiment. 5.4 Temporal Analysis and Trend Identification The model provides multiple temporal perspectives through momentum analysis, rate of change calculations, and trend decomposition. The 20-day momentum indicator helps identify short-term directional changes, while the Hodrick-Prescott filter approximation separates cyclical components from long-term trends. Acceleration analysis through second-order momentum calculations provides early warning signals for potential trend reversals. Positive acceleration during declining conditions may indicate approaching inflection points, while negative acceleration during improving conditions may suggest momentum loss. 5.5 Statistical Confidence and Uncertainty Quantification The model provides comprehensive uncertainty quantification through confidence intervals, volatility measures, and regime stability analysis. The 95% confidence bands help users understand the statistical significance of current readings and identify when conditions reach historically extreme levels. Volatility analysis provides insight into the stability of current conditions, with higher volatility indicating greater uncertainty and potential for rapid changes. The regime stability measure, calculated as the inverse of volatility, helps assess the sustainability of current conditions. 6. Risk Management and Limitations 6.1 Model Limitations and Assumptions The SBO-SAM model operates under several important assumptions that users must understand for proper interpretation. The model assumes that historical relationships between economic variables remain stable over time, though the regime-switching framework helps accommodate some structural changes. The 252-day lookback period provides reasonable statistical power while maintaining sensitivity to changing conditions, but may not capture longer-term structural shifts. The model's reliance on publicly available economic data introduces inherent lags in some components, particularly those based on government statistics. Users should consider these timing differences when interpreting real-time conditions. Additionally, the model's focus on quantitative factors may not fully capture qualitative factors such as regulatory changes, geopolitical events, or technological disruptions that could significantly impact small business conditions. The model's timeframe restrictions ensure statistical validity by preventing application to intraday periods where the underlying economic relationships may be distorted by market microstructure effects, trading noise, and temporal misalignment with the fundamental data sources. Users must utilize daily or longer timeframes to ensure the model's statistical foundations remain valid and interpretable. 6.2 Data Quality and Reliability Considerations The model's accuracy depends heavily on the quality and availability of underlying economic data. Market-based components such as equity indices and bond prices provide real-time information but may be subject to short-term volatility unrelated to fundamental conditions. Economic statistics provide more stable fundamental information but may be subject to revisions and reporting delays. Users should be aware that extreme market conditions may temporarily distort some components, particularly those based on financial market data. The model's statistical normalization helps mitigate these effects, but users should exercise additional caution during periods of market stress or unusual volatility. 6.3 Interpretation Caveats and Best Practices The SBO-SAM model provides statistical analysis and should not be interpreted as investment advice or predictive forecasting. The model's output represents an assessment of current conditions based on historical relationships and may not accurately predict future outcomes. Users should combine the model's insights with other analytical tools and fundamental analysis for comprehensive decision-making. The model's regime classifications are based on historical percentile rankings and may not fully capture the unique characteristics of current economic conditions. Users should consider the broader economic context and potential structural changes when interpreting regime classifications. 7. Academic References and Bibliography Bernanke, B. S., & Blinder, A. S. (1992). The Federal Funds Rate and the Channels of Monetary Transmission. American Economic Review, 82(4), 901-921. Bernanke, B. S., & Gertler, M. (1995). Inside the Black Box: The Credit Channel of Monetary Policy Transmission. Journal of Economic Perspectives, 9(4), 27-48. Boot, A. W. A. (2000). Relationship Banking: What Do We Know? Journal of Financial Intermediation, 9(1), 7-25. Chow, G. C. (1960). Tests of Equality Between Sets of Coefficients in Two Linear Regressions. Econometrica, 28(3), 591-605. Dunkelberg, W. C., & Wade, H. (2023). NFIB Small Business Economic Trends. National Federation of Independent Business Research Foundation, Washington, D.C. Engle, R. F., & Granger, C. W. J. (1987). Co-integration and Error Correction: Representation, Estimation, and Testing. Econometrica, 55(2), 251-276. Fama, E. F. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. Journal of Finance, 25(2), 383-417. Federal Reserve Board. (2024). Senior Loan Officer Opinion Survey on Bank Lending Practices. Board of Governors of the Federal Reserve System, Washington, D.C. Friedman, M. (1957). A Theory of the Consumption Function. Princeton University Press, Princeton, NJ. Gordon, R. J. (1988). The Role of Wages in the Inflation Process. American Economic Review, 78(2), 276-283. Hamilton, J. D. (1989). A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica, 57(2), 357-384. Hurst, H. E. (1951). Long-term Storage Capacity of Reservoirs. Transactions of the American Society of Civil Engineers, 116(1), 770-799. Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47(2), 263-291. Krugman, P. (1987). Pricing to Market When the Exchange Rate Changes. In S. W. Arndt & J. D. Richardson (Eds.), Real-Financial Linkages among Open Economies (pp. 49-70). MIT Press, Cambridge, MA. Markowitz, H. (1952). Portfolio Selection. Journal of Finance, 7(1), 77-91. Mortensen, D. T., & Pissarides, C. A. (1994). Job Creation and Job Destruction in the Theory of Unemployment. Review of Economic Studies, 61(3), 397-415. Shannon, C. E. (1948). A Mathematical Theory of Communication. Bell System Technical Journal, 27(3), 379-423. Sharpe, W. F. (1964). Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk. Journal of Finance, 19(3), 425-442. Tsay, R. S. (2010). Analysis of Financial Time Series (3rd ed.). John Wiley & Sons, Hoboken, NJ. U.S. Small Business Administration. (2024). Small Business Profile. Office of Advocacy, Washington, D.C. 8. Technical Implementation Notes The SBO-SAM model is implemented in Pine Script version 6 for the TradingView platform, ensuring compatibility with modern charting and analysis tools. The implementation follows best practices for financial indicator development, including proper error handling, data validation, and performance optimization. The model includes comprehensive timeframe validation to ensure statistical accuracy and reliability. The indicator operates exclusively on daily (1D) timeframes or higher, including weekly (1W), monthly (1M), and longer periods. This restriction ensures that the statistical analysis maintains appropriate temporal resolution for the underlying economic data sources, which are primarily reported on daily or longer intervals. When users attempt to apply the model to intraday timeframes (such as 1-minute, 5-minute, 15-minute, 30-minute, 1-hour, 2-hour, 4-hour, 6-hour, 8-hour, or 12-hour charts), the system displays a comprehensive error message in the user's selected language and prevents execution. This safeguard protects users from potentially misleading results that could occur when applying daily-based economic analysis to shorter timeframes where the underlying data relationships may not hold. The model's statistical calculations are performed using vectorized operations where possible to ensure computational efficiency. The multi-language support system employs Unicode character encoding to ensure proper display of international characters across different platforms and devices. The alert system utilizes TradingView's native alert functionality, providing users with flexible notification options including email, SMS, and webhook integrations. The alert messages include comprehensive statistical information to support informed decision-making. The model's visualization system employs professional color schemes designed for optimal readability across different chart backgrounds and display devices. The system includes dynamic color transitions based on momentum and volatility, professional glow effects for enhanced line visibility, and transparency controls that allow users to customize the visual intensity to match their preferences and analytical requirements. The clean confidence band implementation provides clear statistical boundaries without visual distractions, maintaining focus on the analytical content.
