Profitable Pair Correlation Divergence Scanner v6This strategy identifies divergence opportunities between two correlated assets using a combination of Z-Score spread analysis, trend confirmation, RSI & MACD momentum checks, correlation filters, and ATR-based stop-loss/take-profit management. It’s optimized for positive P&L and realistic trade execution.
Key Features:
Pair Divergence Detection:
Measures deviation between returns of two assets and identifies overbought/oversold spread conditions using Z-Score.
Trend Alignment:
Trades only in the direction of the primary asset’s trend using a fast EMA vs slow EMA filter.
Momentum Confirmation:
Confirms trades with RSI and MACD to reduce false signals.
Correlation Filter:
Ensures the pair is strongly correlated before taking trades, avoiding noisy signals.
Risk Management:
Dynamic ATR-based stop-loss and take-profit ensures proper reward-to-risk ratio.
Exit Conditions:
Automatically closes positions when Z-Score normalizes, or ATR-based exits are hit.
How It Works:
Calculate Returns:
Computes returns for both assets over the selected timeframe.
Z-Score Spread:
Calculates the spread between returns and normalizes it using moving average and standard deviation.
Trend Filter:
Only takes long trades if the fast EMA is above the slow EMA, and short trades if the fast EMA is below the slow EMA.
Momentum Confirmation:
Confirms trade direction with RSI (>50 for longs, <50 for shorts) and MACD alignment.
Correlation Check:
Ensures the pair’s recent correlation is strong enough to validate divergence signals.
Trade Execution:
Opens positions when Z-Score crosses thresholds and all conditions align. Positions close when Z-Score normalizes or ATR-based SL/TP is hit.
Plot Explanation:
Z-Score: Blue line shows divergence magnitude.
Entry Levels: Red/Green lines mark long/short thresholds.
Exit Zone: Gray lines show normalization zone.
EMA Trend Lines: Purple (fast), Orange (slow) for trend alignment.
Correlation: Teal overlay shows current correlation strength.
Usage Tips:
Use highly correlated pairs for best results (e.g., EURUSD/GBPUSD).
Run on higher timeframe charts (1h or 4h) to reduce noise.
Adjust ATR multiplier based on volatility to avoid premature stops.
Combine with alerts for automated notifications or webhook execution.
Conclusion:
The Profitable Pair Correlation Divergence Scanner v6 is designed for traders who want systematic, low-risk, positive P&L trading opportunities with minimal manual monitoring. By combining trend alignment, momentum confirmation, correlation filters, and dynamic exits, it reduces false signals and improves execution reliability.
Run it on TradingView and watch how it captures divergence opportunities while maintaining positive P&L across trades.
Correlation
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.
AssetCorrelationLibraryLibrary "AssetCorrelationLibrary™"
detectIndicesFutures(ticker)
Detects Index Futures (NQ/ES/YM/RTY + micro variants)
Parameters:
ticker (string) : The ticker string to check (typically syminfo.ticker)
Returns: AssetPairing with secondary and tertiary assets configured
detectMetalsFutures(ticker)
Detects Metal Futures (GC/SI/HG + micro variants)
Parameters:
ticker (string) : The ticker string to check
Returns: AssetPairing with secondary and tertiary assets configured
detectForexFutures(ticker)
Detects Forex Futures (6E/6B + micro variants)
Parameters:
ticker (string) : The ticker string to check
Returns: AssetPairing with secondary and tertiary assets configured
detectEnergyFutures(ticker)
Detects Energy Futures (CL/RB/HO + micro variants)
Parameters:
ticker (string) : The ticker string to check
Returns: AssetPairing with secondary and tertiary assets configured
detectTreasuryFutures(ticker)
Detects Treasury Futures (ZB/ZF/ZN)
Parameters:
ticker (string) : The ticker string to check
Returns: AssetPairing with secondary and tertiary assets configured
detectForexCFD(ticker, tickerId)
Detects Forex CFD pairs (EUR/GBP/DXY, USD/JPY/CHF triads)
Parameters:
ticker (string) : The ticker string to check
tickerId (string) : The full ticker ID (syminfo.tickerid) for primary asset
Returns: AssetPairing with secondary and tertiary assets configured
detectCrypto(ticker, tickerId)
Detects major Crypto assets (BTC, ETH, SOL, XRP, alts)
Parameters:
ticker (string) : The ticker string to check
tickerId (string) : The full ticker ID for primary asset
Returns: AssetPairing with secondary and tertiary assets configured
detectMetalsCFD(ticker, tickerId)
Detects Metals CFD (XAU/XAG/Copper)
Parameters:
ticker (string) : The ticker string to check
tickerId (string) : The full ticker ID for primary asset
Returns: AssetPairing with secondary and tertiary assets configured
detectIndicesCFD(ticker, tickerId)
Detects Indices CFD (NAS100/SP500/DJ30)
Parameters:
ticker (string) : The ticker string to check
tickerId (string) : The full ticker ID for primary asset
Returns: AssetPairing with secondary and tertiary assets configured
detectEUStocks(ticker, tickerId)
Detects EU Stock Indices (GER40/EU50) - Dyad only
Parameters:
ticker (string) : The ticker string to check
tickerId (string) : The full ticker ID for primary asset
Returns: AssetPairing with secondary asset configured (tertiary empty for dyad)
getDefaultFallback(tickerId)
Returns default fallback assets (chart ticker only, no correlation)
Parameters:
tickerId (string) : The full ticker ID for primary asset
