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Correlation

Sector Correlation Matrix [LuxAlgo]The Sector Correlation Matrix indicator provides a quantitative measure of the statistical relationship between a selected asset and a benchmark to help traders identify market synchronization and relative strength. 🔶 USAGE This tool is particularly useful for relative strength analysis. For example, if a stock is "Decoupled" while the benchmark is crashing, it may indicate idiosyncratic strength. Conversely, traders can use "Strong Positive" states to confirm that a price move is part of a broader market trend rather than an isolated event. The indicator categorizes market relationships into five distinct states based on the coefficient value: Strong Positive (> 0.7): High degree of synchronization; the assets move together closely. Moderate Positive (> 0.3): General tendency to move in the same direction. Decoupled / Neutral (-0.3 to 0.3): No significant linear relationship; the asset is moving independently. Moderate Negative (< -0.3): General tendency to move in opposite directions. Strong Negative (< -0.7): High degree of inverse synchronization. 🔶 DETAILS The script utilizes the Pearson Correlation Coefficient to calculate the rolling correlation between the current chart's closing price and a user-defined benchmark (such as SPY, QQQ, or BTC). This statistical method measures the linear correlation between two sets of data, resulting in a value between -1 (perfect inverse correlation) and 1 (perfect positive correlation). The visual output includes a dedicated pane with a correlation oscillator and an on-screen dashboard that provides real-time metrics, allowing for a quick assessment of the current market state without manually comparing multiple charts. // Core Calculation Logic correlationValue = ta.correlation(close, benchClose, corrLenInput) 🔶 SETTINGS 🔹 Logic Settings Benchmark Asset: The ticker symbol to compare the current asset against (e.g., SPY, QQQ, BTCUSDT). Correlation Length: The lookback period used to calculate the Pearson Correlation Coefficient. 🔹 Dashboard Show Dashboard: Enable or disable the on-screen information table. Position: Select the dashboard location (Top Right, Bottom Right, or Bottom Left). Size: Adjust the size of the dashboard text and cells (Tiny to Huge).
Chỉ báo Pine Script®
của LuxAlgo
Correlated Sine Oscillator [LuxAlgo]The Correlated Sine Oscillator indicator identifies and isolates cyclical components within price action to generate a normalized, phase-aligned sinusoid that fluctuates between -1 and 1. 🔶 USAGE The Correlated Sine Oscillator is designed to help traders visualize the underlying rhythm of the market by projecting price data onto a mathematical sine wave. Unlike standard momentum oscillators, this tool uses quadrature components to determine the current "phase" of the market cycle, allowing the oscillator to stay synced with price swings. 🔹 Identifying Cycle Direction The oscillator fluctuates within a fixed range of -1 to 1, centered around a zero line. Signals are generated based on the crossing of this midpoint to identify shifts in the dominant cycle. Bullish Signals: When the oscillator crosses above the zero line, a bullish triangle (▲) appears below the price bar, signaling the start of a rising phase in the cycle. Bearish Signals: When the oscillator crosses below the zero line, a bearish triangle (▼) appears above the price bar, signaling the start of a falling phase in the cycle. Cycle Extremes: While signals occur at the midline, values approaching 1 or -1 represent the peak and trough of the identified cycle, respectively. 🔹 The Phase Multiplier The Phase Multiplier setting is a unique feature that controls how responsively the oscillator adapts to changes in price direction. Since the indicator uses an average of the real and imaginary components to find the phase, increasing this multiplier will result in a smoother, more stable oscillator that is less prone to noise but slightly more lagging. Lowering the multiplier makes the oscillator react faster to price shifts, which can be useful for identifying quick reversals. 🔶 DETAILS The construction of the Correlated Sine Oscillator follows a sophisticated signal processing workflow: Detrending: The script first removes the trend by subtracting a Simple Moving Average (SMA) from the closing price. This isolates the high-frequency fluctuations (cycles) around a mean of zero. Quadrature Projection: The detrended price is projected onto both a Sine and Cosine wave (real and imaginary components) based on the user-defined Cycle Period . Phase Estimation: By calculating the arctangent of these components, the script derives the "Dynamic Phase." This tells us exactly where the current price sits within the theoretical cycle. Correlation: Finally, the script generates a synthetic sine wave that is shifted by the calculated phase, ensuring the output is perfectly correlated with the dominant frequency of the price action. 🔶 SETTINGS Cycle Period: Determines the primary wavelength (in bars) that the indicator looks for in the price data. Setting this to 20 means the oscillator is tuned to find cycles that repeat every 20 bars. Phase Multiplier: A multiplier applied to the Cycle Period to determine the smoothing length of the internal phase components. A higher value leads to a "cleaner" wave, while a lower value follows price more aggressively. Bullish Color: Controls the color of the oscillator and gradient fill when the value is above zero. Bearish Color: Controls the color of the oscillator and gradient fill when the value is below zero.
Chỉ báo Pine Script®
của LuxAlgo
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Rolling Correlation & Breakdown Detector [LuxAlgo]The Rolling Correlation & Breakdown Detector indicator is a specialized "trust meter" designed for statistical arbitrage and pairs trading that monitors the stability of the relationship between two assets to identify when mean-reversion strategies are likely to fail. 🔶 USAGE This indicator functions as a decision filter rather than a standalone entry signal generator. It is intended to be used alongside spread indicators or Z-score models to determine if the underlying relationship between two assets is healthy enough to trade. The script monitors two primary factors: the raw Pearson correlation and the stability of that correlation. This helps traders avoid the "divergence trap," where a price gap looks like a mean-reversion opportunity, but the assets have actually undergone a structural regime shift. 🔹 The Three-State System The indicator categorizes the relationship into three distinct regimes: Healthy (Green): The correlation is high and stable. This is the optimal environment for statistical arbitrage trades. Warning (Orange): The correlation is weakening or becoming volatile. New trades should be approached with caution, and existing positions should be managed tightly. Breakdown (Red): The assets have "divorced." The correlation has collapsed or is moving erratically. In this state, mean-reversion signals should be ignored as the assets are no longer moving in tandem. 🔹 Statistical Arbitrage Filtering Valid Trade: Your spread indicator shows an extreme (e.g., Z-Score > 2) AND the detector is Green. Invalid Trade: Your spread indicator shows an extreme, but the detector is Red. This suggests a "regime shift" where one asset is repricing independently of the other. 🔶 DETAILS While most traders only look at the absolute correlation value, this indicator introduces the concept of Correlation Stability. A pair with a lower but stable correlation is often more tradable than a pair with high correlation that is rapidly collapsing. The script calculates the standard deviation of the correlation over a "Stability Window." If the correlation fluctuates wildly, the stability metric will spike, triggering a Warning or Breakdown state even if the raw correlation value remains relatively high. This acts as a "circuit breaker" to protect traders from macro-driven divergences or structural news events. 🔶 SETTINGS 🔹 General Settings Asset B: The secondary symbol you wish to compare against the current chart symbol. Correlation Length: The rolling lookback window used for the Pearson correlation calculation. Stability Window: The window used to measure the volatility (standard deviation) of the correlation. 🔹 Breakdown Detection Thresholds Healthy Threshold: The minimum correlation value required to be considered in a "Healthy" state. Breakdown Threshold: The level below which the relationship is considered "Broken." Stability Threshold: The maximum allowed volatility in the correlation before the state shifts to a Warning or Breakdown. 🔹 Dashboard * Dashboard: Toggles the visibility of the real-time information table. * Position/Size: Customizes the location and scale of the on-screen dashboard.
Chỉ báo Pine Script®
của LuxAlgo
Cross-Market Regime Scanner [BOSWaves]Cross-Market Regime Scanner - Multi-Asset ADX Positioning with Correlation Network Visualization Overview Cross-Market Regime Scanner is a multi-asset regime monitoring system that maps directional strength and trend intensity across correlated instruments through ADX-based coordinate positioning, where asset locations dynamically reflect their current trending versus ranging state and bullish versus bearish bias. Instead of relying on isolated single-asset trend analysis or static correlation matrices, regime classification, spatial positioning, and intermarket relationship strength are determined through ADX directional movement calculation, percentile-normalized coordinate mapping, and rolling correlation network construction. This creates dynamic regime boundaries that reflect actual cross-market momentum patterns rather than arbitrary single-instrument levels - visualizing trending assets in right quadrants when ADX strength exceeds thresholds, positioning ranging assets in left quadrants during consolidation, and incorporating correlation web topology to reveal which instruments move together or diverge during regime transitions. Assets are therefore evaluated relative to ADX-derived regime coordinates and correlation network position rather than conventional isolated technical indicators. Conceptual Framework Cross-Market Regime Scanner is founded on the principle that meaningful market insights emerge from simultaneous multi-asset regime awareness rather than sequential single-instrument analysis. Traditional trend analysis examines assets individually using separate chart windows, which often obscures the broader cross-market regime structure and correlation patterns that drive coordinated moves. This framework replaces isolated-instrument logic with unified spatial positioning informed by actual ADX directional measurements and correlation relationships. Three core principles guide the design: Asset positioning should be determined by ADX-based regime coordinates that reflect trending versus ranging state and directional bias simultaneously. Spatial mapping must normalize ADX values to place assets within consistent quadrant boundaries regardless of instrument volatility characteristics. Correlation network visualization reveals which assets exhibit coordinated behavior versus divergent regime patterns during market transitions. This shifts regime analysis from isolated single-chart monitoring into unified multi-asset spatial awareness with correlation context. Theoretical Foundation The indicator combines ADX directional movement calculation, coordinate normalization methodology, quadrant-based regime classification, and rolling correlation network construction. A Wilder's smoothing implementation calculates ADX, +DI, and -DI for each monitored asset using True Range and directional movement components. The ADX value relative to a configurable threshold determines X-axis positioning (ranging versus trending), while the difference between +DI and -DI determines Y-axis positioning (bearish versus bullish). Coordinate normalization caps values within fixed boundaries for consistent quadrant placement. Pairwise correlation calculations over rolling windows populate a network graph where line thickness and opacity reflect correlation strength. Five internal systems operate in tandem: Multi-Asset ADX Engine : Computes smoothed ADX, +DI, and -DI values for up to 8 configurable instruments using Wilder's directional movement methodology. Coordinate Transformation System : Converts ADX strength and directional movement into normalized X/Y coordinates with threshold-relative scaling and boundary capping. Quadrant Classification Logic : Maps coordinate positions to four distinct regime states—Trending Bullish, Trending Bearish, Ranging Bullish, Ranging Bearish—with color-coded zones. Historical Trail Rendering : Maintains rolling position history for each asset, drawing gradient-faded trails that visualize recent regime trajectory and velocity. Correlation Network Calculator : Computes pairwise return correlations across all enabled assets, rendering weighted connection lines in circular web topology with strength-based styling. This design allows simultaneous cross-market regime awareness rather than reacting sequentially to individual instrument signals. How It Works Cross-Market Regime Scanner evaluates markets through a sequence of multi-asset spatial processes: Data Request Processing : Security function retrieves high, low, and close values for up to 8 configurable symbols with lookahead offset to ensure confirmed bar data. ADX Calculation Per Asset : True Range computed from high-low-close relationships, directional movement derived from up-moves versus down-moves, smoothed via Wilder's method over configurable period. Directional Index Derivation : +DI and -DI calculated as smoothed directional movement divided by smoothed True Range, scaled to percentage values. Coordinate Transformation : X-axis position equals (ADX - threshold) * 2, capped between -50 and +50; Y-axis position equals (+DI - -DI), capped between -50 and +50. Quadrant Assignment : Positive X indicates trending (ADX > threshold), negative X indicates ranging; positive Y indicates bullish (+DI > -DI), negative Y indicates bearish. Trail History Management : Configurable-length position history maintains recent coordinates for each asset, rendering gradient-faded lines connecting sequential positions. Velocity Vector Calculation : 7-bar coordinate change converted to directional arrow overlays showing regime momentum and trajectory. Return Correlation Processing : Bar-over-bar returns calculated for each asset, pairwise correlations computed over rolling window. Network Graph Construction : Assets positioned in circular topology, correlation lines drawn between pairs exceeding threshold with thickness/opacity scaled by correlation strength, positive correlations solid green, negative correlations dashed red. Risk Regime Scoring : Composite score aggregates bullish risk-on assets (equities, crypto, commodities) minus bullish risk-off assets (gold, dollar, VIX), generating overall market risk sentiment with colored candle overlay. Together, these elements form a continuously updating spatial regime framework anchored in multi-asset momentum reality and correlation structure. Interpretation Cross-Market Regime Scanner should be interpreted as unified spatial regime boundaries with correlation context: Top-Right Quadrant (TREND ▲) : Assets positioned here exhibit ADX above threshold with +DI exceeding -DI - confirmed bullish trending conditions with directional conviction. Bottom-Right Quadrant (TREND ▼) : Assets positioned here exhibit ADX above threshold with -DI exceeding +DI - confirmed bearish trending conditions with directional conviction. Top-Left Quadrant (RANGE ▲) : Assets positioned here exhibit ADX below threshold with +DI exceeding -DI - ranging consolidation with bullish bias but insufficient trend strength. Bottom-Left Quadrant (RANGE ▼) : Assets positioned here exhibit ADX below threshold with -DI exceeding +DI - ranging consolidation with bearish bias but insufficient trend strength. Position Trails : Gradient-faded lines connecting recent coordinate history reveal regime trajectory - curved paths indicate regime rotation, straight paths indicate sustained directional conviction. Velocity Arrows : Directional vectors overlaid on current positions show 7-bar regime momentum - arrow length indicates speed of regime change, angle indicates trajectory direction. Correlation Web : Circular network graph positioned left of main quadrant map displays pairwise asset relationships - solid green lines indicate positive correlation (moving together), dashed red lines indicate negative correlation (diverging moves), line thickness reflects correlation strength magnitude. Asset Dots : Multi-layer glow effects with color-coded markers identify each asset on both quadrant map and correlation web-symbol labels positioned adjacent to current location. Regime Summary Bar : Vertical boxes on right edge display condensed regime state for each enabled asset - box background color reflects quadrant classification, border color matches asset identifier. Risk Regime Candles : Overlay candles on price chart colored by composite risk score - green indicates risk-on dominance (bullish equities/crypto exceeding bullish safe-havens), red indicates risk-off dominance (bullish gold/dollar/VIX exceeding bullish risk assets), gray indicates neutral balance. Quadrant positioning, trail trajectory, correlation network topology, and velocity vectors outweigh isolated single-asset readings. Signal Logic & Visual Cues Cross-Market Regime Scanner presents spatial positioning insights rather than discrete entry signals: Regime Clustering : Multiple assets congregating in same quadrant suggests broad market regime consensus - all assets in TREND ▲ indicates coordinated bullish momentum across instruments. Regime Divergence : Assets splitting across opposing quadrants reveals intermarket disagreement - equities in TREND ▲ while safe-havens in TREND ▼ suggests healthy risk-on environment. Quadrant Transitions : Assets crossing quadrant boundaries mark regime shifts - movement from left (ranging) to right (trending) indicates breakout from consolidation into directional phase. Trail Curvature Patterns : Sharp curves in position trails signal rapid regime rotation, straight trails indicate sustained directional conviction, loops indicate regime uncertainty with back-and-forth oscillation. Velocity Acceleration : Long arrows indicate rapid regime change momentum, short arrows indicate stable regime