Bitcoin vs VUG/VTV Rolling Ratio Analysis | Astral Vision Bitcoin vs VUG/VTV Rolling Ratio Analysis | Astral Vision 🌠💠
This indicator measures the relative momentum of Bitcoin against the VUG/VTV ratio, which is the price of Vanguard's Growth ETF divided by Vanguard's Value ETF. This ratio is one of the most direct and liquid expressions of the growth-versus-value rotation dynamic in institutional equity markets: when VUG outperforms VTV the ratio rises, signaling that markets are rewarding high-multiple, long-duration assets; when VTV outperforms, the ratio falls, signaling a rotation toward defensive, cash-flow-heavy assets typically associated with tightening liquidity or rising rates.
Bitcoin is itself a long-duration, high-beta asset with structural similarities to growth equities: it tends to expand during the same liquidity-driven environments that favor VUG over VTV, and contract during the same risk-off environments that favor VTV over VUG. Tracking the VUG/VTV ratio alongside Bitcoin's own momentum reveals whether the macro environment is confirming or diverging from Bitcoin's price action, which historically has been a more reliable framework for identifying regime shifts than price-only signals.
The indicator operates in three modes.
Average Valuation mode averages the normalized momentum of Bitcoin and the VUG/VTV ratio into a single composite score, comparing it against configurable overbought and oversold percentile thresholds to identify when both are simultaneously extended or depressed.
Average Trend mode compares the composite score against its own midpoint to determine whether aggregate momentum is in its upper or lower historical half.
Double Signal mode plots Bitcoin and the VUG/VTV ratio momentum separately on the same normalized scale, making divergences between the two directly visible.
This is a cross-asset macro momentum and rotation indicator suited for swing trading and position trading on daily timeframes , functioning as a regime filter that connects Bitcoin's price action to one of the most closely watched equity style rotation signals in institutional markets.
How it differs from standard relative strength tools
A standard BTC/VUG ratio chart divides prices directly, producing a series with no statistical normalization and no connection to VTV. This indicator uses percentage returns normalized over a rolling window, making readings statistically consistent across different periods, and compares Bitcoin not against a single equity ETF but against the growth-value spread, which is a second-order signal capturing the macro liquidity regime rather than a single sector's performance. The three-mode architecture further distinguishes it from a simple ratio overlay by providing composite valuation, trend, and component-level views within a single indicator.
Plots 📊
Averaged normalized momentum oscillator with overbought and oversold threshold lines (Average Valuation mode)
Averaged normalized momentum with fill against the 50 midline (Average Trend mode)
Bitcoin and VUG/VTV normalized momentum plotted separately (Double Signal mode)
Overbought and oversold threshold lines (Average Valuation and Double Signal modes)
50 midline reference
Background color on the price chart when average enters extreme zones (Average Valuation mode)
Candle coloring on the price chart reflecting current regime in all three modes
Inputs 🎛️
Mode: Average Valuation, Average Trend, or Double Signal
Normalization Period: rolling window for the min-max normalization of returns
Lookback Period: return calculation window for both Bitcoin and the VUG/VTV ratio
Overbought Threshold: upper percentile level for extreme zone detection
Oversold Threshold: lower percentile level for extreme zone detection
Transparency: opacity of the background color highlight
Colors 🎨
5 Astral Vision presets + custom override. Default: Paradiso.
Disclaimer ⭕️
This indicator is for informational and educational purposes only. It does not constitute financial advice. Past performance is not indicative of future results. Always do your own research before making investment decisions. Indicator
Fractional CUSUM Regime Filter [Jamallo]🔹 Intro
The Fractional CUSUM Regime Filter is a quantitative trend and volatility channel indicator designed to identify structural market regimes with high stability. Unlike traditional filters that suffer from lag or whipsaw signals, this indicator applies a dual-layered preprocessor combining Lopez de Prado’s Fixed-Window Fractional Differentiation (FFD) with a classic Cumulative Sum (CUSUM) statistical trigger.
🔹 Break down
Fractional Differentiation (FFD) : Stationarizes pricing input while retaining long-term historical memory (controlled by the "d" parameter).
Classic CUSUM Filter : Accumulates deviations from a rolling baseline relative to current volatility. When cumulative deviation exceeds the threshold multiplier (h), a new regime change is triggered, and the baseline steps dynamically to the new price level.
Vol-Adjusted Percentile Bands : Linear interpolation percentiles of price deviation relative to the stepped baseline. The distances are "locked" and only update on CUSUM regime triggers to prevent wobbly bands.
🔹 Visual Guide: Indicator Anatomy
Here we have the structure of the indicator, including the CUSUM baseline, the 68% inner percentile band, and the 95% outer percentile band.
How to use: Mean Reversion (Pullbacks to the Mean)
The channel boundaries represent statistical extremes. When the price is pushed outside the bands, it is mathematically overextended and highly likely to revert back to the CUSUM baseline.
Long Setup (Pullbacks in Bullish Trend): During a green CUSUM uptrend, watch for price to pull back to the lower bands ("micro pullback") and enter as it heads back up to the baseline mean.
Short Setup (Pullbacks in Bearish Trend): During a red CUSUM downtrend, watch for price to rally into the upper bands ("micro pullback") and enter as it reverts down to the baseline mean.
🔹 How to use: Trend Following & Risk Management
The indicator is designed to capture sustained macro trends while providing clear risk parameters.
Entering on Breakthroughs : Enter when the BUY/SELL signal flags appear (indicating a new CUSUM regime shift).
Stop Loss Placement : Place stop loss orders just below the opposite outer band or below the stepped baseline.
Letting Winners Run : Ride the trend as long as the CUSUM baseline maintains its colored regime state (Green for Long, Red for Short).
🔹 Settings Parameters
d (0.01 - 0.99) : Differentiation order. Lower values retain more historical memory; higher values approach first-difference.
CUSUM Drift & Threshold : Controls baseline sensitivity to regime changes.
Percentile Lookback & Targets : Set the statistical width of the inner and outer boundaries.
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
Strategy ComparatorStrategy Comparator
Important — script type
This is a Pine v6 indicator() script, NOT a strategy() script. PulseWire's built-in Strategy Tester does NOT apply to this script. All backtesting metrics (equity curve, return, Sharpe, R-Ratio, win rate, profit factor, max drawdown, Calmar) are computed internally and displayed in this script's own ranking table on the chart, plus on-chart entry markers and alerts.
What it does
A single indicator that runs a live backtest of 18 strategy configurations across a grid of 6 assets × 4 timeframes (up to 1h), ranks them by the chosen metric (default R-Ratio ), and surfaces the best (asset, strategy) combination automatically on the chart.
Originality (not a mashup)
This is a self-contained decision framework — not a stack of standard indicators or signals. The three components that make it original:
- Dynamic concurrent backtest engine — the table values are computed live on every bar via request.security across 24 (asset, timeframe) cells, each running all 18 strategy implementations and tracking 6 statistics per strategy. Changing parameters, side, or timeframe re-evaluates the entire grid on the fly. No precomputed values.
- R-Ratio ranking metric — a single number combining return and smoothness via an OLS regression on log-equity vs time in years (R² × slope). Selects strategies that grow linearly rather than via lucky spikes. Available alongside Sharpe, Win Rate, Total Return, Calmar, Profit Factor and Max DD.
- Confidence score (0–100) — a quantitative layer that tests whether the top candidate's edge is meaningful or compatible with noise. Combines: R-Ratio strength (scale-invariant), edge over direction-weighted passive baselines (HODL for long-biased time, REKT for short-biased time), edge over RAND (statistical baseline), win rate excess over 50%, and Kelly fraction = p × (1 − 1/PF). Shown in title as CONF X/100 .
The 18 strategy configurations
- 1. HODL — Always long. Buy & hold reference.
- 2. REKT — Always short. Opposite of HODL.
- 3. RAND — Random positions (-1 / 0 / +1). Statistical baseline.
- 4. BBANDS — Bollinger Bands, 3 selectable behaviors (mean reversion, exit at midline, breakout outside).
- 5. KELTNER — Keltner Channel breakout (EMA ± ATR × multiplier).
- 6. SMA — Two-SMA crossover (fast vs slow).
- 7. MACD — MACD line / signal line crossover.
- 8. MOM — Momentum: current price vs price N bars ago.
- 9. BREAKOUT — Donchian Channel: long above N-bar high, short below N-bar low.
- 10. RSI — RSI thresholds, 3 selectable behaviors.
- 11. RWI — Random Walk Index (fast and slow).
- 12. OUTSIDE — Outside-bar pattern with candle-color direction.
- 13. CONSEC — N consecutive up/down bars for direction.
- 14. SUPER — Native Supertrend (ATR-based trailing stop).
- 15. TRATINGS — Aggregate of 14 sub-indicators (SMA/EMA on 6 periods, HMA, VWMA, Ichimoku, RSI, Stoch, CCI, ADX, AO, MOM, MACD, StochRSI, Williams %R, Bulls/Bears Power, Ultimate Oscillator). MA / Oscillator / Both groups selectable.
- 16. PSAR — Parabolic SAR.
- 17. STOCH — Stochastic Slow with overbought/oversold thresholds.
- 18. WICKS — Cumulative upper-vs-lower wick imbalance over a rolling period.
Every strategy honors the Side input (Long Only / Short Only / Both). In Long Only mode, an opposite-direction signal closes the long position to flat (and symmetric for Short Only).
How to use
1. Add it to your chart.
2. Leave the 6 asset slots empty to test only the chart symbol, or fill them to compare different symbols side by side. Duplicate slots are deduplicated automatically.
3. Read the first row of the ranking table — that's the best combination by the chosen metric averaged over the 4 timeframes.
4. On the chart, the top strategy's long (green label below the bar) and short (red label above the bar) entries are plotted with the strategy name as label. A state machine suppresses consecutive same-direction signals; exits to flat reset the state.
5. Read the CONF X/100 in the title row as a quick "should I trust this?" gauge.
6. Tick Show selected strategy in the Strategy Override group to force a specific strategy (overrides the ranking).
7. Create one PulseWire alert on the indicator with condition "Any alert() function call" — it covers every entry of the currently tracked strategy. Frequency: Once Per Bar Close (no repaint).
Non-repainting
- Markers: rendered once at chart load for the full history, then incrementally only at confirmed real-time bar closes. No intra-bar flicker.
- Alerts: fire only on alert.freq_once_per_bar_close .
Main inputs
- Strategy Override (checkbox + dropdown) — force a specific strategy as TOP regardless of ranking.
- 6 asset slots (optional) — empty falls back to chart symbol.
- 4 timeframe slots (max 1h).
- History (bars per cell) — backtest depth, 100–5000 step 50, default 1000.
- Ranking metric — R-Ratio (default), Sharpe, Win Rate %, Total Return %, Calmar, Profit Factor, Max DD %.
- Side — Long Only / Short Only / Both.
- Top N (markers) — how many top-ranked strategies to plot on the chart (1–10, default 1).
- Top N (table) — how many rows to display in the ranking table (5–98).
- Show signal markers , Enable alerts , Table position , Table size .
- Per-strategy parameters (period, threshold, multiplier, etc.).
Limits
- Up to 4 timeframes and 6 assets, with 1h as the highest allowed timeframe (Pine request.security cap of 40 calls — 24 used).
- History limited to 5000 bars per cell (Pine max_bars_back cap).
- HODL / REKT / RAND are control strategies (baselines): if they appear at the top of the ranking, the technical strategies are underperforming buy & hold or random.
