Mulai

Correlation

Asset Class Correlation MatrixAsset Class Correlation Matrix █ OVERVIEW This indicator displays a Pearson correlation matrix for instruments in the asset class of the symbol you are currently viewing. Open a EUR pair and you see the forex matrix. Open gold and you see the metals matrix. Open Bitcoin and you see the crypto matrix. The relevant basket loads automatically, so there is nothing to configure for the common cases. Each cell shows the rolling correlation between two instruments over a lookback period you control. The goal is to make cross-instrument relationships inside an asset class visible at a glance, rather than checking pairs one at a time, or relying on visual comparison. █ AUTOMATIC ASSET CLASS DETECTION The current symbol is matched to a category in three stages: 1. Exact ticker match against the built-in lists below. 2. Name-fragment match for common broker and CFD names. For example XAU and GOLD map to Metals, US500, SP500, NAS100, US100, US30 and DJ30 map to US Indices, DAX, FTSE and NIKKEI map to Global Indices, and WTI, BRENT and NATGAS map to Energy. 3. Asset type fallback using the instrument type, covering crypto, forex, and stocks. If the current symbol is not already part of a built-in list, it is added as the first row and column of the matrix, so the instrument you are on is always included. If no category can be determined, the table shows a short prompt to use the custom symbol list instead of rendering empty. █ BUILT-IN CATEGORIES Forex: 28 majors and crosses across USD, EUR, GBP, JPY, AUD, CAD, CHF and NZD. US Indices: ES, NQ, YM, EMD, RTY. Global Indices: DAX, Euro Stoxx 50, Nikkei, FTSE, ASX 200, Hang Seng. Metals: Gold, Silver, Copper, Platinum, Palladium. Energy: WTI Crude, Natural Gas, Heating Oil, RBOB Gasoline. Agricultural: Corn, Soybeans, Wheat, Soybean Oil, Soybean Meal, Cocoa, Coffee, Sugar, Cotton, Orange Juice. Livestock: Live Cattle, Lean Hogs, Feeder Cattle. Interest Rates: 2Y, 10Y, 30Y US notes, Euro Bund, Euro Buxl. Crypto: BTC, ETH, BCH, LTC. Stocks: SPX, QQQ, AAPL, MSFT, NVDA, AMZN, GOOGL, META, TSLA, AMD. █ READING THE MATRIX Pearson correlation ranges from -1 to +1. Values near +1 mean the two instruments move strongly together. Values near -1 mean they move strongly opposite to each other, which is still a strong relationship, just inverted. Values near 0 mean little to no linear relationship. The strength of a relationship is the distance from zero, in either direction. A reading of -0.9 is just as tight as +0.9. █ COLORS Positive correlation is shown in green, with a stronger shade above the high threshold and a lighter shade above the moderate threshold. Inverse correlation is shown in purple, using the same two strength levels. Everything between the negative and positive moderate threshold is shown as low. All five colors and both thresholds are adjustable in the settings. The thresholds apply symmetrically to positive and inverse values. █ CUSTOM SYMBOL LIST You can override the auto-detected basket with your own comma-separated list of symbols. Spaces are ignored. The custom list is applied only when the current chart symbol is one of the symbols in the list, which keeps the chart instrument anchored in the matrix. You can include an exchange or broker prefix, for example OANDA:EURUSD. A bare ticker such as GBPJPY inherits the current chart prefix. Bare futures contracts such as ES1! resolve on their native exchange. █ SETTINGS Period: lookback in bars for the correlation calculation. Shorter reacts faster and is noisier. Longer is more stable and slower to update. High and Moderate Correlation Thresholds: the cutoffs for the color bands. Colors: the five correlation colors. Symbol List: the optional custom basket. Table Size: text size of the matrix. █ HOW TO USE IT Add the indicator to any chart in a supported asset class. Use it to find pairs that move together or opposite each other, to check diversification across a basket, to spot when a normally correlated pair is diverging, or to choose hedges and pairs-trade candidates. The left column shows the full applied symbol for each row, so you can confirm exactly which feed each value comes from. █ NOTES AND LIMITATIONS Correlation is period-dependent. For tightly linked instruments, a long lookback pushes most values toward the extremes, while a short lookback spreads them out and reacts faster. Choose the period to match the question you are asking. Correlation measures linear co-movement of closing prices on the chart timeframe. It does not imply causation and does not capture non-linear relationships. Broker naming for CFDs varies widely, so some instruments may not auto-detect. When that happens, use the custom symbol list. A maximum of 28 instruments can be loaded in one matrix.
Indikator Pine Script®
oleh nilstrades_
Intermarket Confluence Engine | AnonycryptousIntermarket Confluence Engine (ICE) | Anonycryptous Description & user manual Why this indicator exists Most indicators analyze one asset in isolation. They look at price, momentum, volume, or volatility — all on the same chart, all based on the same data feed. That is useful, but it leaves out the context that drives markets at a deeper level: the relationship between assets, the macro regime, the direction of capital flow across instruments. ICE approaches the problem differently. Instead of analyzing a single price series, it takes two assets and computes their ratio. That ratio becomes the subject of analysis — not the individual prices. The result is a view of relative strength, regime state, and intermarket context that no single-asset indicator can produce. It runs eight independent analytical engines on that ratio. Each engine returns a directional score. Those scores are weighted based on the selected asset class and combined into a single confluence number from -10 to +10. The dashboard shows the engine breakdown, the macro state, and the current statistical position of the ratio in its historical distribution — all in one compact panel. ICE is not a signal indicator. It does not tell you when to buy or sell. It tells you what the current relationship between two assets looks like across eight independent dimensions, and how much those dimensions agree with each other. Important notice ICE does not generate trading signals. It does not tell you when to buy or sell. It does not predict market direction. It does not guarantee any outcome. All trading decisions remain entirely with the user. Always apply your own judgment and manage your own risk. 1. Overview ICE is a ratio-based intermarket confluence scoring system. It takes two configurable assets, computes their price ratio (Asset A divided by Asset B), and runs that ratio through eight analytical engines simultaneously. The nine engines are: - Relative strength — how much Asset A is outperforming or underperforming Asset B on a rate-of-change basis - Trend — EMA structure and slope direction of the ratio - Momentum — volume-weighted RSI and MACD histogram alignment on the ratio - Volatility — Bollinger Band width, ATR percentile, and squeeze state of the ratio - Statistical extremes — Z-score and historical percentile position of the ratio - Macro regime — direction of DXY, VIX, and 10-year Treasury yields - Liquidity — yield curve proxy using 10-year yield rate of change - Intermarket correlation — rolling correlation between the ratio and each macro feed - Volume participation — OBV slope and relative volume confirmation on both assets Each engine is weighted based on the selected asset class. A custom weighting mode is available for manual control. All weights are normalized so the final score always maps to the -10 to +10 range regardless of class selection. The chart displays the ratio as a line with an EMA stack (21, 50, 200), Bollinger Bands, and statistical deviation bands based on Z-score distance from the historical mean. Signals fire when confluence crosses configurable thresholds. Divergence between the ratio and its volume-weighted RSI is detected mechanically and shown on the chart. 2. The ratio 2.1 What it represents The ratio is simply the price of Asset A divided by the price of Asset B. If Asset A is gold (XAUUSD) and Asset B is silver (XAGUSD), the ratio is the gold/silver ratio — how many ounces of silver one ounce of gold can buy. If Asset A is NQ futures and Asset B is ES futures, the ratio represents the relative performance of tech versus the broad market. The ratio rises when Asset A outperforms Asset B. It falls when Asset B outperforms Asset A. All eight engines work on this ratio, not on the underlying prices. 2.2 What is plotted The ratio line is the primary visual element. It is colored gold when above its 50-period EMA and grey when below. The EMA stack (green for the 21, blue for the 50, white for the 200) shows the structural state of the ratio trend. Two band systems are visible simultaneously: Statistical deviation bands — based on Z-score. The upper band is the historical mean plus 2 standard deviations (configurable). The lower band is the mean minus 2 standard deviations. When the ratio is near or beyond these bands, the Statistical engine activates and the dashboard notes an extreme condition. Bollinger Bands — a separate volatility-based band using a configurable period and multiplier. These bands are lighter and secondary to the statistical bands. Squeeze markers appear as small squares along the statistical mean when the Bollinger Bands are contained inside the Keltner Channel — indicating compressed volatility and a potential breakout. 2.3 Signal markers Signals are plotted directly on the ratio chart using triangles and circles. All markers use plotshape, not labels. Large triangles up (green) — strong bull confluence (score above +6) Large triangles down (red) — strong bear confluence (score below -6) Small triangles up (faded green) — moderate bull confluence (score between +3.5 and +6) Small triangles down (faded red) — moderate bear confluence (score between -3.5 and -6) Cyan circles — bullish momentum divergence aligned with positive score Orange circles — bearish momentum divergence aligned with negative score Purple squares — active volatility squeeze 3. The eight engines 3.1 Relative strength engine This engine measures how much Asset A is outperforming Asset B on a rate-of-change basis. It computes the ROC of each asset independently over a configurable period (default 14) and subtracts them to get a delta. That delta is then Z-score normalized over a longer lookback (default 50) to assess whether the current outperformance is historically significant. The engine also tracks the velocity of the ratio itself — the first derivative of the ratio — and whether the ratio is above its own EMA. Score: +1 when the RS Z-score is above 0.5 and the ratio is above its EMA. -1 when the RS Z-score is below -0.5 and the ratio is below its EMA. 0 otherwise. The dashboard shows the raw RS Z-score in the state section so you can see how far from neutral the relative strength is reading. 3.2 Trend engine The trend engine evaluates the EMA alignment of the ratio across three periods (21, 50, 200), the slope direction using linear regression, and optionally a higher timeframe EMA confirmation. A full bull stack is when EMA 21 is above EMA 50 and EMA 50 is above EMA 200, combined with a positive slope. A full bear stack is the reverse. Transitional states occur when the stack is broken but slope still has a direction. The HTF trend filter uses a configurable higher timeframe (default weekly) and checks whether the chosen asset is above its 50-period EMA on that timeframe. When enabled, the trend engine only scores positively if the HTF also confirms. Score: +1 for confirmed bull trend. -1 for confirmed bear trend. 0 for compression or transition. The trend state shown in the dashboard (Expansion, Contraction, Transitional, Compression) reflects the combination of stack state and slope direction. 3.3 Momentum engine The momentum engine uses a volume-weighted RSI applied to the ratio. The weighting uses the combined average volume of both assets, normalized by its own moving average. This is the same architecture as VW RSI Pro — gains and losses are scaled by relative volume before the RSI calculation, so bars with above-average volume have more influence on the RSI than bars with below-average volume. Alongside the VW RSI, the engine computes MACD histogram acceleration (the change in histogram value, not just its level). This distinguishes between momentum that is building and momentum that is present but decelerating. Score: +1 when VW RSI is above 52 and MACD histogram is positive. -1 when VW RSI is below 48 and MACD histogram is negative. 0 otherwise. The VW RSI value is shown in the state section of the dashboard. Values above 55 are colored green, below 45 red, between them grey. 3.4 Volatility engine The volatility engine assesses whether the ratio is in a phase of compression or expansion, and which direction expansion is occurring. It computes Bollinger Band width relative to its 100-bar average — widening bands indicate expansion, narrowing bands indicate compression. ATR percentile rank over a configurable lookback (default 100 bars) provides a second volatility measure. A squeeze is identified when the Bollinger Bands are fully contained within the Keltner Channel. Score: +1 when volatility is expanding and the ratio is above the Bollinger midline, or when a squeeze releases upward. -1 for the same conditions in the downward direction. 0 during compression or neutral volatility states. The vol state (Squeeze, Breakout, Expansion, Compression, Neutral) is shown in the dashboard state section. Squeeze appears in purple, breakout in gold, expansion in the configured bull color. 3.5 Statistical extremes engine This engine measures where the current ratio stands within its own historical distribution. It computes a Z-score of the ratio over a configurable lookback (default 50) and a historical percentile rank over a longer window (default 252 bars, approximately one year of daily data). When the ratio is more than 1.5 standard deviations above its mean and above the 80th percentile, it is classified as historically expensive — a potential mean reversion candidate to the downside. When it is more than 1.5 standard deviations below its mean and below the 20th percentile, it is historically cheap — a potential mean reversion candidate to the upside. Score: +1 at extreme lows (below mean, below 20th percentile). -1 at extreme highs (above mean, above 80th percentile). 0 within normal range. The Z-score and historical percentile are shown in the dashboard state section. A gold highlight on the Z-score indicates an active extreme condition. The mean reversion probability displayed in the extended panel is a normalized version of the absolute Z-score distance — a rough proxy for how far the ratio has stretched from its historical center. It is not a probability in the statistical sense, but a relative measure of extension. 3.6 Macro regime engine The macro regime engine uses three external data feeds — DXY (dollar index), VIX (volatility index), and TNX (10-year Treasury yield) — loaded via request.security(). It evaluates the trend direction of each feed relative to a smoothed EMA (configurable length, default 20) and classifies the current macro environment. The global regime classification (Risk-On / Risk-Off / Mixed) appears in the dashboard header. It is always based on the same three-signal count regardless of asset class: VIX level, DXY trend, and yield direction. The macro score, however, is class-aware. Each asset class has its own logic: Gold / Silver — risk-off conditions (elevated VIX, falling yields, falling dollar) favor Asset A (gold). Risk-on conditions (low VIX, rising yields, rising dollar) favor Asset B (silver outperforms on industrial demand). Score is +1 for acute risk-off, -1 for sustained risk-on. Crypto — DXY direction is the primary gatekeeper. Falling DXY and falling yields are bullish for crypto. Rising DXY and rising yields are bearish. VIX provides a third signal. Two of the three conditions must align for a score to fire. Forex — trend-following regime logic. Risk-on environments favor the ratio direction, risk-off favors the reverse. Indices — same structure as Forex. Risk-on = positive bias. Commodities — DXY-led. Falling dollar supports commodity ratios. Score: +1 for regime favorable to Asset A. -1 for regime favorable to Asset B. 0 for mixed. 3.7 Liquidity engine The liquidity engine uses the 10-year Treasury yield (TNX) rate of change as a proxy for liquidity conditions. Falling long-term yields indicate looser financial conditions — lower cost of capital, more risk appetite. Rising yields indicate tightening. The TNX rate of change is computed over 20 bars and smoothed with a 10-bar EMA. When the smoothed ROC is below -0.1, conditions are classified as expanding. Above +0.1, contracting. Score logic is class-aware: - Gold / Silver — expanding liquidity (falling yields) is positive for the ratio since gold benefits more from low rates. Contracting is negative. - Crypto — same direction. Loose liquidity benefits risk assets. - Forex — inverted. Rising yields support yield-differential-driven pairs. - Other classes — expansion is positive. Score: +1 for favorable liquidity, -1 for unfavorable, 0 for neutral. 3.8 Intermarket correlation engine This engine computes the rolling Pearson correlation between the ratio and each macro feed (DXY, VIX, TNX) over a configurable window (default 30 bars). It then assesses whether the current correlations match the expected structural behavior for the selected asset class. For the Gold/Silver ratio, for example, historically the ratio is positively correlated with VIX (risk-off pushes gold relative to silver) and negatively correlated with DXY (weaker dollar benefits silver less). When those correlations are in place and above a threshold (±0.15), the engine confirms the macro alignment. A correlation shift is detected when the sign of a correlation flips compared to 10 bars ago — this is flagged in the dashboard as a regime change signal. Score: +1 when correlations confirm expected behavior for Asset A outperformance. -1 when they confirm the reverse. 0 when correlations are below threshold or mixed. 3.9 Volume participation engine This engine measures whether the volume behind the ratio's current move confirms its direction. It uses two inputs: the relative volume difference between Asset A and Asset B, and the slope of the on-balance volume (OBV) calculated on the ratio. The relative volume comparison checks whether Asset A is attracting more volume than Asset B relative to their combined average. When Asset A draws disproportionately more volume, it indicates institutional interest in the primary asset. The OBV slope uses a 20-bar linear regression to determine whether cumulative directional volume is rising or falling. A bullish confirmation requires the OBV slope to be positive, the ratio to be above its 21 EMA, and Asset A to have higher relative volume. A bearish confirmation requires the reverse. When volume diverges from price direction — OBV falling while price rises, or vice versa — this is flagged in the extended panel as a volume divergence warning. Score: +1 when volume participation confirms the ratio move upward. -1 when it confirms downward. 0 when volume is inconclusive or mixed. 4. Adaptive weighting Each engine returns -1, 0, or +1. Each score is multiplied by the engine's weight for the selected asset class. The sum of all nine weighted scores is normalized against the total possible weight to produce the final confluence score on a -10 to +10 scale. Asset class presets: Gold / Silver — statistical extremes and macro regime are weighted most heavily (14 each). This reflects the GSR's mean-reverting nature and strong sensitivity to macro conditions. Volume participation carries moderate weight — on the GSR, volume confirmation is useful but less decisive than macro state. Crypto — liquidity and momentum are weighted most heavily (14 each). Volume participation also carries elevated weight, since capital rotation between an asset and stablecoins is directly visible in relative volume. Forex — trend and correlation are weighted most heavily (14 each). Currency pairs respond to trend conditions and intermarket relationships more reliably than statistical extremes. Indices — momentum and liquidity are weighted most heavily (14 each). Volume participation also carries elevated weight — index futures moves backed by strong volume are more reliable than low-volume drifts. Commodities — relative strength and volatility are weighted most heavily (14 each). Volume participation carries moderate weight since commodity ratio moves are often driven by volume imbalances between the two assets. Custom — all nine weights are individually configurable from 0 to 20. The confidence percentage shown in the dashboard is the spread between the normalized bull and bear score components — a measure of how much the engines agree rather than merely how many fire. 5. Dashboard The dashboard is a single compact panel with four columns and thirteen rows. It shows the complete scoring state, engine breakdown, and market context in one place. Header row — indicator name, asset class, confluence label, and score out of 10. The header color reflects the net score direction. Confidence and regime row — confidence percentage and the global macro regime (Risk-On / Risk-Off / Mixed). Engine scores — eight engines displayed two per row across four columns. Each engine shows its label and its weighted score with direction indicator. A green upward triangle indicates a positive contribution. A red downward triangle indicates a negative contribution. A grey dot indicates a neutral score. State section — trend state, volatility state, VW RSI value, and Z-score. The trend state label (Expansion, Contraction, Transitional, Compression) reflects the combination of EMA alignment and slope. The vol state (Squeeze, Breakout, Expansion, Compression, Neutral) reflects the Bollinger/Keltner relationship. Macro feeds — DXY direction, VIX level, 10-year yield direction, and current divergence state. Brand footer — version reference. The extended macro panel (disabled by default) can be enabled in settings for a second panel showing full correlation values, ATR percentile, statistical state detail, OBV slope, volume participation score, volume divergence flag, and liquidity state. 6. Asset pair configuration 6.1 Gold/Silver ratio (GSR) The gold/silver ratio is the primary design case for ICE. It measures how many ounces of silver are required to buy one ounce of gold. Historically the ratio has ranged between 15 and 120. It is mean-reverting over long cycles but can trend persistently for months or years. Recommended setup: - Asset A: OANDA:XAUUSD - Asset B: OANDA:XAGUSD - Asset class: Gold / Silver The statistical extremes engine is particularly relevant here. When the ratio is near historical highs (above the 80th percentile, Z-score above 1.5), silver has historically outperformed gold significantly over the following months. When near historical lows, gold has tended to recover its premium. The macro regime engine is also central. Acute risk-off events (2008, 2020) spike the GSR rapidly as gold outperforms. Sustained risk-on environments with rising yields and industrial demand tend to compress it. 6.2 Crypto setups For crypto ratio analysis, stablecoin dominance (CRYPTOCAP:USDT.D) as Asset B provides a direct view of capital rotation between an asset and cash equivalents. When the ratio rises, the asset is gaining relative to stablecoins — capital is flowing in. When it falls, capital is rotating out. Recommended setups: - BINANCE:BTCUSDT / CRYPTOCAP:USDT.D — Bitcoin vs stablecoin dominance - BINANCE:SOLUSDT / CRYPTOCAP:USDT.D — SOL vs stablecoin dominance - BINANCE:ETHUSDT / CRYPTOCAP:USDT.D — ETH vs stablecoin dominance - Asset class: Crypto for all of the above BTC.D (Bitcoin dominance, CRYPTOCAP:BTC.D) as Asset B can be used to measure altcoin performance relative to Bitcoin specifically — useful for identifying altseason conditions. 