persistence, arrow direction reveals whether asset moving toward trending or ranging state. Correlation Breakdown Events : Previously strong correlation lines (thick, opaque) suddenly thinning or disappearing indicates relationship decoupling - often precedes major regime transitions. Correlation Inversion Signals : Assets shifting from positive correlation (solid green) to negative correlation (dashed red) marks structural market regime change - historically correlated assets beginning to diverge. Risk Score Extremes : Composite score reaching maximum positive (all risk-on bullish, all risk-off bearish) or maximum negative (all risk-on bearish, all risk-off bullish) marks regime conviction extremes. The primary value lies in simultaneous multi-asset regime awareness and correlation pattern recognition rather than isolated timing signals. Strategy Integration Cross-Market Regime Scanner fits within macro-aware and intermarket analysis approaches: Regime-Filtered Entries : Use quadrant positioning as directional filter for primary trading instrument - favor long setups when asset in TREND ▲ quadrant, short setups in TREND ▼ quadrant. Correlation Confluence Trading : Enter positions when target asset and correlated instruments occupy same quadrant - multiple assets in TREND ▲ provides conviction for long exposure. Divergence-Based Reversal Anticipation : Monitor for regime divergence between correlated assets - if historically aligned instruments split to opposite quadrants, anticipate mean-reversion or regime rotation. Breakout Confirmation via Cross-Asset Validation : Confirm primary instrument breakouts by verifying correlated assets simultaneously transitioning from ranging to trending quadrants. Risk-On/Risk-Off Positioning : Use composite risk score and safe-haven positioning to determine overall market environment - scale risk exposure based on risk regime dominance. Velocity-Based Timing : Enter during periods of high regime velocity (long arrows) when momentum carries assets decisively into new quadrants, avoid entries during low velocity regime uncertainty. Multi-Timeframe Regime Alignment : Apply higher-timeframe regime scanner to establish macro context, use lower-timeframe price action for entry timing within aligned regime structure. Correlation Web Pattern Recognition : Identify regime transitions early by monitoring correlation network topology changes - previously disconnected assets forming strong correlations suggests regime coalescence. Technical Implementation Details Core Engine : Wilder's smoothing-based ADX calculation with separate True Range and directional movement tracking per asset Coordinate Model : Threshold-relative X-axis scaling (trending versus ranging) with directional movement differential Y-axis (bullish versus bearish) Normalization System : Boundary capping at ±50 for consistent spatial positioning regardless of instrument volatility Trail Rendering : Rolling array-based position history with gradient alpha decay and width tapering Correlation Engine : Return-based pairwise correlation calculation over rolling window with configurable lookback Network Visualization : Circular topology with trigonometric positioning, weighted line rendering based on correlation magnitude Risk Scoring : Composite calculation aggregating directional states across classified risk-on and risk-off asset categories Performance Profile : Optimized for 8 simultaneous security requests with efficient array management and conditional rendering Optimal Application Parameters Timeframe Guidance: 1 - 5 min : Micro-regime monitoring for intraday correlation shifts and short-term regime rotations 15 - 60 min : Intraday regime structure with meaningful ADX development and correlation stability 4H - Daily : Swing and position-level macro regime identification with sustained trend classification Weekly - Monthly : Long-term regime cycle tracking with structural correlation pattern evolution Suggested Baseline Configuration: ADX Period : 14 ADX Smoothing : 14 Trend Threshold : 25.0 Trail Length : 15 Correlation Period : 50 Min |Correlation| to Show Line : 0.3 Web Radius : 30 Show Quadrant Colors : Enabled Show Regime Summary Bar : Enabled Show Velocity Arrows : Enabled Show Correlation Web : Enabled These suggested parameters should be used as a baseline; their effectiveness depends on the selected assets' volatility profiles, correlation characteristics, and preferred spatial sensitivity, so fine-tuning is expected for optimal performance. Parameter Calibration Notes Use the following adjustments to refine behavior without altering the core logic: Assets clustering too tightly : Decrease Trend Threshold (e.g., 20) to spread ranging/trending separation, or increase ADX Period for smoother ADX calculation reducing noise. Assets spreading too widely : Increase Trend Threshold (e.g., 30-35) to demand stronger ADX confirmation before classifying as trending, tightening quadrant boundaries. Trail too short to show trajectory : Increase Trail Length (20-25) to visualize longer regime history, revealing sustained directional patterns. Trail too cluttered : Decrease Trail Length (8-12) for cleaner visualization focusing on recent regime state, reducing visual complexity. Unstable ADX readings : Increase ADX Period and ADX Smoothing (18-21) for heavier smoothing reducing bar-to-bar regime oscillation. Sluggish regime detection : Decrease ADX Period (10-12) for faster response to directional changes, accepting increased sensitivity to noise. Too many correlation lines : Increase Min |Correlation| threshold (0.4-0.6) to display only strongest relationships, decluttering network visualization. Missing significant correlations : Decrease Min |Correlation| threshold (0.2-0.25) to reveal weaker but potentially meaningful relationships. Correlation too volatile : Increase Correlation Period (75-100) for more stable correlation measurements, reducing network line flickering. Correlation too stale : Decrease Correlation Period (30-40) to emphasize recent correlation patterns, capturing regime-dependent relationship changes. Velocity arrows too sensitive : Modify 7-bar lookback in code to longer period (10-14) for smoother velocity representation, or increase magnitude threshold for arrow display. Adjustments should be incremental and evaluated across multiple session types rather than isolated market conditions. Performance Characteristics High Effectiveness: Macro-aware trading approaches requiring cross-market regime context for directional bias Intermarket analysis strategies monitoring correlation breakdowns and regime divergences Portfolio construction decisions requiring simultaneous multi-asset regime classification Risk management frameworks using safe-haven positioning and risk-on/risk-off scoring Trend-following systems benefiting from cross-asset regime confirmation before entry Mean-reversion strategies identifying regime extremes via clustering patterns and correlation stress Reduced Effectiveness: Single-asset focused strategies not incorporating cross-market context in decision logic High-frequency trading approaches where multi-security request latency impacts execution Markets with consistently weak correlations where network topology provides limited insight Extremely low volatility environments where ADX remains persistently below threshold for all assets Instruments with erratic or unreliable ADX characteristics producing unstable coordinate positioning Integration Guidelines Confluence : Combine with BOSWaves structure, volume analysis, or primary instrument technical indicators for entry timing within aligned regime Quadrant Respect : Trust signals occurring when primary trading asset occupies appropriate quadrant for intended trade direction Correlation Context : Prioritize setups where target asset exhibits strong correlation with instruments in same regime quadrant Divergence Awareness : Monitor for safe-haven assets moving opposite to risk assets - regime divergence validates directional conviction Velocity Confirmation : Favor entries during periods of strong regime velocity indicating decisive momentum rather than regime oscillation Risk Score Alignment : Scale position sizing and exposure based on composite risk score - larger positions during clear risk-on/risk-off environments Trail Pattern Recognition : Use trail curvature to identify regime stability (straight) versus rotation (curved) versus uncertainty (looped) Multi-Timeframe Structure : Apply higher-timeframe regime scanner for macro filter, lower-timeframe for tactical positioning within established regime Disclaimer Cross-Market Regime Scanner is a professional-grade multi-asset regime visualization and correlation analysis tool. It uses ADX-based coordinate positioning and rolling correlation calculation but does not predict future regime transitions or guarantee relationship persistence. Results depend on selected assets' characteristics, parameter configuration, correlation stability, and disciplined interpretation. Security request timing may introduce minor latency in real-time data retrieval. BOSWaves recommends deploying this indicator within a broader analytical framework that incorporates price structure, volume context, fundamental macro awareness, and comprehensive risk management.
Chỉ báo Pine Script®
của BOSWaves
Volume Weighted Intra Bar CorrelationThis indicator analyzes market character by providing a detailed view of correlation. It uses data from a lower, intra-bar timeframe to separate the total correlation of a single bar into two distinct components. Key Features: 1. **Intra-Bar Correlation Decomposition:** For each bar on the chart, the indicator analyzes the underlying price action on a smaller timeframe ('Intra-Bar Timeframe') and quantifies two types of correlation: - **Between-Bar Correlation (Directional):** Calculated from price movements *between* the intra-bar candles. This component represents the **macro-movement** correlation within the main bar. - **Within-Bar Correlation (Non-Directional):** Calculated from price fluctuations *inside* each intra-bar candle. This component represents the **microstructure/noise** correlation. 2. **Visual Decomposition Logic:** Total Correlation is the primary metric displayed. Since Correlation Coefficients are not linearly additive, this indicator plots the *exact* Total Correlation and partitions the area underneath based on the Covariance Ratio. This ensures the displayed total correlation remains mathematically accurate while showing relative composition. 3. **Dual Display Modes:** The indicator offers two modes to visualize this information: - **Absolute Mode:** Plots the *total* correlation as a stacked column chart, showing the *absolute magnitude* of correlation and the contribution of each component. - **Relative Mode:** Plots the components as a 100% stacked column chart (scaled from 0 to 1), focusing purely on the *energy ratio* of 'between-bar' (macro) and 'within-bar' (micro) correlation. 4. **Calculation Options:** - **Normalization:** An optional 'Normalize' setting calculates an **Exponential Regression Curve** (log-space), making the analysis suitable for comparing assets with different scales (e.g., BTC vs EURUSD). - **Volume Weighting:** An option (`Volume weighted`) applies volume weighting to all mean and covariance calculations. 5. **Correlation Cycle Analysis:** - **Pivot Detection:** Includes a built-in pivot detector that identifies significant turning points (highs and lows) in the *total* correlation line. (Note: This is only visible in 'Absolute Mode'). - **Flexible Pivot Algorithms:** Supports various underlying mathematical models for pivot detection provided by the core library. 6. **Note on Confirmation (Lag):** Pivot signals are confirmed using a lookback method. A pivot is only plotted *after* the `Pivot Right Bars` input has passed, which introduces an inherent lag. 7. **Multi-Timeframe (MTF) Capability:** - **MTF Analysis Lines:** The entire intra-bar analysis can be run on a higher timeframe (using the `Timeframe` input), with standard options to handle gaps (`Fill Gaps`) and prevent repainting (`Wait for...`). - **Limitation:** The Pivot detection (`Calculate Pivots`) is **disabled** if a Higher Timeframe (HTF) is selected. 8. **Integrated Alerts:** Includes alerts for: - Correlation magnitude (High Positive / High Inverse). - Correlation character changes/emerging/fading. - Total Correlation pivot (High/Low) detection. **Caution: Real-Time Data Behavior (Intra-Bar Repainting)** This indicator uses high-resolution intra-bar data. As a result, the values on the **current, unclosed bar** (the real-time bar) will update dynamically as new intra-bar data arrives. This behavior is normal and necessary for this type of analysis. Signals should only be considered final **after the main chart bar has closed.** --- **DISCLAIMER** 1. **For Informational/Educational Use Only:** This indicator is provided for informational and educational purposes only. It does not constitute financial, investment, or trading advice, nor is it a recommendation to buy or sell any asset. 2. **Use at Your Own Risk:** All trading decisions you make based on the information or signals generated by this indicator are made solely at your own risk. 3. **No Guarantee of Performance:** Past performance is not an indicator of future results. The author makes no guarantee regarding the accuracy of the signals or future profitability. 4. **No Liability:** The author shall not be held liable for any financial losses or damages incurred directly or indirectly from the use of this indicator. 5. **Signals Are Not Recommendations:** The alerts and visual signals (e.g., crossovers) generated by this tool are not direct recommendations to buy or sell. They are technical observations for your own analysis and consideration.
Chỉ báo Pine Script®
của AustrianTradingMachine
Volume Weighted LR CorrelationThis indicator analyzes the structural relationship between two assets by decomposing the Total Correlation into three distinct, interpretable components using a Weighted Linear Regression model and a Hybrid Copula Estimator. Key Features: 1. **Hybrid Copula Estimator:** Unlike standard correlation, which often fails on High/Low range data, this indicator fuses two metrics to ensure mathematical rigor: - **Magnitude:** Derived from Rogers-Satchell Volatility (robust to trend). - **Direction:** Derived from Log-Returns. This allows for precise correlation estimates even on intra-bar data. 2. **Three-Component Correlation Decomposition:** The indicator separates correlation based on the 'Estimate Bar Statistics' option. - **Standard Mode (`Estimate Bar Statistics` = OFF):** Calculates correlation based on the selected `Source`. - **Decomposition Mode (`Estimate Bar Statistics` = ON):** The indicator uses a statistical model ('Estimator') to calculate *within-bar* correlation. This separates the relationship into: - **Trend Correlation (Green/Red):** Correlation of the regression slopes. Indicates if assets are trending in the same direction. - **Residual Correlation (Yellow):** Correlation of the noise around the trend (Cointegration). Indicates if assets mean-revert together, even if trends differ. - **Within-Bar Correlation (Blue):** Correlation of the microstructure (intra-bar volatility). 3. **Visual Decomposition Logic:** Total Correlation is the primary metric displayed. Since Correlation Coefficients are not linearly additive, this indicator calculates the *exact* Total Correlation and partitions the area/ratios based on the additive Covariance Decomposition. This ensures the displayed total correlation remains mathematically accurate. 4. **Dual Display Modes:** The indicator offers two modes to visualize this decomposition: - **Absolute Mode:** Displays the *Total Correlation* as the main line, with the background filled by the stacked components (Trend, Residual, Within). Shows the *magnitude* of the relationship. - **Relative Mode:** Displays the **Energy Ratios** (-1.0 to 1.0) of each component using L1-Normalization. This isolates the *structure/quality* of the relationship (e.g., "Is the correlation driven by Trend or just by Noise?"). 5. **Calculation Options:** - **Normalization:** An optional 'Normalize' setting calculates an **Exponential Regression Curve** (log-space), creating a constant percentage variance environment. Essential for comparing assets with different scales (e.g., BTC vs EURUSD). - **Volume Weighting:** An option (`Volume weighted`) applies volume weighting to all regression and covariance calculations. 6. **Correlation Cycle Analysis:** - **Pivot Detection:** Includes a built-in pivot detector that identifies significant turning points (highs and lows) in the *Total Correlation* line. - **Flexible Pivot Algorithms:** Supports various underlying mathematical models for pivot detection provided by the core library. 7. **Note on Confirmation (Lag):** Pivot signals are confirmed using a lookback method. A pivot is only plotted *after* the `Pivot Right Bars` input has passed, which introduces an inherent lag. 8. **Multi-Timeframe (MTF) Capability:** - **MTF Correlation Lines:** The correlation lines can be calculated on a higher timeframe, with standard options to handle gaps (`Fill Gaps`) and prevent repainting (`Wait for...`). - **Limitation:** The Pivot detection (`Calculate Pivots`) is **disabled** if a Higher Timeframe (HTF) is selected. 9. **Integrated Alerts:** Includes comprehensive alerts for: - Correlation magnitude (High Positive / High Inverse). - Correlation character changes/emerging/fading. - Total Correlation pivot (High/Low) detection. --- **DISCLAIMER** 1. **For Informational/Educational Use Only:** This indicator is provided for informational and educational purposes only. It does not constitute financial, investment, or trading advice, nor is it a recommendation to buy or sell any asset. 2. **Use at Your Own Risk:** All trading decisions you make based on the information or signals generated by this indicator are made solely at your own risk. 3. **No Guarantee of Performance:** Past performance is not an indicator of future results. The author makes no guarantee regarding the accuracy of the signals or future profitability. 4. **No Liability:** The author shall not be held liable for any financial losses or damages incurred directly or indirectly from the use of this indicator. 5. **Signals Are Not Recommendations:** The alerts and visual signals (e.g., crossovers) generated by this tool are not direct recommendations to buy or sell. They are technical observations for your own analysis and consideration.
