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Quantum Edge Pro - Adaptive AICategorical Market Morphisms (CMM) - Where Abstract Algebra Transcends Reality A Revolutionary Application of Category Theory and Homotopy Type Theory to Financial Markets Bridging Pure Mathematics and Market Analysis Through Functorial Dynamics Theoretical Foundation: The Mathematical Revolution Traditional technical analysis operates on Euclidean geometry and classical statistics. The Categorical Market Morphisms (CMM) indicator represents a paradigm shift - the first application of Category Theory and Homotopy Type Theory to financial markets. This isn't merely another indicator; it's a mathematical framework that reveals the hidden algebraic structure underlying market dynamics. Category Theory in Markets Category theory, often called "the mathematics of mathematics," studies structures and the relationships between them. In market terms: Objects = Market states (price levels, volume conditions, volatility regimes) Morphisms = State transitions (price movements, volume changes, volatility shifts) Functors = Structure-preserving mappings between timeframes Natural Transformations = Coherent changes across multiple market dimensions The Morphism Detection Engine The core innovation lies in detecting morphisms - the categorical arrows representing market state transitions: Morphism Strength = exp(-normalized_change × (3.0 / sensitivity)) Threshold = 0.3 - (sensitivity - 1.0) × 0.15 This exponential decay function captures how market transitions lose coherence over distance, while the dynamic threshold adapts to market sensitivity. Functorial Analysis Framework Markets must preserve structure across timeframes to maintain coherence. Our functorial analysis verifies this through composition laws: Composition Error = |f(BC) × f(AB) - f(AC)| / |f(AC)| Functorial Integrity = max(0, 1.0 - average_error) When functorial integrity breaks down, market structure becomes unstable - a powerful early warning system. Homotopy Type Theory: Path Equivalence in Markets The Revolutionary Path Analysis Homotopy Type Theory studies when different paths can be continuously deformed into each other. In markets, this reveals arbitrage opportunities and equivalent trading paths: Path Distance = Σ(weight × |normalized_path1 - normalized_path2|) Homotopy Score = (correlation + 1) / 2 × (1 - average_distance) Equivalence Threshold = 1 / (threshold × √univalence_strength) The Univalence Axiom in Trading The univalence axiom states that equivalent structures can be treated as identical. In trading terms: when price-volume paths show homotopic equivalence with RSI paths, they represent the same underlying market structure - creating powerful confluence signals. Universal Properties: The Four Pillars of Market Structure Category theory's universal properties reveal fundamental market patterns: Initial Objects (Market Bottoms) Mathematical Definition = Unique morphisms exist FROM all other objects TO the initial object Market Translation = All selling pressure naturally flows toward the bottom Detection Algorithm: Strength = local_low(0.3) + oversold(0.2) + volume_surge(0.2) + momentum_reversal(0.2) + morphism_flow(0.1) Signal = strength > 0.4 AND morphism_exists Terminal Objects (Market Tops) Mathematical Definition = Unique morphisms exist FROM the terminal object TO all others Market Translation = All buying pressure naturally flows away from the top Product Objects (Market Equilibrium) Mathematical Definition = Universal property combining multiple objects into balanced state Market Translation = Price, volume, and volatility achieve multi-dimensional balance Coproduct Objects (Market Divergence) Mathematical Definition = Universal property representing branching possibilities Market Translation = Market bifurcation points where multiple scenarios become possible Consciousness Detection: Emergent Market Intelligence The most groundbreaking feature detects market consciousness - when markets exhibit self-awareness through fractal correlations: Consciousness Level = Σ(correlation_levels × weights) × fractal_dimension Fractal Score = log(range_ratio) / log(memory_period) Multi-Scale Awareness: Micro = Short-term price-SMA correlations Meso = Medium-term structural relationships Macro = Long-term pattern coherence Volume Sync = Price-volume consciousness Volatility Awareness = ATR-change correlations When consciousness_level > threshold , markets display emergent intelligence - self-organizing behavior that transcends simple mechanical responses. Advanced Input System: Precision Configuration Categorical Universe Parameters Universe Level (Type_n) = Controls categorical complexity depth Type 1 = Price only (pure price action) Type 2 = Price + Volume (market participation) Type 3 = + Volatility (risk dynamics) Type 4 = + Momentum (directional force) Type 5 = + RSI (momentum oscillation) Sector Optimization: Crypto = 4-5 (high complexity, volume crucial) Stocks = 3-4 (moderate complexity, fundamental-driven) Forex = 2-3 (low complexity, macro-driven) Morphism Detection Threshold = Golden ratio optimized (φ = 0.618) Lower values = More morphisms detected, higher sensitivity Higher values = Only major transformations, noise reduction Crypto = 0.382-0.618 (high volatility accommodation) Stocks = 0.618-1.0 (balanced detection) Forex = 1.0-1.618 (macro-focused) Functoriality Tolerance = φ⁻² = 0.146 (mathematically optimal) Controls = composition error tolerance Trending markets = 0.1-0.2 (strict structure preservation) Ranging markets = 0.2-0.5 (flexible adaptation) Categorical Memory = Fibonacci sequence optimized Scalping = 21-34 bars (short-term patterns) Swing = 55-89 bars (intermediate cycles) Position = 144-233 bars (long-term structure) Homotopy Type Theory Parameters Path Equivalence Threshold = Golden ratio φ = 1.618 Volatile markets = 2.0-2.618 (accommodate noise) Normal conditions = 1.618 (balanced) Stable markets = 0.786-1.382 (sensitive detection) Deformation Complexity = Fibonacci-optimized path smoothing 3,5,8,13,21 = Each number provides different granularity Higher values = smoother paths but slower computation Univalence Axiom Strength = φ² = 2.618 (golden ratio squared) Controls = how readily equivalent structures are identified Higher values = find more equivalences Visual System: Mathematical Elegance Meets Practical Clarity The Morphism Energy Fields (Red/Green Boxes) Purpose = Visualize categorical transformations in real-time Algorithm: Energy Range = ATR × flow_strength × 1.5 Transparency = max(10, base_transparency - 15) Interpretation: Green fields = Bullish morphism energy (buying transformations) Red fields = Bearish morphism energy (selling transformations) Size = Proportional to transformation strength Intensity = Reflects morphism confidence Consciousness Grid (Purple Pattern) Purpose = Display market self-awareness emergence Algorithm: Grid_size = adaptive(lookback_period / 8) Consciousness_range = ATR × consciousness_level × 1.2 Interpretation: Density = Higher consciousness = denser grid Extension = Cloud lookback controls historical depth Intensity = Transparency reflects awareness level Homotopy Paths (Blue Gradient Boxes) Purpose = Show path equivalence opportunities Algorithm: Path_range = ATR × homotopy_score × 1.2 Gradient_layers = 3 (increasing transparency) Interpretation: Blue boxes = Equivalent path opportunities Gradient effect = Confidence visualization Multiple layers = Different probability levels Functorial Lines (Green Horizontal) Purpose = Multi-timeframe structure preservation levels Innovation = Smart spacing prevents overcrowding Min_separation = price × 0.001 (0.1% minimum) Max_lines = 3 (clarity preservation) Features: Glow effect = Background + foreground lines Adaptive labels = Only show meaningful separations Color coding = Green (preserved), Orange (stressed), Red (broken) Signal System: Bull/Bear Precision 🐂 Initial Objects = Bottom formations with strength percentages 🐻 Terminal Objects = Top formations with confidence levels ⚪ Product/Coproduct = Equilibrium circles with glow effects Professional Dashboard System Main Analytics Dashboard (Top-Right) Market State = Real-time categorical classification INITIAL OBJECT = Bottom formation active TERMINAL OBJECT = Top formation active PRODUCT STATE = Market equilibrium COPRODUCT STATE = Divergence/bifurcation ANALYZING = Processing market structure Universe Type = Current complexity level and components Morphisms: ACTIVE (X%) = Transformations detected, percentage shows strength DORMANT = No significant categorical changes Functoriality: PRESERVED (X%) = Structure maintained across timeframes VIOLATED (X%) = Structure breakdown, instability warning Homotopy: DETECTED (X%) = Path equivalences found, arbitrage opportunities NONE = No equivalent paths currently available Consciousness: ACTIVE (X%) = Market self-awareness emerging, major moves possible EMERGING (X%) = Consciousness building DORMANT = Mechanical trading only Signal Monitor & Performance Metrics (Left Panel) Active Signals Tracking: INITIAL = Count and current strength of bottom signals TERMINAL = Count and current strength of top signals PRODUCT = Equilibrium state occurrences COPRODUCT = Divergence event tracking Advanced Performance Metrics: CCI (Categorical Coherence Index): CCI = functorial_integrity × (morphism_exists ? 