Quant

Quant Regime Oscillator [JOAT]════════════════════════════════ QUANT REGIME OSCILLATOR ════════════════════════════════ A separate-pane composite oscillator that fuses two classic quant signals — how stretched price is from its own mean, and how strong its momentum is relative to recent volatility — into a single bounded line from -100 to +100 . A Kaufman Efficiency Ratio regime filter then decides whether the market is Trending , Mean-Reverting , or Random , so signals only fire when conditions actually support them. ▎ WHAT IT DOES It condenses mean-deviation and normalized momentum into one clean, smoothed oscillator, classifies the current market regime, and prints sparing BUY / SELL labels only at stretched extremes that align with a trend. A dashboard summarizes every moving part at a glance. ▎ HOW IT WORKS • Z-Score component — price is measured against its moving mean and standard deviation, then clamped at ±3σ and rescaled to ±100. This captures how far price has deviated from equilibrium. • Momentum component — rate-of-change is normalized by its own standard deviation (volatility-adjusted), clamped at ±3σ and rescaled to ±100. This measures thrust independent of raw price size. • Composite blend — the two components are combined using your chosen weights, EMA-smoothed, and clamped into a single -100..+100 oscillator , with an EMA signal line layered on top. • Regime filter — a Kaufman Efficiency Ratio (directional change ÷ total path) scores 0..1. High values = trending; low values = mean-reverting; in-between = random. Direction is read from price versus its mean. • Signal gate — a raw BUY needs the oscillator to cross up over its signal, to have recently visited oversold , and to sit inside a trending-up regime. SELL is the mirror. A cooldown enforces a minimum bar gap so labels stay few and never stack. • Divergence — pivot highs/lows on the oscillator are compared to price pivots to flag regular bullish and bearish divergences. ▎ HOW TO USE IT • Read the oscillator like a bounded momentum gauge — blue above zero, magenta below. Pushes into the dotted ±80 extreme bands mark exhaustion zones. • BUY pills appear at oversold turns inside up-trends; SELL pills at overbought turns inside down-trends. Treat them as context-filtered setups, not standalone triggers. • Use the regime as your playbook: in Trending , favor pullback continuation; in Mean-Rev , fade the band extremes; in Random , stand aside or size down. • The subtle pane background tint mirrors the regime — blue for trending-up, magenta for trending-down, grey for mean-reverting. • Divergence dots on the oscillator hint at weakening thrust; combine with your own structure and risk levels. ▎ KEY SETTINGS • Engine — Z-Score length, Momentum (ROC) length, per-component weights, oscillator smoothing, and signal-line length. • Regime — Efficiency Ratio window plus the Trending and Mean-Revert thresholds that split the three regimes. • Signals — Overbought / Oversold levels, OB/OS recall window, minimum bars between signals (cooldown), and divergence pivot length. • Visuals — toggle the gradient fill, oscillator line, signal line, regime background, and signal markers. • Dashboard — show/hide, position, and text size. ▎ DASHBOARD A compact blue/magenta panel reporting: the current bias (Long / Short / Flat), the composite score, raw Z-Score in σ, the momentum value, the active regime with a strength percentage, the OB/OS state , any live divergence , and the current signal status. ▎ ALERTS • QRO — Long — oscillator crossed up from oversold in a trending-up regime. • QRO — Short — oscillator crossed down from overbought in a trending-down regime. • QRO — Any Signal — fires on either a long or short signal. ▎ NOTES • Works on all timeframes and all assets — the oscillator is self-normalizing, so it adapts to the instrument automatically. • Every visual layer is toggleable for a clean chart; the cooldown keeps markers sparse on any timeframe. • Signals confirm on the closed bar and are non-repainting once the bar completes; divergence markers reference confirmed pivots offset back by the pivot length. For research and education only. This is not financial advice. No indicator can predict the future, and past behavior does not guarantee future results. Always do your own analysis and manage your own risk. Made with passion by JackOfAllTrades ⚡

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của officialjackofalltrades

Quant Confluence Engine [JOAT]Quant Confluence Engine Scores several independent market factors into one weighted composite, so signals fire on agreement across dimensions rather than on any single trigger. What it is Single-factor signals are fragile: a momentum cross, a moving-average flip or a volume spike each fails often on its own. This engine measures several independent factors, normalises them to a common scale, and blends them into one bipolar confluence score. A signal is produced only when enough factors line up, and the transparency of the score lets you see exactly why. It is an original scoring framework, not a bundle of overlaid classic indicators. How it works • The factors — the engine evaluates a set of complementary dimensions, each capturing a different aspect of the tape: trend alignment, momentum, volatility regime, volume behaviour, price structure and stretch relative to a mean. Each factor is computed with a standard, well-understood method and then scaled so it contributes fairly. • Normalisation — every factor is converted to a bounded contribution, so no single input can dominate the composite purely because of its raw magnitude. • Composite score — the contributions are combined into one signed 0-centred score. Positive means the factors lean bullish, negative bearish, and the magnitude expresses how strong the agreement is. • State-machine signals — a Buy fires when the score crosses into sufficient bullish agreement from a non-bullish state; a Sell is the mirror. Because a signal requires a genuine state change, the engine will not re-fire the same direction bar after bar — signals are self-spacing by construction. Trade levels Each signal draws a red risk box to the ATR stop and a green reward box to the third target, with inner dividers and right-edge labels for entry, stop and each take-profit at your R multiples. The dashboard An adjustable factor-grid panel shows each factor's current lean (up or down) alongside a bipolar composite-score headline, the active signal, a conviction reading, and a live first-target-before-stop tally from closed bars only. The grid makes it obvious which factors are driving or vetoing a setup. How to use it • Works on any asset and timeframe; the factors adapt to the data. • Read the grid before acting — a signal backed by broad agreement differs from one carried by a single strong factor. • Raise the agreement requirement for fewer, higher-conviction signals, or lower it for more frequent ones. Settings Per-factor lengths and weights, the agreement threshold, ATR risk multiple and target R multiples, plus visual and dashboard controls. Originality and usefulness The value is the framework itself: a normalised, weighted multi-factor score with a transparent per-factor readout and a state-machine trigger that prevents signal spam. It is designed so a trader can inspect the reasoning, not just accept a label — which is precisely what a confluence approach should offer. Notes and limitations • Confluence reduces some false signals but does not remove them; correlated factors can all be wrong together in unusual conditions. • Weighting is a design choice — different weights suit different markets, so treat the defaults as a starting point. • The tally reflects only past bars on the current chart and is not a prediction. • Educational and analytical tool, not financial advice. — made with passion by officialjackofalltrades

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của officialjackofalltrades

GBR MTF Adaptive Channel [Ghost Lines]The GBR MTF Adaptive Channel solves the biggest problem day traders face: tunnel vision. When you are hyper-focused on the 1m or 5m chart, it is easy to miss that you are driving straight into a massive 4H resistance wall. This indicator projects Higher Timeframe (HTF) volatility channels directly onto your execution chart without any of the visual clutter. 🔥 Core Features: Triple Threat (MTF): Simultaneously plots the adaptive ATR/Keltner channel for your current timeframe, a Medium HTF (e.g., 1H), and a Macro HTF (e.g., 4H). Ghost Lines: No distracting fills or blinding colors. The higher timeframe channels are rendered at 75% transparency using sleek Cyan and Purple, acting as silent guardrails in the background. Dynamic Floating Labels: You never have to guess which line is which. Clean text labels float gracefully on the right side of the chart, clearly identifying the exact timeframe and level (e.g., "4H Resistance"). Institutional Adaptability: The default ATR multiplier is set wider (3.0) to filter out market noise and only highlight true structural extremes. Fully customizable in the settings. 🎯 How to use: Keep this running in the background while using your primary execution setups. Use the Macro (Purple) and Medium (Cyan) outer bands as absolute hard stops or ultimate take-profit targets.

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GBR Quant Master Suite [Smart S/R + Trend]The GBR Quant Master Suite is the ultimate all-in-one algorithmic weapon for professional prop firm traders and aggressive day traders. It combines a highly sensitive multi-timeframe (MTF) trend ribbon with a smart, self-cleaning Support & Resistance network. Built for those who demand raw price action without the visual clutter. 🔥 Core Features: Self-Cleaning Adaptive S/R: Automatically detects major structural pivot points and draws clean support/resistance lines. Auto-Breakout Logic: The moment a level is broken by a candle close, the long line is instantly deleted to keep your chart pristine. It leaves behind a minimal Break ▲ or Break ▼ marker for historical reference. Dynamic Trend Ribbon: A zero-lag, smoothed moving average band that instantly shifts between Bullish (Green) and Bearish (Red) to keep you on the right side of the momentum. Minimal Radar Dashboard: A sleek, transparent, non-intrusive HUD in the bottom right corner that continuously tracks the exact price distance of the closest active Resistance and Support levels. Institutional Grade: Zero noise. No repainting. Just pure market geometry. 🎯 How to execute: Wait for the Trend Ribbon to align with a breakout. If the ribbon turns Green and you get a Break ▲ signal at a major resistance, that is your sniper entry.