6.3 NQ futures setups For Nasdaq and MNQ trading, ratio analysis provides directional and regime context. Recommended setups: - CME_MINI:NQ1! / CME_MINI:ES1! — Nasdaq vs S&P 500. When this ratio rises, tech is outperforming the broad market. A falling ratio suggests defensive rotation or underperformance of growth. Asset class: Indices. - CME_MINI:NQ1! / CME_MINI:RTY1! — Nasdaq vs Russell 2000. Large-cap growth vs small-cap. Risk appetite proxy. Asset class: Indices. - CME_MINI:NQ1! / TVC:DXY — NQ relative to dollar strength. Strong inverse relationship historically. Asset class: Indices. 6.4 Precious metals and commodities - OANDA:XAUUSD / TVC:DXY — gold relative to dollar. One of the cleanest inverse relationships in macro markets. Asset class: Commodities or Gold/Silver. - OANDA:XAUUSD / CME_MINI:ES1! — gold vs equities. Risk-off proxy. When this ratio rises, gold is outperforming stocks. Asset class: Commodities. - TVC:USOIL / TVC:NATGAS — oil vs natural gas relative value. Asset class: Commodities. 6.5 Forex setups For currency pairs, use the pair itself as a ratio — Asset A as the base currency ETF or index, Asset B as the quote. Alternatively, use currency index feeds directly. - FX:EURUSD as a direct entry (ratio of EUR to USD) - TVC:DXY / FX:EURUSD — dollar index vs euro. Asset class: Forex. 7. Macro feeds The three macro feeds are loaded via request.security() and must resolve on TradingView. Default symbols: - DXY: TVC:DXY - VIX: CBOE:VIX - 10-year yield: TVC:TNX These can be changed in the Macro Feeds settings group if alternative data sources are preferred. Each feed can be individually disabled — if all three are disabled, the macro regime, liquidity, and correlation engines return neutral (0) scores. On lower timeframes (1m, 3m), macro feeds may have limited bar history, which can cause some engines to return neutral until sufficient data is loaded. From 15m and higher, all engines should be fully active. On very low timeframes, the statistical engines also require a minimum number of bars before the lookbacks are satisfied. 8. How to use 8.1 Reading the score The confluence score on a -10 to +10 scale communicates direction and intensity simultaneously. It does not communicate timing. A score of +7 with 70% confidence means six or seven engines are aligned in a bullish direction for Asset A relative to Asset B, with the weighted agreement being high. It does not mean a trade should be entered immediately — it means the current relative conditions strongly favor Asset A. A score near 0 with low confidence means the engines are split. This is not a bearish signal — it is the absence of a clear signal. In practice, scores between -3 and +3 with confidence below 40% suggest the ratio is in a mixed or transitional regime. 8.2 Using the score with price action ICE works on the ratio — not on the underlying price. To apply it to a trade on the underlying asset, you need to interpret the score in context. On a BTC/USDT.D ratio chart with a score of -7, the ratio is falling — BTC is losing ground relative to stablecoin dominance. This is a macro tailwind for a bearish BTC view. It does not tell you where to enter or where to put your stop. It tells you the broader relative conditions are bearish. Combine ICE with a price-action tool, a structure indicator, or an entry system applied to the actual trading instrument. ICE provides the regime and relative context. The entry decision remains with the user. 8.3 Divergence signals When the ratio makes a lower low but the VW RSI makes a higher low, a bullish divergence is detected. When the ratio makes a higher high but the VW RSI makes a lower high, a bearish divergence is detected. These are mechanical detections using pivot analysis. Divergence signals that align with the net confluence score carry more weight. A bullish divergence on a ratio that is already scoring positively on four or five engines is a stronger condition than a divergence in an otherwise neutral scoring environment. Cyan circles mark bull divergence, orange circles mark bear divergence. 8.4 Squeeze and volatility breakouts When the volatility engine identifies a squeeze (Bollinger Bands inside the Keltner Channel), a purple square appears along the statistical mean line. This indicates compressed volatility and an elevated probability of a significant directional move. When the squeeze releases, the volatility engine contributes its score in the direction of the breakout. Combined with trend and momentum alignment, a squeeze release can produce a rapid score shift. These moments are marked on the chart and flagged in the dashboard vol state row. 8.5 Statistical extremes The statistical engine is most useful on the Gold/Silver ratio and other fundamentally mean-reverting pairs. When the Z-score exceeds 1.5 and the ratio is in the top 20% of its historical range, the statistical engine scores negatively — signaling that the ratio has historically tended to revert from this level. This is not a timing signal. The ratio can remain at extremes for weeks or months. The statistical engine scores the degree of extension, not the moment of reversal. Use it alongside momentum and trend engines to assess whether the extreme is beginning to resolve. 9. Settings reference Asset configuration - Asset A — the primary asset. Default: XAUUSD. - Asset B — the secondary asset. Default: XAGUSD. The ratio is Asset A divided by Asset B. - Plot ratio line — toggles the main ratio line on the chart. - Plot ratio EMAs — toggles the 21/50/200 EMA stack on the ratio. - Plot std dev bands — toggles the statistical deviation bands and Bollinger Bands. Asset class and weighting - Asset class — selects the weighting preset. Options: Gold/Silver, Crypto, Forex, Indices, Commodities, Custom. - Individual weight inputs — only active in Custom mode. Each engine can be weighted from 0 to 20. Macro feeds - Use DXY / VIX / TNX — individual toggles for each macro feed. - DXY / VIX / TNX symbol — configurable symbols. Defaults: TVC:DXY, CBOE:VIX, TVC:TNX. - Macro smoothing — EMA length for the macro feed trend detection. Default 20. Relative strength engine - ROC length — rate of change period for both assets. Default 14. - RS EMA length — EMA applied to the ratio for trend confirmation. Default 21. - RS Z-score lookback — lookback for normalization of the RS delta. Default 50. Trend engine - Fast / Slow / Macro EMA — the three EMA periods for the ratio. Defaults: 21, 50, 200. - MTF trend filter — enables the higher timeframe confirmation gate. - HTF timeframe — the timeframe used for the HTF EMA check. Default weekly. Momentum engine - RSI length — period for the VW RSI calculation. Default 14. - Volume smoothing — SMA length for volume normalization. Default 14. - Volume weighted RSI — enables volume weighting on the RSI. Default on. - MACD fast / slow / signal — MACD parameters applied to the ratio. Defaults: 12, 26, 9. Volatility engine - BB length / BB multiplier — Bollinger Band parameters. Defaults: 20, 2.0. - ATR length — period for ATR calculation. Default 14. - ATR percentile lookback — historical window for ATR percentile ranking. Default 100. - Squeeze KC length / multiplier — Keltner Channel parameters for squeeze detection. Defaults: 20, 1.5. Statistical extremes engine - Z-score lookback — window for Z-score calculation. Default 50. - Percentile lookback — historical window for percentile ranking. Default 252 (approximately one year of daily data). - Z-score extreme threshold — standard deviations from mean required to classify as extreme. Default 1.5. Correlation engine - Correlation window — rolling window for Pearson correlation. Default 30. Visuals - Bull / bear / neutral color — configurable colors for all directional elements. - Ratio line color — color of the main ratio line. - Show score background — colors the pane background faintly by net score direction. - Background transparency — transparency level for the score background. Default 93. Dashboard - Show dashboard — master toggle. Default on. - Position — Top Left, Top Right, Bottom Left, Bottom Right. Default Bottom Right. - Size — Tiny, Small, Normal. Default Tiny. - Show extended macro panel — enables a second panel with full correlation, volume, and statistical detail. Default off. Recommended for desktop only. 10. Notes - ICE operates on a ratio of two assets. If either asset has no data on the current chart timeframe, the ratio will be unavailable and the engines will not fire. Ensure both symbols resolve correctly in TradingView before interpreting the dashboard. - The macro feeds (DXY, VIX, TNX) are loaded separately via request.security(). On lower timeframes, the feed data may require a few bars to warm up before producing stable readings. All engines should be fully active from the 15m timeframe and above. - The volume used by the momentum engine is the combined average of both asset volumes. On ratio pairs where one or both assets have zero or unavailable volume (such as some index feeds), the volume-weighted RSI falls back to an unweighted RSI automatically. - All statistical calculations (Z-score, percentile rank) require a minimum number of bars equal to the lookback period. On charts with limited history or very short timeframes, these engines may return neutral until sufficient bars are loaded. - The correlation engine requires both assets to have non-constant price series over the correlation window. On very stable or pegged assets, correlation may be undefined and the engine returns neutral. - ICE does not repaint. All scores and signals are based on confirmed bar data. - The indicator is designed for ratio analysis. It can technically be used with a single asset by setting Asset B to a constant reference (such as a stablecoin or index), but it was built around the two-asset ratio concept and performs best in that context. 11. Disclaimer This indicator is provided for educational and informational purposes only. All outputs are based on historical price data and mathematical calculations. Past behavior does not guarantee future results. Trading involves substantial risk of loss. Use at your own discretion.
Indikator Pine Script®
oleh Anonycryptous
Aquila Reale Correlations v1.1🦅 AQUILA REALE CORRELATIONS v1.1 Inter-market correlation dashboard for any two assets, with multi-timeframe analysis, spread monitoring, and regime detection. ═══════════════════════════════════════════════════ 📊 WHAT THIS INDICATOR DOES ═══════════════════════════════════════════════════ Calculates the Pearson correlation coefficient between two user-defined assets across 4 timeframes simultaneously (15M, 1H, 4H, Daily) and presents the result in a single-glance dashboard. Default configuration: XAUUSD vs WTI (FP Markets), but fully configurable for any pair — Gold/DXY, BTC/SPX, EUR/USD vs DXY, equities vs bonds, etc. The dashboard provides: - Multi-timeframe correlation matrix with regime classification (STRONG DIRECT / WEAK DIRECT / NEUTRAL / WEAK INVERSE / STRONG INVERSE) on 15M, 1H, 4H, and Daily - Aggregate count to identify if the correlation regime is consistent across timeframes or in transition - Spread analysis (ratio Asset1 / Asset2) with deviation from medium-term (EMA50) and long-term (EMA200) averages, plus configurable historical thresholds - Regime verdict: DIRECT / INVERSE / NEUTRAL / AMBIGUOUS, declared only when ≥3 of 4 timeframes are aligned - Pre-configured alerts for regime shifts, spread extremes, and 4H correlation crossings ═══════════════════════════════════════════════════ 🎯 USE CASES ═══════════════════════════════════════════════════ 1. Detect regime shifts: Know when historically uncorrelated assets are temporarily moving in lockstep, or vice versa. 2. Validate macro thesis: Confirm your inter-market reading with a numeric measure across multiple timeframes. 3. Identify spread extremes: Spot when asset relationships are stretched (e.g. Gold/Oil ratio above 40 historically signals macroeconomic stress). 4. Filter trade signals: Use the regime verdict as a context filter for other strategies and indicators. ═══════════════════════════════════════════════════ ⚙️ SETTINGS ═══════════════════════════════════════════════════ - Symbols: Asset 1 and Asset 2 (any TradingView symbol) - Correlation period: Default 50 candles (lower = reactive but noisy; higher = stable but lagging) - Strong/Neutral thresholds: Configurable correlation cutoffs (defaults 0.5 / 0.2) - Spread EMAs: Fast and slow EMAs applied to the asset ratio - Historical spread thresholds: Customizable per asset pair - Layout: 3 sections individually toggleable, 9 position options on the chart ═══════════════════════════════════════════════════ 📌 IMPORTANT NOTES ON INTERPRETATION ═══════════════════════════════════════════════════ - Correlation is a STATISTICAL measure, not a directional trading signal. A strong inverse correlation does not mean "go long one and short the other" — it means the two assets are moving in opposite directions over the lookback window. - Strong correlations across multiple timeframes suggest a stable regime; mixed readings across timeframes indicate transition or noise. - For Gold/WTI specifically: the historical baseline correlation is weakly DIRECT (approximately +0.3 to +0.5). Strong INVERSE readings typically reflect temporary supply-side shocks affecting only one asset (e.g. geopolitical events impacting oil supply) and tend to revert over time. - Correlation is a LAGGING indicator. A change in market regime is reflected in the rolling coefficient only after several candles. Use it for context, not for timing entries. - The "AMBIGUOUS" verdict is informative: it tells you the correlation structure is not stable, and inter-market signals should be treated with extra caution. ═══════════════════════════════════════════════════ ⚠️ WARNINGS AND DISCLAIMERS ═══════════════════════════════════════════════════ NOT FINANCIAL ADVICE This script is for educational and informational purposes only. It does not constitute financial, investment, or trading advice. Nothing in this indicator should be interpreted as a recommendation to buy, sell, or hold any financial instrument. NO GUARANTEE OF FUTURE BEHAVIOR Past correlation patterns do not guarantee future behavior. Asset relationships change over time, especially in response to macroeconomic regime shifts, central bank policy changes, geopolitical events, and structural market changes. USE AT YOUR OWN RISK Trading financial instruments (stocks, forex, crypto, futures, CFDs, commodities) carries a high level of risk and may not be suitable for all investors. Before making any trading decision you should carefully consider your investment objectives, level of experience, and risk appetite. You may sustain a loss of some or all of your initial capital. NOT A STANDALONE SYSTEM This indicator does NOT generate buy/sell signals. It provides context for macro analysis. Always combine it with your own technical analysis, fundamental analysis, and proper risk management. DO YOUR OWN RESEARCH Always cross-check signals against multiple tools and your own market analysis. Do not rely on a single indicator for trading decisions. Past performance shown in any chart or example is not indicative of future results. DATA LIMITATIONS Correlation calculations rely on accurate price data from the broker/exchange feed. Results may vary across different data providers. Volume data on forex and CFD instruments comes from individual brokers and may not reflect true market activity. NO AFFILIATION The author is not affiliated with TradingView, any broker, exchange, or financial institution. This script is independent and provided free of charge. LIABILITY The author shall not be held liable for any loss or damage, including but not limited to financial losses, arising from the use of this indicator. By using this script you accept full responsibility for your trading and investment decisions. PAPER TRADE FIRST Always test any indicator and the strategies built around it on a demo account or with paper trading before risking real capital. By using this indicator you acknowledge that you have read, understood, and accepted all the above terms. ═══════════════════════════════════════════════════ 💬 FEEDBACK ═══════════════════════════════════════════════════ This is v1.1, the first public release. Feedback is welcome — please leave a comment if you find bugs, have suggestions, or want to share your results with different asset pairs. Happy charting! 🦅 Born to fly, born to dare.
Indikator Pine Script®
oleh giua64
Cross-Asset Correlation & Cointegration Intelligence [NikaQuant] **Cross-Asset Correlation & Cointegration Intelligence** Track your chart symbol against up to six comparison symbols. The script renders **three synchronized panels** that tell you, in plain numbers: - How coupled the basket is **right now** - Which pairs are genuinely tradeable (and the **expected mean-reversion time**) - How much **gross exposure** you should carry given the current regime ## What It Does - **Intelligence Dashboard** — per-symbol grid: correlation, beta, R², z-score, percentile, stability, lead/lag, spread z, quality score, hedge size, stress-vs-normal correlation delta, signal verdict - **N×N Correlation Matrix** — full 6×6 pairwise heatmap - **Action Center** — regime timer, flip probability, risk-budget advisor, top-5 ranked trades, top-3 cointegrated pair setups, trade playbook ## Why It Is Original Unlike standard correlation heatmap scripts that display a single Pearson value per pair, this script builds a composite intelligence layer across **three independent axes** that no retail correlation indicator combines: **1. Asymmetric (Conditional) Correlation** Splits history into **normal-volatility** and **stress-volatility** regimes using an ATR-median split on the base symbol, and reports the two correlations side by side. This exposes the *"diversification fails when you need it"* amplification that an averaged Pearson value hides — a documented pattern in every crisis since 1998. **2. Cointegration + Half-Life** For all 15 unique pairs, runs an **Engle-Granger two-step** (log-regression then AR(1) on the residual spread) to flag which spreads are genuinely mean-reverting. Cointegrated pairs carry an **Ornstein-Uhlenbeck half-life** t½ = −ln(2) / ln(1 + φ) — the expected mean-reversion time in bars. *Correlation tells you direction; cointegration tells you whether the spread will revert.* **3. Regime Persistence + Flip Probability** Tracks four states (Crisis / Coupled / Mixed / Decoupled) in a **4×4 Markov transition counter**, stores per-regime dwell times, and converts them into flip-probability estimates for the next 10 and 30 bars. You see not just *"we are in X"* but *"X has lasted 47 bars, historical average is 62 bars, probability of flip in 30 bars is 55%."* ## Composite Modules - **Crisis Clock (0–100)** — composite of average absolute correlation, cross-sectional dispersion collapse, and tail-dependence count - **Market Brain** — union-find clustering on positive pairwise correlations, auto-groups symbols that move as one - **Dispersion Trade Detector** — fires when average correlation drops >2σ while realized volatility rises - **Hedge Desk** — converts OLS beta into a **dollar hedge notional** given your base position size - **Effective-N** — correlation-adjusted diversification count (six symbols at ρ=1.0 gives effective N = 1) - **Risk Budget Advisor** — regime + effective-N → suggested gross exposure percentage - **Setup Quality Score** — composite of |corr| × R² × stability, adjusted for regime, clock, and break - **Action List** — scans every symbol and every pair, scores each candidate, ranks them, surfaces the top five with type, target, score, direction, suggested size, rationale ## Per-Symbol Metrics - Rolling Pearson correlation across **three lookbacks** (short, medium, long) — three-block glyph reveals timeframe divergence - **OLS beta** from log returns, **R²** as variance explained - **Z-score** of current correlation vs its own 200-bar distribution - **Percentile rank** of current correlation in its own history - **Stability** from rolling stdev of the correlation itself - **Optimal-lag scanner** across {−5, −3, −1, 0, +1, +3, +5} offsets - **Spread z-score** of the price ratio for pairs signaling - **Asymmetric Δ** = ρ_stress − ρ_normal (positive = hedge fails under stress) ## How To Use It - **Scan the Quality column first.** Anything at or above 60 with a TRACK++ or HEDGE++ signal is a high-confidence setup. - **Cross-check AsymΔ.** Values above +0.3 mean that "hedge" is expected to fail under stress — avoid relying on it in a crisis. - **Use Hedge column values** as the dollar notional to short or long against your base position to neutralize beta. - **Read the matrix** like a portfolio risk report. Clusters of dark-green tiles = diversification is breaking down. Red tiles = inverse pairs. - **In the Action Center**, start at the Risk Budget line, then work top-down through the Action List. Cointegrated pairs marked with a check-mark prefix show expected mean-reversion time in bars. **Recommended timeframes:** intraday or daily charts with at least 250 bars of history across all six symbols. **Recommended markets:** anywhere the base asset has meaningful relationships with a benchmark basket — equity indexes vs sector ETFs, crypto majors vs index proxies, FX vs rates and commodities. **Avoid using when:** fewer than three symbols resolve to valid data; during the first 250 bars after chart load; or on a symbol with gapped or illiquid history that creates artificial correlation jumps. ## Alerts Regime Break · Dispersion Trade Setup · New Cointegrated Pair · High-Quality Setup · Imminent Regime Flip · Crisis Regime Entered · Asymmetric Correlation Amplification · Risk Budget Reduced · Clock Stressed/Critical · Strong Positive/Negative Correlation Crossovers ## Key Settings - **Comparison Symbols 1–6** — the basket (autocomplete from any TV ticker) - **Medium-Term Correlation Period** (50) — primary correlation lookback - **Short / Long Lookbacks** (20 / 200) — timeframe-divergence glyph - **Historical Baseline** (200) — z-score, percentile, stability, regime dwell times, and asymmetric-correlation ATR split - **Strong Correlation** (0.70) — threshold for strong-signal eligibility - **Regime-Break |Z|** (2.00) — flags correlations breaking their range - **Pairs-Trade |Spread Z|** (2.00) — pairs-trade setup threshold - **Min R² for Trust** (0.25), **Min Stability** (0.50) — quality gates - **Cluster Threshold** (0.60) — Market Brain grouping - **Crisis Clock** — Stressed (60), Critical (80) thresholds - **Base Position Size** (10,000) — drives Hedge Desk and Action sizing - **Min Action Quality** (60) — filters the Action List - **Risk Budget per regime** — Crisis 50%, Coupled 75%, Mixed 90%, Decoupled 100% (all user-tunable) - **Display** — position each of three tables independently; Compact or Pro column density; full palette customization ## Notes **No repainting.** All correlations, betas, and regime computations use confirmed bars only. Regime transition counters and dwell-time arrays update only when a bar confirms. **Data integrity.** Six external symbol requests are made with non-forward-looking data fetches, well within TradingView's request limit. **Methods.** Asymmetric correlation uses log returns with indicator weights from an ATR-median volatility split on the base asset. Cointegration is Engle-Granger two-step: log-regression residual, then AR(1) test. A pair is flagged as cointegrated when the AR(1) coefficient is sufficiently negative to indicate mean-reversion. Half-life uses the standard Ornstein-Uhlenbeck solution t½ = −ln(2) / ln(1 + φ). **Warm-up.** The first ~250 bars after chart load are a warm-up period. Several metrics will display "—" until enough history accumulates. **Originality.** All calculations, signal logic, clustering, cointegration testing, and table rendering are original. No third-party code is reused.