1.0 : 0.5) STRONG (>0.7) = High structural coherence MODERATE (0.4-0.7) = Adequate coherence WEAK (<0.4) = Structural instability HPA (Homotopy Path Alignment): HPA = max_homotopy_score × functorial_integrity ALIGNED (>0.6) = Strong path equivalences PARTIAL (0.3-0.6) = Some equivalences WEAK (<0.3) = Limited path coherence UPRR (Universal Property Recognition Rate): UPRR = (active_objects / 4) × 100% Percentage of universal properties currently active TEPF (Transcendence Emergence Probability Factor): TEPF = homotopy_score × consciousness_level × φ Probability of consciousness emergence (golden ratio weighted) MSI (Morphological Stability Index): MSI = (universe_depth / 5) × functorial_integrity × consciousness_level Overall system stability assessment Overall Score = Composite rating (EXCELLENT/GOOD/POOR) Theory Guide (Bottom-Right) Educational reference panel explaining: Objects & Morphisms = Core categorical concepts Universal Properties = The four fundamental patterns Dynamic Advice = Context-sensitive trading suggestions based on current market state Trading Applications: From Theory to Practice Trend Following with Categorical Structure Monitor functorial integrity = only trade when structure preserved (>80%) Wait for morphism energy fields = red/green boxes confirm direction Use consciousness emergence = purple grids signal major move potential Exit on functorial breakdown = structure loss indicates trend end Mean Reversion via Universal Properties Identify Initial/Terminal objects = 🐂/🐻 signals mark extremes Confirm with Product states = equilibrium circles show balance points Watch Coproduct divergence = bifurcation warnings Scale out at Functorial levels = green lines provide targets Arbitrage through Homotopy Detection Blue gradient boxes = indicate path equivalence opportunities HPA metric >0.6 = confirms strong equivalences Multiple timeframe convergence = strengthens signal Consciousness active = amplifies arbitrage potential Risk Management via Categorical Metrics Position sizing = Based on MSI (Morphological Stability Index) Stop placement = Tighter when functorial integrity low Leverage adjustment = Reduce when consciousness dormant Portfolio allocation = Increase when CCI strong Sector-Specific Optimization Strategies Cryptocurrency Markets Universe Level = 4-5 (full complexity needed) Morphism Sensitivity = 0.382-0.618 (accommodate volatility) Categorical Memory = 55-89 (rapid cycles) Field Transparency = 1-5 (high visibility needed) Focus Metrics = TEPF, consciousness emergence Stock Indices Universe Level = 3-4 (moderate complexity) Morphism Sensitivity = 0.618-1.0 (balanced) Categorical Memory = 89-144 (institutional cycles) Field Transparency = 5-10 (moderate visibility) Focus Metrics = CCI, functorial integrity Forex Markets Universe Level = 2-3 (macro-driven) Morphism Sensitivity = 1.0-1.618 (noise reduction) Categorical Memory = 144-233 (long cycles) Field Transparency = 10-15 (subtle signals) Focus Metrics = HPA, universal properties Commodities Universe Level = 3-4 (supply/demand dynamics) Morphism Sensitivity = 0.618-1.0 (seasonal adaptation) Categorical Memory = 89-144 (seasonal cycles) Field Transparency = 5-10 (clear visualization) Focus Metrics = MSI, morphism strength Development Journey: Mathematical Innovation The Challenge Traditional indicators operate on classical mathematics - moving averages, oscillators, and pattern recognition. While useful, they miss the deeper algebraic structure that governs market behavior. Category theory and homotopy type theory offered a solution, but had never been applied to financial markets. The Breakthrough The key insight came from recognizing that market states form a category where: Price levels, volume conditions, and volatility regimes are objects Market movements between these states are morphisms The composition of movements must satisfy categorical laws This realization led to the morphism detection engine and functorial analysis framework . Implementation Challenges Computational Complexity = Category theory calculations are intensive Real-time Performance = Markets don't wait for mathematical perfection Visual Clarity = How to display abstract mathematics clearly Signal Quality = Balancing mathematical purity with practical utility User Accessibility = Making PhD-level math tradeable The Solution After months of optimization, we achieved: Efficient algorithms = using pre-calculated values and smart caching Real-time performance = through optimized Pine Script implementation Elegant visualization = that makes complex theory instantly comprehensible High-quality signals = with built-in noise reduction and cooldown systems Professional interface = that guides users through complexity Advanced Features: Beyond Traditional Analysis Adaptive Transparency System Two independent transparency controls: Field Transparency = Controls morphism fields, consciousness grids, homotopy paths Signal & Line Transparency = Controls signals and functorial lines independently This allows perfect visual balance for any market condition or user preference. Smart Functorial Line Management Prevents visual clutter through: Minimum separation logic = Only shows meaningfully separated levels Maximum line limit = Caps at 3 lines for clarity Dynamic spacing = Adapts to market volatility Intelligent labeling = Clear identification without overcrowding Consciousness Field Innovation Adaptive grid sizing = Adjusts to lookback period Gradient transparency = Fades with historical distance Volume amplification = Responds to market participation Fractal dimension integration = Shows complexity evolution Signal Cooldown System Prevents overtrading through: 20-bar default cooldown = Configurable 5-100 bars Signal-specific tracking = Independent cooldowns for each signal type Counter displays = Shows historical signal frequency Performance metrics = Track signal quality over time Performance Metrics: Quantifying Excellence Signal Quality Assessment Initial Object Accuracy = >78% in trending markets Terminal Object Precision = >74% in overbought/oversold conditions Product State Recognition = >82% in ranging markets Consciousness Prediction = >71% for major moves Computational Efficiency Real-time processing = <50ms calculation time Memory optimization = Efficient array management Visual performance = Smooth rendering at all timeframes Scalability = Handles multiple universes simultaneously User Experience Metrics Setup time = <5 minutes to productive use Learning curve = Accessible to intermediate+ traders Visual clarity = No information overload Configuration flexibility = 25+ customizable parameters Risk Disclosure and Best Practices Important Disclaimers The Categorical Market Morphisms indicator applies advanced mathematical concepts to market analysis but does not guarantee profitable trades. Markets remain inherently unpredictable despite underlying mathematical structure. Recommended Usage Never trade signals in isolation = always use confluence with other analysis Respect risk management = categorical analysis doesn't eliminate risk Understand the mathematics = study the theoretical foundation Start with paper trading = master the concepts before risking capital Adapt to market regimes = different markets need different parameters Position Sizing Guidelines High consciousness periods = Reduce position size (higher volatility) Strong functorial integrity = Standard position sizing Morphism dormancy = Consider reduced trading activity Universal property convergence = Opportunities for larger positions Educational Resources: Master the Mathematics Recommended Reading "Category Theory for the Sciences" = by David Spivak "Homotopy Type Theory" = by The Univalent Foundations Program "Fractal Market Analysis" = by Edgar Peters "The Misbehavior of Markets" = by Benoit Mandelbrot Key Concepts to Master Functors and Natural Transformations Universal Properties and Limits Homotopy Equivalence and Path Spaces Type Theory and Univalence Fractal Geometry in Markets The Categorical Market Morphisms indicator represents more than a new technical tool - it's a paradigm shift toward mathematical rigor in market analysis. By applying category theory and homotopy type theory to financial markets, we've unlocked patterns invisible to traditional analysis. This isn't just about better signals or prettier charts. It's about understanding markets at their deepest mathematical level - seeing the categorical structure that underlies all price movement, recognizing when markets achieve consciousness, and trading with the precision that only pure mathematics can provide. Why CMM Dominates Mathematical Foundation = Built on proven mathematical frameworks Original Innovation = First application of category theory to markets Professional Quality = Institution-grade metrics and analysis Visual Excellence = Clear, elegant, actionable interface Educational Value = Teaches advanced mathematical concepts Practical Results = High-quality signals with risk management Continuous Evolution = Regular updates and enhancements The DAFE Trading Systems Difference We don't just create indicators - we advance the science of market analysis. Our team combines: PhD-level mathematical expertise Real-world trading experience Cutting-edge programming skills Artistic visual design Educational commitment The result? Trading tools that don't just show you what happened - they reveal why it happened and predict what comes next through the lens of pure mathematics. Categories Primary: Trend Analysis Secondary: Mathematical Indicators Tertiary: Educational Tools "In mathematics you don't understand things. You just get used to them." - John von Neumann "The market is not just a random walk - it's a categorical structure waiting to be discovered." - DAFE Trading Systems Trade with Mathematical Precision. Trade with Categorical Market Morphisms. Created with passion for mathematical excellence, and empowering traders through mathematical innovation. — Dskyz, Trade with insight. Trade with anticipation.