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Gabriel's Squeeze Momentum📊 Gabriel’s Squeeze Momentum — Deluxe Volatility + Momentum Suite An advanced, all-in-one squeeze & momentum framework that times volatility compression/expansion and trend shifts, with optional CVD (cumulative volume delta) momentum, ATR zone context, Discontinued Signal Lines (DSL) scalps, Colored DMI trend label, Williams VIX Fix (WVF) low-volatility exhaustion pings, Buff’s VTTI/VPCI volume confirmation, and real-time divergence detection. What it does: Discover Squeezes. They occur when volatility contracts, often preceding significant price moves. Measures momentum with a fast, ATR-normalized linear regression—optionally on Price or CVD—so you see direction and “how hard it’s pushing.” 🧭 Signal Legend ~ Colors the squeeze so you instantly know regime: 🟡 / 🟣 (Tight/Very Tight): Coiled spring; prepare a plan. 🔴 / ⚫ = (Regular/Wide): Watch for Divergences between Price and Momentum. 🟢 (Fired): Expansion started; trade with momentum cross and bias. Adds context bands at ±1/±2/±3 ATR (“trend / expansion / OB-OS”) to filter late or weak signals. DSL (Discontinued Signal Lines) give early scalp flips on momentum vs. adaptive bands. DMI label & triangles communicate trend strength and whether +DI / −DI is in control. Williams VIX Fix flags capitulation/exhaustion style spikes (with optional VIX proxy). VTTI/VPCI modules confirm when volume aligns with price trend or contradicts it. Divergences (regular & hidden) auto-draw with optional live (may repaint) or on-close. 🎢 Squeeze Momentum — How the Logic Works 🎢 The Squeeze Momentum model is built on the principle of volatility compression and expansion. In markets, periods of low volatility are often followed by explosive moves, while high volatility eventually contracts. The “squeeze” seeks to identify these compression phases and prepare traders for the likely expansion that follows. This indicator achieves that by comparing Bollinger Bands (BB) to Keltner Channels (KC). Bands: Bollinger vs. Keltner Bollinger Bands (BB): Calculated using a Simple Moving Average (SMA) of price and standard deviations (σ) of the closing price. The bands expand and contract depending on volatility. Keltner Channels (KC): Built from an SMA plus/minus multiples of the Average True Range (ATR). Unlike some simplified squeeze indicators that approximate ATR, this implementation uses a true ATR-based KC, ensuring accuracy across different assets and timeframes. By comparing whether the Bollinger Bands are inside or outside the Keltner Channels, the indicator identifies different squeeze regimes, each representing a distinct volatility environment. 📦 Regime Colors The squeeze states are color-coded for quick interpretation: 🔹Wide Squeeze (⚫): BB inside KC with a high ATR multiplier. Extremely low volatility, often before major expansion. 🔹Normal Squeeze (🔴): BB inside KC with a moderate ATR multiplier (about 25% more sensitive than Wide). Typical compression setting. 🔹Narrow Squeeze (🟡): BB inside KC with a lower ATR multiplier (about 50% more sensitive than Wide). Signals tighter compression. 🔹Very Narrow Squeeze (🟣): BB inside KC with the lowest ATR multiplier (100% more sensitive than Wide). Indicates extreme coiling. 🔹Fired Squeeze (🟢): BB break outside KC. Marks the release of volatility and potential trend acceleration. This multi-layered system improves upon classical SQZPRO by using precisely calculated Keltner Channels and multiple sensitivity levels, giving traders more granular information about volatility states. 🔒 Multi-Timeframe Support The indicator automatically adjusts squeeze thresholds for different timeframes — hourly, 4-hour, daily, weekly, and monthly charts. Each regime has been manually tuned for its timeframe, allowing traders to use the same tool whether scalping, swing trading, or holding longer-term positions. 🎯 Momentum Core Detecting a squeeze is only half the equation — the indicator also includes a momentum engine to determine direction and strength. Price momentum is measured as the distance of Close from its Highest High and Lowest Low range, smoothed with a Simple Moving Average, and refined with Linear Regression. This value is then divided by ATR, normalizing momentum relative to volatility. Optionally, CVD Mode (Cumulative Volume Delta ÷ Volume) can replace price momentum for assets where order-flow and volume dynamics dominate (e.g., crypto). 🦆 Signal Line Momentum is paired with a Simple Moving Average signal line: 🔹Bullish: Momentum > Signal. 🔹Bearish: Momentum < Signal. This crossover logic provides directional bias and filters for false squeezes. 🚀 When to Use Price vs. CVD CVD Mode (Crypto, FX with tick volume): Best for assets with strong volume/order-flow signals. Price Mode (Equities, Commodities, Higher TFs): Best for assets with irregular or thin volume data. 🛢️ATR Zones (context filter) 🛢️ Its design is straightforward yet effective: it measures the difference between the current price from its highest highs, lowest lows, and a moving average over a chosen period, then expresses that difference in terms of the Average True Range (ATR) over the same period. By normalizing price deviations against volatility, ATR provides a clear sense of how far and how fast price is moving relative to its “normal” range. Interpreting the Zone Positive Values: When it is above zero, price is trading above its HH, LL, and moving average, suggesting bullish momentum. The higher the value, the stronger the momentum relative to volatility. Negative Values: When the Momentum is below zero, price is trading below its HH, LL, and moving average, signaling bearish momentum. The deeper the reading, the stronger the downside pressure. Magnitude Matters: Because the Momentum is expressed in ATR units, traders can immediately gauge whether the move is small (less than 1 ATR), moderate (1–2 ATRs), or extreme (3+ ATRs). This makes it especially useful for assessing overbought or oversold conditions in a normalized way. Strengths: 🔹Volatility-Normalized: Unlike simple squeeze momentum oscillators that have different OB/OS levels, this Momentum adjusts for volatility. This makes signals more consistent across assets with different volatility profiles. 