- All UI labels and tooltips are in English. Translations can be requested in comments if needed. Indicator
ATR Exceedance Probability Model [LuxAlgo]The Volatility Exceedance Probability Model (VEPM) indicator is a comprehensive statistical tool designed to quantify the significance of volatility spikes, determine the likelihood of trend continuation, and categorize market environments into specific regimes.
🔶 USAGE
The indicator provides a multi-layered view of volatility, allowing traders to distinguish between standard market noise and statistically significant "exceedance" events.
🔹 Oscillator Interpretation
The main oscillator plots the current exceedance frequency (the rate at which price or range breaches ATR-based thresholds) against a long-term baseline.
Bullish/Significant Glow: When the Z-Score of the frequency exceeds the sensitivity threshold, the oscillator glows green, indicating a high-probability volatility expansion.
Bearish/Normal Glow: When the frequency falls below the baseline, the oscillator shifts toward red, signaling a contraction in volatility.
Frequency Delta: The area between the current frequency and baseline frequency is filled to highlight the momentum of volatility expansion or exhaustion.
🔹 Chart Visuals & Regimes
The script overlays information directly on the price action to provide context:
ATR Bands: Dynamic bands based on the Average True Range act as the "exceedance" barrier.
Regime Boxes: The indicator automatically identifies "Quiet," "Normal," and "High Vol" regimes. These are visualized as colored boxes (defaulting to High Vol) to show the duration and range of specific volatility climates.
Significance Dots: Circles appear at the top of the chart to mark bars that have breached the volatility threshold.
🔹 Dashboard Metrics
A real-time dashboard provides quantitative data:
Exceedance Freq: The percentage of bars in the short-term window that breached the ATR levels.
Serial Break Prob: The historical probability that a breach will be followed by another breach (continuation).
Clustering Edge: The statistical advantage of volatility clustering; a positive value suggests that volatility is currently feeding on itself.
🔶 DETAILS
The VEPM operates on the principle that volatility is not constant but "clusters" in time. It uses the following logic to derive its metrics:
Exceedance Detection: It calculates whether the current price range (True Range) or price levels (High/Low) exceed a user-defined ATR multiplier.
Statistical Z-Score: By comparing the current frequency of these breaches to a long-term baseline (200 bars by default), the model calculates a Z-Score to determine if the current activity is statistically "abnormal."
Continuation Probability: The model looks back at previous breaches and calculates how often they resulted in immediate follow-through, providing a "Serial Break" percentage.
🔶 SETTINGS
🔹 Core Settings
ATR Length: The lookback period used for the Average True Range calculation.
ATR Multiplier: The threshold used to define what constitutes a "breach" or exceedance.
Breach Detection Method: Choose between comparing the bar's total range to ATR or checking if price levels exceed the previous bar's bands.
🔹 Statistical Windows
Short-Term Window: The period used to calculate the current exceedance frequency.
Baseline Window: The long-term period used to establish the "normal" mean of volatility frequency.
Z-Score Sensitivity: Determines the threshold for identifying statistically significant volatility spikes.
🔹 Visuals
Show ATR Bands: Toggles the visibility of the ATR-based levels on the chart.
Bands Mode: Determines if bands are offset from a central basis (SMA/EMA) or from the bar's High/Low.
Regime Box Options: Toggles background boxes for Quiet, Normal, or High Volatility regimes.
🔹 Dashboard
Dashboard: Enables or disables the on-screen information table.
Position/Size: Controls the location and scale of the dashboard UI.
Indicator
Indicator
Sinc Blackman Spectral Oscillator | Astral Vision Sinc Blackman Spectral Oscillator | Astral Vision 🌠💠
This indicator applies a Sinc filter windowed with a Blackman function to extract a spectrally clean momentum signal from price, isolating cyclical components while suppressing high-frequency noise with a mathematically defined frequency boundary rather than the gradual roll-off of conventional moving averages. The oscillator measures the deviation of price from this filtered baseline, then applies a secondary moving average to the deviation itself to generate a signal line whose crossover drives both the visual output and the equity simulation.
The Blackman window is one of the most effective functions in digital signal processing: it reduces spectral leakage to near zero by tapering the filter kernel smoothly to zero at both ends, preventing frequencies outside the cutoff from bleeding into the filtered output. Applied to a Sinc filter, it produces a near-ideal low-pass filter that separates trend from noise with a precision unachievable by any EMA, WMA, or SMA-based approach.
The indicator operates in two visualization modes:
Oscillator mode displays the momentum and its signal MA directly.
Equity Line mode simulates a long-only strategy that holds Bitcoin when momentum is positive and exits to cash when negative, with a full risk-adjusted performance dashboard.
This is a momentum and trend following indicator suited for swing trading and position trading on daily timeframes . The spectral filtering makes it particularly effective at identifying sustained directional moves while ignoring the short-term price oscillations that generate false signals in standard momentum tools.
How it differs from standard momentum indicators
Standard momentum oscillators such as MACD, RSI, or standard moving average crossovers use filters with poorly defined frequency responses. A simple moving average does not have a clean cutoff frequency: it attenuates some frequencies gradually while letting others through unpredictably, a property known as spectral leakage. The Sinc-Blackman filter defines an explicit cutoff frequency and suppresses everything above it with near-ideal precision. This means the momentum baseline extracted by this indicator is genuinely separated from noise at the mathematical level, not merely smoothed. The configurable smoothing frequency parameter directly controls the spectral cutoff point, giving the user precise control over which price cycles are considered trend and which are considered noise, something no standard PulseWire built-in provides.
Plots 📊
Momentum line with glow effect, colored by position relative to its signal MA (Oscillator mode)
Signal MA with glow effect (Oscillator mode)
Fill between momentum and signal MA highlighting bullish and bearish zones
Zero baseline
Equity curve with glow effect, colored by previous bar's signal direction (Equity Line mode)
Candle coloring on the price chart reflecting the previous confirmed signal in both modes
Performance table in Equity Line mode: Strategy Return, Buy and Hold, vs Buy and Hold, Max Drawdown Total, Max Drawdown per Trade, Sharpe Ratio, Sortino Ratio, Omega Ratio
Inputs 🎛️
Source: price input for the filter
Momentum Length: base length controlling the Sinc filter kernel size
Momentum Smoothing: cutoff frequency parameter controlling the spectral boundary
Post Smoothing: length of the smoothing applied to the filtered deviation
Post Smoothing Style: EMA, DEMA, TEMA, WMA, or SMA
MA Length: length of the signal MA applied to the smoothed momentum
MA Style: EMA, DEMA, TEMA, WMA, or SMA
View: switch between Oscillator and Equity Line
Equity Start Date: starting date for the equity simulation and performance metrics
Colors 🎨
5 Astral Vision presets + custom override. Default: Futura.
Disclaimer ⭕️
This indicator is for informational and educational purposes only. It does not constitute financial advice. Past performance is not indicative of future results. Always do your own research before making investment decisions. Indicator
KNN Machine Learning Mean Reversion Probability [Dots3Red]█ OVERVIEW
This script applies a K-Nearest Neighbors (KNN) machine learning algorithm to estimate the probability that price will revert to its moving average within a defined number of bars. Rather than predicting momentum direction, it asks a more specific question: how likely is it that this extension snaps back?
The model searches historical bars for situations that looked like the current one — same degree of stretch, same RSI exhaustion profile, same volume behavior — and measures how often those situations ended in a reversion to the basis MA. That proportion becomes the live probability shown on your chart.
█ METHODOLOGY
The indicator follows a supervised machine-learning pipeline with five distinct stages.
1 — Labeling (what we are predicting)
Each historical bar receives a label based on what actually happened next. If price was extended above the basis MA and touched it within the Reversion Window — that bar is labeled a successful reversion. If it did not touch — labeled as no reversion. The same logic applies from below. This is the core distinction from momentum KNN indicators: the target is reversion to fair value , not directional price movement.
2 — Feature engineering (what we measure)
Five features capture how stretched current price conditions are, each Z-score normalized to remove scale bias:
• MA Distance — signed % distance of close from the basis MA. The primary extension signal.
• Bollinger Band position — where price sits within the bands, normalizing extension relative to current volatility.
• RSI deviation — how far RSI has moved from neutral (50). Captures momentum exhaustion.
• Body compression — ratio of candle body to total range. Small bodies near extremes signal hesitation and loss of directional conviction.
• Volume fade — declining volume during an extension is a classic exhaustion signature.
3 — Z-score normalization
All five features are standardized using a rolling mean and standard deviation computed on prior bars only (look-ahead free). This ensures the KNN distance calculation is not biased by features of different scales.
4 — KNN engine
The algorithm scans the historical lookback window for the K most similar past bars, measured by Minkowski Distance across all five features simultaneously. Closer neighbors receive exponentially higher voting weight via a Gaussian Kernel , so the prediction is driven by the most relevant historical analogs — not a simple majority vote.
5 — Dual probability output
Two independent probabilities are maintained and tracked separately:
• P(reversion from above) — for overbought / extended-high setups.
• P(reversion from below) — for oversold / extended-low setups.
They are kept separate because bear-side extensions and bull-side extensions have statistically different behavior — bear moves are typically faster and sharper. A signal fires when the relevant probability crosses the user-defined threshold, and only when price is actually extended (see Extension Gate below).
█ WHAT MAKES THIS DIFFERENT
Most published KNN indicators predict momentum direction — will price go up or down next bar? This indicator predicts something more specific: will price return to its average?
The distinction matters for several reasons:
1 — A high momentum reading can persist for many bars. A stretched reading has a natural gravity pulling it back, and measuring the historical probability of that snap is a more tractable problem than direction forecasting.
2 — The two probability channels are trained on separate populations, accounting for the asymmetry between bull and bear extensions.
3 — The Extension Gate ensures signals only appear when there is actually something to revert from — no signals in flat, choppy, low-volatility conditions.
█ EXTENSION GATE
Even if the KNN model outputs a high reversion probability, no signal appears unless price is beyond Gate Multiplier × ATR from the basis MA. This prevents false signals in low-volatility or ranging conditions where mean reversion setups carry no statistical edge.
█ HOW TO USE
Signal shapes (▲ Rev / ▼ Rev)
Fire when P(reversion) crosses the threshold AND price passes the extension gate. The label at the signal bar shows the exact probability at the moment of firing.
Snap zone fill
When a signal is active, the region between current price and the basis MA is shaded. This is the reversion target zone — where price is statistically expected to return. The fill deactivates automatically once price reverts back through the basis.
Bar colors
• Bright green/red — active probability above the threshold on the current price side.
• Dimmed green/red — probability elevated but below threshold, approaching signal territory.
• No color — neutral or low reversion probability.
Background flash
A faint background confirms the exact bar on which a signal fired.
Recommended workflow
1 — Set the Basis MA to your preferred mean reversion average. EMA 20 is a common starting point for intraday and swing setups.
2 — Tune the Reversion Window to match your typical trade hold time in bars.
3 — Adjust the Extension Gate multiplier to the asset's volatility profile. Crypto typically requires higher values than forex or equities.
4 — Use the Probability Threshold to control signal frequency. 0.65 gives moderate frequency; 0.75 and above is more selective.
5 — Combine with volume analysis or candlestick confirmation at signal bars for additional confluence before entering a position.
█ SETTINGS REFERENCE
KNN Engine
• K Neighbors — how many historical analogs vote. Higher = smoother, slower to react.
• Lookback Window — size of the historical search space in bars.
• Reversion Window — bars within which price must touch the MA to count as a reversion.
• Minkowski p — distance metric exponent. 1 = Manhattan, 2 = Euclidean.