Indikator Pine Script®
oleh NikaQuant
3
Alvarium CCHoc indicatorem matricem coloratam viginti instrumentorum futurarum simultanee ostendit. Per coefficientem correlationis Pearsonianae super periodum definitam calculatum, relationem statisticam inter quodque par instrumentorum metitur et resultatum in tabula coloribus distincta in medio chartae depingit. Correlationes fortiter positivae viridi apparent, lectiones neutrales aurantiaco, et correlationes fortiter negativae rubro, cum undecim zonis coloris plene accommodabilibus praecisam potestatem visualem praebentibus. Viginti symbola singulatim per optiones indicatoris eliguntur, permittendo mercatori matricem ad quamcumque combinationem futurarum indicis aequitatis, mercium, monetae, reditus fixi, vel cryptocurrenciae aptare. Tum spatium temporis tum longitudo correlationis etiam mutabilia sunt, analysin per regimina diurna, hebdomadalia, vel intra diem permittentia. This indicator displays a 20x20 correlation heatmap for up to 20 futures instruments simultaneously. Using the Pearson correlation coefficient calculated over a user-defined lookback period, it measures the statistical relationship between each pair of instruments and renders the result as a color-coded table centered on the chart. Strong positive correlations appear in green, neutral readings in orange, and strong negative correlations in red, with 11 fully customizable color bands giving granular control over the visual representation. All 20 symbols are individually selectable via the indicator settings, allowing the trader to tailor the matrix to any combination of equity index, commodity, currency, fixed income, or cryptocurrency futures. The timeframe and correlation length are also adjustable, enabling analysis across daily, weekly, or intraday regimes.
Indikator Pine Script®
oleh AustinM18
Market Correlation Matrix [NikaQuant]Market Correlation Matrix A real-time correlation dashboard that displays Pearson correlation coefficients between the current chart symbol and up to 6 user-defined comparison symbols. ═══ FEATURES ═══ • Track correlations for up to 6 symbols simultaneously (default: BTCUSDT, GOLD, SPX, DXY, US10Y, QQQ) • Heatmap-colored table with intuitive color coding from strong positive (green) to strong negative (red) • Correlation change tracking — see how correlations are shifting over a configurable lookback period • Visual strength bars showing absolute correlation magnitude at a glance • Signal classification labels: STRONG+/-, MOD+/-, WEAK+/-, NEUTRAL • Built-in alerts for strong positive/negative crosses (±0.7) and zero-line crosses • Fully customizable: table position, text size, all colors, border styling ═══ HOW IT WORKS ═══ The indicator calculates Pearson correlation over a user-defined period (default: 50 bars) between the chart’s close price and each comparison symbol’s close price. It then renders a compact table showing: 1. Symbol — Ticker of the comparison asset 2. Corr — Current correlation value (-1 to +1) 3. Change — How much the correlation shifted vs N bars ago (with directional arrows) 4. Strength — Block-style visual bar representing absolute correlation 5. Signal — Classification from NEUTRAL to STRONG+/- ═══ SETTINGS ═══ Symbols: Configure up to 6 comparison symbols (leave blank to skip) Correlation Period: Lookback length for Pearson calculation (default: 50) Change Lookback: Compare current vs past correlation (default: 10 bars) Display: Table position, show/hide title Style: Full color customization for heatmap, headers, borders, and change indicators ═══ ALERTS ═══ • Strong positive correlation cross (above +0.7) per symbol • Strong negative correlation cross (below -0.7) per symbol • Any symbol crossing the zero line ═══ USE CASES ═══ • Monitor intermarket relationships in real time • Identify regime changes when correlations break down or strengthen • Confirm or filter trade setups using cross-asset correlation context • Track USD, bonds, equities, and crypto correlations from a single chart
Indikator Pine Script®
oleh NikaQuant
AG Pro Correlation Breakdown Map [AGPro Series]AG Pro Correlation Breakdown Map Overview / What it does AG Pro Correlation Breakdown Map is an overlay indicator designed to monitor whether a chart symbol is maintaining, weakening, breaking, or repairing its relationship with a benchmark symbol. The default benchmark in this version is Bitcoin via BINANCE:BTCUSDT, which makes the tool especially useful for crypto traders who want to understand whether an altcoin is still moving in line with BTC or beginning to decouple from it. This script does not attempt to answer whether correlation is simply high or low in isolation. Its purpose is more specific: it first checks whether a meaningful benchmark relationship existed, then evaluates whether that relationship is starting to deteriorate, whether the deterioration is becoming a confirmed breakdown, and whether the relationship is later stabilizing again. The result is a regime-style map that helps users read benchmark dependency through distinct states such as coupled, strained, breaking, broken, repairing, and recoupled. This makes the script useful for contextual analysis, benchmark-relative behavior studies, and chart review workflows where users want more than a single rolling-correlation number. Unique Edge The main difference of this script is that it is not a generic correlation line, not a spread-trading engine, and not a simple benchmark overlay. Its focus is the structure of relationship failure. Instead of only plotting short-term correlation, the script combines four layers: 1. prior relationship validation, 2. short-vs-long correlation deterioration, 3. independent price behavior, 4. persistence and repair logic. That combination is what separates a temporary wobble from a more meaningful benchmark breakdown event. This also makes the script distinct from tools that measure correlation pressure or synchronized stress. Correlation Breakdown Map is built around the question: “A relationship existed before, but is it now failing, and if so, how cleanly?” Methodology The script starts by selecting a benchmark series and transforming price data into returns. Users can choose between log returns and percent returns. A short correlation window and a long correlation window are then calculated between the chart symbol and the benchmark. The long window is used to judge whether a stable benchmark relationship has existed, while the short window is used to detect more recent deterioration. The model then evaluates the gap between long and short correlation, along with short-correlation slope behavior. A benchmark relationship is considered more vulnerable when the short window weakens materially relative to the long window and the short-correlation slope also softens. To avoid treating every statistical wobble as a true event, the script also checks for independent price behavior. This layer measures whether the chart symbol is beginning to move in a way that is meaningfully different from the benchmark over a configurable lookback period. Finally, persistence and repair conditions are applied. This allows the script to separate brief instability from a more durable breakdown state, and later identify whether the relationship is beginning to normalize again. Signals & Alerts / States This script is primarily a state-mapping tool rather than a directional buy/sell engine. The core states are: Coupled The chart symbol remains meaningfully aligned with the benchmark relationship structure. Strained The prior relationship still exists, but weakness is starting to appear. Breaking The relationship is under active deterioration and may be transitioning into a more meaningful failure. Broken The chart symbol is behaving as if benchmark linkage has materially weakened. Repairing The breakdown is no longer cleanly expanding, and the relationship may be stabilizing. Recoupled The benchmark relationship has improved enough to suggest that the prior structure is functioning again. The Breakdown Score is used as a compact summary value. It is not intended to be interpreted as a trade signal on its own. It is a regime-strength readout that helps users compare the current condition of the relationship with the underlying state labels. Key Inputs Benchmark Symbol Sets the comparison symbol. The default is BINANCE:BTCUSDT. Benchmark Timeframe Allows users to keep the benchmark on chart timeframe or compare against another timeframe. Source Selects Close, HLC3, or OHLC4 for the benchmark study. Short Correlation Length / Long Correlation Length Define the fast and slow windows used to evaluate current deterioration versus prior relationship structure. Stable Relationship Threshold Controls how strong the historical relationship must be before the script treats later weakness as a true breakdown candidate. Breakdown Threshold / Repair Threshold Control how strict the transition logic is for deterioration and recovery. Min Long/Short Correlation Gap Requires a meaningful difference between longer-term and shorter-term correlation before escalation. Independent Move Threshold Defines how much benchmark-relative price independence is required before the script treats the event as more than a statistical fluctuation. Breakdown Confirmation Bars / Repair Confirmation Bars Control persistence and confirmation sensitivity. Visual Settings Users can customize theme, visual intensity, panel font size, panel position, event visibility, trail visibility, and chart context density. Limitations & Transparency Correlation is a descriptive relationship metric, not a causal model. A relationship breakdown does not automatically imply immediate continuation, reversal, trend acceleration, or trade opportunity. It only means the chart symbol is no longer behaving as consistently relative to the selected benchmark under the current settings. Different assets, timeframes, and volatility regimes can produce different correlation behavior. A benchmark relationship that looks stable on one timeframe may be much less stable on another. Short lookbacks can react faster but may create more noise. Longer lookbacks can be more stable but slower to react. This script should be interpreted in the context of market structure, volatility, liquidity, and the chosen benchmark. It is a framework for reading relationship quality, not a guarantee engine. Risk Disclosure This indicator is for analytical and educational use. It does not provide financial advice, does not predict future price direction, and should not be used in isolation for trading decisions. Users should perform their own analysis, validate settings on the markets they follow, and apply appropriate risk management.
Indikator Pine Script®
oleh AGProLabs
1
Psych Level Mirror - Futures <-> CashPsych Level Mirror — Futures ↔ Cash See the hidden levels that move your market. Futures and cash/CFD instruments trade at different prices due to the spread (carry, funding, roll). A round number on the cash index — like NAS100 20,000 or SPX500 5,600 — doesn't land on a round number on the futures chart. But cash traders are still reacting to those levels. This indicator makes them visible. WHAT IT DOES Psych Level Mirror draws two layers of psychological price levels on your chart: Native psych levels — The round numbers of whatever you're charting (e.g. ES 5650, 5700). These are drawn as full zones with support/resistance state tracking: green when acting as support, red when resistance. Zones flip color when price breaks through and retests — exactly how institutional levels behave in practice. Mirrored companion levels — The round numbers from the other side of the pair, projected onto your chart using the live spread. On an ES chart, you'll see where SPX500 cash round numbers (5,600, 5,650, 5,700) actually sit on the futures price scale. These are marked with a dashed center line and dotted green/red borders so they're visually distinct from your native levels. WHY THIS MATTERS Large portions of the market trade the cash index (CFDs, spot, options on SPX). When NAS100 hits 20,000 or SPX500 hits 5,600, that triggers activity from cash traders — but on your NQ or ES futures chart, that level shows up at an odd number like 20,034 or 5,628. Without this indicator, you'd never know why price stalled or reversed at a seemingly random level. FEATURES Auto-detection — Drop it on any supported chart and it identifies the correct pair. Works on NQ, MNQ, ES, MES, YM, MYM, RTY, M2K, GC, MGC, and their cash/CFD counterparts (NAS100, SPX500, US30, US2000, XAUUSD). Live spread calculation — The futures-cash spread shifts throughout the session. Mirror levels update automatically as the spread moves. State tracking on all levels — Both native and mirrored zones track whether price is above (support/green) or below (resistance/red), and change color on confirmed breaks and retests. Major & minor levels — Major round numbers (e.g. every 1000 on NQ, every 100 on ES) and minor levels (half-steps) are drawn separately with different visual weight. Auto-scaling — Level intervals adjust automatically based on the instrument's price. Works correctly on everything from Russell 2000 (~2,100) to NQ (~20,000) to Gold (~3,000). Manual override — Set a fixed spread offset or force a specific pair via the dropdown if needed. Info table — Shows detection mode, chart side (futures/cash), companion symbol, live spread, and level intervals at a glance. HOW TO READ IT Colored zone (green/red box) = Native psych level — your instrument's own round numbers Dashed center line through zone = Exact round number of the native level Dashed/dotted colored line with tag = Mirrored companion level projected onto your chart Green dotted border (mirror) = Top of the mirrored zone Red dotted border (mirror) = Bottom of the mirrored zone Dark green tag box = Companion level acting as support (price above) Dark red tag box = Companion level acting as resistance (price below) SUPPORTED PAIRS NQ1! / MNQ1! ↔ NAS100 ES1! / MES1! ↔ SPX500 YM1! / MYM1! ↔ US30 RTY1! / M2K1! ↔ US2000 GC1! / MGC1! ↔ XAUUSD Custom ↔ Custom SETTINGS Instrument Pair — Auto (recommended), named preset, or Custom with your own symbols Native Psych Levels — Toggle major/minor, adjust intervals and zone widths, set number of levels above/below Mirrored Levels — Toggle on/off, major-only option Spread — Auto (live) or manual fixed offset NOTES The spread between futures and cash is not constant. It narrows into expiry and fluctuates with funding/carry. Auto mode handles this — levels shift slightly throughout the session, which is accurate to reality. Best used on intraday timeframes (1m–15m) where the spread offset matters most. On daily+ charts the spread becomes negligible relative to the level intervals. This indicator is an overlay — it works alongside your existing setup without conflict. ORIGIN This indicator is built from concepts taught by Student Alpha (Shawn) , who describes the relationship between correlated instruments with a simple but powerful idea: "The tail wags the dog." The cash index and the futures contract are tethered together, but they don't move tick-for-tick in lockstep. When the cash market reacts to a psychological round number, that reaction ripples into the futures — and vice versa. The "tail" (the correlated instrument you're not charting) wags the "dog" (the one you're trading). Understanding where those hidden levels sit on your chart gives you an edge that most traders never see. Psych Level Mirror was built to make that concept visual and automatic — no more manual math, no more guessing where the cash 20,000 lands on your NQ chart.
