STRX - AutoCorrelationSTRX – AutoCorrelation is a quantitative analysis tool designed to monitor real‑time correlations between up to five assets, using the Pearson correlation coefficient over a customizable period and timeframe. The script displays a correlation matrix as a heatmap table together with a structured textual reading, providing an immediate operational view of the strength and direction of relationships across assets.
The “Assets to Correlate” panel allows the user to select up to five instruments (for example Gold, US100, USDX, Silver, Platinum) and to enable or disable each slot individually, so the correlation set can be quickly adapted to different market contexts. Through the “Settings” panel, the user can define an independent calculation timeframe, the lookback period for the correlation, whether to show the textual reading table, and the position of the panel on the chart.
The matrix highlights, for each pair, the normalized correlation value between −1 and +1, with a color scheme that distinguishes positive and negative correlations and emphasizes their intensity. The textual reading table classifies each relationship as “Strong”, “Moderate”, “Weak” or “Negligible” and specifies the sign (“Positive” / “Negative” / “Independent moves”), helping the user assess co‑movements, diversification, hedging behavior and potential concentration of risk across the selected assets.
This indicator is intended as a decision‑support tool for multi‑asset analysis and does not generate standalone entry or exit signals. It does not constitute financial advice or a guarantee of performance; any trading or investment decisions remain solely the responsibility of the user. Indicator
Intermarket Divergence with Reliability ScoringIntermarket Divergence with Reliability Scoring
Related markets tend to move together. When your chart pushes to a new high but a market that usually tracks it does not confirm, that non-confirmation can precede a turn. This script measures divergence between your chart and a chosen leader market — but only when the leader is actually correlated to price, because a decoupled market's "divergence" is meaningless — and then scores, in real time, whether those divergences have been worth trading on your symbol.
WHAT IT PLOTS
A normalised oscillator built from a chosen leader market, its stretch bands, divergence markers and connecting pivot lines, plus a plain-language verdict panel. By default the chart stays minimal — the oscillator and the verdict panel — with optional key-info and per-class tables you can switch on in settings.
WHY THESE COMPONENTS ARE COMBINED, AND HOW THEY WORK TOGETHER
Three classical pieces are fused into one pipeline, not stacked as independent signals:
A z-scored leader series — optionally inverted, optionally lead-lag shifted — the cross-market line your price is compared against.
A live correlation gate — the leader only votes when its trailing correlation to price clears a threshold, so non-confirmations from a decoupled market are ignored.
A binomial-proportion confidence test — asks, for each divergence class, whether the expected move followed more often than a same-zone baseline would deliver.
Part 1 builds the comparison, part 2 keeps only the meaningful divergences, part 3 decides whether the survivors have actually paid on this instrument, by direction. The parts are interdependent — remove any one and the script can no longer answer its core question: is this intermarket divergence worth trading here, and which way?
HOW TO READ IT
The verdict panel translates the statistics into four states:
GREEN, edge confirmed: these divergences have beaten a same-spot baseline here. Worth acting on.
RED, no edge here: they have lost to a coin-flip. Skip them, pick a leader that genuinely leads, or change the timeframe.
AMBER, unproven: edge not statistically established yet; treat as low-confidence.
GREY, learning: still collecting completed samples.
It also shows:
Best signal — names a direction only once that direction is individually proven (its edge lower-bound clears zero). Otherwise it says "none confirmed", so you are never lured by a single noisy number.
Leader link — how tightly the leader currently tracks price (strong, moderate or weak). A weak link means the leader has decoupled and its divergences are unreliable.
Reward : risk — the average best move versus the average worst move after a signal, in ATR. Below 1:1 means signals have hurt more than they helped.
Market regime — reverting markets suit divergence; strong trends punish it.
A divergence is simply price making a higher high or lower low while the leader line does the opposite, counted only when the leader is correlated enough to matter.
