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Ultimate Confluence Oscillator PROUltimate Confluence Oscillator PRO Multi-indicator momentum confluence with real-time bias, divergence, and expansion detection — all in one oscillator. Ultimate Confluence Oscillator PRO is a professional-grade momentum indicator that combines RSI, Stochastic, MACD, divergence analysis, and higher-timeframe context into a single, clean oscillator designed for fast, confident decision-making. Built for crypto, forex, futures, and equities, it helps traders identify when momentum conditions are aligned and when the market is transitioning from compression into expansion. 🔍 What This Indicator Does Combines RSI, Stochastic, and MACD into a unified confluence framework Highlights momentum agreement and disagreement across indicators Detects momentum divergence using both RSI and MACD Identifies compression → expansion conditions Incorporates higher-timeframe trend context for directional awareness 📌 Real-Time Momentum HUD (Built-In) The indicator includes a locked, on-chart information panel that updates in real time: Current RSI value Current Stochastic value Automatic market state classification Bullish Bearish Neutral This allows traders to instantly evaluate momentum at any candle without switching indicators or performing manual checks. Hover → Read → Decide. 📈 Signals & Alerts Confluence-based BUY / SELL markers Custom alert conditions for: Strong momentum confluence RSI divergence MACD divergence Alerts are informational and designed to support — not replace — a trading plan. ⚙️ Key Features Non-repainting logic Works on all timeframes Clean visuals optimized for fast decision-making Fully adjustable inputs Suitable for scalping, intraday, and swing trading 🎯 Best Use Cases Momentum confirmation before entries Filtering low-quality setups in choppy markets Identifying early expansion after consolidation Aligning lower-timeframe trades with broader momentum context ⚠️ Disclaimer This indicator is a technical analysis tool and does not provide financial advice. Always use proper risk management and your own trading plan.
Pine Script® indicator
by EWAIO
3
Black OPS Pro Edition (White Knight) v1.0Black OPS Pro Edition (White Knight) v1.0 Black OPS Pro Edition (White Knight) v1.0 is a professional-grade educational trading tool designed for trend analysis, volatility measurement, and intrabar signal detection. It combines ATR-based volatility tracking, Bollinger Bands, EMA bounces, and stochastic filtering to provide clear visual cues on market movements. Features: ATR & Volatility Analysis: Tracks market volatility and directional movement. Bollinger Bands: Upper, lower, and midline bands with smoothing to identify breakouts and pullbacks. Trend Detection: Automatically identifies bullish, bearish, and neutral trends. EMA Bounces: Detects price interactions with multiple EMA levels (1- 200). Stochastic Filter: Confirms trend signals and helps reduce false alerts. Visual Signals: Up 🚀 and down 💥 arrows for trend flips, plus EMA bounce indicators ⚔️ 🕵️. Dashboard: Displays current volatility and trend strength. Background Coloring: Highlights bullish and bearish periods. Screen-Fixed Disclaimer: Table at the bottom-right with a permanent educational disclaimer. User Customization: Adjust ATR length, volatility lookback, Bollinger Band parameters, EMA settings, and other thresholds to fit your trading style. Disclaimer: For educational purposes only. This script does NOT provide financial advice or guarantee profits. Users are fully responsible for their own trading decisions and risk management. Always perform your own analysis before making trades.
Pine Script® indicator
by Mayo_WBI
1
Composite Index [Auto Signals]Composite Index Description (描述正文): Overview This is an enhanced version of the famous Composite Index (CI) developed by Connie Brown. While the traditional RSI is confined between 0 and 100, often masking true momentum in strong trends, the Composite Index is uncapped and incorporates a momentum component to reveal the market's true structural strength. I have engineered this script to include Automated Signal Markers based on the crossover of the Composite Index and its Slow Moving Average. This helps traders instantly identify momentum shifts and "Timing" entries/exits without manual guesswork. Key Features Uncapped Momentum: Unlike RSI, the CI can go anywhere, preventing the "flattening" effect seen in strong trending markets (e.g., TSLA, NVDA). Automated Signals: ▲ Green Triangle (Launch): Triggers when the Gray CI line crosses ABOVE the Red Slow MA. This indicates bearish momentum is exhausted and bulls are regaining control. ▼ Red Triangle (Warning): Triggers when the Gray CI line crosses BELOW the Red Slow MA. This indicates bullish momentum is failing, serving as an early warning for exits or tightening stops. Classic Formula: Uses the standard Connie Brown parameters (14, 9, 3) + SMA smoothing for reliable divergence detection. How to Use This Indicator This script is best used as a companion to trend indicators like TTM Squeeze or Moving Average Ribbons. For Entries (The "Dip Buy"): In an uptrend, wait for a pullback. When the Green Triangle (▲) appears, it confirms that the pullback is over and momentum has turned back up. For Exits (The "Top"): Look for Divergence. If Price makes a Higher High but the Composite Index makes a Lower High—followed by a Red Triangle (▼)—this is a high-probability sell signal. The "Slow MA" Filter: The signals are generated only when the CI crosses the Slow MA (Red Line). This filters out the noise of minor fluctuations (crossing the Green line) and focuses on significant momentum changes. Settings RSI Period: 14 (Default) Momentum Period: 9 (Default) Signal Logic: Crossover/Crossunder of the Slow MA (33 Period). Disclaimer This tool is for educational purposes only. Always combine momentum signals with price action and structure analysis.
Pine Script® indicator
by liaor
Market Efficiency Ratio [Interakktive]The Market Efficiency Ratio decomposes price movement into two components: net progress vs wasted movement. This tool exposes the underlying math that most traders never see, helping you understand when price is moving efficiently versus chopping sideways. Unlike simple trend indicators, this shows you WHY price movement matters — not just whether it's up or down, but how much of that movement was useful directional progress versus noisy oscillation. █ WHAT IT DOES • Calculates Efficiency Ratio (0–1 or 0–100) measuring directional progress • Exposes Net Displacement (how far price actually moved) • Exposes Path Length (total distance price traveled) • Calculates Chop Cost (wasted movement) • Visual zones for high/mid/low efficiency states █ WHAT IT DOES NOT DO • NO signals, NO entries/exits, NO buy/sell • NO performance claims • NO predictions — purely diagnostic • This is a tool for understanding price behavior █ HOW IT WORKS The efficiency ratio answers one question: "Of all the movement price made, how much was useful progress?" 🔹 THE MATH Over a lookback period of N bars: Net Displacement = |Close - Close | Path Length = Σ |Close - Close | for all bars Efficiency Ratio = Net Displacement / Path Length 🔹 INTERPRETATION • Efficiency = 1.0 (100%): Price moved in a straight line — every tick was progress • Efficiency = 0.5 (50%): Half the movement was wasted in back-and-forth chop • Efficiency = 0.0 (0%): Price ended exactly where it started — all movement was noise 🔹 CHOP COST This is the "wasted movement" — how much price traveled without making progress: Chop Cost = Path Length - Net Displacement Chop % = Chop Cost / Path Length High chop cost means lots of effort for little result — a warning sign for trend traders. █ VISUAL GUIDE Three efficiency zones: • GREEN (≥70): High efficiency — strong directional movement • YELLOW (30-70): Mixed efficiency — some progress, some chop • RED (<30): Low efficiency — mostly noise, little progress █ INPUTS Lookback Length (default: 14) Number of bars to calculate efficiency over. Higher values produce smoother readings but respond slower to changes. Smoothing Length (default: 5) EMA smoothing applied to the output. Reduces noise in the efficiency reading. Apply Smoothing (default: true) Toggle EMA smoothing on/off. Scale Mode (default: 0–100) Display as percentage (0-100) or decimal ratio (0-1). Show Reference Bands (default: true) Display the high/low efficiency threshold lines. Low/High Efficiency Level (default: 30/70) Thresholds for classifying efficiency zones. Overlay Effect (default: None) • None: No overlay • Background Tint: Subtle chart background color in high/low zones • Bar Highlight: Color bars during low efficiency periods Show Data Window Values (default: true) Export all raw values (Net Displacement, Path Length, Efficiency, Chop Cost, Chop %) to the data window for analysis. █ USE CASES This indicator helps traders understand: • Why some trends are "clean" and others are "messy" • When price is consolidating vs trending (without using volume) • The relationship between movement and progress • Why high-chop environments are difficult to trade This is the foundational concept behind more advanced regime detection systems. █ SUITABLE MARKETS Works on: Stocks, Futures, Forex, Crypto Timeframes: All timeframes Note: This is a price-only indicator — no volume required █ DISCLAIMER This indicator is for informational and educational purposes only. It does not constitute financial advice. It does not generate trading signals. Past performance does not guarantee future results. Always conduct your own analysis.
Pine Script® indicator
by Interakktive
1
Custom Reversal Oscillator [wjdtks255]📊 Indicator Overview: Custom Reversal Oscillator This indicator is a momentum-based oscillator designed to identify potential trend reversals by analyzing price velocity and relative strength. It visualizes market exhaustion and recovery through a dynamic histogram and signal dots, similar to premium institutional tools. Key Components Dynamic Histogram (Bottom Bars): Changes color based on momentum strength. Bright Green/Red indicates accelerating momentum, while Darker shades suggest fading strength. Signal Line: A white line tracing the core momentum, helping to visualize the "wave" of the market. Buy/Sell Dots: Small circles at the bottom (Mint) or top (Red) that signal high-probability reversal points when the market is overextended. 📈 Trading Strategy (How to Trade) 1. Long Entry (Buy Signal) Condition 1: The price should ideally be near or above the 200 EMA (for trend following) or showing a Bullish Divergence. Condition 2: The Histogram bars transition from Dark Red to Bright Green. Condition 3: A Mint Buy Dot appears at the bottom of the oscillator (near the -25 level). Entry: Enter on the close of the candle where the Buy Dot is confirmed. 2. Short Entry (Sell Signal) Condition 1: The price is struggling at resistance or showing a Bearish Divergence. Condition 2: The Histogram bars transition from Dark Green to Bright Red. Condition 3: A Red Sell Dot appears at the top of the oscillator (near the +25 level). Entry: Enter on the close of the candle where the Sell Dot is confirmed. 3. Exit & Take Profit Take Profit: Close the position when the Signal Line reaches the opposite extreme or when the histogram color starts to fade (loses its brightness). Stop Loss: Place your stop loss slightly below the recent swing low (for Longs) or above the recent swing high (for Shorts). 💡 Pro Tips for Accuracy Watch for Divergences: The most powerful signals occur when the price makes a lower low, but the Custom Reversal Oscillator makes a higher low. This indicates "Hidden Strength" and a massive reversal is often imminent.
Pine Script® indicator
by wjdtks255
Stochastic X-Score Signal📊 Stochastic X-Score Signal This indicator is designed to analyze market momentum, direction, and strength in a single tool. It combines Z-Score, Stochastic, Trend Filter, ADX/DI, and Volume to filter out high-quality trading signals. 🎯 Key Highlights Measures price deviation using Z-Score Converts data into Stochastic (0–100) to identify Overbought / Oversold Uses HMA + ALMA to separate short-term momentum from long-term trend Offers 4 signal sources, adjustable to different trading styles Includes a Trend Filter to distinguish with-trend vs against-trend signals Confirms real market strength with ADX/DI and Volume Gauge ⚙️ Signal System 🔺 BUY / 🔻 SELL from Reversal, Z-Score, ALMA, or MA Cross With-trend signals = darker colors (stronger confirmation) Against-trend signals = lighter colors (higher risk) 📊 Signal Quality Confirmation ADX > 25 = strong trend DI+ / DI- defines trend direction Volume Candles clearly show buy vs sell pressure 🎨 Visualization On-chart signals (Triangles + Bar Colors) Indicator panel: Z-Score Histogram, Oscillator, ALMA, OB/OS zones Gauge table for instant trend strength reading 🔔 Alerts Included Bullish / Bearish (with-trend & against-trend) MA Golden / Death Cross Strong / Weak Trend alerts High Buy / Sell Volume alerts 💡 Best For Trend & Pullback traders Traders who prefer one powerful indicator instead of many Those who need signals with full market context ⚠️ This indicator is a market analysis tool and does not guarantee profits. Always apply proper risk management when trading. 💬 Interested in our Indicator? Feel free to contact us via INBOX 📱 Facebook Page: Overdue Logic Indicator www.facebook.com
Pine Script® indicator
by overduelogicindicator
Mystic Scales Dual Energy PRO [Destiny Quant]Mystic Scales Dual Energy PRO - Destiny Quant | 【天機衡】雙向能量 English Description Balancing Momentum and Structure. Mystic Scales Dual Energy PRO utilizes a unique split-axis design to evaluate the balance between Market Momentum (WE2) and Market Health (WH1/WH2). It ensures you only execute trades when momentum is supported by a healthy market structure. Custom Thresholds: Fully adjustable Entry/Exit score triggers with built-in hysteresis logic to prevent whipsaws. Structural Health: Monitors DMI flows and Volume Ratios (VR) across Daily, Weekly, and Monthly timeframes. Strategic Confluence: The perfect companion for the Celestial Mirror to confirm high-conviction entries. 中文說明 權衡動能與結構的平衡之衡 【天機衡】雙向能量 PRO 採用獨特的雙軸分離設計，同時權衡 「市場動能 (WE2)」 與 「市場健康度 (WH1/WH2)」。它確保您只在市場結構健康的前提下發動動能交易。 自訂門檻觸發：具備可調式進場/出場分數門檻，並內建遲滯邏輯 (Hysteresis) 有效過濾頻繁洗盤。 結構健康偵測：即時監控日、週、月線級別的 DMI 流向與成交量比率 (VR)。 策略共振：作為【天機鏡】的最佳拍檔，用來確認高勝率的共振進場時機。 🚀 Get Access / 獲取授權 This is an Invite-only script. To unlock the Celestial Mirror, please: Visit the link in my profile. Send a direct message for subscription details. 本指標為 僅限邀請 (Invite-only)。欲獲取授權，請： 點擊我個人主頁的連結（官網/商店）。 透過 TradingView 私訊聯繫我了解訂閱詳情。
Pine Script® indicator
by DestinyQuant
Celestial Mirror AI Score PRO - Destiny QuantCelestial Mirror AI Score PRO - Destiny Quant | 【天機鏡】AI 評分系統 English Description The Strategic Brain of Quantitative Trading. The Celestial Mirror AI Score PRO is a multi-factor weighting engine designed by Destiny Quant Lab. It acts as a digital "Mirror," revealing the hidden truth of market quality. By integrating over 10+ quantitative factors, including the proprietary Zanger Explosion Algorithm, it provides a real-time AI Score (0-99). Institutional Detection: Uses advanced VSA logic to track "Smart Money" footprints. Dual Engine: Switch between "Factor Analysis" (Swing) and "Explosion" (Momentum) modes. Quant Dashboard: Real-time monitoring of momentum, volume structure, and pivot hierarchy. 中文說明 量化交易的策略大腦 【天機鏡】AI 評分系統 PRO 是由 天機量化實驗室 開發的多因子加權引擎。它如同數位之鏡，照見市場體質的虛實。本指標結合了 10 多項量化因子與獨家 Zanger 爆發演算法，將複雜盤面轉化為 0-99 的即時評分。 機構追蹤：透過進階量價分析 (VSA) 偵測大戶資金流向。 雙模式引擎：提供適合波段的「因子分析」與捕捉飆股噴發的「爆發預測」模式。 天機數據面板：即時監測動能、量能與樞軸位置，讓數據一目了然。 🚀 Get Access / 獲取授權 This is an Invite-only script. To unlock the Celestial Mirror, please: Visit the link in my profile. Send a direct message for subscription details. 本指標為 僅限邀請 (Invite-only)。欲獲取授權，請： 點擊我個人主頁的連結（官網/商店）。 透過 TradingView 私訊聯繫我了解訂閱詳情。