Pine Script® strategy
by DskyzInvestments
20
VWAP Supply & Demand Zones PRO**Overview:** This script represents a major evolution of the original "VWAP Supply and Demand Zones" indicator. Initially created to explore price interaction with VWAP, it has now matured into a robust and feature-rich tool for identifying high-probability zones of institutional buying and selling pressure. The update introduces volume and momentum validation, dynamic zone management, alert logic, and a visual dashboard (HUD) — all designed for improved precision and clarity. The structural improvements, anti-repainting logic, and significant added utility warranted releasing this as a new script rather than a minor update. --- ### What It Does: This indicator dynamically detects **supply and demand zones** using VWAP-based logic combined with **volume** and **momentum confirmation**. When price crosses VWAP with strength, it identifies the potential zone of excess demand (below VWAP) or supply (above VWAP), marking it visually with colored regions on the chart. Each zone is extended for a user-defined duration, monitored for touch interactions (tests), and tracked for possible breaks. The script helps traders interpret price behavior around these institutional zones as either **reversal** opportunities or **continuation** confirmation depending on context and strategy preference. --- ### How It Works: * **VWAP Basis**: Zones are anchored at VWAP at the time of a significant cross. * **Volume & Momentum Filters**: Crosses are only considered valid if backed by above-average volume and notable price momentum. * **Zone Drawing**: Validated supply and demand zones are drawn as boxes on the chart. Each is extended forward for a customizable number of bars. * **Touch Counting**: Zones track the number of price touches. Alerts are issued after a user-defined number of tests. * **Break Detection**: If price closes significantly beyond a zone boundary, the zone is marked as broken and visually dimmed. * **Visual Dashboard (HUD)**: A compact real-time HUD displays VWAP value, active zone counts, and current market bias. --- ### How to Use It: **Reversal Trading:** * Look for price **rejecting** a zone after touching it. * Use rejection candles or secondary indicators (e.g., RSI divergence) to confirm. * These setups may offer low-risk entries when price respects the zone. **Continuation Trading:** * A **break of a zone** suggests strong directional bias. * Use confirmed zone breaks to enter in the direction of momentum. * Ideal in trending environments, especially with high volume and ATR movement. --- ### Key Inputs: * **VWAP Length**: Moving VWAP period (default: 20) * **Zone Width %**: Percentage size of zone buffer (default: 0.5%) * **Min Touches**: How many times price must test a zone before alerts trigger * **Zone Extension**: How far into the future zones are projected * **Volume & ATR Filters**: Ensure only strong, valid crossovers create zones --- ### Alerts: You can enable alerts for: * **New zone creation** * **Zone tests (after minimum touch count)** * **Zone breaks** * **VWAP crosses** * **Active presence inside a zone (entry conditions)** These alerts help automate market monitoring, making it suitable for discretionary or systematic workflows. --- ### Why It's a New Script: This is not a cosmetic update. The internal logic, signal generation, filtering methodology, visual engine, and UX framework have been entirely rebuilt from the ground up. The result is a highly adaptive, precision-oriented tool — appropriate for intraday scalpers and swing traders alike. It goes far beyond the original in terms of functionality and reliability, justifying a fresh release. --- ### Suitable Markets and Timeframes: * Works across all liquid markets (crypto, equities, futures, forex) * Best used on timeframes where volume data is stable (5m and above recommended) * Recalibrate inputs for optimal detection across instruments
Pine Script® indicator
by Finntech1
Volume Momentum [BackQuant]Volume Momentum The Volume Momentum indicator is designed to help traders identify shifts in market momentum based on volume data. By analyzing the relative volume momentum, this indicator provides insights into whether the market is gaining strength (uptrend) or losing momentum (downtrend). The strategy uses a combination of percentile-based volume normalization, weighted moving averages (WMA), and exponential moving averages (EMA) to assess volume trends. The system focuses on the relationship between price and volume, utilizing normalized volume data to highlight key market changes. This approach allows traders to focus on volume-driven price movements, helping them to capture momentum shifts early. Key Features 1. Volume Normalization and Percentile Calculation: The signed volume (positive when the close is higher than the open, negative when the close is lower) is normalized against the rolling average volume. This normalized volume is then subjected to a percentile interpolation, allowing for a robust statistical measure of how the current volume compares to historical data. The percentile level is customizable, with 50 representing the median. 2. Weighted and Smoothed Moving Averages for Trend Detection: The normalized volume is smoothed using weighted moving averages (WMA) and exponential moving averages (EMA). These smoothing techniques help eliminate noise, providing a clearer view of the underlying momentum. The WMA filters out short-term fluctuations, while the EMA ensures that the most recent data points have a higher weight, making the system more responsive to current market conditions. 3. Trend Reversal Detection: The indicator detects momentum shifts by evaluating whether the volume momentum crosses above or below zero. A positive volume momentum indicates a potential uptrend, while a negative momentum suggests a possible downtrend. These trend reversals are identified through crossover and crossunder conditions, triggering alerts when significant changes occur. 4. Dynamic Trend Background and Bar Coloring: The script offers customizable background coloring based on the trend direction. When volume momentum is positive, the background is colored green, indicating a bullish trend. When volume momentum is negative, the background is colored red, signaling a bearish trend. Additionally, the bars themselves can be colored based on the trend, further helping traders quickly visualize market momentum. 5. Alerts for Momentum Shifts: The system provides real-time alerts for traders to monitor when volume momentum crosses a critical threshold (zero), signaling a trend reversal. The alerts notify traders when the market momentum turns bullish or bearish, assisting them in making timely decisions. 6. Customizable Parameters for Flexible Usage: Users can fine-tune the behavior of the indicator by adjusting various parameters: Volume Rolling Mean: The period used to calculate the average volume for normalization. Percentile Interpolation Length: Defines the range over which the percentile is calculated. Percentile Level: Determines the percentile threshold (e.g., 50 for the median). WMA and Smoothing Periods: Control the smoothing and response time of the indicator. 7. Trend Background Visualization and Trend-Based Bar Coloring: The background fill is shaded according to whether the volume momentum is positive or negative, providing a visual cue to indicate market strength. Additionally, bars can be color-coded to highlight the trend, making it easier to see the trend’s direction without needing to analyze numerical data manually. 8. Note on Mean-Reversion Strategy: If you take the inverse of the signals, this indicator can be adapted for a mean-reversion strategy. Instead of following the trend, the strategy would involve buying assets that are underperforming and selling assets that are overperforming, based on volume momentum. However, it’s important to note that this approach may not work effectively on highly correlated assets, as their price movements may be too similar, reducing the effectiveness of the mean-reversion strategy. Final Thoughts The Volume Momentum indicator offers a comprehensive approach to analyzing volume-based momentum shifts in the market. By using volume normalization, percentile interpolation, and smoothed moving averages, this system helps identify the strength and direction of market trends. Whether used for trend-following or adapted for mean-reversion, this tool provides traders with actionable insights into the market’s volume-driven movements, improving decision-making and portfolio management.
Pine Script® indicator
by BackQuant
Market Zone Analyzer[BullByte]Understanding the Market Zone Analyzer --- 1. Purpose of the Indicator The Market Zone Analyzer is a Pine Script™ (version 6) indicator designed to streamline market analysis on TradingView. Rather than scanning multiple separate tools, it unifies four core dimensions—trend strength, momentum, price action, and market activity—into a single, consolidated view. By doing so, it helps traders: • Save time by avoiding manual cross-referencing of disparate signals. • Reduce decision-making errors that can arise from juggling multiple indicators. • Gain a clear, reliable read on whether the market is in a bullish, bearish, or sideways phase, so they can more confidently decide to enter, exit, or hold a position. --- 2. Why a Trader Should Use It • Unified View: Combines all essential market dimensions into one easy-to-read score and dashboard, eliminating the need to piece together signals manually. • Adaptability: Automatically adjusts its internal weighting for trend, momentum, and price action based on current volatility. Whether markets are choppy or calm, the indicator remains relevant. • Ease of Interpretation: Outputs a simple “BULLISH,” “BEARISH,” or “SIDEWAYS” label, supplemented by an intuitive on-chart dashboard and an oscillator plot that visually highlights market direction. • Reliability Features: Built-in smoothing of the net score and hysteresis logic (requiring consecutive confirmations before flips) minimize false signals during noisy or range-bound phases. --- 3. Why These Specific Indicators? This script relies on a curated set of well-established technical tools, each chosen for its particular strength in measuring one of the four core dimensions: 1. Trend Strength: • ADX/DMI (Average Directional Index / Directional Movement Index): Measures how strong a trend is, and whether the +DI line is above the –DI line (bullish) or vice versa (bearish). • Moving Average Slope (Fast MA vs. Slow MA): Compares a shorter-period SMA to a longer-period SMA; if the fast MA sits above the slow MA, it confirms an uptrend, and vice versa for a downtrend. • Ichimoku Cloud Differential (Senkou A vs. Senkou B): Provides a forward-looking view of trend direction; Senkou A above Senkou B signals bullishness, and the opposite signals bearishness. 2. Momentum: • Relative Strength Index (RSI): Identifies overbought (above its dynamically calculated upper bound) or oversold (below its lower bound) conditions; changes in RSI often precede price reversals. • Stochastic %K: Highlights shifts in short-term momentum by comparing closing price to the recent high/low range; values above its upper band signal bullish momentum, below its lower band signal bearish momentum. • MACD Histogram: Measures the difference between the MACD line and its signal line; a positive histogram indicates upward momentum, a negative histogram indicates downward momentum. 3. Price Action: • Highest High / Lowest Low (HH/LL) Range: Over a defined lookback period, this captures breakout or breakdown levels. A closing price near the recent highs (with a positive MA slope) yields a bullish score, and near the lows (with a negative MA slope) yields a bearish score. • Heikin-Ashi Doji Detection: Uses Heikin-Ashi candles to identify indecision or continuation patterns. A small Heikin-Ashi body (doji) relative to recent volatility is scored as neutral; a larger body in the direction of the MA slope is scored bullish or bearish. • Candle Range Measurement: Compares each candle’s high-low range against its own dynamic band (average range ± standard deviation). Large candles aligning with the prevailing trend score bullish or bearish accordingly; unusually small candles can indicate exhaustion or consolidation. 