Indikator Pine Script®
oleh jeroenkr
AG Pro Correlation Stress Meter [AGPro Series]AG Pro Correlation Stress Meter Overview / What it does AG Pro Correlation Stress Meter is an overlay indicator designed to estimate when an instrument is becoming increasingly synchronized with a selected benchmark and whether that relationship is developing into a higher-stress market condition. Instead of treating correlation as a standalone number, this script converts multiple correlation-related components into a structured stress framework. The goal is not to predict direction. The goal is to help the user judge whether market behavior is becoming more tightly linked, more fragile, and potentially less independent than usual. The script combines smoothed rolling correlation, short-term correlation acceleration, persistence of elevated correlation, and a simple fragility layer based on price behavior versus an internal backbone EMA. The result is a normalized stress score and a state model that classifies conditions as Stable, Building, Pressured, Stressed, or Critical. Because the script is plotted directly on price, it is intended to function as a context layer. It can be used to evaluate whether a chart is trading in a relatively independent manner or whether it is increasingly behaving like a benchmark-driven instrument. Unique Edge The main difference in this script is that it does not treat correlation as a single readout. It treats correlation as a pressure structure. Many correlation tools stop at the raw coefficient. This script goes further by asking four separate questions: 1. How strong is the current relationship? 2. Is that relationship tightening or loosening? 3. Has elevated correlation persisted for long enough to matter? 4. Is price behavior becoming fragile at the same time? That combination is what makes this script different from many standard overlays, matrix-style correlation displays, or simple coefficient dashboards. It is also different from several other AG Pro scripts in the catalog. Some AG Pro tools focus on trend quality, pullback quality, squeeze behavior, reclaim structure, momentum pressure, or reaction mapping around known reference levels. This script does not focus on any of those themes. Its job is narrower and more diagnostic: it measures how much benchmark-linked stress is building inside the chart. In other words, it is less about trend or structure classification, and more about whether the instrument is becoming increasingly dependent on external benchmark behavior. Methodology The script starts with log returns for both the chart symbol and the selected benchmark symbol. A rolling correlation is then calculated over the chosen correlation window and smoothed to reduce noise. From there, the model evaluates four components: 1. Correlation strength This is the normalized level of the smoothed rolling correlation. Higher positive correlation generally contributes more to the final stress score. 2. Correlation velocity This measures how much the smoothed correlation has changed over a short lookback. A rising relationship can matter even when the absolute coefficient is not yet extreme. 3. Correlation persistence This evaluates how consistently correlation has remained above a user-defined threshold over a recent window. Short spikes and sustained linkage should not be treated as the same condition, so persistence is included as a separate layer. 4. Fragility layer This component looks at whether price is trading below the internal backbone EMA, whether short-term rate of change is weak, how stretched price is relative to the EMA, and whether ATR percentage is elevated. The purpose of this layer is not to predict reversals. Its purpose is to distinguish a calm, orderly correlation regime from a more fragile one. These components are weighted into a composite stress score, then mapped into five states: - Stable - Building - Pressured - Stressed - Critical The script also provides a backdrop layer, optional event labels, a backbone EMA for context, and a compact information panel. Signals & Alerts This script is primarily a state-classification and context tool. It is not a direct entry system and should not be interpreted as a standalone buy or sell engine. Available alert logic includes: - Stress Building - Stress Pressured - Stress Stressed - Stress Critical - Stress Cooling These alerts are designed to notify the user when the internal state model changes. They can be used to monitor regime transitions, benchmark sensitivity changes, or shifts in how tightly a symbol is tracking the selected benchmark. Practical interpretation examples: - Building may suggest that correlation-linked influence is starting to develop. - Pressured may suggest that the relationship is no longer background noise and is becoming relevant to decision-making. - Stressed may suggest that the symbol is trading with notable benchmark dependency. - Critical may suggest that benchmark-linked pressure is unusually elevated relative to the script’s internal framework. - Cooling may suggest that the prior stress state is easing. These are contextual interpretations, not trade instructions. Key Inputs Benchmark Symbol Selects the reference instrument used for the correlation calculation. Benchmark Timeframe Allows the benchmark series to follow the chart timeframe or use a different one. Correlation Length Defines the rolling window used for correlation. Correlation Smoothing Smooths the raw correlation series. Velocity Lookback Controls how quickly changes in correlation are measured. Persistence Window Defines how far back the script checks for sustained elevated correlation. Persistence Threshold Defines what the script considers “elevated” for persistence purposes. Fragility EMA Length Controls the internal backbone EMA used in the fragility layer and optional overlay line. Fragility ROC Length Defines the short-term price change measurement inside the fragility model. ATR Length Controls the volatility input used in the fragility model. Label Trigger State Sets the minimum state required before labels can appear. Minimum Bars Between Labels Reduces label clustering. Background From State Sets the minimum state required before the stress backdrop is shown. Label ATR Offset Controls how far event labels are plotted from price. Panel / Visual Inputs Allow control over panel visibility, panel position, panel theme, panel font size, label size, backdrop visibility, backbone visibility, and backbone label visibility. Limitations & Transparency This script is a contextual model, not a statement of causality. A high reading does not prove that the benchmark is causing the move. It only indicates that the symbol is trading in a way that is more tightly aligned with the selected benchmark according to the model inputs. Correlation is also regime-dependent. A symbol may appear highly linked during one period and much less linked during another. Different benchmarks, timeframes, and windows can produce different readings. The fragility layer is intentionally simple. It is included to refine the stress framework, not to replace full market structure analysis. Users who rely on this script should still examine trend structure, volatility context, liquidity conditions, and the behavior of the benchmark itself. This script also does not claim to identify tops, bottoms, crashes, breakouts, or future returns. It measures an internal definition of correlation-linked stress and presents that information visually. Risk Disclosure This indicator is for analytical and educational use. It does not provide financial advice, investment advice, or guaranteed outcomes. No indicator can remove market risk. Correlation regimes can change quickly, benchmark relationships can decouple without warning, and any model based on historical data can fail in live conditions. This tool should be used as one part of a broader chart review process, not as a substitute for independent judgment, risk management, or position sizing discipline.
Indikator Pine Script®
oleh AGProLabs
2
SMT Divergence [UAlgo]SMT Divergence is a comparative market structure indicator designed to detect disagreement between a primary instrument and a second reference symbol. The script looks for situations where the main chart prints a stronger structural move, while the comparison symbol fails to confirm that move. This kind of disagreement is often referred to as SMT divergence and is commonly used to identify potential weakness in continuation or hidden strength into reversal conditions. The script works by tracking confirmed swing highs and swing lows on both instruments. Once enough pivots are stored, it compares the latest structural movement in the primary chart against the latest comparable movement in the reference symbol. If the primary chart makes a lower low while the comparison symbol makes a higher low, the script identifies bullish SMT divergence. If the primary chart makes a higher high while the comparison symbol makes a lower high, the script identifies bearish SMT divergence. What makes this script especially practical is its flexible comparison logic. It can work in a time matched mode, where the comparison swings must occur near the same bars as the primary swings, or in an independent mode, where the script simply uses the most recent valid swings from both instruments. It also supports inverse correlation logic, allowing the user to compare instruments that normally move in opposite directions. The indicator can then display SMT labels directly on the main chart, draw structure lines between the relevant swing points, show a live information table, and optionally render the comparison symbol as candles inside the oscillator pane. This creates a complete workflow for divergence monitoring instead of only printing occasional labels. In practical use, SMT Divergence can help traders identify moments when the primary symbol appears to be making an aggressive structural move without proper confirmation from a related market. These moments can provide useful context for potential exhaustion, liquidity events, or directional imbalance between correlated instruments. 🔹 Features 🔸 Dual Symbol Market Structure Comparison The script compares the active chart against a second user selected symbol. Both instruments are processed using the same pivot logic, which creates a consistent framework for structural comparison. 🔸 Bullish SMT Detection Bullish SMT is identified when the primary chart makes a lower low while the comparison symbol makes a higher low. This can suggest hidden relative strength in the comparison instrument or possible exhaustion in the primary one. 🔸 Bearish SMT Detection Bearish SMT is identified when the primary chart makes a higher high while the comparison symbol makes a lower high. This can suggest weakening confirmation and possible vulnerability in the primary move. 🔸 Positive and Inverse Correlation Modes The script supports both normal and inverse correlation logic. In normal mode, lows are compared with lows and highs are compared with highs. In inverse mode, lows are compared with highs and highs are compared with lows. This makes the tool flexible enough for both positively correlated and negatively correlated markets. 🔸 Time Matched and Independent Swing Modes The user can choose whether swing comparison should require approximate time alignment or simply use the most recent valid swings from each symbol. This allows the script to be either stricter or more flexible depending on the relationship between the two markets. 🔸 Pivot Strength Control Swing highs and lows are built from confirmed pivots using user defined left and right bar settings. This allows the user to control how sensitive or how selective the swing structure should be. 🔸 Minimum Price Difference Filter A minimum price difference percentage can be required before a divergence is accepted. This helps avoid labeling very small and potentially insignificant swing differences. 🔸 Max Swing Memory Control The script stores a limited number of recent swings for both the primary and comparison symbol. This keeps the logic focused on relevant structure and prevents unnecessary buildup of stale pivots. 🔸 On Chart Labels and Lines Detected SMT events can be labeled directly on the main chart. The script can also draw structure lines across the primary and comparison swing legs used in the divergence. 🔸 Live Info Table An information table can display the active comparison symbol, correlation mode, divergence count, and the latest signal direction. This makes the script easier to monitor in real time. 🔸 Optional Compare Candle Panel The comparison symbol can be plotted as candles in the indicator pane. This allows the user to visually inspect whether the second instrument is confirming or rejecting the move seen in the primary chart. 🔸 Alert Support The script includes separate alerts for bullish SMT, bearish SMT, and any SMT event. 🔹 Calculations 1) Defining Swing and Divergence Objects type SwingPoint int barIdx int barTm float price bool isHigh type SMTDivergence int barIdx int barTm bool isBullish float primaryPrice float comparePrice string primarySymbol string compareSymbol label lbl line ln1 line ln2 type SymbolData float h float l float c This is the structural base of the whole script. A SwingPoint stores one confirmed pivot with its bar index, time, price, and whether it is a high or a low. An SMTDivergence object stores the final event once a divergence is confirmed. It keeps the primary and comparison prices, the involved symbols, the direction, and the visual references used for labels and lines. A SymbolData object is used as a clean container for each symbol’s high, low, and close series. So before any logic runs, the script already has a full structure for swing storage and divergence output. 2) Requesting the Comparison Symbol Data = request.security(compareSymbolInput, timeframe.period, , lookahead = barmerge.lookahead_off) SymbolData primaryData = SymbolData.new(high, low, close) SymbolData compareData = SymbolData.new(compareHigh, compareLow, compareClose) This block loads the second symbol on the same chart timeframe. The primary chart uses the current symbol’s own high, low, and close. The comparison symbol is requested with request.security , and its values are placed into a matching data structure. This means both symbols are analyzed on an equal timeframe basis, which is essential for consistent swing comparison. 3) Finding Pivot Highs and Pivot Lows on Both Symbols float primaryPivotHigh = ta.pivothigh(primaryData.h, pivotLeftBars, pivotRightBars) float primaryPivotLow = ta.pivotlow(primaryData.l, pivotLeftBars, pivotRightBars) float comparePivotHigh = ta.pivothigh(compareData.h, pivotLeftBars, pivotRightBars) float comparePivotLow = ta.pivotlow(compareData.l, pivotLeftBars, pivotRightBars) This is the swing discovery engine. The script uses the same pivot settings for both instruments. A pivot high or pivot low is only confirmed after the required number of bars has passed on each side. This means the divergence engine is built from confirmed structure, not from temporary highs and lows that can disappear before confirmation. 4) Storing Swings in Memory method addSwing(array swings, SwingPoint newSwing) => swings.push(newSwing) if swings.size() > maxSwingsToStore swings.shift() swings if not na(primaryPivotHigh) primarySwings.addSwing(SwingPoint.new(bar_index - pivotOffset, time , primaryPivotHigh, true)) if not na(primaryPivotLow) primarySwings.addSwing(SwingPoint.new(bar_index - pivotOffset, time , primaryPivotLow, false)) if not na(comparePivotHigh) compareSwings.addSwing(SwingPoint.new(bar_index - pivotOffset, time , comparePivotHigh, true)) if not na(comparePivotLow) compareSwings.addSwing(SwingPoint.new(bar_index - pivotOffset, time , comparePivotLow, false)) Once a pivot is confirmed, it is pushed into the appropriate swing array. The true pivot bar is pivotRightBars bars in the past, so the script subtracts that offset from the current bar index and time. Each array only keeps the latest user defined number of swings. So the script maintains a rolling structural memory for both markets without allowing the arrays to grow indefinitely. 5) Retrieving the Latest and Previous Swings method getLastSwing(array swings, bool isHigh) => SwingPoint result = na if swings.size() > 0 for i = swings.size() - 1 to 0 SwingPoint sw = swings.get(i) if sw.isHigh == isHigh result := sw break result method getPreviousSwing(array swings, bool isHigh) => SwingPoint result = na int count = 0 if swings.size() > 1 for i = swings.size() - 1 to 0 SwingPoint sw = swings.get(i) if sw.isHigh == isHigh count += 1 if count == 2 result := sw break result These two helper methods are used to build the swing pairs required for divergence detection. getLastSwing returns the most recent high or low swing of the requested type. getPreviousSwing returns the swing before that. This is important because SMT logic always compares two consecutive structure points on the primary side and two corresponding structure points on the comparison side. 6) Enforcing Maximum Distance Between Swing Points method isWithinRange(SwingPoint sw1, SwingPoint sw2) => not na(sw1) and not na(sw2) and math.abs(sw1.barIdx - sw2.barIdx) <= maxBarsLookback This method makes sure the two swings being compared are not too far apart in time. If the latest and previous swing are separated by more than the allowed bar distance, the setup is ignored. This helps keep the analysis focused on fresh and structurally related moves rather than comparing swings that are too old or too distant to be meaningful together. 7) Time Matched Swing Search method findSwingNearBar(array swings, int targetBar, bool isHigh, int toleranceBars) => SwingPoint result = na int minDiff = 999999 if swings.size() > 0 for i = swings.size() - 1 to 0 SwingPoint sw = swings.get(i) if sw.isHigh == isHigh int diff = math.abs(sw.barIdx - targetBar) if diff <= toleranceBars and diff < minDiff minDiff := diff result := sw result This method is used only when the user selects time matched mode. The idea is to find a comparison symbol swing that occurred near the same bar as the primary swing. The script searches for the nearest swing of the correct type inside the allowed tolerance window. So time matched mode is stricter because it requires approximate timing alignment between the two instruments. 8) Minimum Price Difference Filter method pctDiff(float price1, float price2) => math.abs(price1 - price2) / ((price1 + price2) / 2) * 100 This function measures the percentage difference between two prices. The result is later used as an optional minimum swing magnitude filter. If the primary chart’s new swing differs only slightly from its previous one, the script can ignore that divergence candidate. So the minimum difference filter helps reduce weaker signals that are based on very small structure changes. 9) Bullish SMT Detection Logic if not na(primaryPivotLow) and not na(primaryLastLow) and not na(primaryPrevLow) SwingPoint usedCompareLast = na SwingPoint usedComparePrev = na bool targetSwingHigh = inverseCorrelation ? true : false if useTimeMatching usedCompareLast := compareSwings.findSwingNearBar(primaryLastLow.barIdx, targetSwingHigh, timeToleranceBars) usedComparePrev := compareSwings.findSwingNearBar(primaryPrevLow.barIdx, targetSwingHigh, timeToleranceBars) else usedCompareLast := compareSwings.getLastSwing(targetSwingHigh) usedComparePrev := compareSwings.getPreviousSwing(targetSwingHigh) This is the setup stage for bullish SMT. The script only begins the test when a new primary pivot low has just been confirmed and both the latest and previous primary lows are available. Then it decides what kind of comparison swings are needed. In normal correlation mode, bullish SMT compares primary lows to comparison lows. In inverse correlation mode, bullish SMT compares primary lows to comparison highs. Then the script either uses time matched lookup or independent swing retrieval depending on the chosen mode. So before the bullish condition itself is tested, the script first builds the proper comparison pair according to both timing mode and correlation mode. 10) Bullish SMT Confirmation Conditions if not na(usedCompareLast) and not na(usedComparePrev) if primaryLastLow.isWithinRange(primaryPrevLow) bool primaryLL = primaryLastLow.price < primaryPrevLow.price bool compareHL = inverseCorrelation ? (usedCompareLast.price < usedComparePrev.price) : (usedCompareLast.price > usedComparePrev.price) bool meetsMinDiff = minPriceDiff == 0.0 or (primaryLastLow.price.pctDiff(primaryPrevLow.price) >= minPriceDiff) Bullish SMT is confirmed when three things happen. First, the primary chart must make a lower low: primaryLastLow.price < primaryPrevLow.price Second, the comparison symbol must fail to confirm that weakness. In normal correlation mode this means the comparison symbol makes a higher low. In inverse correlation mode the logic is adjusted accordingly because the relationship is reversed. Third, if the minimum difference filter is enabled, the primary low must differ enough from the previous low. So bullish SMT is essentially a lower low in the main chart that is not properly confirmed by the comparison market. 11) Creating the Bullish Divergence Event if primaryLL and compareHL and meetsMinDiff and primaryLastLow.barIdx != lastBullPrimaryIdx bullishSMT := true lastBullPrimaryIdx := primaryLastLow.barIdx SMTDivergence newDiv = SMTDivergence.new(primaryLastLow.barIdx, primaryLastLow.barTm, true, primaryLastLow.price, usedCompareLast.price, syminfo.tickerid, compareSymbolInput, na, na, na) newDiv.drawVisuals(primaryPrevLow.barTm, primaryPrevLow.price, usedCompareLast.barTm, usedComparePrev.barTm, usedComparePrev.price) divergences.push(newDiv) Once the bullish conditions are met, the script creates a new SMT divergence object. It stores the primary swing information, the comparison swing information, the involved symbols, and the bullish direction. Then it calls the visual drawing method and pushes the divergence into the history array. The duplicate protection check against lastBullPrimaryIdx prevents the same primary swing from being labeled repeatedly. 12) Bearish SMT Detection Logic if not na(primaryPivotHigh) and not na(primaryLastHigh) and not na(primaryPrevHigh) SwingPoint usedCompareLastH = na SwingPoint usedComparePrevH = na bool targetSwingHigh = inverseCorrelation ? false : true if useTimeMatching usedCompareLastH := compareSwings.findSwingNearBar(primaryLastHigh.barIdx, targetSwingHigh, timeToleranceBars) usedComparePrevH := compareSwings.findSwingNearBar(primaryPrevHigh.barIdx, targetSwingHigh, timeToleranceBars) else usedCompareLastH := compareSwings.getLastSwing(targetSwingHigh) usedComparePrevH := compareSwings.getPreviousSwing(targetSwingHigh) This is the mirror setup stage for bearish SMT. It begins only when a new primary pivot high has been confirmed and the required primary highs exist. Then it selects the proper comparison swing type according to the chosen correlation mode. In normal correlation mode, bearish SMT compares highs with highs. In inverse correlation mode, bearish SMT compares highs with lows. So this block prepares the correct structural pair for the bearish test. 13) Bearish SMT Confirmation Conditions if not na(usedCompareLastH) and not na(usedComparePrevH) if primaryLastHigh.isWithinRange(primaryPrevHigh) bool primaryHH = primaryLastHigh.price > primaryPrevHigh.price bool compareLH = inverseCorrelation ? (usedCompareLastH.price > usedComparePrevH.price) : (usedCompareLastH.price < usedComparePrevH.price) bool meetsMinDiff = minPriceDiff == 0.0 or (primaryLastHigh.price.pctDiff(primaryPrevHigh.price) >= minPriceDiff) Bearish SMT is confirmed when the primary chart makes a higher high while the comparison symbol fails to confirm that strength. In normal correlation mode, the comparison symbol must make a lower high. In inverse correlation mode the logic is adjusted to preserve the intended structural disagreement. The minimum difference filter is applied here as well. So bearish SMT is the opposite structure of bullish SMT, focused on unconfirmed upside continuation. 14) Creating the Bearish Divergence Event if primaryHH and compareLH and meetsMinDiff and primaryLastHigh.barIdx != lastBearPrimaryIdx bearishSMT := true lastBearPrimaryIdx := primaryLastHigh.barIdx SMTDivergence newDiv = SMTDivergence.new(primaryLastHigh.barIdx, primaryLastHigh.barTm, false, primaryLastHigh.price, usedCompareLastH.price, syminfo.tickerid, compareSymbolInput, na, na, na) newDiv.drawVisuals(primaryPrevHigh.barTm, primaryPrevHigh.price, usedCompareLastH.barTm, usedComparePrevH.barTm, usedComparePrevH.price) divergences.push(newDiv) Once the bearish rules are satisfied, the script creates a bearish SMT divergence object, draws its visuals, and stores it in the divergence history array. The duplicate protection check against lastBearPrimaryIdx prevents repeated labeling of the same primary high. 15) Drawing Labels and Lines method drawVisuals(SMTDivergence div, int prevTime, float prevPrice, int compareTime, int prevCompareTime, float prevComparePrice) => if showLabels string labelText = div.isBullish ? "🔺 SMT" : "🔻 SMT" string tooltipText = str.format("{0} SMT Divergence {1} vs {2} Price: {3}", div.isBullish ? "Bullish" : "Bearish", div.primarySymbol, div.compareSymbol, str.tostring(div.primaryPrice, format.mintick)) color labelColor = div.isBullish ? bullishColor : bearishColor div.lbl := label.new(div.barIdx, div.primaryPrice, labelText, xloc = xloc.bar_index, yloc = div.isBullish ? yloc.belowbar : yloc.abovebar, color = labelColor, textcolor = color.white, style = div.isBullish ? label.style_label_up : label.style_label_down, tooltip = tooltipText, size = size.small, force_overlay = true) if showLines and not na(prevTime) and not na(prevPrice) color lineColor = div.isBullish ? bullishColor : bearishColor div.ln1 := line.new(prevTime, prevPrice, div.barTm, div.primaryPrice, xloc = xloc.bar_time, color = lineColor, width = 2, style = line.style_solid, force_overlay = true) if not na(prevComparePrice) and not na(prevCompareTime) and not na(compareTime) div.ln2 := line.new(prevCompareTime, prevComparePrice, compareTime, div.comparePrice, xloc = xloc.bar_time, color = lineColor, width = 2, style = line.style_solid) This method creates the chart visuals for each divergence. The label is placed directly at the primary swing location, above price for bearish SMT and below price for bullish SMT. If line drawing is enabled, the script also draws one line across the primary chart’s two relevant swing points and another line across the comparison symbol’s swing leg. So the user can see both the signal marker and the underlying structural disagreement that produced it. 16) Limiting Divergence History if divergences.size() > maxDivergencesToShow SMTDivergence rmDiv = divergences.shift() rmDiv.cleanup() This block controls object history. If the stored divergence count exceeds the chosen limit, the oldest divergence is removed from the array and all its visuals are deleted. This keeps the chart clean and prevents unlimited buildup of old labels and lines. 17) Building the Information Table var table infoTable = table.new(position.top_right, 2, 6, bgcolor = color.new(#2222b3, 10), border_width = 1, border_color = color.new(color.white, 80), frame_width = 2, frame_color = color.new(color.white, 70)) if showTable and barstate.islast table.cell(infoTable, 0, 1, "Primary", text_color = color.gray, text_size = size.tiny) table.cell(infoTable, 1, 1, syminfo.tickerid, text_color = color.white, text_size = size.tiny) table.cell(infoTable, 0, 2, "Compare", text_color = color.gray, text_size = size.tiny) table.cell(infoTable, 1, 2, compareSymbolInput, text_color = color.white, text_size = size.tiny) table.cell(infoTable, 0, 3, "Correlation", text_color = color.gray, text_size = size.tiny) table.cell(infoTable, 1, 3, inverseCorrelation ? "Inverse" : "Positive", text_color = inverseCorrelation ? color.orange : color.aqua, text_size = size.tiny) table.cell(infoTable, 0, 4, "Divergences", text_color = color.gray, text_size = size.tiny) table.cell(infoTable, 1, 4, str.tostring(divergences.size()), text_color = color.white, text_size = size.tiny) This table provides a compact real time summary. It shows: the primary symbol, the comparison symbol, the current correlation mode, the number of stored divergences, and the most recent signal direction. So the table functions as a monitoring dashboard rather than only a decorative element. 18) Determining the Last Signal for the Table string lastSignal = "None" color signalColor = color.gray if divergences.size() > 0 SMTDivergence lastDiv = divergences.get(divergences.size() - 1) lastSignal := lastDiv.isBullish ? "🔺 Bullish" : "🔻 Bearish" signalColor := lastDiv.isBullish ? bullishColor : bearishColor This block determines what the table should show as the latest signal. If at least one divergence has been stored, the script reads the newest one and displays whether it was bullish or bearish, along with the corresponding color. So the info table always reflects the current state of the divergence history. 19) Alert Conditions alertcondition(bullishSMT, title = "Bullish SMT Divergence", message = "🔺 Bullish SMT Divergence detected on {{ticker}}! Primary made Lower Low while {{interval}} comparison made Higher Low.") alertcondition(bearishSMT, title = "Bearish SMT Divergence", message = "🔻 Bearish SMT Divergence detected on {{ticker}}! Primary made Higher High while {{interval}} comparison made Lower High.") alertcondition(bullishSMT or bearishSMT, title = "Any SMT Divergence", message = "SMT Divergence detected on {{ticker}}!") The script provides three alert types. One triggers only on bullish SMT. One triggers only on bearish SMT. One triggers on any SMT event. This makes the indicator useful both for visual study and for live event monitoring. 20) Compare Candle Panel color compareBodyColor = compareClose >= compareOpen ? color.new(#00E676, 0) : color.new(#FF5252, 0) color compareWickColor = compareClose >= compareOpen ? color.new(#00E676, 30) : color.new(#FF5252, 30) color compareBorderColor = compareClose >= compareOpen ? color.new(#00C853, 0) : color.new(#D50000, 0) plotcandle(compareOpen, compareHigh, compareLow, compareClose, title = "Compare Symbol Candles", color = compareBodyColor, wickcolor = compareWickColor, bordercolor = compareBorderColor, display = showCandlePanel ? display.all : display.none) This block renders the comparison symbol as candles inside the indicator pane. The candle colors are determined by the comparison symbol’s own open and close direction. If the user enables the candle panel, this gives a quick visual reference for how the second instrument is behaving without needing to open a separate chart. So the user can study SMT signals and the comparison structure in the same pane.