CHOOSING A LEADER
Pick a market that genuinely leads — a currency, a global risk proxy, a sector index — not one that merely co-moves with your chart. The lead-lag offset lets you test whether it leads. A co-moving index will usually show no edge, which the panel will tell you plainly.
WHAT IS ORIGINAL
Standard intermarket tools just overlay two symbols. This one fuses a live correlation gate into the divergence filter, then keeps a self-updating, confidence-scored, per-class track record against a same-zone baseline — so you see not just that a non-confirmation printed, but whether and how it has paid, and you only trust a direction once it is statistically proven.
UNIVERSAL ACROSS MARKETS
Reads the chart's own price (the source is configurable in settings) plus one leader symbol. Defaults target NIFTY futures with Bank Nifty as the leader. Change the leader to USD/INR (inverse), a sector or overseas index, or any related market for other instruments, in any market.
OUTPUTS FOR OTHER SCRIPTS
Generic EXP_ values — oscillator, signal, probability, edge, edge lower-bound, sample count, regime, leader value and leader correlation — are published to the Data Window so other indicators can read them via input.source().
CONCEPT CREDITS
Average true range — J. W. Wilder. Binomial score confidence interval — E. B. Wilson. Trend-efficiency regime measure — P. Kaufman. Intermarket analysis and divergence — classical technical analysis.
DISCLAIMER
For research and education only. This is not financial advice, not a recommendation to buy or sell, and not a guarantee of future results. All performance shown is in-sample and past-only. Markets carry risk — do your own research and manage your own risk. Indicator
Gold Macro Dashboard [invincible3]Gold Macro Indicator Dashboard
A professional macro-driven gold dashboard designed to evaluate the broader gold market regime using automatically sourced PulseWire data. The indicator combines real yields, dollar strength, rate expectations, risk-off demand, gold breadth, and confirmation ratios into a single 0–100 Gold Macro Score.
The model uses a fixed daily macro timeframe, so dashboard readings stay consistent across intraday, daily, and weekly charts.
Main Features
Fixed Daily macro scoring
0–100 Gold Macro Score oscillator
Macro Regime classification
Macro Strength score
Real Yield driver
DXY / US Dollar driver
Gold liquidity proxy
US 2Y rate outlook
VIX risk-off signal
Cross-currency gold breadth
Gold/Silver ratio
Gold/S&P 500 ratio
Copper/Gold ratio
US 10Y–2Y yield spread
Crypto-style clean dashboard layout
Dark/light theme adaptive colors
No manual macro inputs
Score Interpretation
80–100: Strong Bull
60–80: Bullish
40–60: Neutral
20–40: Bearish
0–20: Strong Bear
How It Works
The composite score is weighted as follows:
Real Yield 10Y: 30%
US Dollar DXY: 25%
Gold liquidity proxy: 15%
US 2Y rate outlook: 10%
Risk-Off VIX: 10%
Gold breadth: 10%
Gold breadth checks whether gold is trending higher across major currencies, including XAUUSD, XAUEUR, XAUJPY, XAUGBP, and XAUCNH.
Use Case
This indicator is designed for traders and investors who want a macro-level view of gold’s trend quality. It can help identify whether gold strength is supported by broad macro conditions or only short-term price movement.
Disclaimer
This is an educational macro model only. It is not financial advice and should not be used as a standalone buy or sell signal. Always combine it with your own risk management, technical analysis, and market research. Indicator
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 PulseWire.
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 PulseWire 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.
Indicator
10Y Yield Spread - Auto FX Pair10Y Yield Spread — Auto FX Pair
Automatically displays the 10-year government bond yield spread for the FX pair currently on your chart. Switch from EURUSD to USDJPY to GBPAUD and the indicator instantly recalculates — no manual reconfiguration needed.