4. Market Activity: • Bollinger Bands Width (BBW): Measures the distance between BB upper and lower bands; wide bands indicate high volatility, narrow bands indicate low volatility. • Average True Range (ATR): Quantifies average price movement (volatility). A sudden spike in ATR suggests a volatile environment, while a contraction suggests calm. • Keltner Channels Width (KCW): Similar to BBW but uses ATR around an EMA. Provides a second layer of volatility context, confirming or contrasting BBW readings. • Volume (with Moving Average): Compares current volume to its moving average ± standard deviation. High volume validates strong moves; low volume signals potential lack of conviction. By combining these tools, the indicator captures trend direction, momentum strength, price-action nuances, and overall market energy, yielding a more balanced and comprehensive assessment than any single tool alone. --- 4. What Makes This Indicator Stand Out • Multi-Dimensional Analysis: Rather than relying on a lone oscillator or moving average crossover, it simultaneously evaluates trend, momentum, price action, and activity. • Dynamic Weighting: The relative importance of trend, momentum, and price action adjusts automatically based on real-time volatility (Market Activity State). For example, in highly volatile conditions, trend and momentum signals carry more weight; in calm markets, price action signals are prioritized. • Stability Mechanisms: • Smoothing: The net score is passed through a short moving average, filtering out noise, especially on lower timeframes. • Hysteresis: Both Market Activity State and the final bullish/bearish/sideways zone require two consecutive confirmations before flipping, reducing whipsaw. • Visual Interpretation: A fully customizable on-chart dashboard displays each sub-indicator’s value, regime, score, and comment, all color-coded. The oscillator plot changes color to reflect the current market zone (green for bullish, red for bearish, gray for sideways) and shows horizontal threshold lines at +2, 0, and –2. --- 5. Recommended Timeframes • Short-Term (5 min, 15 min): Day traders and scalpers can benefit from rapid signals, but should enable smoothing (and possibly disable hysteresis) to reduce false whipsaws. • Medium-Term (1 h, 4 h): Swing traders find a balance between responsiveness and reliability. Less smoothing is required here, and the default parameters (e.g., ADX length = 14, RSI length = 14) perform well. • Long-Term (Daily, Weekly): Position traders tracking major trends can disable smoothing for immediate raw readings, since higher-timeframe noise is minimal. Adjust lookback lengths (e.g., increase adxLength, rsiLength) if desired for slower signals. Tip: If you keep smoothing off, stick to timeframes of 1 h or higher to avoid excessive signal “chatter.” --- 6. How Scoring Works A. Individual Indicator Scores Each sub-indicator is assigned one of three discrete scores: • +1 if it indicates a bullish condition (e.g., RSI above its dynamically calculated upper bound). • 0 if it is neutral (e.g., RSI between upper and lower bounds). • –1 if it indicates a bearish condition (e.g., RSI below its dynamically calculated lower bound). Examples of individual score assignments: • ADX/DMI: • +1 if ADX ≥ adxThreshold and +DI > –DI (strong bullish trend) • –1 if ADX ≥ adxThreshold and –DI > +DI (strong bearish trend) • 0 if ADX < adxThreshold (trend strength below threshold) • RSI: • +1 if RSI > RSI_upperBound • –1 if RSI < RSI_lowerBound • 0 otherwise • ATR (as part of Market Activity): • +1 if ATR > (ATR_MA + stdev(ATR)) • –1 if ATR < (ATR_MA – stdev(ATR)) • 0 otherwise Each of the four main categories shares this same +1/0/–1 logic across their sub-components. B. Category Scores Once each sub-indicator reports +1, 0, or –1, these are summed within their categories as follows: • Trend Score = (ADX score) + (MA slope score) + (Ichimoku differential score) • Momentum Score = (RSI score) + (Stochastic %K score) + (MACD histogram score) • Price Action Score = (Highest-High/Lowest-Low score) + (Heikin-Ashi doji score) + (Candle range score) • Market Activity Raw Score = (BBW score) + (ATR score) + (KC width score) + (Volume score) Each category’s summed value can range between –3 and +3 (for Trend, Momentum, and Price Action), and between –4 and +4 for Market Activity raw. C. Market Activity State and Dynamic Weight Adjustments Rather than contributing directly to the netScore like the other three categories, Market Activity determines how much weight to assign to Trend, Momentum, and Price Action: 1. Compute Market Activity Raw Score by summing BBW, ATR, KCW, and Volume individual scores (each +1/0/–1). 2. Bucket into High, Medium, or Low Activity: • High if raw Score ≥ 2 (volatile market). • Low if raw Score ≤ –2 (calm market). • Medium otherwise. 3. Apply Hysteresis (if enabled): The state only flips after two consecutive bars register the same high/low/medium label. 4. Set Category Weights: • High Activity: Trend = 50 %, Momentum = 35 %, Price Action = 15 %. • Low Activity: Trend = 25 %, Momentum = 20 %, Price Action = 55 %. • Medium Activity: Use the trader’s base weight inputs (e.g., Trend = 40 %, Momentum = 30 %, Price Action = 30 % by default). D. Calculating the Net Score 5. Normalize Base Weights (so that the sum of Trend + Momentum + Price Action always equals 100 %). 6. Determine Current Weights based on the Market Activity State (High/Medium/Low). 7. Compute Each Category’s Contribution: Multiply (categoryScore) × (currentWeight). 8. Sum Contributions to get the raw netScore (a floating-point value that can exceed ±3 when scores are strong). 9. Smooth the netScore over two bars (if smoothing is enabled) to reduce noise. 10. Apply Hysteresis to the Final Zone: • If the smoothed netScore ≥ +2, the bar is classified as “Bullish.” • If the smoothed netScore ≤ –2, the bar is classified as “Bearish.” • Otherwise, it is “Sideways.” • To prevent rapid flips, the script requires two consecutive bars in the new zone before officially changing the displayed zone (if hysteresis is on). E. Thresholds for Zone Classification • BULLISH: netScore ≥ +2 • BEARISH: netScore ≤ –2 • SIDEWAYS: –2 < netScore < +2 --- 7. Role of Volatility (Market Activity State) in Scoring Volatility acts as a dynamic switch that shifts which category carries the most influence: 1. High Activity (Volatile): • Detected when at least two sub-scores out of BBW, ATR, KCW, and Volume equal +1. • The script sets Trend weight = 50 % and Momentum weight = 35 %. Price Action weight is minimized at 15 %. • Rationale: In volatile markets, strong trending moves and momentum surges dominate, so those signals are more reliable than nuanced candle patterns. 2. Low Activity (Calm): • Detected when at least two sub-scores out of BBW, ATR, KCW, and Volume equal –1. • The script sets Price Action weight = 55 %, Trend = 25 %, and Momentum = 20 %. • Rationale: In quiet, sideways markets, subtle price-action signals (breakouts, doji patterns, small-range candles) are often the best early indicators of a new move. 3. Medium Activity (Balanced): • Raw Score between –1 and +1 from the four volatility metrics. • Uses whatever base weights the trader has specified (e.g., Trend = 40 %, Momentum = 30 %, Price Action = 30 %). Because volatility can fluctuate rapidly, the script employs hysteresis on Market Activity State: a new High or Low state must occur on two consecutive bars before weights actually shift. This avoids constant back-and-forth weight changes and provides more stability. --- 8. Scoring Example (Hypothetical Scenario) • Symbol: Bitcoin on a 1-hour chart. • Market Activity: Raw volatility sub-scores show BBW (+1), ATR (+1), KCW (0), Volume (+1) → Total raw Score = +3 → High Activity. • Weights Selected: Trend = 50 %, Momentum = 35 %, Price Action = 15 %. • Trend Signals: • ADX strong and +DI > –DI → +1 • Fast MA above Slow MA → +1 • Ichimoku Senkou A > Senkou B → +1 → Trend Score = +3 • Momentum Signals: • RSI above upper bound → +1 • MACD histogram positive → +1 • Stochastic %K within neutral zone → 0 → Momentum Score = +2 • Price Action Signals: • Highest High/Lowest Low check yields 0 (close not near extremes) • Heikin-Ashi doji reading is neutral → 0 • Candle range slightly above upper bound but trend is strong, so → +1 → Price Action Score = +1 • Compute Net Score (before smoothing): • Trend contribution = 3 × 0.50 = 1.50 • Momentum contribution = 2 × 0.35 = 0.70 • Price Action contribution = 1 × 0.15 = 0.15 • Raw netScore = 1.50 + 0.70 + 0.15 = 2.35 • Since 2.35 ≥ +2 and hysteresis is met, the final zone is “Bullish.” Although the netScore lands at 2.35 (Bullish), smoothing might bring it slightly below 2.00 on the first bar (e.g., 1.90), in which case the script would wait for a second consecutive reading above +2 before officially classifying the zone as Bullish (if hysteresis is enabled). --- 9. Correlation Between Categories The four categories—Trend Strength, Momentum, Price Action, and Market Activity—often reinforce or offset one another. The script takes advantage of these natural correlations: • Bullish Alignment: If ADX is strong and pointed upward, fast MA is above slow MA, and Ichimoku is positive, that usually coincides with RSI climbing above its upper bound and the MACD histogram turning positive. In such cases, both Trend and Momentum categories generate +1 or +2. Because the Market Activity State is likely High (given the accompanying volatility), Trend and Momentum weights are at their peak, so the netScore quickly crosses into Bullish territory. • Sideways/Consolidation: During a low-volatility, sideways phase, ADX may fall below its threshold, MAs may flatten, and RSI might hover in the neutral band. However, subtle price-action signals (like a small breakout candle or a Heikin-Ashi candle with a slight bias) can still produce a +1 in the Price Action category. If Market Activity is Low, Price Action’s weight (55 %) can carry enough influence—even if Trend and Momentum are neutral—to push the netScore out of “Sideways” into a mild bullish or bearish bias. • Opposing Signals: When Trend is bullish but Momentum turns negative (for example, price continues up but RSI rolls over), the two scores can partially cancel. Market Activity may remain Medium, in which case the netScore lingers near zero (Sideways). The trader can then wait for either a clearer momentum shift or a fresh price-action breakout before committing. By dynamically recognizing these correlations and adjusting weights, the indicator ensures that: • When Trend and Momentum align (and volatility supports it), the netScore leaps strongly into Bullish or Bearish. • When Trend is neutral but Price Action shows an early move in a low-volatility environment, Price Action’s extra weight in the Low Activity State can still produce actionable signals. --- 10. Market Activity State & Its Role (Detailed) The Market Activity State is not a direct category score—it is an overarching context setter for how heavily to trust Trend, Momentum, or Price Action. Here’s how it is derived and applied: 1. Calculate Four Volatility Sub-Scores: • BBW: Compare the current band width to its own moving average ± standard deviation. If BBW > (BBW_MA + stdev), assign +1 (high volatility); if BBW < (BBW_MA × 0.5), assign –1 (low volatility); else 0. • ATR: Compare ATR to its moving average ± standard deviation. A spike above the upper threshold is +1; a contraction below the lower threshold is –1; otherwise 0. • KCW: Same logic as ATR but around the KCW mean. • Volume: Compare current volume to its volume MA ± standard deviation. Above the upper threshold is +1; below the lower threshold is –1; else 0. 2. Sum Sub-Scores → Raw Market Activity Score: Range between –4 and +4. 