Pine Script® indicator
by DestinyQuant
BK AK-Zenith💥 Introducing BK AK-ZENITH — Adaptive Rhythm RSI for Peak/Valley Warfare 💥 This is not another generic RSI. This is ZENITH: it measures where momentum is on the scale, then tells you when it’s hitting extremes, when it’s turning, and when price is lying through its teeth with divergence. At its core, ZENITH does one thing ruthlessly well: it matches the oscillator’s period to the market’s current rhythm—adaptive when the market is fast, adaptive when the market is slow—so your signals stop being “late because the settings were wrong.” 🎖 Full Credit — Respect the Origin (AlgoAlpha) The core RSI architecture in this form belongs to AlgoAlpha—one of the best introducers and coders on TradingView. They originated this adaptive/Rhythm-RSI framework and the way it’s presented and engineered. BK AK-ZENITH is my enhancement layer on top of AlgoAlpha’s foundation. I kept the spine intact, and I added tactical systems: clearer Peak/Valley warfare logic, pivot governance (anti-spam), divergence strike markers, momentum flip confirmation, and a war-room readout—so it trades like a weapon, not a toy. Respect where it started: AlgoAlpha built the engine. I tuned it for battlefield use. 🧠 What Exactly is BK AK-ZENITH? BK AK-ZENITH is an Adaptive Period RSI (or fixed if you choose), designed to read momentum like a range of intent rather than a single overbought/oversold gimmick. Core Systems Inside ZENITH ✅ Adaptive Period RSI (Rhythm Engine) Automatically adjusts its internal RSI length to match current market cadence. (Optional fixed length mode if you want static.) ✅ Optional HMA Smoothing Cleaner shape without turning it into a laggy moving average. ✅ Peak / Valley Zones (default 80/20) Hard boundaries that define “true extremes” so you stop treating every wiggle like a signal. ✅ Pivot-Based BUY/SELL Triangles + Cooldown Signals are governed by pivots and a cooldown so it doesn’t machine-gun trash. ✅ Momentum Flip Diamonds (◇) Shows when the oscillator’s slope flips—clean confirmation for “engine change.” ✅ Divergence Lightning (⚡) Exposes when price is performing confidence while momentum is quietly breaking. ✅ War-Room Table / Meter Bias, zone, reading, and adaptive period printed so you don’t “interpret”—you execute. ✅ Alerts Suite Pivots, divergences, zone entries—so the chart calls you, not your emotions. 🎯 How to use it (execution rules) 1) Zones = permission Valley (≤ Valley level): demand territory. Stalk reversal structure; stop chasing breakdown candles. Peak (≥ Peak level): supply territory. Harvest, tighten, stop adding risk at the top. 2) Pivot triangles = the shot clock Your ▲/▼ signals are pivot-confirmed with a cooldown. That’s intentional. This is designed to force patience and prevent overtrading. 3) Divergence = truth serum When price makes the “confident” high/high or low/low but ZENITH disagrees, you’re seeing internal change before the crowd does. Treat divergence as warning + timing context, not a gambling button. 4) Meter/Table = discipline If you can’t summarize the state in one glance, you’ll overtrade. ZENITH prints the state so your brain stops inventing stories. 🔧 Settings that actually matter Adaptive Period ON (default): the whole point of ZENITH Peak/Valley levels: how strict extremes must be Pivot strength + Cooldown: your anti-spam governor Divergence pivot length: controls how “major” divergence must be The “AK” in the name is an acknowledgment of my mentor A.K. His standards—patience, precision, clarity, emotional control—are why this tool is built with governors instead of hype. And above all: all praise to Gd—the true source of wisdom, restraint, and right timing. 👑 King Solomon Lens — ZENITH Discernment Solomon asked Gd for something most people never ask for: not wealth, not victory—discernment. The ability to separate what looks true from what is true. That is exactly what momentum work is supposed to do. 1) Honest weights, honest measures. In Solomon’s world, crooked scales were an abomination because they disguised reality. In trading, the crooked scale is your own excitement: you see one green candle and call it strength. ZENITH forces an honest measure—0 to 100—so you deal in degree, not drama. A Peak is not “bullish.” A Peak is “momentum priced in.” A Valley is not “bearish.” A Valley is “selling pressure reaching exhaustion.” 2) Wisdom adapts to seasons. Solomon’s order wasn’t chaos—there was a time to build, a time to harvest, a time to wait. Markets have seasons too: trend seasons, chop seasons, compression seasons, expansion seasons. Fixed-length RSI pretends every season is the same. ZENITH does not. It listens for rhythm and adjusts its internal timing so your read stays relevant to today’s market tempo—not last month’s. 3) The sword test: revealing what’s hidden. Solomon’s most famous judgment wasn’t about theatrics—it was about revealing the truth beneath appearances. Divergence is that same test in markets: price can perform strength while the engine quietly weakens, or perform weakness while momentum secretly repairs. The ⚡ is not a prophecy. It’s a revelation: “what you see on price is not the full story.” That’s ZENITH discipline: measure → discern → execute. And may Gd bless your judgment to act only when the measure is clean. ⚔️ Final BK AK-ZENITH is a momentum fire-control system: adaptive rhythm + extreme zones + pivot timing + divergence truth. Use it to stop feeling trades and start weighing them. Praise to Gd always. 🙏
Pine Script® indicator
by Ki11a_B
2
Kinetic Elasticity Reversion System - Adaptive Genesis Engine🧬 KERS-AGE - EVOLVED KINETIC ELASTICITY REVERSION SYSTEM EDUCATIONAL GUIDE & THEORETICAL FOUNDATION ⚠️ IMPORTANT DISCLAIMER This indicator and guide are provided for educational and informational purposes only. This is NOT financial advice, investment advice, or a recommendation to buy or sell any security. Trading involves substantial risk of loss. Past performance does not guarantee future results. The performance metrics, win rates, and examples shown are from historical backtesting and do not represent actual trading results. Always conduct your own research, paper trade extensively, and never risk capital you cannot afford to lose. The developers assume no responsibility for any trading losses incurred through use of this indicator. INTRODUCTION KERS-AGE (Kinetic Elasticity Reversion System - Adaptive Genetic Evolution) represents an educational exploration of adaptive trading systems. Unlike traditional indicators with fixed parameters, KERS-AGE demonstrates a dynamic, evolving approach that adjusts to market conditions through genetic algorithms and machine learning techniques. This guide explains the theoretical concepts, technical implementation, and educational examples of how the system operates. CONCEPTUAL FRAMEWORK Traditional Indicators vs. Adaptive Systems: Traditional Indicators: Fixed parameters Single strategy approach Static behavior Designed for specific conditions Require manual optimization Adaptive System Approach (KERS-AGE): Dynamic parameters (adjust based on conditions) Multiple strategies tested simultaneously Pattern recognition (cluster analysis) Regime-aware (speciation) Automated optimization (genetic algorithms) Transparent operation (detailed dashboard) CORE CONCEPTS EXPLAINED 1. THE ELASTICITY ANALOGY 🎯 The indicator models price behavior as if connected to a moving average by an elastic band: Price extends away → Elastic tension builds → Potential reversion point identified Key Measurements: STRETCH: Distance from price to equilibrium (MA) TENSION: Normalized force calculation THRESHOLD: Point where multiple factors align Theoretical Foundation: Markets have historically shown mean-reverting tendencies around fair value. This concept quantifies the deviation and identifies potential reversal zones based on multiple confluence factors. Mathematical Approach: text Tension Score = (Price Distance from MA) / (Band Width) × Volatility Scaling Signal Threshold = Multiple of ATR × Dynamic Volatility Ratio Confluence = Tension Score + Additional Factors 2. THE 6 SIGNAL TYPES 📊 The system recognizes 6 distinct pattern categories: A. ELASTIC SIGNALS Pattern: Price reaches statistical band extremes Theory: Maximum deviation from mean suggests potential reversion Detection: Price touches outer zones (typically 2-3× ATR from MA) Component: Mathematical band extension measurement Historical Context: Often observed in markets with clear swing patterns B. WICK SIGNALS Pattern: Extended rejection wicks on candles Theory: Failed breakout attempts may indicate directional exhaustion Detection: Upper/lower wick exceeding 2× body size Component: Real-time price rejection measurement Historical Context: Common in volatile conditions with rapid reversals C. EXHAUSTION SIGNALS Pattern: Decelerating momentum despite price extension Theory: Velocity and acceleration divergence may precede reversals Detection: Decreasing velocity with negative acceleration Component: Momentum derivative analysis Historical Context: Often seen at trend maturity points D. CLIMAX SIGNALS Pattern: Volume spike at price extreme Theory: Unusual volume at extremes historically correlates with turning points Detection: Volume 1.5-2.5× average at band extreme Component: Volume-price relationship analysis Historical Context: Associated with institutional activity or capitulation E. STRUCTURE SIGNALS Pattern: Fractal pivot formations (swing highs/lows) Theory: Market structure points have historically acted as support/resistance Detection: 2-4 bar pivot patterns Component: Classical technical analysis Historical Context: Universal across timeframes and markets F. DIVERGENCE SIGNALS Pattern: RSI divergence versus price Theory: Momentum divergence has historically preceded price reversals Detection: Price makes new extreme but RSI does not Component: Oscillator divergence detection Historical Context: Considered a leading indicator in technical analysis Pattern Confluence: Historical testing suggests stronger signals when multiple types align: Elastic + Wick + Volume = Higher confluence score Elastic + Exhaustion + Divergence = Multiple confirmation factors Any 3+ types = Increased pattern strength Note: Past pattern performance does not guarantee future occurrence. 3. REGIME DETECTION 🌍 The system attempts to classify market conditions into three behavioral regimes: 📈 TREND REGIME Detection Methodology: text Efficiency Ratio = Net Movement / Total Movement Classification: Efficiency > 0.5 AND Volatility < 1.3 → TREND Characteristics Observed: Directional price movement Relatively lower volatility Defined higher highs/lower lows Persistent directional momentum System Response: Reduces signal frequency Prioritizes trend-specialist strategies Applies additional filtering to counter-trend signals Increases confluence requirements Educational Note: In trending conditions, counter-trend mean reversion signals historically have shown reduced reliability. Users may consider additional confirmation when trend regime is detected. ↔️ RANGE REGIME Detection Methodology: text Classification: Efficiency < 0.5 AND Volatility 0.9-1.4 → RANGE Characteristics Observed: Oscillating price action Defined support/resistance zones Mean-reverting behavior patterns Relatively balanced directional flow System Response: Increases signal frequency Activates range-specialist strategies Adjusts bands relative to volatility Reduces confluence threshold Educational Note: Historical backtesting suggests mean reversion systems have performed better in ranging conditions. This does not guarantee future performance. 🌊 VOLATILE REGIME Detection Methodology: text Classification: DVS (Dynamic Volatility Scaling) > 1.5 → VOLATILE Characteristics Observed: Erratic price swings Expanded ranges Elevated ATR readings Often news or event-driven System Response: Activates volatility-specialist strategies Widens bands automatically Prioritizes wick rejection signals Emphasizes volume confirmation Educational Note: Volatile conditions historically present both opportunity and increased risk. Wider stops may be appropriate for risk management. 4. GENETIC EVOLUTION EXPLAINED 🧬 The system employs genetic algorithms to optimize parameters - an approach used in computational finance research. The Evolution Process: STEP 1: INITIALIZATION text Initial State: System creates 4 starter strategies - Strategy 0: Range-optimized parameters - Strategy 1: Trend-optimized parameters - Strategy 2: Volatility-optimized parameters - Strategy 3: Balanced parameters Each contains 14 adjustable parameters (genes): - Band sensitivity - Extension multiplier - Wick threshold - Momentum threshold - Volume multiplier - Component weights (elastic, wick, momentum, volume, fractal) - Target percentage STEP 2: COMPETITION (Shadow Trading) text Early Bars: All strategies generate signals in parallel - Each tracks hypothetical performance independently - Simulated P&L, win rate, Sharpe ratio calculated - No actual trades executed (educational simulation) - Performance metrics recorded for analysis STEP 3: FITNESS EVALUATION text Fitness Calculation = 0.25 × Win Rate + 0.25 × PnL Score + 0.15 × Drawdown Score + 0.30 × Sharpe Ratio Score + 0.05 × Trade Count Score With Walk-Forward enabled: Fitness = 0.60 × Test Score + 0.40 × Train Score With Speciation enabled: Fitness adjusted by Diversity Penalty STEP 4: SELECTION (Tournament) text Periodically (default every 50 bars): - Randomly select 4 active strategies - Compare fitness scores - Top 2 selected as "parents" STEP 5: CROSSOVER (Breeding) text Parent 1 Fitness: 0.65 Parent 2 Fitness: 0.55 Weight calculation: 0.65/(0.65+0.55) = 54% For each parameter: Child Parameter = (0.54 × Parent1) + (0.46 × Parent2) Example: Band Sensitivity: (0.54 × 1.5) + (0.46 × 2.0) = 1.73 STEP 6: MUTATION text For each parameter: if random(0-1) < Mutation Rate (default 0.15): Add random variation: -12% to +12% Purpose: Prevents premature convergence Enables: Discovery of novel parameter combinations ADAPTIVE MUTATION: If population fitness converges → Mutation rate × 1.5 (Encourages exploration when diversity decreases) STEP 7: INSERTION text New strategy added to population: - Assigned unique ID number - Generation counter incremented - Begins shadow trading - Competes with existing strategies STEP 8: CULLING (Selection Pressure) text Periodically (default every 100 bars): - Identify lowest fitness strategy - Verify not elite (protected top performers) - Verify not last of species - Remove from population Result: Maintains selection pressure Effect: Prevents weak strategies from diluting signals STEP 9: SIGNAL GENERATION LOGIC text When determining signals to display: If Ensemble enabled: - All strategies cast weighted votes - Weights based on fitness scores - Specialists receive boost in matching regime - Signal generated if consensus threshold reached If Ensemble disabled: - Single highest-fitness strategy used STEP 10: ADAPTATION OBSERVATION text Over time: Population characteristics may shift - Lower-performing strategies removed - Higher-performing strategies replicated - Parameters adjust toward observed optima - Fitness scores generally trend upward Long-term: Population reaches maturity - Strategies become specialized - Parameters optimized for recent conditions - Performance stabilizes Educational Context: Genetic algorithms are a recognized computational method for optimization problems. This implementation applies those concepts to trading parameter optimization. Past optimization results do not guarantee future performance. 