• Gaussian Bandwidth — controls how steeply neighbor weight falls with distance.
• Probability Threshold — minimum confidence required to show a signal.
Feature Settings
• Basis MA type / length — the fair value line all features are measured against.
• Bollinger Band mult — standard deviation multiplier for the BB position feature.
• RSI length — period for the RSI exhaustion feature.
• Volume MA length — baseline for the volume fade feature.
Extension Gate
• Require extension gate — toggle the ATR-based signal filter on/off.
• Gate band multiplier — how many ATRs from basis price must be before signaling.
• Gate ATR length — period for the ATR used in the gate calculation.
█ LIMITATIONS
• KNN is a lazy learner — it does not generalize beyond historical patterns in the lookback window. Strong trending regimes or structural breaks can produce elevated false signals.
• The reversion probability reflects historical frequency, not a guarantee of future behavior.
• On low-bar-count charts (e.g. weekly on newer assets), the lookback window may not contain enough samples to produce stable probability estimates.
• Computation scales with lookback window size. Very large windows may slow chart rendering.
█ DISCLAIMER
This indicator is a decision-support tool, not a trading system. It does not constitute financial advice. Always apply proper risk management and combine with your own analysis.
Algorithm: K-Nearest Neighbors (KNN)
Distance metric: Minkowski Distance
Preprocessing: Z-Score Normalization
Target: Probabilistic Mean Reversion Indicator
Indicator
AetherEdge Hybrid Quantum-Inspired Predictor🖊️ Overview
AetherEdge Hybrid Quantum-Inspired Predictor is a next-generation 3-class (UP/DOWN/SIDE) probability prediction engine that fuses three heterogeneous models: a quantum-mechanical wavefunction approach, a K-Nearest Neighbors historical analog search, and a self-learning neural network. Born-rule probabilities derived from complex amplitudes ψ, distance-weighted K-NN voting, and Softmax-based self-optimizing neurons all converge into a single ensemble distribution. With a complete visualization system featuring a radar chart, pie chart, and historical analogs, it decodes the market's "superposition state."
🔶 Key Features
3-Layer Hybrid Architecture: Quantum + KNN + Neural
Quantum Layer: Complex wavefunction ψ, Gaussian amplitudes, decoherence, phase
KNN Layer: K-nearest analog search in 3D feature space
Neural Layer: Online gradient descent + Softmax + Weight decay
3-Class Classification: UP/DOWN/SIDE probability distribution
3 Core Features: Price Structure / Liquidity / Sentiment Proxy
On-Chart Radar Chart: 3-axis visualization
On-Chart Pie Chart: Instant state distribution view
Historical Analogs Display: Top-3 similar past patterns
Quantum Internal State Monitor: Re(ψ), Im(ψ), |ψ|², entropy
State Flip Markers: Auto-detection of directional transitions
Strategy Suggestion Engine: LONG BIAS / SHORT BIAS / RANGE FADE, etc.
🧠 Technical Architecture
This indicator is designed as an ensemble predictor of three heterogeneous models.
Feature Engineering:
Price Structure: EMA20/50/100 + RSI + ATR-deviation composite (tanh normalized)
Liquidity: Close position + wick asymmetry + VWAP deviation + sweep detection
Sentiment Proxy: Vol-Z + price-volume divergence + A/D + volume spike
Quantum Layer:
Wavefunction Construction: ψ_state = Σ A·e^(iθ) per feature
Gaussian Amplitude: A(f, target) = exp(-(f-target)²/(2σ²))
Phase Intensity: θ = f × qPhase × π/2 + offset
Born Rule: P_state = |ψ|² / Σ|ψ|²
Decoherence: P' = (1-γ)·P + γ/3 for quantum→classical transition
Quantum Coherence: 1 - H/log(3) state clarity
KNN Layer:
Euclidean distance search in 3D feature space
Distance-weighted voting w = 1/(1+d)
Aggregates labels (UP/DOWN/SIDE) at past kHorizon-bar future
Records Top-3 analogs (rank, distance, realized return)
Neural Layer:
3-class Softmax classifier (3 inputs → 3 outputs, 12 parameters)
Cross-Entropy Gradient: g = p - target
Update Rule: w_new = w·decay - lr·g·feature
Weight Decay: Prevents overfitting + forgets old patterns
Ensemble Integration:
Weighted average: P = (q·P_q + k·P_k + n·P_n) / Σw
After normalization, max-probability class becomes dominant state
Strategy Engine:
domProb ≥ 0.55 + UP → LONG BIAS
domProb ≥ 0.55 + DOWN → SHORT BIAS
domProb ≥ 0.55 + SIDE → RANGE FADE
confidence < 0.15 → HIGH UNCERTAINTY
⚙️ Recommended Settings & Tuning Guide
Crypto Defaults:
BTC (4H): qWeight=0.35, kWeight=0.35, nWeight=0.30, kK=20
ETH (1H): qWeight=0.30, kWeight=0.40, nWeight=0.30, kHorizon=5
SOL (high-vol): qSigma=1.5, qDecoher=0.15, kThresh=0.5
XRP (short-term): kHorizon=3, kThresh=0.2, nLR=0.02
Quantum Layer:
qWeight=0.20: Conservative (suppress quantum contribution)
qWeight=0.35: Standard
qWeight=0.50: Experimental (quantum-dominant)
qSigma=0.8: Sharp (clear states)
qSigma=1.2: Standard
qSigma=2.0: Smooth (high uncertainty)
qDecoher=0.05: Pure quantum
qDecoher=0.10: Standard
qDecoher=0.30: Strong classical approximation
KNN Layer:
kK=10: Curated matches (sharp)
kK=20: Standard
kK=40: Smooth (conservative)
kLookback=300: Lightweight
kLookback=500: Standard
kLookback=1000: Long-term patterns
kThresh=0.2%: Sensitive (short TF)
kThresh=0.3%: Standard
kThresh=0.5%: Conservative (long TF)
Neural Layer:
nLR=0.005: Cautious learning (stable)
nLR=0.015: Standard
nLR=0.05: Fast adaptation (unstable)
nDecay=0.999: Long-term memory
nDecay=0.997: Standard
nDecay=0.99: Quick forgetting
💡 How to Use in Practice
LONG BIAS + High Q-Coherence: Strongest buy signal, consider entry
SHORT BIAS + High Confidence: Strongest sell signal, build short
RANGE FADE: Range strategies, sell premium, fade both extremes
HIGH UNCERTAINTY: Reduce positions, observe mode
State Flip → UP: Early trend transition, early entry
All Top-3 Analogs Same Direction: Strong historical evidence, raise confidence
3-Model Consensus: Quantum/KNN/Neural all UP → highest confidence
3-Model Divergence: Split opinion, exercise caution
Dir Bias > 0.3: Strong upward bias
Vol Anomaly Detected: Suspend forecasts during normal-time logic
AetherEdge Synergy:
Self-Evolving S/R Grid: LONG BIAS + support reaction = high-win-rate entry
Volatility Regime GAN: SHORT BIAS + EXPAND forecast = powerful drop setup
SMC AI Confidence: 3-model consensus + high-conf zone = conviction entry
Neural Divergence Hunter: State Flip + divergence = reversal confirmation
⚠️ Important Notes
Initial Learning Period: Neural & KNN immature until bar_index > 110
Model Divergence: Split opinions signal weak signal strength
Quantum is Approximation: Mathematical analogy, not actual quantum computing
History Dependent: Cannot handle unprecedented market events
Computation Load: Radar & pie rendering slightly heavy
Repaint: Runs at barstate.islast, displayed only at last bar
Neural Weights: Reset to defaults on chart reload
Confidence < 0.15: Near-uniform distribution, recommend avoiding trades
🚨 Disclaimer
This indicator is an advanced hybrid prediction tool for educational and research purposes only and does not constitute financial advice. "Quantum-inspired" is a mathematical analogy, not actual quantum computing. The 3-model ensemble prediction is a probabilistic method and does not guarantee future price movements. Use with thorough validation and proper risk management. Indicator
AetherEdge Volatility Regime GAN Simulator🖊️ Overview
AetherEdge Volatility Regime GAN Simulator is a next-generation volatility forecasting engine that encodes the current market state as a 6-dimensional feature vector, performs K-NN search against a historical database of up to 500 bars, and generates 100 Monte Carlo paths of likely future trajectories. By integrating Haar wavelet decomposition, Softmax-weighted sampling, and K-means regime clustering, it visualizes the future σ distribution as a complete fan chart and histogram. Inspired by Generative Adversarial Networks, it reconstructs future scenarios from historical market patterns — an innovative simulator for the modern trader.
🔶 Key Features
3-Scale Volatility: Short/Medium/Long σ (annualized)
Haar Wavelet Decomposition: high/mid/low frequency triple-scale
6D Feature Vector: σ-z (×3) + VoV-z + Wavelet-z (×2)
K-NN Analog Search: Top-K nearest historical states
Softmax-Weighted Sampling: Temperature τ controls similarity allocation
Monte Carlo Path Generation: 100 paths build future distribution
K-Means Regime Clustering: CALM/NORMAL/ELEVATED/STRESSED/EXTREME/CRISIS
Fan Chart Visualization: P5-P95, P25-P75, median line
Terminal Distribution Histogram: σ_T probability density
Complete Statistical Dashboard: E , median, CI90, skew, regime distribution
Match Quality Indicator: ⟨d⟩ assesses analog availability
🧠 Technical Architecture
This indicator is designed as a history-based generative model.