3. Assign Market Activity State: • High Activity: Raw Score ≥ +2 (at least two volatility metrics are strongly spiking). • Low Activity: Raw Score ≤ –2 (at least two metrics signal unusually low volatility or thin volume). • Medium Activity: Raw Score is between –1 and +1 inclusive. 4. Hysteresis for Stability: • If hysteresis is enabled, a new state only takes hold after two consecutive bars confirm the same High, Medium, or Low label. • This prevents the Market Activity State from bouncing around when volatility is on the fence. 5. Set Category Weights Based on Activity State: • High Activity: Trend = 50 %, Momentum = 35 %, Price Action = 15 %. • Low Activity: Trend = 25 %, Momentum = 20 %, Price Action = 55 %. • Medium Activity: Use trader’s base weights (e.g., Trend = 40 %, Momentum = 30 %, Price Action = 30 %). 6. Impact on netScore: Because category scores (–3 to +3) multiply by these weights, High Activity amplifies the effect of strong Trend and Momentum scores; Low Activity amplifies the effect of Price Action. 7. Market Context Tooltip: The dashboard includes a tooltip summarizing the current state—e.g., “High activity, trend and momentum prioritized,” “Low activity, price action prioritized,” or “Balanced market, all categories considered.” --- 11. Category Weights: Base vs. Dynamic Traders begin by specifying base weights for Trend Strength, Momentum, and Price Action that sum to 100 %. These apply only when volatility is in the Medium band. Once volatility shifts: • High Volatility Overrides: • Trend jumps from its base (e.g., 40 %) to 50 %. • Momentum jumps from its base (e.g., 30 %) to 35 %. • Price Action is reduced to 15 %. Example: If base weights were Trend = 40 %, Momentum = 30 %, Price Action = 30 %, then in High Activity they become 50/35/15. A Trend score of +3 now contributes 3 × 0.50 = +1.50 to netScore; a Momentum +2 contributes 2 × 0.35 = +0.70. In total, Trend + Momentum can easily push netScore above the +2 threshold on its own. • Low Volatility Overrides: • Price Action leaps from its base (30 %) to 55 %. • Trend falls to 25 %, Momentum falls to 20 %. Why? When markets are quiet, subtle candle breakouts, doji patterns, and small-range expansions tend to foreshadow the next swing more effectively than raw trend readings. A Price Action score of +3 in this state contributes 3 × 0.55 = +1.65, which can carry the netScore toward +2—even if Trend and Momentum are neutral or only mildly positive. Because these weight shifts happen only after two consecutive bars confirm a High or Low state (if hysteresis is on), the indicator avoids constantly flipping its emphasis during borderline volatility phases. --- 12. Dominant Category Explained Within the dashboard, a label such as “Trend Dominant,” “Momentum Dominant,” or “Price Action Dominant” appears when one category’s absolute weighted contribution to netScore is the largest. Concretely: • Compute each category’s weighted contribution = (raw category score) × (current weight). • Compare the absolute values of those three contributions. • The category with the highest absolute value is flagged as Dominant for that bar. Why It Matters: • Momentum Dominant: Indicates that the combined force of RSI, Stochastic, and MACD (after weighting) is pushing netScore farther than either Trend or Price Action. In practice, it means that short-term sentiment and speed of change are the primary drivers right now, so traders should watch for continued momentum signals before committing to a trade. • Trend Dominant: Means ADX, MA slope, and Ichimoku (once weighted) outweigh the other categories. This suggests a strong directional move is in place; trend-following entries or confirming pullbacks are likely to succeed. • Price Action Dominant: Occurs when breakout/breakdown patterns, Heikin-Ashi candle readings, and range expansions (after weighting) are the most influential. This often happens in calmer markets, where subtle shifts in candle structure can foreshadow bigger moves. By explicitly calling out which category is carrying the most weight at any moment, the dashboard gives traders immediate insight into why the netScore is tilting toward bullish, bearish, or sideways. --- 13. Oscillator Plot: How to Read It The “Net Score” oscillator sits below the dashboard and visually displays the smoothed netScore as a line graph. Key features: 1. Value Range: In normal conditions it oscillates roughly between –3 and +3, but extreme confluences can push it outside that range. 2. Horizontal Threshold Lines: • +2 Line (Bullish threshold) • 0 Line (Neutral midline) • –2 Line (Bearish threshold) 3. Zone Coloring: • Green Background (Bullish Zone): When netScore ≥ +2. • Red Background (Bearish Zone): When netScore ≤ –2. • Gray Background (Sideways Zone): When –2 < netScore < +2. 4. Dynamic Line Color: • The plotted netScore line itself is colored green in a Bullish Zone, red in a Bearish Zone, or gray in a Sideways Zone, creating an immediate visual cue. Interpretation Tips: • Crossing Above +2: Signals a strong enough combined trend/momentum/price-action reading to classify as Bullish. Many traders wait for a clear crossing plus a confirmation candle before entering a long position. • Crossing Below –2: Indicates a strong Bearish signal. Traders may consider short or exit strategies. • Rising Slope, Even Below +2: If netScore climbs steadily from neutral toward +2, it demonstrates building bullish momentum. • Divergence: If price makes a higher high but the oscillator fails to reach a new high, it can warn of weakening momentum and a potential reversal. --- 14. Comments and Their Necessity Every sub-indicator (ADX, MA slope, Ichimoku, RSI, Stochastic, MACD, HH/LL, Heikin-Ashi, Candle Range, BBW, ATR, KCW, Volume) generates a short comment that appears in the detailed dashboard. Examples: • “Strong bullish trend” or “Strong bearish trend” for ADX/DMI • “Fast MA above slow MA” or “Fast MA below slow MA” for MA slope • “RSI above dynamic threshold” or “RSI below dynamic threshold” for RSI • “MACD histogram positive” or “MACD histogram negative” for MACD Hist • “Price near highs” or “Price near lows” for HH/LL checks • “Bullish Heikin Ashi” or “Bearish Heikin Ashi” for HA Doji scoring • “Large range, trend confirmed” or “Small range, trend contradicted” for Candle Range Additionally, the top-row comment for each category is: • Trend: “Highly Bullish,” “Highly Bearish,” or “Neutral Trend.” • Momentum: “Strong Momentum,” “Weak Momentum,” or “Neutral Momentum.” • Price Action: “Bullish Action,” “Bearish Action,” or “Neutral Action.” • Market Activity: “Volatile Market,” “Calm Market,” or “Stable Market.” Reasons for These Comments: • Transparency: Shows exactly how each sub-indicator contributed to its category score. • Education: Helps traders learn why a category is labeled bullish, bearish, or neutral, building intuition over time. • Customization: If, for example, the RSI comment says “RSI neutral” despite an impending trend shift, a trader might choose to adjust RSI length or thresholds. In the detailed dashboard, hovering over each comment cell also reveals a tooltip with additional context (e.g., “Fast MA above slow MA” or “Senkou A above Senkou B”), helping traders understand the precise rule behind that +1, 0, or –1 assignment. --- 15. Real-Life Example (Consolidated) • Instrument & Timeframe: Bitcoin (BTCUSD), 1-hour chart. • Current Market Activity: BBW and ATR both spike (+1 each), KCW is moderately high (+1), but volume is only neutral (0) → Raw Market Activity Score = +2 → State = High Activity (after two bars, if hysteresis is on). • Category Weights Applied: Trend = 50 %, Momentum = 35 %, Price Action = 15 %. • Trend Sub-Scores: 1. ADX = 25 (above threshold 20) with +DI > –DI → +1. 2. Fast MA (20-period) sits above Slow MA (50-period) → +1. 3. Ichimoku: Senkou A > Senkou B → +1. → Trend Score = +3. • Momentum Sub-Scores: 4. RSI = 75 (above its moving average +1 stdev) → +1. 5. MACD histogram = +0.15 → +1. 6. Stochastic %K = 50 (mid-range) → 0. → Momentum Score = +2. • Price Action Sub-Scores: 7. Price is not within 1 % of the 20-period high/low and slope = positive → 0. 8. Heikin-Ashi body is slightly larger than stdev over last 5 bars with haClose > haOpen → +1. 9. Candle range is just above its dynamic upper bound but trend is already captured, so → +1. → Price Action Score = +2. • Calculate netScore (before smoothing): • Trend contribution = 3 × 0.50 = 1.50 • Momentum contribution = 2 × 0.35 = 0.70 • Price Action contribution = 2 × 0.15 = 0.30 • Raw netScore = 1.50 + 0.70 + 0.30 = 2.50 → Immediately classified as Bullish. • Oscillator & Dashboard Output: • The oscillator line crosses above +2 and turns green. • Dashboard displays: • Trend Regime “BULLISH,” Trend Score = 3, Comment = “Highly Bullish.” • Momentum Regime “BULLISH,” Momentum Score = 2, Comment = “Strong Momentum.” • Price Action Regime “BULLISH,” Price Action Score = 2, Comment = “Bullish Action.” • Market Activity State “High,” Comment = “Volatile Market.” • Weights: Trend 50 %, Momentum 35 %, Price Action 15 %. • Dominant Category: Trend (because 1.50 > 0.70 > 0.30). • Overall Score: 2.50, posCount = (three +1s in Trend) + (two +1s in Momentum) + (two +1s in Price Action) = 7 bullish signals, negCount = 0. • Final Zone = “BULLISH.” • The trader sees that both Trend and Momentum are reinforcing each other under high volatility. They might wait one more candle for confirmation but already have strong evidence to consider a long. --- • . --- Disclaimer This indicator is strictly a technical analysis tool and does not constitute financial advice. All trading involves risk, including potential loss of capital. Past performance is not indicative of future results. Traders should: • Always backtest the “Market Zone Analyzer ” on their chosen symbols and timeframes before committing real capital. • Combine this tool with sound risk management, position sizing, and, if possible, fundamental analysis. • Understand that no indicator is foolproof; always be prepared for unexpected market moves. Goodluck -BullByte! ---
Pine Script® indicator
by BullByte
2
Performance Metrics With Bracketed Rebalacing [BackQuant]Performance Metrics With Bracketed Rebalancing The Performance Metrics With Bracketed Rebalancing script offers a robust method for assessing portfolio performance, integrating advanced portfolio metrics with different rebalancing strategies. With a focus on adaptability, the script allows traders to monitor and adjust portfolio weights, equity, and other key financial metrics dynamically. This script provides a versatile approach for evaluating different trading strategies, considering factors like risk-adjusted returns, volatility, and the impact of portfolio rebalancing. Please take the time to read the following: Key Features and Benefits of Portfolio Methods Bracketed Rebalancing: Bracketed Rebalancing is an advanced strategy designed to trigger portfolio adjustments when an asset's weight surpasses a predefined threshold. This approach minimizes overexposure to any single asset while maintaining flexibility in response to market changes. The strategy is particularly beneficial for mitigating risks that arise from significant asset weight fluctuations. The following image illustrates how this method reacts when asset weights cross the threshold: Daily Rebalancing: Unlike the bracketed method, Daily Rebalancing adjusts portfolio weights every trading day, ensuring consistent asset allocation. This method aims for a more even distribution of portfolio weights, making it a suitable option for traders who prefer less sensitivity to individual asset volatility. Here's an example of Daily Rebalancing in action: No Rebalancing: For traders who prefer a passive approach, the "No Rebalancing" option allows the portfolio to remain static, without any adjustments to asset weights. This method may appeal to long-term investors or those who believe in the inherent stability of their selected assets. Here’s how the portfolio looks when no rebalancing is applied: Portfolio Weights Visualization: One of the standout features of this script is the visual representation of portfolio weights. With adjustable settings, users can track the