🔹Simplicity: ±1 ATR: trending zone (bulls above +1, bears below −1) ±2 ATR: expansion (keep, add, or trail). Stretch/risk of mean reversion. ±3 ATR: potential exhaustion/mean-revert zone. 🔹Momentum Clarity: By framing momentum in ATR terms, it is easier to distinguish between a small deviation from trend and a genuinely significant move. Sometimes it is a good sign that it trend to ±3/2 ATR, looks for similar directional moves. Color: The script shades +2/+3 (OB) and −2/−3 (OS) areas and provides swing alerts at ±1 ATR. 💚 What Are Discontinued Signal Lines (DSL)? 💚 In technical analysis, one of the most common tools for smoothing out noisy data is the signal line. This concept appears in many indicators, such as the MACD or stochastic oscillator, where the raw value of an indicator is compared to a smoothed version of itself. The signal line acts as a lagging filter, making it easier to identify shifts in momentum, crossovers, and directional changes. While useful, the classic signal line approach has limitations. By design, a single smoothed line introduces lag, which means traders may receive signals later than ideal. Additionally, a one-size-fits-all smoothing process often struggles to adapt to different levels of volatility or rapidly changing market conditions. This is where Discontinued Signal Lines (DSL) come in. DSL is an advanced extension of the traditional signal line concept. Instead of relying on just one smoothed comparison, DSL employs multiple adaptive lines that adjust dynamically to the current state of the indicator. These adaptive lines effectively “discontinue” the dependence on a single, fixed smoothing method, producing a more flexible and nuanced representation of market conditions. How DSL Works? Traditional Signal Line: Compares an the Momentum against its own moving average. Provides crossover signals when the raw indicator value moves above or below the smoothed line. Strength: reduces noise. Weakness: delayed signals and limited adaptability. DSL Extension: Uses multiple adaptive lines that respond differently to the indicator’s current behavior. Instead of one static moving average, the DSL approach creates faster and slower “reaction lines.” These lines adapt dynamically, capturing acceleration or deceleration in the indicator’s state. Result: Traders see how momentum is evolving across multiple adaptive thresholds. This reduces false signals and improves responsiveness in volatile conditions. Benefits of Discontinued Signal Lines 🔹Nuanced Trend Detection DSL doesn’t just flag when momentum changes direction—it shows the quality of that shift, highlighting whether it is gaining strength, losing steam, or consolidating. 🔹Adaptability Across Markets Because DSL adjusts to the Momentum’s own dynamics, it works well across different asset classes and timeframes, from equities and futures to forex and crypto. 🔹Earlier Signal Recognition Multiple adaptive lines allow traders to spot developing trends earlier than with a single smoothed signal line, without being overwhelmed by raw indicator noise. 🔹Better Confirmation DSL is particularly useful for confirmation. If both adaptive lines agree then a fill is applied in the direction, confidence in the trend is higher as the color turns bull/bear. 🔹Practical Uses Momentum Trading: Spot acceleration or deceleration in trend strength. Trend Confirmation: Verify whether a breakout has momentum behind it. Noise Filtering: Smooth out erratic moves while retaining adaptability. ⚖️ Colored Directional Movement Index (CDMI) ⚖️ The Directional Movement Index (DMI), created by J. Welles Wilder, is one of the most respected trend-following indicators in technical analysis. It is actually a family of three separate indicators combined into one: the +DI (Positive Directional Indicator), the –DI (Negative Directional Indicator), and the ADX (Average Directional Index). Together, they measure not only whether the market is trending but also the strength of that trend. Traders have used the DMI for decades to identify trend direction, gauge momentum, and filter out periods of market noise. However, despite its reliability, the traditional DMI can be challenging to interpret. Reading three separate lines at once and extracting meaningful signals requires both experience and careful observation. This complexity often discourages newer traders from fully utilizing its power. The Colored Directional Movement Index (CDMI) is a modern reinterpretation of Wilder’s classic tool. It condenses the same information into a single visual line while using color, shape, and density to communicate what’s happening beneath the surface. The goal is simple: make the DMI’s insights faster to read, easier to act upon, and more intuitive to integrate into trading decisions. Key Features of CDMI 🔹Color Scale for Trend Strength The main triangle changes its base color depending on the strength of the DI reading. Dark Red or Green, colors correspond to stronger trends, while faded Gray or lighter yellow tones signal weaker or fading trends. This makes it visually clear when the market is consolidating versus trending strongly. 🔹Color Density for Momentum Beyond strength, the CDMI uses color density to represent momentum in the trend’s strength. If the ADX is rising (trend gaining momentum), the triangles grows more darker. If the ADX is falling (trend losing momentum), the triangle becomes paler. This provides an instant sense of whether a trend is accelerating or decelerating. 