Indikator Pine Script®
oleh UAlgo
1
Sector Correlation Matrix [LuxAlgo]The Sector Correlation Matrix indicator provides a quantitative measure of the statistical relationship between a selected asset and a benchmark to help traders identify market synchronization and relative strength. 🔶 USAGE This tool is particularly useful for relative strength analysis. For example, if a stock is "Decoupled" while the benchmark is crashing, it may indicate idiosyncratic strength. Conversely, traders can use "Strong Positive" states to confirm that a price move is part of a broader market trend rather than an isolated event. The indicator categorizes market relationships into five distinct states based on the coefficient value: Strong Positive (> 0.7): High degree of synchronization; the assets move together closely. Moderate Positive (> 0.3): General tendency to move in the same direction. Decoupled / Neutral (-0.3 to 0.3): No significant linear relationship; the asset is moving independently. Moderate Negative (< -0.3): General tendency to move in opposite directions. Strong Negative (< -0.7): High degree of inverse synchronization. 🔶 DETAILS The script utilizes the Pearson Correlation Coefficient to calculate the rolling correlation between the current chart's closing price and a user-defined benchmark (such as SPY, QQQ, or BTC). This statistical method measures the linear correlation between two sets of data, resulting in a value between -1 (perfect inverse correlation) and 1 (perfect positive correlation). The visual output includes a dedicated pane with a correlation oscillator and an on-screen dashboard that provides real-time metrics, allowing for a quick assessment of the current market state without manually comparing multiple charts. // Core Calculation Logic correlationValue = ta.correlation(close, benchClose, corrLenInput) 🔶 SETTINGS 🔹 Logic Settings Benchmark Asset: The ticker symbol to compare the current asset against (e.g., SPY, QQQ, BTCUSDT). Correlation Length: The lookback period used to calculate the Pearson Correlation Coefficient. 🔹 Dashboard Show Dashboard: Enable or disable the on-screen information table. Position: Select the dashboard location (Top Right, Bottom Right, or Bottom Left). Size: Adjust the size of the dashboard text and cells (Tiny to Huge).
Indikator Pine Script®
oleh LuxAlgo
Correlated Sine Oscillator [LuxAlgo]The Correlated Sine Oscillator indicator identifies and isolates cyclical components within price action to generate a normalized, phase-aligned sinusoid that fluctuates between -1 and 1. 🔶 USAGE The Correlated Sine Oscillator is designed to help traders visualize the underlying rhythm of the market by projecting price data onto a mathematical sine wave. Unlike standard momentum oscillators, this tool uses quadrature components to determine the current "phase" of the market cycle, allowing the oscillator to stay synced with price swings. 🔹 Identifying Cycle Direction The oscillator fluctuates within a fixed range of -1 to 1, centered around a zero line. Signals are generated based on the crossing of this midpoint to identify shifts in the dominant cycle. Bullish Signals: When the oscillator crosses above the zero line, a bullish triangle (▲) appears below the price bar, signaling the start of a rising phase in the cycle. Bearish Signals: When the oscillator crosses below the zero line, a bearish triangle (▼) appears above the price bar, signaling the start of a falling phase in the cycle. Cycle Extremes: While signals occur at the midline, values approaching 1 or -1 represent the peak and trough of the identified cycle, respectively. 🔹 The Phase Multiplier The Phase Multiplier setting is a unique feature that controls how responsively the oscillator adapts to changes in price direction. Since the indicator uses an average of the real and imaginary components to find the phase, increasing this multiplier will result in a smoother, more stable oscillator that is less prone to noise but slightly more lagging. Lowering the multiplier makes the oscillator react faster to price shifts, which can be useful for identifying quick reversals. 🔶 DETAILS The construction of the Correlated Sine Oscillator follows a sophisticated signal processing workflow: Detrending: The script first removes the trend by subtracting a Simple Moving Average (SMA) from the closing price. This isolates the high-frequency fluctuations (cycles) around a mean of zero. Quadrature Projection: The detrended price is projected onto both a Sine and Cosine wave (real and imaginary components) based on the user-defined Cycle Period . Phase Estimation: By calculating the arctangent of these components, the script derives the "Dynamic Phase." This tells us exactly where the current price sits within the theoretical cycle. Correlation: Finally, the script generates a synthetic sine wave that is shifted by the calculated phase, ensuring the output is perfectly correlated with the dominant frequency of the price action. 🔶 SETTINGS Cycle Period: Determines the primary wavelength (in bars) that the indicator looks for in the price data. Setting this to 20 means the oscillator is tuned to find cycles that repeat every 20 bars. Phase Multiplier: A multiplier applied to the Cycle Period to determine the smoothing length of the internal phase components. A higher value leads to a "cleaner" wave, while a lower value follows price more aggressively. Bullish Color: Controls the color of the oscillator and gradient fill when the value is above zero. Bearish Color: Controls the color of the oscillator and gradient fill when the value is below zero.
Indikator Pine Script®
oleh LuxAlgo
1
Rolling Correlation & Breakdown Detector [LuxAlgo]The Rolling Correlation & Breakdown Detector indicator is a specialized "trust meter" designed for statistical arbitrage and pairs trading that monitors the stability of the relationship between two assets to identify when mean-reversion strategies are likely to fail. 🔶 USAGE This indicator functions as a decision filter rather than a standalone entry signal generator. It is intended to be used alongside spread indicators or Z-score models to determine if the underlying relationship between two assets is healthy enough to trade. The script monitors two primary factors: the raw Pearson correlation and the stability of that correlation. This helps traders avoid the "divergence trap," where a price gap looks like a mean-reversion opportunity, but the assets have actually undergone a structural regime shift. 🔹 The Three-State System The indicator categorizes the relationship into three distinct regimes: Healthy (Green): The correlation is high and stable. This is the optimal environment for statistical arbitrage trades. Warning (Orange): The correlation is weakening or becoming volatile. New trades should be approached with caution, and existing positions should be managed tightly. Breakdown (Red): The assets have "divorced." The correlation has collapsed or is moving erratically. In this state, mean-reversion signals should be ignored as the assets are no longer moving in tandem. 🔹 Statistical Arbitrage Filtering Valid Trade: Your spread indicator shows an extreme (e.g., Z-Score > 2) AND the detector is Green. Invalid Trade: Your spread indicator shows an extreme, but the detector is Red. This suggests a "regime shift" where one asset is repricing independently of the other. 🔶 DETAILS While most traders only look at the absolute correlation value, this indicator introduces the concept of Correlation Stability. A pair with a lower but stable correlation is often more tradable than a pair with high correlation that is rapidly collapsing. The script calculates the standard deviation of the correlation over a "Stability Window." If the correlation fluctuates wildly, the stability metric will spike, triggering a Warning or Breakdown state even if the raw correlation value remains relatively high. This acts as a "circuit breaker" to protect traders from macro-driven divergences or structural news events. 🔶 SETTINGS 🔹 General Settings Asset B: The secondary symbol you wish to compare against the current chart symbol. Correlation Length: The rolling lookback window used for the Pearson correlation calculation. Stability Window: The window used to measure the volatility (standard deviation) of the correlation. 🔹 Breakdown Detection Thresholds Healthy Threshold: The minimum correlation value required to be considered in a "Healthy" state. Breakdown Threshold: The level below which the relationship is considered "Broken." Stability Threshold: The maximum allowed volatility in the correlation before the state shifts to a Warning or Breakdown. 🔹 Dashboard * Dashboard: Toggles the visibility of the real-time information table. * Position/Size: Customizes the location and scale of the on-screen dashboard.
Indikator Pine Script®
oleh LuxAlgo
Cross-Market Regime Scanner [BOSWaves]Cross-Market Regime Scanner - Multi-Asset ADX Positioning with Correlation Network Visualization Overview Cross-Market Regime Scanner is a multi-asset regime monitoring system that maps directional strength and trend intensity across correlated instruments through ADX-based coordinate positioning, where asset locations dynamically reflect their current trending versus ranging state and bullish versus bearish bias. Instead of relying on isolated single-asset trend analysis or static correlation matrices, regime classification, spatial positioning, and intermarket relationship strength are determined through ADX directional movement calculation, percentile-normalized coordinate mapping, and rolling correlation network construction. This creates dynamic regime boundaries that reflect actual cross-market momentum patterns rather than arbitrary single-instrument levels - visualizing trending assets in right quadrants when ADX strength exceeds thresholds, positioning ranging assets in left quadrants during consolidation, and incorporating correlation web topology to reveal which instruments move together or diverge during regime transitions. Assets are therefore evaluated relative to ADX-derived regime coordinates and correlation network position rather than conventional isolated technical indicators. Conceptual Framework Cross-Market Regime Scanner is founded on the principle that meaningful market insights emerge from simultaneous multi-asset regime awareness rather than sequential single-instrument analysis. Traditional trend analysis examines assets individually using separate chart windows, which often obscures the broader cross-market regime structure and correlation patterns that drive coordinated moves. This framework replaces isolated-instrument logic with unified spatial positioning informed by actual ADX directional measurements and correlation relationships. Three core principles guide the design: Asset positioning should be determined by ADX-based regime coordinates that reflect trending versus ranging state and directional bias simultaneously. Spatial mapping must normalize ADX values to place assets within consistent quadrant boundaries regardless of instrument volatility characteristics. Correlation network visualization reveals which assets exhibit coordinated behavior versus divergent regime patterns during market transitions. This shifts regime analysis from isolated single-chart monitoring into unified multi-asset spatial awareness with correlation context. Theoretical Foundation The indicator combines ADX directional movement calculation, coordinate normalization methodology, quadrant-based regime classification, and rolling correlation network construction. A Wilder's smoothing implementation calculates ADX, +DI, and -DI for each monitored asset using True Range and directional movement components. The ADX value relative to a configurable threshold determines X-axis positioning (ranging versus trending), while the difference between +DI and -DI determines Y-axis positioning (bearish versus bullish). Coordinate normalization caps values within fixed boundaries for consistent quadrant placement. Pairwise correlation calculations over rolling windows populate a network graph where line thickness and opacity reflect correlation strength. Five internal systems operate in tandem: Multi-Asset ADX Engine : Computes smoothed ADX, +DI, and -DI values for up to 8 configurable instruments using Wilder's directional movement methodology. Coordinate Transformation System : Converts ADX strength and directional movement into normalized X/Y coordinates with threshold-relative scaling and boundary capping. Quadrant Classification Logic : Maps coordinate positions to four distinct regime states—Trending Bullish, Trending Bearish, Ranging Bullish, Ranging Bearish—with color-coded zones. Historical Trail Rendering : Maintains rolling position history for each asset, drawing gradient-faded trails that visualize recent regime trajectory and velocity. Correlation Network Calculator : Computes pairwise return correlations across all enabled assets, rendering weighted connection lines in circular web topology with strength-based styling. This design allows simultaneous cross-market regime awareness rather than reacting sequentially to individual instrument signals. How It Works Cross-Market Regime Scanner evaluates markets through a sequence of multi-asset spatial processes: Data Request Processing : Security function retrieves high, low, and close values for up to 8 configurable symbols with lookahead offset to ensure confirmed bar data. ADX Calculation Per Asset : True Range computed from high-low-close relationships, directional movement derived from up-moves versus down-moves, smoothed via Wilder's method over configurable period. Directional Index Derivation : +DI and -DI calculated as smoothed directional movement divided by smoothed True Range, scaled to percentage values. Coordinate Transformation : X-axis position equals (ADX - threshold) * 2, capped between -50 and +50; Y-axis position equals (+DI - -DI), capped between -50 and +50. Quadrant Assignment : Positive X indicates trending (ADX > threshold), negative X indicates ranging; positive Y indicates bullish (+DI > -DI), negative Y indicates bearish. Trail History Management : Configurable-length position history maintains recent coordinates for each asset, rendering gradient-faded lines connecting sequential positions. Velocity Vector Calculation : 7-bar coordinate change converted to directional arrow overlays showing regime momentum and trajectory. Return Correlation Processing : Bar-over-bar returns calculated for each asset, pairwise correlations computed over rolling window. Network Graph Construction : Assets positioned in circular topology, correlation lines drawn between pairs exceeding threshold with thickness/opacity scaled by correlation strength, positive correlations solid green, negative correlations dashed red. Risk Regime Scoring : Composite score aggregates bullish risk-on assets (equities, crypto, commodities) minus bullish risk-off assets (gold, dollar, VIX), generating overall market risk sentiment with colored candle overlay. Together, these elements form a continuously updating spatial regime framework anchored in multi-asset momentum reality and correlation structure. Interpretation Cross-Market Regime Scanner should be interpreted as unified spatial regime boundaries with correlation context: Top-Right Quadrant (TREND ▲) : Assets positioned here exhibit ADX above threshold with +DI exceeding -DI - confirmed bullish trending conditions with directional conviction. Bottom-Right Quadrant (TREND ▼) : Assets positioned here exhibit ADX above threshold with -DI exceeding +DI - confirmed bearish trending conditions with directional conviction. Top-Left Quadrant (RANGE ▲) : Assets positioned here exhibit ADX below threshold with +DI exceeding -DI - ranging consolidation with bullish bias but insufficient trend strength. Bottom-Left Quadrant (RANGE ▼) : Assets positioned here exhibit ADX below threshold with -DI exceeding +DI - ranging consolidation with bearish bias but insufficient trend strength. Position Trails : Gradient-faded lines connecting recent coordinate history reveal regime trajectory - curved paths indicate regime rotation, straight paths indicate sustained directional conviction. Velocity Arrows : Directional vectors overlaid on current positions show 7-bar regime momentum - arrow length indicates speed of regime change, angle indicates trajectory direction. Correlation Web : Circular network graph positioned left of main quadrant map displays pairwise asset relationships - solid green lines indicate positive correlation (moving together), dashed red lines indicate negative correlation (diverging moves), line thickness reflects correlation strength magnitude. Asset Dots : Multi-layer glow effects with color-coded markers identify each asset on both quadrant map and correlation web-symbol labels positioned adjacent to current location. Regime Summary Bar : Vertical boxes on right edge display condensed regime state for each enabled asset - box background color reflects quadrant classification, border color matches asset identifier. Risk Regime Candles : Overlay candles on price chart colored by composite risk score - green indicates risk-on dominance (bullish equities/crypto exceeding bullish safe-havens), red indicates risk-off dominance (bullish gold/dollar/VIX exceeding bullish risk assets), gray indicates neutral balance. Quadrant positioning, trail trajectory, correlation network topology, and velocity vectors outweigh isolated single-asset readings. Signal Logic & Visual Cues Cross-Market Regime Scanner presents spatial positioning insights rather than discrete entry signals: Regime Clustering : Multiple assets congregating in same quadrant suggests broad market regime consensus - all assets in TREND ▲ indicates coordinated bullish momentum across instruments. Regime Divergence : Assets splitting across opposing quadrants reveals intermarket disagreement - equities in TREND ▲ while safe-havens in TREND ▼ suggests healthy risk-on environment. Quadrant Transitions : Assets crossing quadrant boundaries mark regime shifts - movement from left (ranging) to right (trending) indicates breakout from consolidation into directional phase. Trail Curvature Patterns : Sharp curves in position trails signal rapid regime rotation, straight trails indicate sustained directional conviction, loops indicate regime uncertainty with back-and-forth oscillation. Velocity Acceleration : Long arrows indicate rapid regime change momentum, short arrows indicate stable regime persistence, arrow direction reveals whether asset moving toward trending or ranging state. Correlation Breakdown Events : Previously strong correlation lines (thick, opaque) suddenly thinning or disappearing indicates relationship decoupling - often precedes major regime transitions. Correlation Inversion Signals : Assets shifting from positive correlation (solid green) to negative correlation (dashed red) marks structural market regime change - historically correlated assets beginning to diverge. Risk Score Extremes : Composite score reaching maximum positive (all risk-on bullish, all risk-off bearish) or maximum negative (all risk-on bearish, all risk-off bullish) marks regime conviction extremes. The primary value lies in simultaneous multi-asset regime awareness and correlation pattern recognition rather than isolated timing signals. Strategy Integration Cross-Market Regime Scanner fits within macro-aware and intermarket analysis approaches: Regime-Filtered Entries : Use quadrant positioning as directional filter for primary trading instrument - favor long setups when asset in TREND ▲ quadrant, short setups in TREND ▼ quadrant. Correlation Confluence Trading : Enter positions when target asset and correlated instruments occupy same quadrant - multiple assets in TREND ▲ provides conviction for long exposure. Divergence-Based Reversal Anticipation : Monitor for regime divergence between correlated assets - if historically aligned instruments split to opposite quadrants, anticipate mean-reversion or regime rotation. Breakout Confirmation via Cross-Asset Validation : Confirm primary instrument breakouts by verifying correlated assets simultaneously transitioning from ranging to trending quadrants. Risk-On/Risk-Off Positioning : Use composite risk score and safe-haven positioning to determine overall market environment - scale risk exposure based on risk regime dominance. Velocity-Based Timing : Enter during periods of high regime velocity (long arrows) when momentum carries assets decisively into new quadrants, avoid entries during low velocity regime uncertainty. Multi-Timeframe Regime Alignment : Apply higher-timeframe regime scanner to establish macro context, use lower-timeframe price action for entry timing within aligned regime structure. Correlation Web Pattern Recognition : Identify regime transitions early by monitoring correlation network topology changes - previously disconnected