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GBR Quant Trend & Reversal GBR Quant Trend & Reversal is a zero-noise, institutional-grade indicator built for prop firm traders and aggressive day traders. Unlike lagging kernel regressions, this dynamic ribbon is highly sensitive to real-time price action, getting you into the move earlier. 🔥 Core Features: Dynamic Trend Ribbon: Instantly shifts between UV Green (Bullish) and UV Red (Bearish) the moment price crosses the moving average band. Multi-Timeframe (MTF) Ready: Trade on a 5m chart while plotting a 1H or 4H trend ribbon to stay aligned with the macro order flow. Historical Reversal Zones: Automatically plots dashed UV lines at major structural pivot highs and lows. Use these visual markers to backtest past liquidity sweeps and identify future high-probability reversal zones. Clean UI: Stripped of unnecessary clutter. Just raw data and striking UV colors.

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Brownian Motion Residual [JOAT]BROWNIAN MOTION RESIDUAL A regime classifier rooted in the sqrt(T) scaling law of geometric Brownian motion. Under a true random walk, the standard deviation of T-bar returns scales as σ₁ · √T — that is the central fact of Brownian motion in continuous time. Markets violate this scaling in revealing ways: when they trend, dispersion at long horizons grows faster than √T; when they mean-revert, it grows slower. Brownian Motion Residual measures that violation across three horizons simultaneously, aggregates it, and surfaces a single Z-like residual that classifies the market into Strong MR / MR / Random / Trend / Strong Trend. The sqrt(T) scaling law, restated For a Brownian process with per-bar volatility σ₁: σ(T-bar return) = σ₁ · √T For a real market the observed σ at horizon T can be measured directly. The residual is the deviation of the observed value from the Brownian-implied value: residual(T) = σ_observed(T) − σ₁ · √T When the residual is positive , dispersion at T is greater than Brownian predicts — the market is trending (price travels further than a random walk in T bars). When it is negative , dispersion is less than Brownian predicts — the market is mean-reverting (price ends up closer to home than a random walk would). Optional normalisation by σ₁ · √T turns the residual into a unit-less percentage of expected dispersion, so the same threshold values are meaningful across instruments and timeframes. Three horizons, weighted blend A single horizon is noisy. Brownian Motion Residual reads three horizons simultaneously (default 5 / 20 / 100 bars), each independently toggleable and weighted (default 1.0 each). The horizons are aggregated into a single residual line — the script's headline metric. Toggling off the short horizon makes the read smoother and slower; toggling off the long horizon makes it more reactive. Configurable. A configurable EMA on top of the aggregated residual suppresses single-bar noise without lagging the regime view. Two-tier classification The aggregated residual is mapped to one of five regimes by two symmetric thresholds (default ±1 mild, ±2 strong): Strong Trend — residual > +2. Aggressive momentum regime. Trend — residual between +1 and +2. Trending. Random — residual between −1 and +1. Brownian-like. MR — residual between −2 and −1. Mean-reverting. Strong MR — residual < −2. Aggressive reversion regime. Visual system Slope-coloured residual line with configurable width and optional area fill under it (transparency configurable). Zero line and ±1 / ±2 threshold lines (toggleable). Background tint by regime (subtle 88 transparency default) — teal trend, lavender MR, mint random. Per-horizon plots (toggleable, off by default) — each horizon's residual as a faint dotted overlay; useful for seeing which horizon is driving the read. Regime-change dots above the line at every confirmed flip. A locked Aurora palette (teal trend / lavender MR / mint random on a deep-night ground) gives the pane a distinctive structural identity. Dashboard Monospaced table, positionable to any of nine corners, with vertical row-fade. Surfaces: Aggregated residual (raw and smoothed). Regime classification with glyph. σ₁ value (the Brownian anchor). Per-horizon residuals (h1 / h2 / h3) when enabled. Bars in current regime. Distance to nearest threshold. Optional fancy Unicode header for the institutional aesthetic. Alerts Three alert conditions, each independently controllable: Regime Change (any classification flip) Strong threshold cross (±2) Mild threshold cross (±1) — off by default How to read it Three reads, in order of conviction: Strong Trend / Strong MR entry — the highest-conviction read. The market has decisively departed from Brownian scaling in one direction. Pair with a momentum tool in Trend regimes, a reversion tool in MR regimes. Residual crossing zero — the regime fault line. Even before crossing a threshold, a sustained sign flip means the underlying distribution has rotated; the next threshold cross will confirm the new regime. Per-horizon disagreement (when enabled) — when the short horizon is in trend regime but the long horizon is in MR regime, the market is in a nested state: short-term momentum inside a longer reversion. This is the textbook setup for fade-the-extreme intraday plays inside a wider range. Suggested settings Defaults (σ₁ window 100, observed σ window 60, horizons 5/20/100, equal weights, log returns ON, normalisation ON) are tuned for 15m–4H on liquid markets. For lower timeframes drop horizon 3 to 50. For HTF (daily+) raise horizon 3 to 200 and σ₁ window to 200. Log returns are theoretically correct and the recommended default — the script's regime classification depends on the scaling law, which assumes log returns; switch off only for research. Originality The √T Brownian scaling law is textbook continuous-time finance — the central piece of Bachelier's 1900 thesis and the foundation of every diffusion model in pricing. The implementation here — the per-horizon σ measurement pipeline, the σ₁-anchored Brownian baseline with optional normalisation, the three-horizon weighted aggregation, the EMA-smoothed residual classifier with two-tier thresholds, the per-horizon overlay layer, the regime-tinted background, and the dashboard — is JOAT-original. No third-party code reused. The use of residual against Brownian as a regime classifier is the original quantitative contribution. Limitations The √T law is exact only for Brownian motion — real markets have fat tails, autocorrelation, and discrete bars, so the measured "residual" is always non-zero even in a regime that looks random. The thresholds (±1 / ±2) are calibrated to be the regime boundaries empirically; tighten or loosen if your instrument has unusual variance behaviour. Per-horizon σ values need their respective windows populated to be meaningful — early bars give a warm-up read. — -made with passion by jackofalltrades