🔹Directional Triangles for Trend Direction To replace the separate +DI and –DI lines, the CDMI plots small triangle shapes along the bottom axis. An upward-facing triangle indicates that +DI is dominant, confirming bullish direction. A downward-facing triangle signals –DI dominance, confirming bearish direction. This way, both strength and direction are shown without the clutter of multiple overlapping lines. 🔹Label Display for Detailed Values For traders who want precise data alongside the visuals, CDMI includes a label that shows: Current trend strength (ADX value). Current +DI and –DI values. Momentum status of the ADX (rising or falling). Historical values of DMI readings, so traders can track how the indicator has evolved over time. Tooltips are also available to explain “How to read the colored DMI line”, making this version more beginner-friendly. Why CDMI Matters The CDMI retains the proven reliability of Wilder’s DMI while solving its biggest drawback—interpretation difficulty. Instead of juggling three separate plots, traders get a single, information-rich line supplemented with intuitive shapes and labels. This streamlined format makes trend verification, momentum analysis, and signal confirmation much faster. For trading applications, the CDMI can help: Confirm Entries by showing whether the market is trending strongly enough to justify a position. Avoid False Signals by filtering out periods of low ADX (weak trend). Enhance Timing by tracking momentum shifts in trend strength. By simplifying the complexity of the original DMI into an elegant, color-coded tool, the CDMI makes one of technical analysis’ most advanced indicators practical for everyday use. 😅 The VIX, the Williams Vix Fix, and Market Bottoms 😎 The VIX, formally known as the CBOE Volatility Index, has long been considered one of the most reliable indicators for spotting major market bottoms. Often referred to as the “fear gauge,” it measures the market’s expectation of volatility in the S&P 500 over the next 30 days. When fear grips investors and volatility spikes, the VIX rises sharply. Historically, these moments of extreme fear often coincide with powerful buying opportunities, as markets have a tendency to rebound once panic selling exhausts itself. Larry Williams, a well-known trader and author, developed the Williams Vix Fix as a way to replicate the insights of the VIX across any tradable asset. While the VIX itself is tied specifically to S&P 500 options, Williams wanted a tool that could capture similar panic-driven dynamics in stocks, futures, forex, and other markets where the VIX is not directly applicable. His “fix” uses price action and volatility formulas to approximate the same emotional extremes reflected in the official VIX, creating almost identical results in practice. This makes the Williams Vix Fix a powerful addition to the trader’s toolbox, allowing the same principle that works on U.S. equities to be applied universally. One of the most important characteristics of both the VIX and the Williams Vix Fix is that they are far more reliable at signaling market bottoms than market tops. The reason is psychological as much as it is mathematical. At market bottoms, fear and panic are widespread. Retail investors often capitulate, selling in a frenzy as prices drop. This panic drives volatility higher, producing the spikes we see in the VIX. At the same time, professional traders and institutions—those with larger capital and more disciplined strategies—tend to step in when volatility is stretched. They buy when others are fearful, using the panic of retail investors as an opportunity to acquire assets at discounted prices. This confluence of retail panic and institutional buying power is what makes the VIX such a strong bottom-finding tool. In contrast, at market tops, the dynamic is very different. Tops tend not to be marked by panic or fear. Instead, they form quietly as enthusiasm fades, liquidity dries up, and buying interest wanes. Investors are often complacent, assuming prices will continue to rise, while professional money begins distributing their positions. Because there is no surge in fear, volatility remains muted, and the VIX does not offer a clear warning. This is why traders who rely on the VIX or the Williams Vix Fix must understand its limitations: it is exceptional for detecting bottoms but less useful for anticipating tops. For traders, the lesson is straightforward. When you see the VIX or Williams Vix Fix spiking to extreme levels, it often indicates a high-probability environment for a rebound. These tools should not be used in isolation, but when combined with support levels, sentiment indicators, and market breadth, they can provide some of the most reliable bottom-fishing signals available. While no indicator is perfect, few have stood the test of time as consistently as the VIX—and thanks to Williams’ adaptation, its power can now be applied to nearly every market. Indicator Signals (Great in risk-off charts): 🔹Flags spike events (tops/bottoms) with both original and filtered (AE/FE) criteria. 🔹Great as a risk overlay: tighten stops into AE/FE, or require “no spike” to enter. 