Returns: AssetPairing with chart ticker as primary, empty secondary/tertiary (no correlation)
applySessionModifierWithBackadjust(tickerStr, sessionType)
Applies futures session modifier to ticker WITH back adjustment
Parameters:
tickerStr (string) : The ticker to modify
sessionType (string) : The session type (syminfo.session)
Returns: Modified ticker string with session and backadjustment.on applied
applySessionModifierNoBackadjust(tickerStr, sessionType)
Applies futures session modifier to ticker WITHOUT back adjustment
Parameters:
tickerStr (string) : The ticker to modify
sessionType (string) : The session type (syminfo.session)
Returns: Modified ticker string with session and backadjustment.off applied
isTriadMode(pairing)
Checks if a pairing represents a valid triad (3 assets)
Parameters:
pairing (AssetPairing) : The AssetPairing to check
Returns: True if tertiary is non-empty (triad mode), false for dyad
getAssetTicker(tickerId)
Extracts clean ticker string from full ticker ID
Parameters:
tickerId (string) : The full ticker ID (e.g., "BITGET:BTCUSDT.P")
Returns: Clean ticker string (e.g., "BTCUSDT.P")
resolveTriad(chartTickerId, pairing)
Resolves triad asset assignments with proper inversion flags
Parameters:
chartTickerId (string) : The current chart's ticker ID (syminfo.tickerid)
pairing (AssetPairing) : The detected AssetPairing
Returns: Tuple
resolveDyad(chartTickerId, pairing)
Resolves dyad asset assignment with proper inversion flag
Parameters:
chartTickerId (string) : The current chart's ticker ID
pairing (AssetPairing) : The detected AssetPairing (dyad: tertiary is empty)
Returns: Tuple
resolveAssets(ticker, tickerId, assetType, sessionType, useBackadjust)
Main auto-detection entry point. Detects asset category and returns fully resolved config.
Parameters:
ticker (string) : The ticker string to check (typically syminfo.ticker)
tickerId (string) : The full ticker ID (typically syminfo.tickerid)
assetType (string) : The asset type (typically syminfo.type)
sessionType (string) : The session type for futures (typically syminfo.session)
useBackadjust (bool) : Whether to apply back adjustment for futures session alignment
Returns: AssetConfig with fully resolved assets, inversion flags, and detection status
resolveCurrentChart()
Simplified auto-detection using current chart's syminfo values
Returns: AssetConfig with fully resolved assets, inversion flags, and detection status
AssetPairing
Core asset pairing structure for triad/dyad configurations
Fields:
primary (series string) : The primary (chart) asset ticker ID
secondary (series string) : The secondary correlated asset ticker ID
tertiary (series string) : The tertiary correlated asset ticker ID (empty for dyad)
invertSecondary (series bool) : Whether secondary asset should be inverted for divergence calc
invertTertiary (series bool) : Whether tertiary asset should be inverted for divergence calc
AssetConfig
Full asset resolution result with mode detection and computed values
Fields:
detected (series bool) : Whether auto-detection succeeded
isTriadMode (series bool) : True if triad (3 assets), false if dyad (2 assets)
primary (series string) : The resolved primary asset ticker ID
secondary (series string) : The resolved secondary asset ticker ID
tertiary (series string) : The resolved tertiary asset ticker ID (empty for dyad)
invertSecondary (series bool) : Computed inversion flag for secondary asset
invertTertiary (series bool) : Computed inversion flag for tertiary asset
assetCategory (series string) : String describing the detected asset category
Note to potential users.
I did not really intend to make this public but i have to in order to avoid any potential compliance issues with the TradingView Moderation Team and the House Rules.
However if you are to use this library, you cannot make your code closed source / invite only as it is intellectual property. The only exception to this is if I am credited in the header of your code and i explicitly give permission to do so.
As per the TradingView house rules, you are completely FREE to do with this as you like, provided the script stays private.
Use the @fstarcapital tag to give credits
❤️ from cephxs
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
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.
Asset Correlation Matrix [PEARSON|BETA|R2]The Market Dilemma: The Liquidity Trap and The Illusion of Diversification
One of the most expensive mistakes in modern trading is the assumption that holding different asset classes—such as Technology Stocks, Crypto, and Commodities—automatically provides safety. In stable economic times, this may be true. However, in environments defined by high liquidity stress or macroeconomic shocks, the correlations between these seemingly distinct assets tend to converge mathematically to 1.0. This phenomenon is known in quantitative finance as "Systemic Coupling." When this occurs, technical analysis on individual charts loses its predictive power because the asset is no longer trading on its own idiosyncratic fundamentals (e.g., earnings or user growth) but is merely acting as a high-beta proxy for global liquidity flows. This toolkit solves this problem by providing an institutional-grade framework to quantify exactly how much "independence" your assets truly possess at any given moment. It objectively separates a "Stock Picker's Market," where individual analysis works, from a "Macro Regime," where only the broader trend matters.
