Inizia

Zscore

Axis Pressure Cartography [JOAT]Axis Pressure Cartography Introduction Axis Pressure Cartography is an open-source normalized pressure oscillator that measures how far price has deviated from a configurable moving average and classifies the resulting state into constructive, compressed, or overheated conditions. Core Concepts 1. Normalized MA distance Price is measured against a configurable moving average and normalized against recent maximum deviation so different volatility regimes remain interpretable on the same scale. 2. State persistence Pressure states are held using a confirmation window rather than flipping instantly on small changes, which reduces noise and creates a more stable regime read. 3. Envelope logic Pressure envelopes define structural bounds around the oscillator so extreme expansion can be recognized as a separate state from ordinary constructive trend pressure. Features Normalized pressure oscillator and signal Compression, expansion, and overheated-state classification Envelope rails and pressure background tint Compact institutional dashboard Confirmed alerts for flips, releases, and exhaustion transitions Disclaimer This indicator is educational only. Extreme pressure can persist longer than expected, and compression can resolve in either direction.
Indicatore Pine Script®
di officialjackofalltrades
Z-Score Mean Reversion ProZ-Score Mean Reversion Pro is a Strategy designed to identify high-probability mean reversion setups by combining statistical Z-Score extremes with RSI momentum confirmation, Bollinger Band volatility filtering, and EMA trend alignment. It provides clean buy/sell signals, visual trade zones, dashboard data, and alert outputs to assist in short-term reversal trading, intraday setups, and swing mean reversion trades. 🔑 FEATURES Z-Score Mean Reversion Engine: Detects statistically stretched price moves and highlights potential reversal zones when price deviates too far from its rolling mean. Multi-Layer Signal Confirmation: Combines RSI exhaustion, Bollinger Band Width volatility checks, and EMA trend filtering to reduce false signals and improve trade quality. Built-In Risk Management: Includes ATR-based stop loss and take profit logic, plus optional mean reversion exits when price returns toward equilibrium. ⚠️ NOTES & DISCLAIMERS This script works best on 5m, 15m, 1H, and 4H charts across Forex, Crypto, Indices, and liquid Stocks. Best suited for mean reversion traders looking to capture exhaustion moves and short-term reversals. ATR exits are ideal for structured risk management, while Z-Score exits are better for pure statistical reversion setups. Always use risk management (Stop Loss). This script is for educational and research purposes only. Always forward test before using live capital.
Strategia Pine Script®
di ayusattv
Impulse Structure Zones [JOAT]Impulse Structure Zones Introduction Impulse Structure Zones (ISZ) is an open-source, institutional-grade zone engine that detects statistically significant price impulses using a Z-Score methodology, identifies the origin candle of each impulse as an order block, and grades each zone using a multi-factor wick rejection scoring system. Bullish and bearish zones are tracked in parallel arrays with full lifecycle management — creation, extension, mitigation detection, and rejection confirmation — all rendered as clean, non-repainting boxes on the chart with a mid-line bisecting each zone. The core problem ISZ solves is the manual process of locating high-probability order block zones on a chart. Institutional price delivery frequently originates from specific candles where large orders were placed — the last opposing candle before a strong directional move. ISZ automates the detection of those moves, marks the origin candles, and then monitors each zone to fire a graded rejection signal when price returns to test the level. Grades A, B, and C communicate signal quality based on wick dominance, proportional wick depth, volume confirmation, and candle size relative to ATR. Core Concepts 1. Z-Score Impulse Detection Price change is measured bar-by-bar as a percentage move and normalized into a Z-Score against a rolling mean and standard deviation window: float pxChg = (close - close ) / close * 100.0 float zscore = (pxChg - avgChg) / math.max(stdChg, 0.0001) A bar qualifies as an impulse when the absolute Z-Score exceeds the user-defined threshold (default: 1.5). This isolates moves that are statistically unusual relative to recent activity — the same principle used in quantitative strategies to filter meaningful displacement from noise. All signals are gated on barstate.isconfirmed to prevent repainting. 2. Order Block Identification When a bullish impulse is confirmed, ISZ scans back through recent bars to locate the last bearish candle (close < open) before the move. That candle's high and low become the order block zone boundaries. For bearish impulses, the last bullish candle is used. This matches the ICT definition of an order block — the final imbalance candle before institutional displacement. 3. Zone Lifecycle Management Each zone is stored as a user-defined type (UDT) containing the box object, mid-line, price boundaries, birth bar, direction, mitigation flag, and rejection flag. Zones extend rightward on each bar until price closes beyond the zone (mitigation), at which point the box is frozen and marked as mitigated. A maximum zone count is enforced and oldest zones are trimmed to maintain chart performance. 4. A/B/C Rejection Grading When price returns to test a live zone and a rejection candle forms, ISZ grades the signal quality using four independent scoring factors: Wick dominance ratio: The rejection wick length divided by candle body size Proportional wick depth: The wick as a percentage of the total candle range Volume confirmation: Current bar volume compared to the 20-bar average Candle size vs ATR: Whether the rejection candle is of meaningful size relative to recent volatility A total score of 6+ = Grade A, 4-5 = Grade B, below 4 = Grade C. Grade is displayed as a label on the rejection bar. Features Z-Score Impulse Engine: Statistically filters price moves against a rolling mean/standard deviation window — configurable length and threshold Automatic Order Block Detection: Last opposing candle before each confirmed impulse identified and stored as a zone Bidirectional Zone Tracking: Bullish (demand) and bearish (supply) zones managed in separate arrays with independent colors A/B/C Rejection Grading: Four-factor scoring system labels each zone test with a quality grade Zone Mitigation Detection: Zones that are fully closed through are frozen and visually distinguished from active zones Mid-Line Reference: Each zone box includes a dashed mid-line at the 50% level — institutional equilibrium reference ATR Proximity Filter: Rejection signals only fire when price is within a configurable ATR multiple of the zone Volume Confirmation: Optional volume filter requires above-average volume at rejection for grading Non-Repainting: All signals gated on barstate.isconfirmed — no look-ahead bias Zone History Limit: Oldest zones automatically removed when the maximum count is reached to maintain performance Dashboard (Top Right): Active bull/bear zone counts, last signal grade, last impulse Z-Score, and ATR — updated on each bar Live Z-Score Candle Gradient Coloring: Impulse candles colored teal or rose based on Z-Score strength — immediately identifies statistically significant displacement bars on the chart ATR Band Plots Around EMA 750: Visual upper and lower extremity zones drawn as ATR-based bands around the 750-period EMA — communicates when price is at macro stretch relative to the long-term anchor RR Trade Boxes on Rejection Signals: Auto-generated SL/TP boxes on every rejection signal — 1.5× ATR stop loss with 3:1 reward-to-risk ratio, extending forward from the signal bar Session Win Rate Tracking: Asia, London, and NY win rates tracked independently for rejection trades — outcome recorded against each signal's ATR-based TP/SL levels Best Session Highlight: Dashboard automatically identifies and highlights the highest win-rate session across all three windows Expanded Dashboard (9 Rows): Dashboard expanded to 9 rows — now includes live Z-Score reading, total impulse count, and full session win rate breakdown alongside existing zone and signal data Input Parameters Z-Score Settings: Z-Score Length: Rolling window for mean and standard deviation calculation (default: 20) Z-Score Threshold: Minimum absolute Z-Score required to qualify as an impulse (default: 1.5) Zone Settings: Max Active Zones: Maximum number of zones tracked simultaneously per direction (default: 8) Bull Zone Color / Bear Zone Color: Independent colors per direction Rejection Settings: ATR Proximity (multiplier): How close price must be to a zone to trigger rejection check (default: 0.5) ATR Length: Period for ATR calculation (default: 14) Require Volume Confirmation: Toggle — above-average volume required for Grade A How to Use This Indicator Step 1: Identify Active Zones Active bullish zones (demand) appear below price in teal. Active bearish zones (supply) appear above price in rose. Mitigated zones are visually dimmed. Focus on zones that have not yet been tested — these are the most relevant levels for future price interaction. Step 2: Wait for Price to Return to the Zone ISZ does not generate entry signals on impulse creation. It monitors active zones for return tests. When price pulls back into a zone, watch for the rejection grading label to appear. Step 3: Grade the Signal An A-grade rejection at a fresh, unmitigated zone is the highest-quality setup. B-grade is acceptable