5. SPECIATION (Niche Specialization) 🐟🦎🦅 Inspired by biological speciation theory applied to algorithmic trading. The Three Species: RANGE SPECIALISTS 📊 text Optimized for: Sideways market conditions Parameter tendencies: - Tighter bands (1.0-1.5× ATR) - Higher sensitivity to elastic stretch - Emphasis on fractal structure - More frequent signal generation Typically emerge when: - Range regime detected - Clear support/resistance present - Mean reversion showing historical success Historical backtesting observations: - Win rates often in 55-65% range - Smaller reward/risk ratios (0.5-1.5R) - Higher trade frequency TREND SPECIALISTS 📈 text Optimized for: Directional market conditions Parameter tendencies: - Wider bands (2.0-2.5× ATR) - Focus on momentum exhaustion - Emphasis on divergence patterns - More selective signal generation Typically emerge when: - Trend regime detected - Strong directional movement observed - Counter-trend exhaustion signals sought Historical backtesting observations: - Win rates often in 40-55% range - Larger reward/risk ratios (1.5-3.0R) - Lower trade frequency VOLATILITY SPECIALISTS 🌊 text Optimized for: High-volatility conditions Parameter tendencies: - Expanded bands (1.5-2.0× ATR) - Priority on wick rejection patterns - Strong volume confirmation requirement - Very selective signals Typically emerge when: - Volatile regime detected - High DVS ratio (>1.5) - News-driven or event-driven conditions Historical backtesting observations: - Win rates often in 50-60% range - Variable reward/risk ratios (1.0-2.5R) - Opportunistic trade timing Species Protection Mechanism: text Minimum Per Species: Configurable (default 2) If Range specialists = 1: → Preferential spawning of Range type → Protection from culling process Purpose: Ensures coverage across regime types Theory: Markets cycle between behavioral states Goal: Prevent extinction of specialized approaches Fitness Sharing: text If Species has 4 members: Individual Fitness × 1 / (4 ^ 0.3) Individual Fitness × 0.72 Purpose: Creates pressure toward species diversity Effect: Prevents single approach from dominating population Educational Note: Speciation is a theoretical framework for maintaining strategy diversity. Past specialization performance does not guarantee future regime classification accuracy or signal quality. 6. WALK-FORWARD VALIDATION 📈 An out-of-sample testing methodology used in quantitative research to reduce overfitting risk. The Overfitting Problem: text Hypothetical Example: In-Sample Backtest: 85% win rate Out-of-Sample Results: 35% win rate Explanation: Strategy may have optimized to historical noise rather than repeatable patterns Walk-Forward Methodology: Timeline Structure: text ┌──────────────────────────────────────────────────────┐ │ Train Window │ Test Window │ Train │ Test │ │ (200 bars) │ (50 bars) │ (200) │ (50) │ └──────────────────────────────────────────────────────┘ In-Sample Out-of-Sample IS OOS (Optimize) (Validate) Cycle 2... TRAIN PHASE (In-Sample): text Example Bars 1-200: Strategies optimize parameters - Performance tracked - Not yet used for primary fitness - Learning period TEST PHASE (Out-of-Sample): text Example Bars 201-250: Strategies use optimized parameters - Performance tracked separately - Validation period - Out-of-sample evaluation FITNESS CALCULATION EXAMPLE: text Train Win Rate: 65% Test Win Rate: 58% Composite Fitness: = (0.40 × 0.65) + (0.60 × 0.58) = 0.26 + 0.35 = 0.61 Note: Test results weighted 60%, Train 40% Theory: Out-of-sample may better indicate forward performance OVERFIT DETECTION MECHANISM: text Gap = Train WR - Test WR = 65% - 58% = 7% If Gap > Overfit Threshold (default 25%): Fitness Penalty = Gap × 2 Example with 30% gap: Strategy shows: Train 70%, Test 40% Gap: 30% → Potential overfit flagged Penalty: 30% × 2 = 60% fitness reduction Result: Strategy likely to be culled WINDOW ROLLING: text Example Bar 250: Test window complete → Reset both windows → Start new cycle → Previous results retained for analysis Cycle Count increments Historical performance tracked across multiple cycles Educational Context: Walk-forward analysis is a recognized approach in quantitative finance research for evaluating strategy robustness. However, past out-of-sample performance does not guarantee future results. Market conditions can change in ways not represented in historical data. 7. CLUSTER ANALYSIS 🔬 An unsupervised machine learning approach for pattern recognition. The Concept: text Scenario: System identifies a price pivot that wasn't signaled → Extract pattern characteristics → Store features for analysis → Adjust detection for similar future patterns Implementation: STEP 1: FEATURE EXTRACTION text When significant move occurs without signal: Extract 5-dimensional feature vector: Feature Vector = Example: Observed Pattern: STEP 2: CLUSTER ASSIGNMENT text Compare to existing cluster centroids using distance metric: Cluster 0: Cluster 1: ← Minimum distance Cluster 2: ... Assign to nearest cluster STEP 3: CENTROID UPDATE text Old Centroid 1: New Pattern: Decay Rate: 0.95 Updated Centroid: = 0.95 × Old + 0.05 × New = Exponential moving average update = STEP 4: PROFIT TRACKING text Cluster Average Profit (hypothetical): Old Average: 2.5R New Observation: 3.2R Updated: 0.95 × 2.5 + 0.05 × 3.2 = 2.535R STEP 5: LEARNING ADJUSTMENT text If Cluster Average Profit > Threshold (e.g., 2.0R): Cluster Learning Boost += increment (e.g., 0.1) (Maximum cap: 2.0) Effect: Future signals resembling this cluster receive adjustment STEP 6: SCORE MODIFICATION text For signals matching cluster characteristics: Base Score × Cluster Learning Boost Example: Base Score: 5.2 Cluster Boost: 1.3 Adjusted Score: 5.2 × 1.3 = 6.76 Result: Pattern more likely to generate signal Cluster Interpretation Example: text CLUSTER 0: "High elastic, low volume" Centroid: Avg Profit: 3.5R (historical backtest) Interpretation: Pure elastic signals in ranges historically favorable CLUSTER 1: "Wick rejection, volatile" Centroid: Avg Profit: 2.8R (historical backtest) Interpretation: Wick signals in volatility showed positive results CLUSTER 2: "Exhaustion divergence" Centroid: Avg Profit: 4.2R (historical backtest) Interpretation: Momentum exhaustion in trends performed well Learning Progress Metrics: text Missed Total: 47 Clusters Updated: 142 Patterns Learned: 28 Interpretation: - System identified 47 significant moves without signals - Clusters updated 142 times (incremental refinement) - Made 28 parameter adjustments - Theoretically improving pattern recognition Educational Note: Cluster analysis is a recognized machine learning technique. This implementation applies it to trading pattern recognition. Past cluster performance does not guarantee future pattern profitability or accurate classification. 8. ENSEMBLE VOTING 🗳️ A collective decision-making approach common in machine learning. The Wisdom of Crowds Concept: text Single Model: - May have blind spots - Subject to individual bias - Limited perspective Ensemble of Models: - Blind spots may offset - Biases may average out - Multiple perspectives considered Implementation: STEP 1: INDIVIDUAL VOTES text Example Bar 247: Strategy 0 (Range): LONG (fitness: 0.65) Strategy 1 (Trend): FLAT (fitness: 0.58) Strategy 2 (Volatile): LONG (fitness: 0.52) Strategy 3 (Balanced): SHORT (fitness: 0.48) Strategy 4 (Range): LONG (fitness: 0.71) Strategy 5 (Trend): FLAT (fitness: 0.55) STEP 2: WEIGHT CALCULATION text Base Weight = Fitness Score If strategy's species matches current regime: Weight × Specialist Boost (configurable, default 1.5) If strategy has recent positive performance: Weight × Recent Performance Factor Example for Strategy 0: Base: 0.65 Range specialist in Range regime: 0.65 × 1.5 = 0.975 Recent performance adjustment: 0.975 × 1.13 = 1.10 STEP 3: WEIGHTED TALLYING text LONG votes: S0: 1.10 + S2: 0.52 + S4: 0.71 = 2.33 SHORT votes: S3: 0.48 = 0.48 FLAT votes: S1: 0.58 + S5: 0.55 = 1.13 Total Weight: 2.33 + 0.48 + 1.13 = 3.94 STEP 4: CONSENSUS CALCULATION text LONG %: 2.33 / 3.94 = 59.1% SHORT %: 0.48 / 3.94 = 12.2% FLAT %: 1.13 / 3.94 = 28.7% Minimum Consensus Setting: 60% Result: NO SIGNAL (59.1% < 60%) STEP 5: SIGNAL DETERMINATION text If LONG % >= Min Consensus: → Display LONG signal → Show consensus percentage in dashboard If SHORT % >= Min Consensus: → Display SHORT signal If neither threshold reached: → No signal displayed Practical Examples: text Strong Consensus (85%): 5 strategies LONG, 0 SHORT, 1 FLAT → High agreement among models Moderate Consensus (62%): 3 LONG, 2 SHORT, 1 FLAT → Borderline agreement No Consensus (48%): 3 LONG, 2 SHORT, 1 FLAT → Insufficient agreement, no signal shown Educational Note: Ensemble methods are widely used in machine learning to improve model robustness. This implementation applies ensemble concepts to trading signals. Past ensemble performance does not guarantee future signal quality or profitability. 9. THOMPSON SAMPLING 🎲 A Bayesian reinforcement learning technique for balancing exploration and exploitation. The Exploration-Exploitation Dilemma: text EXPLOITATION: Use what appears to work Benefit: Leverages observed success patterns Risk: May miss better alternatives EXPLORATION: Try less-tested approaches Benefit: May discover superior methods Risk: May waste resources on inferior options Thompson Sampling Solution: STEP 1: BETA DISTRIBUTIONS text For each signal type, maintain: Alpha = Successes + 1 Beta = Failures + 1 Example for Elastic signals: 15 wins, 10 losses Alpha = 16, Beta = 11 STEP 2: PROBABILITY SAMPLING text Rather than using simple Win Rate = 15/25 = 60% Sample from Beta(16, 11) distribution: Possible samples: 0.55, 0.62, 0.58, 0.64, 0.59... Rationale: Incorporates uncertainty - Type with 5 trades: High uncertainty, wide sample variation - Type with 50 trades: Lower uncertainty, narrow sample range STEP 3: TYPE PRIORITIZATION text Example Bar 248: Elastic sampled: 0.62 Wick sampled: 0.58 Exhaustion sampled: 0.71 ← Highest this sample Climax sampled: 0.52 Structure sampled: 0.63 Divergence sampled: 0.45 Exhaustion type receives temporary boost STEP 4: SIGNAL ADJUSTMENT text If current signal is Exhaustion type: Score × (0.7 + 0.71 × 0.6) Score × 1.126 If current signal is other type with lower sample: Score × (0.7 + sample × 0.6) (smaller adjustment) STEP 5: OUTCOME FEEDBACK text When trade completes: If WIN: Alpha += 1 (Beta unchanged) If LOSS: Beta += 1 (Alpha unchanged) Effect: Shifts probability distribution for future samples Educational Context: Thompson Sampling is a recognized Bayesian approach to the multi-armed bandit problem. This implementation applies it to signal type selection. The mathematical optimality assumes stationary distributions, which may not hold in financial markets. Past sampling performance does not guarantee future type selection accuracy. 10. DYNAMIC VOLATILITY SCALING (DVS) 📉 An adaptive approach where parameters adjust based on current vs. baseline volatility. The Adaptation Problem: text Fixed bands (e.g., always 1.5 ATR): In low volatility environment (vol = 0.5): Bands may be too wide → fewer signals In high volatility environment (vol = 2.0): Bands may be too tight → excessive signals The DVS Approach: STEP 1: BASELINE ESTABLISHMENT text Calculate volatility over baseline period (default 100 bars): Method options: ATR / Close, Parkinson, or Garman-Klass Example average volatility = 1.2% This represents "normal" for recent conditions STEP 2: CURRENT VOLATILITY text Current bar volatility = 1.8% STEP 3: DVS RATIO text DVS Ratio = Current / Baseline = 1.8 / 1.2 = 1.5 Interpretation: Volatility currently 50% above baseline STEP 4: BAND ADJUSTMENT text Base Band Width: 1.5 ATR Adjusted Band Width: Upper: 1.5 × DVS = 1.5 × 1.5 = 2.25 ATR Lower: Same Result: Bands expand 50% to accommodate higher volatility STEP 5: THRESHOLD ADJUSTMENT text Base Thresholds: Wick: 0.15 Momentum: 0.6 Adjusted: Wick: 0.15 / DVS = 0.10 (easier to trigger in high vol) Momentum: 0.6 × DVS = 0.90 (harder to trigger in high vol) DVS Calculation Methods: text ATR RATIO (Simplest): DVS = (ATR / Close) / SMA(ATR / Close, 100) PARKINSON (Range-based): σ = √(∑(ln(H/L))² / (4×n×ln(2))) DVS = Current σ / Baseline σ GARMAN-KLASS (Comprehensive): σ = √(0.5×(ln(H/L))² - (2×ln(2)-1)×(ln(C/O))²) DVS = Current σ / Baseline σ ENSEMBLE (Robust): DVS = Median(ATR_Ratio, Parkinson, Garman_Klass) Educational Note: Dynamic volatility scaling is an approach to normalize indicators across varying market conditions. The effectiveness depends on the assumption that recent volatility patterns continue, which is not guaranteed. Past volatility adjustment performance does not guarantee future normalization accuracy. 11. PRESSURE KERNEL 💪 A composite measurement attempting to quantify directional force beyond simple price movement. Components: 1. CLOSE LOCATION VALUE (CLV) text CLV = ((Close - Low) - (High - Close)) / Range Examples: Close at top of range: CLV = +1.0 (bullish position) Close at midpoint: CLV = 0.0 (neutral) Close at bottom: CLV = -1.0 (bearish position) 2. WICK ASYMMETRY text Wick Pressure = (Lower Wick - Upper Wick) / Range Additional factors: If Lower Wick > Body × 2: +0.3 (rejection boost) If Upper Wick > Body × 2: -0.3 (rejection penalty) 3. BODY MOMENTUM text Body Ratio = Body Size / Range Body Momentum = Close > Open ? +Body Ratio : -Body Ratio Strong bullish candle: +0.9 Weak bullish candle: +0.2 Doji: 0.0 4. PATH ESTIMATE text Close Position = (Close - Low) / Range Open Position = (Open - Low) / Range Path = Close Position - Open Position Additional adjustments: If closed high with lower wick: +0.2 If closed low with upper wick: -0.2 5. MOMENTUM CONFIRMATION text Price Change / ATR Examples: +1.5 ATR move: +1.0 (capped) +0.5 ATR move: +0.5 -0.8 ATR move: -0.8 COMPOSITE CALCULATION: text Pressure = CLV × 0.25 + Wick Pressure × 0.25 + Body Momentum × 0.20 + Path Estimate × 0.15 + Momentum Confirm × 0.15 Volume context applied: If Volume > 1.5× avg: × 1.3 If Volume < 0.5× avg: × 0.7 Final smoothing: 3-period EMA Pressure Interpretation: text Pressure > 0.3: Suggests buying pressure → May support LONG signals → May reduce SHORT signal strength Pressure < -0.3: Suggests selling pressure → May support SHORT signals → May reduce LONG signal strength -0.3 to +0.3: Neutral range → Minimal directional bias Educational Note: The Pressure Kernel is a custom composite indicator combining multiple price action metrics. These weightings are theoretical constructs. Past pressure readings do not guarantee future directional movement or signal quality. USAGE GUIDE - EDUCATIONAL EXAMPLES Getting Started: STEP 1: Add Indicator Open TradingView Add KERS-AGE to chart Allow minimum 100 bars for initialization Verify dashboard displays Gen: 1+ STEP 2: Initial Observation Period text First 200 bars: - System is in learning phase - Signal frequency typically low - Population evolution occurring - Fitness scores generally increasing Recommendation: Observe without trading during initialization STEP 3: Signal Evaluation Criteria text Consider evaluating signals based on: - Confidence percentage - Grade assignment (A+, A, B+, B, C) - Position within bands - Historical win rate shown in dashboard - Train vs. Test performance gap Example Signal Evaluation Checklist: Educational Criteria to Consider: Signal appeared (⚡ arrow displayed) Confidence level meets personal threshold Grade meets personal quality standard Ensemble consensus (if enabled) meets threshold Historical win rate acceptable Test performance reasonable vs. Train Price location at band extreme Regime classification appropriate for strategy If trending: Signal direction aligns with personal analysis Stop loss distance acceptable for risk tolerance Position size appropriate (example: 1-2% account risk) Note: This is an educational checklist, not trading advice. Users should develop their own criteria based on personal risk tolerance and strategy. Risk Management Educational Examples: POSITION SIZING EXAMPLE: text Hypothetical scenario: Account: $10,000 Risk tolerance: 1.5% per trade = $150 Indicated stop distance: 1.5 ATR = $300 per contract Calculation: $150 / $300 = 0.5 contracts This is an educational example only, not a recommendation. STOP LOSS EXAMPLES: text System provides stop level (red line) Typically calculated as 1.5 ATR from entry Alternative approaches users might consider: LONG: Below