Scientific Foundation: Why Logarithmic Returns Matter
Standard retail indicators often calculate correlation based on simple percentage price changes. This approach is mathematically flawed over longer timeframes due to the compounding effect. This algorithm is grounded in Modern Portfolio Theory (MPT) and utilizes Logarithmic Returns (continuously compounded returns). As established in academic literature by Hudson & Gregoriou (2015), log returns provide time-additivity and numerical stability. This ensures that the statistical relationship measured over a rolling 60-day window is accurate and not distorted by volatility spikes, providing a professional basis for risk modeling.
The Three Pillars of Analysis: Understanding the Metrics
To fully understand market behavior, one must look at the relationship between an asset and a benchmark from three distinct mathematical angles. This indicator allows you to switch between these institutional metrics:
1. Pearson Correlation (Directional Alignment):
This is the classic measure of linear dependence, ranging from -1.0 to +1.0. Its primary value lies in identifying Regime Changes . When the correlation is high (above 0.8), the asset has lost its autonomy and is "locked" with the benchmark. When the correlation drops or turns negative, the asset is "decoupled." This mode is essential for hedging strategies. If you are long Bitcoin and short the Nasdaq to hedge, but their correlation drops to zero, your hedge has mathematically evaporated. This mode warns you of such structural breaks.
2. Beta Sensitivity (Volatility Adjusted Risk):
While Correlation asks "Are they moving together?", Beta asks "How violently are they moving together?". Beta adjusts the correlation by the relative volatility of the asset versus the benchmark. A Beta of 1.5 implies that for every 1% move in the S&P 500, the asset is statistically likely to move 1.5%. This is the single most important metric for Position Sizing . In high-beta regimes, you must reduce position size to maintain constant risk. This mode visualizes when an asset transitions from being a "Defensive Haven" (Beta < 1.0) to a "High Risk Vehicle" (Beta > 1.0).
3. Explained Variance / R-Squared (The Truth Serum):
This is the most advanced metric in the toolkit, rarely found in retail indicators. R-Squared ranges from 0% to 100% and answers the question of causality: "How much of the asset's price movement is purely explained by the movement of the benchmark?" If R2 is 85%, it mathematically proves that 85% of the price action is external noise driven by the market, and only 15% is driven by the asset's own news or chart pattern. Institutional traders use this to filter trades: They seek Low R-Squared environments for alpha generation (breakouts) and avoid High R-Squared environments where they would simply be trading the index with higher fees.
The Theory of "Invisible Gravity" and Macro Benchmarking
While comparing assets to the S&P 500 is standard, the theoretical value of this matrix expands significantly when utilizing Macro Benchmarks like US Treasury Yields (US10Y). According to Discounted Cash Flow (DCF) theory, the value of long-duration assets (like Tech Stocks or Crypto) is inversely related to the risk-free rate. By setting the benchmark to yields, this indicator makes this theoretical concept visible. A strong Negative Correlation confirms that asset appreciation is being driven by "cheap money" (falling yields). However, a sudden flip to Positive Correlation against yields signals a profound shift in market mechanics, often indicating that inflation fears are being replaced by growth fears or monetary debasement. This visualizes the "Denominator Effect" in real-time.
Visualizing Market Breadth and Internal Health
Beyond individual lines, the "Breadth Mode" aggregates the data into a histogram to diagnose the health of a trend. A healthy rally is supported by broad participation, meaning high correlation across risk assets. A dangerous, exhausted rally is characterized by Divergence : Price makes a new high, but the Correlation Breadth (the number of assets participating in the move) collapses. This is often the earliest warning signal of a liquidity withdrawal before a reversal occurs.
References
Markowitz, H. (1952). Portfolio Selection. The Journal of Finance.
Sharpe, W. F. (1964). Capital Asset Prices: A Theory of Market Equilibrium.
Hudson, R., & Gregoriou, A. (2015). Calculating and Comparing Security Returns: Logarithmic vs Simple Returns.
Disclaimer: This indicator is for educational purposes only. Past performance is not indicative of future results.
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.
Triple Correlation Signal by COCOSTATriple Correlation Signal by COCOSTA
Concept
Bitcoin experiences violent swings driven by large liquidations and panic selling. During these chaotic market events, Bitcoin often decouples from its usual correlation patterns with traditional assets like gold, copper, and equity indices.
This indicator identifies these critical moments when an asset simultaneously loses correlation with three major reference assets—a phenomenon that typically signals oversold conditions and extreme market dislocations .
How It Works
The Triple Correlation Signal monitors the correlation coefficient between your primary asset and three customizable assets. Simply apply it to any chart—the signals will trigger based on that asset's correlation behavior.
Default Setup: Bitcoin (BTC1!)
Gold (GC1!) - Safe-haven asset correlation
Copper (HG1!) - Industrial/economic growth correlation
NASDAQ-100 (US100) - Technology/equity market correlation
When all three correlations fall below zero simultaneously , the indicator triggers a signal. This rare multi-asset decorrelation event suggests that the asset has decoupled far beyond normal trading ranges—often indicating extreme selling pressure that has pushed prices to unreasonable levels .