What it shows
The spread between the base currency's 10Y yield and the quote currency's 10Y yield, expressed in basis points. For example:
USD/JPY → US10Y minus JP10Y
EUR/USD → DE10Y (Bund) minus US10Y
GBP/AUD → GB10Y minus AU10Y
A positive spread (green) means the base currency offers a yield premium — historically a tailwind for the pair. A negative spread (red) means the opposite. The wider the spread, the stronger the rates differential pushing the pair.
Why it matters
Yield differentials are one of the most reliable medium-term drivers of major FX pairs. When the spread trends one way and the pair trends the other, you're often looking at a setup waiting to resolve — either the pair catches up, or the spread breaks. Particularly powerful for:
Carry trade decisions (positive spreads = positive carry on the long side)
Filtering FX trades against the macro backdrop
Spotting divergences between price and rates
Anticipating central bank repricing impact across pairs
Confirming or fading reactions to CPI, NFP, and rate decisions
Supported pairs
All combinations of the 8 majors: USD, EUR, JPY, GBP, AUD, NZD, CAD, CHF. Auto-detection works on standard naming (EURUSD, EUR/USD, OANDA:USDJPY, FX:GBPJPY, etc.). For exotic symbols or futures, switch to Manual mode and pick base/quote from the dropdowns.
Yield benchmarks used
USD → US10Y (US Treasury)
EUR → DE10Y (German Bund, eurozone benchmark)
JPY → JP10Y (JGB)
GBP → GB10Y (Gilt)
AUD → AU10Y
NZD → NZ10Y
CAD → CA10Y
CHF → CH10Y
Settings
Moving average : configurable SMA overlay (default 50) to smooth the spread trend
Zero line : visual reference for spread sign change
Background fill : green above zero, red below — instant regime read
End label : shows current pair and live spread value in bps
Pair detection : Auto (reads chart ticker) or Manual (override with dropdowns)
Recommended setup
Works on any FX chart timeframe from 15m to daily. For best results, pair this with my companion indicator Bond Yield Strength — 10Y Majors to see the full rates landscape alongside the specific spread of your pair.
Notes
DE10Y (German Bund) is used as the EUR proxy — it's the de facto eurozone benchmark used by rates desks globally. Data availability for some symbols depends on your PulseWire plan; if a yield doesn't render, your plan may not include that exchange.
Feedback and suggestions welcome. Indicator
Bond Yield Strength - 10Y MajorsBond Yield Strength — 10Y Majors
Track intraday momentum across the 8 major bond markets in a single panel. This indicator plots the change in 10-year government bond yields for USD, EUR, JPY, GBP, AUD, NZD, CAD and CHF, normalized to a common starting point so you can instantly see which currencies are catching a bid in the rates market — and which are getting sold.
What it shows
Each line represents how far a country's 10Y yield has moved (in basis points or %) since the start of the current period. All 8 lines start at zero on each reset, making relative strength immediately readable. A line climbing above zero means yields are rising in that country (typically bullish for the currency); a line falling below zero means yields are dropping.
The eight benchmarks tracked:
USD → US10Y (US Treasury)
EUR → DE10Y (German Bund, the eurozone benchmark)
JPY → JP10Y (JGB)
GBP → GB10Y (Gilt)
AUD → AU10Y
NZD → NZ10Y
CAD → CA10Y
CHF → CH10Y
Why it's useful
Rate differentials drive FX. When US yields rip while Bunds stay flat, EUR/USD usually feels it. This indicator gives you that read at a glance, without flipping between 8 separate charts. Particularly useful for:
Spotting which currencies have a yield tailwind heading into a session
Confirming or fading FX moves against the rates backdrop
Watching the reaction to central bank decisions, CPI releases, and bond auctions across all majors simultaneously
Identifying outliers (one yield diverging from the pack often precedes an FX move)
Settings
Display unit : basis points (default, the standard rates unit) or percent
Reset period : Daily, Weekly, or Monthly — choose your lookback horizon
Line thickness : 1 to 4
End-of-line labels : toggle currency tags with live values at the right edge of the chart
Day separators : optional dashed verticals at each session boundary
Recommended setup
Apply on a 24-hour symbol (e.g. FX:EURUSD or any major forex pair) on a 15m to 1h timeframe — bond symbols themselves don't trade overnight, so the chart's time axis needs to come from a continuously-quoted instrument. You can hide the underlying price plot via the chart's visibility toggle to keep only the yield indicator on screen.
Notes
DE10Y (German Bund) is used as the EUR proxy — it's the de facto eurozone benchmark used by rates desks globally. Data availability for some symbols depends on your PulseWire plan; if a yield doesn't render, your plan may not include that exchange.
Feedback and suggestions welcome. Indicator
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 Indicator
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. Indicator
BTC Fundamental Value Hypothesis [OmegaTools]BTC Fundamental Value Hypothesis is a macro-valuation and regime-detection model designed to contextualize Bitcoin’s price through relative market-cap comparisons against major capital reservoirs: Gold, Silver, the Altcoin market, and large-cap equities. Instead of relying on traditional on-chain metrics or purely technical signals, this tool frames BTC as an asset competing for global liquidity and “store-of-value mindshare”, then estimates an implied fair value based on how BTC historically coexists (or diverges) from these benchmark universes.
Core concept: relative market-cap anchoring
The indicator builds a reference-based fair price by translating external market capitalizations into implied BTC valuation using a dominance framework. In practice, you choose one or more reference universes (Gold, Silver, Altcoins, Stocks). For each selected universe, the script computes how large BTC “should be” relative to that universe (dominance ratio), and converts that into an implied BTC price. The final fair price is the average of the implied prices from the enabled universes.
Two dominance modes: automatic vs manual
1. Automatic Dominance % (default)
When enabled, the model estimates dominance ratios dynamically using a 252-period simple moving average of BTC market cap divided by each reference market cap. This produces an adaptive baseline that follows structural changes over time and reduces sensitivity to short-term spikes.
2. Manual Dominance %
If you prefer a discretionary macro thesis, you can directly input dominance parameters for each reference universe. This is useful when you want to stress-test scenarios (e.g., “BTC should converge toward X% of Gold’s market cap”) or align the model with a specific long-term adoption narrative.
Reference universes and data construction
- BTC market cap: pulled from CRYPTOCAP:BTC.
- Gold and Silver market caps: derived from the corresponding futures symbols (GC1!, SI1!) multiplied by an assumed total above-ground quantity (constant tonnage converted to troy ounces). This provides a practical and tradable proxy for spot valuation context.
- Altcoin market cap: pulled from CRYPTOCAP:TOTAL2 (total crypto market excluding BTC).
- Stocks market cap proxy (Σ3): a deliberately conservative equity benchmark built from three mega-cap stocks (AAPL, MSFT, AMZN) using total shares outstanding (request.financial) multiplied by price. This avoids index licensing complexity while still tracking a meaningful slice of global equity beta/liquidity.
Valuation output: overvalued vs undervalued (log-based)
The valuation readout is expressed as a percentage derived from the logarithmic distance between BTC price and the model’s fair price. This choice makes valuation comparable across long time horizons and reduces distortion during exponential growth phases. A positive valuation indicates BTC trading below the model’s implied value (undervalued), while a negative valuation indicates trading above it (overvalued).
Oscillator: relative momentum and regime confirmation
In addition to fair value, the indicator includes a momentum differential oscillator built from RSI(50):
- BTC RSI is compared to the average RSI of the selected reference universes.
- The oscillator highlights when BTC strength is leading or lagging the broader macro benchmarks.
- Color is rendered through a gradient to provide immediate regime readability (risk-on vs risk-off behavior, expansion vs contraction phases).
Visualization and UI components
- Fair Price overlay: the computed fair price is plotted directly on the BTC chart for immediate comparison with spot price action.
- Valuation shading: the area between price and fair price is filled to visually emphasize dislocation and potential mean-reversion zones.