with additional confluence. C-grade rejections at already-tested zones carry the least weight. Use the grade in combination with your own bias and higher-timeframe analysis. Step 4: Monitor the Dashboard The dashboard shows active zone counts, last Z-Score, last grade, and ATR. A high Z-Score at impulse creation indicates an unusually strong move — those zones tend to attract more significant future tests. Indicator Limitations Z-Score impulse detection requires sufficient historical bars (at least 2× the Z-Score length) to produce accurate statistics — on very short chart histories the first few zones may form under unstable conditions Order block detection scans back a fixed number of bars (configurable). In fast-moving markets where multiple candles are the same color, the scan may place the zone further back than an analyst would manually Rejection grading uses volume data. On instruments with synthetic or unreliable volume (e.g., some CFDs, synthetic indices), the volume scoring component will not reflect true market activity Zones do not account for gap fills, overnight moves, or after-hours sessions — a zone that appears unmitigated on the chart may have been effectively traded through outside of regular hours depending on the instrument The A/B/C grading is a quantitative scoring system, not a certainty measure. Grade A signals do not guarantee price continuation in the expected direction Originality Statement ISZ combines Z-Score statistical impulse detection with origin-candle order block identification and a multi-factor rejection grading system in a single, self-contained indicator. This combination is original for the following reasons: The use of a Z-Score normalized against a rolling mean and standard deviation — rather than a fixed pip or percentage threshold — makes impulse detection adaptive to current market volatility. The same threshold parameter behaves consistently across instruments and timeframes without requiring manual recalibration The A/B/C grading system applies four independent quantitative factors (wick dominance, wick proportion, volume, candle size) simultaneously to classify signal quality at the point of zone interaction — rather than simply marking every return to a zone as equal Zone lifecycle management (create → extend → mitigate → reject → trim) is handled automatically through UDT arrays with in-place field mutation, eliminating the need for manual zone maintenance or re-drawing The combination of impulse detection, zone creation, and rejection grading in a single engine — with a unified dashboard — removes the need to layer multiple indicators to accomplish the same workflow Disclaimer This indicator is provided for educational and informational purposes only. It is not financial advice or a recommendation to buy or sell any financial instrument. Trading involves substantial risk of loss. Order block zones are historical reference levels and do not guarantee that price will react at those levels. A/B/C grades reflect quantitative scoring and do not predict future price movement. Always apply proper risk management. The author is not responsible for any trading losses resulting from the use of this indicator. -Made with passion by jackofalltrades
Indicatore Pine Script®
di officialjackofalltrades
Deviation Lens [JOAT]Deviation Lens Introduction Deviation Lens is an open-source multi-dimensional statistical displacement tool that applies Z-Score analysis simultaneously to three market dimensions: price level, close-to-close price change, and volume. Rather than using arbitrary overbought/oversold thresholds derived from historical maxima and minima, Deviation Lens computes exactly how many standard deviations each dimension is from its recent rolling mean. This provides a precise, adaptive, distribution-aware measure of how statistically extreme current market conditions are. The core insight is that markets are mean-reverting systems over short time horizons. Statistical extremes — conditions where price, momentum, or volume are far from their recent averages — represent transient states. The further from the mean, the greater the statistical probability that conditions will normalize. Deviation Lens quantifies this probability directly, from 0% (at the mean) to 99.7% (at three standard deviations), and displays it as a live reversal probability for every bar. Core Concepts 1. Three-Dimensional Z-Score Calculation Three independent Z-Scores are computed on every bar: The Price Z-Score measures how far the current close is from the rolling mean close in standard deviation units. This captures whether the current price level is statistically cheap or expensive relative to recent history. The Change Z-Score measures how far the current bar's close-to-close price change is from the rolling mean change — quantifying momentum extremity rather than price level extremity. The Volume Z-Score measures how far the current volume is from the rolling mean volume. High-volume Z-Score values identify bars where unusual institutional participation is statistically evident: priceZ = priceStd > 0 ? (close - priceMean) / priceStd : 0.0 changeZ = changeStd > 0 ? (chg - changeMean) / changeStd : 0.0 volumeZ = volStd > 0 ? (volume - volMean) / volStd : 0.0 2. Reversal Probability Mapping The absolute Z-Score is mapped to a reversal probability percentage based on the properties of the normal distribution. A Z-Score of 1.0 corresponds to 68.3% of values lying within one standard deviation — meaning only 31.7% of readings exceed this level, implying a 68.3% probability of mean reversion. A Z-Score of 2.0 corresponds to 95.4%, and 3.0 to 99.7%: calcRevProb(float z) => float absZ = math.abs(z) absZ >= 3.0 ? 99.7 : absZ >= 2.5 ? 98.8 : absZ >= 2.0 ? 95.4 : absZ >= 1.5 ? 86.6 : absZ >= 1.0 ? 68.3 : absZ >= 0.5 ? 38.3 : 0.0 This probability is displayed in the dashboard alongside the live Z-Score value, giving the trader both the raw statistical reading and its corresponding reversal likelihood. 3. Composite Z-Score and Zone Classification The three individual Z-Scores are combined into a composite score using configurable weights for each dimension. The composite is then classified into a zone: EXTREME (above the configurable extreme threshold), ELEVATED, NEUTRAL, or the opposing directional equivalents. Zone classification determines the dashboard color coding and alert triggers: composite = (priceZ * wPrice + changeZ * wChange + volumeZ * wVolume) / totalWeight 4. Divergence and Hidden Divergence Detection Deviation Lens monitors for two divergence conditions. Standard divergence occurs when the Z-Score direction disagrees with the price direction — price makes a higher high but the Z-Score makes a lower high (bearish divergence), or price makes a lower low but the Z-Score makes a higher low (bullish divergence). Hidden divergence occurs when the Z-Score makes an extreme move while price action is relatively contained — a potential continuation pattern. Divergence events are labeled directly on the chart with bold, clearly sized labels: bullDiv = close > close and priceZ < priceZ // Price up, Z down = bull div bearDiv = close < close and priceZ > priceZ // Price down, Z up = bear div Labels: BULL DIV, BEAR DIV (size.small), H.BULL, H.BEAR (size.tiny for hidden divergence). 5. Multi-Dimensional Dashboard The institutional dashboard presents all three Z-Scores, the composite Z-Score, current zone classification, reversal probability, and divergence status simultaneously. The layout is designed so the most actionable information — Zone and Rev. Probability — is displayed at the largest text size, with supporting metrics at smaller sizes. Features Three independent Z-Scores: Price level, price change (momentum), and volume — each computed on its own rolling mean and standard deviation Configurable Z-Score weights: The composite score uses adjustable per-dimension weights allowing emphasis on price, momentum, or volume depending on trading context Live reversal probability: Probability percentage mapped directly from the Z-Score using normal distribution properties (68.3% at 1σ through 99.7% at 3σ) Zone classification: Composite Z-Score classified as Extreme, Elevated, or Neutral in both directions with color-coded dashboard display Divergence labels (BULL DIV / BEAR DIV): Z-Score vs price direction disagreement labeled on-chart at size.small Hidden divergence labels (H.BULL / H.BEAR): Z-Score extreme with contained price action labeled at size.tiny Configurable extreme and elevated thresholds: Both Z-Score thresholds independently adjustable Institutional dashboard (top right): 14-row table with Price Z, Change Z, Volume Z, Composite Z, Zone, Reversal Probability, and divergence status Adaptive thresholds: All calculations normalize to the rolling lookback period, adapting to current instrument and timeframe volatility Alerts: Separate alertconditions for extreme bull and extreme bear composite Z-Score readings Input Parameters Z-Score Settings: Z-Score Length: Rolling window for all three Z-Score calculations (default: 20) Extreme Threshold: Z-Score magnitude classified as Extreme zone (default: 2.0) Elevated Threshold: Z-Score magnitude classified as Elevated zone (default: 1.0) Dimension Weights: Price Weight: Relative weight of the price Z-Score in composite (default: 1.0) Change Weight: Relative weight of the momentum Z-Score in composite (default: 1.0) Volume Weight: Relative weight of the volume Z-Score in composite (default: 0.5) Divergence: Divergence Lookback: Bars back for divergence comparison (default: 5) Show Divergence Labels toggle Display: Show Dashboard toggle Bull and Bear color inputs How to Use This Indicator Step 1: Read the Composite Zone The