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của officialjackofalltrades

Multi-Factor Divergence Matrix Multi-Factor Divergence Matrix OVERVIEW Most divergence tools read one oscillator against price. The Multi-Factor Divergence Matrix reads fifteen independent lenses at once, standardizes them onto a single shared standard-deviation (sigma) scale, and then organizes them into a structure: lenses roll up into 14 aspects (distinct questions), aspects roll up into 6 families (factor classes), and families roll up into one composite. Divergence is detected five different ways on that construction, and a built-in calibration harness scores whether each method has actually carried any edge on your instrument. The core idea: a price move is more trustworthy when many independent reads confirm it, and a divergence is more meaningful when it shows up across different kinds of information — not just three flavours of momentum that all say the same thing. WHY THE COMPONENTS BELONG IN ONE SCRIPT (mashup rationale) This is a deliberate multi-factor engine, not indicators stacked side by side. Every part answers the same question — is this price move confirmed, and by how broad a set of independent reads? — and each fixes a blind spot of the others: A single oscillator can only diverge one way. Fifteen lenses across six families let price be unconfirmed by momentum, by trend efficiency, by location, by volatility, by order flow, or by cross-asset carry — independently. Raw factor-stacking double-counts. Standardizing every lens to one sigma scale makes them directly comparable, and grouping correlated lenses into aspects (then families) means consensus is counted where it carries independent information, not where it merely repeats. One detection method misses what another catches. Pivot divergence is precise but lags; slope fires earlier; correlation is continuous; sequential catches structured exhaustion; the intra-family split is often the very first crack. Run together, they cover the ways divergence actually appears. Assertions are cheap. The calibration harness ties the whole construction back to realized forward outcomes, per method, so the tool reports whether its own signals carry edge rather than claiming they do. Remove any one layer and the central question is answered less completely — which is what makes them one tool. HOW IT WORKS The 15 lenses → 14 aspects → 6 families Momentum — oscillatory (RSI + Know Sure Thing), velocity (low-lag two-pole strength), stationary (fractional-difference of log price) Trend / Efficiency — path quality (Kaufman efficiency ratio), extension (SAR distance in ATR units), rollover (dual-horizon efficiency gap) Location / Mean — volume-anchored (VWAP deviation), geometric (linear-regression deviation) Volatility — realized expansion (directional range), implied-vs-realized (variance-risk-premium spread) Flow / Volume — net pressure (cumulative signed-volume delta, lower-timeframe estimated), volume-weighted (Money Flow Index) Cross-Asset — carry (futures-vs-spot basis), fear (volatility-index vs price) Each lens is z-scored over a rolling window (up = bullish). Correlated lenses that answer the same question (e.g. RSI and KST) are averaged into one aspect — the anti-redundancy step. A family agrees only when a majority of its filled aspects align; when its aspects disagree it is flagged SPLIT. Two consensus axes, both at family resolution, auto-scaled by timeframe Extreme-count — how many families are stretched to their extreme. Divergence-count — how many families are diverging from price right now. Five detection methods Pivot — regular, hidden, exaggerated (equal-extreme) and triple divergence on the composite. Slope — price-vs-composite regression-slope sign disagreement (fires earlier than pivots). Correlation — rolling price-composite correlation flipping negative (continuous, always-on). Sequential — a structured RSI exhaustion pattern (three deeper pushes, then a turn). Leading — the intra-family SPLIT, often the first warning before a family flips. Calibration. Each event is a directional hypothesis, queued and resolved a fixed horizon later versus an ATR threshold, then compared with the unconditional same-horizon base rate. The dashboard reports, per method: number of events, Hit %, and Edge = Hit − Base. Events are logged and resolved on confirmed bars only. HOW TO USE The dashboard has two modes. Compact (default) shows the decision essentials: the composite zone, the two consensus counts (Stretched X/6 · Diverging Y/6), a one-line family summary (bull / bear / split), and the single best-calibrated method with its Edge. Pro expands this to every family row (vote arrow, aspect agreement, SPLIT flag) and every per-method calibration class. In both, a high divergence-count backed by clean family agreement is strong context; the Edge figure tells you whether that read has actually preceded a move on this symbol and timeframe. Treat consensus as context, never a standalone trigger. UNIVERSAL ACROSS MARKETS Price, high, low, the VWAP source, the spot reference symbol and the volatility symbol are all inputs, so the engine runs on any instrument and timeframe. Volume-based lenses (VWAP, flow, MFI) need real traded volume — use the futures contract, not a cash index. Defaults target NSE NIFTY index futures intraday with an NSE:NIFTY spot reference and NSE:INDIAVIX; lenses without data quietly drop out and the consensus scales to whatever stays active. ORIGINALITY The individual techniques are public and credited below. The original work is the integration: standardizing fifteen heterogeneous reads onto one sigma axis, the aspect → family → composite roll-up that counts agreement only where it is independent, the dual extreme-and-divergence consensus, the surfacing of intra-family disagreement as a leading signal, and the forward base-rate calibration over every detection method. No third-party Pine code is reused. CONCEPT CREDIT RSI, Parabolic SAR, ATR, DMI — J. Welles Wilder. Know Sure Thing — Martin J. Pring. Efficiency Ratio — Perry J. Kaufman. Money Flow Index — Quong & Soudack. VWAP and cumulative volume delta — standard public market-microstructure concepts. Fractional differentiation — the long-memory / stationarity literature (Hosking 1981; adapted for finance by M. López de Prado). Two-pole low-pass smoothing — John F. Ehlers. The basis is explained by the cost-of-carry framework (N. Kaldor 1939; H. Working 1948–49). Variance risk premium — the implied-minus-realized literature. Linear regression and price/oscillator divergence are long-established public techniques. Not affiliated with, nor endorsed by, any third party. HONESTY / LIMITATIONS Consensus is context, not a trigger. Independence is managed, not perfect — lenses inside a family still share inputs, which is exactly why consensus counts families and aspects rather than raw lenses, and why a high count is never proof. The Edge figures are in-sample, close-to-close, with overlapping forward windows and no costs — descriptive context, not a verified backtest. An Edge near zero, negative, or unstable across timeframes is the harness honestly telling you the method has no reliable edge on that instrument; do not tune parameters until it turns green — that is curve-fitting. Divergence and reversals confirm a few bars after their pivot (inherent to honest pivot detection). Nothing here predicts price. DISCLAIMER Research and educational tool only. NOT financial advice and NO guarantee of profitability or accuracy. Indicators describe past behaviour; they do not predict the future. Trading carries risk of loss. Test out-of-sample and make your own decisions. The author accepts no liability for any use of this script.

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của Market_Logic_India

Fractional-Diff Momentum Oscillator Fractional-Diff Momentum Oscillator What it does The Fractional-Diff Momentum Oscillator is a momentum line, scaled in standard-deviation (σ) units around a zero balance, built on the fractional differencing of price — the smallest amount of differencing that makes price statistically stationary while still keeping its memory. Why this is different (and original) Almost every momentum oscillator works on returns — that is, price differenced once (integer order d = 1). Returns are stationary, but differencing once erases the series' memory, throwing away the slow, persistent structure that carries trend information. Raw price (order d = 0) keeps all the memory but is non-stationary and unusable by most statistical tools. Fractional differencing uses a non-integer order (typically 0.3–0.6) that sits between the two: it removes just enough drift to reach stationarity while retaining long memory. The result is a momentum series that is both well-behaved and information-rich. This transform is standard in quantitative research but rare on retail charts, where oscillators almost universally difference once and discard the signal. That is what makes this original: it is a momentum oscillator built on a memory-preserving transform rather than plain returns. How it works The fixed-width fractional-difference weights are generated recursively — w(0) = 1, w(k) = −w(k−1)·(d − k + 1)/k — and truncated once they fall below a tolerance, giving a finite window. Those weights are convolved with log-price to produce the fractionally-differenced series. That series is then z-scored over the normalization window and lightly smoothed into the σ oscillator you see, centred on zero. How to use it Zero is the balance line. Above zero = net up-momentum; below = net down-momentum. Dashed σ bands mark stretched momentum; dotted bands mark extremes prone to exhaustion (red on top, green on the bottom in the standard reading). Zero-crosses (triangles) are momentum-flip events. Divergences (circles) warn when price makes a new extreme that momentum does not confirm. Read the EDGE row. The dashboard runs a live forward-return harness: for every momentum flip it checks whether a favourable move (≥ k×ATR within the horizon) actually occurred, and compares that Hit % against the unconditional Base %. EDGE = Hit − Base is the honest measure of whether the signal adds information on your instrument and timeframe. If EDGE is near zero, the signal is not helping there — and the tool says so. Settings guide 01 · Data & Differencing — source, log-price toggle, differencing order d, weight tolerance, max window, and a universal price source for the harness. 02 · Normalization — z-score window and output smoothing. 03 · Calibration — horizon, favourable-move threshold (×ATR), base-rate window. 04 · Bands — momentum and extreme σ bands; divergence pivot. 05 · Display & Theme — visual style (gradient area + glow / histogram / line), regime tint, dashboard, colors. Non-repaint The weights are fixed and the convolution reads only closed bars — no recalculation of past values, no future leak. Concept credit Fixed-width window fractional differentiation — Marcos López de Prado, Advances in Financial Machine Learning (2018). Fractional integration in time series — Hosking (1981); Granger & Joyeux (1980). Disclaimer For research and education only. Not financial advice, not a recommendation, and not a guarantee of future results. All statistics shown are in-sample, close-to-close, and exclude costs — a study aid, not a backtest. Do your own research and manage your own risk.

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của Market_Logic_India

Advanced Fear & Greed Cycle (Quant Model)## Overview The **Advanced Fear & Greed Cycle (Quant Model) v6** is a pure quantitative oscillator designed to decode market sentiment by measuring the architectural divergence between smart money accumulation and retail distribution. Fully upgraded to Pine Script v6, this script addresses standard oscillator limitations by implementing dynamic time-frequency normalization ($0-100$ fixed scale). Unlike standard sentiment proxies, this model filters out price-action noise by isolating volume flows, directional volatility, and mean-reversion extensions simultaneously. --- ## Mathematical Architecture & Core Engines ### 1. Directional Volatility Engine Standard models treat volatility expansions as pure panic. This algorithm isolates **Directional Volatility**: - A 14-period Average True Range (ATR) is mathematically normalized over a dynamic 90-day rolling quarter (`lookback`). - **Trend Filter:** Volatility is converted into the `vol_fear` metric **only** if the closing price is below its 14-period Simple Moving Average (`is_descending`). Upside expansions (bullish breakouts) are correctly filtered out to prevent false panic readings. ### 2. Normalized Volume & Flow Sentiment Liquidity and order-flow tracking are computed via a three-layered matrix: - Normalized Volume spikes relative to the quarterly window. - Inside-candle Selling Pressure ( AMEX:HIGH - Close$ versus the overall candle range). - A normalized On-Balance Volume (OBV) structure to track mathematical capital inflows and outflows. ### 3. Boundary-Proof Macro Extension (Mayer Proxy) To track cyclical overextensions, the script calculates the asset's percentage distance from its long-term moving average (SMA 200 on Daily, SMA 40 on Weekly charts). To solve the scale break-out issue (where different assets experience wildly different percentage extensions), a **MinMax Normalization** is applied. This compresses the structural extension into a bound $0-100\%$ scale (`extension_norm`) based on the rolling quarter's extremes. --- ## The Greed Score Synthesizer The final plotting line is the **Greed Score**, a mathematically symmetric index calculated as: $$\text{Greed Score} = \frac{(100 - \text{Fear Index}) + \text{Extension Norm}}{2}$$ This creates a fixed-bound oscillator ($0$ to $100$) that charts three distinct market phases: - 🟢 **INSTITUTIONAL ACCUMULATION (Green Zone / < 20):** High systemic fear combined with compressed macro price extensions (< 25%). Smart money absorbs panicking retail order flow near historical value areas. - ⚪ **NEUTRAL REGIME (Gray Line):** Symmetrical equilibrium where supply and demand are balanced. - 🔴 **RETAIL FOMO / BUBBLE (Red Zone / > 80):** Zero systemic fear combined with extreme quarterly price overextensions. Retail traders buying the top driven by euphoria, highlighting distribution blocks. --- ## Display Dashboard & Custom Parameters The top-right informational panel provides real-time diagnostic outputs of the quantitative data (Current Cycle State, Exact Greed Score, and Normalized Extension %). Traders can adjust the `Soglia Bolla Normalizzata` input to calibrate the macro-exhaustion scanner to specific asset classes (Equities, Forex, or Cryptocurrencies). --- Disclaimer: This tool calculates mathematical probabilities based on normalized historical structures. It does not provide definitive buy/sell signals or financial advice. Always integrate sound risk management protocols.