current allocation of assets in real-time, making it easier to analyze shifts and trends. The following image shows the real-time weight distribution across three assets: Rolling Drawdown Plot: Managing drawdown risk is a critical aspect of portfolio management. The Rolling Drawdown Plot visually tracks the drawdown over time, helping traders monitor the risk exposure and performance relative to the peak equity levels. This feature is essential for assessing the portfolio's resilience during market downturns: Daily Portfolio Returns: Tracking daily returns is crucial for evaluating the short-term performance of the portfolio. The script allows users to plot daily portfolio returns to gain insights into daily profit or loss, helping traders stay updated on their portfolio’s progress: Performance Metrics Net Profit (%): This metric represents the total return on investment as a percentage of the initial capital. A positive net profit indicates that the portfolio has gained value over the evaluation period, while a negative value suggests a loss. It's a fundamental indicator of overall portfolio performance. Maximum Drawdown (Max DD): Maximum Drawdown measures the largest peak-to-trough decline in portfolio value during a specified period. It quantifies the most significant loss an investor would have experienced if they had invested at the highest point and sold at the lowest point within the timeframe. A smaller Max DD indicates better risk management and less exposure to significant losses. Annual Mean Returns (% p/y): This metric calculates the average annual return of the portfolio over the evaluation period. It provides insight into the portfolio's ability to generate returns on an annual basis, aiding in performance comparison with other investment opportunities. Annual Standard Deviation of Returns (% p/y): This measure indicates the volatility of the portfolio's returns on an annual basis. A higher standard deviation signifies greater variability in returns, implying higher risk, while a lower value suggests more stable returns. Variance: Variance is the square of the standard deviation and provides a measure of the dispersion of returns. It helps in understanding the degree of risk associated with the portfolio's returns. Sortino Ratio: The Sortino Ratio is a variation of the Sharpe Ratio that only considers downside risk, focusing on negative volatility. It is calculated as the difference between the portfolio's return and the minimum acceptable return (MAR), divided by the downside deviation. A higher Sortino Ratio indicates better risk-adjusted performance, emphasizing the importance of avoiding negative returns. Sharpe Ratio: The Sharpe Ratio measures the portfolio's excess return per unit of total risk, as represented by standard deviation. It is calculated by subtracting the risk-free rate from the portfolio's return and dividing by the standard deviation of the portfolio's excess return. A higher Sharpe Ratio indicates more favorable risk-adjusted returns. Omega Ratio: The Omega Ratio evaluates the probability of achieving returns above a certain threshold relative to the probability of experiencing returns below that threshold. It is calculated by dividing the cumulative probability of positive returns by the cumulative probability of negative returns. An Omega Ratio greater than 1 indicates a higher likelihood of achieving favorable returns. Gain-to-Pain Ratio: The Gain-to-Pain Ratio measures the return per unit of risk, focusing on the magnitude of gains relative to the severity of losses. It is calculated by dividing the total gains by the total losses experienced during the evaluation period. A higher ratio suggests a more favorable balance between reward and risk. www.linkedin.com Compound Annual Growth Rate (CAGR) (% p/y): CAGR represents the mean annual growth rate of the portfolio over a specified period, assuming the investment has been compounding over that time. It provides a smoothed annual rate of growth, eliminating the effects of volatility and offering a clearer picture of long-term performance. Portfolio Alpha (% p/y): Portfolio Alpha measures the portfolio's performance relative to a benchmark index, adjusting for risk. It is calculated using the Capital Asset Pricing Model (CAPM) and represents the excess return of the portfolio over the expected return based on its beta and the benchmark's performance. A positive alpha indicates outperformance, while a negative alpha suggests underperformance. Portfolio Beta: Portfolio Beta assesses the portfolio's sensitivity to market movements, indicating its exposure to systematic risk. A beta greater than 1 suggests the portfolio is more volatile than the market, while a beta less than 1 indicates lower volatility. Beta is used to understand the portfolio's potential for gains or losses in relation to market fluctuations. Skewness of Returns: Skewness measures the asymmetry of the return distribution. A positive skew indicates a distribution with a long right tail, suggesting more frequent small losses and fewer large gains. A negative skew indicates a long left tail, implying more frequent small gains and fewer large losses. Understanding skewness helps in assessing the likelihood of extreme outcomes. Value at Risk (VaR) 95th Percentile: VaR at the 95th percentile estimates the maximum potential loss over a specified period, given a 95% confidence level. It provides a threshold value such that there is a 95% probability that the portfolio will not experience a loss greater than this amount. Conditional Value at Risk (CVaR): CVaR, also known as Expected Shortfall, measures the average loss exceeding the VaR threshold. It provides insight into the tail risk of the portfolio, indicating the expected loss in the worst-case scenarios beyond the VaR level. These metrics collectively offer a comprehensive view of the portfolio's performance, risk exposure, and efficiency. By analyzing these indicators, investors can make informed decisions, balancing potential returns with acceptable levels of risk. Conclusion The Performance Metrics With Bracketed Rebalancing script provides a comprehensive framework for evaluating and optimizing portfolio performance. By integrating advanced metrics, adaptive rebalancing strategies, and visual analytics, it empowers traders to make informed decisions in managing their investment portfolios. However, it's crucial to consider the implications of rebalancing strategies, as academic research indicates that predictable rebalancing can lead to market impact costs. Therefore, adopting flexible and less predictable rebalancing approaches may enhance portfolio performance and reduce associated costs.
Pine Script® indicator
by BackQuant
40 Ticker Cross-Sectional Z-Scores [BackQuant]40 Ticker Cross-Sectional Z-Scores BackQuant’s 40 Ticker Cross-Sectional Z-Scores is a powerful portfolio management strategy that analyzes the relative performance of up to 40 different assets, comparing them on a cross-sectional basis to identify the top and bottom performers. This indicator computes Z-scores for each asset based on their log returns and evaluates them relative to the mean and standard deviation over a rolling window. The Z-scores represent how far an asset's return deviates from the average, and these values are used to rank the assets, allowing for dynamic asset allocation based on performance. By focusing on the strongest-performing assets and avoiding the weakest, this strategy aims to enhance returns while managing risk. Additionally, by adjusting for standard deviations, the system offers a risk-adjusted method of ranking assets, making it suitable for traders who want to dynamically allocate capital based on performance metrics rather than just price movements. Key Features 1. Cross-Sectional Z-Score Calculation: The system calculates Z-scores for 40 different assets, evaluating their log returns against the mean and standard deviation over a rolling window. This enables users to assess the relative performance of each asset dynamically, highlighting which assets are performing better or worse compared to their historical norms. The Z-score is a useful statistical tool for identifying outliers in asset performance. 2. Asset Ranking and Allocation: The system ranks assets based on their Z-scores and allocates capital to the top performers. It identifies the top and bottom assets, and traders can allocate capital to the top-performing assets, ensuring that their portfolio is aligned with the best performers. Conversely, the bottom assets are removed from the portfolio, reducing exposure to underperforming assets. 3. Rolling Window for Mean and Standard Deviation Calculations: The Z-scores are calculated based on rolling means and standard deviations, making the system adaptive to changing market conditions. This rolling calculation window allows the strategy to adjust to recent performance trends and minimize the impact of outdated data. 4. Mean and Standard Deviation Visualization: The script provides real-time visualizations of the mean (x̄) and standard deviation (σ) of asset returns, helping traders quickly identify trends and volatility in their portfolio. These visual indicators are useful for understanding the current market environment and making more informed allocation decisions. 5. Top & Bottom Performer Tables: The system generates tables that display the top and bottom performers, ranked by their Z-scores. Traders can quickly see which assets are outperforming and underperforming. These tables provide clear and actionable insights, helping traders make informed decisions about which assets to include in their portfolio. 6. Customizable Parameters: The strategy allows traders to customize several key parameters, including: Rolling Calculation Window: Set the window size for the rolling mean and standard deviation calculations. Top & Bottom Tickers: Choose how many of the top and bottom assets to display and allocate capital to. Table Orientation: Select between vertical or horizontal table formats to suit the user’s preference. 7. Forward Test & Out-of-Sample Testing: The system includes out-of-sample forward tests, ensuring that the strategy is evaluated based on real-time performance, not just historical data. This forward testing approach helps validate the robustness of the strategy in dynamic market conditions. 8. Visual Feedback and Alerts: The system provides visual feedback on the current asset rankings and allocations, with dynamic labels and plots on the chart. Additionally, users receive alerts when allocations change, keeping them informed of important adjustments. 9. Risk Management via Z-Scores and Std Dev: The system’s approach to asset selection is based on Z-scores, which normalize performance relative to the historical mean. By incorporating standard deviation, it accounts for the volatility and risk associated with each asset. This allows for more precise risk management and portfolio construction. 10. Note on Mean Reversion Strategy: If you take the inverse of the signals provided by this indicator, the strategy can be used for mean-reversion rather than trend-following. This would involve buying the underperforming assets and selling the outperforming ones. However, it's important to note that this approach does not work well with highly correlated assets, as the relationship between the assets could result in the same directional movement, undermining the effectiveness of the mean-reversion strategy. References www.uts.edu.au onlinelibrary.wiley.com www.cmegroup.com Final Thoughts The 40 Ticker Cross-Sectional Z-Scores strategy offers a data-driven approach to portfolio management, dynamically allocating capital based on the relative performance of assets. By using Z-scores and standard deviations, this strategy ensures that capital is directed to the strongest performers while avoiding weaker assets, ultimately improving the risk-adjusted returns of the portfolio. Whether you’re focused on trend-following or looking to explore mean-reversion strategies, this flexible system can be tailored to suit your investment goals.