Signal Visualization
The indicator displays signals as vertical lines that span the full chart height when all three correlations drop below zero. A semi-transparent red background also highlights periods when the signal condition is active. This neutral visual representation avoids implying a specific directional bias.
Universal Application
This indicator works on any ticker or asset class . Simply change the chart to your desired asset and adjust the three correlation symbols to match different market combinations:
Stocks: Compare against sector indices, VIX, and bond futures
Commodities: Compare against currencies, equity indices, and related commodities
Forex: Compare against central bank proxies, commodity indices, and equity markets
Why Use BTC1! (CME Bitcoin Futures)
For Bitcoin specifically, use BTC1! (CME Bitcoin Futures) rather than spot BTCUSD. Since traditional assets like gold (GC1!) and copper (HG1!) trade on CME with market hours, using BTC1! ensures synchronized trading sessions and accurate correlation measurements . The 24/7 spot market can create timing mismatches that distort correlation readings.
Trading Application
Signal triggers = Potential capitulation events and oversold extremes
Best used with other confirmation indicators (support levels, RSI, volume analysis)
Customizable correlation length (default: 62 bars) and asset symbols to match any strategy
Finding Your Edge
Experiment with different asset combinations for your trading interest. If you discover particularly effective correlation combinations—especially for underexplored assets—feel free to reach out. Your insights help COCOSTA continuously improve market analysis tools.
Key Insight
When massive liquidations force panic selling, assets temporarily break their normal relationships with other markets. The Triple Correlation Signal catches these precise moments—your edge in identifying when any asset has been sold below reasonable value.
Created by COCOSTA | Advanced Market Analysis Tools
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.
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.
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.
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.
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.
AI-Weighted RSI (Zeiierman)█ Overview
AI-Weighted RSI (Zeiierman) is an adaptive oscillator that enhances classic RSI by applying a correlation-weighted prediction layer. Instead of looking only at RSI values directly, this indicator continuously evaluates how other price- and volume-based features (returns, volatility, volume shifts) correlate with RSI, and then weights them accordingly to project the next RSI state.
The result is a smoother, forward-looking RSI framework that adapts to market conditions in real time.
By leveraging feature correlation instead of static formulas, AI-Weighted RSI behaves like a lightweight learning model, adjusting its emphasis depending on which features are most aligned with RSI behavior during the current regime.
█ How It Works
⚪ Feature Extraction
Each bar, the script computes features: log returns, RSI itself, ATR% (volatility), volume, and volume log-change.
⚪ Correlation Screening
Over a rolling learning window, it measures the correlation of each feature against RSI. The strongest relationships are ranked and selected.
⚪ Adaptive Weighting
Features are standardized (z-scored), then combined using their signed correlations as weights, building a rolling, adaptive prediction of RSI.
⚪ Prediction to RSI Weight
The predicted RSI is mapped back into a “weight” scale (±2 by default). Above 0 = bullish bias, below 0 = bearish bias, with color-graded fills to visualize overbought/oversold pressure.
⚪ Signal Line
A smoothing option (signal length) overlays a moving average of the AI-Weighted RSI for clearer trend confirmation.
█ Why AI-Weighted RSI
⚪ Adaptive to Market Regime
Because the model re-evaluates correlations continuously, it naturally shifts which features dominate, sometimes volatility explains RSI best, sometimes volume, sometimes returns.
⚪ Forward-Looking Bias
Instead of simply reflecting RSI, the model provides a projection, helping anticipate shifts in momentum before RSI itself flips.
█ How to Use
⚪ Directional Bias
Read the RSI relative to 0. Above = bullish momentum bias, below = bearish.
⚪ Overbought / Oversold Zones
Shaded fills beyond +0.5 or -0.5 highlight extremes where RSI pressure often exhausts.
⚪ Divergences
When price makes new highs/lows but AI-Weighted RSI fails to confirm, it often signals weakening momentum.
█ Settings
RSI Length: Lookback for the core RSI calculation.
Signal Length: Smoothing applied to the AI-Weighted RSI output.
Learning Window: Bars used for correlation learning and z-scoring.
-----------------
Disclaimer
The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
CME FX Futures Correlation MatrixThis indicator calculates the correlation between major CME FX futures and displays it in a visual table. It shows how closely pairs like EUR/USD, GBP/USD, USD/JPY, USD/CHF, USD/CAD, AUD/USD, and NZD/USD move together or in opposite directions.
The indicator inherits the timeframe of the chart it’s applied to.
Color coding:
Red: strong correlation (absolute value > 80%), both positive and negative
Green: moderate/low correlation
How to launch it
Apply the indicator to a CME chart (e.g., EUR/USD futures).
Set Numbers of Bars Back to the desired lookback period (default 100).
The table appears in the center of the chart, showing correlation percentages between all major FX futures.
PPP – Info Table (Anchor + Corr/Alpha/Beta) v3PPP – Info Table (Anchor + Corr/Alpha/Beta)
- By P3 Analytics, run by Puranam Pradeep Picasso Sharma
🔎 Overview
This indicator creates a clean, dynamic information table on your chart that lets you quickly analyze how your chosen asset is performing relative to BTC, ETH, or any other benchmarks.