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Gemini Quant OS 6-in-1 Signal Gemini Quant OS — 6-in-1 Core Overview Gemini Quant OS — 6-in-1 Core is a multi-market analytical framework designed to help traders identify market structure, trend conditions, volatility expansion, and potential trading opportunities across multiple asset classes. The framework supports: Forex Gold Indices Crypto Commodities Equities This script is part of the broader Gemini Quant OS ecosystem. --- Core Concepts The framework combines multiple technical components into a single structured decision-making system, including: trend strength analysis EMA structure volatility expansion ATR-based risk zones breakout confirmation adaptive market regime filtering momentum alignment multi-timeframe bias detection The dashboard dynamically adapts to different market conditions and attempts to classify whether the market is: trending ranging volatile mixed --- Features Multi-market compatibility Signal dashboard Adaptive market bias Buy/Sell signal detection ATR-based SL/TP framework Trend and momentum filtering Volatility condition analysis --- Best Use Cases Best suited for: H1–D1 analysis swing trading momentum trading volatility expansion environments structured discretionary trading --- Notes This script is designed as an educational and analytical framework. Not financial advice.

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của Super_Trader_HK

Volume-Weighted Information Rate [AlgoPoint]🧠 The Mathematical Engine VWIR doesn't just track where the price is going; it measures the **quality** of the move using three core pillars: 1. Shannon Entropy (Information Density): By calculating the probability distribution of log returns, the script measures market "uncertainty." Low entropy indicates that the market has reached a consensus, meaning "clear information" is flowing into the price. 2. Volume-Weighted Conviction: We multiply information clarity by relative volume. If a move happens with low volume and high entropy, it is flagged as **Market Noise**. If it happens with high volume and low entropy, it is flagged as an **Institutional Surge**. 3. Z-Score Normalization: The raw data is transformed into standard deviations. This allows the indicator to maintain consistent sensitivity across all asset classes—be it Crypto, Forex, or Equities. 💎 Key Features * Info Dashboard: Real-time calculation of **Information Clarity (%)** and Market Efficiency. It tells you instantly if the current environment is "Pure Noise" or a "High-Signal" trend. * Institutional Surge Signals: The oscillator identifies "Exhaustion" and "Surge" points when the Z-Score crosses the $\pm2.0$ thresholds. * Alerts: Fully integrated with JSON placeholders for webhook automation and standard alerts for manual traders. 📈 How to Use * Trend Confirmation: Look for a VWIR Z-Score breakout accompanied by "Information Clarity" above 70%. This confirms institutional participation. * Mean Reversion: When the Z-Score reaches extreme levels (Exhaustion), expect a cooling-off period or a sharp reversal as "Information" becomes overpriced. * Noise Filter: Avoid trading when the Dashboard displays "Pure Noise." This protects your capital during choppy, low-probability conditions.

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Smart Quant Money Execution Signals [Rehankhanani]The Smart Quant Money Execution Signals indicator is a powerful multi-factor trading system designed to help traders identify high-probability market opportunities using institutional-grade logic. This indicator combines trend, momentum, strength, and price structure into a single unified framework, eliminating the need to rely on multiple separate tools. Built using a 5-factor confirmation model, the system integrates EMA trend analysis, RSI momentum, ADX strength, MACD confirmation, and Smoothed Heiken Ashi (SHA) to filter out low-quality setups and highlight only strong trading conditions. Unlike traditional indicators that rely on a single signal, this system uses a scoring-based probability engine, ensuring that trades are only generated when multiple confirmations align. This significantly improves signal quality and reduces noise in volatile markets. ⚙️ Key Features ✔ Multi-Factor Signal Engine (EMA + RSI + ADX + MACD + SHA) ✔ Smart Probability Scoring System (0% – 100%) ✔ Strong Buy / Sell Signals (80%+ Confidence) ✔ Automatic Entry, Stop Loss, and Take Profit Levels ✔ Dynamic Risk-to-Reward Ratio Calculation ✔ Smoothed Heiken Ashi for Trend Clarity ✔ Real-Time Professional Dashboard ✔ Clean BUY / SELL Labels on Chart ✔ Candle Highlighting for Signal Confirmation 🎯 How It Works The indicator evaluates 5 core market conditions: 📈 Trend Direction (EMA 9/21) 📊 Momentum (RSI) 📉 Trend Strength (ADX) 🔄 Momentum Confirmation (MACD) 🕯️ Price Structure (Smoothed Heiken Ashi) Each condition contributes 20% to the overall score, creating a total probability rating: 80% – 100% → Strong Trade Signal 60% – 80% → Moderate Setup Below 60% → No Trade / Low Probability Signals are only triggered when multiple factors align, ensuring higher reliability. 💡 Advantages of This Indicator 🔹 Reduces False Signals By combining multiple indicators, the system filters out weak setups and focuses only on high-quality trades. 🔹 Institutional-Level Logic This is not a basic crossover system — it mimics how professional traders analyze markets using confluence. 🔹 All-in-One Solution No need to switch between multiple indicators — everything is integrated into one clean system. 🔹 Clear Trade Execution Entry, Stop Loss, and Take Profit levels are automatically calculated, helping traders manage risk effectively. 🔹 Improved Trend Clarity Smoothed Heiken Ashi removes market noise and makes trend direction easier to identify. 🔹 Probability-Based Decision Making Instead of guessing, traders can rely on a structured probability model. 🧠 Best For ✔ Intraday Traders ✔ Swing Traders ✔ Crypto / Forex / Indices / Stocks ✔ Traders looking for high-probability setups ✔ Traders who prefer structured decision-making ⚠️ Important Note This indicator is designed to assist decision-making and does not guarantee profits. Always apply proper risk management and trading discipline. "Trade smarter, not harder — let multi-factor confirmation guide your decisions with precision and confidence."