Feature Engineering:
Log-return ret = ln(close/close )
3-scale stdev × √(annFactor) for annualized σ
Haar wavelet: |W_s| = √(s/2) × |mean_recent - mean_older|
200-bar z-score normalization (outlier robust)
6D Feature Vector:
z1=σS, z2=σM, z3=σL, z4=VoV, z5=W_hi, z6=W_mid
K-NN Search:
Euclidean distance d = √Σ(z_now - z_hist)²
Top kNeighbors selected
Softmax Sampling:
w_i = exp(-d_i / τ) / Σexp(-d_j / τ)
Low τ → focus on closest, High τ → diversify
Monte Carlo Path Generation:
Per simulation: cumulative probability samples a neighbor
σ-ratio projection pathVol = volS × (histFutVol / nVol)
Innovation noise + noiseAmp × volS × U(-0.5, 0.5)
Quantile Computation:
P5/P25/P50/P75/P95 extracted at each time step
Fan chart + smooth polyline rendering
K-Means Clustering:
2D space (σ-z, VoV-z) classified into 4-6 regimes
Lloyd's algorithm for kmIters iterations
σ-z ascending sort (regime 0 = calmest)
Match Quality Metric:
⟨d⟩ < 1.0: TIGHT (high reliability)
⟨d⟩ < 2.5: LOOSE (moderate)
⟨d⟩ ≥ 2.5: POOR (no analog → warning)
⚙️ Recommended Settings & Tuning Guide
Crypto Defaults:
BTC (Daily): volLen=20, annFactor=365, historyLen=500
ETH (4H): volLen=14, volMed=42, kNeighbors=15
SOL (high-vol): volLen=10, nSims=200, noiseAmp=0.12
XRP (short-term): volLen=14, forecastLen=10, kNeighbors=15
History Depth:
historyLen=200: lightweight, recent only
historyLen=500: standard (recommended)
historyLen=1000+: long-term pattern reference
K-NN Settings:
kNeighbors=10: curated, sharp forecast
kNeighbors=20: standard (balanced)
kNeighbors=50: smooth, conservative
Temperature Parameter:
tempSample=0.3: elite concentration (focus on nearest)
tempSample=1.0: standard (recommended)
tempSample=3.0: diversification (high uncertainty)
Monte Carlo:
nSims=50: lightweight, low-resolution
nSims=100: standard
nSims=200+: high-resolution histogram
Noise Amplitude:
noiseAmp=0.05: conservative (history-faithful)
noiseAmp=0.08: standard
noiseAmp=0.15: exploratory (unknown scenarios)
Regime Count:
nRegimes=3: coarse (simple)
nRegimes=4: standard
nRegimes=6: granular (CALM→CRISIS)
💡 How to Use in Practice
EXPAND forecast + STRESSED: Buy options / strengthen hedges
CONTRACT forecast + CALM: Range strategies / sell premium
Narrow CI90 + TIGHT match: High-confidence forecast, execute strategy
Wide CI90 + POOR match: Unknown territory, exercise caution
P95 > 2× volS: Tail-risk alert, reduce position
Regime Transition Detection:
CALM → ELEVATED: Caution mode
STRESSED → EXTREME: Crisis approaching
EXTREME → STRESSED: Storm passing
Skew Interpretation:
right-tail: Upside risk dominant (vol spike possible)
symmetric: Standard scenario
left-tail: Downside risk dominant (vol crash possible)
AetherEdge Synergy:
SMC AI Confidence Engine: CONTRACT forecast + high-conf zone = compression breakout setup
Self-Evolving S/R Grid: EXPAND forecast + line cluster = breakout preparation
Neural Divergence Hunter: STRESSED + divergence = elevated reversal probability
All-in-One Dashboard: Regime + direction = comprehensive judgment
⚠️ Important Notes
History Dependency: Waits for bar_index > 210 to accumulate history (no early rendering)
POOR Match Warning: ⟨d⟩ > 3.0 indicates low forecast reliability
History-Based Prediction: Cannot handle unprecedented market events
Fan Chart Width: Wide CI90 indicates high uncertainty
Repaint Behavior: Runs only on barstate.islast, displayed at last bar
Computation Load: Large nSims × forecastLen may slow rendering
Cluster Initialization Sensitivity: K-means convergence depends on initial values (increase kmIters for stability)
🚨 Disclaimer
This indicator is an advanced volatility-simulation tool for educational and research purposes only and does not constitute financial advice. GAN-style Monte Carlo prediction is a stochastic simulation method and does not guarantee future volatility. Historical patterns may not repeat — particularly during unprecedented events, the forecast may fail. Use with thorough validation and proper risk management.
Indicator
AetherEdge Multi-Feature Neural Divergence Hunter🖊️ Overview
AetherEdge Multi-Feature Neural Divergence Hunter is a revolutionary divergence engine that simultaneously detects divergences across 6 independent oscillators and self-evolves neural weights based on each feature's empirical accuracy. Every feature (RSI/MACD/Stoch/CCI/MFI/MOM) detects both Regular and Hidden divergences in parallel, and Softmax-normalized dynamic weights evaluate confluence. With its 3D visual encoding (color × size × shape), the type and strength of every signal is identifiable at a glance — a next-generation divergence hunter.
🔶 Key Features
6 Parallel Oscillator Detection: RSI / MACD Hist / Stochastic / CCI / MFI / Momentum
Up to 12 Divergence Variants (2 types × 6 features) tracked simultaneously
Softmax Neural Weight Learning: Auto-redistribution based on empirical accuracy
Reward-Based Weight Updates: Bonus for correct, penalty for wrong
Confluence Counting: Separate Regular/Hidden tracking, combined for trigger
3D Visual Encoding:
Color: REG Bull/Bear, HID Bull/Bear, MIXED — 5-class system
Size: tiny→huge based on confluence count (5 levels)
Shape: Regular=triangle, Hidden=arrow, Mixed=label
Composite Neural Oscillator: Weighted average centered display
Confluence Glow: Background opacity reflects strength
Per-Feature Accuracy Tracking: Individual hit-rate records
Dynamic Divergence Lines: Width and style identify type
🧠 Technical Architecture
This indicator is a neural-weight-learning system processing 6 oscillators in parallel.
Feature Normalization:
RSI/Stoch/MFI: Native 0-100
MACD Hist/CCI/Momentum: Normalized to 0-100 over 100-bar range
Pivot Storage Method (Pine v5 compatible):
Eliminates dynamic history access by storing previous-pivot oscillator values
12 slots managed: prevPL_rsi, prevPL_macd, ...
Divergence Detection (per feature):
Regular Bull: Price LL + Oscillator HL
Hidden Bull: Price HL + Oscillator LL
Regular Bear: Price HH + Oscillator LH
Hidden Bear: Price LH + Oscillator HH
Distance filter: minBarsBet ≤ pivot gap ≤ maxBarsBet
Neural Weight Learning:
Evaluates return after rewardLook bars per signal
Correct (ret > 0): w += learnRate × |ret| × 100
Wrong (ret < 0): w -= learnRate × |ret| × 50 (asymmetric penalty)
Weight constraint:
Softmax Normalization (temperature τ):
w_i = exp((rawW_i - max) / τ) / Σexp(...)
Low τ → concentrate on best; High τ → uniform
Composite Neural Oscillator:
nnOsc = Σ(value_i × w_i) / Σw_i - 50
3-bar EMA smoothing, 5-zone state detection
3D Visual Encoding:
Color = type, Size = strength, Shape = category — 3-dimensional information transmission
⚙️ Recommended Settings & Tuning Guide
Crypto Defaults:
BTC (4H): pivotLen=5, minConfluence=2, learnRate=0.08 (standard)
ETH (1H): pivotLen=4, minConfluence=2, rewardLook=8
SOL (high-vol): pivotLen=6, minConfluence=3, rewardLook=12
XRP (short-term): pivotLen=3, minConfluence=2, rewardLook=5
Pivot Sensitivity:
pivotLen=3: high-sensitivity, noisy
pivotLen=5: standard
pivotLen=8–10: HTF, certainty-focused
Confluence Threshold:
minConfluence=1: many signals, false positives
minConfluence=2: standard (recommended)
minConfluence=3: curated, fewer
minConfluence=4+: ultra-curated, rare
Learning Parameters:
learnRate=0.05: conservative, stable
learnRate=0.08: standard
learnRate=0.15: fast, unstable risk
softmaxTemp=0.5: elite concentration (focus on best 1-2)
softmaxTemp=1.5: standard (balanced)
softmaxTemp=3.0: uniform (diversity)
Feature Selection Strategy:
Oscillator-focus: RSI + Stoch + MFI (momentum group)
Trend-focus: MACD + CCI + Momentum
Full parallel: All 6 ON (recommended, NN auto-curates)
💡 How to Use in Practice
5+ Confluence + REG MIXED: Strongest reversal signal (huge size emphasis)
3 REG Bull + NN 80%: High-probability reversal-buy candidate
3 HID Bull: Trend-continuation pullback buy
MIXED (REG+HID): Transition zone / unstable market
Neural Oscillator Usage:
EXTREME OB (>35): Overheated, watch for reversal
EXTREME OS (<-35): Bottom zone, watch for bounce
OS zero-cross + divergence: Direction confirmation
Learning Maturity Check:
Per-feature Acc% > 60%: feature is reliable
Acc% < 45%: feature is failing, ignore
Many Pending: still learning, wait
AetherEdge Synergy:
SMC AI Confidence Engine: A+ zone + 5× confluence = ultimate alignment
NeuraNet Predictor: Direction match + REG MIXED = double confirmation
RL Signal Optimizer: CONFIDENT + divergence = strong entry
All-in-One Dashboard: HIGH-CONVICTION + huge label = supreme reversal signal
⚠️ Important Notes
Pivot Lag: Divergence confirms pivotLen bars later (no real-time detection)
Weight Reset: Chart reload resets weight learning
Learning Maturity: Per-feature Acc% reliable only after Signals ≥ 20
Overfitting Risk: High learnRate + long runtime may over-skew weights
Hidden Divergence Nature: Continuation signal — avoid counter-trend misuse
MIXED Interpretation: Simultaneous REG+HID = transitional confusion, be cautious
Feature Disabling: Turning off unused features improves weight allocation
🚨 Disclaimer
This indicator is an advanced divergence-analysis tool for educational and research purposes only and does not constitute financial advice. Neural weight learning is a stochastic optimization method and does not guarantee future profits. Divergences suggest reversal or continuation possibilities but do not guarantee outcomes. Use with thorough validation and proper risk management.
Indicator
AetherEdge RL Signal Optimizer🖊️ Overview
AetherEdge RL Signal Optimizer is a truly self-optimizing signal engine powered by a complete Q-Learning (TD-learning) implementation. It discretizes the market into 18 states, explores Long/Short/Skip actions through trial-and-error, updates Q-values via the Bellman equation, and dynamically balances exploration vs exploitation through ε-greedy decay. Combined with statistical TP/SL learning from MFE/MAE tracking, it is a living reinforcement-learning system where both policy and risk-management evolve with every trade.
🔶 Key Features
Full Q-Learning Implementation (TD(0)): 18 states × 3 actions Q-table
ε-Greedy Exploration: Auto-decay from 30% → 5%, solving the exploration/exploitation dilemma
State Discretization: Trend(3) × RSI(3) × Volatility(2) = 18 states
Reward Function: n-step ATR-normalized return + skip penalty
MFE/MAE Learning: Statistical TP/SL estimation from up to 200 trades
Dynamic TP/SL: Quantile-based optimal levels (TP=70%, SL=85% confidence)
Q-Spread Confidence: Best vs second-best Q gap as conviction proxy
Q-Table Heatmap: All 18 states visualized with color-graded Q-values
Learning Progress Bar: EXPLORING → MIXING → EXPLOITING phases
Performance Tracking: Win rate, avg reward, recent 50-reward MA
Current-State Highlight: On-chart directional box
🧠 Technical Architecture
This indicator is a complete reinforcement-learning agent running natively on PulseWire.