assets forming strong correlations suggests regime coalescence. Technical Implementation Details Core Engine : Wilder's smoothing-based ADX calculation with separate True Range and directional movement tracking per asset Coordinate Model : Threshold-relative X-axis scaling (trending versus ranging) with directional movement differential Y-axis (bullish versus bearish) Normalization System : Boundary capping at ±50 for consistent spatial positioning regardless of instrument volatility Trail Rendering : Rolling array-based position history with gradient alpha decay and width tapering Correlation Engine : Return-based pairwise correlation calculation over rolling window with configurable lookback Network Visualization : Circular topology with trigonometric positioning, weighted line rendering based on correlation magnitude Risk Scoring : Composite calculation aggregating directional states across classified risk-on and risk-off asset categories Performance Profile : Optimized for 8 simultaneous security requests with efficient array management and conditional rendering Optimal Application Parameters Timeframe Guidance: 1 - 5 min : Micro-regime monitoring for intraday correlation shifts and short-term regime rotations 15 - 60 min : Intraday regime structure with meaningful ADX development and correlation stability 4H - Daily : Swing and position-level macro regime identification with sustained trend classification Weekly - Monthly : Long-term regime cycle tracking with structural correlation pattern evolution Suggested Baseline Configuration: ADX Period : 14 ADX Smoothing : 14 Trend Threshold : 25.0 Trail Length : 15 Correlation Period : 50 Min |Correlation| to Show Line : 0.3 Web Radius : 30 Show Quadrant Colors : Enabled Show Regime Summary Bar : Enabled Show Velocity Arrows : Enabled Show Correlation Web : Enabled These suggested parameters should be used as a baseline; their effectiveness depends on the selected assets' volatility profiles, correlation characteristics, and preferred spatial sensitivity, so fine-tuning is expected for optimal performance. Parameter Calibration Notes Use the following adjustments to refine behavior without altering the core logic: Assets clustering too tightly : Decrease Trend Threshold (e.g., 20) to spread ranging/trending separation, or increase ADX Period for smoother ADX calculation reducing noise. Assets spreading too widely : Increase Trend Threshold (e.g., 30-35) to demand stronger ADX confirmation before classifying as trending, tightening quadrant boundaries. Trail too short to show trajectory : Increase Trail Length (20-25) to visualize longer regime history, revealing sustained directional patterns. Trail too cluttered : Decrease Trail Length (8-12) for cleaner visualization focusing on recent regime state, reducing visual complexity. Unstable ADX readings : Increase ADX Period and ADX Smoothing (18-21) for heavier smoothing reducing bar-to-bar regime oscillation. Sluggish regime detection : Decrease ADX Period (10-12) for faster response to directional changes, accepting increased sensitivity to noise. Too many correlation lines : Increase Min |Correlation| threshold (0.4-0.6) to display only strongest relationships, decluttering network visualization. Missing significant correlations : Decrease Min |Correlation| threshold (0.2-0.25) to reveal weaker but potentially meaningful relationships. Correlation too volatile : Increase Correlation Period (75-100) for more stable correlation measurements, reducing network line flickering. Correlation too stale : Decrease Correlation Period (30-40) to emphasize recent correlation patterns, capturing regime-dependent relationship changes. Velocity arrows too sensitive : Modify 7-bar lookback in code to longer period (10-14) for smoother velocity representation, or increase magnitude threshold for arrow display. Adjustments should be incremental and evaluated across multiple session types rather than isolated market conditions. Performance Characteristics High Effectiveness: Macro-aware trading approaches requiring cross-market regime context for directional bias Intermarket analysis strategies monitoring correlation breakdowns and regime divergences Portfolio construction decisions requiring simultaneous multi-asset regime classification Risk management frameworks using safe-haven positioning and risk-on/risk-off scoring Trend-following systems benefiting from cross-asset regime confirmation before entry Mean-reversion strategies identifying regime extremes via clustering patterns and correlation stress Reduced Effectiveness: Single-asset focused strategies not incorporating cross-market context in decision logic High-frequency trading approaches where multi-security request latency impacts execution Markets with consistently weak correlations where network topology provides limited insight Extremely low volatility environments where ADX remains persistently below threshold for all assets Instruments with erratic or unreliable ADX characteristics producing unstable coordinate positioning Integration Guidelines Confluence : Combine with BOSWaves structure, volume analysis, or primary instrument technical indicators for entry timing within aligned regime Quadrant Respect : Trust signals occurring when primary trading asset occupies appropriate quadrant for intended trade direction Correlation Context : Prioritize setups where target asset exhibits strong correlation with instruments in same regime quadrant Divergence Awareness : Monitor for safe-haven assets moving opposite to risk assets - regime divergence validates directional conviction Velocity Confirmation : Favor entries during periods of strong regime velocity indicating decisive momentum rather than regime oscillation Risk Score Alignment : Scale position sizing and exposure based on composite risk score - larger positions during clear risk-on/risk-off environments Trail Pattern Recognition : Use trail curvature to identify regime stability (straight) versus rotation (curved) versus uncertainty (looped) Multi-Timeframe Structure : Apply higher-timeframe regime scanner for macro filter, lower-timeframe for tactical positioning within established regime Disclaimer Cross-Market Regime Scanner is a professional-grade multi-asset regime visualization and correlation analysis tool. It uses ADX-based coordinate positioning and rolling correlation calculation but does not predict future regime transitions or guarantee relationship persistence. Results depend on selected assets' characteristics, parameter configuration, correlation stability, and disciplined interpretation. Security request timing may introduce minor latency in real-time data retrieval. BOSWaves recommends deploying this indicator within a broader analytical framework that incorporates price structure, volume context, fundamental macro awareness, and comprehensive risk management.
Indikator Pine Script®
oleh BOSWaves
Volume Weighted Intra Bar CorrelationThis indicator analyzes market character by providing a detailed view of correlation. It uses data from a lower, intra-bar timeframe to separate the total correlation of a single bar into two distinct components. Key Features: 1. **Intra-Bar Correlation Decomposition:** For each bar on the chart, the indicator analyzes the underlying price action on a smaller timeframe ('Intra-Bar Timeframe') and quantifies two types of correlation: - **Between-Bar Correlation (Directional):** Calculated from price movements *between* the intra-bar candles. This component represents the **macro-movement** correlation within the main bar. - **Within-Bar Correlation (Non-Directional):** Calculated from price fluctuations *inside* each intra-bar candle. This component represents the **microstructure/noise** correlation. 2. **Visual Decomposition Logic:** Total Correlation is the primary metric displayed. Since Correlation Coefficients are not linearly additive, this indicator plots the *exact* Total Correlation and partitions the area underneath based on the Covariance Ratio. This ensures the displayed total correlation remains mathematically accurate while showing relative composition. 3. **Dual Display Modes:** The indicator offers two modes to visualize this information: - **Absolute Mode:** Plots the *total* correlation as a stacked column chart, showing the *absolute magnitude* of correlation and the contribution of each component. - **Relative Mode:** Plots the components as a 100% stacked column chart (scaled from 0 to 1), focusing purely on the *energy ratio* of 'between-bar' (macro) and 'within-bar' (micro) correlation. 4. **Calculation Options:** - **Normalization:** An optional 'Normalize' setting calculates an **Exponential Regression Curve** (log-space), making the analysis suitable for comparing assets with different scales (e.g., BTC vs EURUSD). - **Volume Weighting:** An option (`Volume weighted`) applies volume weighting to all mean and covariance calculations. 5. **Correlation Cycle Analysis:** - **Pivot Detection:** Includes a built-in pivot detector that identifies significant turning points (highs and lows) in the *total* correlation line. (Note: This is only visible in 'Absolute Mode'). - **Flexible Pivot Algorithms:** Supports various underlying mathematical models for pivot detection provided by the core library. 6. **Note on Confirmation (Lag):** Pivot signals are confirmed using a lookback method. A pivot is only plotted *after* the `Pivot Right Bars` input has passed, which introduces an inherent lag. 7. **Multi-Timeframe (MTF) Capability:** - **MTF Analysis Lines:** The entire intra-bar analysis can be run on a higher timeframe (using the `Timeframe` input), with standard options to handle gaps (`Fill Gaps`) and prevent repainting (`Wait for...`). - **Limitation:** The Pivot detection (`Calculate Pivots`) is **disabled** if a Higher Timeframe (HTF) is selected. 8. **Integrated Alerts:** Includes alerts for: - Correlation magnitude (High Positive / High Inverse). - Correlation character changes/emerging/fading. - Total Correlation pivot (High/Low) detection. **Caution: Real-Time Data Behavior (Intra-Bar Repainting)** This indicator uses high-resolution intra-bar data. As a result, the values on the **current, unclosed bar** (the real-time bar) will update dynamically as new intra-bar data arrives. This behavior is normal and necessary for this type of analysis. Signals should only be considered final **after the main chart bar has closed.** --- **DISCLAIMER** 1. **For Informational/Educational Use Only:** This indicator is provided for informational and educational purposes only. It does not constitute financial, investment, or trading advice, nor is it a recommendation to buy or sell any asset. 2. **Use at Your Own Risk:** All trading decisions you make based on the information or signals generated by this indicator are made solely at your own risk. 3. **No Guarantee of Performance:** Past performance is not an indicator of future results. The author makes no guarantee regarding the accuracy of the signals or future profitability. 4. **No Liability:** The author shall not be held liable for any financial losses or damages incurred directly or indirectly from the use of this indicator. 5. **Signals Are Not Recommendations:** The alerts and visual signals (e.g., crossovers) generated by this tool are not direct recommendations to buy or sell. They are technical observations for your own analysis and consideration.
Indikator Pine Script®
oleh AustrianTradingMachine
Volume Weighted LR CorrelationThis indicator analyzes the structural relationship between two assets by decomposing the Total Correlation into three distinct, interpretable components using a Weighted Linear Regression model and a Hybrid Copula Estimator. Key Features: 1. **Hybrid Copula Estimator:** Unlike standard correlation, which often fails on High/Low range data, this indicator fuses two metrics to ensure mathematical rigor: - **Magnitude:** Derived from Rogers-Satchell Volatility (robust to trend). - **Direction:** Derived from Log-Returns. This allows for precise correlation estimates even on intra-bar data. 2. **Three-Component Correlation Decomposition:** The indicator separates correlation based on the 'Estimate Bar Statistics' option. - **Standard Mode (`Estimate Bar Statistics` = OFF):** Calculates correlation based on the selected `Source`. - **Decomposition Mode (`Estimate Bar Statistics` = ON):** The indicator uses a statistical model ('Estimator') to calculate *within-bar* correlation. This separates the relationship into: - **Trend Correlation (Green/Red):** Correlation of the regression slopes. Indicates if assets are trending in the same direction. - **Residual Correlation (Yellow):** Correlation of the noise around the trend (Cointegration). Indicates if assets mean-revert together, even if trends differ. - **Within-Bar Correlation (Blue):** Correlation of the microstructure (intra-bar volatility). 3. **Visual Decomposition Logic:** Total Correlation is the primary metric displayed. Since Correlation Coefficients are not linearly additive, this indicator calculates the *exact* Total Correlation and partitions the area/ratios based on the additive Covariance Decomposition. This ensures the displayed total correlation remains mathematically accurate. 4. **Dual Display Modes:** The indicator offers two modes to visualize this decomposition: - **Absolute Mode:** Displays the *Total Correlation* as the main line, with the background filled by the stacked components (Trend, Residual, Within). Shows the *magnitude* of the relationship. - **Relative Mode:** Displays the **Energy Ratios** (-1.0 to 1.0) of each component using L1-Normalization. This isolates the *structure/quality* of the relationship (e.g., "Is the correlation driven by Trend or just by Noise?"). 5. **Calculation Options:** - **Normalization:** An optional 'Normalize' setting calculates an **Exponential Regression Curve** (log-space), creating a constant percentage variance environment. Essential for comparing assets with different scales (e.g., BTC vs EURUSD). - **Volume Weighting:** An option (`Volume weighted`) applies volume weighting to all regression and covariance calculations. 6. **Correlation Cycle Analysis:** - **Pivot Detection:** Includes a built-in pivot detector that identifies significant turning points (highs and lows) in the *Total Correlation* line. - **Flexible Pivot Algorithms:** Supports various underlying mathematical models for pivot detection provided by the core library. 7. **Note on Confirmation (Lag):** Pivot signals are confirmed using a lookback method. A pivot is only plotted *after* the `Pivot Right Bars` input has passed, which introduces an inherent lag. 8. **Multi-Timeframe (MTF) Capability:** - **MTF Correlation Lines:** The correlation lines can be calculated on a higher timeframe, with standard options to handle gaps (`Fill Gaps`) and prevent repainting (`Wait for...`). - **Limitation:** The Pivot detection (`Calculate Pivots`) is **disabled** if a Higher Timeframe (HTF) is selected. 9. **Integrated Alerts:** Includes comprehensive alerts for: - Correlation magnitude (High Positive / High Inverse). - Correlation character changes/emerging/fading. - Total Correlation pivot (High/Low) detection. --- **DISCLAIMER** 1. **For Informational/Educational Use Only:** This indicator is provided for informational and educational purposes only. It does not constitute financial, investment, or trading advice, nor is it a recommendation to buy or sell any asset. 2. **Use at Your Own Risk:** All trading decisions you make based on the information or signals generated by this indicator are made solely at your own risk. 3. **No Guarantee of Performance:** Past performance is not an indicator of future results. The author makes no guarantee regarding the accuracy of the signals or future profitability. 4. **No Liability:** The author shall not be held liable for any financial losses or damages incurred directly or indirectly from the use of this indicator. 5. **Signals Are Not Recommendations:** The alerts and visual signals (e.g., crossovers) generated by this tool are not direct recommendations to buy or sell. They are technical observations for your own analysis and consideration.
Indikator Pine Script®
oleh AustrianTradingMachine
Volume Weighted CorrelationThis indicator analyzes the structural relationship between two assets by decomposing the Total Correlation into two distinct, interpretable components: "Between-Bar" (Inter-Bar) and "Within-Bar" (Intra-Bar) correlation. Key Features: 1. **Hybrid Copula Estimator:** Unlike standard correlation, which often fails on High/Low range data, this indicator fuses two metrics to ensure mathematical rigor: - **Magnitude:** Derived from Rogers-Satchell Volatility. - **Direction:** Derived from Log-Returns. This allows for precise correlation estimates even on intra-bar data. 2. **Two-Component Correlation Decomposition:** The indicator separates correlation based on the 'Estimate Bar Statistics' option. - **Standard Mode (`Estimate Bar Statistics` = OFF):** Calculates correlation based on the selected `Source` (Close-to-Close). - **Decomposition Mode (`Estimate Bar Statistics` = ON):** The indicator uses a statistical model ('Estimator') to calculate *within-bar* correlation. This separates the relationship into: - **Between-Bar Correlation (Green/Red):** Correlation of the price paths (means). Indicates if the macro movements of the assets are aligned (Inter-Bar correlation). - **Within-Bar Correlation (Blue):** Correlation of the microstructure (Intra-Bar volatility/noise). 3. **Visual Decomposition Logic:** Total Correlation is the primary metric displayed. Since Correlation Coefficients are not linearly additive, this indicator calculates the *exact* Total Correlation and partitions the area/ratios based on the additive Covariance Decomposition (`CovTot = CovBtw + CovWtn`). This ensures the displayed total correlation remains mathematically accurate. 4. **Dual Display Modes:** The indicator offers two modes to visualize this decomposition: - **Absolute Mode:** Displays the *Total Correlation* as the main line, with the background filled by the stacked components (Between vs. Within). Shows the *magnitude* of the relationship. - **Relative Mode:** Displays the **Energy Ratios** (-1.0 to 1.0) of each component using L1-Normalization. This isolates the *structure/quality* of the relationship (e.g., "Is the correlation driven by price movement or just by volatility coupling?"). 5. **Calculation Options:** - **Normalization:** An optional 'Normalize' setting calculates an **Exponential Regression Curve** (log-space), creating a constant percentage variance environment. Essential for comparing assets with different scales (e.g., BTC vs EURUSD). - **Volume Weighting:** An option (`Volume weighted`) applies volume weighting to all mean and covariance calculations. 6. **Correlation Cycle Analysis:** - **Pivot Detection:** Includes a built-in pivot detector that identifies significant turning points (highs and lows) in the *Total Correlation* line. (Note: This is only visible in 'Absolute Mode'). - **Flexible Pivot Algorithms:** Supports various underlying mathematical models for pivot detection provided by the core library. 7. **Note on Confirmation (Lag):** Pivot signals are confirmed using a lookback method. A pivot is only plotted *after* the `Pivot Right Bars` input has passed, which introduces an inherent lag. 8. **Multi-Timeframe (MTF) Capability:** - **MTF Correlation Lines:** The correlation lines can be calculated on a higher timeframe, with standard options to handle gaps (`Fill Gaps`) and prevent repainting (`Wait for...`). - **Limitation:** The Pivot detection (`Calculate Pivots`) is **disabled** if a Higher Timeframe (HTF) is selected. 9. **Integrated Alerts:** Includes comprehensive alerts for: - Correlation magnitude (High Positive / High Inverse). - Character changes (Inter-Bar vs. Intra-Bar dominance). - Total Correlation pivot (High/Low) detection. --- **DISCLAIMER** 1. **For Informational/Educational Use Only:** This indicator is provided for informational and educational purposes only. It does not constitute financial, investment, or trading advice, nor is it a recommendation to buy or sell any asset. 2. **Use at Your Own Risk:** All trading decisions you make based on the information or signals generated by this indicator are made solely at your own risk. 3. **No Guarantee of Performance:** Past performance is not an indicator of future results. The author makes no guarantee regarding the accuracy of the signals or future profitability. 4. **No Liability:** The author shall not be held liable for any financial losses or damages incurred directly or indirectly from the use of this indicator. 5. **Signals Are Not Recommendations:** The alerts and visual signals (e.g., crossovers) generated by this tool are not direct recommendations to buy or sell. They are technical observations for your own analysis and consideration.