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của rehankhanani

Yield Curve Monitor What you see in the preview image The chart displays the complete US Treasury yield curve as a smooth log-scaled polyline drawn to the right of the last bar, with all eleven maturities from 1-month T-bills to 30-year bonds plotted at their actual yield values. The solid line shows today's curve; the dashed line shows the curve from 21 trading days ago for direct visual comparison. Each tenor is labeled with its name and yield in percent. To the right, a comprehensive dashboard table lists every maturity with current yield, the change in basis points versus the historical reference date, and a relative magnitude bar. Below the maturity rows the four most-watched yield-curve spreads are shown (2s10s, 3m10y, 2s30s, 5s30s) with their current value, change versus history, and inversion status. The footer classifies both the current and historical curves into one of five regimes — Steep, Normal, Flat, Inverted, or Humped — using a user-selectable slope metric. What this indicator does This is a complete US Treasury yield-curve workstation built into a single TradingView pane. It solves a problem that ordinary time-series indicators cannot: the yield curve is fundamentally a cross-sectional object — yield as a function of maturity at a single moment — but standard charts plot variables against time. By rendering the curve as a polyline anchored to the right of the last bar, with maturity on a logarithmic x-axis and yield on the price axis, the indicator gives you a real, geometrically faithful view of curve shape, alongside a complete data table and historical comparison. Eleven maturities are pulled from TradingView's TVC feed at daily resolution: US01MY, US03MY, US06MY, US01Y, US02Y, US03Y, US05Y, US07Y, US10Y, US20Y, US30Y. Each tenor is requested twice — once for the current bar and once for a user-defined lookback offset — giving the indicator a paired snapshot that drives every visualization and metric. If a specific tenor is unavailable on your data plan (US20Y is the most common gap), that point silently drops from the curve and shows an em dash in the table — no errors are raised. Settings explained Display group Curves geplottet (Curves to plot) — choose which yield curves are drawn to the right of the last bar: , , or . The dashboard table always shows both datasets regardless of this setting; this toggle controls only the visual polyline. The tenor labels next to the points always reflect the curve being plotted (or the current curve when both are shown).Current onlyHistoric onlyBoth Slope-Metrik (Shape-Klassifikation) — selects which spread drives the Steep / Normal / Flat / Inverted / Humped classification shown in the dashboard footer. Six options are available: (the default, broadest possible measure), (classic NBER recession lead-indicator), (the Federal Reserve's preferred recession signal), (long-end steepness), (pure long-end term-premium proxy), and (full-spectrum slope excluding T-bills). Each metric has its own empirically calibrated thresholds (see the Methodology section), so the classification remains meaningful regardless of which spread you choose. Alerts also fire on the selected spread.30y - 3m10y - 2y (2s10s)10y - 3m (3m10y)30y - 5y (5s30s)30y - 10y (10s30s)30y - 2y (2s30s) Dashboard anzeigen — show or hide the data table. Tabellen-Position — five anchor points for the dashboard: top-left, top-right, middle-right, bottom-right, bottom-left. Historic Lookback (Daily Bars) — the number of trading days back used for the historical comparison. Default is 21, which approximates one trading month. Useful presets: 21 ≈ 1 month, 63 ≈ 1 quarter, 126 ≈ 6 months, 252 ≈ 1 year. Maximum 504 bars (≈ 2 years). Style group Five customizable colors mapped to semantic roles (Bull/Steep/Current, Bear/Inversion, Neutral/Normal, Warning/Flat/Hump, Historic). One additional input controls the polyline line width (1 to 5). All defaults match a dark theme; the colors can be repointed to fit any chart style. Alerts group Two alert toggles: one fires when the user-selected slope metric crosses below zero (inversion event), the other when it crosses back above zero (re-steepening event). Both alerts trigger on confirmed daily-bar closes only — there is no intrabar repainting. What the dashboard shows The table is divided into three blocks. Maturity block (eleven rows) — one row per tenor showing the current yield to three decimal places, the change in basis points versus the lookback date with directional color (green up, red down), and a magnitude bar built from filled and empty Unicode block characters that visualizes the yield's size relative to the highest yield in the curve. This block lets you read absolute levels and recent moves at a glance. Key Spreads block (four rows) — the four most-watched curve spreads (2s10s, 3m10y, 2s30s, 5s30s), each with current value, change in basis points versus the lookback date, and an OK / INVERTED status flag. These four are shown unconditionally regardless of which slope metric you selected for shape classification, so you always have the full institutional toolkit visible. Curve Shape block (one or two rows) — one row per plotted curve (Current and/or Historic, depending on the curve toggle). Each row shows the regime label (STEEP, NORMAL, FLAT, INVERTED, or HUMPED) on a colored background matching the regime, the current slope value of the selected metric, and the metric's name. This lets you compare regime states between today and the lookback date directly: the curve may have shifted from FLAT to STEEP, for example, even if both versions are visible on the chart. Methodology — shape classification The indicator uses the user-selected spread as its primary slope input, with four absolute thresholds calibrated empirically from US Treasury data since 1990: 30y - 3m Steep : +1.5% Normal > +0.3% Flat > -0.3% Inverted ≤ -0.3% 10y - 2y (2s10s) +1.0% +0.2% -0.2% -0.2% 10y - 3m (3m10y) +1.5% +0.3% -0.3% -0.3% 30y - 5y (5s30s) +0.7% +0.15% -0.15% -0.15% 30y - 10y (10s30s) +0.5% +0.1% -0.1% -0.1% 30y - 2y (2s30s) +1.0% +0.2% -0.2% -0.2% The thresholds reflect each spread's historical distribution: the long-end spreads (5s30s, 10s30s) have far narrower ranges than the broad measures (30y-3m, 3m10y), so applying a single global threshold would over-classify the long-end as STEEP at almost every reading. By calibrating per metric, the classification stays meaningful no matter which spread the user selects. A separate Hump detection runs alongside: if the maximum yield in the curve falls in the belly (2Y, 3Y, 5Y, or 7Y) and the overall slope is below twice the Normal threshold, the curve is classified as HUMPED. This identifies the canonical inverted-belly shape that often signals a near-term policy-rate peak followed by expected easing. How to read the curve A Steep curve typically associates with early-cycle expansions, accommodative monetary policy, or rising inflation expectations — investors demand higher compensation to lend further out. A Normal curve is the long-term default state of healthy bond markets. A Flat curve indicates near-equality of short and long expectations, typically near cyclical turning points. An Inverted curve, where long yields trade below short yields, has preceded every US recession since the 1960s with an average lead time of roughly 12 to 18 months — this is the signal to watch for. A Humped shape, where the belly trades above both ends, typically reflects market pricing of a near-term hiking cycle followed by expected cuts. The dashed historical curve makes regime transitions visually obvious. If the solid current curve sits below the dashed reference across all tenors, yields have fallen broadly (a bond rally). If the curves cross — for example, the short end is up but the long end is down — the curve has flattened or inverted further during the lookback window. If the spread between them widens at the long end, term premium is expanding. Repainting and data behavior All calls use . Alerts trigger only on confirmed daily-bar closes. There is no repainting on confirmed bars.request.securitylookahead = barmerge.lookahead_off When the chart is on an intraday timeframe, the displayed yields reflect the most recent closed daily bar — during the US trading session this means yesterday's settlement until the new daily close prints. This is correct, non-repainting behavior and is consistent with how all daily-resolution data is served on TradingView. The pane scales automatically to the yield range using two invisible anchor plots (min and max across both current and historical curves). This ensures the polyline always uses the full vertical space without manual axis adjustment, regardless of the absolute level of yields. Limitations The classification thresholds are static absolutes, not adaptive. In persistent low-rate regimes (such as 2010-2021) the STEEP threshold may register slightly too generously; in high-rate regimes the FLAT threshold may understate compression. The numerical slope value is always shown alongside the label, so you can apply your own judgment when the regime label feels off. The Hump detection uses a simple argmax check on the belly tenors rather than a full curvature metric (such as the 2 × 5Y minus 2Y minus 10Y butterfly). It is most reliable when the selected slope metric spans the full curve (30y-3m, 3m10y, 2s10s); for narrow long-end metrics (5s30s, 10s30s) the belly lies outside the metric's span and Hump classification can be misleading. When in doubt, switch the slope metric to for the most robust shape reading.30y - 3m Only US Treasuries are supported in this version. The architecture leaves headroom in the request.security budget (22 of 40 calls used) for adding additional sovereign curves (Bunds, Gilts, JGBs) in future updates. Two alert conditions are exposed for the user-selected slope metric: Selected Spread Inversion — fires when the chosen spread crosses below zero (e.g., from positive to negative 2s10s). Useful as an early-warning trigger in macro frameworks. Selected Spread Re-Steepening — fires when the chosen spread crosses back above zero. The bull-versus-bear-steepener distinction (which end of the curve is moving) requires looking at the individual yields in the dashboard at the moment the alert fires. Alerts respect the user's slope-metric selection — switching from 2s10s to 3m10y in the settings will redirect the alerts to the new spread automatically. Recommended use Place the indicator on a daily chart of a broad US equity index (SPX, ES1!, SPY) to visualize how historical yield-curve regimes have aligned with equity-market phases — the inversion shading and shape labels make prior recession signals immediately visible. For fixed-income traders, place it directly on a Treasury futures chart (ZN1!, ZB1!) to use the live curve view as a directional input alongside the underlying price action. For macro discretionary traders, the configurable slope metric lets you align the shape classification with whichever spread your framework prioritizes — Fed-watchers typically use 3m10y, recession-modelers use 2s10s, term-premium analysts use 5s30s or 10s30s. Originality This indicator combines three distinct visualizations of the same dataset — a cross-sectional polyline curve drawn to the right of the last bar, a comprehensive numerical dashboard with eleven tenors and four spreads, and a configurable shape classification with per-metric calibrated thresholds — into a single, self-contained workspace. The log-scaled cross-sectional curve drawing using Pine v6's polyline objects, the historical comparison overlay, and the user-selectable slope metric with empirically calibrated thresholds per spread are not, to my knowledge, available in this combination in other public yield-curve scripts on the platform.