Zone row in the dashboard shows the current composite Z-Score classification. EXTREME readings at the top of the scale indicate the highest statistical probability of mean reversion. NEUTRAL readings indicate current conditions are close to the mean and have low statistical directional edge from this tool alone. Step 2: Check Reversal Probability The Rev. Probability row translates the Z-Score magnitude directly into a percentage. A reading above 95% means the current composite Z-Score is in the outer 5% of its historical distribution — a statistical extreme that has preceded mean reversion 95% of the time in the measured period. Step 3: Assess Each Dimension Independently The three individual Z-Score rows reveal which dimension is driving the composite. A high composite driven entirely by volume Z-Score is a different setup than one driven by price Z-Score. Understanding which dimension is extreme helps filter entries: a price Z-Score extreme without supporting momentum or volume Z-Score extremes may be a lower-conviction reading. Step 4: React to Divergence Labels BULL DIV and BEAR DIV labels appear when Z-Score momentum diverges from price direction. These signal that the statistical driver of a move is weakening even as price continues. H.BULL and H.BEAR hidden divergence labels flag potential continuation setups where Z-Score is extreme but price is not. Step 5: Combine with Structural Context Deviation Lens produces the highest value when its extreme readings coincide with a structural confluence point — an order block, session low, or structure level. A 99.7% reversal probability at a tested support zone is a higher-conviction setup than the same reading in open air. Indicator Limitations All Z-Scores are computed relative to the rolling lookback window. The lookback defines what "normal" means. A very short lookback will produce extreme readings frequently; a very long lookback will rarely reach the extreme threshold. Calibration to the instrument and timeframe is required The reversal probability percentages are derived from the normal distribution assumption. Price change and volume distributions are not perfectly normal — they exhibit fat tails and skew. The probabilities are approximations, not precise statistical guarantees The composite Z-Score uses equal weights by default. Changing dimension weights significantly alters which market conditions produce extreme readings. Weight adjustments should be based on the specific instrument's characteristics Divergence detection uses a simple lookback comparison, not a peak-detection algorithm. In choppy markets, divergence labels may appear frequently without providing actionable signals Originality Statement Deviation Lens is original in its simultaneous, weighted multi-dimensional Z-Score framework that maps composite statistical extremity directly to a reversal probability percentage. This indicator is published because: Applying Z-Score analysis to three independent market dimensions simultaneously — price level, momentum (close-to-close change), and volume — rather than a single oscillator provides a richer statistical picture of current market extremity than any single-dimension Z-Score tool The direct mapping of Z-Score magnitude to reversal probability percentages using normal distribution properties gives traders an immediately interpretable statistic rather than a raw number requiring subjective interpretation The composite weighted Z-Score system, where each dimension's contribution to the overall reading is configurable, allows the indicator to be tuned toward price-mean-reversion strategies, momentum exhaustion strategies, or volume anomaly detection depending on the trader's methodology The combined detection of standard divergence and hidden divergence between the Z-Score and price direction provides trend continuation and reversal signals from the same framework Disclaimer This indicator is provided for educational and informational purposes only. It is not financial advice or a recommendation to buy or sell any financial instrument. Trading involves substantial risk of loss. Z-Score readings and reversal probability percentages are statistical tools based on historical distributions and do not guarantee any future price behavior. The normal distribution assumption applied to price and volume data is an approximation. Always use proper risk management. The author is not responsible for any trading losses resulting from the use of this indicator. -Made with passion by jackofalltrades
Indicatore Pine Script®
di officialjackofalltrades
Confluence Signal Engine [JOAT]Confluence Signal Engine Introduction Most traders encounter a common trap: stacking multiple indicators that all claim to measure something different, yet each one is ultimately derived from the same price data. The result is not confirmation — it is correlated noise presented as agreement. The Confluence Signal Engine was built to address this directly. This indicator assigns a composite score to the current market condition by evaluating six deliberately chosen dimensions of market behavior. Each dimension is designed to measure a fundamentally different property of price action. When multiple dimensions agree, that agreement carries more weight than any single indicator firing alone. The result is a single, normalised score between -1 and +1, accompanied by a visual confidence meter and a score breakdown table so you can see exactly what is driving the signal. This is an overlay indicator — it plots directly on the price chart. --- Core Concepts The Six Scoring Dimensions Each dimension returns one of three values: +1 (bullish contribution), -1 (bearish contribution), or 0 (neutral / insufficient data). These are summed and divided by 6.0 to produce the composite score. D1 — EMA Alignment (Trend Direction) Compares a fast EMA to a slow EMA. If the fast EMA is above the slow EMA, the trend dimension scores +1. If below, it scores -1. This is the structural backbone — a baseline read on which side of the trend the price currently sits. D2 — Price Z-Score (Statistical Deviation) Calculates how many standard deviations the current close is from a baseline EMA. A Z-score below the negative threshold suggests the price has deviated far enough below the mean to be considered statistically stretched — a potential reversion candidate, scored +1. A Z-score above the positive threshold scores -1. This dimension does not measure trend; it measures relative price position against recent statistical norms. D3 — Volume Pressure (Demand Validation) Uses a Volume RSI (RSI applied to volume over 8 bars, divided by 50) as a proxy for whether volume activity is elevated. When volume pressure exceeds the threshold, the candle's direction (close vs. open) determines the score: a bullish candle in high-volume conditions scores +1; a bearish candle scores -1. When volume is not elevated, this dimension returns 0, contributing nothing. This prevents volume noise on low-activity bars from polluting the signal. D4 — RSI Momentum (Momentum Quality) Evaluates both the current RSI value and its slope. A rising RSI above 50 scores +1 — confirming that momentum is positive and strengthening. A falling RSI below 50 scores -1. This differs from a simple RSI threshold because the slope requirement means momentum must be actively moving in the scored direction, not merely sitting above or below a level. D5 — Structural Position (Range Placement) Compares the current close to the midpoint of the highest high and lowest low over a configurable lookback period. Closing above the midpoint scores +1; closing below scores -1. This is a simple but useful structural context: is price holding in the upper or lower half of its recent range? D6 — Volatility Context (Environment Quality) Divides a fast ATR by a slow ATR to produce a volatility ratio. A low ratio (calm, contracting volatility) scores +1 — historically a more favorable environment for trend continuation. A high ratio (expanding, elevated volatility) scores -1, flagging that the current environment may be erratic. A ratio between the two thresholds is neutral. This dimension does not predict price direction; it assesses whether current conditions are conducive to acting on the other signals. --- Composite Score and Confidence compositeScore = (D1 + D2 + D3 + D4 + D5 + D6) / 6.0 confidence = math.abs(compositeScore) * 100 The composite score ranges from -1.0 (all six dimensions bearish) to +1.0 (all six dimensions bullish). The confidence value is simply the absolute magnitude — a score of ±1.0 represents 100% agreement across all dimensions, while a score near 0 represents disagreement or neutrality. Signal thresholds: Score > buy threshold (default 0.3) → bullish signal Score < sell threshold (default -0.3) → bearish signal Score > high-confidence threshold (default ±0.6) → high-confidence signal Signals are gated by barstate.isconfirmed — they only fire on fully closed bars, preventing intra-bar repainting. State tracking also prevents the same directional signal from repeating consecutively without a change in direction first. --- Visual Components 24-Cell Gradient Confidence Meter A horizontal bar of 24 cells is displayed at the bottom of the chart. The left side is the bearish extreme, the center is neutral, and the right side is the bullish extreme. The current composite score position is highlighted within the meter, giving a continuous visual read of where the market sits in the conviction range — not just whether a signal has fired, but how strongly. Score Breakdown Table A table showing three columns for each dimension: dimension name, dimension number, and its current score (+1, -1, or 0). This allows you to see exactly