Indikator Pine Script®
oleh AustrianTradingMachine
PineStats█ OVERVIEW PineStats is a comprehensive statistical analysis library for Pine Script v6, providing 104 functions across 6 modules. Built for quantitative traders, researchers, and indicator developers who need professional-grade statistics without reinventing the wheel. For building mean-reversion strategies, analyzing return distributions, measuring correlations, or testing for market regimes. █ MODULES CORE STATISTICS (20 functions) • Central tendency: mean, median, WMA, EMA • Dispersion: variance, stdev, MAD, range • Standardization: z-score, robust z-score, normalize, percentile • Distribution shape: skewness, kurtosis PROBABILITY DISTRIBUTIONS (17 functions) • Normal: PDF, CDF, inverse CDF (quantile function) • Power-law: Hill estimator, MLE alpha, survival function • Exponential: PDF, CDF, rate estimation • Normality testing: Jarque-Bera test ENTROPY (9 functions) • Shannon entropy (information theory) • Tsallis entropy (non-extensive, fat-tail sensitive) • Permutation entropy (ordinal patterns) • Approximate entropy (regularity measure) • Entropy-based regime detection PROBABILITY (21 functions) • Win rates and expected value • First passage time estimation • TP/SL probability analysis • Conditional probability and Bayes updates • Streak and drawdown probabilities REGRESSION (19 functions) • Linear regression: slope, intercept, forecast • Goodness of fit: R², adjusted R², standard error • Statistical tests: t-statistic, p-value, significance • Trend analysis: strength, angle, acceleration • Quadratic regression CORRELATION (18 functions) • Pearson, Spearman, Kendall correlation • Covariance, beta, alpha (Jensen's) • Rolling correlation analysis • Autocorrelation and cross-correlation • Information ratio, tracking error █ QUICK START import HenriqueCentieiro/PineStats/1 as stats // Z-score for mean reversion z = stats.zscore(close, 20) // Test if returns are normally distributed returns = (close - close ) / close isGaussian = stats.is_normal(returns, 100, 0.05) // Regression channel = stats.linreg_channel(close, 50, 2.0) // Correlation with benchmark spyReturns = request.security("SPY", timeframe.period, close/close - 1) beta = stats.beta(returns, spyReturns, 60) █ USE CASES ✓ Mean Reversion — z-scores, percentiles, Bollinger-style analysis ✓ Regime Detection — entropy measures, correlation regimes ✓ Risk Analysis — drawdown probability, VaR via quantiles ✓ Strategy Evaluation — expected value, win rates, R:R analysis ✓ Distribution Analysis — normality tests, fat-tail detection ✓ Multi-Asset — beta, alpha, correlation, relative strength █ NOTES • All functions return `na` on invalid inputs • Designed for Pine Script v6 • Fully documented in the library header • Part of the Pine ecosystem: PineStats, PineQuant, PineCriticality, PineWavelet █ REFERENCES • Abramowitz & Stegun — Normal CDF approximation • Acklam's algorithm — Inverse normal CDF • Hill estimator — Power-law tail estimation • Tsallis statistics — Non-extensive entropy Full documentation in the library header. mean(src, length)   Calculates the arithmetic mean (simple moving average) over a lookback period   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 1)   Returns: Arithmetic mean of the last `length` values, or `na` if inputs invalid wma_custom(src, length)   Calculates weighted moving average with linearly decreasing weights   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 1)   Returns: Weighted moving average, or `na` if inputs invalid ema_custom(src, length)   Calculates exponential moving average   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 1)   Returns: Exponential moving average, or `na` if inputs invalid median(src, length)   Calculates the median value over a lookback period   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 1)   Returns: Median value, or `na` if inputs invalid variance(src, length)   Calculates population variance over a lookback period   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 1)   Returns: Population variance, or `na` if inputs invalid stdev(src, length)   Calculates population standard deviation over a lookback period   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 1)   Returns: Population standard deviation, or `na` if inputs invalid mad(src, length)   Calculates Median Absolute Deviation (MAD) - robust dispersion measure   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 1)   Returns: MAD value, or `na` if inputs invalid data_range(src, length)   Calculates the range (highest - lowest) over a lookback period   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 1)   Returns: Range value, or `na` if inputs invalid zscore(src, length)   Calculates z-score (number of standard deviations from mean)   Parameters:      src (float) : Source series      length (simple int) : Lookback period for mean and stdev calculation (must be >= 2)   Returns: Z-score, or `na` if inputs invalid or stdev is zero zscore_robust(src, length)   Calculates robust z-score using median and MAD (resistant to outliers)   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 2)   Returns: Robust z-score, or `na` if inputs invalid or MAD is zero normalize(src, length)   Normalizes value to range using min-max scaling   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 1)   Returns: Normalized value in , or `na` if inputs invalid or range is zero percentile(src, length)   Calculates percentile rank of current value within lookback window   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 1)   Returns: Percentile rank (0 to 100), or `na` if inputs invalid winsorize(src, length, lower_pct, upper_pct)   Winsorizes values by clamping to percentile bounds (reduces outlier impact)   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 1)      lower_pct (simple float) : Lower percentile bound (0-100, e.g., 5 for 5th percentile)      upper_pct (simple float) : Upper percentile bound (0-100, e.g., 95 for 95th percentile)   Returns: Winsorized value clamped to bounds skewness(src, length)   Calculates sample skewness (measure of distribution asymmetry)   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 3)   Returns: Skewness value (negative = left tail, positive = right tail), or `na` if invalid kurtosis(src, length)   Calculates excess kurtosis (measure of distribution tail heaviness)   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 4)   Returns: Excess kurtosis (>0 = heavy tails, <0 = light tails), or `na` if invalid count_valid(src, length)   Counts non-na values in lookback window (useful for data quality checks)   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 1)   Returns: Count of valid (non-na) values sum(src, length)   Calculates sum over lookback period   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 1)   Returns: Sum of values, or `na` if inputs invalid cumsum(src)   Calculates cumulative sum (running total from first bar)   Parameters:      src (float) : Source series   Returns: Cumulative sum change(src, length)   Returns the change (difference) from n bars ago   Parameters:      src (float) : Source series      length (simple int) : Number of bars to look back (must be >= 1)   Returns: Current value minus value from `length` bars ago roc(src, length)   Calculates Rate of Change (percentage change from n bars ago)   Parameters:      src (float) : Source series      length (simple int) : Number of bars to look back (must be >= 1)   Returns: Percentage change as decimal (0.05 = 5%), or `na` if invalid normal_pdf_standard(x)   Calculates the standard normal probability density function (PDF)   Parameters:      x (float) : The value to evaluate   Returns: PDF value at x for standard normal N(0,1) normal_pdf(x, mu, sigma)   Calculates the normal probability density function (PDF)   Parameters:      x (float) : The value to evaluate      mu (float) : Mean of the distribution (default: 0)      sigma (float) : Standard deviation (default: 1, must be > 0)   Returns: PDF value at x for normal N(mu, sigma²) normal_cdf_standard(x)   Calculates the standard normal cumulative distribution function (CDF)   Parameters:      x (float) : The value to evaluate   Returns: Probability P(X <= x) for standard normal N(0,1) @description Uses Abramowitz & Stegun approximation (formula 7.1.26), accurate to ~1.5e-7 normal_cdf(x, mu, sigma)   Calculates the normal cumulative distribution function (CDF)   Parameters:      x (float) : The value to evaluate      mu (float) : Mean of the distribution (default: 0)      sigma (float) : Standard deviation (default: 1, must be > 0)   Returns: Probability P(X <= x) for normal N(mu, sigma²) normal_inv_standard(p)   Calculates the inverse standard normal CDF (quantile function)   Parameters:      p (float) : Probability value (must be in (0, 1))   Returns: x such that P(X <= x) = p for standard normal N(0,1) @description Uses Acklam's algorithm, accurate to ~1.15e-9 normal_inv(p, mu, sigma)   Calculates the inverse normal CDF (quantile function)   Parameters:      p (float) : Probability value (must be in (0, 1))      mu (float) : Mean of the distribution      sigma (float) : Standard deviation (must be > 0)   Returns: x such that P(X <= x) = p for normal N(mu, sigma²) power_law_alpha(src, length, tail_pct)   Estimates power-law exponent (alpha) using Hill estimator   Parameters:      src (float) : Source series (typically absolute returns or drawdowns)      length (simple int) : Lookback period (must be >= 10 for reliable estimates)      tail_pct (simple float) : Percentage of data to use for tail estimation (default: 0.1 = top 10%)   Returns: Estimated alpha (tail index), typically 2-4 for financial data @description Alpha < 2 indicates infinite variance (very heavy tails) @description Alpha < 3 indicates infinite kurtosis @description Alpha > 4 suggests near-Gaussian behavior power_law_alpha_mle(src, length, x_min)   Estimates power-law alpha using maximum likelihood (Clauset method)   Parameters:      src (float) : Source series (positive values expected)      length (simple int) : Lookback period (must be >= 20)      x_min (float) : Minimum threshold for power-law behavior   Returns: Estimated alpha using MLE power_law_pdf(x, alpha, x_min)   Calculates power-law probability density (Pareto Type I)   Parameters:      x (float) : Value to evaluate (must be >= x_min)      alpha (float) : Power-law exponent (must be > 1)      x_min (float) : Minimum value / scale parameter (must be > 0)   Returns: PDF value power_law_survival(x, alpha, x_min)   Calculates power-law survival function P(X > x)   Parameters:      x (float) : Value to evaluate (must be >= x_min)      alpha (float) : Power-law exponent (must be > 1)      x_min (float) : Minimum value / scale parameter (must be > 0)   Returns: Probability of exceeding x power_law_ks(src, length, alpha, x_min)   Tests if data follows power-law using simplified Kolmogorov-Smirnov   Parameters:      src (float) : Source series      length (simple int) : Lookback period      alpha (float) : Estimated alpha from power_law_alpha()      x_min (float) : Threshold value   Returns: KS statistic (lower = better fit, typically < 0.1 for good fit) is_power_law(src, length, tail_pct, ks_threshold)   Simple test if distribution appears to follow power-law   Parameters:      src (float) : Source series      length (simple int) : Lookback period      tail_pct (simple float) : Tail percentage for alpha estimation      ks_threshold (simple float) : Maximum KS statistic for acceptance (default: 0.1)   Returns: true if KS test suggests power-law fit exp_pdf(x, lambda)   Calculates exponential probability density function   Parameters:      x (float) : Value to evaluate (must be >= 0)      lambda (float) : Rate parameter (must be > 0)   Returns: PDF value exp_cdf(x, lambda)   Calculates exponential cumulative distribution function   Parameters:      x (float) : Value to evaluate (must be >= 0)      lambda (float) : Rate parameter (must be > 0)   Returns: Probability P(X <= x) exp_lambda(src, length)   Estimates exponential rate parameter (lambda) using MLE   Parameters:      src (float) : Source series (positive values)      length (simple int) : Lookback period   Returns: Estimated lambda (1/mean) jarque_bera(src, length)   Calculates Jarque-Bera test statistic for normality   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 10)   Returns: JB statistic (higher = more deviation from normality) @description Under normality, JB ~ chi-squared(2). JB > 6 suggests non-normality at 5% level is_normal(src, length, significance)   Tests if distribution is approximately normal   Parameters:      src (float) : Source series      length (simple int) : Lookback period      significance (simple float) : Significance level (default: 0.05)   Returns: true if Jarque-Bera test does not reject normality shannon_entropy(src, length, n_bins)   Calculates Shannon entropy from a probability distribution   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 10)      n_bins (simple int) : Number of histogram bins for discretization (default: 10)   Returns: Shannon entropy in bits (log base 2) @description Higher entropy = more randomness/uncertainty, lower = more predictability shannon_entropy_norm(src, length, n_bins)   Calculates normalized Shannon entropy   Parameters:      src (float) : Source series      length (simple int) : Lookback period      n_bins (simple int) : Number of histogram bins   Returns: Normalized entropy where 0 = perfectly predictable, 1 = maximum randomness tsallis_entropy(src, length, q, n_bins)   Calculates Tsallis entropy with q-parameter   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 10)      q (float) : Entropic index (q=1 recovers Shannon entropy)      n_bins (simple int) : Number of histogram bins   Returns: Tsallis entropy value @description q < 1: emphasizes rare events (fat tails) @description q = 1: equivalent to Shannon entropy @description q > 1: emphasizes common events optimal_q(src, length)   Estimates optimal q parameter from kurtosis   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: Estimated q value that best captures the distribution's tail behavior @description Uses relationship: q ≈ (5 + kurtosis) / (3 + kurtosis) for kurtosis > 0 tsallis_q_gaussian(x, q, beta)   Calculates Tsallis q-Gaussian probability density   Parameters:      x (float) : Value to evaluate      q (float) : Tsallis q parameter (must be < 3)      beta (float) : Width parameter (inverse temperature, must be > 0)   Returns: q-Gaussian PDF value @description q=1 recovers standard Gaussian permutation_entropy(src, length, order)   Calculates permutation entropy (ordinal pattern complexity)   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 20)      order (simple int) : Embedding dimension / pattern length (2-5, default: 3)   Returns: Normalized permutation entropy @description Measures complexity of temporal ordering patterns @description 0 = perfectly predictable sequence, 1 = random approx_entropy(src, length, m, r)   Calculates Approximate Entropy (ApEn) - regularity measure   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 50)      m (simple int) : Embedding dimension (default: 2)      r (simple float) : Tolerance as fraction of stdev (default: 0.2)   Returns: Approximate entropy value (higher = more irregular/complex) @description Lower ApEn indicates more self-similarity and predictability entropy_regime(src, length, q, n_bins)   Detects market regime based on entropy level   Parameters:      src (float) : Source series (typically returns)      length (simple int) : Lookback period      q (float) : Tsallis q parameter (use optimal_q() or default 1.5)      n_bins (simple int) : Number of histogram bins   Returns: Regime indicator: -1 = trending (low entropy), 0 = transition, 1 = ranging (high entropy) entropy_risk(src, length)   Calculates entropy-based risk indicator   Parameters:      src (float) : Source series (typically returns)      length (simple int) : Lookback period   Returns: Risk score where 1 = maximum divergence from Gaussian 1 hit_rate(src, length)   Calculates hit rate (probability of positive outcome) over lookback   Parameters:      src (float) : Source series (positive values count as hits)      length (simple int) : Lookback period   Returns: Hit rate as decimal hit_rate_cond(condition, length)   Calculates hit rate for custom condition over lookback   Parameters:      condition (bool) : Boolean series (true = hit)      length (simple int) : Lookback period   Returns: Hit rate as decimal expected_value(src, length)   Calculates expected value of a series   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: Expected value (mean) expected_value_trade(win_prob, take_profit, stop_loss)   Calculates expected value for a trade with TP and SL levels   Parameters:      win_prob (float) : Probability of hitting TP (0-1)      take_profit (float) : Take profit in price units or %      stop_loss (float) : Stop loss in price units or % (positive value)   Returns: Expected value per trade @description EV = (win_prob * TP) - ((1 - win_prob) * SL) breakeven_winrate(take_profit, stop_loss)   Calculates breakeven win rate for given TP/SL ratio   Parameters:      take_profit (float) : Take profit distance      stop_loss (float) : Stop loss distance   Returns: Required win rate for breakeven (EV = 0) reward_risk_ratio(take_profit, stop_loss)   Calculates the reward-to-risk ratio   Parameters:      take_profit (float) : Take profit distance      stop_loss (float) : Stop loss distance   Returns: R:R ratio fpt_probability(src, length, target, max_bars)   Estimates probability of price reaching target within N bars   Parameters:      src (float) : Source series (typically returns)      length (simple int) : Lookback for volatility estimation      target (float) : Target move (in same units as src, e.g., % return)      max_bars (simple int) : Maximum bars to consider   Returns: Probability of reaching target within max_bars @description Based on random walk with drift approximation fpt_mean(src, length, target)   Estimates mean first passage time to target level   Parameters:      src (float) : Source series (typically returns)      length (simple int) : Lookback for volatility estimation      target (float) : Target move   Returns: Expected number of bars to reach target (can be infinite) fpt_historical(src, length, target)   Counts historical bars to reach target from each point   Parameters:      src (float) : Source series (typically price or returns)      length (simple int) : Lookback period      target (float) : Target move from each starting point   Returns: Array of first passage times (na if target not reached within lookback) tp_probability(src, length, tp_distance, sl_distance)   Estimates probability of hitting TP before SL   Parameters:      src (float) : Source series (typically returns)      length (simple int) : Lookback for estimation      tp_distance (float) : Take profit distance (positive)      sl_distance (float) : Stop loss distance (positive)   Returns: Probability of TP being hit first trade_probability(src, length, tp_pct, sl_pct)   Calculates complete trade probability and EV analysis   Parameters:      src (float) : Source series (typically returns)      length (simple int) : Lookback period      tp_pct (float) : Take profit percentage      sl_pct (float) : Stop loss percentage   Returns: Tuple: cond_prob(condition_a, condition_b, length)   Calculates conditional probability P(B|A) from historical data   Parameters:      condition_a (bool) : Condition A (the given condition)      condition_b (bool) : Condition B (the outcome)      length (simple int) : Lookback period   Returns: P(B|A) = P(A and B) / P(A) bayes_update(prior, likelihood, false_positive)   Updates probability using Bayes' theorem   Parameters:      prior (float) : Prior probability P(H)      likelihood (float) : P(E|H) - probability of evidence given hypothesis      false_positive (float) : P(E|~H) - probability of evidence given hypothesis is false   Returns: Posterior probability P(H|E) streak_prob(win_rate, streak_length)   Calculates probability of N consecutive wins given win rate   Parameters:      win_rate (float) : Single-trade win probability      streak_length (simple int) : Number of consecutive wins   Returns: Probability of streak losing_streak_prob(win_rate, streak_length)   Calculates probability of experiencing N consecutive losses   Parameters:      win_rate (float) : Single-trade win probability      streak_length (simple int) : Number of consecutive losses   Returns: Probability of losing streak drawdown_prob(src, length, dd_threshold)   Estimates probability of drawdown exceeding threshold   Parameters:      src (float) : Source series (returns)      length (simple int) : Lookback period      dd_threshold (float) : Drawdown threshold (as positive decimal, e.g., 0.10 = 10%)   Returns: Historical probability of exceeding drawdown threshold prob_to_odds(prob)   Calculates odds from probability   Parameters:      prob (float) : Probability (0-1)   Returns: Odds (prob / (1 - prob)) odds_to_prob(odds)   Calculates probability from odds   Parameters:      odds (float) : Odds ratio   Returns: Probability (0-1) implied_prob(decimal_odds)   Calculates implied probability from decimal odds (betting)   Parameters:      decimal_odds (float) : Decimal odds (e.g., 2.5 means $2.50 return per $1 bet)   Returns: Implied probability logit(prob)   Calculates log-odds (logit) from