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của SmartMoneyHour-GMB-Quant

Monte Carlo Risk Geometry Simulator [Aslan]Thanks to @KioseffTrading for the polyline retracing system and the plotting system as a whole🙏 ♦️ What This Script Does This is a Monte Carlo simulator for visualising and calculating the probability of a return based on risk geometry of the model (Risk %, RR, WR). It assesses the probability of returns by generating hundreds or thousands of possible outcomes using your win rate, risk-reward, and position sizing. Each line you see is a different plausible “future,” showing how your account could realistically evolve. 🔶 How To Use It Input your strategy stats, run a large number of simulations, and focus on three things: how wide the equity curves spread, how deep drawdowns get, and the percentage of profitable outcomes. Then adjust your model and repeat. 🔷 Application in Prop Firm evaluations Using the threshold system, you can see what risk geometry is most likely to pass a prop firm evaluation. Suprisingly, the most probable geometry for passing an eval can sometimes have a negative expected value! ♦️ Bottom Line This script helps you move from “how much can I make?” to “how likely am I to profit?” 🔎 Monte Carlo Simulations Explained Monte Carlo simulations are a method of modeling uncertainty by running many random versions of the same system to see all possible outcomes. In trading, instead of assuming one fixed result, it repeatedly simulates sequences of wins and losses based on your strategy’s statistics (like win rate and risk-reward). This creates a distribution of potential equity curves, showing not just what did happen, but could happen. It’s essentially a way to test probability and survival under randomness rather than relying on a single backtest. Monte Carlo simulations are widely used on quant trading desks around the world to model uncertainty, test strategy robustness, and estimate the probability distribution of trading outcomes under real-world randomness.

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Haar Wavelet Range Filter [Jamallo]Intro Haar Wavelet Range Filter Standard moving averages often struggle with a fundamental trade-off: they are either too fast (reacting to every bit of market noise) or too slow (lagging behind actual trend shifts). This indicator solves that problem by moving away from time-based averaging and into the frequency domain . By utilizing a Maximal Overlap Discrete Wavelet Transform (MODWT) with a Haar basis, this filter decomposes price action into specific "energy levels." It isolates the core trend from the high-frequency "chatter" (wicks and micro-jitters). The result is a highly responsive, shift-invariant trend line that stays flat during consolidation and moves decisively when a true volatility-backed shift occurs. How It Works The indicator processes price through a multi-stage signal processing pipeline: Stage 1 — MODWT Haar Decomposition Instead of a simple windowed average, the engine performs a wavelet decomposition. It splits the price into Scaling coefficients (the smooth trend) and Detail coefficients (the noise/volatility). Unlike standard DWT, this uses MODWT , making it shift-invariant. This means the filter doesn't "jump" or repaint when new bars arrive; the relationship between price and the wavelet remains constant over time. The Decomposition Level (1–5) dictates the scale of the trend, ranging from ultra-fast 2-bar captures to macro 32-bar structural cycles. Stage 2 — Adaptive Detail-Energy Deadband The "step-like" nature of the filter is driven by an automated threshold. The indicator measures the Detail Energy—essentially the absolute volatility of the noise at your chosen scale. Stage 3 — Non-Parametric Percentile Bands Rather than using standard deviation (which assumes price follows a normal "Bell Curve" distribution), this indicator uses Linear Interpolation Percentiles.

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của Jamallo22

Iteratively Reweighted Least Squares Range Filter [Jamallo]Intro Iteratively Reweighted Least Squares Range Filter Most filters treat every bar equally. This one doesn't. The IRLS Range Filter uses a robust statistical engine to continuously evaluate how much each bar in the lookback window should be trusted — down-weighting noisy data and rejecting outliers entirely — before computing a consensus price level. The result is a smoother, more honest trend line that is far less susceptible to wicks, gaps, and volatile noise than conventional moving averages. How It Works The indicator runs in three stages: Stage 1 — IRLS Consensus Engine . Every bar in the rolling window is assigned one of three trust states based on how far its price deviates from the current consensus, measured in adaptive sigma units. High Trust bars (within Lo-Trust Band σ) receive full weight. Lo Trust bars receive half weight. Rejected bars receive none. A Schmitt-trigger hysteresis mechanism prevents bars from flickering between states on minor fluctuations. The weighted average of all contributing bars becomes the consensus price. This residual-based weighting scheme is adapted from the IRLS framework described in Li & Deng (2026), arXiv:2603.08158. Stage 2 — Deadband Quantizer . The continuous consensus is passed through a step filter. The output only moves when the consensus has shifted by more than one deadband unit (Step Size × σ). This eliminates micro-jitter and produces a clean staircase line with fewer false trend flips. Stage 2.5 — Sample-and-Hold Bands. The volatility channel is only updated when the filter takes a new step, so band width reflects conditions at the last decisive move rather than real-time noise. An EMA-style smoother (Band Smooth) prevents abrupt band jumps between steps. Reference Li, J., & Deng, F. (2026). Outlier-robust Autocovariance Least Square Estimation via Iteratively Reweighted Least Square. arXiv preprint arXiv:2603.08158v1.

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của Jamallo22

Adaptive Keltner Channel [NovaLens]Adaptive Keltner Channel detects when price is doing something exceptional versus simply moving within its expected range. It wraps price in ATR-based bands around an EMA center, optionally adjusts band width to the current volatility regime, and includes built-in squeeze detection and context-aware center reclaim signals. ════════════════════════════════════════════ ◉ HOW IT WORKS The indicator builds on the Keltner Channel concept introduced by Chester Keltner (1960) and refined into the modern ATR-based variant by Linda Bradford Raschke: Center line : EMA of close Upper band : Center + multiplier x ATR Lower band : Center - multiplier x ATR In static mode, the multiplier is fixed: Upper = EMA(Close, Length) + Multiplier x ATR Lower = EMA(Close, Length) - Multiplier x ATR In adaptive mode, the multiplier scales with the current ATR percentile rank. When ATR is low relative to recent history, the channel widens to filter routine noise. When ATR is already elevated, the channel tightens so a band break still represents a genuine expansion rather than getting absorbed inside an overly wide envelope. This is the opposite of how Bollinger Bands behave. Bollinger Bands widen mechanically as volatility rises because they use standard deviation. The adaptive Keltner deliberately tightens its multiplier in high-vol regimes to preserve the usefulness of a breakout signal across different conditions. The script also includes squeeze detection: when Bollinger Bands contract inside the Keltner Channel, it flags that volatility has compressed. Compressed conditions can precede larger-than-normal moves, though the squeeze does not indicate which direction or guarantee that the expansion will follow through. ════════════════════════════════════════════ ◈ HOW TO READ IT Band interactions: Price above upper band: upside expansion beyond the recent channel range Price below lower band: downside expansion beyond the recent channel range Center reclaims and losses: Price reclaims center after a lower-band touch: downside extension did not hold Price loses center after an upper-band touch: upside extension did not hold These are not simple center crosses. The indicator tracks which band was touched most recently and only fires a reclaim or loss signal when that context is present. A center cross after sideways consolidation is a different event from a center cross following a failed band extension. This logic filters out a significant amount of the noise that makes naive center-cross signals unreliable. Squeeze and trend: Orange fill and center color: squeeze is active, volatility compressed Squeeze intensity builds visually as the compression persists Green center line: short-term trend pressure is up Red center line: short-term trend pressure is down Teal X-cross marker below bar: squeeze just released The outer bands are intentionally neutral gray. Direction comes from price behavior, center slope, and the interaction signals. ════════════════════════════════════════════ ✦ HOW WE USE IT: VOLATILITY CONTEXT We use this channel as a volatility context layer rather than a standalone signal source. The question it answers: is the current price behavior routine or exceptional for this market's recent history? When the channel is in squeeze state and the info panel reads "Compressed" with the bar count climbing, that is a low-conviction environment for new directional entries but a high-attention environment for preparation. On BTC 4H with Balanced settings, in our observation squeeze conditions often persist for 8-15 bars while the fill gradually deepens to orange. During that window, we are watching, not acting. When the squeeze releases and price pushes through a band with the center slope already confirming, that combination (squeeze release, band break, center slope alignment) carries more weight than a random band break during an already-volatile session. No single element alone is the trigger. On the reversion side, when price flushes to the lower band and then reclaims the center, the state-aware logic recognizes that the last band touched was the lower band. It treats that center cross as a meaningful reclaim rather than noise. This matters because price often consolidates between a band touch and the center for several bars. A naive center-cross indicator would miss the connection. This one remembers. The practical workflow: use the channel to characterize whether price is inside its expected range, expanding beyond it, or compressed. Layer your directional signals on top of that context. ════════════════════════════════════════════ ✦ OTHER APPLICATIONS Breakout context : band breaks after compression, confirmed by center slope and price acceptance on subsequent bars Mean reversion : in sideways conditions, band touches can frame moves back toward the center, especially when the center line is flat rather than sloped Trend pullbacks : the center line as a dynamic support/resistance during directional moves Risk management : use the center or opposite band as a trailing reference that adapts to current volatility Alert-driven workflow : set alerts on squeeze start, squeeze release, breakout up/down, or center reclaim/loss and check the chart only when conditions change ════════════════════════════════════════════ ⚙ GETTING STARTED Adaptive Keltner Channel ships with reactivity presets: Reactive : EMA 10 / Mult 1.5 / ATR 10. Tighter bands that respond quickly. Useful when you want the channel to track recent behavior more closely. Balanced : EMA 20 / Mult 2.0 / ATR 14. The default. A reasonable starting point for most assets. Start here unless you have a specific reason not to. Smooth : EMA 50 / Mult 2.5 / ATR 20. Wider and calmer. Useful on higher timeframes or when you want the channel to only flag larger-scale events. Custom : Full manual control for specific setups. Core settings: Reactivity : selects the behavior profile. Start with Balanced. Adaptive Band Width : on = regime-aware scaling, off = classic static Keltner. The difference is most visible when the market transitions between calm and volatile periods. Adaptive Lookback : how far back ATR is ranked. 100 bars covers roughly 5 months on daily, 4 days on 1H. Increase for assets with long volatility cycles. Decrease for assets that shift regimes quickly. Adaptive tuning: Adaptive Max Multiplier (calm) : scales your base multiplier up in quiet conditions. Default 1.25 means the effective multiplier becomes 25% wider than the base (e.g., base 2.0 becomes 2.5). Increase if you are seeing too many false band touches during low-vol periods. Adaptive Min Multiplier (vol) : scales your base multiplier down in volatile conditions. Default 0.75 means the effective multiplier becomes 25% narrower than the base (e.g., base 2.0 becomes 1.5). Decrease if breakouts are still not clearing the bands during high-vol sessions. Squeeze settings: Show Squeeze Detection : enables Bollinger-inside-Keltner squeeze logic BB Length / BB Multiplier : default 20/2.0 matches standard Bollinger. Generally no reason to change unless comparing against a non-standard Bollinger on the same chart. Display: Show Info Panel : 4-row panel showing current state, volatility, trend direction, and price location as a channel percentage. Hover any label for a plain-English explanation. Show Squeeze Background : subtle background tint during active squeezes. Off by default. Light Theme Mode : adapts panel colors for light backgrounds. ════════════════════════════════════════════ △ LIMITATIONS Lagging structure : the center is an EMA and the bands use historical ATR. Sharp reversals can outrun the channel. This is inherent to any channel overlay. Squeeze is not directional : a squeeze tells you volatility has contracted, not which way the next move goes. The release can fail, reverse, or stall. False band breaks : in noisy conditions, price can poke through a band and return inside on the next bar. Adaptive scaling is designed to help with this, but does not eliminate it. Mean-reversion risk in trends : using center reclaims as reversion entries during a strong trend can stay wrong longer than expected. The center keeps moving with the trend. History requirement : adaptive scaling needs enough bars to fill the Adaptive Lookback window (default 100 bars) before it adjusts properly. Squeeze detection also needs sufficient Bollinger and Keltner history. Early bars on any chart will have less reliable readings. ════════════════════════════════════════════ 🔔 ALERTS Six alert conditions are built in so you can monitor without watching the chart: Breakout Up / Breakout Down : price closes beyond the upper or lower band Squeeze Started : Bollinger Bands have contracted inside the Keltner Channel Squeeze Released : squeeze just ended, volatility expanding Center Reclaim : price crossed above the center after previously touching the lower band Center Loss : price crossed below the center after previously touching the upper band All alerts fire on bar close by default for confirmed signals. ════════════════════════════════════════════ ⌁ NOTES Original concept by Chester Keltner (1960), modern ATR-based variant by Linda Bradford Raschke Uses ATR-based bands with optional volatility-regime adaptation via percentile ranking Includes Bollinger-inside-Keltner squeeze detection with visual intensity buildup Center reclaim/loss signals use state-aware logic that tracks which band was last touched Values update in real time on the current bar and are confirmed at bar close Open-source: inspect the logic directly in the Pine editor