which dimensions are contributing to the composite and which are neutral or conflicting. Gradient Bar Coloring Price bars are colored using a gradient that interpolates from a neutral color toward the signal color, weighted by the absolute value of the composite score. A high-confidence bull signal produces a strong green bar; a low-confidence or mixed signal produces a muted or neutral color. This keeps bar coloring proportional to actual conviction rather than using a binary flip. --- Features Six-dimension composite scoring system covering trend, statistics, volume, momentum, structure, and volatility Composite score normalised to with confidence percentage Non-repainting: all signals confirmed on bar close via barstate.isconfirmed State-tracked signals prevent repeated same-direction firing 24-cell gradient confidence meter with continuous position display Score breakdown table showing each dimension's individual contribution Gradient bar coloring proportional to conviction level Configurable thresholds for all six dimensions and signal levels --- Input Parameters EMA Fast / Slow (default 21 / 55) — D1 trend alignment Z-Score Baseline EMA (default 50) — the mean used for Z-score calculation Z-Score Window (default 50) — standard deviation lookback Z-Score Threshold (default 1.5) — how many standard deviations trigger the score Volume RSI Length (default 8) — RSI period applied to volume Volume Threshold (default 1.2) — Volume RSI / 50 must exceed this to activate D3 RSI Length (default 14) — standard RSI period for D4 Structure Lookback (default 20) — bars used to define the high/low range for D5 ATR Fast / Slow (default 14 / 50) — periods for the volatility ratio in D6 Volatility Thresholds (default 0.8 / 1.5) — low and high boundaries for the ATR ratio Buy Threshold (default 0.3) — minimum composite score to generate a long signal Sell Threshold (default -0.3) — maximum composite score to generate a short signal High-Confidence Threshold (default ±0.6) — score level at which a signal is classified as high-confidence --- How to Use Apply to any chart. The overlay paints directly on price bars. Watch the confidence meter for the current composite score position. A score pressed toward either extreme with multiple dimensions aligned is a higher-quality read than one sitting near center. Use the score breakdown table to understand why the composite score is what it is. If only 2 of 6 dimensions are contributing, the signal is weaker regardless of whether it crossed the threshold. High-confidence signals (score beyond ±0.6 by default) indicate that four or more of the six dimensions are in agreement. These can be treated as stronger setups than threshold-level signals. Combine the composite score read with your own price action, support/resistance, or higher-timeframe context before entering a trade. This indicator is a confluence tool, not a standalone entry system. If several dimensions are conflicting (score near 0), the market is not in a clear state — no action is the appropriate response. --- Limitations No indicator can predict future price. The composite score reflects current market conditions based on recent historical data — not what will happen next. Z-score and structural position dimensions are mean-reverting in nature, while EMA alignment and RSI momentum are trend-following. In strongly trending markets, D2 and D5 may produce persistent bearish readings even during a healthy uptrend, suppressing the composite score. This is by design — the indicator is more suited to environments where confluence across all dimensions is achievable. Volume RSI (D3) is only reliable on instruments and timeframes with consistent, meaningful volume data. On synthetic instruments, indices, or very low-timeframe charts, volume data may be unreliable and D3's contribution should be weighted accordingly. The volatility context dimension (D6) measures the environment , not direction. A low-volatility score of +1 does not mean the market is about to move up — only that conditions are historically more favorable for clean signals. Signal state tracking prevents consecutive same-direction signals, which reduces noise but also means the indicator will not re-fire during a prolonged trending move. This is a deliberate design choice but should be understood before use. Default thresholds were chosen for general applicability. Different asset classes, timeframes, and volatility regimes may benefit from threshold adjustment. Past signal quality on any given instrument does not guarantee future performance. --- Originality Statement The core innovation of this indicator is the deliberate selection of six dimensions that measure fundamentally different market properties rather than multiple views of the same property. Standard multi-indicator approaches tend to combine RSI, MACD, and Stochastic — all of which are momentum oscillators derived from price, generating correlated signals that appear independent but are not. This indicator separates the problem into distinct domains: trend direction (EMA alignment), statistical deviation from the mean (Z-score), demand-side pressure (Volume RSI), momentum quality and direction (RSI slope + level), structural placement within recent range (midpoint comparison), and environmental favorability (ATR ratio). Because these dimensions are largely uncorrelated with each other, genuine multi-dimension agreement represents a qualitatively different kind of confluence than stacking three oscillators. The 24-cell gradient meter goes further — it provides a continuous conviction read rather than a binary signal, treating market condition as a spectrum. --- Disclaimer This indicator is provided for educational and informational purposes only. It does not constitute financial advice, investment advice, or a recommendation to buy or sell any security. All trading involves risk, including the possible loss of principal. Past indicator performance does not guarantee future results. Always conduct your own research and consult a qualified financial professional before making any trading decisions. -Made with passion by officialjackofalltrades
Indicatore Pine Script®
di officialjackofalltrades
Swing-Level Z-Score Oscillator▶️Overview The Swing-Level Z-Score Oscillator is an innovative indicator that bridges the gap between classic market structure and statistical probability. Instead of relying on traditional moving averages as a baseline, this oscillator evaluates price extremes relative to recent structural pivot levels (Swing Highs and Swing Lows). By transforming these structural deviations into a standardized Z-Score, it provides a highly intuitive, context-aware perspective on Overbought (OB) and Oversold (OS) conditions. ▶️How It Works (The Logic) Traditional oscillators often lag or provide false signals during strong trends. This script tackles that issue through a unique three-step process: Dynamic Baseline : The algorithm constantly scans for recent Pivot Highs and Pivot Lows. It takes the average of the last N pivots to establish a dynamic "horizontal zone" of recent historical interest. This acts as our expected mean. Error & Volatility: It measures the distance (error) between the current Close price and this expected mean. To understand the significance of this distance, it calculates the rolling standard deviation of these errors. Z-Score Normalization: Finally, it divides the current error by the standard deviation. The result is a clean Z-Score that tells you exactly how many standard deviations the current price has stretched away from recent structural levels. ▶️Key Features Actionable Market Context: Because the baseline is built on actual price pivots rather than arbitrary averages, the oscillator respects current market structure (support/resistance). Intelligent Gradient UI: The indicator features a dynamic color-coding system. The histogram and signal line smoothly fade based on the intensity of the momentum. Vivid Extreme Alerts: When the Z-Score stretches beyond the critical ±2.0 Sigma threshold, the histogram flashes vivid Cyan (Overbought) or Neon Pink (Oversold), immediately catching your attention. Plug-and-Play Presets: Don't want to mess with settings? Use the "Operating Mode" dropdown to quickly switch between Short-term, Standard, and Long-term presets tailored to different trading styles. Fully customizable options are also available. ▶️How to Trade with It Mean Reversion (Fade the Extremes): When the histogram hits the vivid ±2.0 zones, the price is statistically overextended relative to recent swing levels. Look for exhaustion price action (like pin bars) combined with a hook back toward the center line to trade reversions. Pullbacks in a Trend: During a clear trend, look for the oscillator to reset back to the Center Line (0) or the ±1 Sigma lines. These often represent optimal, low-risk entry points (buy the dip/sell the rally) before the trend resumes. Momentum Breakouts: A sudden, aggressive spike that blasts through the ±2 Sigma line can indicate a genuine structural breakout with heavy momentum, rather than a mere overextension. ▶️Settings & Customization If you select "Custom" in the Operating Mode, you can fine-tune: Left/Right Bars: Adjusts the sensitivity of the pivot detection. Lower numbers catch micro-swings, while higher numbers catch major structural points. StdDev Length: The lookback period for calculating the variance of the errors. Past Pivots Count (N): Determines how many historical pivots are used to calculate the "Expected Value" baseline. Disclaimer: This script is for educational and analytical purposes only. Always combine oscillator readings with broader price action analysis and proper risk management.