With a single glance, you can see:
% change from today’s open (for the anchor asset, BTC, and ETH)
Previous day % change (self + benchmarks)
Correlation, Beta, and Alpha statistics for the selected window (1W, 1M, 1Y)
Anchor values at any bar you choose (via Bars Back or Anchor Time)
Perfect for traders who want to measure coin strength vs benchmarks and make better rotation, risk, or hedging decisions.
📊 Key Metrics
Correlation (Corr): How closely the asset moves with the benchmark.
+1 = moves together, 0 = no relation, -1 = moves opposite.
Beta (β): Sensitivity of returns vs the benchmark.
β = 1 → moves 1:1 with BTC.
β > 1 → more volatile (amplifies BTC moves).
β < 1 → less volatile (defensive).
Alpha (α): Excess return beyond what Beta predicts.
Positive α = outperforming benchmark-adjusted expectation.
Negative α = underperforming.
⚙️ Features
Flexible Anchor Mode:
Bars Back → quickly step through bars.
Time → pin analysis to a specific historical candle.
Customizable Benchmarks: Default BTC & ETH (futures), but replaceable with any ticker.
Adjustable Stats Window:
1 Week, 1 Month, 1 Year (auto-scales if using chart timeframe).
Compact Mode for a smaller table layout.
Dark/Light Theme, font size, corner placement, transparency, and decimal control.
Runs efficiently with minimal chart clutter.
🧑💻 About P3 Analytics
This indicator is developed under P3 Analytics, a research & trading technology initiative led by Puranam Pradeep Picasso Sharma.
P3 Analytics builds tools that merge machine learning, statistics, and trading strategy into accessible products for traders across crypto, equities, forex, and commodities.
✅ How to Use
Add indicator to your chart.
In settings:
Pick your benchmarks (default = BTCUSDT.P, ETHUSDT.P).
Choose your anchor (Bars Back or Time).
Set window length for correlation/alpha/beta.
Read the table:
Left side = your asset.
Right side = benchmarks.
Colors: Green = positive % change, Red = negative.
🚀 Why Use This?
Quickly compare your asset vs BTC/ETH without juggling multiple charts.
Spot whether a coin is truly leading or just following BTC.
Identify outperformance (alpha) coins for rotation or trend plays.
Manage risk by knowing which assets are high beta (high leverage-like moves).
✦ Indicator by P3 Analytics
✦ Created & published by Puranam Pradeep Picasso Sharma
Rolling Performance Toolkit (Returns, Correlation and Sharpe)This script provides a flexible toolkit for evaluating rolling performance metrics between any asset and a benchmark.
Features:
Library-based: Built on a custom utilities library for consistent return and statistics calculations.
Rolling Window Control: Choose the lookback period (in days) to calculate metrics.
Multiple Modes: Toggle between Rolling Returns, Rolling Correlation, and Rolling Sharpe Ratio.
Benchmark Comparison: Compare your selected ticker against a benchmark (default: S&P 500 / SPX), but you can easily switch to any symbol.
Risk-Free Rate Options: Choose from zero, a constant annual % rate, or a proxy symbol (default: US03M – 3-Month Treasury Yield).
Annualized Sharpe: Sharpe ratios are annualized by default (×√252) for intuitive interpretation.
This tool is useful for traders and investors who want to monitor relative performance, diversification benefits, or risk-adjusted returns over time.
utilitiesLibrary for commonly used utilities, for visualizing rolling returns, correlations and sharpe
Crypto Strength MatrixOverview
The "Crypto Strength Matrix" is a custom Pine Script v5 indicator designed for cryptocurrency traders to assess the relative strength of major crypto market segments against traditional markets (e.g., the U.S. Dollar Index) and Bitcoin dominance. This indicator plots the strength of Altcoins (excluding ETH and SOL), Ethereum (ETH), Solana (SOL), the Dollar Index (DXY) versus Altcoins, and Bitcoin Dominance (DOM) on a 0-100 scale, using the Relative Strength Index (RSI) methodology. It provides a visual and intuitive way to identify overbought (>70) or oversold (<30) conditions across these assets, helping traders spot potential entry or exit points in the crypto market.
How It Works
The indicator fetches real-time data from various crypto and forex symbols available on TradingView, including:
CRYPTOCAP:TOTAL2 (total altcoin market cap),
CRYPTOCAP:ETH and CRYPTOCAP:SOL (market caps of ETH and SOL),
CRYPTO:ETHUSD and CRYPTO:SOLUSD (ETH and SOL prices),
CRYPTOCAP:BTC.D (Bitcoin dominance),
TVC:DXY (U.S. Dollar Index).
Calculations:
Altcoin Strength (OTH): Measures the RSI of the normalized market cap of all altcoins excluding ETH and SOL (calculated as TOTAL2 - ETH - SOL), relative to the total altcoin market cap. This reflects the strength of smaller altcoins.
ETH Strength: Computes the RSI of ETH/USD price adjusted by the DXY, isolating ETH's performance against the dollar.
SOL Strength: Similar to ETH, calculates the RSI of SOL/USD price adjusted by the DXY, focusing on Solana's strength.
DXY vs Altcoins: Uses the RSI of the DXY divided by the normalized total altcoin market cap, indicating the dollar's strength relative to altcoins.
Bitcoin Dominance (DOM): Directly applies RSI to Bitcoin dominance data, showing BTC's market control.