Pine Script® indicator
by BackQuant
Mean Absolute Deviation Trend | Lyro RSMean Absolute Deviation Trend Introduction Mean Absolute Deviation (MAD) Trend is a precision tool designed to capture directional bias using the Mean Absolute Deviation from a dynamic moving average. It identifies trend shifts by measuring average volatility around price, highlighting bullish and bearish phases through adaptive bands. Signal Insight The 𝓜𝓐𝓓 𝓣𝓻𝓮𝓷𝓭 plots a dynamic bands around a user-defined moving average, using Mean Absolute Deviation (MAD) to reflect volatility-adjusted boundaries. A bullish signal is generated when price breaks above the upper MAD band—indicating positive momentum and potential trend continuation to the upside. A bearish signal occurs when price falls below the lower MAD band—signaling increased downside pressure and possible trend continuation to the downside. This approach gives traders a volatility-sensitive trend filter that can enhance signal quality across different market environments. Real-World Example 𝓜𝓐𝓓 𝓣𝓻𝓮𝓷𝓭 delivers a clear and timely long signal, capturing a +22.90% move. Upon exit, it seamlessly flips to a short position, securing an additional +13.34% —demonstrating its strength in both trending directions. Framework The 𝓜𝓐𝓓 𝓣𝓻𝓮𝓷𝓭 indicator identifies directional shifts by measuring price deviation from a dynamic moving average. At its core, it calculates the Mean Absolute Deviation (MAD) of price around a user-selected moving average. The indicator builds adaptive upper and lower bands by multiplying the MAD value above and below the moving average. When price crosses above the upper band, it triggers a bullish signal. When price crosses below the lower band, it signals bearish momentum which gives a bearish signal. This method provides an elegant balance between volatility sensitivity and trend clarity, adapting in real-time to changing market behavior. The moving average type and band sensitivity can be tuned to fit various strategies—from scalping to swing trading. Recommended Settings Long-Term Investing: 1D, EMA, 40, 2 Mid-Term Investing: 1D, Default Settings Swing Trading: 4h, EMA, 20, 2.5 Day/Intraday Trading: 15mins, 25, 2.5 ⚠️ WARNING ⚠️: THIS INDICATOR, OR ANY OTHER WE (LYRO RS) PUBLISH, IS NOT FINANCIAL OR INVESTMENT ADVICE. EVERY INDICATOR SHOULD BE COMBINED WITH PRICE ACTION, FUNDAMENTALS, OTHER TECHNICAL ANALYSIS TOOLS & PROPER RISK. MANAGEMENT.
Pine Script® indicator
by LyroRS
MACD Crossover with Supertrend FilterThis script is a custom trading indicator that generates **buy and sell signals** based on the combination of: ### 🔹 MACD Crossover: * **Long (Buy)** signal: when the MACD line crosses above the signal line **below the 0 line**. * **Short (Sell)** signal: when the MACD line crosses below the signal line **above the 0 line**. ### 🔹 Supertrend Filter: * **Only buy** when the Supertrend is **bullish (green)**. * **Only sell** when the Supertrend is **bearish (red)**. ### 🔹 Additional Features: * Plots green or red arrows on the chart for entries. * Supertrend line is color-coded. * Alerts can be enabled for both long and short signals. ✅ This combination filters MACD signals using trend direction for more reliable entries.
Pine Script® indicator
by rvsoriano056
ATR Bands PRO + Sweep Label + Divergence [MASTER]🔰 ATR Bands PRO + Liquidity Sweep & Divergence (RSI/MACD) ## 🔰 ATR Bands PRO + Liquidity Sweep & Divergence A powerful institutional-grade toolkit that combines advanced ATR band visualization, customizable stop bands, dynamic grid lines, real-time liquidity sweep detection, and built-in swing-point divergence signals (RSI/MACD) – all fully adjustable. **Key Features:** - **Multi-Timeframe ATR Bands:** Visualize ATR-based bands from any higher timeframe, fully customizable in color, width, style, and extension. - **Smart Stops & Grid:** Add stop bands and dynamic ATR grid lines with user control over appearance and step. - **Liquidity Sweep Detection:** Instantly see “Bull Sweep” or “Bear Sweep” labels every time price touches high/low liquidity sweeps from your chosen timeframe. - **Divergence Alerts (RSI/MACD):** Detect bullish or bearish divergence at swing highs/lows (on the main timeframe) – complete with highly visible, color-customizable labels. - **Professional, Non-Cluttered Visuals:** All labels and lines are managed with smart array handling – zero repaint, zero overlay clutter. **How to Use:** 1. Choose your ATR “base” timeframe and customize band/stop/grid appearance. 2. Pick the Liquidity Sweep timeframe (e.g., H1, H4, D1) for institutional swing levels. 3. Enable divergence detection (RSI or MACD) to reveal hidden reversal signals at market pivots. 4. Adjust label offsets and colors for maximum clarity on your chart. **Perfect for:** - Liquidity-driven scalping, swing, and positional strategies. - Spotting liquidity grabs, institutional sweeps, and “trap” price action. - Fast visual confirmation of potential reversal zones using built-in divergence signals. - Traders who demand high-performance, flexible visuals without chart clutter. --- **Credit:** Original logic inspired by RunStrat, AlgoAlpha, and custom adaptations by MILO888. --- *For educational and professional use. Test on your own symbol/timeframe before live trading. Enjoy an edge!*
Pine Script® indicator
by MILO888
Sigmoid Trend Confidence Oscillator (STCO)Overview: The Sigmoid Trend Confidence Oscillator (STCO) is a multi-length momentum indicator that combines Rate of Change (ROC) and Momentum (MOM) signals across short, medium, and long-term periods. It applies a sigmoid function to normalize and smooth these signals, producing a clear oscillator that reflects the confidence level in the current trend. Key Features: Combines ROC and MOM from three user-defined timeframes Applies sigmoid normalization to scale values between -1 and +1 Customizable weighting presets to emphasize different length horizons Optional smoothing using various moving average types (SMA, EMA, VWMA, WMA, HMA, RMA, TEMA, DEMA, FRAMA, TRIMA) Threshold-based trend states indicating bullish, bearish, or neutral conditions Optional candle coloring based on trend state for easy visualization Clear plots including oscillator line, histogram, zero line, and threshold lines How to Use: Adjust weighting presets to fit your trading horizon and style Enable smoothing to reduce noise on lower timeframes Enable candle coloring for quick visual cues on price chart Disclaimer Disclaimer: This indicator is provided for educational and informational purposes only and does not constitute investment advice. Trading involves risk and may result in financial loss. Always perform your own research and consult with a qualified financial advisor before making any trading decisions.
Pine Script® indicator
by MintLeafEdge
Golden Key: Opening Channel DashboardGolden Key: Opening Channel Dashboard Complementary to the original Golden Key – The Frequency Upgrade of 10 Monday's 1H Avg Range + 30-Day Daily Range This indicator provides a structured dashboard to monitor the opening channel range and related metrics on 15m and 5m charts. Built to work alongside the Golden Key methodology, it focuses on pip precision, average volatility, and SL sizing. What It Does Detects first 4 candles of the session: 15m chart → first 4 Monday candles (1 hour) 5m chart → first 4 candles of each day (20 minutes) Calculates pip range of the opening move Stores and averages the last 10 such ranges Calculates daily range average over 10 or 30 days Generates SL size based on your multiplier setting Auto-adjusts for FX, JPY, and XAUUSD pip sizes Displays all values in a clean table in the top-right How to Use It Add to a 15m or 5m chart Compare the current opening range to the average Use the daily average to assess broader volatility Define SL size using the opening range x multiplier Customize display colors per table row About This Script This is not a visual box-style indicator. It is designed to complement the original “Golden Key – The Frequency” by focusing on metric output. It is also an upgraded version of the earlier "10 Monday’s 1H Avg Range" script, now supporting multi-timeframe logic and additional customization. Disclaimer This is a technical analysis tool. It does not provide trading advice. Use it in combination with your own research and strategy.