Indicatore Pine Script®
di SatohK
Smoothed ZLEMA Z-scoreThis indicator computes a Z-score of price using a Zero Lag EMA (ZLEMA) instead of a standard moving average, then applies a second ZLEMA pass to smooth the result. The output oscillates around zero — crossings of the zero line generate buy/sell signals with a configurable cooldown filter. Primary utility is as a mean-reversion and momentum-shift detector rather than a trend-following tool. Its edge comes from answering one specific question: has price moved so far from its dynamic equilibrium that a reversal is statistically due? Because the mean is a ZLEMA (Zero Lag EMA) rather than a simple or standard EMA, it tracks price closely without the usual lag tax. This makes the Z-score more reactive — it rises and falls faster than a Bollinger-based oscillator, meaning it spends less time stuck at extremes after the actual turning point has already passed. The smoothing pass on top then removes the noise that would otherwise cause whipsaws on that fast signal. So you get the best of both worlds: fast mean-tracking, clean signal output. Practical applications: Entries on mean-reversion setups — when the Z-score crosses back to zero from a stretched extreme (±1.5 to ±2.5 zone), it signals that price has returned to statistical normalcy, which is often the safest entry point. Momentum confirmation — on a breakout, a zero-cross in the direction of the break confirms that price is not just stretching — it's shifting its mean relationship entirely. Divergence reads — if price makes a new high but the Z-score makes a lower high, the move is losing statistical intensity even if it hasn't reversed yet. The indicator is genuinely timeframe-agnostic but behaves differently at each scale: The key insight on timeframes: the Z-score's absolute value at any point is what matters more than the signal itself. On lower timeframes, ±1.5 is already extreme. On daily charts, the oscillator needs to push beyond ±2 before a zero-cross signal carries real weight. The Mathematics — What It's Actually Measuring: The Z-score tells you how unusual the current price is relative to its recent self, expressed in standard deviation units. This is a statistical normalization — it strips away the actual price level and volatility of the asset, leaving only the relative displacement. The ZLEMA twist modifies both the mean and the variance calculation. In a standard Bollinger-based Z-score, a simple moving average is the center. Here, the center is a zero-lag EMA — which means: The mean itself moves toward current price faster Variance is computed against that faster mean, so it captures dispersion around a more current reference point The result is a Z-score that doesn't have the "stale mean" problem that plagues traditional oscillators in trending conditions Visually, think of it this way: a standard deviation band around a slow SMA creates a wide tunnel that price has to escape to trigger a reading. A ZLEMA-based deviation band is a narrower, faster-moving tunnel that hugs price more tightly, so genuine outlier moves register more immediately. The final smoothing pass converts the noisy raw Z-score (which can spike and collapse within a few bars) into a cleaner wave that crosses zero in a more deliberate, readable way. The quality of a zero-cross signal is directly proportional to how deep the Z-score was before crossing. A cross from −2.2 back above zero is a high-conviction mean-reversion. A cross from −0.4 is statistical noise dressed as a signal — the cooldown filter is your defense against those. The fill coloring reinforces this: orange above zero, blue below. When the fill is deep and the cross happens, the visual contrast of the color flipping is itself a useful alert. This indicator does not predict direction in a vacuum. A Z-score of +2.5 does not guarantee a decline — in a strong trending market, price can "ride the band" with the Z-score hovering at elevated levels for extended periods. The zero-cross signal is most reliable in mean-reverting market regimes (range-bound conditions, consolidation phases, overnight sessions in futures). In strong trends, signals against the trend direction should be filtered by a higher-timeframe trend indicator before acting on them. How to Use It: The indicator is best read as a mean-reversion oscillator with momentum context: Z-score rising through zero from below = price reclaiming its ZLEMA mean, potential momentum shift upward Z-score falling through zero from above = price breaking down through its mean Extreme values (±2 or beyond) suggest price is statistically stretched and likely to revert The cooldown parameter is important to tune — on trending instruments, increase it to avoid whipsaws; on ranging instruments, reducing it captures more turns The ZLEMA backbone makes this more responsive than a traditional Bollinger-based Z-score, which can lag by several bars in fast-moving markets. Disclaimer: This indicator and any signals it generates are provided strictly for educational and informational purposes. Nothing presented here constitutes financial advice, investment advice, or a recommendation to buy or sell any financial instrument. Good Luck !!
Indicatore Pine Script®
di kingshukghosh71
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.
Indicatore Pine Script®
di Iamalala
4
Statistical Reversion Engine [JOAT]Statistical Reversion Engine Introduction The Statistical Reversion Engine (SRE) is an advanced open-source mean reversion indicator that combines statistical deviation bands, premium/discount zone analysis, DCA level calculation, Z-score measurement, and enhanced reversion probability scoring to identify high-probability mean reversion opportunities. This indicator quantifies price deviation from statistical mean using multiple calculation methods (SMA, EMA, VWAP, HMA) and provides probabilistic assessment of reversion likelihood through multi-factor analysis including deviation magnitude, volatility regime, and historical reversion patterns. Unlike basic Bollinger Band indicators that simply plot standard deviation bands, SRE employs a sophisticated statistical framework that calculates Z-scores, premium/discount percentages, enhanced reversion probability (incorporating volatility and premium factors), and tracks historical reversion speed to provide traders with quantitative mean reversion intelligence. The indicator also generates DCA (Dollar Cost Averaging) levels with volatility-adjusted spacing for systematic position building. Why This Indicator Exists This indicator addresses the challenge of identifying when price has deviated sufficiently from mean to warrant mean reversion trades. Traditional mean reversion indicators lack probabilistic quantification and don't account for volatility regime or historical reversion patterns. SRE systematically reveals: Multiple Mean Calculations: SMA, EMA, VWAP (session/continuous), HMA for flexible mean definition Statistical Deviation Bands: 1σ, 2σ, 3σ bands with customizable multipliers Z-Score Calculation: Quantifies deviation in standard deviation units Premium/Discount Analysis: Percentage deviation from mean with zone classification Enhanced Reversion Probability: Multi-factor scoring (Z-score + premium + volatility) DCA Level Generation: Volatility-adjusted levels for systematic position building Historical Reversion Tracking: Measures average bars to return to mean after extreme deviation Each component provides unique intelligence. Mean calculation defines center, deviation bands show extremes, Z-score quantifies magnitude, premium/discount shows percentage, probability scores likelihood, DCA levels provide entry framework, and historical tracking provides context. Core Components Explained 1. Flexible Mean Calculation System SRE supports four mean calculation methods: f_calculate_mean(string type, int length) => float result = close if type == "SMA" result := ta.sma(close, length) else if type == "EMA" result := ta.ema(close, length) else if type == "VWAP" result := session_reset ? ta.vwap(hlc3) : ta.vwma(hlc3, length) else if type == "HMA" result := ta.hma(close, length) result Mean selection impacts reversion behavior: - SMA: Simple average, slower to respond - EMA: Exponential weighting, faster response - VWAP: Volume-weighted, institutional reference - HMA: Hull Moving Average, smoothest with minimal lag 2. Statistical Deviation Band System Three deviation bands calculated using standard deviation: float mean_line = f_calculate_mean(mean_type, mean_length) float stdev = f_calculate_stdev(close, deviation_period) float upper_band_1 = mean_line + (stdev * band_multiplier_1) // 1σ float lower_band_1 = mean_line - (stdev * band_multiplier_1) float upper_band_2 = mean_line + (stdev * band_multiplier_2) // 2σ float lower_band_2 = mean_line - (stdev * band_multiplier_2) float upper_band_3 = mean_line + (stdev * band_multiplier_3) // 3σ float lower_band_3 = mean_line - (stdev * band_multiplier_3) Default multipliers: 1.0, 2.0, 3.0 (customizable) - 1σ: 68% of price action (normal range) - 2σ: 95% of price action (extended range) - 3σ: 99.7% of price action (extreme range) 3. Z-Score Calculation & Classification Z-score quantifies deviation in standard deviation units: f_calculate_zscore(float price, float mean, float stdev) => float zscore = stdev > 0 ? (price - mean) / stdev : 0.0 zscore float zscore = f_calculate_zscore(close, mean_line, stdev) Z-score interpretation: - |Z| < 1.0: Normal deviation (40% reversion probability) - |Z| 1.0-1.5: Moderate deviation (60% reversion probability) - |Z| 1.5-2.0: Extended deviation (75% reversion probability) - |Z| 2.0-2.5: Extreme deviation (85% reversion probability) - |Z| > 3.0: 3-sigma event (95% reversion probability) 4. Premium/Discount Zone Analysis Percentage deviation from mean with zone classification: f_calculate_premium_discount(float price, float mean) => float pct = mean > 0 ? ((price - mean) / mean) * 100 : 0.0 pct float premium_discount_pct = f_calculate_premium_discount(close, mean_line) string current_zone = premium_discount_pct >= premium_threshold * 2 ? "Extreme Premium" : premium_discount_pct >= premium_threshold ? "Premium" : premium_discount_pct <= discount_threshold * 2 ? "Extreme Discount" : premium_discount_pct <= discount_threshold ? "Discount" : "Fair Value" Zone classification (default thresholds): - Extreme Premium: >3.0% above mean (strong sell zone) - Premium: 1.5-3.0% above mean (sell zone) - Fair Value: -1.5% to +1.5% (neutral zone) - Discount: -3.0% to -1.5% below mean (buy zone) - Extreme Discount: <-3.0% below mean (strong buy zone) 5. Enhanced Reversion Probability Scoring Multi-factor probability calculation: f_enhanced_reversion_prob(float z, float premium_pct, float vol_rank) => float base_prob = f_reversion_probability(z) // Adjust for premium/discount magnitude float premium_factor = math.abs(premium_pct) > 3 ? 1.2 : math.abs(premium_pct) > 2 ? 1.1 : math.abs(premium_pct) > 1 ? 1.0 : 0.9 // Adjust for volatility (lower vol = higher reversion probability) float vol_factor = vol_rank < 30 ? 1.2 : vol_rank < 50 ? 1.1 : vol_rank < 70 ? 1.0 : 0.85 math.min(base_prob * premium_factor * vol_factor, 99) Enhanced probability accounts for: - Base Z-score probability - Premium/discount magnitude (larger deviation = higher probability) - Volatility regime (lower volatility = more predictable reversion) 6. Volatility-Adjusted DCA Level Generation DCA levels automatically adjust spacing based on volatility: float current_atr = ta.atr(14) float atr_pct = close > 0 ? (current_atr / close) * 100 : 0 float vol_multiplier = atr_pct > 3 ? 1.5 : atr_pct > 2 ? 1.2 : atr_pct > 1 ? 1.0 : 0.8 for i = 1 to dca_levels float adjusted_spacing = (dca_spacing * vol_multiplier) / 100 float buy_level = mean_line * (1 - adjusted_spacing * i) float sell_level = mean_line * (1 + adjusted_spacing * i) array.push(dca_buy_levels, buy_level) array.push(dca_sell_levels, sell_level) Volatility adjustment: - High vol (ATR% >3): 1.5x spacing (wider levels) - Elevated vol (ATR% 2-3): 1.2x spacing - Normal vol (ATR% 1-2): 1.0x spacing (default) - Low vol (ATR% <1): 0.8x spacing (tighter levels) 7. Historical Reversion Speed Tracking Measures average bars to return to mean after extreme deviation: var array reversion_times = array.new_int(0) var bool tracking_reversion = false var int reversion_start_bar = 0 if math.abs(zscore) >= 2.5 and not tracking_reversion tracking_reversion := true reversion_start_bar := bar_index if tracking_reversion and math.abs(zscore) < 0.5 int reversion_time = bar_index - reversion_start_bar array.push(reversion_times, reversion_time) tracking_reversion := false float avg_reversion_time = array.size(reversion_times) > 0 ? array.avg(reversion_times) : na Average reversion time provides context for expected holding period. Visual Elements Mean Line: Electric lime line showing statistical mean Deviation Bands: 1σ (lime), 2σ (violet), 3σ (deep violet) with gradient fills Premium/Discount Zones: Background coloring (violet for premium, lime for discount) DCA Levels: Dotted lines with "B1, B2, B3..." (buy) and "S1, S2, S3..." (sell) labels Z-Score Label: Current Z-score displayed on price Gradient Zone Fills: Progressive transparency between bands Mean Reversion Signals: Triangle markers for strong buy/sell setups Reversion Probability Heatmap: Background intensity based on enhanced probability Dashboard: Real-time metrics including zone, P/D%, Z-score, reversion probability, mean value, distance, enhanced probability, deviation percentile, mean trend, nearest DCA, average reversion time, bars since extreme Input Parameters Mean Calculation: Mean Type: SMA, EMA, VWAP, HMA (default: VWAP) Mean Length: Period for mean calculation (default: 20) Session Reset (VWAP): Toggle session anchoring (default: true) Deviation Bands: Band 1 Multiplier: 1σ multiplier (default: 1.0) Band 2 Multiplier: 2σ multiplier (default: 2.0) Band 3 Multiplier: 3σ multiplier (default: 3.0) Deviation Period: Standard deviation calculation period (default: 20) Premium/Discount: Premium Threshold (%): Threshold for premium zone (default: 1.5%) Discount Threshold (%): Threshold for discount zone (default: -1.5%) DCA Levels: Enable DCA Levels: Toggle DCA display (default: true) Number of DCA Levels: Levels to generate (default: 5) DCA Spacing (%): Base spacing between levels (default: 1.5%) Visualization: Show Deviation Bands: Toggle band display (default: true) Show Band Fills: Toggle gradient fills (default: true) Show Premium/Discount Zones: Toggle background coloring (default: true) Show Z-Score Label: Toggle Z-score display (default: true) How to Use This Indicator Step 1: Identify Current Zone Check dashboard "Zone" row. Extreme Discount = strong buy zone, Extreme Premium = strong sell zone. Step 2: Assess Z-Score Magnitude |Z| >2.0 indicates extended deviation. |Z| >3.0 is 3-sigma event (rare, high reversion probability). Step 3: Check Enhanced Reversion Probability Dashboard shows enhanced probability accounting for volatility and premium factors. >80% is high probability. Step 4: Monitor Mean Trend "Rising" mean suggests uptrend, "Falling" suggests downtrend. Trade with mean trend for higher probability. Step 5: Use DCA Levels for Entry Enter positions at DCA levels (B1, B2, B3 for longs; S1, S2, S3 for shorts) to average into position. Step 6: Wait for Strong Signals Triangle markers appear when: - Extreme zone + enhanced probability >80% + band crossover - These are highest conviction mean reversion setups Best Practices Mean reversion works best in ranging markets - avoid strong trends 3-sigma events (|Z| >3.0) have highest reversion probability but occur rarely Use DCA levels to build positions systematically rather than all-in entries Enhanced probability >80% indicates high-quality setup Mean trend provides context - reversion against trend is lower probability Volatility-adjusted DCA spacing prevents over-concentration in high vol Average reversion time helps set realistic profit target timeframes Combine with higher timeframe trend - mean reversion with trend is safer Deviation percentile >90% indicates extreme deviation Bars since extreme >50 suggests extended deviation may persist Indicator Limitations Mean reversion fails during strong trending markets 3-sigma events can persist longer than expected during major news DCA levels don't account for fundamental catalysts Enhanced probability is statistical, not deterministic Historical reversion time doesn't guarantee future reversion speed VWAP mean resets daily - may not be appropriate for all timeframes Standard deviation assumes normal distribution - markets have fat tails Premium/discount thresholds may need adjustment for different instruments Technical Implementation Built with Pine Script v6 using: Four mean calculation methods (SMA, EMA, VWAP, HMA) Three-tier deviation band system with customizable multipliers Z-score calculation with standard deviation Premium/discount percentage with zone classification Enhanced reversion probability (Z-score + premium + volatility) Volatility-adjusted DCA level generation Historical reversion speed tracking with arrays Deviation percentile ranking Mean trend detection (fast vs slow mean) Gradient zone fills with progressive transparency Reversion probability heatmap