recent swing low SHORT: Above recent swing high Users should determine stops based on personal risk management. TAKE PROFIT EXAMPLES: text System provides target level (green line) Typically calculated as price stretch × 60% Alternative approaches users might consider: Scale out: Partial exit at 1R, remainder at 2R Trailing stop: Adjust stop after profit threshold Users should determine targets based on personal strategy. Educational Note: These are theoretical examples for educational purposes. Actual position sizing and risk management should be determined by each user based on their individual risk tolerance, account size, and trading plan. OPTIMIZATION BY MARKET TYPE - EDUCATIONAL SUGGESTIONS RANGE-BOUND MARKETS Suggested Settings for Testing: Population Size: 6-8 Min Confluence: 5.0-6.0 Min Consensus: 70% Enable Speciation: Consider enabling Min Per Species: 2 Theoretical Rationale: More strategies may provide better coverage Moderate confluence may generate more signals Higher consensus may filter quality Speciation may encourage range specialist emergence Historical Backtest Observations: Win rates in testing: Varied, often 50-65% range Reward/risk ratios observed: 0.5-1.5R Signal frequency: Relatively frequent Disclaimer: Past backtesting results do not guarantee future performance. TRENDING MARKETS Suggested Settings for Testing: Population Size: 4-5 Min Confluence: 6.0-7.0 Consider enabling MTF filter MTF Timeframe: 3-5× current timeframe Specialist Boost: 1.8-2.0 Theoretical Rationale: Fewer strategies may adapt faster Higher confluence may filter counter-trend noise MTF may reduce counter-trend signals Specialist boost may prioritize trend specialists Historical Backtest Observations: Win rates in testing: Varied, often 40-55% range Reward/risk ratios observed: 1.5-3.0R Signal frequency: Less frequent Disclaimer: Past backtesting results do not guarantee future performance. VOLATILE MARKETS (e.g., Cryptocurrency) Suggested Settings for Testing: Base Length: 25-30 Band Multiplier: 1.8-2.0 DVS: Consider enabling (Ensemble method) Consider enabling Volume Filter Volume Multiplier: 1.5-2.0 Theoretical Rationale: Longer base may smooth noise Wider bands may accommodate larger swings DVS may be critical for adaptation Volume filter may confirm genuine moves Historical Backtest Observations: Win rates in testing: Varied, often 45-60% range Reward/risk ratios observed: 1.0-2.5R Signal frequency: Moderate Disclaimer: Cryptocurrency markets are highly volatile and risky. Past backtesting results do not guarantee future performance. SCALPING (1-5min timeframes) Suggested Settings for Testing: Base Length: 15-20 Train Window: 150 Test Window: 30 Spawn Interval: 30 Min Confluence: 5.5-6.5 Consider enabling Ensemble Min Consensus: 75% Theoretical Rationale: Shorter base may increase responsiveness Shorter windows may speed evolution cycles Quick spawning may enable rapid adaptation Higher confluence may filter noise Ensemble may reduce false signals Historical Backtest Observations: Win rates in testing: Varied, often 50-65% range Reward/risk ratios observed: 0.5-1.0R Signal frequency: Frequent but filtered Disclaimer: Scalping involves high frequency trading with increased transaction costs and slippage risk. Past backtesting results do not guarantee future performance. SWING TRADING (4H-Daily timeframes) Suggested Settings for Testing: Base Length: 25-35 Train Window: 300 Test Window: 100 Population Size: 7-8 Consider enabling Walk-Forward Cooldown: 8-10 bars Theoretical Rationale: Longer timeframe may benefit from longer lookbacks Larger windows may improve robustness testing More population may increase stability Walk-forward may be valuable for multi-day holds Longer cooldown may reduce overtrading Historical Backtest Observations: Win rates in testing: Varied, often 45-60% range Reward/risk ratios observed: 2.0-4.0R Signal frequency: Infrequent but potentially higher quality Disclaimer: Swing trading involves overnight and weekend risk. Past backtesting results do not guarantee future performance. DASHBOARD GUIDE - INTERPRETATION EXAMPLES Reading Each Section: HEADER: text 🧬 KERS-AGE EVOLVED 📈 TREND Regime indication: Color coding suggests current classification (Green = Range, Orange = Trend, Purple = Volatile) POPULATION: text Pop: 6/6 Gen: 42 Interpretation: - Population at target size - System at generation 42 - May indicate mature evolution SPECIES (if enabled): text R:2 T:3 V:1 Interpretation: - 2 Range specialists - 3 Trend specialists - 1 Volatility specialist In TREND regime this distribution may be expected WALK-FORWARD (if enabled): text Phase: 🧪 TEST Cycles: 5 Train: 65% Test: 58% Considerations: - Currently in test phase - Completed 5 full cycles - 7% performance gap between train and test - Gap under default 25% overfit threshold ENSEMBLE (if enabled): text Vote: 🟢 LONG Consensus: 72% Interpretation: - Weighted majority voting LONG - 72% agreement level - Exceeds default 60% consensus threshold SELECTED STRATEGY: text ID:23 Trades: 47 Win%: 58% P&L: +8.3R Fitness: 0.62 Information displayed: - Strategy ID 23, Trend specialist - 47 historical simulated trades - 58% historical win rate - +8.3R historical cumulative reward/risk - 0.62 fitness score Note: These are historical simulation metrics SIGNAL QUALITY: text Conf: 78% Grade: B+ Elastic: ████████░░ Wick: ██████░░░░ Momentum: ███████░░░ Pressure: ███████░░░ Information displayed: - 78% confluence score - B+ grade assignment - Elastic component strongest - Visual representation of component strengths LEARNING (if enabled): text Missed: 47 Learned: 28 Interpretation: - System identified 47 moves without signals - 28 pattern adjustments made - Suggests ongoing learning process POSITION: text POS: 🟢 LONG Score: 7.2 Current state: - Simulated long position active - 7.2 confluence score - Monitor for potential exit signal Educational Note: Dashboard displays are for informational and educational purposes. All performance metrics are historical simulations and do not represent actual trading results or future expectations. FREQUENTLY ASKED QUESTIONS - EDUCATIONAL RESPONSES Q: Why aren't signals showing? A: Several factors may affect signal generation: System may still be initializing (check Gen: counter) Confluence score may be below threshold Ensemble consensus (if enabled) may be below requirement Current regime may naturally produce fewer signals Filters may be active (volume, noise reduction) Consider adjusting settings or allowing more time for evolution. Q: The win rate seems low compared to backtesting? A: Consider these factors: First 200 bars typically represent learning period Focus on TEST % rather than TRAIN % for realistic expectations Trend regime historically shows 40-55% win rates in backtesting Different market conditions may affect performance System emphasizes reward/risk ratio alongside win rate Past performance does not guarantee future results Q: Should I take all signals? A: This is a personal decision. Some users may consider: Taking higher grades (A+, A) in any regime Being more selective in trend regimes Requiring higher ensemble consensus Only trading during specific regimes Paper trading extensively before live trading Each user should develop their own signal selection criteria. Q: Signals appear then disappear? A: This may be expected behavior: Default requires 2-bar persistence Designed to filter brief spikes Confirmation delay intended to reduce false signals Wait for persistence requirement to be met This is an intentional feature, not a malfunction. Q: Test % much lower than Train %? A: This may indicate: Overfit detection system functioning Gap exceeding threshold triggers penalty Strategy may be optimizing to in-sample noise System designed to cull such strategies Walk-forward protection working as intended This is a safety feature to reduce overfitting risk. Q: The population keeps culling strategies? A: This is part of normal evolution: Lower-performing strategies removed periodically Higher-performing strategies replicate Population quality theoretically improves over time Total culled count shows selection pressure This is expected evolutionary behavior. Q: Which timeframe works best? A: Backtesting suggests 15min to 4H may be suitable ranges: Lower timeframes may be noisier, may need more filtering Higher timeframes may produce fewer signals Extensive historical testing recommended for chosen asset Each asset may behave differently Consider paper trading across multiple timeframes Personal testing is recommended for your specific use case. Q: Does it work on all asset types? A: Historical testing suggests: Cryptocurrency: Consider longer Base Length (25-30) due to volatility Forex: Standard settings may be appropriate starting point Stocks: Standard settings, possibly smaller population (4-5) Indices: Trend-focused settings may be worth testing Each asset class has unique characteristics. Extensive testing recommended. Q: Can settings be changed after initialization? A: Yes, but considerations: Population will reset Strategies restart evolution Learning progress resets Consider testing new settings on separate chart first May want to compare performance before committing Settings changes restart the evolutionary process. Q: Walk-Forward enabled or disabled? A: Educational perspective: Walk-Forward adds out-of-sample validation May reduce overfitting risk Results may be more conservative Considered best practice in quantitative research Requires more bars for meaningful data Recommended for those concerned about robustness Individual users should assess based on their needs. Q: Ensemble mode or single strategy? A: Trade-offs to consider: Ensemble approach: Requires consensus threshold May have higher consistency Typically fewer signals Multiple perspectives considered Single strategy approach: More signals (varying quality) Faster response to conditions Higher variability More active signal generation Personal preference and risk tolerance should guide this choice. ADVANCED CONSIDERATIONS Evolution Time: Consider allowing 200+ bars for population maturity Regime Awareness: Historical performance varies by regime classification Confluence Range: Testing suggests 70-85% may be informative range Ensemble Levels: 80%+ consensus historically associated with stronger agreement Out-of-Sample Focus: Test performance may be more indicative than train performance Learning Metrics: "Learned" count shows pattern adjustment over time Pressure Levels: >0.4 pressure historically added confirmation DVS Monitoring: >1.5 DVS typically widens bands and affects frequency Species Balance: Healthy distribution might be 2-2-2 or 3-2-1, avoid 6-0-0 Timeframe Testing: Match to personal trading style, test thoroughly Volume Importance: May be more critical for stocks/crypto than forex MTF Utility: Historically more impactful in trending conditions Grade Significance: A+ in trend regime historically rare and potentially significant Risk Parameters: Standard risk management suggests 1-2% per trade maximum Stop Levels: System stops are pre-calculated, widening may affect reward/risk THEORETICAL FOUNDATIONS Genetic Algorithms in Finance: Traditional Optimization Approaches: Grid search: Exhaustive but computationally expensive Gradient descent: Efficient but prone to local optima Random search: Simple but inefficient Genetic Algorithm Characteristics: Explores parameter space through evolutionary process Balances exploration (mutation) and exploitation (selection) Mitigates local optima through population diversity Parallel evaluation via population approach Inspired by biological evolution principles Academic Context: Genetic algorithms are studied in computational finance literature for parameter optimization. Effectiveness varies based on problem characteristics and implementation. Ensemble Methods in Machine Learning: Single Model Limitations: May overfit to specific patterns Can have blind spots in certain conditions May be brittle to distribution shifts Ensemble Theoretical Benefits: Variance reduction through averaging Robustness through diversity Improved generalization potential Widely used (Random Forests, Gradient Boosting, etc.) Academic Context: Ensemble methods are well-studied in machine learning literature. Performance benefits depend on base model diversity and correlation structure. Walk-Forward Analysis: Alternative Approaches: Simple backtest: Risk of overfitting to full dataset Single train/test split: Limited validation Cross-validation: May violate time-series properties Walk-Forward Characteristics: Continuous out-of-sample validation Respects temporal ordering Attempts to detect strategy degradation Used in quantitative trading research Academic Context: Walk-forward analysis is discussed in quantitative finance literature as a robustness check. However, it assumes future regimes will resemble recent test periods, which is not guaranteed. FINAL EDUCATIONAL SUMMARY KERS-AGE demonstrates an adaptive systems approach to technical analysis. Rather than fixed rules, it implements: ✓ Evolutionary Optimization: Parameter adaptation through genetic algorithms ✓ Regime Classification: Attempted market condition categorization ✓ Out-of-Sample Testing: Walk-forward validation methodology ✓ Pattern Recognition: Cluster analysis and learning systems ✓ Ensemble Methodology: Collective decision-making framework ✓ Full Transparency: Comprehensive dashboard and metrics This indicator is an educational tool demonstrating advanced algorithmic concepts. Critical Reminders: The system: ✓ Attempts to identify potential reversal patterns ✓ Adapts parameters to changing conditions ✓ Provides multiple filtering mechanisms ✓ Offers detailed performance metrics Users must understand: ✓ No system guarantees profitable results ✓ Past performance does not predict future results ✓ Extensive testing and validation recommended ✓ Risk management is user's responsibility ✓ Market conditions can change unpredictably ✓ This is educational software, not financial advice Success in trading requires: Proper education, risk management, discipline, realistic expectations, and personal responsibility for all trading decisions. For Educational Use 🧬 KERS-AGE Development Team ⚠️ FINAL DISCLAIMER This indicator and documentation are provided strictly for educational and informational purposes. NOT FINANCIAL ADVICE: Nothing in this guide constitutes financial advice, investment advice, trading advice, or any recommendation to buy, sell, or hold any security or to engage in any trading strategy. NO GUARANTEES: No representation is made that any account will or is likely to achieve profits or losses similar to those shown in backtests, examples, or historical data. Past performance is not indicative of future results. SUBSTANTIAL RISK: Trading stocks, forex, futures, options, and cryptocurrencies involves substantial risk of loss and is not suitable for every investor. The high degree of leverage can work against you as well as for you. YOUR RESPONSIBILITY: You are solely responsible for your own investment and trading decisions. You should conduct your own research, perform your own analysis, and consult with qualified financial advisors before making any trading decisions. NO LIABILITY: The developers, contributors, and distributors of this indicator disclaim all liability for any losses or damages, direct or indirect, that may result from use of this indicator or reliance on any information provided. PAPER TRADE FIRST: Users are strongly encouraged to thoroughly test this indicator in a paper trading environment before risking any real capital. By using this indicator, you acknowledge that you have read this disclaimer, understand the risks involved in trading, and agree that you are solely responsible for your own trading decisions and their outcomes. Educational Software Only | Trade at Your Own Risk | Not Financial Advice Taking you to school. — Dskyz , Trade with insight. Trade with anticipation.