🤯 Volume Comfirmation: VTTI & VPCI (Buff Dormeier) 🤯 Volume Trend Technical Indicator (VTTI) The Volume Trend Technical Indicator (VTTI) is a momentum-style tool that analyzes how volume trends interact with price movement. Unlike basic volume measures that simply report how many shares or contracts were traded, the VTTI evaluates whether volume is expanding or contracting in the same direction as the prevailing price trend. The underlying logic is that healthy trends are supported by rising volume, while weakening trends often occur on shrinking volume. At its core, VTTI looks at the rate of change in volume compared to price movements. By smoothing and normalizing these relationships, the indicator helps traders determine whether momentum is accelerating, decelerating, or diverging. Rising VTTI: Suggests that volume is confirming the current price trend, strengthening the case for continuation. Flips BG Green after crossing it's signal. Falling VTTI: Indicates that the trend may be losing participation, often a sign of possible consolidation or reversal. Flips BG Red after crossing it's signal. Traders often use VTTI to filter entries and exits. For example, if price breaks out but VTTI does not rise above zero, the breakout may lack conviction. On the other hand, when both price and VTTI are aligned, probability of continuation improves. Volume Price Confirmation Indicator (VPCI) The Volume Price Confirmation Indicator (VPCI), developed by Buff Dormeier, takes the relationship between price and volume a step further. While traditional indicators like On-Balance Volume (OBV) or Chaikin Money Flow look at cumulative patterns, VPCI breaks price and volume into trend and volatility components and then recombines them to measure how well they confirm each other. In essence, VPCI asks: “Does volume confirm what price is signaling?” The formula integrates: Price Trend Component – whether the market is trending upward or downward. Volume Trend Component – whether trading activity supports that price trend. Volatility Adjustments – to account for irregular swings. The resulting oscillator fluctuates around a zero line: Positive VPCI: Indicates that price and volume trends are in agreement (bullish confirmation). Negative VPCI: Suggests that price and volume are diverging (bearish warning or false move). Crossovers of Zero: Can serve as potential buy or sell signals, depending on context. A key strength of VPCI is its sensitivity to divergence. When prices continue rising but VPCI begins falling, it often foreshadows a weakening rally. Conversely, a rising VPCI during a flat or down market can highlight early accumulation. VTTI (Entry Signal) vs. VPCI (Exit Signal) While both indicators study price-volume dynamics, their focus differs: VTTI is simpler, emphasizing the trend of volume relative to price for momentum confirmation. VPCI is more advanced, decomposing both price and volume into multiple components to produce a nuanced oscillator. Used together, they provide complementary insights. VTTI helps quickly spot whether volume is supporting a move, while VPCI offers deeper confirmation and highlights subtle divergences. Note: The Up/Down Volume Alert works better on the 4 HR, for Daily scalps or 30 minute for HR scalps. Intraday it's 2/10 minute. 🦅 Divergence toolkit 🦅 Divergences in Technical Analysis Divergence occurs when the price action of an asset moves in one direction while a technical indicator, such as RSI, MACD, or Momentum, moves in the opposite direction. This disagreement between price and indicator often signals a shift in underlying market dynamics. Traders use divergences to anticipate either potential reversals or continuations in trends. There are two main types of divergences: regular divergences, which typically precede reversals, and hidden divergences, which suggest continuation of the current trend. Regular Divergence (Reversal Signals) A regular divergence occurs when price and indicator disagree during a trend extension. These divergences signal that momentum is no longer fully supporting the current trend and that a reversal may be imminent. 🔹Regular Bullish Divergence Price Action: Forms a lower low. Indicator: Forms a higher low. Interpretation: Price is making new lows, but the indicator is gaining strength. This suggests that selling pressure is weakening, and a reversal to the upside may occur. Example: RSI rising while price dips to fresh lows. 🔹Regular Bearish Divergence Price Action: Forms a higher high. Indicator: Forms a lower high. Interpretation: Price is reaching new highs, but the indicator shows weakening momentum. This implies that buying pressure is fading, warning of a potential downside reversal. Example: MACD histogram falling while price makes higher highs. Regular divergences are often spotted near the end of trends and are most powerful when aligned with key support/resistance levels or overbought/oversold conditions. Hidden Divergence (Continuation Signals) A hidden divergence occurs during retracements within a trend. Unlike regular divergences, hidden divergences suggest that the prevailing trend still has strength and is likely to continue. 🔹Hidden Bullish Divergence Price Action: Forms a higher low. Indicator: Forms a lower low. Interpretation: Price is retracing within an uptrend, but the indicator is overshooting downward. This shows that momentum remains intact, supporting continuation upward. 🔹Hidden Bearish Divergence Price Action: Forms a lower high. Indicator: Forms a higher high. Interpretation: Price is retracing within a downtrend, while the indicator overshoots upward. This indicates that bearish momentum remains strong, supporting continuation downward. Hidden divergences often appear during pullbacks, helping traders time entries in the direction of the prevailing trend. Practical Use of Divergences 🔹Trend Reversal Alerts – Regular divergences are early warnings that a trend may be ending. 🔹Trend Continuation Signals – Hidden divergences help confirm that retracements are simply pauses, not full reversals. 🔹Confluence with Other Tools – Divergences are more reliable when combined with support/resistance, candlestick patterns, or volume analysis. 🔹Multi-Timeframe Analysis – Spotting divergences on higher timeframes often produces stronger signals. 