background Comprehensive dashboard with 12 metrics The code is fully open-source and can be modified to suit individual trading styles. Originality Statement This indicator is original in its comprehensive statistical mean reversion approach. While Bollinger Bands and mean reversion are established concepts, this indicator is justified because: It combines four mean calculation methods with three-tier deviation bands Enhanced reversion probability incorporates Z-score, premium magnitude, and volatility regime Volatility-adjusted DCA level generation adapts to market conditions Historical reversion speed tracking provides empirical context Premium/discount zone classification adds percentage-based perspective Mean trend detection (fast vs slow) provides directional context Deviation percentile ranking shows historical extremity Integration of statistical measures (Z-score, stdev, percentile) with practical tools (DCA levels, signals) Each component contributes unique information: mean defines center, deviation bands show extremes, Z-score quantifies magnitude, premium/discount shows percentage, enhanced probability scores likelihood, DCA levels provide framework, historical tracking provides context, and mean trend shows direction. The indicator's value lies in presenting these complementary perspectives simultaneously with unified statistical framework. Disclaimer This indicator is provided for educational and informational purposes only. It is not financial advice. Mean reversion probabilities do not guarantee outcomes. Trading involves substantial risk of loss. Past performance does not guarantee future results. Always use proper risk management and never risk more than you can afford to lose. -Made with passion by officialjackofalltrades
Indicatore Pine Script®
di officialjackofalltrades
2
Liquidity Structure & Order Flow [UAlgo]Liquidity Structure & Order Flow is a range based market participation tool that combines a custom volume profile, value area analysis, liquidity void detection, and unusual volume tagging into a single chart overlay. Its goal is to show not only where volume has concentrated across price, but also how that activity was distributed between estimated buying pressure and selling pressure inside the recent market structure. The script begins by scanning a rolling lookback range, then divides that vertical price space into a configurable number of bins. Each bin becomes a price segment that stores estimated buy volume, sell volume, and total volume. From there, the script builds a profile that highlights the Point of Control, the value area, and the internal order flow balance across the studied range. What makes this indicator especially useful is that it does more than draw a standard profile. It also identifies areas where participation is abnormally thin relative to both the Point of Control and local neighboring bins. These low participation areas are marked as liquidity voids, helping the user see where the market moved through price with relatively little acceptance. In addition, the script monitors the current bar for unusually large activity using a volume z score and a directional delta ratio filter. When a bar shows both exceptional size and meaningful directional imbalance, the script prints a bubble style marker above or below price. This gives the user a way to spot unusual participation events as they happen. The result is a tool that can be used for profile analysis, liquidity mapping, imbalance recognition, and structural context. It helps answer several practical questions at once: where the market accepted price, where it rejected or skipped through price, where the strongest concentration of activity formed, and whether recent candles are showing exceptional directional participation. 🔹 Features 🔸 Custom Range Based Volume Profile The script constructs a manual volume profile over the selected lookback period. Instead of relying on a built in profile engine, it divides the recent range into user defined price bins and allocates each bar’s volume into those bins. This gives full control over how the profile is built and how the final distribution is interpreted. 🔸 Buy Volume and Sell Volume Estimation Every candle contributes both buy side and sell side estimates. The script uses the candle’s open, high, low, and close to derive a buy volume ratio, then splits total volume into buy volume and sell volume accordingly. This creates a practical order flow style approximation that is more informative than total volume alone. 🔸 Proportional Price Overlap Allocation When a candle spans multiple bins, the script distributes its buy volume, sell volume, and total volume proportionally according to how much of the candle overlaps each bin. This produces a more realistic internal structure than simply dropping the full bar volume into one price row. 🔸 Point of Control Detection The indicator finds the bin with the highest total volume and marks it as the Point of Control. This gives the user an immediate view of the strongest participation price inside the studied range. 🔸 Value Area Calculation After the Point of Control is found, the script expands upward and downward through neighboring bins until the selected percentage of total profile volume is captured. This defines Value Area High and Value Area Low, allowing the user to distinguish the central acceptance region from the rest of the range. 🔸 Profile Coloring by Participation Side The profile is drawn as stacked horizontal boxes showing estimated buy side participation and sell side participation inside each row. Bins inside the value area use stronger coloring, while bins outside the value area use softer coloring. This makes the internal structure easy to read visually. 🔸 Liquidity Void Detection The script scans for bins with unusually weak participation outside the value area. A bin qualifies as a liquidity void candidate only if it is both small relative to the Point of Control and also weaker than its nearby neighbors. Consecutive weak bins are grouped into a larger void zone and labeled directly on the chart. 🔸 Unusual Volume Bubble Markers Current bar activity is evaluated using a long period volume average and standard deviation. If the bar’s volume is statistically unusual and its estimated delta ratio is large enough, the script prints a directional bubble marker. Positive directional activity is shown below price, and negative directional activity is shown above price. 🔸 Optional Profile and Void Display The user can independently control whether the volume profile, liquidity voids, and unusual volume markers are shown. This makes the script flexible enough for both full structure analysis and lighter chart layouts. 🔸 Extendable Structural Levels The Point of Control, Value Area High, and Value Area Low can be drawn as either compact structure references or extended lines, depending on the chosen setting. This allows the user to decide whether the levels should function as local annotations or ongoing chart references. 🔸 Useful for Acceptance and Imbalance Analysis The combination of profile structure, value area, void zones, and unusual activity markers gives the indicator a broader purpose than a standard profile. It can help identify accepted price, skipped price, directional participation, and possible future reaction areas. 🔹 Calculations 1) Building the Volume Profile Container type PriceBin float price float buyVol = 0.0 float sellVol = 0.0 float totalVol = 0.0 type VolumeProfile float highPrice = na float lowPrice = na float binSize = na array bins float pocPrice = na float pocVol = 0.0 float vah = na float val = na float totalVol = 0.0 This is the foundation of the whole script. Each PriceBin stores one price level area inside the profile. It contains: the row midpoint price, estimated buy volume, estimated sell volume, and total volume. The VolumeProfile structure stores the full profile state: the highest price of the lookback range, the lowest price of the lookback range, the bin size, the array of bins, and the final analytical values such as Point of Control, Value Area High, Value Area Low, and total profile volume. So before any analysis happens, the script defines a complete custom data model for price distribution and order flow style estimation. 