State Space (18 states):
Trend Index: EMA20-50 spread (Bear/Flat/Bull)
RSI Index: Low/Mid/High
Volatility Index: ATR short/long ratio (Low/High)
Combined: state = trend×6 + rsi×2 + vol
Action Space (3 actions): 0=Long, 1=Short, 2=Skip
Q-Update (Bellman Equation):
Q(s,a) ← Q(s,a) + α ×
Reward Function:
Long → r = (close - close ) / ATR
Short → r = -(close - close ) / ATR
Skip → r = skipPenalty (-0.05)
ε-Greedy Policy:
With probability ε: random exploration; else greedy
Linear decay: epsStart → epsEnd over epsDecayLen
Pseudo-random: sin(seed×12.9898 + 78.233) for reproducibility
TP/SL Learning Engine:
Track MFE/MAE for trackBars after each signal
Separate Long/Short buffers, max 200 trades each
TP = 70th percentile of MFE; SL = 85th percentile of MAE
Safe defaults when data insufficient (TP=2.0×, SL=1.5×ATR)
Q-Spread Confidence: Signal fires only if best - second_best ≥ minConfidence
State Coverage Tracking: Percentage of (state, action) cells visited
⚙️ Recommended Settings & Tuning Guide
Crypto Defaults:
BTC (4H): α=0.15, γ=0.90, epsDecay=500 (standard)
ETH (1H): α=0.20, γ=0.85, epsDecay=300 (fast-learn)
SOL (high-vol): α=0.10, γ=0.92, epsDecay=800 (careful)
XRP (short-term): α=0.25, epsDecay=200, rewardLook=2
Q-Learning Hyperparameters:
α (Learning Rate):
0.05–0.10: conservative, noise-resilient
0.15: standard
0.25–0.40: fast adaptation, unstable risk
γ (Discount):
0.80: short-term view, immediate reward
0.90: standard
0.95–0.99: long-term strategy, delayed reward
ε Decay:
200: rapid learning, low data
500: standard
1000+: thorough exploration, HTF use
TP/SL Optimization:
tpConfidence=0.5: conservative TP (early profit)
tpConfidence=0.7: standard
tpConfidence=0.9: greedy TP (miss risk)
slConfidence=0.85: standard (tolerate 85% drawdowns)
Min Q-Spread:
0.05: many signals, noise included
0.15: standard
0.30: ultra-curated, fewer opportunities
💡 How to Use in Practice
EXPLOITING phase (ε≤0.08): Maximum signal trust, live trading
EXPLORING phase (ε>0.20): Learning, signals are reference only
MIXING phase: Transitional, observe carefully
CONFIDENT + LONG/SHORT: Sufficient Q-spread, entry candidate
UNCERTAIN: Ambiguous state, skip recommended
Q-Table Observation:
All-green rows: bullish bias learned
High Visits = high cell reliability
"—" displayed: unvisited cells, insufficient data
Trade Scenarios:
Bull · RSI↓ · LoVol with max Q : Pullback-buy pattern learned
Bear · RSI↑ · HiVol with max Q : Bounce-sell pattern learned
Learned TP/SL with 1:2.5 R:R: Trade only after statistical edge confirmed
AetherEdge Synergy:
SMC AI Confidence Engine: A+ zone + RL Long = double rationale
NeuraNet Predictor: Direction match + RL CONFIDENT = high probability
Self-Evolving S/R Grid: Strong line touch + RL signal = supreme alignment
All-in-One Dashboard: HIGH-CONVICTION + RL EXPLOITING = ultimate confluence
⚠️ Important Notes
Learning Reset Issue: Q-table resets on chart reload or timeframe change—relearning required
Initial Learning Period: Run at least epsDecayLen × 1.5 bars before live use
State Coverage: <50% means many unvisited states; 80%+ recommended for stability
Overfitting Risk: High α over-fits to recent noise; HTF requires lower α
MFE/MAE Buffer: At least 20+ trades needed for reliable TP/SL
Pseudo-Random: bar_index-based, so same moment yields same exploration
Signal Latency: Operates on barstate.isconfirmed; signals confirm after bar close
🚨 Disclaimer
This indicator is a reinforcement-learning demonstration for educational and research purposes only and does not constitute financial advice. Q-Learning is a stochastic optimization method and does not guarantee future profits. Learning outcomes depend strongly on environment and data; past performance does not predict future results. Use with thorough validation and proper risk management. Indicator
GLI Trend Analysis | Astral Vision GLI Trend Analysis | Astral Vision 🌠💠
This indicator plots the Global Liquidity Index and its exponential moving average, using the EMA crossover as a directional trend signal for the global monetary environment. The GLI is constructed from the same comprehensive 21-source aggregation used across the Astral Vision liquidity suite: 17 major central bank balance sheets converted to USD via live FX rates, minus the Fed's non-stimulative liabilities (Reverse Repo Facility and Treasury General Account), plus M2 money supply for the US, EU, China, and Japan.
Calculation ⚙️
The GLI is computed as:
Fed balance sheet, minus RRP (Reverse Repo: overnight cash parked at the Fed by money market funds, which drains liquidity from the financial system), minus TGA (Treasury General Account: the US government's cash balance at the Fed, which also drains liquidity when it grows), plus the balance sheets of the Bank of Japan, People's Bank of China, Bank of England, ECB, Reserve Bank of India, Bank of Canada, Reserve Bank of Australia, Swiss National Bank, Central Bank of Russia, Central Bank of Brazil, Bank of Korea, Reserve Bank of New Zealand, Sveriges Riksbank, and Bank Negara Malaysia, each converted to USD by multiplying by the corresponding live FX rate, plus M2 money supply for the US, EU (converted via EURUSD), China (converted via CNYUSD), and Japan (converted via JPYUSD).
The subtraction of RRP and TGA from the Fed balance sheet is a critical correction absent from simpler GLI implementations. The Fed's balance sheet includes liabilities that do not actually inject money into the financial system: when RRP balances are high, money market funds are lending cash back to the Fed overnight, effectively withdrawing it from circulation. Similarly, a growing TGA means the government is holding more cash at the Fed rather than spending it into the economy. Subtracting both produces a more accurate measure of net liquidity actually available to financial markets.
An EMA of configurable length is applied to the resulting GLI series. When GLI is above its EMA, global liquidity is in an uptrend relative to its own smoothed baseline, historically associated with expanding risk appetite and upward pressure on Bitcoin and other risk assets. When GLI is below its EMA, the trend is contractionary.
Both the GLI line and the background color on the price chart are shifted forward in time by a configurable offset in bars, operationalizing the empirically documented lead-lag relationship between global liquidity inflections and Bitcoin price response.
Plots 📊
GLI line colored by its position relative to the EMA, shifted forward by the configurable offset
EMA line as a neutral reference
Background color on the price chart reflecting GLI trend direction, shifted forward by the same offset
Inputs 🎛️
EMA Length: smoothing period for the GLI trend baseline
Lead Offset: bars to shift both the GLI signal and the background color forward in time
Colors 🎨
5 Astral Vision presets + custom override. Default: Infinito.
Purpose 🎯
A standard EMA crossover applied to Bitcoin price measures Bitcoin's own momentum without any external reference. This indicator applies the same crossover logic to global central bank liquidity, producing a regime signal that is causally upstream of Bitcoin price rather than derived from it. The forward shift separates this tool from coincident liquidity indicators by explicitly positioning the signal as a leading reference, reflecting the time lag between liquidity creation and its transmission into asset prices. The RRP and TGA correction further distinguishes it from simpler GLI charts available elsewhere, which overstate liquidity by including Fed liabilities that do not reach financial markets.
Disclaimer ⭕️
This indicator is for informational and educational purposes only. It does not constitute financial advice. Past performance is not indicative of future results. Always do your own research before making investment decisions. Indicator
AetherEdge SMC AI Confidence Engine🖊️ Overview
AetherEdge SMC AI Confidence Engine is a next-generation SMC engine fusing Smart Money Concepts—the language of institutional footprints—with 8-factor AI confidence scoring and kNN pattern matching. It auto-detects the three pillars of smart-money zones (Order Blocks, Fair Value Gaps, Liquidity Sweeps) and instantly delivers an A+ to D conviction grade by integrating eight weighted factors: structure integrity, displacement, volume, MTF consensus, kNN similarity, imbalance, freshness, and confluence. A data-driven SMC intelligence that answers the eternal question: "Which zone is real?"
🔶 Key Features
3 Core SMC Zone Detection: Order Block / Fair Value Gap / Liquidity Sweep
8-Factor AI Scoring: Structure / Displacement / Volume / MTF / kNN / Imbalance / Freshness / Confluence
kNN Pattern Matching: 5-D feature space, K-nearest historical analogs
A+/A/B/C/D Grades: Intuitive five-tier conviction ranking
3-Layer MTF Consensus: 1H / 4H / D triple confirmation
BOS/CHoCH Detection: Auto-identifies structural breaks
Mitigation Tracking: Records zone-touch events; Fresh/Mitigated state
Confluence Detection: Auto-bonuses overlapping zones
Dynamic Visualization: Transparency, border & size scale with score
Top-Zone Detail Panel: Full 8-factor breakdown with bar visualization
Ranking Table: Up to 10 zones sorted by composite score
Custom Weights: Tune all 8 factors to fit your trading style
🧠 Technical Architecture
This indicator elevates SMC theory into a statistical / machine-learning framework, delivering industry-leading smart-money intelligence.
Zone Detection Logic:
Order Block: Strong displacement (ATR×1.5+) + volume Z>0.5 + BOS triggers backward scan to last opposite-colored candle
FVG: 3-bar gap exceeding ATR × fvgMinSize
Liquidity Sweep: Recent swing pierced by sweepTol × ATR then reversal close
8-Factor Scoring (each normalized 0–100):
Structure Integrity — trend alignment + BOS bonus
Displacement Strength — birth-time displacement magnitude
Volume Profile — birth-time volume Z-score
MTF Consensus — sum of 3 HTF EMA20/50 slopes (–3 to +3)
kNN Pattern Match — 5-D feature K-NN average forward magnitude
Imbalance Ratio — zone size ÷ ATR
Freshness/Age — exp(-age/150) decay, –30 if mitigated
Confluence Score — bonus per overlapping zone
kNN Engine:
Features: displacement / volZ / bodyRatio / atrRatio / rangeRatio
300-bar history buffer, K=5 nearest by Euclidean distance
Average 5-bar-forward absolute move scored probabilistically
Weighted Composite: composite = Σ(factor_i × weight_i) / Σweights
Dynamic Update: All active zones rescored every confirmed bar
Grade Mapping: ≥85=A+, ≥75=A, ≥65=B, ≥50=C, <50=D
⚙️ Recommended Settings & Tuning Guide
Crypto Defaults:
BTC (4H–D): Pivot=5, OBLookback=50, MTF=4H/D/W
ETH (1H–4H): Pivot=5, fvgMinSize=0.3, MTF=1H/4H/D (standard)
SOL (high-vol): Pivot=7, sweepTol=0.15, fvgMinSize=0.4
XRP (short-swing): Pivot=3, OBLookback=30, MTF=15M/1H/4H
Pivot Length (structure sensitivity):
3: agile, noisy
5: standard
7–10: major-structure only, HTF use
FVG Min Size:
0.2: catch fine gaps
0.3: standard
0.5: strong FVGs only
Weight Tuning by Style:
Trend Follower: Structure↑, MTF↑, Displacement↑
Counter-Trader: kNN↑, Freshness↑, Imbalance↑
Scalper: Volume↑, Displacement↑, Freshness↑
Swing Trader: MTF↑, Confluence↑, Structure↑
Min Score Display:
40: standard (hide D)
65: B+ only, clean view
75: A+ only, ultra-curated
kNN Config:
K=3: sharp similarity, unstable
K=5: standard
K=10–15: smooth, averaged
💡 How to Use in Practice
A+ Zones (≥85%): Gold-bordered ultimate-conviction zones, primary thesis
A Zones (75–85%): High quality, multi-factor agreement, entry candidates
B Zones (65–75%): Reference level, combine with other evidence
C/D (<65%): Information only, generally ignore
Fresh + A+: Untouched apex zone, optimal ambush
Mitigated + High Score: Past battlefield, high re-test reaction probability
Confluence Areas: OB + FVG + Sweep overlap = institutional decision zones
Trade Scenarios:
Bull OB A+ + MTF Strong Bull: Golden pullback long
Bear FVG A + Liquidity Sweep up-pierce: Short opportunity
BOS Up + Recent A+ Zone: Trend-confirmation entry
AetherEdge Synergy:
Self-Evolving S/R Grid: A+ zone + strong S/R line overlap = ultimate alignment
LSTM Forecaster: Bullish prediction + Bull OB A+ = dual AI conviction
NeuraNet Predictor: Direction match + zone reach = high-probability entry
All-in-One Dashboard: HIGH-CONVICTION + A+ zone = supreme alignment
Multi-TF Workflow: Identify A+ zones on D/4H → precise entry on 1H/15M
⚠️ Important Notes
kNN Warm-up: <300 bars yields unstable similarity, fixed 50-score
Zone Cap: Oldest zones auto-pruned beyond maxZones
Weight Normalization: Auto-normalized regardless of total—relative influence
MTF Lag: HTF references confirmed bars; first few bars unfinalized
Detection Delay: Pivots confirm pivotlen bars later; real-time slight delay
Zone Overlap: Same OB may be detected multiple times—use as confluence signal
🚨 Disclaimer
This indicator is an SMC analysis support tool provided for educational and research purposes only and does not constitute financial advice. AI confidence scores are probabilistic indicators based on mathematical approximation and do not guarantee future profits. Scores are one input among many—combine with independent analysis and proper risk management.