Pine Script® indicator
by DskyzInvestments
RunRox - Pairs Screener📊 Pairs Screener is part of our premium suite for pair trading. This indicator is designed to scan and rank the most profitable and optimal pairs for the Pairs Strategy. The screener can backtest multiple metrics on deep historical data and display results for many pairs against one base asset at the same time. This allows you to quickly detect market inefficiencies and select the most promising pairs for live trading. HOW DOES THIS STRATEGY WORK⁉️ The core idea of the strategy is described in detail in our main indicator Pairs Strategy from the same product line. There you can find a full explanation of the concept, the math behind pair trading, and the internal logic of the engine. The Pairs Screener is built on top of the same core technology as the main indicator and uses the same internal logic and calculations. It is designed as a key companion tool to the main strategy: it helps you find tradeable pairs, evaluate current deviations, sort and filter lists of candidates, and much more. All of these features will be described in this post. ✅ KEY FEATURES More than 400+ assets available for scanning Forex assets Crypto assets Lower Timeframe Backtester Strategy support Invert signals mode Hedge Coefficient (position size balancing between both legs) 6 hedge modes Stop Loss support Take Profit support Whitelist with your own custom asset list Blacklist to exclude unwanted assets Custom filters 12 tracking metrics for pair evaluation Customizable alerts And many other tools for fine-tuning your search The screener runs backtests simultaneously across a large number of assets and calculates metrics automatically. This helps you very quickly find pairs with strong structural relationships or current inefficiencies that can be used as the basis for your pair trading strategies. ⚙️ MAIN SETTINGS The first section controls the core parameters of the screener: Score, correlation, asset groups for scanning, and other base settings. All major crypto and forex symbols are embedded directly into the screener. Since there are more than 400 assets, it is technically impossible to analyze everything at once, so we grouped them into batches of 40 assets per group. The workflow is simple: Open the chart of the asset you want to use as the base ticker. In the screener settings choose the market (Crypto or Forex). Select a Group (for example, Group 1) and the indicator will scan all assets inside that group against your base ticker. Then you switch to Group 2, Group 3, etc., and repeat the scan. Embedded universe: 400+ assets total 350+ Crypto – split into 10 groups 70+ Forex – split into 3 groups Below is a description of each setting. 🔸 Exclude Dates Allows you to specify a period that should be excluded from analysis. Useful for removing abnormal spikes, news events, or any non-typical segments that distort the statistics for your pairs. 🔸 Market Defines which universe will be used to build pairs with the current main asset: Crypto – 350+ crypto symbols Forex – 70+ FX symbols Whitelist – your own custom list of assets 🔸 Group Selects the asset group to scan. As mentioned above, assets are split into groups of about 40 instruments: 350+ Crypto → 10 groups 70+ Forex → 3 groups The screener will calculate all metrics only for the group you select. 🔸 Lower Timeframe This option enables deep history analysis. Each TradingView plan has a limit on the number of visible bars (for example, 5,000 bars on the basic plan). In standard mode you would only get statistics for the last 5,000 bars of your current timeframe. If you want a deeper backtest on a lower timeframe, you can do the following: Suppose your target timeframe for analysis is 5 minutes. Switch your chart to a 30-minute timeframe. Enable Lower Timeframe in the indicator. Select 5 minutes as the lower timeframe inside the screener. In this mode the screener can reconstruct and analyze up to 99,000 bars of data for your assets. This allows you to evaluate pairs on a much deeper history and see whether the results are stable over a larger sample. 🔸 Method Here you choose the deviation model: preferred Z-Score or S-Score for your analysis, plus you can enable Invert to search for negatively correlated pairs and calculate their profit correctly. 🔸 Period This is the lookback period for Z/S Score. It defines how many bars are used to calculate the deviation metric for each pair. 🔸 Correlation Period This is the number of bars used to calculate correlation between the base asset and each candidate in the group. The resulting correlation value is also displayed in the results table. 🔀 HEDGE COEFFICIENT The next block of settings is related to the hedge coefficient. This defines how much margin is allocated to each leg of the pair. The classic approach in pair trading is to split the position equally between both assets. For example, if you allocate 100 USD to a trade , the standard model would open 50 USD long on one asset and 50 USD short on the other. This works well for pairs with similar volatility , such as BTCUSDT / ETHUSDT However, if you use a pair like BTCUSDT / DOGEUSDT , the volatility of these assets is very different. They can still be correlated, but their amplitude is not the same. While Bitcoin might move 2% , Dogecoin can move 10% over the same period. Because of that, for pairs with strongly different volatility, we can use a hedge coefficient and, for example, enter with 30 USD on one leg and 70 USD on the other, taking the volatility difference into account. This is the main idea behind the Hedge Coefficient section and its primary use. The indicator includes 6 methods of calculating the coefficient: Cumulative RMA Beta OLS Beta TLS Beta EMA RMA Range RMA Delta Each method uses a different formula to compute the hedge coefficient and to size the position based on different metrics of the assets. We leave it to the trader to decide which algorithm works best for their specific pair and style. Below are the settings inside this section: 🔹 Method When Auto Hedge is enabled, you can select which method to use from the list above. The chosen method will automatically calculate the hedge coefficient between the two legs. 🔹 Hedge Coefficient This is the manual hedge ratio per trade when Auto Hedge is disabled. By default it is set to 1, which means the position is opened 50/50 between the two assets. 🔹 Min Allowed Hedge Coef. This is the minimum allowed hedge coefficient. By default it is 0.2, which means the model will not go below a 20% / 80% split between the legs. 🔹 MA Length For methods that use moving averages (for example Beta EMA), this parameter sets the period used to calculate the hedge coefficient. 💰 STRATEGY SETTINGS This section defines the base backtesting settings for all assets in the screener. Here you configure entries, exits, Stop Loss, and other parameters used to find the most optimal pairs for your strategy. 🔸 Commission % In this field you set your broker’s fee percentage per trade. The indicator automatically calculates the correct commission for each leg of every trade. You only need to input the real commission rate that your broker charges for volume. No additional manual calculations are required. 🔸 Qty $ The margin amount used for backtesting across all assets in the screener. This margin is split between both legs of the pair either equally or according to the selected hedge coefficient. 🔸 Entry The Z/S Score deviation level at which the backtest opens a trade for each pair. 🔸 Exit The Z/S Score level at which the backtest closes trades for the tested assets. 🔸 Stop Loss PnL threshold at which a trade is force-closed during the historical test. 🔸 Cooldown Number of bars the strategy will wait after a Stop Loss before opening the next trade. This block gives you flexible control over how your strategy is tested on 400+ assets, helping you standardize the rules and compare pairs under the exact same conditions. 🗒️ WHITELIST In this section you can define your own custom list of assets for monitoring and backtesting. This is useful if you want to work with symbols that are not included in the built-in lists, such as exotic crypto from smaller exchanges, specific stocks, or any custom universe 🔹 Exchange Prefix Enter the exchange prefix used for your tickers. Example: BINANCE, OANDA, etc. 🔹 Ticker Postfix Enable this option if the tickers require a postfix. Example 1: .P for Binance Futures perpetual contracts. Example 2: USDT if you only provide the base asset in the ticker list. 🔹 Ticker List Enter a comma-separated list of tickers to analyze. Example 1: BTCUSDT, ETHUSDT, BNBUSDT (when the exchange prefix is set). Example 2: BTC, ETH, BNB (when using postfix USDT). Example 3: BINANCE:BTCUSDT.P, OANDA:EURUSD (when different exchanges are used and the prefix option is disabled). This gives you full flexibility to build a screener universe that matches exactly the assets you trade. ⛔ BLACKLIST In this section you can enable a blacklist of unwanted assets that should be skipped during analysis. Enter a comma-separated list of tickers to exclude from the screener: Example 1: BTCUSDT, ETHUSDT Example 2: BTC, ETH (all tickers that contain these symbols will be excluded) This helps you quickly remove illiquid, noisy, or unwanted instruments from the results without changing your main groups or whitelist. 📈 DASHBOARD This section controls the results dashboard: table position, style, and sorting logic. Here is what you can configure: Result Table – position of the results table on the chart. Background / Text – colors and opacity for the table background and text. Table Size – overall size of the results table (from 0 to 30). Show Results – how many rows (pairs) to display in the table. Sort by (stat) – which metric to use for sorting the results. Available options: Profit Factor, Profit, Winrate, Correlation, Score. This lets you quickly focus on the most interesting pairs according to the exact metric that matters most for your strategy. 📎 FILTER SETTINGS This section lets you filter the results table by metric values. For example, you can show only pairs with a minimum correlation of 0.8 to focus on more stable relationships. 🔸 Min Correlation Minimum allowed correlation between the two assets over the selected lookback period. 🔸 Min Score Minimum absolute Score (Z-Score or S-Score) required to include a pair in the results. For example, 2.0 means only pairs with Score >= 2.0 or <= -2.0 will be displayed. 🔸 Min Winrate Minimum win rate percentage for a pair to be included in the table. 🔸 Min Profit Factor Minimum profit factor required for a pair to stay in the results. These filters help you quickly narrow the list down to pairs that meet your quality criteria and match your risk profile. 📌 COLUMN SELECTION This section lets you fully customize which metrics are displayed in the results table. You can enable or hide any column to focus only on the data you need to identify the best pairs for trading. The screener allows you to show up to 12 metrics at the same time, which gives a detailed view of pair quality. Available columns: 🔹 Exchange Prefix Show the exchange prefix in the ticker. 🔹 Correlation Correlation between the two assets’ prices over the lookback period. 🔹 Score Current Score value (Z-Score or S-Score). On lower timeframe research, Score is not displayed. 🔹 Spread Shows spread as % change since entry. Positive value = profit on the main position. 🔹 Unrealized PnL Shows unrealized PnL as a $ value based on current prices. 🔹 Profit Total profit from all trades: Gross Profit − Gross Loss. 🔹 Winrate Percentage of profitable trades out of all executed trades. 🔹 Profit Factor Gross Profit / Gross Loss. 🔹 Trades Total number of trades. 🔹 Max Drawdown Maximum observed loss from peak to trough before a new peak is made. 🔹 Max Loss Largest loss recorded on a single trade. 🔹 Long/Short Profit Separate profit/loss for long trades and short trades. 🔹 Avg. Trade Time Average duration of trades. All these metrics are designed to help you quickly identify the strongest pairs for your strategy. You can change colors, opacity, and hide any columns that are not relevant to your workflow. 🔔 ALERT The alert system in this screener works in a specific way. Alerts are tied directly to the filters you set in the Filter Settings section: Minimum Correlation Minimum Score Minimum Winrate Minimum Profit Factor You can configure alerts to trigger when a new pair appears that matches all your filter conditions. 💡 Example You set: Minimum Score = 3 Then you create an alert based on the screener. When any pair reaches a Score greater than +3 or less than −3, you will receive a notification. This is how alerts work in this screener. The idea is to deliver the most relevant information about the current market situation without forcing you to watch the screener all the time. Supported placeholders for alert messages: {{ticker_1}} – main ticker (the one on the chart). {{ticker_2}} – the paired ticker listed in the table. {{corr}} – correlation value. {{score}} – Score value (Z-Score or S-Score). {{time}} – bar open time (UTC). {{timenow}} – alert trigger time (UTC). You can use these placeholders to build alert text or JSON payloads in any format required by your tools. The screener is designed to significantly enhance your pair trading workflow: it helps you quickly identify working pairs and current market inefficiencies, and with the alert system you can react to opportunities without constantly sitting in front of the screen. Always remember that past performance does not guarantee future results. Use the screener data within a risk-controlled trading system and adjust position sizing according to your own risk management rules.
Pine Script® indicator
by RunRox
RunRox - Pairs Strategy🧬 Pairs Strategy is a new indicator by RunRox included in our premium subscription. It is a specialized tool for trading pairs, built around working with two correlated instruments at the same time. The indicator is designed specifically for pair trading logic: it helps track the relationship between two assets, identify statistical deviations, and generate signals for opening and managing long/short combinations on both legs of the pair. Below in this description I will go through the core functions of the indicator and the main concepts behind the strategy so you can clearly understand how to apply it in your trading. 📌 CONCEPT The core idea of pair trading is to find and trade correlated instruments that usually move in a similar way. When these two assets temporarily diverge from each other, a trading opportunity appears. In such moments, the relatively overvalued asset is sold (short leg), and the relatively undervalued asset is bought (long leg). When the spread between them narrows and both instruments revert back toward their typical relationship (mean), the position is closed and the trader captures the profit from this convergence. In practice, one leg of the pair can end up in a loss while the other generates a larger profit. Due to the difference in performance between the two assets, the combined result of the pair trade can still be positive. ✅ KEY FEATURES: 2 deviation types (Z-Score and S-Score) Invert signals mode Hedge Coefficient (position size balancing between both legs) 6 hedge modes Entries based on Score or RSI Extra entries based on Score or Spread Stop Loss Take Profit RSI Filter RSI Pivot Mode Built-in Backtester Strategy Lower Timeframe Backtester Strategy Live trade panel for current position Equity curve chart 21 performance metrics in the backtester 2 alert types *And many more fine-tuning options for pair trading 🔗 SCORE Score is the core deviation metric between the two assets in the pair. For example, if you are trading ETHUSDT/BTCUSDT, the indicator analyzes the relationship ETH/BTC, and when one leg temporarily diverges from the other, this difference is reflected in the Score value. In other words, Score shows how much the current spread between the two instruments deviates from its typical state and is used as the main signal source for pair entries and exits. In the screenshot above you can see how Score looks in our indicator. Depending on how large the difference is between the two assets, the Score value can move in a range from −N to +N When Score is in the −N zone, this is a 🟢 long zone for the first asset and a short zone for the second. Using the ETH/BTC example: when Score is deeply negative, you open a long on ETH and a short on BTC at the same time, then close both legs when Score returns back to the 0 zone (balance between the two assets). When Score is in the +N zone, this is a 🔴 short zone for the first asset and a long zone for the second. In the same ETH/BTC example: when Score is strongly positive, you short ETH and long BTC, and again close both positions when Score comes back to the neutral 0 zone. ☯️ Z/S SCORE Inside the indicator we added two different formulas for calculating the spread between the two legs of the pair: Z-Score and S-Score. These approaches measure deviation in different ways and can produce slightly different signals depending on the chosen pair and its behavior. This allows you to switch between Z-Score and S-Score and choose the method that gives more stable and cleaner signals for your specific instruments. As you can see in the screenshot above, we used the same pair but applied different Score types to measure the spread and deviation from the norm. 🟣 Z-Score – generated 9 entry signals . It reacts to price fluctuations more smoothly and usually stays within a range of approximately −8 to +8 . 🟠 S-Score – generated 5 entry signals . It reacts to price changes more aggressively and produces wider deviations, often reaching −15 to +15 . This gives traders the choice between a more sensitive but smoother model (Z-Score) and a more selective, stronger-deviation model (S-Score) ⁉️ HOW DOES THE STRATEGY WORK Here is a basic example of how you can trade this pair trading strategy using our indicator and its signals. In the classic approach the trade consists of one initial entry and several scale-ins (averaging) if the spread continues to move against the position. The first entry is opened when Score reaches a standard deviation of −2 or +2. If price does not revert to the mean and moves further against the position so that Score expands to −3 or +3, the strategy performs the first scale-in. If Score extends to −4 or +4, a second scale-in is added. If the spread grows even more and Score reaches −5 or +5, a third scale-in is executed. In our indicator the number of averaging steps can be up to 4 scale-ins . After that the position waits until Score returns back to the 0 level , where the whole pair position is closed. This is the standard model of classical pair trading. However there are many variations: using Stop Loss and Take Profit, exiting earlier or later than the 0 zone, scaling in not by Score but by Spread, since Score is not linear while Spread is linear, entering when RSI on both tickers shows opposite extremes, for example RSI 20 on one asset and RSI 80 on the other, and so on. The number of possible trading styles for this strategy is very large. We designed the indicator to cover as many of these variations as possible and added flexible tools so you can build your own pair trading logic on top of it. Below is an example of a classic pair trade with two entries: one main entry and one extra entry (scale-in) . The pair SUIUSDT / PENGUUSDT shows a high correlation, and on one of the trades the sequence looked like this: A −2 Score deviation occurred into the long zone and triggered the Main Entry . 🔹 Main Entry Long SUIUSDT – Margin: 5,000 USD, Entry price: 1.5708 Short PENGUUSDT – Margin: 5,000 USD, Entry price: 0.011793 Price then moved further against the position, Score went deeper into deviation, and the strategy added one extra entry. 🔸 Extra Entry Long SUIUSDT – Margin: 5,000 USD, Entry price: 1.5938 Short PENGUUSDT – Margin: 5,000 USD, Entry price: 0.012173 The trade was closed when Score reverted back toward the 0 zone (mean reversion of the spread): ❎ Exit SUIUSDT P&L: −403.34 USD, Exit price: 1.5184 PENGUUSDT P&L: +743.73 USD, Exit price: 0.011089 ✅ Total P&L: +340.39 USD With a total margin of 10,000 USD used per side (20,000 USD combined), this trade yielded around +1.7% on the deployed margin. On different assets the size and speed of the spread movement will vary, but the principle remains the same. This is just one example to illustrate how the strategy works in practice using simplified theoretical balances. ⚙️ MAIN SETTINGS After explaining how the strategy works, we can move to the indicator settings and their logic. The first block is Main Settings, which controls how the pair is built, how the spread is calculated, and how the backtest is performed. The core idea of the indicator is to backtest historical data, generate entry signals, show open-position parameters, and provide all necessary metrics for both discretionary and algorithmic trading. This is a complete framework for analyzing a pair of assets and building a trading system around them. Below I will go through the main parameters one by one. 