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Iteratively Reweighted Least Squares (IRLS) [Jamallo]Author's Note: This script is published as a unique mathematical contribution to TradingView's open-source public library. It is intended to introduce a novel application of a robust statistical method for the community and developers to study, adapt, and build upon, rather than to serve as a standalone, out-of-the-box trading strategy. Introduction Almost all moving averages and smoothing filters in technical analysis treat historical price data equally or apply a fixed mathematical decay (like an EMA). The problem? A massive, anomalous wick or a sudden volatility spike will inevitably drag the average away from the true underlying market consensus. Enter Iteratively Reweighted Least Squares (IRLS) . IRLS is a robust statistical method that calculates a "consensus" price by actively identifying and down-weighting outliers. Instead of letting a large wick distort the line, the algorithm assigns less weight to prices that deviate furthest from the current estimate. The result is a filter that cuts through noise, ignores price-distant spikes, and naturally locks onto the dominant, high-density price levels. How It Works The indicator uses the Hardy weight function to determine how heavily each historical candle influences the current estimate. On every bar, the algorithm checks the distance of each sample from the current consensus and iteratively refines the line until it converges on a robust mean. Epsilon — the outlier rejection scale — is derived dynamically from the average High–Low range, keeping the filter dimensionless and consistent across all instruments and timeframes. Parameters Window Size (N) : The rolling lookback window of historical samples the kernel considers. Larger values produce a smoother, slower-responding line. Sparsity (s/N) : The core behavioral control. Dictates the fraction of the window allowed to "vote" on the estimate. Low Sparsity (e.g., 0.1) : Only the 10% of samples closest to the current estimate participate. Produces a snappy, selective line that locks tightly onto the most dominant price cluster. High Sparsity (e.g., 1.0) : All samples participate, resulting in a smoother, more conventional robust mean. Gamma (ε scale) : Controls the strength of outlier rejection. Lower values enforce harsh, median-like rejection. Higher values soften the rejection toward a standard weighted mean. Iterations : The number of reweighting convergence passes per bar. 2–3 is sufficient for practical convergence. Potential Applications The Hardy IRLS filter provides a unique lens into market structure by shifting the focus from simple time-averaged prices to spatial price consensus. Because it rejects price-distant wicks and noise spikes by design, it can serve as a foundation for: Custom trailing stops Dynamic support and resistance trackers Baseline trend or regime filters Feel free to inspect the open-source code, experiment with extreme sparsity and gamma settings, and integrate the IRLS core into your own quantitative projects. References Li Shuang, "Sparse Representation of Hardy Function by Iteratively Reweighted Least Squares," 2020 International Symposium on Computer Engineering and Intelligent Communications (ISCEIC), IEEE, 2020. DOI: 10.1109/ISCEIC51027.2020.00020

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của Jamallo22

Quantum Scanner Equalizer [Point algo]The Quantum State Scanner is an advanced quantitative dashboard designed to provide a comprehensive "Health Check" of any financial instrument. Instead of relying on a single timeframe or a single mathematical model, the Quantum Scanner aggregates data across 12 distinct lookback periods (based on the Fibonacci sequence) to calculate the market's true state. By normalizing complex data into a simplified 0-100 Equalizer HUD, traders can instantly identify whether a move is backed by broad-spectrum consensus or is simply a low-conviction anomaly. The 5 Pillars of Quantum Analysis The scanner calculates five core "Quantum Pillars" before aggregating them into a final Master Score: 1. Trend (TRND): Analyzes the relationship between Price, VWMA, and EMA across 12 periods. Considers whether price is structurally bullish or bearish. 2. Momentum (MOM): Uses a Stoch-RSI derivation to measure the speed of price movement and identify overbought/oversold exhaustion zones. 3. Volatility (VOLA): Employs a Bollinger/Keltner Squeeze engine. Higher scores indicate expanding volatility (expansion), while lower scores indicate a squeeze (contraction). 4. Volume Delta (VOLU): Measures the internal force of the market by calculating Buy vs. Sell volume pressure. 5. Structure (STRC): A price-action engine that scans for liquidity sweeps (High/Low breaks) and candle rejections to determine structural strength. How to Read the Scanner 0-40 (Red Zone): Strong Bearish Consensus. 40-60 (Yellow Zone): Neutral / Indecision / Consolidation. 60-100 (Green Zone): Strong Bullish Consensus. High Conviction Signals: When the Master Score exceeds 80 or drops below 20, the background of the chart will dynamically highlight, signaling a potential "Power Move" or trend ignition. Key Features 1. Dynamic HUD: A sleek, minimalist "Equalizer" UI positioned to the right of your price action. 2. Aggregated Logic: Processes data from lookback periods 5 through 987 simultaneously. 3. Right-Offset Control: Easily move the dashboard to prevent it from overlapping with your candles. Disclaimer: Educational Purpose Only: This indicator ("The Script") is provided for educational and informational purposes only. It does not constitute financial, investment, tax, or legal advice. No Guarantee of Results: Past performance, whether actual or simulated by backtesting, is not indicative of future results. The mathematical models used (including GARCH, Ito’s Lemma, and Volume-Weighted calculations) are theoretical and do not account for market slippage, liquidity gaps, or "black swan" events. Assumption of Risk: Trading involves a substantial risk of loss and is not suitable for every investor. You are solely responsible for your own trading decisions. Pointalgo and its creators shall not be held liable for any financial losses or damages resulting from the use of this script.