probability   Parameters:      prob (float) : Probability (must be in (0, 1))   Returns: Log-odds inv_logit(log_odds)   Calculates probability from log-odds (inverse logit / sigmoid)   Parameters:      log_odds (float) : Log-odds value   Returns: Probability (0-1) linreg_slope(src, length)   Calculates linear regression slope   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 2)   Returns: Slope coefficient (change per bar) linreg_intercept(src, length)   Calculates linear regression intercept   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 2)   Returns: Intercept (predicted value at oldest bar in window) linreg_value(src, length)   Calculates predicted value at current bar using linear regression   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: Predicted value at current bar (end of regression line) linreg_forecast(src, length, offset)   Forecasts value N bars ahead using linear regression   Parameters:      src (float) : Source series      length (simple int) : Lookback period for regression      offset (simple int) : Bars ahead to forecast (positive = future)   Returns: Forecasted value linreg_channel(src, length, mult)   Calculates linear regression channel with bands   Parameters:      src (float) : Source series      length (simple int) : Lookback period      mult (simple float) : Standard deviation multiplier for bands   Returns: Tuple: r_squared(src, length)   Calculates R-squared (coefficient of determination)   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: R² value where 1 = perfect linear fit adj_r_squared(src, length)   Calculates adjusted R-squared (accounts for sample size)   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: Adjusted R² value std_error(src, length)   Calculates standard error of estimate (residual standard deviation)   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: Standard error residual(src, length)   Calculates residual at current bar   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: Residual (actual - predicted) residuals(src, length)   Returns array of all residuals in lookback window   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: Array of residuals t_statistic(src, length)   Calculates t-statistic for slope coefficient   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: T-statistic (slope / standard error of slope) slope_pvalue(src, length)   Approximates p-value for slope t-test (two-tailed)   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: Approximate p-value is_significant(src, length, alpha)   Tests if regression slope is statistically significant   Parameters:      src (float) : Source series      length (simple int) : Lookback period      alpha (simple float) : Significance level (default: 0.05)   Returns: true if slope is significant at alpha level trend_strength(src, length)   Calculates normalized trend strength based on R² and slope   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: Trend strength where sign indicates direction trend_angle(src, length)   Calculates trend angle in degrees   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: Angle in degrees (positive = uptrend, negative = downtrend) linreg_acceleration(src, length)   Calculates trend acceleration (second derivative)   Parameters:      src (float) : Source series      length (simple int) : Lookback period for each regression   Returns: Acceleration (change in slope) linreg_deviation(src, length)   Calculates deviation from regression line in standard error units   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: Deviation in standard error units (like z-score) quadreg_coefficients(src, length)   Fits quadratic regression and returns coefficients   Parameters:      src (float) : Source series      length (simple int) : Lookback period (must be >= 4)   Returns: Tuple: for y = a*x² + b*x + c quadreg_value(src, length)   Calculates quadratic regression value at current bar   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: Predicted value from quadratic fit correlation(x, y, length)   Calculates Pearson correlation coefficient between two series   Parameters:      x (float) : First series      y (float) : Second series      length (simple int) : Lookback period (must be >= 3)   Returns: Correlation coefficient covariance(x, y, length)   Calculates sample covariance between two series   Parameters:      x (float) : First series      y (float) : Second series      length (simple int) : Lookback period (must be >= 2)   Returns: Covariance value beta(asset, benchmark, length)   Calculates beta coefficient (slope of regression of y on x)   Parameters:      asset (float) : Asset returns series      benchmark (float) : Benchmark returns series      length (simple int) : Lookback period   Returns: Beta coefficient @description Beta = Cov(asset, benchmark) / Var(benchmark) alpha(asset, benchmark, length, risk_free)   Calculates alpha (Jensen's alpha / intercept)   Parameters:      asset (float) : Asset returns series      benchmark (float) : Benchmark returns series      length (simple int) : Lookback period      risk_free (float) : Risk-free rate (default: 0)   Returns: Alpha value (excess return not explained by beta) spearman(x, y, length)   Calculates Spearman rank correlation coefficient   Parameters:      x (float) : First series      y (float) : Second series      length (simple int) : Lookback period (must be >= 3)   Returns: Spearman correlation @description More robust to outliers than Pearson correlation kendall_tau(x, y, length)   Calculates Kendall's tau rank correlation (simplified)   Parameters:      x (float) : First series      y (float) : Second series      length (simple int) : Lookback period (must be >= 3)   Returns: Kendall's tau correlation_change(x, y, length, change_period)   Calculates change in correlation over time   Parameters:      x (float) : First series      y (float) : Second series      length (simple int) : Lookback period for correlation      change_period (simple int) : Period over which to measure change   Returns: Change in correlation correlation_regime(x, y, length, ma_length)   Detects correlation regime based on level and stability   Parameters:      x (float) : First series      y (float) : Second series      length (simple int) : Lookback period for correlation      ma_length (simple int) : Moving average length for smoothing   Returns: Regime: -1 = negative, 0 = uncorrelated, 1 = positive correlation_stability(x, y, length, stability_length)   Calculates correlation stability (inverse of volatility)   Parameters:      x (float) : First series      y (float) : Second series      length (simple int) : Lookback for correlation      stability_length (simple int) : Lookback for stability calculation   Returns: Stability score where 1 = perfectly stable relative_strength(asset, benchmark, length)   Calculates relative strength of asset vs benchmark   Parameters:      asset (float) : Asset price series      benchmark (float) : Benchmark price series      length (simple int) : Smoothing period   Returns: Relative strength ratio (normalized) tracking_error(asset, benchmark, length)   Calculates tracking error (standard deviation of excess returns)   Parameters:      asset (float) : Asset returns      benchmark (float) : Benchmark returns      length (simple int) : Lookback period   Returns: Tracking error (annualize by multiplying by sqrt(252) for daily data) information_ratio(asset, benchmark, length)   Calculates information ratio (risk-adjusted excess return)   Parameters:      asset (float) : Asset returns      benchmark (float) : Benchmark returns      length (simple int) : Lookback period   Returns: Information ratio capture_ratio(asset, benchmark, length, up_capture)   Calculates up/down capture ratio   Parameters:      asset (float) : Asset returns      benchmark (float) : Benchmark returns      length (simple int) : Lookback period      up_capture (simple bool) : If true, calculate up capture; if false, down capture   Returns: Capture ratio autocorrelation(src, length, lag)   Calculates autocorrelation at specified lag   Parameters:      src (float) : Source series      length (simple int) : Lookback period      lag (simple int) : Lag for autocorrelation (default: 1)   Returns: Autocorrelation at specified lag partial_autocorr(src, length)   Calculates partial autocorrelation at lag 1   Parameters:      src (float) : Source series      length (simple int) : Lookback period   Returns: PACF at lag 1 (equals ACF at lag 1) autocorr_test(src, length, max_lag)   Tests for significant autocorrelation (Ljung-Box inspired)   Parameters:      src (float) : Source series      length (simple int) : Lookback period      max_lag (simple int) : Maximum lag to test   Returns: Sum of squared autocorrelations (higher = more autocorrelation) cross_correlation(x, y, length, lag)   Calculates cross-correlation at specified lag   Parameters:      x (float) : First series      y (float) : Second series (lagged)      length (simple int) : Lookback period      lag (simple int) : Lag to apply to y (positive = y leads x)   Returns: Cross-correlation at specified lag cross_correlation_peak(x, y, length, max_lag)   Finds lag with maximum cross-correlation   Parameters:      x (float) : First series      y (float) : Second series      length (simple int) : Lookback period      max_lag (simple int) : Maximum lag to search (both directions)   Returns: Tuple:
Perpustakaan Pine Script®
oleh HenriqueCentieiro
1
Gold Inverse Correlation TrackerGold Inverse Correlation Tracker - Professional Multi-Asset Analysis What This Indicator Does: This indicator monitors the real-time correlation between Gold and five key financial assets that historically move inversely (opposite) to gold prices. It displays these relationships across three different timeframes simultaneously, giving you both short-term trading signals and long-term trend confirmation. The indicator tracks: US Dollar Index (DXY) - Historical correlation: -0.63 Real Interest Rates (TIPS) - Historical correlation: -0.82 (strongest inverse relationship) 10-Year Treasury Yield - Nominal interest rate proxy S&P 500 (SPX) - Equity market sentiment (variable correlation) VIX - Volatility index (optional, flight-to-safety indicator) Why Inverse Correlations Matter for Gold Trading: Understanding inverse correlations is critical for gold traders because: Predictive Power - When assets move opposite to gold consistently, you can use their strength/weakness to predict gold's next move Hedging Opportunities - Strong inverse correlations let you hedge gold positions by trading the inverse asset Regime Detection - When correlations break down, it signals a market regime change or increased uncertainty Confirmation Signals - Multiple strong inverse correlations validate your gold trade thesis Risk Management - Knowing what moves against gold helps you understand your portfolio's true exposure The Science Behind the Numbers: Real interest rates have the strongest inverse correlation to gold (approximately -0.82) because: Gold pays no yield or dividend When real rates rise, the opportunity cost of holding gold increases Investors shift to interest-bearing assets when they offer positive real returns When real rates go negative, gold becomes relatively more attractive The US Dollar shows strong inverse correlation (approximately -0.63) because: Gold is priced in US dollars globally A stronger dollar makes gold more expensive for foreign buyers, reducing demand A weaker dollar makes gold cheaper internationally, increasing demand Both compete as reserve assets and stores of value Why the Indicator is Weighted This Way: Three Timeframe Approach: Short-term (20 periods) - Captures recent correlation shifts for day trading and swing trading Medium-term (50 periods) - The primary signal - balances noise reduction with responsiveness Long-term (100 periods) - Confirms structural correlation trends for position trading Correlation Thresholds: Strong Inverse (<-0.7) - Statistically significant inverse relationship; highest confidence for inverse trades Moderate Inverse (<-0.3) - Meaningful inverse relationship; still useful but less reliable Weak Inverse (<0.0) - Slight inverse tendency; correlation may be breaking down Positive (>0.0) - Assets moving together; inverse relationship has failed How to Use This Indicator: For Inverse Trading Strategies: When DXY shows RED correlation (<-0.7), consider shorting DXY when gold is strong When Real Rates show RED correlation, rising rates = falling gold (and vice versa) When multiple assets show strong inverse correlation, confidence is highest For Regime Detection: All RED = Classic gold market behavior; correlations intact Mixed colors = Transitional market; be cautious All GREEN/GRAY = Correlation breakdown; paradigm shift occurring For Hedging: Use assets with strong inverse correlation to hedge gold positions When correlation weakens, reduce hedge size When correlation strengthens, increase hedge effectiveness Alert System: The indicator includes built-in alerts for: Individual assets crossing strong inverse threshold Multiple assets simultaneously showing strong inverse correlation (highest probability setup) Correlation breakdowns that may signal regime changes Color Guide: RED - Strong inverse correlation (<-0.7) - Best inverse trading opportunity ORANGE - Moderate inverse (<-0.3) - Useful but less reliable YELLOW - Weak inverse (<0.0) - Correlation weakening GRAY - Weak positive (0.0 to 0.7) - Assets moving together GREEN - Strong positive (>0.7) - Inverse relationship broken Recommended Settings: Day Trading (1H-4H charts): Short: 14 periods Medium: 30 periods Long: 60 periods Swing Trading (Daily charts): Short: 20 periods (default) Medium: 50 periods (default) Long: 100 periods (default) Position Trading (Weekly charts): Short: 10 periods Medium: 20 periods Long: 50 periods Pro Tips: Watch for divergences - when gold moves but correlations don't confirm Correlation breakdowns often precede major trend reversals The Medium-term (50p) correlation is plotted on the chart as your primary reference Use the Status column for quick assessment of each asset's relationship Set alerts for "Multiple Strong Inverse" to catch highest-probability setups Important Notes: This indicator is designed for Gold charts only (XAUUSD, GLD, GC1!, etc.) Correlations are not static - they change over time based on market conditions A correlation of -0.82 means 82% of gold's price movements can be explained by real interest rates Always combine with other technical analysis and fundamental factors Past correlations do not guarantee future relationships Based on Research: The correlation coefficients used in this indicator are based on peer-reviewed research: Erb & Harvey (1997-2012): Real rates to gold correlation of -0.82 World Gold Council (2024): US Dollar to gold correlation of -0.63 Multiple academic studies confirming gold's inverse relationship with opportunity cost assets Use this indicator to trade smarter, hedge better, and understand the macro forces driving gold prices.
Indikator Pine Script®
oleh bigcitytom
Gold Projection DivergenceGOLD PROJECTION DIVERGENCE Oscillator Companion for the Gold Macro Projection Model OVERVIEW The Gold Projection Divergence oscillator quantifies how far gold is trading from its projected fair value. While the main indicator shows where gold should be, this oscillator shows how extreme the mispricing is—providing precise timing signals for entries and exits. HOW IT WORKS The oscillator calculates the difference between actual gold price and the projected value, then normalizes it as a Z-score . This statistical measure shows how many standard deviations gold is trading away from its projected fair value. Z > +2 — Gold is 2+ standard deviations above fair value (extremely overvalued) Z > +1 — Gold is moderately overvalued Z = 0 — Gold is trading at projected fair value Z < -1 — Gold is moderately undervalued Z < -2 — Gold is 2+ standard deviations below fair value (extremely undervalued) VISUAL ELEMENTS Histogram — Color-coded divergence magnitude Yellow Line — Smoothed Z-score Dashed Lines — +2 and -2 standard deviation levels Dotted Lines — +1 and -1 standard deviation levels Triangle Markers — Extreme crossover signals Circle Markers — Zero-line crossings HISTOGRAM COLORS Dark Red — Z > +2 (extreme overvaluation) Orange — Z between +1 and +2 Light Orange — Z between 0 and +1 Light Green — Z between -1 and 0 Green — Z between -2 and -1 Lime — Z < -2 (extreme undervaluation) COMPONENT TABLE The breakdown table shows divergence from each individual factor: Silver — Is gold over/undervalued relative to silver? M2 — Is gold over/undervalued relative to money supply? DXY — Is gold over/undervalued relative to dollar strength? Equity — Is gold over/undervalued relative to stocks? TIPS — Is gold over/undervalued relative to real rates? TRADING APPLICATIONS Mean Reversion Strategy Enter LONG when Z < -2 and begins rising Enter SHORT when Z > +2 and begins falling Use zero-line crossings for trend confirmation Trend Following Filter Only take long trades when Z < 0 (undervalued) Only take short trades when Z > 0 (overvalued) Divergence Confirmation Bearish: Price makes new highs while Z-score makes lower highs Bullish: Price makes new lows while Z-score makes higher lows ALERTS Extreme Undervaluation — Z crosses below -2 Extreme Overvaluation — Z crosses above +2 Moderate Undervaluation — Z crosses below -1 Moderate Overvaluation — Z crosses above +1 Divergence Turned Positive — Crossed above zero Divergence Turned Negative — Crossed below zero COMBINED USAGE For best results, use both indicators together : Main Indicator — Visual context of actual vs. projected on price chart Divergence Oscillator — Precise measurement for timing decisions The main indicator shows where gold should be; the oscillator shows how extreme the mispricing is and when to act. Disclaimer: This indicator is for educational purposes only. Past correlations do not guarantee future relationships. Market conditions can alter historical relationships. Always use proper risk management.
Indikator Pine Script®
oleh bigcitytom
Gold Macro Projection ModelGOLD MACRO PROJECTION MODEL Multi-Factor Fair Value Estimation for Gold OVERVIEW The Gold Macro Projection Model estimates gold's fair value based on its historical relationships with key macroeconomic drivers. By synthesizing data from silver , M2 money supply , the US Dollar Index , TIPS (real rates proxy) , and major equity indices , this indicator projects where gold should theoretically be trading—helping traders identify potential overvaluation and undervaluation conditions. HOW IT WORKS This indicator employs three complementary projection methodologies : Correlation-Weighted Z-Score Composite (50% weight) Calculates rolling correlations between gold and each input factor. Factors with stronger correlations receive more influence. Each factor is normalized to a z-score, combined into a composite, then converted back to gold's price scale. Silver/Gold Ratio Mean Reversion (35% weight) The silver/gold ratio historically exhibits mean-reverting behavior. This component projects gold's implied price based on current silver prices and the historical average ratio. M2 Money Supply Relationship (15% weight) Gold tracks monetary expansion over long time horizons. This anchors the projection to the fundamental relationship between gold and the monetary base. INPUT FACTORS Silver — Strong positive correlation; precious metals move together M2 Money Supply — Positive correlation; gold as inflation hedge US Dollar Index (DXY) — Typically negative correlation; inverse relationship TIPS ETF — Real interest rate proxy; gold responds to real yields Equity Indices — Variable correlation; risk-on/risk-off dynamics VISUAL ELEMENTS Yellow Line — Actual gold price Aqua Line — Projected fair value Green Fill — Gold trading below projection (potentially undervalued) Red Fill — Gold trading above projection (potentially overvalued) Aqua Bands — Standard deviation envelope around projection INFO TABLE The indicator displays a real-time information panel showing: Current actual vs. projected price Divergence percentage and Z-score Rolling correlations for each factor Dynamic weight allocation Buy/Sell signal based on divergence extremes SIGNAL INTERPRETATION STRONG BUY — Z-score below -2 (extremely undervalued) BUY — Z-score between -2 and -1 (moderately undervalued) NEUTRAL — Z-score between -1 and +1 (fairly valued) SELL — Z-score between +1 and +2 (moderately overvalued) STRONG SELL — Z-score above +2 (extremely overvalued) SETTINGS Correlation Period — Lookback for correlation calculations (default: 60) Regression Period — Lookback for mean/standard deviation (default: 120) Smoothing Period — EMA smoothing for projection line (default: 10) Auto Weights — Toggle between correlation-based or manual weights Band Multiplier — Standard deviation multiplier for bands (default: 1.5) ALERTS Gold Extremely Undervalued — Z crosses below -2 Gold Extremely Overvalued — Z crosses above +2 Gold Crossed Above Projection Gold Crossed Below Projection BEST PRACTICES Use on daily timeframe for most reliable signals Combine with the companion Gold Divergence Oscillator for timing Disclaimer: This indicator is for educational purposes only. Past correlations do not guarantee future relationships. Always use proper risk management.
Indikator Pine Script®
oleh bigcitytom