Each metric is plotted as a line with a unique color (OTH in aqua, ETH in teal, SOL in purple, DXY in green, DOM in orange) and labeled at the end of the chart for easy identification. Horizontal lines at 70 (overbought), 50 (neutral), and 30 (oversold) provide reference levels.
How to Use
Add the Indicator: Apply the "Crypto Strength Matrix" to a cryptocurrency chart (e.g., BTC/USD or ETH/USD) on a daily or 4-hour timeframe for optimal results.
Interpret the Lines:
OTH (Altcoins excluding ETH and SOL): A value above 70 suggests strong momentum in smaller altcoins, while below 30 indicates weakness. Monitor for divergence with ETH and SOL.
ETH and SOL: High values (>70) signal potential overbought conditions for these assets, while low values (<30) may indicate oversold opportunities.
DXY: Rising above 70 may suggest a stronger dollar, potentially pressuring crypto prices, while below 30 could indicate a weakening dollar, favoring crypto.
DOM: A value above 70 reflects strong Bitcoin dominance, often leading to altcoin underperformance, while below 30 may signal altcoin season.
Combine with Price Action: Use the indicator alongside candlestick patterns or volume analysis to confirm trade signals.
Adjust RSI Length: The default RSI length is 14, but you can tweak this input in the indicator settings to suit your trading style (e.g., 7 for shorter-term, 21 for longer-term trends).
Monitor Trends: Look for crossovers between lines (e.g., OTH rising above DXY) or alignment with the 50 neutral line to gauge market shifts.
Tips
Timeframe Selection: Daily charts provide a broad market view, while 4-hour charts offer more frequent signals. Avoid very short timeframes (e.g., 5m) due to noise.
Contextual Awareness: Combine with macroeconomic news (e.g., U.S. dollar strength) and Bitcoin price movements for better decision-making.
Risk Management: Use the indicator as a supplementary tool, not a standalone signal, and always set stop-losses based on your risk tolerance.
This indicator is ideal for crypto traders seeking a comprehensive view of market dynamics without the complexity of multiple charts. Enjoy trading with the "Crypto Strength Matrix"!
Correlation Heatmap Matrix [TradingFinder] 20 Assets Variable🔵 Introduction
Correlation is one of the most important statistical and analytical metrics in financial markets, data mining, and data science. It measures the strength and direction of the relationship between two variables.
The correlation coefficient always ranges between +1 and -1 : a perfect positive correlation (+1) means that two assets or currency pairs move together in the same direction and at a constant ratio, a correlation of zero (0) indicates no clear linear relationship, and a perfect negative correlation (-1) means they move in exactly opposite directions.
While the Pearson Correlation Coefficient is the most common method for calculation, other statistical methods like Spearman and Kendall are also used depending on the context.
In financial market analysis, correlation is a key tool for Forex, the Stock Market, and the Cryptocurrency Market because it allows traders to assess the price relationship between currency pairs, stocks, or coins. For example, in Forex, EUR/USD and GBP/USD often have a high positive correlation; in stocks, companies from the same sector such as Apple and Microsoft tend to move similarly; and in crypto, most altcoins show a strong positive correlation with Bitcoin.
Using a Correlation Heatmap in these markets visually displays the strength and direction of these relationships, helping traders make more accurate decisions for risk management and strategy optimization.
🟣 Correlation in Financial Markets
In finance, correlation refers to measuring how closely two assets move together over time. These assets can be stocks, currency pairs, commodities, indices, or cryptocurrencies. The main goal of correlation analysis in trading is to understand these movement patterns and use them for risk management, trend forecasting, and developing trading strategies.
🟣 Correlation Heatmap
A correlation heatmap is a visual tool that presents the correlation between multiple assets in a color-coded table. Each cell shows the correlation coefficient between two assets, with colors indicating its strength and direction. Warm colors (such as red or orange) represent strong negative correlation, cool colors (such as blue or cyan) represent strong positive correlation, and mid-range tones (such as yellow or green) indicate correlations that are close to neutral.
🟣 Practical Applications in Markets
Forex : Identify currency pairs that move together or in opposite directions, avoid overexposure to similar trades, and spot unusual divergences.
Crypto : Examine the dependency of altcoins on Bitcoin and find independent movers for portfolio diversification.
Stocks : Detect relationships between stocks in the same industry or find outliers that move differently from their sector.
🟣 Key Uses of Correlation in Trading
Risk management and diversification: Select assets with low or negative correlation to reduce portfolio volatility.
Avoiding overexposure: Prevent opening multiple positions on highly correlated assets.
Pairs trading: Exploit temporary deviations between historically correlated assets for arbitrage opportunities.
Intermarket analysis: Study the relationships between different markets like stocks, currencies, commodities, and bonds.
Divergence detection: Spot when two typically correlated assets move apart as a possible trend change signal.
Market forecasting: Use correlated asset movements to anticipate others’ behavior.
Event reaction analysis: Evaluate how groups of assets respond to economic or political events.
❗ Important Note
It’s important to note that correlation does not imply causation — it only reflects co-movement between assets. Correlation is also dynamic and can change over time, which is why analyzing it across multiple timeframes provides a more accurate picture. Combining correlation heatmaps with other analytical tools can significantly improve the precision of trading decisions.