🔹 Exclude Dates Allows you to exclude abnormal periods in the pair’s history to remove outlier trades from the backtest. This is useful when the market experienced extreme news events, listing spikes, or other non-typical situations that distort statistics. 🔹 Pair Here you select the second asset for your pair. For example, if your main chart is BTCUSDT, in this field you choose a correlated asset such as ETHUSDT, and the working pair becomes BTCUSDT / ETHUSDT. The indicator then calculates spread, Score, and all related metrics based on this asset combination. 🔹 Lower Timeframe This is a special mode for backtesting on a lower timeframe while using a higher timeframe chart to extend the history limit. For example, if your TradingView plan provides only 5,000 bars of history on the current timeframe, you can switch your chart to a higher timeframe and select a lower timeframe in this setting. The indicator will then reconstruct the pair logic using up to 99,000 bars of lower timeframe data for backtesting. This allows you to test the pair on a much longer historical period and find more stable combinations of assets. 🔹 Method Here you choose which deviation model you want to use: Z-Score or S-Score. Both methods calculate spread deviation but use different formulas, which can give different signal behavior depending on the pair. Examples of these two methods are shown earlier in this description. 🔹 Period This parameter defines how many bars are used to calculate the average deviation for the pair. If you set Period = 300, the indicator looks back 300 bars and calculates the typical spread deviation over that window. For example, if the average deviation over 300 bars is around 1%, then a move to 2% or more will push Z/S Score closer to its boundary levels, since such a deviation is considered abnormal for that lookback period. A larger Period means that only bigger deviations will be treated as anomalies. A smaller Period makes the model more sensitive and treats smaller deviations as anomalies. This allows you to tune how aggressive or conservative your pair trading signals should be. 🔹 Invert This setting is used for negatively correlated pairs. Some instruments have a positive correlation in the range from +0.8 to +1.0 (strong positive correlation), while others show a negative correlation from −0.8 to −1.0, meaning they usually move in opposite directions. A classic example is the pair EURUSD and DXY. As shown in the screenshot above, these instruments often have strong negative correlation due to macro factors and typically move in opposite directions: when EURUSD is rising, DXY is falling, and vice versa. Such pairs can also be traded with our indicator. To do this, we use the Invert option, which effectively flips one of the assets (as shown in the screenshot below). After inversion, both instruments are brought to a “same-direction” behavior from the model’s point of view. From there, you trade the pair in the same way as a positively correlated one: you open both legs in the same direction (both long or both short) depending on the spread and Score, and then wait for the spread between the inverted pair to converge back toward its mean. 🔀 HEDGE COEFFICIENT The next block of settings is related to the hedge coefficient. This defines how much margin is allocated to each leg of the pair. The classic approach in pair trading is to split the position equally between both assets. For example, if you allocate 100 USD to a trade , the standard model would open 50 USD long on one asset and 50 USD short on the other. This works well for pairs with similar volatility , such as BTCUSDT / ETHUSDT However, if you use a pair like BTCUSDT / DOGEUSDT , the volatility of these assets is very different. They can still be correlated, but their amplitude is not the same. While Bitcoin might move 2% , Dogecoin can move 10% over the same period. Because of that, for pairs with strongly different volatility, we can use a hedge coefficient and, for example, enter with 30 USD on one leg and 70 USD on the other, taking the volatility difference into account. This is the main idea behind the Hedge Coefficient section and its primary use. The indicator includes 6 methods of calculating the coefficient: Cumulative RMA Beta OLS Beta TLS Beta EMA RMA Range RMA Delta Each method uses a different formula to compute the hedge coefficient and to size the position based on different metrics of the assets. We leave it to the trader to decide which algorithm works best for their specific pair and style. Below are the settings inside this section: 🔹 Method When Auto Hedge is enabled, you can select which method to use from the list above. The chosen method will automatically calculate the hedge coefficient between the two legs. 🔹 Hedge Coefficient This is the manual hedge ratio per trade when Auto Hedge is disabled. By default it is set to 1, which means the position is opened 50/50 between the two assets. 🔹 Min Allowed Hedge Coef. This is the minimum allowed hedge coefficient. By default it is 0.2, which means the model will not go below a 20% / 80% split between the legs. 🔹 MA Length For methods that use moving averages (for example Beta EMA), this parameter sets the period used to calculate the hedge coefficient. 🛠️ STRATEGY SETTINGS The next important block is Strategy Settings . Here you define the core parameters used for backtesting: trading commission, position size, entry / exit logic, Stop Loss, Take Profit, and other rules that describe how you want the strategy to operate. Below are all parameters with a detailed explanation. 🔸 Commission % In this field you set your broker’s fee percentage per trade . The indicator automatically calculates the correct commission for each leg of every trade. You only need to input the real commission rate that your broker charges for volume. No additional manual calculations are required. 🔸 Main Entry Mode There are two options for the main entry: Score - This is the primary entry method based on Z/S Score. When Score reaches the deviation level defined in the settings below, the strategy opens the first position. For example, if you set “Entry at 2 deviations”, the trade will be opened when Score hits ±2. RSI Only - Alternative entry method based on RSI divergence between the two assets. The exact RSI levels are defined in the RSI settings section below. For example, if you set the entry threshold at 30, then when one asset has RSI below 30 and the second one has RSI above 70, the first entry will be triggered. 🔸 Extra Entries Mode This defines how scale-ins (averaging) are executed. There are two modes: Score - Works the same way as the main entry, but for additional entries. For example, the main entry can be at 2 deviations, the first scale-in at 3, the second at 4, etc. Spread - This mode uses the Spread (difference between the two assets) starting from the main entry moment. As the spread continues to widen, the strategy can add extra entries based on spread growth rather than Score. Since Score is a non-linear metric and Spread is linear, in some configurations averaging by Spread can produce better results than averaging by Score. This is pair- and strategy-dependent. 🔸 Entry parameters Deviation / Spread threshold Entry size Main Entry – first field (deviation / spread), second field (position size) Entry 2 – first field (deviation / spread), second field (position size) Entry 3 – first field (deviation / spread), second field (position size) Entry 4 – first field (deviation / spread), second field (position size) This allows you to define up to four scaling steps with different triggers and different sizing. 🔸 Exit Level This parameter defines at what Score level you want to exit the trade. By default it is 0, which means the backtester closes the position when Score returns to the neutral (0) zone. You can also use positive or negative values. Example: Assume your main entry is configured at a 3 deviation. You can exit at the 0 level, or you can set Exit Level = 2. If your initial entry was at −3, the position will be closed when Score reaches +2. If your initial entry was at +3, the position will be closed when Score reaches −2. This approach can increase the profit per trade due to a larger captured spread, but it may also increase the holding time of the position. 🔸 Stop Loss Here you define the maximum loss per trade in PnL units. If a trade reaches the negative PnL value specified in this field and the Stop Loss option is enabled, the indicator will close the trade at a loss. The Cooldown parameter sets a pause after a losing trade: the strategy will wait a specified number of bars before opening the next trade. 🔸 Take Profit Works similar to Stop Loss but for profit targets. You set the desired PnL value you want to reach. The trade will be closed when either the Take Profit target is hit or when Score reaches the exit level defined in the settings, whichever occurs first (depending on your configuration). 🔸 Show Qty in currency When enabled, trade size is displayed in currency (USD) instead of token quantity. This is useful for quickly understanding position size in monetary terms. You will see this in the Current Trade panel, which is described later. 🔸 Size Rounding Controls how many decimal places are used when rounding position size (from 0 to 10 digits after the decimal). This is also used for the Current Trade panel so you can adjust how detailed or compact the size display should be. 📊 RSI FILTERS This section is used for additional trade filtering. RSI can be used in two ways: as a primary entry signal, or as an extra filter for entries based on Z/S Score. If in the Strategy Settings the Main Entry Mode is set to RSI, then RSI becomes the main trigger for opening a position. In this case a trade is opened when the RSI of the two assets reaches opposite zones. Example: If the threshold is set to 30, then: when one asset has RSI below 30, and the second asset has RSI above 70 (100 − 30), the strategy opens the first entry. All extra entries after that will be executed either by Spread or by Z/S Score, depending on your Extra Entries Mode. Below are the parameters in this block: RSI Length – standard RSI period setting. RSI Pivot Mode – when enabled, RSI is used as an additional filter together with Z/S Score. The indicator looks for a reversal pattern on RSI (pivot behavior). If RSI forms a reversal structure, the trade is allowed to open. If not, the signal is skipped until a proper RSI pivot is formed. Entry RSI Filter – here you define the RSI thresholds used for RSI-based entries. These are the same boundary levels described in the example above. Overall, this section helps filter out lower-quality trades using additional RSI conditions or lets you build RSI-only entry logic based on extreme levels. 🎨 MAIN CHART STYLING This section controls the visual appearance of trades on the main chart. You can customize how the second asset line is drawn, as well as the icons for entries, scale-ins, and exits, including their size and style. ▫️ Price Line This is the line that shows the price of the second asset and the relative difference between the two instruments. You can adjust the line thickness and color to make it more readable on your chart. ▫️ Adjust Price Line by Hedge Coefficient When this option is enabled, the second asset’s line is normalized by the hedge coefficient. If you turn it off, the hedge coefficient will not be applied to the second asset’s line, and it will be displayed in raw form. ▫️ Entry Label Here you can customize how the entry markers look: choose the color, icon style, and size of the label that marks each trade entry and scale-in on the chart. ▫️ Exit Label Similarly, you can define the color, icon style, and size of the label used for exits. This helps visually separate entries and exits and makes it easier to read the trade history directly from the chart. 🎯 INDICATOR PANEL This section controls the settings of the indicator panel, which works like an oscillator and allows you to visualize multiple metrics in one place. You can flexibly enable, style, and scale each parameter. 🔹 Score Displays the main deviation metric between the two assets. You can customize the color and line thickness of the Score plot. 🔹 Spread Shows the spread between the two assets. It starts calculating from the moment the trade is opened. You can adjust its color and thickness for better visibility. 🔹 Total Profit Displays the cumulative profit for this pair and strategy as a line that grows (or falls) over time. Color, opacity, and line thickness can be customized. 🔹 Unrealized PNL Once a trade is opened, this line shows the current PnL of the active position. It also lets you see historical drawdowns on the pair. Color and thickness can be adjusted. 🔹 Released PNL Shows the realized PnL of each closed trade as bars. Useful for quickly evaluating the result of every individual trade in the backtest. 🔹 Correlation Plots the correlation coefficient between the two assets as a graph, so you can visually track how stable or unstable the relationship between them is over time. 🔹 Hedge Coefficient Shows the hedge coefficient as a line, which helps understand how the model is rebalancing exposure between the two legs depending on their behavior. For each metric there is also a 📎 Stretch option. Stretch allows you to compress or expand the scale of a specific line to visually align metrics with different ranges on the same panel and make the chart easier to read. 📈 PROFIT CHART Since TradingView does not natively support proper backtesting for pair trading, this indicator includes its own profit curve for the pair. You can visually see how the strategy performed over historical data: whether there were deep drawdowns, abnormal profit spikes, or stable equity growth over time. This makes it much easier to evaluate the quality of the pair and the strategy on history. In the settings of this section you can flexibly customize how the profit chart is displayed: labels, position of the panel, padding, and other visual details. Everything depends on your personal preferences, so we give full control over styling: you can adjust the look of the profit chart to match your layout or completely hide it from the chart if you do not need it. 📌 CURRENT TRADE This section controls the current trade table. When there is an active trade on the chart, the panel displays all key information for the open position: direction for each ticker (long or short), required position size for each leg, entry price for both assets, and real-time PnL for each leg separately, so you always have a clear view of the current situation. The main thing you can do with this table is customize its appearance: you can change the size, position on the chart, background and text colors, as well as separate coloring for positive / negative PnL and different colors for long and short positions. 📅 BACKTEST RESULTS The next key block is Backtest Results. This results table with detailed metrics gives you an extended view of how the pair and strategy perform: win rate, profit factor, long/short breakdown, and more than 20 additional stats that help you evaluate the potential of your setup. ⚠️ First of all, it is important to note ⚠️ past performance does not guarantee future results. Every trader must keep this in mind and factor these risks into their strategy. The table shows metrics in three cuts: All Entries Main Entries Extra Entries (scale-ins) Core metrics: Profit – total profit for each entry type. Winrate – win rate for this pair. Profit Factor – ratio of gross profit to gross loss for the strategy. Trades – number of trades in the backtest. Wins – number of winning trades. Losses – number of losing trades. Long Profit – profit generated by long positions. Short Profit – profit generated by short positions. Longs – total number of long trades. Shorts – total number of short trades. Avg. Time – average time spent in a trade. Additional metrics for a deeper evaluation of the pair: Correlation – current correlation between the two assets in the pair. Bars Processed – number of bars used in the analysis. Max Drawdown – maximum historical drawdown of the strategy. Biggest Loss – the largest single losing trade in the backtest. Recommended Hedge – recommended hedge coefficient based on historical behavior. Max Spread – maximum positive spread observed in history. Min Spread – maximum negative spread observed in history. Avg. Max Spread – average of positive extreme spread values (above 0). Avg. Min Spread – average of negative extreme spread values (below 0). Avg Positive Spread – average positive spread across all trades (only values above 0). Avg Negative Spread – average negative spread across all trades (only values below 0). Current Spread – current spread between the assets when a trade is open. These metrics together allow you to quickly assess how stable the pair is, how the risk/return profile looks, and whether the strategy parameters are suitable for live trading. You can fully customize this results table to fit your workflow: hide metrics you don’t need, change colors, opacity, and other visual styles, and reorder the focus of the stats according to your trading style. This way the backtest block can show only the metrics that matter to you most and remain clean and readable during analysis. 📣 ALERTS The next section is dedicated to alerts. Here you can configure all signals you need, both for manual trading and for full automation of this pair trading strategy. This block is designed to cover most practical use cases. The indicator supports two alert modes: Single Alert – one universal custom alert for all events. Two Alerts – separate alerts for each ticker so you can receive different messages per asset. Available alert events: Main Entry – when the main entry is triggered. Entry 2 – when the first scale-in is executed. Entry 3 – when the second scale-in is executed. Entry 4 – when the third scale-in is executed. Exit Alert – when the position is closed. StopLoss Alert – when Stop Loss is hit. TakeProfit Alert – when Take Profit is hit. All alerts are fully customizable and support a set of placeholders for building structured messages or JSON payloads. 🔹1 Alert Type List of supported placeholders: {{event}} – trigger name ('Entry 1', 'Exit'). {{dir_1}} – 'Long' or 'Short' for the main ticker. {{dir_2}} – 'Long' or 'Short' for the other ticker. {{action_1}} – 'Buy', 'Sell' or 'Close' for the main ticker. {{action_2}} – 'Buy', 'Sell' or 'Close' for the other ticker. {{price_1}} – price for the main ticker. {{price_2}} – price for the other ticker. {{qty_1}} – order size for the main ticker. {{qty_2}} – order size for the other ticker. {{ticker_1}} – main ticker (e.g. 'BTCUSD'). {{ticker_2}} – other ticker (e.g. 'ETHUSD'). {{time}} – candle open time in UTC. {{timenow}} – signal time in UTC. 🔹2 Alert Type List of supported placeholders: {{event}} – trigger name ('Entry 1', 'Exit', 'SL', 'TP'). {{action}} – 'Buy', 'Sell' or 'Close'. {{price}} – order price. {{qty}} – order size. {{ticker}} – ticker (e.g. 'BTCUSD'). {{time}} – candle open time in UTC. {{timenow}} – signal time in UTC. You can use these placeholders to build any JSON structure or custom alert text required by your trading bot, exchange API, or automation service. In this post I’ve explained how the indicator works, the core concept behind this pair trading strategy, and shown practical examples of trades together with a detailed breakdown of each unique feature inside the tool. We have invested a lot of work into building this indicator and we truly hope it will help you trade pair strategies more efficiently and more profitably by giving you structured, strategy-specific information that is difficult to obtain in any other way. ⚠️ Please also remember that past performance does not guarantee future results. Always evaluate the risks, the robustness of your setup, and your own risk tolerance before entering any position, and make independent, well-considered decisions when using this or any other strategy.