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của pointalgo

Black-Scholes Quantum Gap [Pointalgo]Market Pricing Inefficiency & Probabilistic Value Engine Black-Scholes Quantum Gap is a quantitative indicator designed to measure the difference between current market price and a theoretical fair value estimate derived from probabilistic pricing concepts. The tool helps traders identify discount (undervalued) and premium (overvalued) conditions in the market using a structured, model-driven approach. What This Indicator Does This indicator builds a dynamic pricing benchmark and compares it with live market price to generate a relative valuation gap. The result is displayed as a momentum-style histogram showing: • When price is trading at a discount • When price is trading at a premium • When the market is near equilibrium Core Concept (High-Level) The engine uses a probabilistic pricing framework to estimate a reference value for the current market. It then calculates the deviation between: → Observed market price → Model-derived fair value This deviation is normalized into a percentage gap, which becomes the core signal of the indicator. To improve usability, the gap is smoothed and statistically bounded to highlight extreme conditions. Visual Interpretation Histogram (BSM Alpha Gap) • Green Zone → Discount / Undervalued Conditions • Red Zone → Premium / Overvalued Conditions The further the histogram moves from zero, the stronger the deviation from equilibrium. Equilibrium Line The zero line represents a fair value balance point where price and model value are aligned. Extreme Zones Upper and lower bands represent statistical extremes of the gap: • Upper Band → Extreme Discount Zone • Lower Band → Extreme Premium Zone These zones help identify potential reversal or mean-reversion areas. Signals BUY Signal Triggered when the gap moves into extreme discount territory and begins to revert upward. Indicates potential: • undervaluation • accumulation zones • mean reversion opportunities SELL Signal Triggered when the gap moves into extreme premium territory and begins to revert downward. Indicates potential: • overvaluation • distribution zones • downside mean reversion Dashboard The built-in panel provides real-time insights: • Market State → Discount / Premium / Equilibrium • Probability Metric → A model-derived probability estimate reflecting market positioning This helps traders quickly assess current conditions without additional tools. Key Features • Quant-based valuation gap analysis • Dynamic equilibrium detection • Statistical extreme zones • Built-in signal generation • Smoothed output for clarity • Real-time dashboard panel • Works across all liquid markets How to Use 1. Identify Market Condition Check whether the market is: • Discount → potential buying interest • Premium → potential selling pressure • Neutral → range-bound behavior 2. Watch Extreme Zones Focus on moments when the gap reaches statistical extremes. These areas often indicate: • exhaustion • imbalance • potential reversal zones 3. Confirm with Price Action Use this indicator alongside: • support/resistance • trend structure • volume Do not rely on signals in isolation. 4. Multi-Timeframe Approach Higher timeframe → macro valuation bias Lower timeframe → precise entry timing Best Use Cases • Index Futures • Large-Cap Stocks • Forex Pairs • Crypto (high liquidity pairs) Disclaimer This indicator is based on quantitative modeling and statistical assumptions. It does not predict future prices and should not be considered financial advice. Always apply proper risk management and confirm signals with additional analysis.

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của pointalgo

Open Interest Saturation Oscillator This is the Open Interest indicator Bounded and Normalized . In order to make rapid Quant Trading decisions, I have inspired myself by other Open Interest indicators and developed this changing oscillator, that follow the rules of Open Interest! The indicator is best used for Crypto currency pairs, however, anyone is open to test the indicators limits. Best use cases for the indicator are as follow: 1. Rising OI Osc Value + Up-trending Market = New Longs Coming into the Market (Strong Bull, Aggressive Buying) 2. Rising OI Osc Value + Down-trending Market = New Shorts Coming into the Market (Strong Bear, Aggressive Selling) 3. Falling OI Osc Value + Up-trending Market = Long Orders being closed (Weak Bulls / Corrective move) 4. Falling OI Osc Value + Down-trending Market = Market is losing fuel / Preparing for a violent Bounce I developed the indicator with the help and consultation with AI! Please find it useful in your trading and may the profits be with you!

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của ErnestsAdlers

EMA 20/50 Optimal Distance (M15) EMA 20/50 Optimal Distance (M15) is a quantitative context filter designed to evaluate market direction and trading conditions in XAUUSD using exponential moving averages. This indicator uses the 20 EMA and 50 EMA on the 15-minute timeframe to define two key components: 1. Market bias (direction) 2. Risk level based on EMA separation --- DIRECTION LOGIC --- - BUY: EMA 20 > EMA 50 and price is above both averages. - SELL: EMA 20 < EMA 50 and price is below both averages. - NO BIAS: When price is between EMAs or alignment is unclear. --- EMA DISTANCE (NORMALIZED) --- The distance is calculated as the absolute difference between EMA 20 and EMA 50, normalized for XAUUSD volatility. Classification: - High Risk: Distance < 0.8 → Weak or ranging market conditions. - Medium Risk: Distance between 0.8 and 1.5 → Acceptable but not optimal conditions. - Low Risk (Optimal): Distance > 1.5 → Strong trend with higher probability of continuation. --- --- WHAT MAKES THIS INDICATOR DIFFERENT --- Unlike standard EMA crossover tools, this script introduces a quantitative layer by measuring the distance between moving averages to classify market conditions. It is not designed to generate entry signals, but to provide a structured and measurable framework for: - Filtering trades based on market quality - Categorizing setups by risk conditions - Supporting data-driven decision making --- --- PURPOSE --- This indicator is specifically built as a base tool for backtesting and quantitative strategy development. It is intended to be used in combination with market structure models (such as BOS/CHOCH) and lower timeframe execution strategies. The goal is to help traders: - Collect structured data - Identify statistical edge - Optimize entries, trade management, and sessions based on real performance metrics --- Designed for intraday trading in Gold (XAUUSD). EMA 20/50 – Distancia Óptima (M15) es un indicador diseñado para evaluar el contexto direccional y la calidad del entorno de mercado en XAUUSD mediante medias móviles exponenciales. Utiliza la EMA 20 y EMA 50 en temporalidad de 15 minutos para definir el sesgo de mercado y el nivel de riesgo según la distancia entre ambas. Este indicador no genera entradas, sino que funciona como filtro cuantitativo para backtesting y desarrollo de estrategias basadas en estructura de mercado (BOS/CHOCH). Permite analizar condiciones de mercado, clasificar operaciones y optimizar decisiones en base a datos reales.

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của Jose-Adames

VWAP Z-Score & Exhaustion Matrix [AlgoPoint]AlgoPoint VWAP Z-Score & Order Flow Matrix Overview The AlgoPoint VWAP Z-Score Matrix is a quantitative oscillator designed to identify statistical extremes in price action relative to a dynamic volume-weighted baseline. By combining a Rolling VWAP, Volume-Weighted Standard Deviation, Volume Exhaustion, and an Intrabar Order Flow Delta proxy, this indicator provides a comprehensive framework for modeling mean-reversion (fade) setups. Mathematical Core & Components This indicator relies on three primary quantitative mechanics:Rolling VWAP & Z-Score: Unlike a traditional anchored VWAP that resets daily, this script calculates a Rolling VWAP over a user-defined lookback window. The standard deviation is volume-weighted, ensuring that high-volume nodes have a proportional impact on the variance. The Z-Score normalizes the price deviation from the VWAP, creating a 0-centered oscillator. - Volume Exhaustion: The script compares the current bar's volume against a Simple Moving Average (SMA) of volume. If the current volume is lower than the average, it flags a state of "exhaustion," indicating a potential deceleration in the current price push. - Order Flow Delta Proxy: To evaluate microstructure without requiring lower timeframe data, the indicator estimates intrabar buying and selling pressure. It apportions the total bar volume into "Up Volume" and "Down Volume" based on where the close occurs relative to the high-low range. The net difference establishes the Volume Delta. Visual Elements & Interpretation Dual-Gradient Bar Coloring: The main chart candles and the oscillator line are dynamically colored using a dual-gradient system. The color smoothly transitions from a neutral gray at the mean (0) to green at negative extremes and red at positive extremes. Reference Thresholds: Default extreme limits are set at +2.5 and -2.5 Z-Scores.Exhaustion Nodes: Circular nodes appear on the oscillator line when the price reaches an extreme Z-Score simultaneously with volume exhaustion. Quant Dashboard: A real-time table displaying the current Z-Score, Volume Status (Active/Exhausted), Order Flow Delta (Net Buyers/Sellers), and the absolute Rolling VWAP price. Signal Generation "Fade Long" and "Fade Short" labels are generated only when all structural conditions are met and the bar is confirmed (barstate.isconfirmed) Fade Short: Z-Score > Upper Threshold AND Volume is Exhausted AND Delta is Negative (Sellers taking control). Fade Long: Z-Score < Lower Threshold AND Volume is Exhausted AND Delta is Positive (Buyers taking control). Alerts The indicator includes standard alert conditions and dynamic JSON webhook strings for automated trading systems, providing real-time data on the asset, price, Z-Score, and Delta values upon signal generation.

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