Indicator
AetherEdge Adaptive LSTM-inspired Forecaster🖊️ Overview
AetherEdge Adaptive LSTM-inspired Forecaster is a next-generation price forecasting engine that recreates the core machinery of the LSTM (Long Short-Term Memory) neural network—the cornerstone of deep learning—directly in Pine Script. Featuring a multi-layered memory structure of forget, input, output gates and cell state, augmented by an Attention mechanism that dynamically references the most relevant past moments, plus residual connections that bypass training instability, it fuses multi-timeframe features to render 1–30 bar price forecasts and uncertainty bands with exceptional fidelity.
🔶 Key Features
LSTM-style Cell: Full implementation of forget/input/output gates + cell state
Parallel Hidden Units: 3–16 units with diverse time constants τ for layered memory
Attention Mechanism: Similarity + recency-decayed weighted context
Residual Connection: Skip connection bypassing vanishing-gradient pitfalls
Multi-Timeframe Features: HTF (1H) + HHTF (4H) hierarchical inputs
8-D Input Vector: retZ / volZ / RSI / Slope / Mom / HTF Ret / HTF Bias / HHTF Bias
Multi-Step Forecast: 1–30 bar projection lines
Uncertainty Bands: √t-decay confidence intervals (inner 1σ / outer 2σ)
3 Band Methods: Residual Std / ATR / Hybrid
Hidden Unit Visualization: τ, Cell, Hidden & activity bars per unit
Attention Heatmap: 30-bar attention weight distribution
Hit Score: Real-time evaluation of last forecast accuracy
Bullish/Bearish Flip & Band-Breach Alerts
🧠 Technical Architecture
This indicator is an industry-leading neural forecasting engine fully reconstructing the mathematical essence of LSTM, the apex of RNN-family models, in Pine Script.
LSTM Cell Equations (per unit u):
Forget Gate f_t = σ(W_f · x_t + b_f) — past memory retention
Input Gate i_t = σ(W_i · x_t + b_i) — new information intake
Candidate g_t = tanh(W_g · x_t + b_g) — new memory candidate
Output Gate o_t = σ(W_o · x_t + b_o) — exposure to hidden state
Cell Update c_t = f_t ⊙ c_{t-1} + i_t ⊙ g_t
Hidden h_t = o_t ⊙ tanh(c_t)
τ Diversity: Each unit assigned distinct τ, processing short-to-long memory in parallel
Pseudo-Weight Generation: Deterministic sin/cos phase for stable initialization
MTF Influence Boost: Input gate amplified by HHTF Bias magnitude
Attention:
Score = h_curr × h_past − |h_curr − h_past|×0.5 + recency decay
Softmax normalization (temperature-adjustable) → context = weighted mean
Residual Path: Upper-half units add raw retZ × residualMix × (1-τ)
Output Projection: h_aggregated × σ_returns × 0.5 + Attention + Residual contributions
Uncertainty Model: σ_h = σ_base × fanOut^h × √h (band widens with horizon)
Decay Forecast: Multi-step uses stepRet × 0.82^(h-1) (extrapolation decay suppresses overshoot)
⚙️ Recommended Settings & Tuning Guide
Crypto Defaults:
BTC (4H–D): Hidden=8, Seq=30, Forecast=10 (standard)
ETH (1H–4H): Hidden=10, Seq=40, MTF Weight=0.4
SOL (high-vol): Hidden=12, Forget=0.88, fanOut=1.20
XRP (short-bias): Hidden=6, Seq=20, Forecast=5
Hidden Units (Memory Capacity):
3–5: Lightweight, short-pattern specialized
8: Standard (recommended)
12–16: Rich memory, heavier compute
Sequence Length (Attention Range):
20: Recency-focused
30: Standard
50–100: Long context, lower TFs
Learning Rate α (lrBase):
0.05–0.10: Conservative, smooth
0.15: Standard
0.25–0.50: Agile, noisier
Forget Gate Base:
0.85–0.90: Agile forgetting, regime-shift responsive
0.92: Standard
0.95–0.98: Long memory, range-favoring
Attention Temperature:
0.5–1.0: Sharp focus, specific-pattern referencing
1.5: Standard
2.5–5.0: Even attention, stable but dull
Band Method:
Residual Std: Empirical, most precise
ATR: Simple, valid early
Hybrid: Geometric mean, robust
💡 How to Use in Practice
Strong Bullish Forecast: σ-breach prediction + positive attention = long entry
Strong Bearish Forecast: Opposite, short consideration
Band Breach: Price exceeded ±2σ band, overheating warning
Forecast Flip: expectedRet zero-cross, early regime-shift signal
Hit Score > 70%: Last forecast accurate, current trustworthy
Hit Score < 40%: Sudden volatility regime, low confidence—wait
Hidden Unit Activity: Multiple units same direction = strong consensus; mixed = regime transition
Attention Heatmap purple-dominant: Strong reference to past similar pattern, repeating-scenario likely
Attention dispersed: No precedent, novel territory—tread cautiously
AetherEdge Synergy:
KNN Regime Classifier: Trending▲ + LSTM bullish forecast = dual AI conviction
NeuraNet Predictor: Direction agreement amplifies signal
Self-Evolving S/R Grid: LSTM band edges ≈ S/R lines = strong reaction candidates
All-in-One Dashboard: HIGH-CONVICTION + Hit Score > 70% = ultimate alignment
Multi-TF: 4H LSTM direction → 1H strategy selection → 15M precision entry
⚠️ Important Notes
Pseudo-Weights: No pre-trained weights; deterministic phase-based approximation
Learning Reset: Cell states wipe on reload; rebuild required
First 50 Bars: States unfinalized, forecasts invalid
Compute Load: Hidden=16 + Seq=100 is heavy; use 8/30 on low-spec
MTF lookahead_off: No future-peeking, real-time integrity guaranteed
Multi-Step Drift: Error accumulates with horizon; Forecast=5–10 recommended
Bands are Probabilistic: σ-range exceedance can occur; not absolute ceilings/floors
🚨 Disclaimer
This indicator is a deep-learning-inspired forecasting model provided for educational and research purposes only and does not constitute financial advice. LSTM-style predictions are mathematical approximations and do not guarantee future profits. Forecast accuracy depends on market conditions, asset, and timeframe. Combine with independent analysis and proper risk management. Indicator
AetherEdge KNN Regime Classifier🖊️ Overview
AetherEdge KNN Regime Classifier is an innovative memory-based machine learning regime classifier that fully implements the k-Nearest Neighbors (k-NN) algorithm in Pine Script. It memorizes up to 2,000 historical patterns, retrieves the k most similar past patterns matching the current 8-dimensional feature vector, and classifies the market into five regimes (Trending Up / Trending Down / Range / Breakout / Reversal) in real time via majority voting. Each regime's forward returns and historical win-rate are statistically tracked, providing multi-dimensional data-driven trade support.
🔶 Key Features
True k-NN Implementation: k tunable from 3–50, odd numbers preferred
Memory Bank: Up to 2,000 patterns stored, FIFO auto-discard
3 Distance Metrics: Euclidean / Manhattan / Cosine
2 Voting Schemes: Majority / Distance-weighted (1/d)
5 Regime Classification: Trending▲ / Trending▼ / Range / Breakout / Reversal
Forward-Looking Validation: Each pattern verified against N-bar future return
8-D Feature Vector: ADX/DMI/BBW/RSI/Volume/Slope/Position/ROC
Live Win-Rate Table: Statistical edge of each regime visualized
KNN Distance Heatmap: Nearest-to-farthest distance distribution
Vote Distribution Bars: % bars showing votes per regime
Confidence Computation: Auto-derived from vote concentration
Regime Background Tinting: Instant chart-wide regime visibility
High-Confidence Regime Shift Alert: Triggered at Confidence > 85%
🧠 Technical Architecture
This indicator is a top-tier regime classification engine fully implementing k-NN, the flagship of memory-based (lazy) learning.
k-NN Algorithm:
Compute query vector q =
Calculate distance d(q, m_i) to all memory patterns
Select top-k by ascending distance
Vote: argmax_l Σ w_i ×
Distance Metrics:
Euclidean: √Σ(q_i - m_i)² — standard geometric
Manhattan: Σ|q_i - m_i| — outlier-robust
Cosine: 1 - (q·m)/(|q||m|) — direction-focused
Weighting Schemes:
Majority: Plain vote
Distance-weighted: w = 1/(d + ε) — closer = stronger
Feature Engineering (8-D):
f1: ADX strength (trend presence)
f2: DMI differential (direction)
f3: BBW ratio (volatility expansion)
f4: RSI normalized (momentum)
f5: Volume z-score (liquidity)
f6: Linear Regression Slope (structural tilt)
f7: Range Position (within high-low)
f8: ROC (rate of change)
Regime Labeling Rules:
Breakout: BBW > 1.5×avg AND Volume > 1.8×avg AND |ROC| > 1.0
Reversal: RSI extreme AND ROC sign flipped
Trending Up: ADX > 25 AND DI+ > DI- AND Slope > 0
Trending Down: ADX > 25 AND DI- > DI+ AND Slope < 0
Range: ADX < 18 AND BBW < 1.2×avg
Forward-Looking Validation: Win/loss decided after N (default 5) bars
Memory Management: Staging queue → committed after forward window → FIFO once size exceeded
⚙️ Recommended Settings & Tuning Guide
Crypto Defaults:
BTC (4H–D): k=15, Memory=500, Forward=5 (standard)
ETH (1H–4H): k=11, Cosine, Forward=8
SOL (high-vol): k=21, Manhattan (outlier-robust)
XRP (range-heavy): k=9, Forward=3 for short-horizon edge
k Tuning:
k=3–7: High sensitivity, noisy
k=11–15: Balanced (recommended)
k=21–31: Smooth, may dilute rare patterns
Memory Size:
300: Lightweight, recent-trend focused
500: Standard
1000–2000: Long-term stats, heavy compute
Forward Bars:
3: Scalping
5: Standard
10–15: Swing-oriented
Distance Metric Selection:
Euclidean: Standard, balanced
Manhattan: High-vol assets, outlier-heavy
Cosine: Pattern-shape focus, magnitude-agnostic
💡 How to Use in Practice
Trending▲ + Confidence > 70%: Optimal trend-following entry
Breakout detected: Activate volatility-breakout strategies
Reversal warning: Reduce trend-follow size, consider counter-trend
Range: Range/grid strategies fit
Win-Rate Table analysis: Discover statistically dominant regimes for your asset
Avg Ret monitoring: Trending▲ with high +Avg Ret = trend-follow valid
Confidence < 50%: Vote dispersion, regime unclear—reduce size
Heatmap green-dominant: Abundant similar patterns, high prediction trust
Heatmap red-dominant: Uncharted territory—proceed cautiously
AetherEdge Synergy:
NeuraNet Predictor: KNN Trending▲ + NeuraNet > 0.7 = dual AI conviction
Self-Evolving S/R Grid: Breakout regime + line breach = strong entry
All-in-One Dashboard: HIGH-CONVICTION + high KNN confidence = ultimate alignment
Multi-TF: 4H KNN regime → 1H strategy selection → 15M precision entry
⚠️ Important Notes
Learning Reset: Memory wipes on chart reload; rebuild required
Min Samples (50): Classification disabled prior; wait phase mandatory
FIFO Memory: Old patterns auto-discarded for regime adaptation
Compute Load: Memory=2000 + k=50 is heavy; use 500/15 on low-spec
Feature NaN: Features undefined initially; accumulation starts after structLen+50 bars
Rare regimes: Reversal/Breakout occur infrequently; small N = unstable stats
Forward Window Lag: Most recent N bars not yet labeled; memory commit delayed
🚨 Disclaimer
This indicator is a machine learning demonstration provided for educational and research purposes only and does not constitute financial advice. k-NN classification results derive from statistical similarity to past patterns and do not guarantee future profits. Reliability depends on market conditions, asset, and timeframe. Combine with independent analysis and proper risk management. Indicator
AetherEdge NeuraNet Trend Predictor🖊️ Overview
AetherEdge NeuraNet Trend Predictor is a next-generation AI-driven trend forecasting indicator that fully implements a true Multi-Layer Perceptron (MLP) neural network in Pine Script. Nine-dimensional features flow through input → hidden → output layers, with weights self-updating every bar via backpropagation + Nesterov momentum + L2 regularization. This is the pinnacle of machine learning engines on PulseWire—a system that perpetually evolves with market structure changes.