- Oscillator panel: a zero-centered oscillator with filled bands helps you identify persistent trend regimes versus transitional conditions.
- Summary table: a right-side table displays the current valuation (over/under) and, when Automatic mode is enabled, the live dominance ratios used in the model (BTC/GOLD, BTC/SILVER, BTC/ALTC, BTC/STOCKS).
How to use it (practical workflows)
- Macro valuation context: use fair price as a structural anchor to assess whether BTC is trading at a premium or discount relative to external liquidity baselines.
- Regime filtering: combine valuation with the oscillator to distinguish “cheap but weak” from “cheap and strengthening” (and the inverse for tops).
- Mean-reversion mapping: large, persistent deviations from fair value often highlight speculative extremes or capitulation zones; this can support systematic entries/exits, position sizing, or hedging decisions.
- Scenario analysis: switch to Manual Dominance % to model adoption outcomes, policy-driven shifts, or multi-year re-rating assumptions.
Important notes and limitations (read before use)
- This is a hypothesis-driven macro model, not a literal intrinsic value calculation. Results depend on dominance assumptions, proxies, and data availability.
- Gold/Silver market caps are approximations based on futures pricing and fixed supply constants; real-world supply dynamics, above-ground estimates, and spot/futures basis can differ.
- The Stocks (Σ3) benchmark is a proxy and intentionally not “the whole market”. It is designed to represent a large-cap liquidity reference, not total equity capitalization.
- Always validate signals with additional context (market structure, volatility regime, risk management rules). This indicator is best used as a macro layer in a broader decision framework.
Designed for clarity, macro discipline, and repeatability
BTC Fundamental Value Hypothesis by OmegaTools is built for traders and investors who want a clean, data-driven way to interpret BTC through the lens of competing asset classes and capital flows. It is particularly effective on higher timeframes (Daily/Weekly) where macro relationships are more stable and valuation signals are less noisy.
© OmegaTools, Eros Indicator
Ultimate Major Contextual Dashboard (Multi-Asset)Overview : The Ultimate Major Dashboard is a performance-optimized market overview tool designed to provide a consolidated snapshot of the 7 major Forex pairs and Gold. It aggregates correlation, trend, momentum, and volatility data into a single, clean table, allowing users to view broader market context without switching charts.
Technical Logic & Components : This indicator utilizes a modular function to analyze EURUSD, GBPUSD, USDJPY, USDCHF, AUDUSD, USDCAD, NZDUSD, and XAUUSD across four key dimensions:
Intermarket Correlation (Pearson Coefficient): Uses ta.correlation() to compare each asset against the symbol currently on your main chart.
Logic: Values above 0.7 (Dark Green) suggest a strong positive relationship, while values below -0.7 (Dark Red) suggest inverse behavior. This is calculated over a rolling 50-period window to balance stability with current market sensitivity.
Trend Bias (EMA-200): Evaluates the long-term trend by checking price position relative to the 200-period Exponential Moving Average.
Visuals: An upward arrow (⬆) indicates price is above the EMA; a downward arrow (⬇) indicates it is below.
Momentum (RSI-14): Calculates the Relative Strength Index. The dashboard automatically highlights readings above 70 (OB) or below 30 (OS) to help identify potential momentum extremes.
Volatility (ATR-14): Displays the Average True Range as a reference for the current active range of each market, helping users compare volatility levels across the majors.
How to Interpret the Dashboard
Asset Alignment: Correlation values help identify when pairs are moving in "unison" versus when a specific currency is diverging from the group.
Directional Context: Combining the Trend (EMA) and Momentum (RSI) columns provides a quick view of whether a market is trending strongly or reaching an exhaustion point.
Volatility Benchmarking: The ATR values offer perspective on which pairs are currently the most active, assisting in market comparison based on volatility preference.
Data Handling & Customization
Multi-Symbol Sync: Data is fetched using request.security(). The calculations are synchronized with the chart's current bar state for real-time accuracy.