Chỉ báo Pine Script®
của AustrianTradingMachine
Volume Weighted CorrelationThis indicator analyzes the structural relationship between two assets by decomposing the Total Correlation into two distinct, interpretable components: "Between-Bar" (Inter-Bar) and "Within-Bar" (Intra-Bar) correlation. Key Features: 1. **Hybrid Copula Estimator:** Unlike standard correlation, which often fails on High/Low range data, this indicator fuses two metrics to ensure mathematical rigor: - **Magnitude:** Derived from Rogers-Satchell Volatility. - **Direction:** Derived from Log-Returns. This allows for precise correlation estimates even on intra-bar data. 2. **Two-Component Correlation Decomposition:** The indicator separates correlation based on the 'Estimate Bar Statistics' option. - **Standard Mode (`Estimate Bar Statistics` = OFF):** Calculates correlation based on the selected `Source` (Close-to-Close). - **Decomposition Mode (`Estimate Bar Statistics` = ON):** The indicator uses a statistical model ('Estimator') to calculate *within-bar* correlation. This separates the relationship into: - **Between-Bar Correlation (Green/Red):** Correlation of the price paths (means). Indicates if the macro movements of the assets are aligned (Inter-Bar correlation). - **Within-Bar Correlation (Blue):** Correlation of the microstructure (Intra-Bar volatility/noise). 3. **Visual Decomposition Logic:** Total Correlation is the primary metric displayed. Since Correlation Coefficients are not linearly additive, this indicator calculates the *exact* Total Correlation and partitions the area/ratios based on the additive Covariance Decomposition (`CovTot = CovBtw + CovWtn`). This ensures the displayed total correlation remains mathematically accurate. 4. **Dual Display Modes:** The indicator offers two modes to visualize this decomposition: - **Absolute Mode:** Displays the *Total Correlation* as the main line, with the background filled by the stacked components (Between vs. Within). Shows the *magnitude* of the relationship. - **Relative Mode:** Displays the **Energy Ratios** (-1.0 to 1.0) of each component using L1-Normalization. This isolates the *structure/quality* of the relationship (e.g., "Is the correlation driven by price movement or just by volatility coupling?"). 5. **Calculation Options:** - **Normalization:** An optional 'Normalize' setting calculates an **Exponential Regression Curve** (log-space), creating a constant percentage variance environment. Essential for comparing assets with different scales (e.g., BTC vs EURUSD). - **Volume Weighting:** An option (`Volume weighted`) applies volume weighting to all mean and covariance calculations. 6. **Correlation Cycle Analysis:** - **Pivot Detection:** Includes a built-in pivot detector that identifies significant turning points (highs and lows) in the *Total Correlation* line. (Note: This is only visible in 'Absolute Mode'). - **Flexible Pivot Algorithms:** Supports various underlying mathematical models for pivot detection provided by the core library. 7. **Note on Confirmation (Lag):** Pivot signals are confirmed using a lookback method. A pivot is only plotted *after* the `Pivot Right Bars` input has passed, which introduces an inherent lag. 8. **Multi-Timeframe (MTF) Capability:** - **MTF Correlation Lines:** The correlation lines can be calculated on a higher timeframe, with standard options to handle gaps (`Fill Gaps`) and prevent repainting (`Wait for...`). - **Limitation:** The Pivot detection (`Calculate Pivots`) is **disabled** if a Higher Timeframe (HTF) is selected. 9. **Integrated Alerts:** Includes comprehensive alerts for: - Correlation magnitude (High Positive / High Inverse). - Character changes (Inter-Bar vs. Intra-Bar dominance). - Total Correlation pivot (High/Low) detection. --- **DISCLAIMER** 1. **For Informational/Educational Use Only:** This indicator is provided for informational and educational purposes only. It does not constitute financial, investment, or trading advice, nor is it a recommendation to buy or sell any asset. 2. **Use at Your Own Risk:** All trading decisions you make based on the information or signals generated by this indicator are made solely at your own risk. 3. **No Guarantee of Performance:** Past performance is not an indicator of future results. The author makes no guarantee regarding the accuracy of the signals or future profitability. 4. **No Liability:** The author shall not be held liable for any financial losses or damages incurred directly or indirectly from the use of this indicator. 5. **Signals Are Not Recommendations:** The alerts and visual signals (e.g., crossovers) generated by this tool are not direct recommendations to buy or sell. They are technical observations for your own analysis and consideration.
Chỉ báo Pine Script®
của AustrianTradingMachine
PineStats█ OVERVIEW PineStats is a comprehensive statistical analysis library for Pine Script v6, providing 104 functions across 6 modules. Built for quantitative traders, researchers, and indicator developers who need professional-grade statistics without reinventing the wheel. For building mean-reversion strategies, analyzing return distributions, measuring correlations, or testing for market regimes. █ MODULES CORE STATISTICS (20 functions) • Central tendency: mean, median, WMA, EMA • Dispersion: variance, stdev, MAD, range • Standardization: z-score, robust z-score, normalize, percentile • Distribution shape: skewness, kurtosis PROBABILITY DISTRIBUTIONS (17 functions) • Normal: PDF, CDF, inverse CDF (quantile function) • Power-law: Hill estimator, MLE alpha, survival function • Exponential: PDF, CDF, rate estimation • Normality testing: Jarque-Bera test ENTROPY (9 functions) • Shannon entropy (information theory) • Tsallis entropy (non-extensive, fat-tail sensitive) • Permutation entropy (ordinal patterns) • Approximate entropy (regularity measure) • Entropy-based regime detection PROBABILITY (21 functions) • Win rates and expected value • First passage time estimation • TP/SL probability analysis • Conditional probability and Bayes updates • Streak and drawdown probabilities REGRESSION (19 functions) • Linear regression: slope, intercept, forecast • Goodness of fit: R², adjusted R², standard error • Statistical tests: t-statistic, p-value, significance • Trend analysis: strength, angle, acceleration • Quadratic regression CORRELATION (18 functions) • Pearson, Spearman, Kendall correlation • Covariance, beta, alpha (Jensen's) • Rolling correlation analysis • Autocorrelation and cross-correlation • Information ratio, tracking error █ QUICK START import HenriqueCentieiro/PineStats/1 as stats // Z-score for mean reversion z = stats.zscore(close, 20) // Test if returns are normally distributed returns = (close - close ) / close isGaussian = stats.is_normal(returns, 100, 0.05) // Regression channel = stats.linreg_channel(close, 50, 2.0) // Correlation with benchmark spyReturns = request.security("SPY", timeframe.period, close/close - 1) beta = stats.beta(returns, spyReturns, 60) █ USE CASES ✓ Mean Reversion — z-scores, percentiles, Bollinger-style analysis ✓ Regime Detection — entropy measures, correlation regimes ✓ Risk Analysis — drawdown probability, VaR via quantiles ✓ Strategy Evaluation — expected value, win rates, R:R analysis ✓ Distribution Analysis — normality tests, fat-tail detection ✓ Multi-Asset — beta, alpha, correlation, relative strength █ NOTES • All functions return `na` on invalid inputs • Designed for Pine Script v6 • Fully documented in the library header • Part of the Pine ecosystem: PineStats, PineQuant, PineCriticality, PineWavelet █ REFERENCES • Abramowitz & Stegun — Normal CDF approximation • Acklam's algorithm — Inverse normal CDF • Hill estimator — Power-law tail estimation • Tsallis statistics — Non-extensive entropy Full documentation in the library header. mean(src, length)   Calculates the arithmetic mean (simple moving average) over a lookback period   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 1)   Returns: Arithmetic mean of the last `length` values, or `na` if inputs invalid wma_custom(src, length)   Calculates weighted moving average with linearly decreasing weights   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 1)   Returns: Weighted moving average, or `na` if inputs invalid ema_custom(src, length)   Calculates exponential moving average   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 1)   Returns: Exponential moving average, or `na` if inputs invalid median(src, length)   Calculates the median value over a lookback period   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 1)   Returns: Median value, or `na` if inputs invalid variance(src, length)   Calculates population variance over a lookback period   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 1)   Returns: Population variance, or `na` if inputs invalid stdev(src, length)   Calculates population standard deviation over a lookback period   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 1)   Returns: Population standard deviation, or `na` if inputs invalid mad(src, length)   Calculates Median Absolute Deviation (MAD) - robust dispersion measure   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 1)   Returns: MAD value, or `na` if inputs invalid data_range(src, length)   Calculates the range (highest - lowest) over a lookback period   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 1)   Returns: Range value, or `na` if inputs invalid zscore(src, length)   Calculates z-score (number of standard deviations from mean)   Parameters:      src (float) : Source series      length (simple int) : Lookback period for mean and stdev calculation (must be >= 2)   Returns: Z-score, or `na` if inputs invalid or stdev is zero zscore_robust(src, length)   Calculates robust z-score using median and MAD (resistant to outliers)   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 2)   Returns: Robust z-score, or `na` if inputs invalid or MAD is zero normalize(src, length)   Normalizes value to range using min-max scaling   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 1)   Returns: Normalized value in , or `na` if inputs invalid or range is zero percentile(src, length)   Calculates percentile rank of current value within lookback window   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 1)   Returns: Percentile rank (0 to 100), or `na` if inputs invalid winsorize(src, length, lower_pct, upper_pct)   Winsorizes values by clamping to percentile bounds (reduces outlier impact)   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 1)      lower_pct (simple float) : Lower percentile bound (0-100, e.g., 5 for 5th percentile)      upper_pct (simple float) : Upper percentile bound (0-100, e.g., 95 for 95th percentile)   Returns: Winsorized value clamped to bounds skewness(src, length)   Calculates sample skewness (measure of distribution asymmetry)   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 3)   Returns: Skewness value (negative = left tail, positive = right tail), or `na` if invalid kurtosis(src, length)   Calculates excess kurtosis (measure of distribution tail heaviness)   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 4)   Returns: Excess kurtosis (>0 = heavy tails, <0 = light tails), or `na` if invalid count_valid(src, length)   Counts non-na values in lookback window (useful for data quality checks)   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 1)   Returns: Count of valid (non-na) values sum(src, length)   Calculates sum over lookback period   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 1)   Returns: Sum of values, or `na` if inputs invalid cumsum(src)   Calculates cumulative sum (running total from first bar)   Parameters:      src (float) : Source series   Returns: Cumulative sum change(src, length)   Returns the change (difference) from n bars ago   Parameters:      src (float) : Source series      length (simple int) : Number of bars to look back (must be >= 1)   Returns: Current value minus value from `length` bars ago roc(src, length)   Calculates Rate of Change (percentage change from n bars ago)   Parameters:      src (float) : Source series      length (simple int) : Number of bars to look back (must be >= 1)   Returns: Percentage change as decimal (0.05 = 5%), or `na` if invalid normal_pdf_standard(x)   Calculates the standard normal probability density function (PDF)   Parameters:      x (float) : The value to evaluate   Returns: PDF value at x for standard normal N(0,1) normal_pdf(x, mu, sigma)   Calculates the normal probability density function (PDF)   Parameters:      x (float) : The value to evaluate      mu (float) : Mean of the distribution (default: 0)      sigma (float) : Standard deviation (default: 1, must be > 0)   Returns: PDF value at x for normal N(mu, sigma²) normal_cdf_standard(x)   Calculates the standard normal cumulative distribution function (CDF)   Parameters:      x (float) : The value to evaluate   Returns: Probability P(X <= x) for standard normal N(0,1) @description Uses Abramowitz & Stegun approximation (formula 7.1.26), accurate to ~1.5e-7 normal_cdf(x, mu, sigma)   Calculates the normal cumulative distribution function (CDF)   Parameters:      x (float) : The value to evaluate      mu (float) : Mean of the distribution (default: 0)      sigma (float) : Standard deviation (default: 1, must be > 0)   Returns: Probability P(X <= x) for normal N(mu, sigma²) normal_inv_standard(p)   Calculates the inverse standard normal CDF (quantile function)   Parameters:      p (float) : Probability value (must be in (0, 1))   Returns: x such that P(X <= x) = p for standard normal N(0,1) @description Uses Acklam's algorithm, accurate to ~1.15e-9 normal_inv(p, mu, sigma)   Calculates the inverse normal CDF (quantile function)   Parameters:      p (float) : Probability value (must be in (0, 1))      mu (float) : Mean of the distribution      sigma (float) : Standard deviation (must be > 0)   Returns: x such that P(X <= x) = p for normal N(mu, sigma²) power_law_alpha(src, length, tail_pct)   Estimates power-law exponent (alpha) using Hill estimator   Parameters:      src (float) : Source series (typically absolute returns or drawdowns)      length (simple int) : Lookback period (must be >= 10 for reliable estimates)      tail_pct (simple float) : Percentage of data to use for tail estimation (default: 0.1 = top 10%)   Returns: Estimated alpha (tail index), typically 2-4 for financial data @description Alpha < 2 indicates infinite variance (very heavy tails) @description Alpha < 3 indicates infinite kurtosis @description Alpha > 4 suggests near-Gaussian behavior power_law_alpha_mle(src, length, x_min)   Estimates power-law alpha using maximum likelihood (Clauset method)   Parameters:      src (float) : Source series (positive values expected)      length (simple int) : Lookback period (must be >= 20)      x_min (float) : Minimum threshold for power-law behavior   Returns: Estimated alpha using MLE power_law_pdf(x, alpha, x_min)   Calculates power-law probability density (Pareto Type I)   Parameters:      x (float) : Value to evaluate (must be >= x_min)      alpha (float) : Power-law exponent (must be > 1)      x_min (float) : Minimum value / scale parameter (must be > 0)   Returns: PDF value power_law_survival(x, alpha, x_min)   Calculates power-law survival function P(X > x)   Parameters:      x (float) : Value to evaluate (must be >= x_min)      alpha (float) : Power-law exponent (must be > 1)      x_min (float) : Minimum value / scale parameter (must be > 0)   Returns: Probability of exceeding x power_law_ks(src, length, alpha, x_min)   Tests if data follows power-law using simplified Kolmogorov-Smirnov   Parameters:      src (float) : Source series      length (simple int) : Lookback period      alpha (float) : Estimated alpha from power_law_alpha()      x_min (float) : Threshold value   Returns: KS statistic (lower = better fit, typically < 0.1 for good fit) is_power_law(src, length, tail_pct, ks_threshold)   Simple test if distribution appears to follow power-law   Parameters:      src (float) : Source series      length (simple int) : Lookback period      tail_pct (simple float) : Tail percentage for alpha estimation      ks_threshold (simple float) : Maximum KS statistic for acceptance (default: 0.1)   Returns: true if KS test suggests power-law fit exp_pdf(x, lambda)   Calculates exponential probability density function   Parameters:      x (float) : Value to evaluate (must be >= 0)      lambda (float) : Rate parameter (must be > 0)   Returns: PDF value exp_cdf(x, lambda)   Calculates exponential cumulative distribution function   Parameters:      x (float) : Value to evaluate (must be >= 0)      lambda (float) : Rate parameter (must be > 0)   Returns: Probability P(X <= x) exp_lambda(src, length)   Estimates exponential rate parameter (lambda) using MLE   Parameters:      src (float) : Source series (positive values)      length (simple int) : Lookback period   Returns: Estimated lambda (1/mean) jarque_bera(src, length)   Calculates Jarque-Bera test statistic for normality   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 10)   Returns: JB statistic (higher = more deviation from normality) @description Under normality, JB ~ chi-squared(2). JB > 6 suggests non-normality at 5% level is_normal(src, length, significance)   Tests if distribution is approximately normal   Parameters:      src (float) : Source series      length (simple int) : Lookback period      significance (simple float) : Significance level (default: 0.05)   Returns: true if Jarque-Bera test does not reject normality shannon_entropy(src, length, n_bins)   Calculates Shannon entropy from a probability distribution   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 10)      n_bins (simple int) : Number of histogram bins for discretization (default: 10)   Returns: Shannon entropy in bits (log base 2) @description Higher entropy = more randomness/uncertainty, lower = more predictability shannon_entropy_norm(src, length, n_bins)   Calculates normalized Shannon entropy   Parameters:      src (float) : Source series      length (simple int) : Lookback period      n_bins (simple int) : Number of histogram bins   Returns: Normalized entropy where 0 = perfectly predictable, 1 = maximum randomness tsallis_entropy(src, length, q, n_bins)   Calculates Tsallis entropy with q-parameter   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 10)      q (float) : Entropic index (q=1 recovers Shannon entropy)      n_bins (simple int) : Number of histogram bins   Returns: Tsallis entropy value @description q < 1: emphasizes rare events (fat tails) @description q = 1: equivalent to Shannon entropy @description q > 1: emphasizes common events optimal_q(src, length)   Estimates optimal q parameter from kurtosis   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: Estimated q value that best captures the distribution's tail behavior @description Uses relationship: q ≈ (5 + kurtosis) / (3 + kurtosis) for kurtosis > 0 tsallis_q_gaussian(x, q, beta)   Calculates Tsallis q-Gaussian probability density   Parameters:      x (float) : Value to evaluate      q (float) : Tsallis q parameter (must be < 3)      beta (float) : Width parameter (inverse temperature, must be > 0)   Returns: q-Gaussian PDF value @description q=1 recovers standard Gaussian permutation_entropy(src, length, order)   Calculates permutation entropy (ordinal pattern complexity)   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 20)      order (simple int) : Embedding dimension / pattern length (2-5, default: 3)   Returns: Normalized permutation entropy @description Measures complexity of temporal ordering patterns @description 0 = perfectly predictable sequence, 1 = random approx_entropy(src, length, m, r)   Calculates Approximate Entropy (ApEn) - regularity measure   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 50)      m (simple int) : Embedding dimension (default: 2)      r (simple float) : Tolerance as fraction of stdev (default: 0.2)   Returns: Approximate entropy value (higher = more irregular/complex) @description Lower ApEn indicates more self-similarity and predictability entropy_regime(src, length, q, n_bins)   Detects market regime based on entropy level   Parameters:      src (float) : Source series (typically returns)      length (simple int) : Lookback period      q (float) : Tsallis q parameter (use optimal_q() or default 1.5)      n_bins (simple int) : Number of histogram bins   Returns: Regime indicator: -1 = trending (low entropy), 0 = transition, 1 = ranging (high entropy) entropy_risk(src, length)   