🔶 Key Features
True MLP Implementation: 9-H-1 architecture (hidden 3–12 neurons)
Online Learning: Deterministic gradient descent updating weights every bar
Nesterov-Style Momentum: Accelerated convergence via gradient inertia
L2 Regularization: Overfitting suppression
Xavier Initialization: Theoretically optimal weight init
Binary Cross-Entropy Loss: Optimal for probabilistic prediction
9-D Feature Engine: Price/EMA/RSI/ATR/Volume/MACD/BB/DMI/Structure
Robust Normalization: z-score based statistical standardization
Live Accuracy Tracking: Cumulative prediction accuracy %
Loss EMA Monitoring: Visualized learning progress
Network Visualization: Real-time input → hidden activation → output
Probability Bar (right edge): 10-tier prediction visualization
Confidence Computation: Auto-calculated via |prob - 0.5| × 2
AI VERDICT: 5-tier decision support messaging
🧠 Technical Architecture
This indicator is an advanced neural network implementation grounded in genuine deep learning theory.
Network Structure:
Input layer: 9 neurons (feature vector)
Hidden layer: 3–12 neurons (tanh activation)
Output layer: 1 neuron (sigmoid, probability)
Total params: 9×H + H + H + 1
Feature Engineering (9-D):
f1: Price/EMA Fast deviation
f2: EMA Spread (Fast vs Slow)
f3: RSI normalized
f4: ATR% z-score
f5: Volume z-score (log-transformed)
f6: MACD Histogram normalized
f7: Bollinger Band Position
f8: DMI differential
f9: Structural position (high/low range)
Forward Pass:
h_j = tanh(Σ x_i × W1_ij + b1_j)
prob = sigmoid(Σ h_j × W2_j + b2)
Backpropagation:
Output error: err = ŷ - y (BCE gradient)
Hidden error: δ = err × W2 × (1 - h²) (tanh derivative)
L2-regularized gradient: g = δ × x + λ × W
Nesterov update: v = β×v - η×g; W += v
Target Signal: Next bar direction (close > close )
Warmup: Signals activate after 200 bars of training
Learning Statistics: Train Steps / Accuracy / Loss EMA / 100-bar Loss
⚙️ Recommended Settings & Tuning Guide
Crypto Defaults:
BTC (1H–4H): Standard (Hidden=6, η=0.05, β=0.9)
ETH (15M–1H): Hidden=8, η=0.03 for precision
SOL (high-vol): λ=0.005 to enhance overfitting suppression
XRP (range): Hidden=4, η=0.07 for rapid adaptation
Learning Rate (η):
0.01–0.03: Stability-focused, long-term trends
0.05: Balanced (recommended)
0.1–0.2: Fast adaptation for regime shifts
Hidden Neurons:
3–4: Simple, low overfit risk
6: Balanced (recommended)
8–12: High capacity, requires sufficient data
Warmup Period:
200 bars: Standard
500 bars: High-precision focus
100 bars: Quick activation
Thresholds:
Conservative: Long=0.75, Short=0.25, MinConf=0.7
Standard: Long=0.65, Short=0.35, MinConf=0.6
Aggressive: Long=0.6, Short=0.4, MinConf=0.5
💡 How to Use in Practice
VERDICT = STRONG LONG CONVICTION: Neural net at >80% conviction—strongest entry
Accuracy > 55%: Network in hot streak, high signal trust
Accuracy < 50%: Regime shift in progress, stand aside
Loss EMA declining: Learning healthy, precision improving
Confidence > 70%: High-quality signal, full position consideration
Feature panel watch: All features unidirectional = multi-faceted consensus
Hidden activations: ●●● (strong) aligned = internal network consensus
Probability bar extreme (>90%): Watch for reversal risk
AetherEdge Synergy:
All-in-One Dashboard HIGH-CONVICTION + NeuraNet STRONG = dual AI confirmation
Self-Evolving S/R Grid strong line + NeuraNet aligned = ML × RL fusion
Liquidity Sweep Bull Sweep + NeuraNet > 0.8 = institutional + AI sync
Multi-TF: 4H for direction → 1H for NeuraNet wait → 15M for precision entry
⚠️ Important Notes
Learning Reset: Weights initialize on every chart reload—warmup mandatory
Initial Warmup: Minimum 200 bars required before predictions
Accuracy Metric: Cumulative; treat <100 samples as preliminary
Overfit Risk: Excess Hidden + low λ = overfitting—balance carefully
Diverging η: η > 0.2 risks weight explosion—use cautiously
Regime Changes: Sudden market shifts temporarily reduce Accuracy, auto-recovers
Compute Load: Hidden=12 is heavy—use 6 on low-spec setups
🚨 Disclaimer
This indicator is a machine learning demonstration provided for educational and research purposes only and does not constitute financial advice. Neural network predictions are statistical estimates based on historical patterns and do not guarantee future profits. Do not blindly trust AI model outputs—combine with independent analysis and proper risk management. Indicator
Internal & External MSSHere is a quick breakdown of what it does and its key features:
Dual Tracking (Internal vs. External): It calculates two different sets of market swings simultaneously. It tracks smaller, short-term price movements (Internal MSS) and larger, structural price movements (External MSS).
Overlap Prevention: To keep your chart clean, if a short-term Internal shift and a long-term External shift occur at the exact same price level, the script is programmed to only draw the more significant External level.
Wick vs. Body Calculation: You can toggle the settings to define market structure using either the extreme highs/lows of the candle wicks, or the real bodies (open/close) of the candles.
Smart Label Placement: Breakout lines are drawn horizontally from the broken swing point, and text labels (like "E-MSS" or "i-MSS") automatically float above or below the lines so they don't collide with the drawings.
Fully Customizable Visuals: Through the settings menu, you can easily change the line colors, thickness, dashed/solid styles, and the exact text used for the labels without needing to edit the code again. Indicator
Indicator
ICT Killzones [by fantasio]# ICT Killzones & Pivots
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**ICT Killzones & Pivots** visualizes the main institutional trading sessions based on Michael Huddleston's ICT (Inner Circle Trader) methodology: Asia, London, NY AM, NY Lunch and NY PM.
**🕐 Killzones**
Colored session boxes with customizable labels, transparency and text display.
**📐 Pivots**
High/low pivot lines per session, with optional midpoint. Extend until mitigated or past mitigation, for the most recent session or all of them. Optional price labels on left or right side of the chart.
**📅 Day / Week / Month**
Opening price lines with optional High/Low levels and period separators.
**⏰ Opening Prices & Timestamps**
Up to 8 custom opening price lines (True Day Open, etc.) and 4 vertical timestamp lines.
**📊 Range Table**
Per-session range display with configurable average length.
**⚙️ Settings**
Timezone, timeframe limit, line styles, label size and transparency are all fully customizable. Drawings above the selected timeframe are automatically hidden.
**🔔 Alerts**
Pivot breaks and HTF High/Low breaks supported.
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*ICT session and pivot concepts are based on the methodology developed by Michael Huddleston (Inner Circle Trader).*
Indicator
Cloud Institutional Bands | Rainbow MatrixGENERAL OVERVIEW
The Cloud Institutional Bands is a statistical price-envelope indicator that maps institutional accumulation and exhaustion zones using deviation channels built on a Log-Normal regression anchored to a dynamic VWAP. Instead of treating the chart as a series of fixed support and resistance levels, the indicator continuously classifies the current price into one of four statistical regimes — and colors the chart accordingly.
The main goal of this indicator is to give traders a clean, automatic read on how stretched price is relative to its own statistical baseline — without having to manually identify trend strength, overextension, or exhaustion zones bar by bar. Every band you see on the chart represents a specific deviation from the volume-weighted regression base, and every color tells you which statistical zone is currently active.
It plots four pairs of deviation bands (eight bands in total: four above the regression base and four below), each calibrated to a Fibonacci-proportioned sigma multiplier. Combined with the dynamic VWAP and the Zone Info Panel, the indicator gives a complete read on directional bias, statistical position, and proximity to extreme zones — all from a single visual.
This indicator was developed for traders who already understand band-based indicators (Bollinger, Keltner, Donchian) and want a statistically corrected envelope that handles asymmetric price distributions properly, particularly during volatility expansion phases.
WHAT IS THE THEORY BEHIND THIS INDICATOR?
Most envelope indicators on PulseWire — Bollinger Bands, Keltner Channels, and their derivatives — share a common architectural choice: they apply standard deviation directly to the price series, using a Simple Moving Average (or similar linear estimator) as the central tendency. This treats price as a symmetric variable.
The problem: price is not symmetric. Price has a hard floor at zero and unbounded upside. Its returns follow a log-normal distribution, not a normal one. Applying linear statistics to asymmetric data introduces a systematic bias — bands that are too wide on one side and too narrow on the other, especially during volatility expansion. This bias becomes most visible at exactly the moments traders need accuracy most: trend climaxes, blow-off tops, capitulation lows.
This indicator addresses that bias by performing the regression in log space. The price series is first transformed via the natural logarithm, the linear regression is fitted on the log-prices, the standard deviation of the residuals is computed, and the resulting deviation bands are exponentiated back to price space. The math is standard — what makes it useful is applying it to a series that actually follows the underlying distribution it assumes.
Why traders use it: each band represents a probabilistic boundary. When price sits between the regression base and the first deviation band, it is statistically inside its normal operating range — equilibrium. When price crosses into the second band, the move has crossed into directional territory. The third band marks the threshold beyond which most of the impulse has already happened — exhaustion. The fourth band marks the tail of the distribution — a Black Swan event in Taleb's sense — where less than 1% of candles reach under normal conditions.
The dynamic VWAP overlay adds a second dimension: directional bias. While the regression bands tell you how stretched price is, the VWAP tells you whether the volume-weighted average favors buyers or sellers. Together they give a two-axis read on every bar: directional bias plus statistical zone.
CLOUD INSTITUTIONAL BANDS FEATURES
The indicator includes 6 main features:
Log-Normal Regression Engine
Fibonacci-Proportioned Deviation Bands
Dynamic VWAP with Glow
Rainbow Zone Fills
Zone Info Panel (HUD)
Black Swan Alerts
Multilingual interface and full customization across all visual layers.