🔵 How to Use
The Correlation Heatmap Matrix indicator is designed to analyze and manage the relationships between multiple assets at once. After adding the tool to your chart, start by selecting the assets you want to compare (up to 20).
Then, choose the Correlation Period that fits your trading strategy. Shorter periods (e.g., 20 bars) are more sensitive to recent price movements, making them suitable for short-term trading, while longer periods (e.g., 100 or 200 bars) provide a broader view of correlation trends over time.
The indicator outputs a color-coded matrix where each cell represents the correlation between two assets. Warm colors like red and orange signal strong negative correlation, while cool colors like blue and cyan indicate strong positive correlation. Mid-range tones such as yellow or green suggest correlations that are close to neutral. This visual representation makes it easy to spot market patterns at a glance.
One of the most valuable uses of this tool is in portfolio risk management. Portfolios with highly correlated assets are more vulnerable to market swings. By using the heatmap, traders can find assets with low or negative correlation to reduce overall risk.
Another key benefit is preventing overexposure. For example, if EUR/USD and GBP/USD have a high positive correlation, opening trades on both is almost like doubling the position size on one asset, increasing risk unnecessarily. The heatmap makes such relationships clear, helping you avoid them.
The indicator is also useful for pairs trading, where a trader identifies assets that are usually correlated but have temporarily diverged — a potential arbitrage or mean-reversion opportunity.
Additionally, the tool supports intermarket analysis, allowing traders to see how movements in one market (e.g., crude oil) may impact others (e.g., the Canadian dollar). Divergence detection is another advantage: if two typically aligned assets suddenly move in opposite directions, it could signal a major trend shift or a news-driven move.
Overall, the Correlation Heatmap Matrix is not just an analytical indicator but also a fast, visual alert system for monitoring multiple markets at once. This is particularly valuable for traders in fast-moving environments like Forex and crypto.
🔵 Settings
🟣 Logic
Correlation Period : Number of bars used to calculate correlation between assets.
🟣 Display
Table on Chart : Enable/disable displaying the heatmap directly on the chart.
Table Size : Choose the table size (from very small to very large).
Table Position : Set the table location on the chart (top, middle, or bottom in various alignments).
🟣 Symbol Custom
Select Market : Choose the market type (Forex, Stocks, Crypto, or Custom).
Symbol 1 to Symbol 20: In custom mode, you can define up to 20 assets for correlation calculation.
🔵 Conclusion
The Correlation Heatmap Matrix is a powerful tool for analyzing correlations across multiple assets in Forex, crypto, and stock markets. By displaying a color-coded table, it visually conveys both the strength and direction of correlations — warm colors for strong negative correlation, cool colors for strong positive correlation, and mid-range tones such as yellow or green for near-zero or neutral correlation.
This helps traders select assets with low or negative correlation for diversification, avoid overexposure to similar trades, identify arbitrage and pairs trading opportunities, and detect unusual divergences between typically aligned assets. With support for custom mode and up to 20 symbols, it offers high flexibility for different trading strategies, making it a valuable complement to technical analysis and risk management.
Correlation HeatMap [TradingFinder] Sessions Data Science Stats🔵 Introduction
n financial markets, correlation describes the statistical relationship between the price movements of two assets and how they interact over time. It plays a key role in both trading and investing by helping analyze asset behavior, manage portfolio risk, and understand intermarket dynamics. The Correlation Heatmap is a visual tool that shows how the correlation between multiple assets and a central reference asset (the Main Symbol) changes over time.
It supports four market types forex, stocks, crypto, and a custom mode making it adaptable to different trading environments. The heatmap uses a color-coded grid where warmer tones represent stronger negative correlations and cooler tones indicate stronger positive ones. This intuitive color system allows traders to quickly identify when assets move together or diverge, offering real-time insights that go beyond traditional correlation tables.
🟣 How to Interpret the Heatmap Visually ?
Each cell represents the correlation between the main symbol and one compared asset at a specific time.
Warm colors (e.g. red, orange) suggest strong negative correlation as one asset rises, the other tends to fall.
Cool colors (e.g. blue, green) suggest strong positive correlation both assets tend to move in the same direction.
Lighter shades indicate weaker correlations, while darker shades indicate stronger correlations.
The heatmap updates over time, allowing users to detect changes in correlation during market events or trading sessions.
One of the standout features of this indicator is its ability to overlay global market sessions such as Tokyo, London, New York, or major equity opens directly onto the heatmap timeline. This alignment lets traders observe how correlation structures respond to real-world session changes. For example, they can spot when assets shift from being inversely correlated to moving together as a new session opens, potentially signaling new momentum or macro flow. The customizable symbol setup (including up to 20 compared assets) makes it ideal not only for forex and crypto traders but also for multi-asset and sector-based stock analysis.
🟣 Use Cases and Advantages
Analyze sector rotation in equities by tracking correlation to major indices like SPX or DJI.
Monitor altcoin behavior relative to Bitcoin to find early entry opportunities in crypto markets.
Detect changes in currency alignment with DXY across trading sessions in forex.
Identify correlation breakdowns during market volatility, signaling possible new trends.