Pine Script® indicator
by RunRox
MACD + Divergence Indicator [Dynamic Filter]Title: MACD + Divergence Description: This is an enhanced momentum analysis suite based on the classic Moving Average Convergence Divergence (MACD). It addresses the common weakness of the standard MACD—false signals during low-volatility consolidation—by integrating a Dynamic Volatility Filter and a Multi-Timeframe (MTF) Dashboard. The Problem It Solves: Standard MACD indicators often generate "whipsaw" crossovers when the market is ranging (moving sideways). Traders often struggle to identify these consolidation zones until it is too late. This script solves this by calculating a dynamic "Consolidation Zone" based on Standard Deviation, visually warning traders when momentum is too weak to be reliable. Key Features: 1. Dynamic Consolidation Filter (The Grey Zone) The script calculates Upper and Lower bands around the MACD line using Standard Deviation (Volatility). Grey Fill: When the MACD line is inside the grey bands, the market is in a "Squeeze" or low-volatility consolidation. Crossovers in this zone are often lower probability. Breakout: When the MACD line exits the bands, it indicates a volatility expansion and a potentially stronger trend. 2. Automated Divergence Detection Automatically scans for both Regular (Reversal) and Hidden (Continuation) divergences between Price and Momentum. Bullish: Marked with Green lines/labels. Bearish: Marked with Red lines/labels. Customization: You can choose to calculate divergence based on the MACD Line or the Histogram via settings. 3. Multi-Timeframe (MTF) Dashboard A customizable information table (optional) displays the MACD state across 4 different timeframes (e.g., 15m, 1H, 4H, Daily). It checks for Trend Alignment (e.g., are all timeframes Bullish?) to help you trade in the direction of the higher timeframes. 4. Enhanced Visuals 4-Color Histogram: Visualizes momentum growing (bright) vs. momentum fading (pale) for both bullish and bearish phases. Line Highlights: The MACD and Signal lines are clearly distinct, with configurable smoothing options (EMA/SMA). Settings Guide: Consolidation Filter: Increase the Dynamic Filter Multiplier (Default: 0.5) to widen the grey zone if you want to filter out more noise. Oscillator Source: Switch between "MACD Line" or "Histogram" for divergence detection depending on your strategy. Table: You can toggle the dashboard on/off or change its position to fit your chart layout. Credits: Base MACD logic derived from standard technical analysis concepts. Dynamic filtering logic adapted from volatility band theories.
Pine Script® indicator
by tonngnh3
RSI Distribution [Kodexius]RSI Distribution is a statistics driven visualization companion for the classic RSI oscillator. In addition to plotting RSI itself, it continuously builds a rolling sample of recent RSI values and projects their distribution as a forward drawn histogram, so you can see where RSI has spent most of its time over the selected lookback window. The indicator is designed to add context to oscillator readings. Instead of only treating RSI as a single point estimate that is either “high” or “low”, you can evaluate the current RSI level relative to its own recent history. This makes it easier to recognize when the market is operating inside a familiar regime, and when RSI is pushing into rarer tail conditions that tend to appear during momentum bursts, exhaustion, or volatility expansion. To complement the histogram, the script can optionally overlay a Gaussian curve fitted to the sample mean and standard deviation. It also runs a Jarque Bera normality check, based on skewness and excess kurtosis, and surfaces the result both visually and in a compact dashboard. On the oscillator panel itself, RSI is presented with a clean gradient line and standard overbought and oversold references, with fills that become more visible when RSI meaningfully extends beyond key thresholds. 🔹 Features 1. Distribution Histogram of Recent RSI Values The script stores the last N RSI values in an internal sample and uses that rolling window to compute a frequency distribution across a user selected number of bins. The histogram is drawn into the future by a configurable width in bars, which keeps it readable and prevents it from colliding with the active RSI plot. The result is a compact visual summary of where RSI clusters most often, whether it is spending more time near the center, or shifting toward higher or lower regimes. 2. Gaussian Overlay for Shape Intuition If enabled, a fitted bell curve is drawn on top of the histogram using the sample mean and standard deviation. This overlay is not intended as a direct trading signal. Its purpose is to provide a fast visual comparator between the empirical RSI distribution and a theoretical normal shape. When the histogram diverges strongly from the curve, you can quickly spot skew, heavy tails, or regime changes that often occur when market structure or volatility conditions shift. 3. Jarque Bera Normality Check With Clear PASS/FAIL Feedback The script computes skewness and excess kurtosis from the RSI sample, then forms the Jarque Bera statistic and compares it to a fixed 95% critical value. When the distribution is closer to normal under this test, the status is marked as PASS, otherwise it is marked as FAIL. This result is displayed in the dashboard and can also influence the histogram styling, giving immediate feedback about whether the recent RSI behavior resembles a bell shaped distribution or a more distorted, regime driven profile. Jarque Bera is a goodness of fit test that evaluates whether a dataset looks consistent with a normal distribution by checking two shape properties: skewness (asymmetry) and kurtosis (tail heaviness, expressed here as excess kurtosis where a perfect normal has 0). Under the null hypothesis of normality, skewness should be near 0 and excess kurtosis should be near 0. The test combines deviations in both into a single statistic, which is then compared to a chi square threshold. A PASS in this script means the sample does not show strong evidence against normality at the chosen threshold, while a FAIL means the sample is meaningfully skewed, heavy tailed, or both. In practical trading terms, a FAIL often suggests RSI is behaving in a regime where extremes and asymmetry are more common, which is typical during strong trends, volatility expansions, or one sided market pressure. It is still a statistical diagnostic, not a prediction tool, and results can vary with lookback length and market conditions. 4. Integrated Stats Dashboard A compact table in the top right summarizes key distribution moments and the normality result: Mean, StdDev, Skewness, Kurtosis, and the JB statistic with PASS/FAIL text. Skewness is color coded by sign to quickly distinguish right skew (more time at higher RSI) versus left skew (more time at lower RSI), which can be helpful when diagnosing trend bias and momentum persistence. 5. RSI Visual Quality and Context Zones RSI is plotted with a gradient color scheme and standard overbought and oversold reference lines. The overbought and oversold areas are filled with a smart gradient so visual emphasis increases when RSI meaningfully extends beyond the 70 and 30 regions, improving readability without overwhelming the panel. 🔹 Calculations This section summarizes the main calculations and transformations used internally. 1. RSI Series RSI is computed from the selected source and length using the standard RSI function: rsi_val = ta.rsi(rsi_src, rsi_len) 2. Rolling Sample Collection A float array stores recent RSI values. Each bar appends the newest RSI, and if the array exceeds the configured lookback, the oldest value is removed. Conceptually: rsi_history.push(rsi_val) if rsi_history.size() > lookback rsi_history.shift() This maintains a fixed size window that represents the most recent RSI behavior. 3. Mean, Variance, and Standard Deviation The script computes the sample mean across the array. Variance is computed as sample variance using (n - 1) in the denominator, and standard deviation is the square root of that variance. These values serve both the dashboard display and the Gaussian overlay parameters. 4. Skewness and Excess Kurtosis Skewness is calculated from the standardized third central moment with a small sample correction. Kurtosis is computed as excess kurtosis (kurtosis minus 3), so the normal baseline is 0. These two metrics summarize asymmetry and tail heaviness, which are the core ingredients for the Jarque Bera statistic. 5. Jarque Bera Statistic and Decision Rule Using skewness S and excess kurtosis K, the Jarque Bera statistic is computed as: JB = (n / 6.0) * (S^2 + 0.25 * K^2) Normality is flagged using a fixed critical value: is_normal = JB < 5.991 This produces a simple PASS/FAIL classification suitable for fast chart interpretation. 6. Histogram Binning and Scaling The RSI domain is treated as 0 to 100 and divided into a configurable number of bins. Bin size is: bin_size = 100.0 / bins Each RSI sample maps to a bin index via floor(rsi / bin_size), with clamping to ensure the index stays within valid bounds. The script counts occurrences per bin, tracks the maximum frequency, and normalizes each bar height by freq/max_freq so the histogram remains visually stable and comparable as the window updates. 7. Gaussian Curve Overlay (Optional) The Gaussian overlay uses the normal probability density function with mu as the sample mean and sigma as the sample standard deviation: normal_pdf(x) = (1 / (sigma * sqrt(2*pi))) * exp(-0.5 * ((x - mu)/sigma)^2) For drawing, the script samples x across the histogram width, evaluates the PDF, and normalizes it relative to its peak so the curve fits within the same visual height scale as the histogram.
Pine Script® indicator
by UnknownUnicorn113699460
Amihud Illiquidity Ratio [MarkitTick]💡This indicator implements the Amihud Illiquidity Ratio, a financial metric designed to measure the price impact of trading volume. It assesses the relationship between absolute price returns and the volume required to generate that return, providing traders with insight into the "stress" levels of the market liquidity. Concept and Originality Standard volume indicators often look at volume in isolation. This script differentiates itself by contextualizing volume against price movement. It answers the question: "How much did the price move per unit of volume?" Furthermore, unlike static indicators, this implementation utilizes dynamic percentile zones (Linear Interpolation) to adapt to the changing volatility profile of the specific asset you are viewing. Methodology The calculation proceeds in three distinct steps: 1. Daily Return: The script calculates the absolute percentage change of the closing price relative to the previous close. 2. Raw Ratio: The absolute return is divided by the volume. I have introduced a standard scaling factor (1,000,000) to the calculation. This resolves the issue of the values being astronomically small (displayed as roughly 0) without altering the fundamental logic of the Amihud ratio (Absolute Return / Volume). - High Ratio: Indicates that price is moving significantly on low volume (Illiquid/Thin Order Book). - Low Ratio: Indicates that price requires massive volume to move (Liquid/Deep Order Book). 3. Dynamic Regimes: The script calculates the 75th and 25th percentiles of the ratio over a lookback period. This creates adaptive bands that define "High Stress" and "Liquid" zones relative to recent history. How to Use Traders can use this tool to identify market fragility: - High Stress Zone (Red Background): When the indicator crosses above the 75th percentile, the market is in a High Illiquidity Regime. Price is slipping easily. This is often observed during panic selling or volatile tops where the order book is thin. - Liquid Zone (Green Background): When the indicator drops below the 25th percentile, the market is in a Liquid Regime. The market is absorbing volume well, which is often characteristic of stable trends or accumulation phases. - Dashboard: A visual table on the chart displays the current Amihud Ratio and the active Market Regime (High Stress, Normal, or Liquid). Inputs - Calculation Period: The lookback length for the average illiquidity (Default: 20). - Smoothing Period: The length of the additional moving average to smooth out noise (Default: 5). - Show Quant Dashboard: Toggles the visibility of the on-screen information table. ● How to read this chart • Spike in Illiquidity (Red Zones) Price is moving on "thin air." Expect high volatility or potential reversals. • Low Illiquidity (Green/Stable Zones) The market is deep and liquid. Trends here are more sustainable and reliable. • Divergence Watch for price making new highs while liquidity is drying up—a classic sign of an exhausted trend. Example: ● Chart Overview The chart displays the Amihud Illiquidity indicator applied to a Gold (XAUUSD) 4-hour timeframe. Top Pane: Price action with manual text annotations highlighting market reversals relative to liquidity zones. Bottom Pane: The specific technical indicator defined in the logic. It features a Blue Line (Raw Illiquidity), a Red Line (Signal/Smoothed), and dynamic background coloring (Red and Green vertical strips). ● Deep Visual Analysis • High Stress Regime (Red Zones) Visual Event: In the bottom pane, the background periodically shifts to a translucent red. Technical Logic: This event is triggered when the amihudAvg (the smoothed illiquidity ratio) exceeds the 75th percentile ( hZone ) of the lookback period. Forensic Interpretation: The logic calculates the absolute price change relative to volume. A spike into the red zone indicates that price is moving significantly on relatively lower volume (high price impact). Visually, the chart shows these red zones aligning with local price peaks (volatility expansion), leading to the bearish reversal marked by the red box in the top pane. • Liquid Regime (Green Zones) Visual Event: The background shifts to a translucent green in the bottom pane. Technical Logic: This triggers when the amihudAvg falls below the 25th percentile ( lZone ). Forensic Interpretation: This state represents a period where large volumes are absorbed with minimal price impact (efficiency). On the chart, this green zone corresponds to the consolidation trough (green box, top pane), validating the annotated accumulation phase before the bullish breakout. • Indicator Lines Blue Line: This is the illiquidityRaw value. It represents the raw daily return divided by volume. Red Line: This is the smoothedVal , a Simple Moving Average (SMA) of the raw data, used to filter out noise and define the trend of liquidity stress. ● Anomalies & Critical Data • The Reversal Pivot The transition from the "High Stress" (Red) background to the "Liquid" (Green) background serves as a visual proxy for market regime change. The chart shows that as the Red zones dissipate (volatility contraction), the market enters a Green zone (efficient liquidity), which acted as the precursor to the sustained upward trend on the right side of the chart. ● About Yakov Amihud Yakov Amihud is a leading researcher in market liquidity and asset pricing. • Brief Background Professor of Finance, affiliated with New York University (NYU). Specializes in market microstructure, liquidity, and quantitative finance. His work has had a major impact on both academic research and practical investment models. ● The Amihud (2002) Paper In 2002, he published his influential paper: “Illiquidity and Stock Returns: Cross-Section and Time-Series Effects” . • Key Contributions Introduced the Amihud Illiquidity Measure, a simple yet powerful proxy for market liquidity. Demonstrated that less liquid stocks tend to earn higher expected returns as compensation for liquidity risk. The measure became one of the most widely used liquidity metrics in finance research. ● Why It Matters in Practice Used in quantitative trading models. Applied in portfolio construction and risk management. Helpful as a liquidity filter to avoid assets with excessive price impact. In short: Yakov Amihud established a practical and robust link between liquidity and returns, making his 2002 work a cornerstone in modern financial economics. Disclaimer: All provided scripts and indicators are strictly for educational exploration and must not be interpreted as financial advice or a recommendation to execute trades. I expressly disclaim all liability for any financial losses or damages that may result, directly or indirectly, from the reliance on or application of these tools. Market participation carries inherent risk where past performance never guarantees future returns, leaving all investment decisions and due diligence solely at your own discretion.