Calculates entropy-based risk indicator   Parameters:      src (float) : Source series (typically returns)      length (simple int) : Lookback period   Returns: Risk score where 1 = maximum divergence from Gaussian 1 hit_rate(src, length)   Calculates hit rate (probability of positive outcome) over lookback   Parameters:      src (float) : Source series (positive values count as hits)      length (simple int) : Lookback period   Returns: Hit rate as decimal hit_rate_cond(condition, length)   Calculates hit rate for custom condition over lookback   Parameters:      condition (bool) : Boolean series (true = hit)      length (simple int) : Lookback period   Returns: Hit rate as decimal expected_value(src, length)   Calculates expected value of a series   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: Expected value (mean) expected_value_trade(win_prob, take_profit, stop_loss)   Calculates expected value for a trade with TP and SL levels   Parameters:      win_prob (float) : Probability of hitting TP (0-1)      take_profit (float) : Take profit in price units or %      stop_loss (float) : Stop loss in price units or % (positive value)   Returns: Expected value per trade @description EV = (win_prob * TP) - ((1 - win_prob) * SL) breakeven_winrate(take_profit, stop_loss)   Calculates breakeven win rate for given TP/SL ratio   Parameters:      take_profit (float) : Take profit distance      stop_loss (float) : Stop loss distance   Returns: Required win rate for breakeven (EV = 0) reward_risk_ratio(take_profit, stop_loss)   Calculates the reward-to-risk ratio   Parameters:      take_profit (float) : Take profit distance      stop_loss (float) : Stop loss distance   Returns: R:R ratio fpt_probability(src, length, target, max_bars)   Estimates probability of price reaching target within N bars   Parameters:      src (float) : Source series (typically returns)      length (simple int) : Lookback for volatility estimation      target (float) : Target move (in same units as src, e.g., % return)      max_bars (simple int) : Maximum bars to consider   Returns: Probability of reaching target within max_bars @description Based on random walk with drift approximation fpt_mean(src, length, target)   Estimates mean first passage time to target level   Parameters:      src (float) : Source series (typically returns)      length (simple int) : Lookback for volatility estimation      target (float) : Target move   Returns: Expected number of bars to reach target (can be infinite) fpt_historical(src, length, target)   Counts historical bars to reach target from each point   Parameters:      src (float) : Source series (typically price or returns)      length (simple int) : Lookback period      target (float) : Target move from each starting point   Returns: Array of first passage times (na if target not reached within lookback) tp_probability(src, length, tp_distance, sl_distance)   Estimates probability of hitting TP before SL   Parameters:      src (float) : Source series (typically returns)      length (simple int) : Lookback for estimation      tp_distance (float) : Take profit distance (positive)      sl_distance (float) : Stop loss distance (positive)   Returns: Probability of TP being hit first trade_probability(src, length, tp_pct, sl_pct)   Calculates complete trade probability and EV analysis   Parameters:      src (float) : Source series (typically returns)      length (simple int) : Lookback period      tp_pct (float) : Take profit percentage      sl_pct (float) : Stop loss percentage   Returns: Tuple: cond_prob(condition_a, condition_b, length)   Calculates conditional probability P(B|A) from historical data   Parameters:      condition_a (bool) : Condition A (the given condition)      condition_b (bool) : Condition B (the outcome)      length (simple int) : Lookback period   Returns: P(B|A) = P(A and B) / P(A) bayes_update(prior, likelihood, false_positive)   Updates probability using Bayes' theorem   Parameters:      prior (float) : Prior probability P(H)      likelihood (float) : P(E|H) - probability of evidence given hypothesis      false_positive (float) : P(E|~H) - probability of evidence given hypothesis is false   Returns: Posterior probability P(H|E) streak_prob(win_rate, streak_length)   Calculates probability of N consecutive wins given win rate   Parameters:      win_rate (float) : Single-trade win probability      streak_length (simple int) : Number of consecutive wins   Returns: Probability of streak losing_streak_prob(win_rate, streak_length)   Calculates probability of experiencing N consecutive losses   Parameters:      win_rate (float) : Single-trade win probability      streak_length (simple int) : Number of consecutive losses   Returns: Probability of losing streak drawdown_prob(src, length, dd_threshold)   Estimates probability of drawdown exceeding threshold   Parameters:      src (float) : Source series (returns)      length (simple int) : Lookback period      dd_threshold (float) : Drawdown threshold (as positive decimal, e.g., 0.10 = 10%)   Returns: Historical probability of exceeding drawdown threshold prob_to_odds(prob)   Calculates odds from probability   Parameters:      prob (float) : Probability (0-1)   Returns: Odds (prob / (1 - prob)) odds_to_prob(odds)   Calculates probability from odds   Parameters:      odds (float) : Odds ratio   Returns: Probability (0-1) implied_prob(decimal_odds)   Calculates implied probability from decimal odds (betting)   Parameters:      decimal_odds (float) : Decimal odds (e.g., 2.5 means $2.50 return per $1 bet)   Returns: Implied probability logit(prob)   Calculates log-odds (logit) from probability   Parameters:      prob (float) : Probability (must be in (0, 1))   Returns: Log-odds inv_logit(log_odds)   Calculates probability from log-odds (inverse logit / sigmoid)   Parameters:      log_odds (float) : Log-odds value   Returns: Probability (0-1) linreg_slope(src, length)   Calculates linear regression slope   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 2)   Returns: Slope coefficient (change per bar) linreg_intercept(src, length)   Calculates linear regression intercept   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 2)   Returns: Intercept (predicted value at oldest bar in window) linreg_value(src, length)   Calculates predicted value at current bar using linear regression   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: Predicted value at current bar (end of regression line) linreg_forecast(src, length, offset)   Forecasts value N bars ahead using linear regression   Parameters:      src (float) : Source series      length (simple int) : Lookback period for regression      offset (simple int) : Bars ahead to forecast (positive = future)   Returns: Forecasted value linreg_channel(src, length, mult)   Calculates linear regression channel with bands   Parameters:      src (float) : Source series      length (simple int) : Lookback period      mult (simple float) : Standard deviation multiplier for bands   Returns: Tuple: r_squared(src, length)   Calculates R-squared (coefficient of determination)   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: R² value where 1 = perfect linear fit adj_r_squared(src, length)   Calculates adjusted R-squared (accounts for sample size)   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: Adjusted R² value std_error(src, length)   Calculates standard error of estimate (residual standard deviation)   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: Standard error residual(src, length)   Calculates residual at current bar   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: Residual (actual - predicted) residuals(src, length)   Returns array of all residuals in lookback window   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: Array of residuals t_statistic(src, length)   Calculates t-statistic for slope coefficient   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: T-statistic (slope / standard error of slope) slope_pvalue(src, length)   Approximates p-value for slope t-test (two-tailed)   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: Approximate p-value is_significant(src, length, alpha)   Tests if regression slope is statistically significant   Parameters:      src (float) : Source series      length (simple int) : Lookback period      alpha (simple float) : Significance level (default: 0.05)   Returns: true if slope is significant at alpha level trend_strength(src, length)   Calculates normalized trend strength based on R² and slope   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: Trend strength where sign indicates direction trend_angle(src, length)   Calculates trend angle in degrees   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: Angle in degrees (positive = uptrend, negative = downtrend) linreg_acceleration(src, length)   Calculates trend acceleration (second derivative)   Parameters:      src (float) : Source series      length (simple int) : Lookback period for each regression   Returns: Acceleration (change in slope) linreg_deviation(src, length)   Calculates deviation from regression line in standard error units   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: Deviation in standard error units (like z-score) quadreg_coefficients(src, length)   Fits quadratic regression and returns coefficients   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 4)   Returns: Tuple: for y = a*x² + b*x + c quadreg_value(src, length)   Calculates quadratic regression value at current bar   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: Predicted value from quadratic fit correlation(x, y, length)   Calculates Pearson correlation coefficient between two series   Parameters:      x (float) : First series      y (float) : Second series      length (simple int) : Lookback period (must be >= 3)   Returns: Correlation coefficient covariance(x, y, length)   Calculates sample covariance between two series   Parameters:      x (float) : First series      y (float) : Second series      length (simple int) : Lookback period (must be >= 2)   Returns: Covariance value beta(asset, benchmark, length)   Calculates beta coefficient (slope of regression of y on x)   Parameters:      asset (float) : Asset returns series      benchmark (float) : Benchmark returns series      length (simple int) : Lookback period   Returns: Beta coefficient @description Beta = Cov(asset, benchmark) / Var(benchmark) alpha(asset, benchmark, length, risk_free)   Calculates alpha (Jensen's alpha / intercept)   Parameters:      asset (float) : Asset returns series      benchmark (float) : Benchmark returns series      length (simple int) : Lookback period      risk_free (float) : Risk-free rate (default: 0)   Returns: Alpha value (excess return not explained by beta) spearman(x, y, length)   Calculates Spearman rank correlation coefficient   Parameters:      x (float) : First series      y (float) : Second series      length (simple int) : Lookback period (must be >= 3)   Returns: Spearman correlation @description More robust to outliers than Pearson correlation kendall_tau(x, y, length)   Calculates Kendall's tau rank correlation (simplified)   Parameters:      x (float) : First series      y (float) : Second series      length (simple int) : Lookback period (must be >= 3)   Returns: Kendall's tau correlation_change(x, y, length, change_period)   Calculates change in correlation over time   Parameters:      x (float) : First series      y (float) : Second series      length (simple int) : Lookback period for correlation      change_period (simple int) : Period over which to measure change   Returns: Change in correlation correlation_regime(x, y, length, ma_length)   Detects correlation regime based on level and stability   Parameters:      x (float) : First series      y (float) : Second series      length (simple int) : Lookback period for correlation      ma_length (simple int) : Moving average length for smoothing   Returns: Regime: -1 = negative, 0 = uncorrelated, 1 = positive correlation_stability(x, y, length, stability_length)   Calculates correlation stability (inverse of volatility)   Parameters:      x (float) : First series      y (float) : Second series      length (simple int) : Lookback for correlation      stability_length (simple int) : Lookback for stability calculation   Returns: Stability score where 1 = perfectly stable relative_strength(asset, benchmark, length)   Calculates relative strength of asset vs benchmark   Parameters:      asset (float) : Asset price series      benchmark (float) : Benchmark price series      length (simple int) : Smoothing period   Returns: Relative strength ratio (normalized) tracking_error(asset, benchmark, length)   Calculates tracking error (standard deviation of excess returns)   Parameters:      asset (float) : Asset returns      benchmark (float) : Benchmark returns      length (simple int) : Lookback period   Returns: Tracking error (annualize by multiplying by sqrt(252) for daily data) information_ratio(asset, benchmark, length)   Calculates information ratio (risk-adjusted excess return)   Parameters:      asset (float) : Asset returns      benchmark (float) : Benchmark returns      length (simple int) : Lookback period   Returns: Information ratio capture_ratio(asset, benchmark, length, up_capture)   Calculates up/down capture ratio   Parameters:      asset (float) : Asset returns      benchmark (float) : Benchmark returns      length (simple int) : Lookback period      up_capture (simple bool) : If true, calculate up capture; if false, down capture   Returns: Capture ratio autocorrelation(src, length, lag)   Calculates autocorrelation at specified lag   Parameters:      src (float) : Source series      length (simple int) : Lookback period      lag (simple int) : Lag for autocorrelation (default: 1)   Returns: Autocorrelation at specified lag partial_autocorr(src, length)   Calculates partial autocorrelation at lag 1   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: PACF at lag 1 (equals ACF at lag 1) autocorr_test(src, length, max_lag)   Tests for significant autocorrelation (Ljung-Box inspired)   Parameters:      src (float) : Source series      length (simple int) : Lookback period      max_lag (simple int) : Maximum lag to test   Returns: Sum of squared autocorrelations (higher = more autocorrelation) cross_correlation(x, y, length, lag)   Calculates cross-correlation at specified lag   Parameters:      x (float) : First series      y (float) : Second series (lagged)      length (simple int) : Lookback period      lag (simple int) : Lag to apply to y (positive = y leads x)   Returns: Cross-correlation at specified lag cross_correlation_peak(x, y, length, max_lag)   Finds lag with maximum cross-correlation   Parameters:      x (float) : First series      y (float) : Second series      length (simple int) : Lookback period      max_lag (simple int) : Maximum lag to search (both directions)   Returns: Tuple:
Thư viện Pine Script®
của HenriqueCentieiro
1
Gold Inverse Correlation TrackerGold Inverse Correlation Tracker - Professional Multi-Asset Analysis What This Indicator Does: This indicator monitors the real-time correlation between Gold and five key financial assets that historically move inversely (opposite) to gold prices. It displays these relationships across three different timeframes simultaneously, giving you both short-term trading signals and long-term trend confirmation. The indicator tracks: US Dollar Index (DXY) - Historical correlation: -0.63 Real Interest Rates (TIPS) - Historical correlation: -0.82 (strongest inverse relationship) 10-Year Treasury Yield - Nominal interest rate proxy S&P 500 (SPX) - Equity market sentiment (variable correlation) VIX - Volatility index (optional, flight-to-safety indicator) Why Inverse Correlations Matter for Gold Trading: Understanding inverse correlations is critical for gold traders because: Predictive Power - When assets move opposite to gold consistently, you can use their strength/weakness to predict gold's next move Hedging Opportunities - Strong inverse correlations let you hedge gold positions by trading the inverse asset Regime Detection - When correlations break down, it signals a market regime change or increased uncertainty Confirmation Signals - Multiple strong inverse correlations validate your gold trade thesis Risk Management - Knowing what moves against gold helps you understand your portfolio's true exposure The Science Behind the Numbers: Real interest rates have the strongest inverse correlation to gold (approximately -0.82) because: Gold pays no yield or dividend When real rates rise, the opportunity cost of holding gold increases Investors shift to interest-bearing assets when they offer positive real returns When real rates go negative, gold becomes relatively more attractive The US Dollar shows strong inverse correlation (approximately -0.63) because: Gold is priced in US dollars globally A stronger dollar makes gold more expensive for foreign buyers, reducing demand A weaker dollar makes gold cheaper internationally, increasing demand Both compete as reserve assets and stores of value Why the Indicator is Weighted This Way: Three Timeframe Approach: Short-term (20 periods) - Captures recent correlation shifts for day trading and swing trading Medium-term (50 periods) - The primary signal - balances noise reduction with responsiveness Long-term (100 periods) - Confirms structural correlation trends for position trading Correlation Thresholds: Strong Inverse (<-0.7) - Statistically significant inverse relationship; highest confidence for inverse trades Moderate Inverse (<-0.3) - Meaningful inverse relationship; still useful but less reliable Weak Inverse (<0.0) - Slight inverse tendency; correlation may be breaking down Positive (>0.0) - Assets moving together; inverse relationship has failed How to Use This Indicator: For Inverse Trading Strategies: When DXY shows RED correlation (<-0.7), consider shorting DXY when gold is strong When Real Rates show RED correlation, rising rates = falling gold (and vice versa) When multiple assets show strong inverse correlation, confidence is highest For Regime Detection: All RED = Classic gold market behavior; correlations intact Mixed colors = Transitional market; be cautious All GREEN/GRAY = Correlation breakdown; paradigm shift occurring For Hedging: Use assets with strong inverse correlation to hedge gold positions When correlation weakens, reduce hedge size When correlation strengthens, increase hedge effectiveness Alert System: The indicator includes built-in alerts for: Individual assets crossing strong inverse threshold Multiple assets simultaneously showing strong inverse correlation (highest probability setup) Correlation breakdowns that may signal regime changes Color Guide: RED - Strong inverse correlation (<-0.7) - Best inverse trading opportunity ORANGE - Moderate inverse (<-0.3) - Useful but less reliable YELLOW - Weak inverse (<0.0) - Correlation weakening GRAY - Weak positive (0.0 to 0.7) - Assets moving together GREEN - Strong positive (>0.7) - Inverse relationship broken Recommended Settings: Day Trading (1H-4H charts): Short: 14 periods Medium: 30 periods Long: 60 periods Swing Trading (Daily charts): Short: 20 periods (default) Medium: 50 periods (default) Long: 100 periods (default) Position Trading (Weekly charts): Short: 10 periods Medium: 20 periods Long: 50 periods Pro Tips: Watch for divergences - when gold moves but correlations don't confirm Correlation breakdowns often precede major trend reversals The Medium-term (50p) correlation is plotted on the chart as your primary reference Use the Status column for quick assessment of each asset's relationship Set alerts for "Multiple Strong Inverse" to catch highest-probability setups Important Notes: This indicator is designed for Gold charts only (XAUUSD, GLD, GC1!, etc.) Correlations are not static - they change over time based on market conditions A correlation of -0.82 means 82% of gold's price movements can be explained by real interest rates Always combine with other technical analysis and fundamental factors Past correlations do not guarantee future relationships Based on Research: The correlation coefficients used in this indicator are based on peer-reviewed research: Erb & Harvey (1997-2012): Real rates to gold correlation of -0.82 World Gold Council (2024): US Dollar to gold correlation of -0.63 Multiple academic studies confirming gold's inverse relationship with opportunity cost assets Use this indicator to trade smarter, hedge better, and understand the macro forces driving gold prices.