Use correlation shifts as confirmation for trade setups or to hedge multi-asset exposure
🔵 How to Use
Correlation is one of the core concepts in financial analysis and allows traders to understand how assets behave in relation to one another. The Correlation Heatmap extends this idea by going beyond a simple number or static matrix. Instead, it presents a dynamic visual map of how correlations shift over time.
In this indicator, a Main Symbol is selected as the reference point for analysis. In standard modes such as forex, stocks, or crypto, the symbol currently shown on the main chart is automatically used as the main symbol. This allows users to begin correlation analysis right away without adjusting any settings.
The horizontal axis of the heatmap shows time, while the vertical axis lists the selected assets. Each cell on the heatmap shows the correlation between that asset and the main symbol at a given moment.
This approach is especially useful for intermarket analysis. In forex, for example, tracking how currency pairs like OANDA:EURUSD EURUSD, FX:GBPUSD GBPUSD, and PEPPERSTONE:AUDUSD AUDUSD correlate with TVC:DXY DXY can give insight into broader capital flow.
If these pairs start showing increasing positive correlation with DXY say, shifting from blue to light green it could signal the start of a new phase or reversal. Conversely, if negative correlation fades gradually, it may suggest weakening relationships and more independent or volatile movement.
In the crypto market, watching how altcoins correlate with Bitcoin can help identify ideal entry points in secondary assets. In the stock market, analyzing how companies within the same sector move in relation to a major index like SP:SPX SPX or DJ:DJI DJI is also a highly effective technique for both technical and fundamental analysts.
This indicator not only visualizes correlation but also displays major market sessions. When enabled, this feature helps traders observe how correlation behavior changes at the start of each session, whether it's Tokyo, London, New York, or the opening of stock exchanges. Many key shifts, breakouts, or reversals tend to happen around these times, and the heatmap makes them easy to spot.
Another important feature is the market selection mode. Users can switch between forex, crypto, stocks, or custom markets and see correlation behavior specific to each one. In custom mode, users can manually select any combination of symbols for more advanced or personalized analysis. This makes the heatmap valuable not only for forex traders but also for stock traders, crypto analysts, and multi-asset strategists.
Finally, the heatmap's color-coded design helps users make sense of the data quickly. Warm colors such as red and orange reflect stronger negative correlations, while cool colors like blue and green represent stronger positive relationships. This simplicity and clarity make the tool accessible to both beginners and experienced traders.
🔵 Settings
Correlation Period: Allows you to set how many historical bars are used for calculating correlation. A higher number means a smoother, slower-moving heatmap, while a lower number makes it more responsive to recent changes.
Select Market: Lets you choose between Forex, Stock, Crypto, or Custom. In the first three options, the chart’s active symbol is automatically used as the Main Symbol. In Custom mode, you can manually define the Main Symbol and up to 20 Compared Symbols.
Show Open Session: Enables the display of major trading sessions such as Tokyo, London, New York, or equity market opening hours directly on the timeline. This helps you connect correlation shifts with real-world market activity.
Market Mode: Lets you select whether the displayed sessions relate to the forex or stock market.
🔵 Conclusion
The Correlation Heatmap is a robust and flexible tool for analyzing the relationship between assets across different markets. By tracking how correlations change in real time, traders can better identify alignment or divergence between symbols and gain valuable insights into market structure.
Support for multiple asset classes, session overlays, and intuitive visual cues make this one of the most effective tools for intermarket analysis.
Whether you’re looking to manage portfolio risk, validate entry points, or simply understand capital flow across markets, this heatmap provides a clear and actionable perspective that you can rely on.
M2 Global Liquidity Index [Extended + Empirical BTC Offset]M2 Global Liquidity Index
This script visualizes global M2 liquidity based on major economic zones (USA, China, Eurozone, Japan, UK), with the option to include extended countries such as Switzerland, Canada, India, Russia, Brazil, South Korea, Mexico, and South Africa.
The indicator includes an empirically derived offset to reflect how Bitcoin historically reacts with a time lag—typically around 12 weeks—after shifts in global liquidity.
Features:
Predefined empirical offset options ranging from 12 to 120 days
Automatic offset adjustment when applied to the weekly chart
Optional inclusion of extended global M2 sources
Important:
This indicator is intended only for use on the weekly chart. It provides meaningful and accurate results exclusively in this time frame, due to the nature of the offset-based correlation logic.
Use cases:
Macro-level analysis of Bitcoin’s price movements
Identifying early signs of potential market tops or bottoms in relation to liquidity flows
Two assets correlation trackerHi, I made this simple script to see how two correlated assets are actually performing short-term. The idea comes from correlation between BTC and ETH that that usually stands 0.9 (Pearson's correlation). However Pearson's correlation doesn't indicate the relative price difference and cannot be used trading correlation when used alone.
My approach is simple - we can select the date/time from which we will start tracking the price change, and instead of tracking the price, we track 100 USD worth of BTC and ETH and how this investment perform. This gives us the price difference relative to a point in the near future, I would suggest using latest trend shift, for example.
On example of how this can be used: If we see that BTC is falling slower than ETH since trend shift, we can long BTC and short ETH in equal parts and expect to gain from the difference while hedging potential loss.