🕭🔔🛎️ Alert 🛎️🔔🕭 🔹Squeeze 🟢 Fired Squeeze ⚫ Low (Wide) Squeeze / 🔴 Normal / 🟡 Tight / 🟣 Very Tight 🔹Momentum 🐂 Bullish Trend Reversal (Crossover of Momentum and Signal from sub −2) 🐻 Bearish Trend Reversal (Crossover of Momentum and Signal from above +2) 📈 Bullish Swing (cross above +1 ATR) / 📉 Bearish Swing (cross below −1 ATR) 🔹DSL 💚 Bullish DSL Scalp / 💔 Bearish DSL Scalp 🔹Volume 🎯 Strong Up Volume (VPCI > 0 and VTTI up) ⏳ Strong Down Volume (VPCI < 0 and VTTI down) 🔹Divergences 🦅 Bullish, 🦆 Bearish, 🦅 Bullish Hidden, 🦆 Bearish Hidden Management: Search Vanguard ETFs in your browser, look up full list of VOO holdings. Download it, or copy paste all the ticker symbols. Place that with a AI, just ask it to place , in between each ticker. NVDA, TSLA, AVGO, etc. Create a new watchlist, in the + add all tickers separated by commas. Place a watchlist alert ⚠️ only available for premium + subscribers. Practical playbook 1) Classic Squeeze Break Setup: 🔴(D)/🟡(2D)/🟣(3D) squeeze → wait for 🟢(1HR) Fired. Confirm: Momentum > Signal and above +1 ATR (or DMI strong & rising). Manage: add on pullbacks that hold +1 ATR; scale near +2 ATR or WVF AE/FE. 2) DSL Scalp in Trend Setup: Clear trend (DMI strong) + DSL bull/bear trigger in the direction of trend. Filter: avoid tight/very tight yellow/purple unless you want micro-scalps. Exit: opposite DSL or ATR midline loss. 3) Mean-Reversion Fade Setup: Momentum extended to ±3 ATR, WVF spike, and a regular divergence. Entry: Counter signal only when mom crosses back through ±3 ATR toward mid. Exit early if squeeze ⚫/🔴, Momentum may extend to ±3/2 ATR in the same direction. Risk: reduce size; this is a fade, not trend following. 4) Volume-Confirmed Breakout Setup: Squeeze → 🟢 Fired + VPCI > 0 and VTTI up → trend continuation. Manage: trail behind +1 ATR (long) or −1 ATR (short). 9 SMA works good. Inputs at a glance (key ones) Mode: Price or CVD momentum; Squeeze Sensitivity (σ); Momentum Length; Signal Length; ATR Smoothing. 🧮 Colors: SQZMOM: per squeeze regime, momentum, ATR fills. DSL: On/Off, Fast/Slow, Length. ATR Zones: Bullish/Bearish levels (±1), ±2/±3 zone lines & fills. DMI: Lengths, key & weak thresholds, label on/off. WVF/VIX: Lookbacks, bands, AE/FE toggles, VIX proxy symbol. VTTI/VPCI: Fast/slow/signal (VTTI), Short/Long (VPCI), and volume source (Tick/CVD/NVI/PVI/OBV/PVT/AccDist/VWAP). Divergences: Regular/Hidden toggles, Sensitivity %, Lifetime, Live vs On-Close, Lines/Labels. 🔎 Suggested defaults (feel free to tweak) Calibration: Size Momentum, so that when it's above zero the asset is trending up. For the signal, it can be kept the same or lower. Intraday (60–240m): σ = 2.0, 18~20, 3~5, DSL Fast, DMI key 23, weak 17. Daily/Weekly: keep σ = 2.0, consider DSL Slow, DMI key 25, weak 20, widen ATR filters; lean on VPCI/VTTI (4-HR). CVD mode: use where tick/volume quality is high (index futures, liquid equities, crypto majors). 🪟 Tips & caveats Swing Screener: Favor liquid underlyings (index futures/ETFs, large caps). Large-Cap, 2 M Vol, Mid-Cap, 500K Vol. Squeeze: BB( 20) upper < KC (20) upper, and BB (20) lower > KC (20) lower. Optional: Price above 9 SMA, 21 SMA, and 50 SMA, they are my SMA of choice. 200 SMA too, unless you are willing to fish in a bear market. Vice-versa for shorts. Optional: ADX 4 HR > 17, or 23 depending on what you are looking for. Scalp Screener: Same as above, change the D 9 SMA to 5, and the BB/KC from D to 1 HR. Scalps may last 2~3 days. Position Screener: Change all daily setting to W, aside from Volume. Optional: PEG < 1.5, FCF > 0, ROA > 8% or ROE > 6%. Good with Moving averages (9/21/50) and low-volume zones. Position size by IV, ATR, and account risk. Consider stop/hedge rules around ±2/±3 ATR. Let alerts stage your watchlist; act only on combined squeeze + momentum signals. Divergences in live mode can repaint (Real-Time); for algo or alerts, use on-close. Tight/Very tight squeezes are great for scalps but choppy; combine with DMI rising + VPCI>0. ±3 ATR is exhaustion context, not an auto-fade—look for WVF/Div/DSL confirmation. For alerts, pair “Fired Squeeze + Bullish Swing” (or bearish) to avoid false starts. 🎯 How to Trade Entry ~ Recap: Tight/very tight squeeze → fires → momentum crosses up (or DSL bull). Exit/Flip: Momentum crosses down into/after expansion or hits +2/+3 ATR with fade signs. Filter: Avoid fresh longs at +3 ATR; avoid fresh shorts at −3 ATR unless fading with confirmation. 📐 Options Integrations ✅ Risk Reversal/Modified Risk Reversal (Bullish: Short Put + Long Call) Use when: Squeeze fires up from 🟡/🟣 and momentum crosses above signal (or zero/DSL). Playbook Entry: On or just after the bullish fire and momentum upcross. DMI or Volume supports trend as well. Structure: Sell a put at/just below the −2 ATR reference (or recent swing support). Buy a call at/above the breakout zone (prior high/mid-range +1 to +2 ATR). A classic risk reversal is a long call plus a short put. That’s a very bullish structure—you gain if the price rallies (via the call), and you collect a premium by selling a put. But it has a naked downside risk. The modified risk reversal fixes that by adding a long lower put (making the short put into a defined put credit spread). Management: If momentum stays above signal, ride toward +2 → +3 ATR. Sell the put near the current price → receive big premium. Buy the lower put → spend part of that premium (risk cap). Buy the call above the current price → spend more, but the short put premium mostly pays for it. Exits/Adjust: Momentum downcross or squeeze flips back on (new compression) → reduce. If price retests −1/−2 ATR and holds, you can roll the short put down/out. Breakout = Big Success; No Breakout = you keep the initial credit. Reversal = Max loss is capped by the long lower put. ✅ Iron Condor (Neutral: Short OTM Put Spread + Short OTM Call Spread) Use when: Squeeze is active (🟡/🟣), momentum is flat near zero, and there is no directional edge. 🟢 lasts for around 5~8 bars typically. I measure the historical duration of it, and wait for a range period to occur. Playbook Entry: During compression, set wings outside ±2 ATR (or recent range extremes). I prefer identifying boxes where the rectangle pattern occurs on the chart. Management: Time decay works while price remains trapped in the coil. High-winrate ~80%, but 1 loser can wipe most of the gains. Exits/Adjust: If a squeeze fires and momentum breaks hard one way, close the losing side, consider converting to a vertical or rotating to a directional spread aligned with momentum. 