Chỉ báo Pine Script®
của bigcitytom
Gold Projection DivergenceGOLD PROJECTION DIVERGENCE Oscillator Companion for the Gold Macro Projection Model OVERVIEW The Gold Projection Divergence oscillator quantifies how far gold is trading from its projected fair value. While the main indicator shows where gold should be, this oscillator shows how extreme the mispricing is—providing precise timing signals for entries and exits. HOW IT WORKS The oscillator calculates the difference between actual gold price and the projected value, then normalizes it as a Z-score . This statistical measure shows how many standard deviations gold is trading away from its projected fair value. Z > +2 — Gold is 2+ standard deviations above fair value (extremely overvalued) Z > +1 — Gold is moderately overvalued Z = 0 — Gold is trading at projected fair value Z < -1 — Gold is moderately undervalued Z < -2 — Gold is 2+ standard deviations below fair value (extremely undervalued) VISUAL ELEMENTS Histogram — Color-coded divergence magnitude Yellow Line — Smoothed Z-score Dashed Lines — +2 and -2 standard deviation levels Dotted Lines — +1 and -1 standard deviation levels Triangle Markers — Extreme crossover signals Circle Markers — Zero-line crossings HISTOGRAM COLORS Dark Red — Z > +2 (extreme overvaluation) Orange — Z between +1 and +2 Light Orange — Z between 0 and +1 Light Green — Z between -1 and 0 Green — Z between -2 and -1 Lime — Z < -2 (extreme undervaluation) COMPONENT TABLE The breakdown table shows divergence from each individual factor: Silver — Is gold over/undervalued relative to silver? M2 — Is gold over/undervalued relative to money supply? DXY — Is gold over/undervalued relative to dollar strength? Equity — Is gold over/undervalued relative to stocks? TIPS — Is gold over/undervalued relative to real rates? TRADING APPLICATIONS Mean Reversion Strategy Enter LONG when Z < -2 and begins rising Enter SHORT when Z > +2 and begins falling Use zero-line crossings for trend confirmation Trend Following Filter Only take long trades when Z < 0 (undervalued) Only take short trades when Z > 0 (overvalued) Divergence Confirmation Bearish: Price makes new highs while Z-score makes lower highs Bullish: Price makes new lows while Z-score makes higher lows ALERTS Extreme Undervaluation — Z crosses below -2 Extreme Overvaluation — Z crosses above +2 Moderate Undervaluation — Z crosses below -1 Moderate Overvaluation — Z crosses above +1 Divergence Turned Positive — Crossed above zero Divergence Turned Negative — Crossed below zero COMBINED USAGE For best results, use both indicators together : Main Indicator — Visual context of actual vs. projected on price chart Divergence Oscillator — Precise measurement for timing decisions The main indicator shows where gold should be; the oscillator shows how extreme the mispricing is and when to act. Disclaimer: This indicator is for educational purposes only. Past correlations do not guarantee future relationships. Market conditions can alter historical relationships. Always use proper risk management.
Chỉ báo Pine Script®
của bigcitytom
Gold Macro Projection ModelGOLD MACRO PROJECTION MODEL Multi-Factor Fair Value Estimation for Gold OVERVIEW The Gold Macro Projection Model estimates gold's fair value based on its historical relationships with key macroeconomic drivers. By synthesizing data from silver , M2 money supply , the US Dollar Index , TIPS (real rates proxy) , and major equity indices , this indicator projects where gold should theoretically be trading—helping traders identify potential overvaluation and undervaluation conditions. HOW IT WORKS This indicator employs three complementary projection methodologies : Correlation-Weighted Z-Score Composite (50% weight) Calculates rolling correlations between gold and each input factor. Factors with stronger correlations receive more influence. Each factor is normalized to a z-score, combined into a composite, then converted back to gold's price scale. Silver/Gold Ratio Mean Reversion (35% weight) The silver/gold ratio historically exhibits mean-reverting behavior. This component projects gold's implied price based on current silver prices and the historical average ratio. M2 Money Supply Relationship (15% weight) Gold tracks monetary expansion over long time horizons. This anchors the projection to the fundamental relationship between gold and the monetary base. INPUT FACTORS Silver — Strong positive correlation; precious metals move together M2 Money Supply — Positive correlation; gold as inflation hedge US Dollar Index (DXY) — Typically negative correlation; inverse relationship TIPS ETF — Real interest rate proxy; gold responds to real yields Equity Indices — Variable correlation; risk-on/risk-off dynamics VISUAL ELEMENTS Yellow Line — Actual gold price Aqua Line — Projected fair value Green Fill — Gold trading below projection (potentially undervalued) Red Fill — Gold trading above projection (potentially overvalued) Aqua Bands — Standard deviation envelope around projection INFO TABLE The indicator displays a real-time information panel showing: Current actual vs. projected price Divergence percentage and Z-score Rolling correlations for each factor Dynamic weight allocation Buy/Sell signal based on divergence extremes SIGNAL INTERPRETATION STRONG BUY — Z-score below -2 (extremely undervalued) BUY — Z-score between -2 and -1 (moderately undervalued) NEUTRAL — Z-score between -1 and +1 (fairly valued) SELL — Z-score between +1 and +2 (moderately overvalued) STRONG SELL — Z-score above +2 (extremely overvalued) SETTINGS Correlation Period — Lookback for correlation calculations (default: 60) Regression Period — Lookback for mean/standard deviation (default: 120) Smoothing Period — EMA smoothing for projection line (default: 10) Auto Weights — Toggle between correlation-based or manual weights Band Multiplier — Standard deviation multiplier for bands (default: 1.5) ALERTS Gold Extremely Undervalued — Z crosses below -2 Gold Extremely Overvalued — Z crosses above +2 Gold Crossed Above Projection Gold Crossed Below Projection BEST PRACTICES Use on daily timeframe for most reliable signals Combine with the companion Gold Divergence Oscillator for timing Disclaimer: This indicator is for educational purposes only. Past correlations do not guarantee future relationships. Always use proper risk management.
Chỉ báo Pine Script®
của bigcitytom
Global Macro Scanner & Relative PerformanceDescription: This indicator is an all-in-one Macro Dashboard that allows traders to track money flow across major global asset classes in real-time. It combines a floating data table with a normalized percentage-performance chart. Features: Macro Dashboard (Table): Displays the current value, daily % change, and status (Inflow/Outflow) for 9 key economic sectors: US M2 Supply: Tracks monetary inflation/tightening. DXY (US Dollar): Currency strength. Bonds (AGG): US Aggregate Bond market. Stocks (VT): Total World Stock Index. Real Estate (VNQ): Vanguard Real Estate ETF. Commodities: Oil (WTI), Gold, and Silver. Crypto: Total Crypto Market Cap. Relative Performance Chart (Lines): Instead of plotting raw prices (which have vastly different scales), this script plots the Percentage Return relative to a baseline. Lookback Period: You can set a lookback (default 100 bars). The script sets the price 100 bars ago as "0%" and plots how much each asset has gained or lost since then. Comparison: This allows you to visually see which assets are outperforming or underperforming relative to each other over the same time period. Visual Aids: Dynamic Labels: Each line is tagged with a label at the current candle so you can identify assets without needing a legend. Colors: Each asset has a distinct, fixed color for consistency between the table and the chart. How to use: Add the script to your chart. Adjust the "Lookback" setting in the inputs to change the starting point of the comparison (e.g., set it to the start of the year to see Year-to-Date performance). Use the dashboard to spot daily money flow rotation (e.g., Money moving out of Stocks and into Gold).
Chỉ báo Pine Script®
của armigoldman
QuantLabs MASM Correlation TableThe Market is a graph. See the flows: The QuantLabs MASM is not a standard correlation table. It is an Alpha-Grade Scanner architected to reveal the hidden "hydraulic" relationships between global macro assets in real-time. Rebuilt from the ground up for Version 3, this engine pushes the absolute limits of the Pine Script™ runtime. It utilizes a proprietary Logarithmic Math Engine, Symmetric Compute Optimization, and a futuristic "Ghost Mode" interface to deliver a 15x15 real-time correlation matrix with zero lag. Under the Hood: The Quant Architecture We stripped away standard libraries to build a lean, high-performance engine designed for institutional-grade accuracy. 1. Alpha Math Engine (Logarithmic Returns) Most tools calculate correlation based on Price, which generates spurious signals (e.g., "Everything is correlated in a bull run"). The Solution: Our engine computes Logarithmic Returns (log(close/close )) by default. This measures the correlation of change (Velocity & Vector), not price levels. The Result: A mathematically rigorous view of statistical relationships that filters out the noise of general market drift. Dual-Core: Toggle seamlessly between "Alpha Mode" (Log Returns) for verified stats and "Visual Mode" (Price) for trend alignment. Calculation Modes: Pearson (Standard), Euclidean (Distance), Cosine (Vector), Manhattan (Grid). 2. Symmetric Compute Optimization Calculating a 15x15 matrix requires evaluating 225 unique relationships per bar, which often crashes memory limits. The Fix: The V3 Engine utilizes Symmetric Logic, recognizing that Correlation(A, B) == Correlation(B, A). The Gain: By computing only the lower triangle of the matrix and mirroring pointers to the upper triangle, we reduced computational load by 50%, ensuring a lightning-fast data feed even on lower timeframes. 3. Context-Aware "Ghost Mode" The UI is designed for professional traders who need focus, not clutter. Smart Detection: The matrix automatically detects your current chart's Ticker ID. If you are trading QQQ, the matrix will visually highlight the Nas100 row and column, making them opaque and bright while dimming the rest. Dynamic Transparency: Irrelevant data ("Noise" < 0.3 correlation) fades into the background. Only significant "Alpha Signals" (> 0.7) glow with full Neon Saturation. Key Features Dominant Flow Scanner: The matrix scans all 105 unique pairs every tick and prints the #1 Strongest Correlation at the bottom of the pane (e.g., DOMINANT FLOW: Bitcoin ↔ Nas100 ). Streak Counter: A "Stubbornness" metric that tracks how many consecutive days a strong correlation has persisted. Instantly identify if a move is a "flash event" or a "structural trend." Neon Palette: Proprietary color mapping using Electric Blue (+1.0) for lockstep correlation and Deep Red (-1.0) for inverse hedging. Usage Guide Placement: Best viewed in a bottom pane (Footer). Assets: Pre-loaded with the Essential 15 Macro Drivers (Indices, BTC, Gold, Oil, Rates, FX, Key Sectors). Fully editable via settings (Ticker|Name). Reading the Grid: 🔵 Bright Blue: Assets moving in lockstep (Risk-On). 🔴 Bright Red: Assets moving perfectly opposite (Hedge/Risk-Off). ⚫ Faded/Black: No statistical relationship (Decoupled). Key Improvements Made: Formatting: Added clear bullet points and bolding to make it scannable. Clarity: Clarified the "Logarithmic Returns" section to explain why it matters (Velocity vs. Price Levels). Tone: Maintained the "high-tech/quant" vibe but removed slightly clunky phrases like "spurious signals" (unless you prefer that academic tone, in which case I left it in as it fits the persona). Structure: Grouped the "Modes" under the Math Engine for better logic. Created and designed by QuantLabs
Chỉ báo Pine Script®
của CustomQuantLabs
SMT Divergence [Kodexius]SMT Divergence is a correlation-based divergence detector built around the Smart Money Technique concept: when two normally correlated instruments should be making similar swing progress, but one prints a new extreme while the other fails to confirm it. This “disagreement” can be a valuable contextual signal around liquidity runs, distribution phases, and potential reversal or continuation points. The script compares the chart symbol (primary) with a user-selected comparison symbol (for example BTC vs ETH, ES vs NQ, EUR/USD vs GBP/USD) and automatically scans both instruments for confirmed swing highs and swing lows using pivot logic. Once swings are established, it checks for classic SMT conditions: Primary makes a new swing extreme while the comparison symbol forms a non-confirming swing . To support a wider range of markets, the indicator includes an Inverse Correlation option for pairs that typically move opposite to each other (for example DXY vs EUR/USD). With this enabled, the divergence rules are logically flipped so that the script still detects “non-confirmation” in a way that is consistent with the pair’s relationship. The indicator is designed to be readable and actionable. It can draw divergence labels directly on the main chart, connect the relevant swing points with lines, show a compact information table with the last signal and settings, and optionally render the comparison symbol as a mini candle chart in the indicator pane for quick visual validation. 🔹 Features 🔸 Two-Symbol SMT Analysis (Primary vs Compare) Select any comparison symbol to evaluate correlation structure and divergence. The script fetches the comparison OHLC data using the current chart timeframe to keep both series aligned for analysis. 🔸 Inverse Correlation Mode For inversely correlated pairs, enable “Inverse Correlation” so the script interprets confirmation appropriately (for example, a higher low on the comparison instrument might be expected to correspond to a lower low on the primary, depending on the relationship). This helps avoid false conclusions when the pair naturally moves opposite. 🔸 Pivot-Based Swing with Adjustable Sensitivity Swings are detected using confirmed pivots (left bars and right bars). This provides cleaner structural swing points compared with raw candle-to-candle comparisons, and it lets you control sensitivity for different market conditions and timeframes. The script also limits stored swing history to keep performance stable. 🔸 Flexible Detection Mode: Time Matched or Independent Swings You can choose how swings are paired across instruments: Time Matched searches for a comparison swing that occurred at the same pivot time as the primary swing. Independent Swings compares each symbol’s own last two swings without requiring an exact time match. 🔸 Range Control and Noise Filtering To reduce weak or irrelevant signals: “Max Bars Between Swings” ensures the two swings being compared are close enough in structure to be meaningful. “Min Price Diff (%)” can require a minimum percentage change between the primary’s last two swing prices to confirm the move is significant. 🔸 Clear Visual Output with Tooltips When a divergence is detected, the script can print a label (“SMT”) with bullish or bearish styling and a tooltip that includes the symbol pair and the primary swing price for quick context. 🔸 Divergence Lines for Context Optional lines connect the relevant swing points, making it easier to see the exact structure that triggered the signal. One line can be drawn on the main chart and another in the indicator pane for the comparison series. 🔸 Info Table (At a Glance) A compact table can display the active symbols, correlation mode, total divergences stored, and the most recent signal type. 🔸 Alerts Included Built-in alert conditions are provided for bullish SMT, bearish SMT, and any SMT event so you can automate notifications without editing the code. 🔸 Optional Comparison Candle Panel If enabled, the indicator can plot the comparison symbol as candles in the indicator pane. This is useful for confirming whether the divergence is happening around major levels, consolidations, or impulsive legs on the secondary instrument. 🔹 Calculations This section summarizes the core logic used by the script. 1. Data Synchronization (Comparison Symbol) The comparison instrument is requested on the chart’s current timeframe so swing calculations are performed consistently: = request.security(compareSymbolInput, timeframe.period, ) This ensures pivots and swing times are derived from the same bar cadence as the primary chart. 