Dynamic TF: Users can select the analysis timeframe (60, 240, D, W) via the settings menu.
Flexibility: The dashboard position can be toggled between all four corners of the chart to avoid overlapping with price action.
Disclaimer
This tool is provided for analytical and educational purposes only. It does not generate trading signals and should not be considered financial advice. Indicator
TFPS_EngineLibrary "TFPS_Engine"
f_calculate_lead_lag(series1, series2, length, max_lag)
Parameters:
series1 (float)
series2 (float)
length (int)
max_lag (int)
f_calculate_pressure_score(spx_ticker, vix_ticker, dxy_ticker, us10y_ticker, benchmark_source, trend_lookback, score_smoothing, use_dynamic_weights, corr_lookback, w_spx, w_vix, w_dxy, w_us10y, zscore_lookback, max_lag)
Parameters:
spx_ticker (string)
vix_ticker (string)
dxy_ticker (string)
us10y_ticker (string)
benchmark_source (float)
trend_lookback (int)
score_smoothing (simple int)
use_dynamic_weights (bool)
corr_lookback (int)
w_spx (float)
w_vix (float)
w_dxy (float)
w_us10y (float)
zscore_lookback (int)
max_lag (int)
LeadLagOutput
Fields:
best_lag (series int)
max_corr (series float)
TFPS_Output
Fields:
historical_score (series float)
smoothed_score (series float)
z_score (series float)
regime_signal (series int)
lead_lag_bars (series int)
lead_lag_corr (series float)
weight_spx (series float)
weight_vix (series float)
weight_dxy (series float)
weight_us10y (series float) Library
Strength Comparison @joshuuuexample:
if you want to find the stronger/weaker pair between eurusd and gbpusd, what you can do is check the eurgbp charts. if eurgbp is bullish, that means, that longs longs on eurusd are better than on gbpusd.
Unfortunately, there is no such thing to compare for example usoil with ukoil, or us100 with us500.
That's where this indicator comes in handy. You can choose whatever two symbols you want, that are supported by pulsewire and you will get a chart, which shows symbol1/symbol2.
Now you can use normal market structure, or the ema option, to find out the stronger symbol.
This can also help predicting the so called SMT Divergences, taught by ICT.
⚠️ Open Source ⚠️
Coders and TV users are authorized to copy this code base, but a paid distribution is prohibited. A mention to the original author is expected, and appreciated.
⚠️ Terms and Conditions ⚠️
This financial tool is for educational purposes only and not financial advice. Users assume responsibility for decisions made based on the tool's information. Past performance doesn't guarantee future results. By using this tool, users agree to these terms. Indicator
Correlation with Matrix TableCorrelation coefficient is a measure of the strength of the relationship between two values. It can be useful for market analysis, cryptocurrencies, forex and much more.
Since it "describes the degree to which two series tend to deviate from their moving average values" (1), first of all you have to set the length of these moving averages. You can also retrieve the values from another timeframe, and choose whether or not to ignore the gaps.
After selecting the reference ticker, which is not dependent from the chart you are on, you can choose up to eight other tickers to relate to it. The provided matrix table will then give you a deeper insight through all of the correlations between the chosen symbols.
Correlation values are scored on a scale from 1 to -1
A value of 1 means the correlation between the values is perfect.
A value of 0 means that there is no correlation at all.
A value of -1 indicates that the correlation is perfectly opposite.
For a better view at a glance, eight level colors are available and it is possible to modify them at will. You can even change level ranges by setting their threshold values. The background color of the matrix's cells will change accordingly to all of these choices.
The default threshold values, commonly used in statistics, are as follows:
None to weak correlation: 0 - 0.3
Weak to moderate correlation: 0.3 - 0.5
Moderate to high correlation: 0.5 - 0.7
High to perfect correlation: 0.7 - 1
Remember to be careful about spurious correlations, which are strong correlations without a real causal relationship.
(1) www.pulsewire.com Indicator
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