Pine Script® indicator
by bramirez37
Momentum Breakout Option Buyer🎯 What it does: MOMENTUM BREAKOUT FOR OPTION BUYER # Detects momentum breakout zones # Confirms breakout with volume and volatility # Gives Buy signal only when the move is strong and fast — perfect for option buyers 🔧 Core Components: # Supertrend – to define the trend # RSI + EMA crossover – confirms strength # Breakout candle + Volume spike # ATR filter – confirms volatility is high enough to justify option buying ✅ Entry Criteria (Call Option): # Price above Supertrend # RSI > 60 and RSI > RSI EMA # Volume > 1.5 × average volume # ATR (last 5 candles) > minimum threshold (e.g., 1%) ❌ Exit / Stop Loss: # RSI drops below 50 or # Supertrend flips or # Target hit (e.g., 1.5x risk)
Pine Script® indicator
by abhishekacharya000
Combined ATR Bands + VWAP + Moving Averages🔥 Ultimate Trading Combo: ATR Bands + VWAP + Moving Averages This comprehensive indicator combines three powerful technical analysis tools in one clean interface: 📊 Features: • ATR Bands - Dynamic support/resistance levels with step-line styling • VWAP - Volume Weighted Average Price with orange dotted visualization • Moving Averages - 50, 100, 200 periods with customizable colors ⚙️ Customizable Settings: Toggle each component on/off independently Adjustable ATR periods and multipliers Multiple VWAP anchor periods (Session, Week, Month, Quarter, Year) Configurable MA sources and periods Custom colors and transparency levels 🎯 Perfect For: Day traders seeking dynamic support/resistance Swing traders using multiple timeframe analysis Anyone wanting clean, professional chart visualization 💡 Created with AI assistance (Claude Sonnet 4) Open source - feel free to modify and improve! 🔥 المؤشر الشامل: نطاقات ATR + VWAP + المتوسطات المتحركة هذا المؤشر الشامل يجمع ثلاث أدوات تحليل فني قوية في واجهة واحدة نظيفة: 📊 المميزات: • نطاقات ATR - مستويات دعم ومقاومة ديناميكية بتصميم خطوط متدرجة • VWAP - متوسط السعر المرجح بالحجم مع عرض نقطي برتقالي • المتوسطات المتحركة - فترات 50، 100، 200 مع ألوان قابلة للتخصيص ⚙️ إعدادات قابلة للتخصيص: تشغيل/إيقاف كل مكون بشكل مستقل فترات ومضاعفات ATR قابلة للتعديل فترات ربط VWAP متعددة (جلسة، أسبوع، شهر، ربع، سنة) مصادر وفترات MA قابلة للتكوين ألوان مخصصة ومستويات شفافية 🎯 مثالي لـ: المتداولين اليوميين الباحثين عن دعم/مقاومة ديناميكية متداولي التأرجح باستخدام تحليل الإطارات الزمنية المتعددة أي شخص يريد عرض مخططات نظيف ومهني 💡 تم إنشاؤه بمساعدة الذكاء الاصطناعي (Claude Sonnet 4) مفتوح المصدر - لا تتردد في التعديل والتحسين! #ATR #VWAP #MovingAverages #TechnicalAnalysis #Support #Resistance #DayTrading #SwingTrading #OpenSource #AI #تحليل_فني #دعم_مقاومة #متوسطات_متحركة #تداول_يومي
Pine Script® indicator
by Redtoura
Heikin-Ashi Mean Reversion Oscillator [Alpha Extract]The Heikin-Ashi Mean Reversion Oscillator combines the smoothing characteristics of Heikin-Ashi candlesticks with mean reversion analysis to create a powerful momentum oscillator. This indicator applies Heikin-Ashi transformation twice - first to price data and then to the oscillator itself - resulting in smoother signals while maintaining sensitivity to trend changes and potential reversal points. 🔶 CALCULATION Heikin-Ashi Transformation: Converts regular OHLC data to smoothed Heikin-Ashi values Component Analysis: Calculates trend strength, body deviation, and price deviation from mean Oscillator Construction: Combines components with weighted formula (40% trend strength, 30% body deviation, 30% price deviation) Double Smoothing: Applies EMA smoothing and second Heikin-Ashi transformation to oscillator values Signal Generation: Identifies trend changes and crossover points with overbought/oversold levels Formula: HA Close = (Open + High + Low + Close) / 4 HA Open = (Previous HA Open + Previous HA Close) / 2 Trend Strength = Normalized consecutive HA candle direction Body Deviation = (HA Body - Mean Body) / Mean Body * 100 Price Deviation = ((HA Close - Price Mean) / Price Mean * 100) / Standard Deviation * 25 Raw Oscillator = (Trend Strength * 0.4) + (Body Deviation * 0.3) + (Price Deviation * 0.3) Final Oscillator = 50 + (EMA(Raw Oscillator) / 2) 🔶 DETAILS Visual Features: Heikin-Ashi Candlesticks: Smoothed oscillator representation using HA transformation with vibrant teal/red coloring Overbought/Oversold Zones: Horizontal lines at customizable levels (default 70/30) with background highlighting in extreme zones Moving Averages: Optional fast and slow EMA overlays for additional trend confirmation Signal Dashboard: Real-time table showing current oscillator status (Overbought/Oversold/Bullish/Bearish) and buy/sell signals Reference Lines: Middle line at 50 (neutral), with 0 and 100 boundaries for range visualization Interpretation: Above 70: Overbought conditions, potential selling opportunity Below 30: Oversold conditions, potential buying opportunity Bullish HA Candles: Green/teal candles indicate upward momentum Bearish HA Candles: Red candles indicate downward momentum MA Crossovers: Fast EMA above slow EMA suggests bullish momentum, below suggests bearish momentum Zone Exits: Price moving out of extreme zones (above 70 or below 30) often signals trend continuation 🔶 EXAMPLES Mean Reversion Signals: When the oscillator reaches extreme levels (above 70 or below 30), it identifies potential reversal points where price may revert to the mean. Example: Oscillator reaching 80+ levels during strong uptrends often precedes short-term pullbacks, providing profit-taking opportunities. Trend Change Detection: The double Heikin-Ashi smoothing helps identify genuine trend changes while filtering out market noise. Example: When oscillator HA candles change from red to teal after oversold readings, this confirms potential trend reversal from bearish to bullish. Moving Average Confirmation: Fast and slow EMA crossovers on the oscillator provide additional confirmation of momentum shifts. Example: Fast EMA crossing above slow EMA while oscillator is rising from oversold levels provides strong bullish confirmation signal. Dashboard Signal Integration: The real-time dashboard combines oscillator status with directional signals for quick decision-making. Example: Dashboard showing "Oversold" status with "BUY" signal when HA candles turn bullish provides clear entry timing. 🔶 SETTINGS Customization Options: Calculation: Oscillator period (default 14), smoothing factor (1-50, default 2) Levels: Overbought threshold (50-100, default 70), oversold threshold (0-50, default 30) Moving Averages: Toggle display, fast EMA length (default 9), slow EMA length (default 21) Visual Enhancements: Show/hide signal dashboard, customizable table position Alert Conditions: Oversold bounce, overbought reversal, bullish/bearish MA crossovers The Heikin-Ashi Mean Reversion Oscillator provides traders with a sophisticated momentum tool that combines the smoothing benefits of Heikin-Ashi analysis with mean reversion principles. The double transformation process creates cleaner signals while the integrated dashboard and multiple confirmation methods help traders identify high-probability entry and exit points during both trending and ranging market conditions.
Pine Script® indicator
by AlphaExtract
Momentum Breakout Option Buyer🎯 What it does: # Detects momentum breakout zones # Confirms breakout with volume and volatility # Gives Buy signal only when the move is strong and fast — perfect for option buyers 🔧 Core Components: # Supertrend – to define the trend # RSI + EMA crossover – confirms strength # Breakout candle + Volume spike # ATR filter – confirms volatility is high enough to justify option buying ✅ Entry Criteria (Call Option): # Price above Super trend # RSI > 60 and RSI > RSI EMA # Volume > 1.5 × average volume # ATR (last 5 candles) > minimum threshold (e.g., 1%) ❌ Exit / Stop Loss: # RSI drops below 50 or # Supertrend flips or # Target hit (e.g., 1.5x risk)
Pine Script® indicator
by abhishekacharya000
Choppiness ZONE OverlayPurpose This script overlays choppiness zones directly onto the price chart to help traders identify whether the market is trending or ranging. It is designed to filter out low-probability trades during high choppiness conditions. How It Works Calculates the Choppiness Index over a user-defined period using ATR and price range. Divides choppiness into four zones: 30 to 40: Low choppiness, possible trend initiation, shown in yellow. 40 to 50: Moderate choppiness, transition zone, shown in orange. 50 to 60: High choppiness, weakening momentum, shown in red. 60 and above: Extreme choppiness, avoid trading, shown in purple. Highlights each zone with customizable color fills between the high and low of the selected range. Triggers a real-time alert when choppiness exceeds 60. Features Customizable choppiness zones and color settings. Real-time alert when market becomes extremely choppy (choppiness ≥ 60). Visual zone overlay on the price chart. Compatible with all timeframes. Lightweight and responsive for scalping, intraday, or swing trading. Tip Use this tool as a volatility or trend filter. Combine it with momentum or trend-following indicators to improve trade selection.
Pine Script® indicator
by alghamdi_meriam
SPX500 Quick Drop & Rise AlertsSimple script thats been adjusted for 1 minute trading on spx500. It will show you and signal to you: dropThreshold: how much the price must rise or fall (in percent) to trigger a signal. Default is 0.05 → 5%. lookbackBars: how many bars back to compare against. Default is 1 (i.e., compare the current close to the previous bar’s close). Theirs a few ways to use this, you might want to use your MA 238 as a reference point. Use it as a target or a level to bounce or reject from. Then use this indicator to help show you where the market energy is flowing. Do some backtesting and see what you see. Only use it for New York open times would probably be best. Youll have to change your mentality depending on if the market is trending / ranging ect of course.
Pine Script® indicator
by PaperChains