4HR-Bullish, closing one wing: Tip: Align daily/weekly context with your intraday entries. 9 > 50 on Weekly, similar on Daily. Sell premium into compression; switch to directional spreads on expansion and momentum confirmation. ✅ Naked Call/Puts (Directional: 10~30 Delta Calls) Stick to naked calls and puts when the squeezes are fired from either 🔴 or ⚫. Look for Strikes slightly out of the money with an OI and Volume spread less than <10%. If Strike Date is >45, manage 21 Days before expiration. Scalp: Expiration Strikes of 1/4 of the Squeeze period. Leap: Expiration Strikes of 1.75x of the Squeeze period. 📐 Futures Integrations Playbook Entry: Verify if the squeeze on the hourly is red or green, and enter on the 2- or 5-minute during a similar squeeze state. Trend-Following: Traditional 2 Renko Block above 21 SMA and Momentum is bullish, or vice versa. (2~ES, 5~NQ) Structure: Go long at/just below the ATR reference (or recent swing support). Exit below the breakout zone (prior high/mid-range +1 to +2 ATR). Management: If momentum stays above +1 ATR ride toward +2 → +3 ATR, etc. House-money, should be kept. Exits/Adjust: Momentum downcross or squeeze flips back on (new compression) → exit. On Renko Charts, lower the sensitivity to 0.7~1. If price retests 0/−1/−2 ATR and holds, you can enter when the 9 SMA flips. The 50 SMA is better for Daily and up; I wouldn't trade against it then. 📌 FOMO Trading Playbook Credits & License Credits: @JF10R (Multi-Timeframe Squeeze), @BigBeluga (DSL), @OskarGallard (Colored DMI base), @ChrisMoody (WVF ideas), @PineCodersTASC (VTTI/VPCI), @EliCobra (Divergence toolkit). License: Mozilla Public License 2.0 (MPL-2.0). Author: © GabrielAmadeusLau
مؤشر Pine Script®
من ‎GabrielAmadeusLau‎
9
eORB - Day EditionThe eORB – Day Edition (Enhanced Opening Range Breakout) is a powerful intraday trading indicator designed for Algo Trading, Scalpers, Day Traders, and ORB-based strategies. It combines classic ORB logic with advanced filters, multiple exit strategies, and smart risk management tools. The default setup is optimised for a 3-minute ETHUSD chart. Key Features:- # Opening Range Breakout (ORB) - Defines intraday high/low for the first X minutes. - Automatically updates breakout levels. - Optional buffer (%) for precision entries. # Day & Session Filters - Enable/disable trading on specific weekdays. - Flexible session time configuration. # EMA Crossover - Option to trade based on EMA crossover with ORB levels. # Breakout Candle Logic - Detects breakout candle high/low for secondary confirmation. # RSI Filter - Confirms signals using RSI thresholds (customisable). # Exit Strategies - ORB High/Low Exit - Buffer Exit - Trailing Stop Loss (TSL) with activation, lock, and increments - Target & Stoploss (fixed points) - Universal Exit (UTC time-based) with background highlight # Trade Sync Logic - Prevents consecutive Buy → Buy or Sell → Sell without the opposite signal in between. # Alerts Ready - Buy, Sell, and Exit conditions are available for alerts. - Compatible with TradingView alert system (popup, email, SMS, webhook). How to Use:- 1. Add indicator to the chart. 2. Set ORB Time & Session (e.g., 3 min ORB at market open). 3. Enable/disable filters (EMA, RSI, Breakout candle). 4. Configure exits (TSL, Target, Stoploss, Universal Exit). 5. Add alerts for automation or notifications. - This indicator is ideal for Crypto, Nifty, BankNifty, Index Futures, and Stocks, but it can be applied to any asset. - The default settings are optimised for ETHUSD. How it Works – eORB Day Edition:- Step 1 – Define the Range - At market open, the indicator records the Opening Range High & Low for the first X minutes (configurable by the user). - This creates a price boundary (box) that acts as support and resistance for the rest of the session. - Optional buffers can be added to make signals more reliable. Step 2 – Generate the Signal - When price (or EMA, if enabled) crosses above the Opening Range High, a Buy signal is generated. - When price (or EMA) crosses below the Opening Range Low, a Sell signal is generated. - Extra filters like RSI and Breakout Candle confirmation can be turned on to reduce false breakouts. - Built-in sync logic ensures signals alternate properly (no double Buy or double Sell without the opposite in between). Step 3 – Manage the Exit - Trades can exit using multiple methods: - Target (fixed profit in points) - Stoploss (fixed risk in points) - Trailing Stop-loss (TSL) that locks profit and trails as price moves further in your favour - ORB/Buffer exit when price re-enters the range - Universal Exit at a fixed UTC time to close all positions for the day - Exits are visualised on the chart with shapes, labels, and optional background highlights. In simple terms:- Step 1: DEFINE - Opening Range (first X minutes) → Marks High & Low → Creates breakout zone Step 2: SIGNAL - Price / EMA crosses High (+ Buffer) → BUY - Price / EMA crosses Low (- Buffer) → SELL - + Optional filters: RSI, Breakout Candle Step 3: EXIT - Target | Stoploss | Trailing Stoploss | Universal Exit Important Note on Alert Setup - When using the RSI filter, signals may fluctuate in some edge cases where RSI hovers near the Buy or Sell level. - To avoid this, it is recommended to use “Once Per Bar Close” as the alert trigger, since signals confirm only after the bar closes (especially helpful when Breakout Candle High/Low Crossover is enabled). - If you choose not to use RSI, you can safely use “Once Per Bar” alerts, even when the Breakout Candle High/Low Crossover option is enabled. Disclaimer:- - This tool is for educational and research purposes only. - It does not guarantee profits. Always backtest and use proper risk management before live trading. The author is not responsible for financial losses. Developer: @ikunalsingh Built using AI + the best of human logic.