2. Swing Detection via Confirmed Pivots Swings are detected using pivot logic with user-defined left and right bars: primaryPivotHigh = ta.pivothigh(high, pivotLeftBars, pivotRightBars) primaryPivotLow = ta.pivotlow(low, pivotLeftBars, pivotRightBars) Because pivots are confirmed only after the “right bars” have closed, the script stores each swing using an offset so the swing’s bar index and time reflect where the pivot actually occurred, not where it was confirmed. 3. Swing Storage and Retrieval Both symbols maintain arrays of SwingPoint objects. Each new swing is pushed into the array, and older swings are dropped once the array exceeds the configured maximum. This makes the divergence engine predictable and prevents uncontrolled memory growth. The script then retrieves the last and previous swing highs and lows (per symbol) to evaluate structure. 4. Matching Logic (Time Matched vs Independent) When “Time Matched” is selected, the script searches the comparison swing array for a pivot that occurred at the exact same timestamp as the primary swing. When “Independent Swings” is selected, it simply uses the comparison symbol’s last two swings of the same type. 5. Bullish SMT Condition (LL vs HL) A bullish SMT event is defined as: Primary forms a lower low (last low < previous low) Comparison forms a higher low (last low > previous low) If inverse correlation is enabled, the comparison condition flips to maintain logical confirmation rules The two primary swings must be within the configured bar distance window Optional minimum percentage difference must be satisfied A simple anti duplication rule prevents repeated triggers on the same structure These checks are implemented directly in the bullish detection block. 6. Bearish SMT Condition (HH vs LH) A bearish SMT event is defined as: Primary forms a higher high (last high > previous high) Comparison forms a lower high (last high < previous high) Inverse correlation flips the comparison rule Range checks, minimum difference filtering, and duplicate protection apply similarly These checks are implemented in the bearish detection block. 7. Percentage Difference Filter The optional “Min Price Diff (%)” filter measures the relative distance between the last two primary swing prices. This prevents very small structural changes from being treated as valid SMT signals. priceDiffPerc = math.abs(lastSwing.price - prevSwing.price) / prevSwing.price * 100.0 The divergence condition is only allowed to trigger if this value exceeds the user defined threshold. priceOk = priceDiffPerc >= minPriceDiff This filter is especially useful on higher timeframes or during low volatility conditions, where micro structure noise can otherwise produce misleading signals. 8. Visualization and Output When a divergence is confirmed, the script: Stores the event in a divergence array (limited by “Max Divergences to Display”) Draws a directional SMT label with a tooltip (optional) Draws connecting lines using time based coordinates for clean alignment (optional) It also updates an information table on the last bar only, and exposes alertconditions for automation workflows.
Chỉ báo Pine Script®
của UnknownUnicorn113699460
2
Pair Correlation Master [Macro]The Main Idea Trading represents a constant battle between Systemic Flows (the whole market moving together) and Idiosyncratic Moves (one specific asset moving on its own). This tool allows you to monitor a "basket" of 4 assets simultaneously (e.g., the major USD pairs). It answers the most important question in forex and multi-asset trading: "Is this move happening because the Dollar is weak, or because the Euro is strong?" It separates the "Signal" (the unique move) from the "Noise" (the herd movement). 1. The Chart Lines: The "Race" (Macro Trend) Think of the lines on your chart as a long-distance race. They visualize the performance of all 4 assets over the last 200 candles (adjustable). - Bunched Together: If all lines are moving in the same direction, the market is highly correlated. (e.g., "The Dollar is selling off everywhere"). - Fanning Out: If the lines are spreading apart, specific currencies are outperforming others. - The Zero Line: This is the starting line. --- Above 0: The pair is in a macro uptrend. --- Below 0: The pair is in a macro downtrend. 2. The Dashboard: The "Health Check" (Micro Data) The table in the top right gives you the immediate statistics for right now. - A. The Z-Score (The Rubber Band) This measures how "stretched" price is compared to its normal behavior. - White (< 2.0): Normal trading activity. - Orange (> 2.0): The price is stretching. Warning sign. - Red (> 3.0): Critical Stretch. The rubber band is pulled to its limit. Statistically, a pullback or pause is highly likely. B. The Star (★) The script automatically calculates the average behavior of your group. If one asset is behaving completely differently from the rest, it marks it with a Star (★). - Example: EURUSD, GBPUSD, and NZDUSD are flat, but AUDUSD is rallying hard. AUDUSD gets the ★. This is where the unique opportunity lies. 🎯 Best Uses: 4H & Daily Timeframes This indicator is tuned for "Macro" analysis. It works best on the "4-Hour" and "Daily" charts to filter out intraday noise and capture swing trading moves. - Strategy 1: The "Rubber Band" Snap (Mean Reversion) - Setup: Look for a Z-Score in the RED (> 3.0) on the Daily timeframe. - Action: This indicates an unsustainable move. Look for reversals or exhaustion patterns to trade against the trend back toward the mean. - Strategy 2: The "Lone Wolf" (Trend Following) - Setup: Look for the asset with the Star (★). - Action: If the whole basket is flat (Balanced), but the Star asset is breaking out, that creates a high-quality trend trade because that specific currency has its own catalyst (News/Earnings). - Strategy 3: Systemic Flows (Basket Trading) - Setup: The dashboard footer says "⚠️ SYSTEMIC MOVE." - Action: This means everything is moving together (e.g., a massive USD crash). Don't look for unique setups; just join the trend on the strongest pair. Dashboard Footer Key The bottom of the table summarizes the current state of the market for you: - Balanced / Rangebound: The market is quiet. Good for range trading. - Focus: : Trade this specific pair. It is moving independently. - Systemic Move: The whole basket is moving violently. Trade the momentum. p.s. Suggestion - apply and use on the chart rather than an oscillator.
Chỉ báo Pine Script®
của siLve_R88
RCV Essentials════════════════════════════════════════════ RCV ESSENTIALS - MULTI-TIMEFRAME & SESSION ANALYSIS TOOL ════════════════════════════════════════════ 📊 WHAT THIS INDICATOR DOES This professional-grade indicator combines two powerful analysis modules: 1. TRADING SESSION TRACKER - Visualizes high/low ranges for major global market sessions (NY Open, London Open, Asian Session, etc.) 2. MULTI-TIMEFRAME CANDLE DISPLAY - Shows up to 8 higher timeframes simultaneously on your chart (15m, 30m, 1H, 4H, 1D, 1W, 1M, 3M) ════════════════════════════════════════════ 🎯 KEY FEATURES ════════════════════════════════════════════ TRADING SESSIONS MODULE: ✓ Track up to 6 custom trading sessions simultaneously ✓ Real-time high/low range detection during active sessions ✓ Pre-configured for NYO (7-9am), LNO (2-3am), Asian Session (4:30pm-12am) ✓ 60+ global timezone options ✓ Customizable colors, labels, and transparency ✓ Daily divider lines (optional Sunday skip for traditional markets) ✓ Only displays on ≤30m timeframes for optimal clarity MULTI-TIMEFRAME CANDLES MODULE: ✓ Display 1-8 higher timeframes with up to 10 candles each ✓ Real-time candle updates (non-repainting) ✓ Fully customizable colors (separate bullish/bearish for body/border/wick) ✓ Adjustable candle width, spacing, and positioning ✓ Smart label system (top/bottom/both, aligned or follow candles) ✓ Automatic timeframe validation (only shows TFs higher than chart) ✓ Memory-optimized with automatic cleanup ════════════════════════════════════════════ 🔧 HOW IT WORKS ════════════════════════════════════════════ TECHNICAL IMPLEMENTATION: Session Tracking Algorithm: • Detects session start/end using time() function with timezone support • Continuously monitors and updates high/low during active session • Finalizes range when session ends using var persistence • Draws boxes using real-time bar_index positioning • Maintains session ranges across multiple days for reference Multi-Timeframe System: • Uses ta.change(time()) detection to identify new MTF candle formation • Constructs candles using custom Type definitions (Candle, CandleSet, Config) • Stores OHLC data in arrays with automatic size management • Renders using box objects (bodies) and line objects (wicks) • Updates current candle every tick; historical candles remain static • Calculates dynamic positioning based on user settings (offset, spacing, width) Object-Oriented Architecture: • Custom Type "Candle" - Stores OHLC values, timestamps, visual elements • Custom Type "CandleSet" - Manages arrays of candles + settings per timeframe • Custom Type "Config" - Centralizes all display configuration • Efficient memory management via unshift() for new candles, pop() for old Performance Optimizations: • var declarations minimize recalculation overhead • Conditional execution (sessions only on short timeframes) • Maximum display limits prevent excessive object creation • Timeframe validation at barstate.isfirst reduces redundant checks ════════════════════════════════════════════ 📈 HOW TO USE ════════════════════════════════════════════ SETUP: 1. Add indicator to chart (works best on 1m-30m timeframes) 2. Open Settings → "Trading Sessions" group - Enable desired sessions (NYO, LNO, AS, or custom) - Select your timezone from 60+ options - Adjust colors and transparency 3. Open Settings → "Multi-TF Candles" group - Enable timeframes (TF1-TF8) - Configure each timeframe and display count - Customize colors and layout READING THE CHART: • Session boxes show high/low ranges during active sessions • MTF candles display to the right of current price • Labels identify each timeframe (15m, 1H, 4H, etc.) • Real-time updates on the most recent MTF candle TRADING APPLICATIONS: Session Breakout Strategy: → Identify session high/low (e.g., Asian session 16:30-00:00) → Wait for break above/below range → Confirm with higher timeframe candle close → Enter in breakout direction, stop at opposite side of range Multi-Timeframe Confirmation: → Spot setup on primary chart (e.g., 5m) → Verify 15m, 1H, 4H candles align with trade direction → Only take trades where higher TFs confirm → Exit when higher TF candles show reversal Combined Session + MTF: → Asian session establishes range overnight → London Open breaks Asian high → Confirm with bullish 15m + 1H candles → Enter long with stop below Asian high ════════════════════════════════════════════ 🎨 ORIGINALITY & INNOVATION ════════════════════════════════════════════ What makes this indicator original: 1. INTEGRATED DUAL-MODULE DESIGN Unlike separate session or MTF indicators, this combines both in a single performance-optimized script, enabling powerful correlation analysis between session behavior and timeframe structure. 2. ADVANCED RENDERING SYSTEM Uses custom Pine Script v5 Types with dynamic box/line object management instead of basic plot functions. This enables: • Precise visual control over positioning and spacing • Real-time updates without repainting • Efficient memory handling via automatic cleanup • Support for 8 simultaneous timeframes with independent settings 3. INTELLIGENT SESSION TRACKING The algorithm continuously recalculates ranges bar-by-bar during active sessions, then preserves the final range. This differs from static zone indicators that simply draw fixed boxes at predefined levels. 4. MODULAR ARCHITECTURE Custom Type definitions (Candle, CandleSet, Config) create extensible, maintainable code structure while supporting complex multi-timeframe operations with minimal performance impact. 5. PROFESSIONAL FLEXIBILITY Extensive customization: 6 configurable sessions, 8 timeframe slots, 60+ timezones, granular color/sizing/spacing controls, multiple label positioning modes—adaptable to any market or trading style. 6. SMART VISUAL DESIGN Automatic timeframe validation, dynamic label alignment options, and intelligent spacing calculations ensure clarity even with multiple timeframes displayed simultaneously. ════════════════════════════════════════════ ⚙️ CONFIGURATION OPTIONS ════════════════════════════════════════════ TRADING SESSIONS: • Session 1-6: On/Off toggles • Time Ranges: Custom start-end times • Labels: Custom text for each session • Colors: Individual color per session • Timezone: 60+ options (Americas, Europe, Asia, Pacific, Africa) • Range Transparency: 0-100% • Outline: Optional border • Label Display: Show/hide session names • Daily Divider: Dotted lines at day changes • Skip Sunday: For traditional markets vs 24/7 crypto MULTI-TF CANDLES: • Timeframes 1-8: Enable/disable individually • Timeframe Selection: Any TF (seconds to months) • Display Count: 1-10 candles per timeframe • Bullish Colors: Body/Border/Wick (independent) • Bearish Colors: Body/Border/Wick (independent) • Candle Width: 1-10+ bars • Right Margin: 0-200+ bars from edge • TF Spacing: Gap between timeframe groups • Label Color: Any color • Label Size: Tiny/Small/Normal/Large/Huge • Label Position: Top/Bottom/Both • Label Alignment: Follow Candles or Align ════════════════════════════════════════════ 📋 TECHNICAL SPECIFICATIONS ════════════════════════════════════════════ • Pine Script Version: v5 • Chart Overlay: True • Max Boxes: 500 • Max Lines: 500 • Max Labels: 500 • Max Bars Back: 5000 • Update Frequency: Real-time (every tick) • Timeframe Compatibility: Chart TF must be lower than selected MTFs • Session Display: Activates only on ≤30 minute timeframes • Memory Management: Automatic cleanup via array operations
Chỉ báo Pine Script®
của RCVisionaries