LOG-NORMAL REGRESSION ENGINE
🔹 What It Does
The core of the indicator. Every bar, the engine performs four operations:
◇ Transforms the price series (hlc3) into log space via the natural logarithm.
◇ Fits a linear regression through the log-prices over the configured lookback window.
◇ Computes the standard deviation of the residuals — the gap between actual log-price and the regression line.
◇ Exponentiates the regression line and the deviation bands back to price space.
The result is a statistical baseline (the Base Line) and four pairs of deviation bands that respect the asymmetric nature of price distribution.
🔹 Method
The regression base is calculated using a standard linear regression on the log-price series. This is the classic least-squares fit — every bar in the lookback window contributes equally. The result is a baseline that represents where the market would be statistically if it were tracking its own trend perfectly.
🔹 Period
The Band Period input sets the rolling lookback window for both the regression and the standard deviation calculation. Larger values produce smoother, wider bands that respond slowly to new price action. Smaller values produce tighter, more reactive bands that follow recent volatility more closely. The default is 200 bars, calibrated for the 223-minute Bitcoin chart. For other instruments and timeframes, the period should be adjusted to match the natural cycle length of the asset.
FIBONACCI-PROPORTIONED DEVIATION BANDS
🔹 The Four Sigma Multipliers
Instead of plotting bands at integer multiples of the standard deviation (1σ, 2σ, 3σ), this indicator uses Fibonacci-inspired proportions:
◇ ±1.50σ — Breathing Zone (yellow above, green below)
◇ ±1.85σ — Alert Zone (orange above, teal below)
◇ ±2.75σ — Exhaustion Zone (red above, blue below)
◇ ±3.85σ — Black Swan Zone (purple above, aqua below)
Each multiplier corresponds to a different probabilistic regime:
◇ Breathing Zone: equilibrium. Most candles operate inside this range. Low conviction, no signal.
◇ Alert Zone: directional move in progress. Trend is asserting itself. Watch for follow-through.
◇ Exhaustion Zone: most of the impulse has already happened. Pullback probability rising. New entries in trend direction have unfavorable risk-reward.
◇ Black Swan Zone: statistical extreme. Less than 1% of candles reach this band under normal market conditions. Elevated probability of either mean reversion or volatility regime change.
snapshot
DYNAMIC VWAP WITH GLOW
🔹 What It Does
A Volume-Weighted Moving Average is plotted alongside the regression bands, using the same lookback window. The VWAP renders in teal when price trades above it (bullish bias) and in red when price trades below (bearish bias).
🔹 Glow Effect
The VWAP line carries a proximity-based glow: the closer price gets to the VWAP, the more intense the glow becomes. This visual cue prepares the eye for proximity to a high-liquidity zone, where reactions often occur.
🔹 Why It Matters
The regression bands tell you how stretched price is. The VWAP tells you what bias the volume-weighted average favors. Together they give a complete read on every bar:
◇ Price above VWAP and inside Breathing Zone: healthy uptrend.
◇ Price above VWAP and at +2.75σ: uptrend in exhaustion.
◇ Price below VWAP and at -3.85σ: capitulation or imminent reversal.
RAINBOW ZONE FILLS
🔹 What They Show
The space between adjacent deviation bands is filled with a semi-transparent color matching the zone palette. This makes the current zone immediately visible without having to read the Z-Score number — the chart background tells you the regime at a glance.
🔹 Toggleable
Fills can be turned off for traders who prefer to see only the band lines themselves. The lines alone (with the Base Line and VWAP) still provide all the information; the fills are a visual aid to make zone identification faster.
ZONE INFO PANEL (HUD)
🔹 What It Shows
A compact corner panel reports three live values:
◇ ZONE — the name of the currently active zone (e.g., "ALERT — HIGH RISK ZONE", "EQUILIBRIUM — BASE LINE")
◇ DEV. — the current Z-Score, expressed in standard deviations (e.g., "+2.44σ")
◇ VWAP — the current position relative to the dynamic VWAP ("VWAP: BUY ZONE" or "VWAP: SELL ZONE")
🔹 Why It Helps
The HUD removes the need to interpret colors and band positions visually. It tells you in plain language where price is, how stretched it is, and which direction the volume-weighted bias is leaning. Useful for live trading where decisions need to happen quickly.
🔹 Customization
The HUD can be positioned in any of the four chart corners and rendered in any of five font sizes. The display language is controlled by the System Language input.
snapshot
BLACK SWAN ALERTS
🔹 What Triggers
The indicator fires an alert when price touches the ±3.85σ band — the Black Swan zone. Two separate alerts are available: one for the upper extreme (potential capitulation top), one for the lower extreme (potential capitulation bottom).
🔹 How They Fire
Alerts are gated by barstate.isconfirmed, which means they only trigger on the close of the bar that touched the band — not intra-bar. This prevents false signals from wicks that get rejected before the bar closes.
🔹 Frequency
Each alert uses alert.freq_once_per_bar, ensuring no duplicate firings on the same candle.
MULTILINGUAL INTERFACE
The indicator supports five languages for the HUD display and alert messages: English (default), Português, Español, Русский, and 中文 (Chinese). Code, comments, and configuration tooltips remain in English regardless of the selected language.
For reference, the English text of all multilingual UI strings used in the HUD and alerts:
◇ BLACK SWAN — EXTREME HIGH / BLACK SWAN — EXTREME LOW
◇ BUYING EXHAUSTION / SELLING EXHAUSTION
◇ ALERT — HIGH RISK ZONE / ALERT — LOW RISK ZONE
◇ INSTITUTIONAL BREATHING ZONE
◇ EQUILIBRIUM — BASE LINE
◇ VWAP: BUY ZONE / VWAP: SELL ZONE
◇ ZONE: / DEV.: / VWAP:
◇ Black Swan Alert High: "Price at 4th standard deviation — EXTREME HIGH. High probability of severe reversal."
◇ Black Swan Alert Low: "Price at 4th standard deviation — EXTREME LOW. High probability of explosive reversal."
HOW TO USE
This indicator is not a signal generator. It is a state classifier: it tells you which statistical zone the current price is in, and how that zone relates to the volume-weighted bias.
🔹 Reading the Chart
◇ Identify the current Z-Score from the Zone Info Panel.
◇ Note the active zone color in the panel and on the chart fills.
◇ Combine with VWAP position for directional context.
🔹 Tactical Reading
◇ Z-Score between -1.50 and +1.50: market is in equilibrium. Mean-reversion strategies have higher edge than breakout strategies.
◇ Z-Score crossing ±1.85: breakout in progress. Trend-following entries have higher edge than fade entries.
◇ Z-Score at ±2.75: trend is mature. Trailing stops should be tightened. New entries in trend direction have unfavorable risk-reward.
◇ Z-Score touching ±3.85: Black Swan touch. Statistically the tail. Mean reversion has elevated probability — but Black Swans can also indicate regime change, where volatility expands and a new range opens. Use the Black Swan Alert to catch these events.
🔹 Multi-Timeframe Reading
◇ On lower timeframes (1m, 5m, 15m), the bands react to micro-trends and serve as dynamic support and resistance.
◇ On higher timeframes (1h, 4h, daily), the bands map macro regime — the outer bands at higher timeframes represent multi-day exhaustion zones.
INPUTS EXPLAINED
🔹 System Language
Display language for the HUD and alert messages. Options: English (default), Português, Español, Русский, 中文 (Chinese).
🔹 Band Period (bars)
Rolling lookback for the Log-Normal regression and the VWAP. Range 50–500, default 200. Higher values produce smoother, wider bands; lower values produce tighter, more reactive bands.
🔹 Show Thermal Zone Fills
Toggle for the semi-transparent rainbow fills between adjacent bands.
🔹 Show Black Swan Glow (4th Std Dev)
Toggle for the glow effect on the outermost ±3.85σ bands. The glow intensifies as price approaches the band.
🔹 Show Base Line (Gravitational Center)
Toggle for the regression central line — the statistical baseline around which the bands are computed.
🔹 Show Dynamic VWAP (Macro)
Toggle for the volume-weighted reference line with proximity glow.
🔹 Show Zone Info Panel
Toggle for the corner HUD reporting current zone, Z-Score, and VWAP position.
🔹 Panel Position
Position of the HUD on the chart. Four corners available: Top Right (default), Top Left, Bottom Right, Bottom Left.
🔹 Font Size
HUD font size. Options: Tiny (default), Small, Normal, Large, Huge.
🔹 Black Swan Alert (4th Std Dev touch)
Toggle for the alerts that fire when price touches the ±3.85σ band. Two alerts: one for the upper extreme, one for the lower extreme.
IMPORTANT NOTES
The Cloud Institutional Bands works on any timeframe. The Band Period default of 200 is calibrated for the 223-minute chart and may need adjustment for other timeframes — a good rule of thumb is to set the period to approximately one full daily cycle for the chart timeframe (e.g., 288 bars for 5-minute charts, 96 bars for 15-minute charts).
The indicator works best on instruments with reliable volume data: crypto perpetual contracts, large-cap equities, major forex pairs. On low-volume instruments, the dynamic VWAP component becomes less reliable, though the regression bands continue to function correctly.
Alerts fire once per confirmed bar. Historical bars never repaint after they close. The live bar updates intra-bar as expected for a real-time indicator.
The four sigma multipliers (1.50, 1.85, 2.75, 3.85) are intentionally non-standard. They are Fibonacci-inspired proportions, not arbitrary choices, and they map to four behavioral regimes derived from observation rather than to integer statistical thresholds.
Pine Script v6. Open-source under Mozilla Public License 2.0.
UNIQUENESS
The Cloud Institutional Bands is unique in three ways. First, it performs the regression in log space, addressing the asymmetric nature of price distribution that linear estimators (such as the Simple Moving Average used by Bollinger Bands) fail to account for. This produces bands that behave correctly during volatility expansion phases, where standard envelopes show systematic bias. Second, it uses Fibonacci-proportioned sigma multipliers (1.50, 1.85, 2.75, 3.85) instead of integer steps, mapping the bands to four behavioral regimes — breathing, alert, exhaustion, and Black Swan — that correspond to observable phases of institutional order flow rather than to arbitrary thresholds. Third, it integrates a dynamic VWAP overlay with proximity-based glow alongside the regression bands, giving traders a two-axis read on every bar: how stretched price is statistically, and which direction the volume-weighted bias favors. The combination of log-space regression, Fibonacci sigma calibration, and integrated VWAP context produces a statistical envelope that behaves differently from standard band-based indicators, particularly at trend climaxes and capitulation events where standard envelopes are least reliable. Indicator
Precision ORB FrameworkPrecision ORB Clean v17 is part of the Precision ORB Framework — a structured futures trading model developed around opening range behavior, VWAP positioning, liquidity sweeps, market acceptance, and clean breakout confirmation.
This framework was built specifically for NQ and index futures execution, combining:
• Opening Range Breakouts (ORB)
• VWAP trend alignment
• Liquidity sweep detection
• Acceptance candle confirmation
• Trend bias filtering
• REV? rejection warnings
• Dynamic TP1 / TP2 targeting
• Price discovery grid targets
The companion “Precision Entry Assistant” indicator was developed alongside this framework to assist with lower timeframe execution, market state filtering, and cleaner trade timing.
This is not intended to be a prediction tool or signal-selling script. The framework is designed to help traders structure decision-making around momentum, liquidity, volatility expansion, and disciplined execution.
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