مؤشر Pine Script®
من ‎ikunalsingh‎
ORB Breakout Strategy with reversalORB 1,5,15,30,60min with reversals, its my first strategy Im not 100% sure it works well. Im not a programmer nor a profitable trader. Max stoploss in points sets maximum fixed stoploss Stop offset sets additional points below/above signal bar RR Ratio is pretty self explanatory, it sets target based on stoploss American session is time when positions can be opened ORB SessionIs basically almost the same but when the time runs it closes all positions\ ORB candle timeframe is the time which orb is measured Enable reverse position enables reversing positions on stoploss of first position, stoploss of reverse position is based on max stoploss and target is set by RR times max stoploss Im sharing this to share this with my friends, discuss some things and dont have to test it manually. I made it all myself and with help of AI Sorry for bad english
استراتيجية Pine Script®
من ‎Ciuchers‎
Kairos AR EdgeEN Kairos AR Edge is a closed-source (invite-only) Forex indicator providing statistical analysis of Asian session box breakouts and relative currency strength across 28 major pairs. Unlike standard breakout or trend-following tools, it consolidates breakout behavior into a single overview, helping traders quickly identify directional bias and strong/weak currencies. This aggregation provides unique insight not easily obtained from separate pair analysis. Important Clarification: Reversal and Continuation percentages are calculated for the pair on which the indicator is applied , showing how often a breakout returns (Reversal) or continues (Continuation) within the selected session window. The Currency Strength Table is independent of these percentages. It scores each currency from -7 to +7 based on participation in Asian box breakouts across all 28 pairs, providing a relative strength overview regardless of the active pair. The -7/+7 scale is derived from historical breakout occurrences and provides a quick reference for currency strength ranking Indicator operates on two levels: Session Bias Statistics: Builds an Asian session box for the active pair and analyzes breakout behavior. Users can select: Reversal Mode : Percentage of breakouts that return to the opposite side within the selected timeframe Continuation Mode : Percentage of breakouts that continue in the same direction within the timeframe Currency Strength Table: Aggregates breakout behavior across all 28 pairs to provide a relative currency strength score (-7 to +7) Visual Tools: Optional pivot-based bullish/bearish triggers and automatic session box visualization provide additional informational support. Main Features: Customizable Asian session box (start/end times and timezone) Reversal or Continuation statistical mode Automatic update of high/low levels Currency Strength Table (-7 to +7) Statistical table with historical breakout percentages Optional visual triggers (pivot-based patterns) Light/Dark theme support Originality and Value: Consolidates 28 pairs into a single view for immediate identification of market bias Provides statistical insight into breakout behavior, not just trend-following or generic breakout alerts Offers a quick-reference Currency Strength Table to identify strong/weak currencies without tracking multiple pairs individually Important Notes: Statistics are based on historical data only – no guarantee of future results Educational and informational purposes only; not financial or trading advice Closed-source indicator with invite-only access. Access requests can be made by contacting the author or following the link in the Author’s Instructions field IT Kairos AR Edge è un indicatore closed-source (invite-only) che fornisce analisi statistica sulle rotture del box della sessione asiatica e forza relativa delle valute su 28 coppie principali. A differenza dei normali strumenti di breakout o trend-following, consolida il comportamento dei breakout in un’unica panoramica, aiutando i trader a identificare rapidamente bias direzionali e valute forti/deboli. Questa aggregazione offre insight unici non facilmente ottenibili analizzando coppie singole. Chiarimento importante: Le percentuali di Reversal e Continuation si riferiscono solo alla coppia su cui l’indicatore è applicato , calcolando quante volte una rottura ritorna (Reversal) o continua (Continuation) entro la finestra di sessione selezionata. La Tabella di Forza Valute è indipendente da queste percentuali. Assegna a ciascuna valuta un punteggio da -7 a +7 in base alla partecipazione ai breakout del box asiatico su tutte le 28 coppie, fornendo un quadro della forza relativa indipendentemente dalla coppia attiva. Il punteggio -7/+7 deriva dai breakout storici e fornisce un riferimento rapido per la forza delle valute. Lo script opera su due livelli: Statistiche Bias di Sessione: Costruisce il box della sessione asiatica per la coppia attiva e analizza i breakout. Modalità selezionabili: Reversal : Percentuale di breakout che tornano verso il lato opposto entro la finestra temporale Continuation : Percentuale di breakout che proseguono nella stessa direzione entro la finestra Tabella di Forza Valute: Aggrega il comportamento dei breakout su tutte le 28 coppie, assegnando un punteggio da -7 a +7 per ciascuna valuta in base alla sua forza relativa Strumenti Visivi: Box della sessione asiatica aggiornato automaticamente e trigger opzionali basati su pattern pivot, fornendo supporto informativo aggiuntivo. Funzionalità principali: Box della sessione asiatica personalizzabile (orari e timezone) Modalità statistica: Reversal o Continuation Aggiornamento automatico dei livelli high/low Tabella di Forza Valute (-7 a +7) Tabella statistica con percentuali di rottura storiche Trigger visivi opzionali (pattern pivot) Supporto tema chiaro/scuro Originalità e Valore: Consolida 28 coppie in un’unica panoramica per identificare immediatamente bias di mercato Fornisce insight statistico sui breakout, non solo trend-following o alert generici Tabella di Forza Valute rapida per identificare valute forti/deboli senza controllare molteplici coppie Nota importante: Le statistiche si basano solo su dati storici – nessuna garanzia di risultati futuri Strumento educativo e informativo; non costituisce consiglio finanziario o di trading Indicatore closed-source con accesso su invito. Le richieste di accesso possono essere fatte contattando l’autore o seguendo il link nelle istruzioni dell’autore
مؤشر Pine Script®
من ‎diegoforex9‎