Omega Correlation [OmegaTools]Omega Correlation (Ω CRR) is a cross-asset analytics tool designed to quantify both the strength of the relationship between two instruments and the tendency of one to move ahead of the other. It is intended for traders who work with indices, futures, FX, commodities, equities and ETFs, and who require something more robust than a simple linear correlation line. The indicator operates in two distinct modes, selected via the “Show” parameter: Correlation and Anticipation. In Correlation mode, the script focuses on how tightly the current chart and the chosen second asset move together. In Anticipation mode, it shifts to a lead–lag perspective and estimates whether the second asset tends to behave as a leader or a follower relative to the symbol on the chart. In both modes, the core inputs are the chart symbol and a user-selected second symbol. Internally, both assets are transformed into normalized log-returns: the script computes logarithmic returns, removes short-term mean and scales by realized volatility, then clips extreme values. This normalisation allows the tool to compare behaviour across assets with different price levels and volatility profiles. In Correlation mode, the indicator computes a composite correlation score that typically ranges between –1 and +1. Values near +1 indicate strong and persistent positive co-movement, values near zero indicate an unstable or weak link, and values near –1 indicate a stable anti-correlation regime. The composite score is constructed from three components. The first component is a normalized return co-movement measure. After transforming both instruments into normalized returns, the script evaluates how similar those returns are bar by bar. When the two assets consistently deliver returns of similar sign and magnitude, this component is high and positive. When they frequently diverge or move in opposite directions, it becomes negative. This captures short-term co-movement in a volatility-adjusted way. The second component focuses on high–low swing alignment. Rather than looking only at closes, it examines the direction of changes in highs and lows for each bar. If both instruments are printing higher highs and higher lows together, or lower highs and lower lows together, the swing structure is considered aligned. Persistent alignment contributes positively to the correlation score, while repeated mismatches between the swing directions reduce it. This helps differentiate between superficial price noise and structural similarity in trend behaviour. The third component is a classical Pearson correlation on closing prices, computed over a longer lookback. This serves as a stabilising backbone that summarises general co-movement over a broader window. By combining normalized return co-movement, swing alignment and standard price correlation with calibrated weights, the Correlation mode provides a richer view than a single linear measure, capturing both short-term dynamic interaction and longer-term structural linkage. In Anticipation mode, Omega Correlation estimates whether the second asset tends to lead or lag the current chart. The output is again a continuous score around the range. Positive values suggest that the second asset is acting more as a leader, with its past moves bearing informative value for subsequent moves of the chart symbol. Negative values indicate that the second asset behaves more like a laggard or follower. Values near zero suggest that no stable lead–lag structure can be identified. The anticipation score is built from four elements inspired by quantitative lead–lag and price discovery analysis. The first element is a residual lead correlation, conceptually similar to Granger-style logic. The script first measures how much of the chart symbol’s normalized returns can be explained by its own lagged values. It then removes that component and studies the correlation between the residuals and lagged returns of the second asset. If the second asset’s past returns consistently explain what the chart symbol does beyond its own autoregressive behaviour, this residual correlation becomes significantly positive. The second element is an asymmetric lead–lag structure measure. It compares the strength of relationships in both directions across multiple lags: the correlation of the current symbol with lagged versions of the second asset (candidate leader) versus the correlation of lagged values of the current symbol with the present values of the second asset. If the forward direction (second asset leading the first) is systematically stronger than the backward direction, the structure is skewed toward genuine leadership of the second asset. The third element is a relative price discovery score, constructed by building a dynamic hedge ratio between the two prices and defining a spread. The indicator looks at how changes in each asset contribute to correcting deviations in this spread over time. When the chart symbol tends to do most of the adjustment while the second asset remains relatively stable, it suggests that the second asset is taking a greater role in determining the equilibrium price and the chart symbol is adjusting to it. The difference in adjustment intensity between the two instruments is summarised into a single score. The fourth element is a breakout follow-through causality component. The script scans for breakout events on the second asset, where its price breaks out of a recent high or low range while the chart symbol has not yet done so. It then evaluates whether the chart symbol subsequently confirms the breakout direction, remains neutral, or moves against it. Events where the second asset breaks and the first asset later follows in the same direction add positive contribution, while failed or contrarian follow-through reduce this component. The contribution is also lightly modulated by the strength of the breakout, via the underlying normalized return. The four elements of the Anticipation mode are combined into a single leading correlation score, providing a compact and interpretable measure of whether the second asset currently behaves as an effective early signal for the symbol you trade. To aid interpretation, Omega Correlation builds dynamic bands around the active series (correlation or anticipation). It estimates a long-term central tendency and a typical deviation around it, plotting upper and lower bands that highlight unusually high or low values relative to recent history. These bands can be used to distinguish routine fluctuations from genuinely extreme regimes. The script also computes percentile-based levels for the correlation series and uses them to track two special price levels on the main chart: lost correlation levels and gained correlation levels. When the correlation drops below an upper percentile threshold, the current price is stored as a lost correlation level and plotted as a horizontal line. When the correlation rises above a lower percentile threshold, the current price is stored as a gained correlation level. These levels mark zones where a historically strong relationship between the two markets broke down or re-emerged, and can be used to frame divergence, convergence and spread opportunities. An information panel summarises, in real time, whether the second asset is behaving more as a leading, lagging or independent instrument according to the anticipation score, and suggests whether the current environment is more conducive to de-alignment, re-alignment or classic spread behaviour based on the correlation regime. This makes the tool directly interpretable even for users who are not familiar with all the underlying statistical details. Typical applications for Omega Correlation include intermarket analysis (for example, index vs index, commodity vs related equity sector, FX vs bonds), dynamic hedge sizing, regime detection for algorithmic strategies, and the identification of lead–lag structures where a macro driver or benchmark can be monitored as an early signal for the instrument actually traded. The indicator can be applied across intraday and higher timeframes, with the understanding that the strength and nature of relationships will differ across horizons. Omega Correlation is designed as an advanced analytical framework, not as a standalone trading system. Correlation and lead–lag relationships are statistical in nature and can change abruptly, especially around macro events, regime shifts or liquidity shocks. A positive anticipation reading does not guarantee that the second asset will always move first, and a high correlation regime can break without warning. All outputs of this tool should be combined with independent analysis, sound risk management and, when appropriate, backtesting or forward testing on the user’s specific instruments and timeframes. The intention behind Omega Correlation is to bring techniques inspired by quantitative research, such as normalized return analysis, residual correlation, asymmetric lead–lag structure, price discovery logic and breakout event studies, into an accessible TradingView indicator. It is intended for traders who want a structured, professional way to understand how markets interact and to incorporate that information into their discretionary or systematic decision-making processes.
Chỉ báo Pine Script®
của OmegaTools
Pair Cointegration & Static Beta Analyzer (v6)Pair Cointegration & Static Beta Analyzer (v6) This indicator evaluates whether two instruments exhibit statistical properties consistent with cointegration and tradable mean reversion. It uses long-term beta estimation, spread standardization, AR(1) dynamics, drift stability, tail distribution analysis, and a multi-factor scoring model. 1. Static Beta and Spread Construction A long-horizon static beta is estimated using covariance and variance of log-returns. This beta does not update on every bar and is used throughout the entire model. Beta = Cov(r1, r2) / Var(r2) Spread = PriceA - Beta * PriceB This “frozen” beta provides structural stability and avoids rolling noise in spread construction. 2. Correlation Check Log-price correlation ensures the instruments move together over time. Correlation ≥ 0.85 is required before deeper cointegration diagnostics are considered meaningful. 3. Z-Score Normalization and Distribution Behavior The spread is standardized: Z = (Spread - MA(Spread)) / Std(Spread) The following statistical properties are examined: Z-Mean: Should be close to zero in a stationary process Z-Variance: Measures amplitude of deviations Tail Probability: Frequency of |Z| being larger than a threshold (e.g. 2) These metrics reveal whether the spread behaves like a mean-reverting equilibrium. 4. Mean Drift Stability A rolling mean of the spread is examined. If the rolling mean drifts excessively, the spread may not represent a stable long-term equilibrium. A normalized drift ratio is used: Mean Drift Ratio = Range( RollingMean(Spread) ) / Std(Spread) Low drift indicates stable long-run equilibrium behavior. 5. AR(1) Dynamics and Half-Life An AR(1) model approximates mean reversion: Spread(t) = Phi * Spread(t-1) + error Mean reversion requires: 0 < Phi < 1 Half-life of reversion: Half-life = -ln(2) / ln(Phi) Valid half-life for 10-minute bars typically falls between 3 and 80 bars. 6. Composite Scoring Model (0–100) A multi-factor weighted scoring system is applied: Component Score Correlation 0–20 Z-Mean 0–15 Z-Variance 0–10 Tail Probability 0–10 Mean Drift 0–15 AR(1) Phi 0–15 Half-Life 0–15 Score interpretation: 70–100: Strong Cointegration Quality 40–70: Moderate 0–40: Weak A pair is classified as cointegrated when: Total Score ≥ Threshold (default = 70) 7. Main Cointegration Panel Displays: Static beta Log-price correlation Z-Mean, Z-Variance, Tail Probability Drift Ratio AR(1) Phi and Half-life Composite score Overall cointegration assessment 8. Beta Hedge Position Sizing (Average-Price Based) To provide a more stable hedge ratio, hedge sizing is computed using average prices, not instantaneous prices: AvgPriceA = SMA(PriceA, N) AvgPriceB = SMA(PriceB, N) Required B per 1 A = Beta * (AvgPriceA / AvgPriceB) Using averaged prices results in a smoother, more reliable hedge ratio, reducing noise from bar-to-bar volatility. The panel displays: Required B security for 1 A security (average) This represents the beta-neutral quantity of B required to hedge one unit of A. Overview of Classical Stationarity & Cointegration Methods The principal econometric tools commonly used in assessing stationarity and cointegration include: Augmented Dickey–Fuller (ADF) Test Phillips–Perron (PP) Test KPSS Test Engle–Granger Cointegration Test Phillips–Ouliaris Cointegration Test Johansen Cointegration Test Since these procedures rely on regression residuals, matrix operations, and distribution-based critical values that are not supported in TradingView Pine Script, a practical multi-criteria scoring approach is employed instead. This framework leverages metrics that are fully computable in Pine and offers an operational proxy for evaluating cointegration-like behavior under platform constraints. References Engle & Granger (1987), Co-integration and Error Correction Poterba & Summers (1988), Mean Reversion in Stock Prices Vidyamurthy (2004), Pairs Trading Explanation structured with assistance from OpenAI’s ChatGPT Regards.
Chỉ báo Pine Script®
của Noldo
Rolling Correlation vs Another Symbol (SPY Default)This indicator visualizes the rolling correlation between the current chart symbol and another selected asset, helping traders understand how closely the two move together over time. It calculates the Pearson correlation coefficient over a user-defined period (default 22 bars) and plots it as a color-coded line: • Green line → positive correlation (move in the same direction) • Red line → negative correlation (move in opposite directions) • A gray dashed line marks the zero level (no correlation). The background highlights periods of strong relationship: • Light green when correlation > +0.7 (strong positive) • Light red when correlation < –0.7 (strong negative) Use this tool to quickly spot diversification opportunities, confirm hedges, or understand how assets interact during different market regimes.
Chỉ báo Pine Script®
của EuerMarit
cd_correlation_analys_Cxcd_correlation_analys_Cx General: This indicator is designed for correlation analysis by classifying stocks (487 in total) and indices (14 in total) traded on Borsa İstanbul (BIST) on a sectoral basis. Tradingview's sector classifications (20) have been strictly adhered to for sector grouping. Depending on user preference, the analysis can be performed within sectors, between sectors, or manually (single asset). Let me express my gratitude to the code author, @fikira, beforehand; you will find the reason for my thanks in the context. Details: First, let's briefly mention how this indicator could have been prepared using the classic method before going into details. Classically, assets could be divided into groups of forty (40), and the analysis could be performed using the built-in function: ta.correlation(source1, source2, length) → series float. I chose sectoral classification because I believe there would be a higher probability of assets moving together, rather than using fixed-number classes. In this case, 21 arrays were formed with the following number of elements: (3, 11, 21, 60, 29, 20, 12, 3, 31, 5, 10, 11, 6, 48, 73, 62, 16, 19, 13, 34 and indices (14)). However, you might have noticed that some arrays have more than 40 elements. This is exactly where @Fikira's indicator came to the rescue. When I examined their excellent indicator, I saw that it could process 120 assets in a single operation. (I believe this was the first limit overrun; thanks again.) It was amazing to see that data for 3 pairs could be called in a single request using a special method. You can find the details here: When I adapted it for BIST, I found it sufficient to call data for 2 pairs instead of 3 in a single go. Since asset prices are regular and have 2 decimal places, I used a fixed multiplier of $10^8$ and a fixed decimal count of 2 in Fikira's formulas. With this method, the (high, low, open, close) values became accessible for each asset. The summary up to this point is that instead of the ready-made formula + groups of 40, I used variable-sized groups and the method I will detail now. Correlation/harmony/co-movement between assets provides advantages to market participants. Coherent assets are expected to rise or fall simultaneously. Therefore, to convert co-movement into a mathematical value, I defined the possible movements of the current candle relative to the previous candle bar over a certain period (user-defined). These are: Up := high > high and low > low Down := high < high and low < low Inside := high <= high and low >= low Outside := high >= high and low <= low and NOT Inside. Ignore := high = low = open = close If both assets performed the same movement, 1 was added to the tracking counter. If (Up-Up), (Down-Down), (Inside-Inside), or (Outside-Outside), then counter := counter + 1. If the period length is 100 and the counter is 75, it means there is 75% co-movement. Corr = counter / period ($75/100$) Average = ta.sma(Corr, 100) is obtained. The highest coefficients recorded in the array are presented to the user in a table. From the user menu options, the user can choose to compare: • With assets in its own sector • With assets in the selected sector • By activating the confirmation box and manually entering a single asset for comparison. Table display options can be adjusted from the Settings tab. In the attached examples: Results for AKBNK stock from the Finance sector compared with GARAN stock from the same sector: Timeframe: Daily, Period: 50 => Harmony 76% (They performed the same movement in 38 out of 50 bars) Comment: Opposite movements at swing high and low levels may indicate a change in the direction of the price flow (SMT). Looking at ASELS from the Electronic Technology sector over the last 30 daily candles, they performed the same movements by 40% with XU100, 73.3% (22/30) with XUTEK (Technology Index), and 86.9% according to the averages. Comment: It is more appropriate to follow ASELS stock with XUTEK (Technology index) instead of the general index (XU100). Opposite movements at swing high and low levels may indicate a change in the direction of the price flow (SMT). Again, when ASELS stock is taken on H1 instead of daily, and the length is 100 instead of 30, the harmony rate is seen to be 87%. Please share your thoughts and criticisms regarding the indicator, which I prepared with a bit of an educational purpose specifically for BIST. Happy trading.