Pine Script® indicator
by MarkitTick
FVG MTF Consensus OscillatorFVG MTF Consensus Oscillator A multi-timeframe, multi-component oscillator that combines momentum, deviation, and slope analysis across multiple timeframes using Zeiierman's Chebyshev-filtered trend calculation. This indicator identifies potential turning points with zone-based signal classification and timeframe consensus filtering. Backed by ML/Deep Learning evaluation on ES Futures data from 2015-2024. 🎯 Concept Traditional oscillators suffer from two major weaknesses: Single measurement - relying on one metric makes them susceptible to noise Single timeframe - missing the bigger picture leads to fighting the trend The FVG MTF Consensus Oscillator addresses both issues by combining three independent measurements across three timeframes into a weighted consensus signal. The Three Components Momentum - How fast is the trend moving? Deviation - How far has price stretched from the trend? Slope - What is the short-term directional bias? The Three Timeframes TF1 (Chart) - Your current chart timeframe (lowest weight) TF2 (Medium) - Typically 1H or 4H (medium weight) TF3 (High) - Typically 4H or Daily (highest weight) By requiring agreement across multiple components AND multiple timeframes, the oscillator filters out noise while capturing meaningful, high-probability market movements. 🔧 How It Works The Core: Chebyshev Type 1 Filter At its heart, this indicator uses a Chebyshev Type 1 low-pass filter (inspired by Zeiierman's FVG Trend) to extract a clean trend line from price action. Unlike simple moving averages, the Chebyshev filter offers: Sharper cutoff between trend and noise Minimal lag for a given smoothness level Controlled overshoot via the ripple parameter Three Oscillator Components 1. Momentum Component Momentum = Current Trend Value - Previous Trend Value Measures the velocity of the trend. High positive values indicate strong upward acceleration, while high negative values show downward acceleration. 2. Deviation Component Deviation = Close Price - Trend Value Measures how far price has stretched away from the trend line. Useful for identifying overextended conditions and mean reversion opportunities. 3. Slope Component Slope = Change in Trend over 3 bars Captures the short-term directional bias of the trend itself, helping confirm trend changes. Normalization & Component Consensus Each component is individually normalized to a -100 to +100 scale using adaptive scaling. The oscillator output is a weighted average of all three components, allowing you to emphasize different aspects based on your trading style. Multi-Timeframe Weighting The final oscillator value combines all three timeframes using configurable weights: Combined = (TF1 × Weight1 + TF2 × Weight2 + TF3 × Weight3) / Total Weight Default weights (1, 2, 3) ensure higher timeframes have more influence, keeping you aligned with the dominant trend while timing entries on lower timeframes. 📊 Zone System The oscillator uses a fuzzy zone system to classify market conditions: ZoneRangeInterpretationSignal ColorNeutral-5 to +5No clear bias, avoid tradingGrayContinuation±5 to ±25Trend pullback, continuation setupsAquaDeep Swing±25 to ±50Extended move, stronger setupsGreenReversalBeyond ±50Extreme extension, reversal potentialOrange When "Show Zone Background" is enabled, the background shading darkens as the oscillator moves into more extreme zones, providing instant visual feedback. 📈 Signal Interpretation Turn Signals The indicator plots triangular markers when the oscillator changes direction: ▲ Triangle Up (bottom): Oscillator turning up from a low ▼ Triangle Down (top): Oscillator turning down from a high Signal Quality by Zone Not all signals are equal. The signal color indicates which zone the turn occurred in: ColorZoneProbabilityBest UseGrayNeutralLowAvoid or use very tight stopsAquaContinuationModerateTrend continuation entriesGreenDeep SwingHigherSwing trade entriesOrangeReversalHighestCounter-trend with caution Timeframe Consensus Filter Signals only fire when the required number of timeframes agree on direction. With default settings (TF Consensus = 2), at least 2 of 3 timeframes must be moving in the same direction for a signal to trigger. This prevents: Taking longs when higher timeframes are bearish Taking shorts when higher timeframes are bullish Whipsaws during timeframe disagreement Trend Coloring The combined oscillator line changes color based on trend direction: Light purple (RGB 240, 174, 252): Majority of timeframes trending up Dark purple (RGB 84, 19, 95): Majority of timeframes trending down Info Table When MTF is enabled, a table in the top-right corner displays: Current oscillator values for each timeframe (TF1, TF2, TF3) Combined value (CMB) Color coding: Green = rising, Red = falling ⚙️ Settings Guide Timeframe Settings SettingDefaultDescriptionEnable Multi-TimeframeOnMaster switch for MTF functionalityTF1 (Chart)"" (current)First timeframe, typically your chart TFTF2 (Medium)60Second timeframe, typically 1HTF3 (High)240Third timeframe, typically 4HTF1/TF2/TF3 Weight1 / 2 / 3Influence of each TF on combined signal Timeframe Tips: Keep TF1 ≤ TF2 ≤ TF3 (ascending order) For day trading: 5m / 15m / 1H For swing trading: 1H / 4H / Daily For position trading: 4H / Daily / Weekly Display Settings SettingDefaultDescriptionShow All TimeframesOffDisplay individual TF oscillator linesShow Combined LineOnDisplay the weighted combined oscillatorShow Zone BackgroundOffShade background based on current zone Trend Filter Settings SettingDefaultDescriptionTrend Ripple4.0Filter responsiveness (1-10). Higher = faster but more overshootTrend Cutoff0.1Cutoff frequency (0.01-0.5). Lower = smoother trendNormalization Length50Lookback for scaling. Longer = more stable Component Weights SettingDefaultDescriptionMomentum Weight1.0Emphasis on trend speedDeviation Weight1.0Emphasis on price stretch from trendSlope Weight1.0Emphasis on short-term trend direction Component Tips: For trend-following: Increase Momentum and Slope weights For mean reversion: Increase Deviation weight Set any weight to 0 to disable that component Zone Thresholds SettingDefaultDescriptionNeutral Zone5Inner boundary (±5 = neutral)Continuation Zone25Middle boundary for continuation setupsDeep Swing Zone50Outer boundary for reversal zone Adjust based on instrument volatility. More volatile instruments may need wider zones. Signal Filters SettingDefaultDescriptionSignal Cooldown3Minimum bars between signalsMin Turn Size2.0Minimum oscillator change for valid turnTF Consensus Required2Minimum TFs agreeing for signal (1-3) 💡 Usage Examples Example 1: Trend Continuation (Dip Buying) Setup: Uptrend confirmed by higher timeframes Check the info table - TF2 and TF3 should show green (rising) Wait for TF1 to pull back, oscillator enters Continuation zone Enter on Aqua ▲ signal (turn up with TF consensus) Stop below recent swing low Target: Previous high or next resistance Why it works: You're buying a dip in an established uptrend with multi-timeframe confirmation. Example 2: Deep Swing Entry Setup: Extended move showing exhaustion Oscillator reaches Deep Swing zone (±25 to ±50) At least 2 TFs start showing the same direction Enter on Green signal indicating momentum exhaustion Use tighter stop as the move is already extended Target: Return to Continuation zone or trend line Why it works: Extended moves tend to mean-revert. The zone system identifies these opportunities. Example 3: Reversal Setup (Advanced) Setup: Extreme extension with diverging timeframes Oscillator reaches Reversal zone (beyond ±50) Watch for TF1 to turn while TF3 is still extended Enter on Orange signal - this is counter-trend! Use smaller position size and wider stops Target: Return to Deep Swing or Continuation zone Why it works: Extreme extensions eventually correct. The orange signal marks high-probability reversal points. Example 4: Avoiding Bad Trades What to avoid: Gray signals in Neutral zone - No edge, random noise Signals against TF3 direction - Fighting the dominant trend Signals without TF consensus - Timeframe disagreement = choppy market Multiple signals in quick succession - Let cooldown filter work 🔬 Multi-Timeframe Analysis Tips Reading the Info Table The info table shows real-time oscillator values: | TF1 | TF2 | TF3 | CMB | | 23.5 | 45.2 | 67.8 | 52.1 | All green: Strong uptrend across all timeframes All red: Strong downtrend across all timeframes Mixed colors: Potential transition or consolidation Timeframe Alignment States TF1TF2TF3Interpretation↑↑↑Strong bull - look for long entries↓↓↓Strong bear - look for short entries↑↑↓Pullback in downtrend - caution on longs↓↓↑Pullback in uptrend - caution on shorts↑↓↑Choppy - reduce position size↓↑↓Choppy - reduce position size The Power of Consensus With TF Consensus = 2, signals only fire when 2+ timeframes agree. This single filter eliminates most whipsaws and keeps you aligned with the dominant trend. For more conservative trading, set TF Consensus = 3 (all timeframes must agree). ⚠️ Important Notes This indicator does not predict the future. It measures current market conditions and momentum across multiple timeframes. Always use proper risk management. No indicator is 100% accurate. Combine with price action. The oscillator works best when confirmed by support/resistance, candlestick patterns, or other confluence factors. Respect the higher timeframe. When TF3 disagrees, trade smaller or sit out. Zone signals are probabilistic. Orange (reversal) signals have higher probability but aren't guaranteed reversals. Adjust settings per instrument. Default settings are optimized for ES Futures but may need tuning for other markets. 🧪 ML/Deep Learning Background The default parameters and zone thresholds were evaluated using machine learning techniques on ES Futures data spanning 2015-2024. This included: Optimization of component weights Zone threshold calibration Timeframe weight balancing Signal filter tuning While past performance doesn't guarantee future results, the parameters represent a data-driven starting point rather than arbitrary defaults. 🙏 Credits This indicator is inspired by Zeiierman's Multitimeframe Fair Value Gap (FVG) indicator, specifically utilizing concepts from his Chebyshev Type 1 filter implementation for trend calculation. Original indicator: Multitimeframe Fair Value Gap – FVG (Zeiierman) 📝 Changelog v1.0 Initial release Three-component consensus oscillator (Momentum, Deviation, Slope) Multi-timeframe support with weighted combination Fuzzy zone classification system Configurable component and timeframe weights TF consensus filter for signal quality Signal cooldown and minimum turn size filters Real-time info table with TF values Optional zone background shading
Pine Script® indicator
by xriswart
Adaptive Z-Score Oscillator [QuantAlgo]🟢 Overview The Adaptive Z-Score Oscillator transforms price action into statistical significance measurements by calculating how many standard deviations the current price deviates from its moving average baseline, then dynamically adjusting threshold levels based on historical distribution patterns. Unlike traditional oscillators that rely on fixed overbought/oversold levels, this indicator employs percentile-based adaptive thresholds that automatically calibrate to changing market volatility regimes and statistical characteristics. By offering both adaptive and fixed threshold modes alongside multiple moving average types and customizable smoothing, the indicator provides traders and investors with a robust framework for identifying extreme price deviations, mean reversion opportunities, and underlying trend conditions through the visualization of price behavior within a statistical distribution context. 🟢 How It Works The indicator begins by establishing a dynamic baseline using a user-selected moving average type applied to closing prices over the specified length period, then calculates the standard deviation to measure price dispersion: basis = ma(close, length, maType) stdev = ta.stdev(close, length) The core Z-Score calculation quantifies how many standard deviations the current price sits above or below the moving average basis, creating a normalized oscillator that facilitates cross-asset and cross-timeframe comparisons: zScore = stdev != 0 ? (close - basis) / stdev : 0 smoothedZ = ma(zScore, smooth, maType) The adaptive threshold mechanism employs percentile calculations over a historical lookback period to determine statistically significant extreme zones. Rather than using fixed levels like ±2.0, the indicator identifies where a specified percentage of historical Z-Score readings have fallen, automatically adjusting to market regime changes: upperThreshold = adaptive ? ta.percentile_linear_interpolation(smoothedZ, percentilePeriod, upperPercentile) : fixedUpper lowerThreshold = adaptive ? ta.percentile_linear_interpolation(smoothedZ, percentilePeriod, lowerPercentile) : fixedLower The visualization architecture creates a four-tier coloring system that distinguishes between extreme conditions (beyond the adaptive thresholds) and moderate conditions (between the midpoint and threshold levels), providing visual gradation of statistical significance through opacity variations and immediate recognition of distribution extremes. 🟢 How to Use This Indicator ▶ Overbought and Oversold Identification: The indicator identifies potential overbought conditions when the smoothed Z-Score crosses above the upper threshold, indicating that price has deviated to a statistically extreme level above its mean. Conversely, oversold conditions emerge when the Z-Score crosses below the lower threshold, signaling statistically significant downward deviation. In adaptive mode (default), these thresholds automatically adjust to the asset's historical behavior, i.e., during high volatility periods, the thresholds expand to accommodate wider price swings, while during low volatility regimes, they contract to capture smaller deviations as significant. This dynamic calibration reduce false signals that plague fixed-level oscillators when market character shifts between volatile and ranging conditions. ▶ Mean Reversion Trading Applications: The Z-Score framework excels at identifying mean reversion opportunities by highlighting when price has stretched too far from its statistical equilibrium. When the oscillator reaches extreme bearish levels (below the lower threshold with deep red coloring), it suggests price has become statistically oversold and may snap back toward the mean, presenting potential long entry opportunities for mean reversion traders. Symmetrically, extreme bullish readings (above the upper threshold with bright green coloring) indicate potential short opportunities or long exit points as price becomes statistically overbought. The moderate zones (lighter colors between midpoint and threshold) serve as early warning areas where traders can prepare for potential reversals, while exits from extreme zones (crossing back inside the thresholds) often provide confirmation that mean reversion is underway. ▶ Trend and Distribution Analysis: Beyond discrete overbought/oversold signals, the histogram's color pattern and shape reveal the underlying trend structure and distribution characteristics. Sustained periods where the Z-Score oscillates primarily in positive territory (green bars) indicate a bullish trend where price consistently trades above its moving average baseline, even if not reaching extreme levels. Conversely, predominant negative readings (red bars) suggest bearish trend conditions. The distribution shape itself provides insight into market behavior, e.g., a narrow, centered distribution clustering near zero indicates tight ranging conditions with price respecting the mean, while a wide distribution with frequent extreme readings reveals volatile trending or choppy conditions. Asymmetric distributions skewed heavily toward one side demonstrate persistent directional bias, whereas balanced distributions suggest equilibrium between bulls and bears. ▶ Built-in Alerts: Seven alert conditions enable automated monitoring of statistical extremes and trend transitions. Enter Overbought and Enter Oversold alerts trigger when the Z-Score crosses into extreme zones, providing early warnings of potential reversal setups. Exit Overbought and Exit Oversold alerts signal when price begins reverting from extremes, offering confirmation that mean reversion has initiated. Zero Cross Up and Zero Cross Down alerts identify transitions through the neutral line, indicating shifts between above-mean and below-mean price action that can signal trend changes. The Extreme Zone Entry alert fires on any extreme threshold penetration regardless of direction, allowing unified monitoring of both overbought and oversold opportunities. ▶ Color Customization: Six visual themes (Classic, Aqua, Cosmic, Ember, Neon, plus Custom) accommodate different chart backgrounds and aesthetic preferences, ensuring optimal contrast and readability across trading platforms. The bar transparency control (0-90%) allows fine-tuning of visual prominence, with minimal transparency creating bold, attention-grabbing bars for primary analysis, while higher transparency values produce subtle background context when using the oscillator alongside other indicators. The extreme and moderate zone coloring system uses automatic opacity variation to create instant visual hierarchy, with darkest colors highlight the most statistically significant deviations demanding immediate attention, while lighter shades mark developing conditions that warrant monitoring but may not yet justify action. Optional candle coloring extends the Z-Score color scheme directly to the price candles on the main chart, enabling traders to instantly recognize statistical extremes and trend conditions without needing to reference the oscillator panel, creating a unified visual experience where both price action and statistical analysis share the same color language.
Pine Script® indicator
by QuantAlgo
10
USD Liquidity Regime for BTC Perps (Dual) V1USD Liquidity Regime for BTC Perps (Dual) This intents to be a BTC Perps USD Liquidity Regime macro indicator. As it names states it is designed for BTCUSDT perpetual futures traders. It attempts to tracks USD strength (DXY, UUP, yields, VIX composite) as liquidity proxy: Lower index = weak USD = Risk-On (green background/histogram = long tailwind for BTC). Higher = strong USD = Risk-Off (red = caution longs, shorts favor). How to use: Green background/histogram: Favor longs — rallies likely, dips bought. Red: Caution longs — corrections hurt, short bias possible. Blue line (index) vs red SMA: Crosses signal regime shifts. Histogram strength: Bigger bars = stronger bias. This is not intended as financial advise or trigger signal tool. This is a work in progress Its value is limited, if you do not understand any or some of the words above please do not use this indicator. If you did, then you understand you are not supposed to use this alone to make decisions. Feel free to ask any questions, this is a work in progress. Feel free to suggest improvements. Educational macro context tool — not signals/advice. Ok for avoiding going against the USD trend dominance by following liquidity. By @frank_vergaram
Pine Script® indicator
by frank_vergaram