Chỉ báo Pine Script®
của cdikici71
2
Fair Value Lead-Lag Model [BackQuant]Fair Value Lead-Lag Model A cross-asset model that estimates where price "should" be relative to a chosen reference series, then tracks the deviation as a normalized oscillator. It helps you answer two questions: 1) is the asset rich or cheap vs its driver, and 2) is the driver leading or lagging price over the next N bars. Concept in one paragraph Many assets co-move with a macro or sector driver. Think BTC vs DXY, gold vs real yields, a stock vs its sector ETF. This tool builds a rolling fair value of the charted asset from a reference series and shows how far price is above or below that fair value in standard deviation units. You can shift the reference forward or backward to test who leads whom, then use the deviation and its bands to structure mean-reversion or trend-following ideas. What the model does Reference mapping : Pulls a reference symbol at a chosen timeframe, with an optional lead or lag in bars to test causality. Fair value engine : Converts the reference into a synthetic fair value of the chart using one of four methods: Ratio : price/ref with a rolling average ratio. Good when the relationship is proportional. Spread : price minus ref with a rolling average spread. Good when the relationship is additive. Z-Score : normalizes both series, aligns on standardized units, then re-projects to price space. Good when scale drifts. Beta-Adjusted : rolling regression style. Uses covariance and variance to compute beta, then builds a fair value = mean(price) + beta * (ref − mean(ref)). Deviation and bands : Computes a z-scored deviation of price vs fair value and plots sigma bands (±1, ±2, ±3) around the fair value line on the chart. Correlation context : Shows rolling correlation so you can judge if deviations are meaningful or just noise when co-movement is weak. Visuals : Fair value line on price chart with sigma envelopes. Deviation as a column oscillator and optional line. Threshold shading beyond user-set upper and lower levels. Summary table with reference, deviation, status, correlation, and method. Why this is useful Mean reversion framework : When correlation is healthy and deviation stretches beyond your sigma threshold, probability favors reversion toward fair value. This is classic pairs logic adapted to a driver and a target. Trend confirmation : If price rides the fair value line and deviation stays modest while correlation is positive, it supports trend persistence. Pullbacks to negative deviation in an uptrend can be buyable. Lead-lag discovery : Shift the reference forward by +N bars. If correlation improves, the reference tends to lead. Shift backward for the reverse. Use the best setting for planning early entries or hedges. Regime detection : Large persistent deviations with falling correlation hint at regime change. The relationship you relied on may be breaking down, so reduce confidence or switch methods. How to use it step by step Pick a sensible reference : Choose a macro, index, currency, or sector driver that logically explains the asset’s moves. Example: gold with DXY, a semiconductor stock with SOXX. Test lead-lag : Nudge Lead/Lag Periods to small positive values like +1 to +5 to see if the reference leads. If correlation improves, keep that offset. If correlation worsens, try a small negative value or zero. Select a method : Start with Beta-Adjusted when the relationship is approximately linear with drift. Use Ratio if the assets usually move in proportional terms. Use Spread when they trade around a level difference. Use Z-Score when scales wander or volatility regimes shift. Tune windows : Rolling Window controls how quickly fair value adapts. Shorter equals faster but noisier. Normalization Period controls how deviations are standardized. Longer equals stabler sigma sizing. Correlation Length controls how co-movement is measured. Keep it near the fair value window. Trade the edges : Mean reversion idea : Wait for deviation beyond your Upper or Lower Threshold with positive correlation. Fade back toward fair value. Exit at the fair value line or the next inner sigma band. Trend idea : In an uptrend, buy pullbacks when deviation dips negative but correlation remains healthy. In a downtrend, sell bounces when deviation spikes positive. Read the table : Deviation shows how many sigmas you are from fair value. Status tells you overvalued or undervalued. Correlation color hints confidence. Method tells you the projection style used. Reading the display Fair value line on price chart: the model’s estimate of where price should trade given the reference, updated each bar. Sigma bands around fair value: a quick sense of residual volatility. Reversions often target inner bands first. Deviation oscillator : above zero means rich vs fair value, below zero means cheap. Color bins intensify with distance. Correlation line (optional): scale is folded to match thresholds. Higher values increase trust in deviations. Parameter tips Start with Rolling Window 20 to 30, Normalization Period 100, Correlation Length 50. Upper and Lower Threshold at ±2.0 are classic. Tighten to ±1.5 for more signals or widen to ±2.5 to focus on outliers. When correlation drifts below about 0.3, treat deviations with caution. Consider switching method or reference. If the fair value line whipsaws, increase Rolling Window or move to Beta-Adjusted which tends to be smoother. Playbook examples Pairs-style reversion : Asset is +2.3 sigma rich vs reference, correlation 0.65, trend flat. Short the deviation back toward fair value. Cover near the fair value line or +1 sigma. Pro-trend pullback : Uptrend with correlation 0.7. Deviation dips to −1.2 sigma while price sits near the −1 sigma band. Buy the dip, target the fair value line, trail if the line is rising. Lead-lag timing : Reference leads by +3 bars with improved correlation. Use reference swings as early cues to anticipate deviation turns on the target. Caveats The model assumes a stable relationship over the chosen windows. Structural breaks, policy shocks, and index rebalances can invalidate recent history. Correlation is descriptive, not causal. A strong correlation does not guarantee future convergence. Do not force trades when the reference has low liquidity or mismatched hours. Use a reference timeframe that captures real overlap. Bottom line This tool turns a loose cross-asset intuition into a quantified, visual fair value map. It gives you a consistent way to find rich or cheap conditions, time mean-reversion toward a statistically grounded target, and confirm or fade trends when the driver agrees.
Chỉ báo Pine Script®
của BackQuant
Pairs Trading Scanner [BackQuant]Pairs Trading Scanner What it is This scanner analyzes the relationship between your chart symbol and a chosen pair symbol in real time. It builds a normalized “spread” between them, tracks how tightly they move together (correlation), converts the spread into a Z-Score (how far from typical it is), and then prints clear LONG / SHORT / EXIT prompts plus an at-a-glance dashboard with the numbers that matter. Why pairs at all? Markets co-move. When two assets are statistically related, their relationship (the spread) tends to oscillate around a mean. Pairs trading doesn’t require calling overall market direction you trade the relative mispricing between two instruments. This scanner gives you a robust, visual way to find those dislocations, size their significance, and structure the trade. How it works (plain English) Step 1 Pick a partner: Select the Pair Symbol to compare against your chart symbol. The tool fetches synchronized prices for both. Step 2 Build a spread: Choose a Spread Method that defines “relative value” (e.g., Log Spread, Price Ratio, Return Difference, Price Difference). Each lens highlights a different flavor of divergence. Step 3 Validate relationship: A rolling Correlation checks if the pair is moving together enough to be tradable. If correlation is weak, the scanner stands down. Step 4 Standardize & score: The spread is normalized (mean & variability over a lookback) to form a Z-Score . Large absolute Z means “stretched,” small means “near fair.” Step 5 Signals: When the Z-Score crosses user-defined thresholds with sufficient correlation , entries print: LONG = long chart symbol / short pair symbol, SHORT = short chart symbol / long pair symbol, EXIT = mean reversion into the exit zone or correlation failure. Core concepts (the three pillars) Spread Method Your definition of “distance” between the two series. Guidance: Log Spread: Focuses on proportional differences; robust when prices live on different scales. Price Ratio: Classic relative value; good when you care about “X per Y.” Return Difference: Emphasizes recent performance gaps; nimble for momentum-to-mean plays. Price Difference: Straight subtraction; intuitive for similar-scale assets (e.g., two ETFs). Correlation A rolling score of co-movement. The scanner requires it to be above your Min Correlation before acting, so you’re not trading random divergence. Z-Score “How abnormal is today’s spread?” Positive = chart richer than pair; negative = cheaper. Thresholds define entries/exits with transparent, statistical context. What you’ll see on the chart Correlation plot (blue line) with a dashed Min Correlation guide. Above the line = green zone for signals; below = hands off. Z-Score plot (white line) with colored, dashed Entry bands and dotted Exit bands. Zero line for mean. Normalized spread (yellow) for a quick “shape read” of recent divergence swings. Signal markers : LONG (green label) when Z < –Entry and corr OK, SHORT (red label) when Z > +Entry and corr OK, EXIT (gray label) when Z returns inside the Exit band or correlation drops below the floor. Background tint for active state (faint green for long-spread stance, faint red for short-spread stance). The two built-in dashboards Statistics Table (top-right) Pair Symbol Your chosen partner. Correlation Live value vs. your minimum. Z-Score How stretched the spread is now. Current / Pair Prices Real-time anchors. Signal State NEUTRAL / LONG / SHORT. Price Ratio Context for ratio-style setups. Analysis Table (bottom-right) Avg Correlation Typical co-movement level over your window. Max |Z| The recent extremes of dislocation. Spread Volatility How “lively” the spread has been. Trade Signal A human-readable prompt (e.g., “LONG A / SHORT B” or “NO TRADE” / “LOW CORRELATION”). Risk Level LOW / MEDIUM / HIGH based on current stretch (absolute Z). Signals logic (plain English) Entry (LONG): The spread is unusually negative (chart cheaper vs pair) and correlation is healthy. Expect mean reversion upward in the spread: long chart, short pair. Entry (SHORT): The spread is unusually positive (chart richer vs pair) and correlation is healthy. Expect mean reversion downward in the spread: short chart, long pair. Exit: The spread relaxes back toward normal (inside your exit band), or correlation deteriorates (relationship no longer trusted). A quick, repeatable workflow 1) Choose your pair in context (same sector/theme or known macro link). Think: “Do these two plausibly co-move?” 2) Pick a spread lens that matches your narrative (ratio for relative value, returns for short-term performance gaps, etc.). 3) Confirm correlation is above your floor no corr, no trade. 4) Wait for a stretch (Z beyond Entry band) and a printed LONG / SHORT . 5) Manage to the mean (EXIT band) or correlation failure; let the scanners’ state/labels keep you honest. Settings that matter (and why) Spread Method Defines the “mispricing” you care about. Correlation Period Longer = steadier regime read, shorter = snappier to regime change. Z-Score Period The window that defines “normal” for the spread; it sets the yardstick. Use Percentage Returns Normalizes series when using return-based logic; keep on for mixed-scale assets. Entry / Exit Thresholds Set your stretch and your target reversion zone. Wider entries = rarer but stronger signals. Minimum Correlation The gatekeeper. Raising it favors quality over quantity. Choosing pairs (practical cheat sheet) Same family: two index ETFs, two oil-linked names, two gold miners, two L1 tokens. Hedge & proxy: stock vs. sector ETF, BTC vs. BTC index, WTI vs. energy ETF. Cross-venue or cross-listing: instruments that are functionally the same exposure but price differently intraday. Reading the cues like a pro Divergence shape: The yellow normalized spread helps you see rhythm fast spike and snap-back versus slow grind. Corr-first discipline: Don’t fight the “Min Correlation” line. Good pairs trading starts with a relationship you can trust. Exit humility: When Z re-centers, let the EXIT do its job. The edge is the journey to the mean, not overstaying it. Frequently asked (quick answers) “Long/Short means what exactly?” LONG = long the chart symbol and short the pair symbol. SHORT = short the chart symbol and long the pair symbol. “Do I need same price scales?” No. The spread methods normalize in different ways; choose the one that fits your use case (log/ratio are great for mixed scales). “What if correlation falls mid-trade?” The scanner will neutralize the state and print EXIT . Relationship first; trade second. Field notes & patterns Snap-back days: After a one-sided session, return-difference spreads often flag cleaner intraday mean reversions. Macro rotations: Ratio spreads shine during sector re-weights (e.g., value vs. growth ETFs); look for steady corr + elevated |Z|. Event bleed-through: If one symbol reacts to news and its partner lags, Z often flags a high-quality, short-horizon re-centering. Display controls at a glance Show Statistics Table Live state & key numbers, top-right. Show Analysis Table Context/risk read, bottom-right. Show Correlation / Spread / Z-Score Toggle the sub-charts you want visible. Show Entry/Exit Signals Turn markers on/off as needed. Coloring Adjust Long/Short/Neutral and correlation line colors to match your theme. Alerts (ready to route to your workflow) Pairs Long Entry Z falls through the long threshold with correlation above minimum. Pairs Short Entry Z rises through the short threshold with correlation above minimum. Pairs Trade Exit Z returns to neutral or the relationship fails your correlation floor. Correlation Breakdown Rolling correlation crosses your minimum; relationship caution. Final notes The scanner is designed to keep you systematic: require relationship (correlation), quantify dislocation (Z-Score), act when stretched, stand down when it normalizes or the relationship degrades. It’s a full, visual loop for relative-value trading that stays out of your way when it should and gets loud only when the numbers line up.
Chỉ báo Pine Script®
của BackQuant
1
Bitcoin vs. Gold correlation with lagBTC vs Gold (Lag) + Correlation — multi-timeframe, publication notes What it does Plots Gold on the same chart as Bitcoin, with a configurable lead/lag. Lets you choose how the series is displayed: Gold shifted forward (+lag on chart) — shows gold ahead of BTC on the time axis (visual offset). Gold aligned to BTC (gold lag) — standard alignment; gold is lagged for calculation and plotted in place. BTC 200D Lag (BTC shifted forward) — visualizes BTC shifted forward (like popular “BTC 200D Lag” charts). Computes Pearson correlations between BTC (no lag) and Gold (with lag) over multiple lookback windows equivalent to: 30d, 60d, 90d, 180d, 365d, 2y (730d), 3y (1095d), 5y (1825d). Shows a table with the correlation values, automatically scaled to the current timeframe. Why this is useful A common macro claim is that BTC tends to follow Gold with a delay (e.g., ~200 trading days). This tool lets you: Visually advance Gold (or BTC) to see that lead-lag relationship on the chart. Quantify the relationship with rolling correlations. Switch timeframes (D/W/M/…): everything automatically stays in sync. Quick start Open a BTC chart (any exchange). Add the indicator. Set Gold symbol (default TVC:GOLD; alternatives: OANDA:XAUUSD, COMEX:GC1!, etc.). Choose Lag value and Lag unit (Days/Weeks/Months/Years/Bars). Pick Visual Mode: To mirror those “BTC 200D Lag” posts: choose “BTC 200D Lag (BTC shifted forward)” with 200 Days. To view Gold 200D ahead of BTC: select “Gold shifted forward (+lag on chart)” with 200 Days. Keep Rebase to 100 ON for an apples-to-apples visual scale. (You can move the study to the left price scale if needed.) Inputs Gold symbol: external series to pair with BTC. Lag value: numeric value. Lag unit: Days, Weeks, Months (≈30d), Years (≈365d), or direct Bars. Visual mode: Gold shifted forward (+lag on chart) → gold is offset to the right by the lag (visual only). Gold aligned to BTC (gold lag) → standard plot (no visual offset); correlations still use lagged gold. BTC 200D Lag (BTC shifted forward) → BTC is offset to the right by the lag (visual only). Rebase to 100 (visual): rescales each series to 100 on its first valid bar for clearer comparison. Show gold without lag (debug): optional reference line. Show price tag for gold (lag): toggles the track price label. Timeframe handling The study uses the current chart timeframe for both BTC and Gold (timeframe.period). Lag in time units (Days/Weeks/Months/Years) is internally converted to an integer number of bars of the active timeframe (using timeframe.in_seconds). Example: on W (weekly), 200 days ≈ 29 bars. On intraday timeframes, days are converted proportionally. Correlation math Correlation = ta.correlation(BTC, Gold_lagged, length_in_bars) Lookback lengths are the bar-equivalents of 30/60/90/180/365/730/1095/1825 days in the active timeframe. Important: correlations are computed on prices (not returns). If you prefer returns-based correlation (often more statistically robust), duplicate the script and replace price inputs with change(close) or ta.roc(close, 1). Reading the table Window: nominal day label (e.g., 30d, 1y, 5y). Bars (TF): how many bars that window equals on the current timeframe. Correlation: Pearson coefficient . Background tint shows intensity and sign. Tips & caveats Visual offsets (offset=) move series on screen only; they don’t affect the math. The math always uses BTC (no lag) × Gold (lagged). With large lags on high timeframes, early bars will be na (normal). Scroll forward / reduce lag. If your Gold feed doesn’t load, try an alternative symbol that your plan supports. Rebase to 100 helps visibility when BTC ($100k) and Gold ($2k) share a scale. Months/Years use 30/365-day approximations. For exact control, use Days or Bars. Correlations on very short lengths or sparse data can be unstable; consider the longer windows for sturdier signals. This is a visual/analytical tool, not a trading signal. Always apply independent risk management. Suggested setups Replicate “BTC 200D Lag” charts: Visual Mode: BTC 200D Lag (BTC shifted forward) Lag: 200 Days Rebase: ON Gold leads BTC (Gold ahead): Visual Mode: Gold shifted forward (+lag on chart) Lag: 200 Days Rebase: ON Compatibility: Pine v6, overlay study. Best with: BTCUSD (any exchange) + a reliable Gold feed. Author’s note: Lead-lag relationships are not stable over time; treat correlations as descriptive, not predictive.
Chỉ báo Pine Script®
của viniciusbazan