2) Initializing the Bins Across the Lookback Range method initBins(VolumeProfile this, float h, float l, int numBins) => this.highPrice := h this.lowPrice := l this.binSize := (h - l) / numBins this.pocPrice := na this.pocVol := 0.0 this.totalVol := 0.0 this.vah := na this.val := na this.bins := array.new() for i = 0 to numBins - 1 this.bins.push(PriceBin.new(price = l + i * this.binSize + (this.binSize / 2))) This method creates the working profile rows. First, it stores the high and low of the selected lookback period. Then it calculates binSize , which is the vertical price height of each row. That is simply the full range height divided by the number of bins. After resetting all major profile outputs, the script creates a fresh bin array. Each new bin is assigned a midpoint price: l + i * this.binSize + (this.binSize / 2) That midpoint becomes the visual and analytical center of the row. In practical terms, this is where the script transforms the raw market range into a structured ladder of price rows that can later receive allocated volume. 3) Locating the Correct Bin for a Price method getBinIndex(VolumeProfile this, float p) => if na(this.lowPrice) or na(this.binSize) or this.binSize == 0 0 else int idx = math.floor((p - this.lowPrice) / this.binSize) math.max(0, math.min(idx, this.bins.size() - 1)) This helper method maps any price to its correct row index inside the profile. It works by measuring how far the price sits above the profile low, then dividing that distance by the bin size. The result is the raw row index. After that, the value is clamped so it always stays inside the valid bin range. This is important because the script repeatedly needs to know which rows are touched by each candle’s low and high. Without this mapping step, the profile could not distribute volume across price space correctly. 4) Estimating Buy Volume and Sell Volume From Candle Structure float hlR = high - low float bVR = hlR == 0 ? 0.5 : (close - low + high - open) / (2 * hlR) float currentBuyVol = volume * bVR float currentSellVol = volume * (1 - bVR) float delta = currentBuyVol - currentSellVol This snippet explains how the script approximates order flow direction on the current bar. First, it measures the candle range from high to low. Then it computes a buy volume ratio using the relative location of the open and close inside that range: (close - low + high - open) / (2 * hlR) This ratio becomes a practical estimate of how much of the bar’s total volume behaved like buying pressure versus selling pressure. If the candle closes stronger and opens higher inside its range, the ratio leans more bullish. If the candle structure is weaker, the ratio leans more bearish. That ratio is then used to split total volume into: currentBuyVol and currentSellVol Finally, the script calculates delta as the difference between estimated buy volume and estimated sell volume. This is not true transaction tagged exchange delta, but it is a useful chart based directional participation model. 5) Distributing Candle Volume Across Touched Price Rows method addBarVolume(VolumeProfile this, float h, float l, float c, float o, float v) => float hlRange = h - l float buyVolRatio = hlRange == 0 ? 0.5 : (c - l + h - o) / (2 * hlRange) float buyV = v * buyVolRatio float sellV = v * (1 - buyVolRatio) int startIdx = this.getBinIndex(l) int endIdx = this.getBinIndex(h) for i = startIdx to endIdx if i >= 0 and i < this.bins.size() PriceBin b = this.bins.get(i) float binTop = b.price + (this.binSize / 2) float binBot = b.price - (this.binSize / 2) float overlapTop = math.min(h, binTop) float overlapBot = math.max(l, binBot) float overlap = math.max(0.0, overlapTop - overlapBot) float weight = hlRange > 0 ? overlap / hlRange : (1.0 / (endIdx - startIdx + 1)) b.buyVol += buyV * weight b.sellVol += sellV * weight b.totalVol += v * weight this.bins.set(i, b) this.totalVol += v * weight This is one of the most important calculations in the entire script. For each candle inside the lookback period, the script first computes estimated buy volume and sell volume. Then it finds which profile rows are touched by the candle’s low and high. For every touched row, it measures how much of the candle overlaps that specific row. That overlap becomes a weighting factor: weight = overlap / hlRange If a candle overlaps a row heavily, that row receives a larger share of the bar’s volume. If the overlap is small, the row receives only a small share. The script then adds weighted buy volume, weighted sell volume, and weighted total volume into that bin. This is much more realistic than assigning all volume to a single row because it respects the actual price space the candle traveled through. 6) Determining the Point of Control method calcValueArea(VolumeProfile this, float pct) => float midPrice = (this.highPrice + this.lowPrice) / 2 float maxVol = -1.0 float bestPrice = na int pocIdx = -1 for i = 0 to this.bins.size() - 1 PriceBin b = this.bins.get(i) if b.totalVol > maxVol maxVol := b.totalVol bestPrice := b.price pocIdx := i else if b.totalVol == maxVol and maxVol > 0 if math.abs(b.price - midPrice) < math.abs(bestPrice - midPrice) bestPrice := b.price pocIdx := i this.pocVol := maxVol this.pocPrice := bestPrice This is the first phase of the value area calculation. The script scans all bins and finds the row with the greatest total volume. That row becomes the Point of Control. If two rows have the same maximum volume, the script breaks the tie by choosing the one closer to the middle of the full lookback range. That tie handling matters because it avoids unstable selection when multiple bins have identical strength. After the winning row is found, the script stores: the Point of Control volume in pocVol and the Point of Control price in pocPrice So the Point of Control is not simply a visual midpoint. It is the actual strongest participation row in the profile. 7) Expanding Upward and Downward to Build the Value Area float targetVol = this.totalVol * pct / 100.0 float currentVol = 0.0 if pocIdx >= 0 and pocIdx < this.bins.size() currentVol := this.bins.get(pocIdx).totalVol int upIdx = pocIdx + 1 int dnIdx = pocIdx - 1 while currentVol < targetVol and (upIdx < this.bins.size() or dnIdx >= 0) float upVol = upIdx < this.bins.size() ? this.bins.get(upIdx).totalVol : -1.0 float dnVol = dnIdx >= 0 ? this.bins.get(dnIdx).totalVol : -1.0 After finding the Point of Control, the script calculates the target volume required for the value area. For example, if the input is 70 percent, the target becomes 70 percent of total profile volume. The expansion begins from the Point of Control row itself. currentVol starts with the Point of Control row’s own total volume. Then the script looks one row up and one row down, repeatedly expanding until the accumulated volume reaches the target. This is the standard logic of building a value area around the strongest participation center. 8) Deciding Whether to Expand Up or Down if upVol > dnVol and upVol != -1.0 currentVol += upVol upIdx += 1 else if dnVol > upVol and dnVol != -1.0 currentVol += dnVol dnIdx -= 1 else if upVol == dnVol and upVol != -1.0 if currentVol + upVol > targetVol if math.abs(this.bins.get(upIdx).price - midPrice) < math.abs(this.bins.get(dnIdx).price - midPrice) currentVol += upVol upIdx += 1 else currentVol += dnVol dnIdx -= 1 else currentVol += upVol + dnVol upIdx += 1 dnIdx -= 1 This block decides which side to include next in the value area. If the row above has more volume than the row below, the script expands upward. If the row below has more volume, it expands downward. If both sides are equal, it uses distance to the overall midpoint as a tie breaker when necessary. This is important because value area growth should follow participation strength, not arbitrary direction. The final result is a value area that naturally wraps around the highest volume concentration. 9) Final VAH and VAL Assignment int finalUpIdx = math.max(pocIdx, upIdx - 1) int finalDnIdx = math.min(pocIdx, dnIdx + 1) this.vah := finalUpIdx < this.bins.size() ? this.bins.get(finalUpIdx).price : this.highPrice this.val := finalDnIdx >= 0 ? this.bins.get(finalDnIdx).price : this.lowPrice Once expansion is complete, the script converts the final included rows into value area boundaries. The highest included row becomes Value Area High. The lowest included row becomes Value Area Low. These values define the central price zone where the chosen percentage of the profile’s total volume was traded. So VAH and VAL are directly derived from the row by row structure of the profile, not from any fixed percentage of price range. 10) Detecting Unusual Volume Activity float volSma = ta.sma(volume, 200) float volStdev = ta.stdev(volume, 200) float zScore = volStdev == 0 ? 0 : (volume - volSma) / volStdev float deltaRatio = volume > 0 ? math.abs(delta) / volume : 0 bool isUnusual = zScore > zScoreThreshold and deltaRatio >= deltaRatioThreshold This block evaluates whether the current bar is unusually active. First, the script computes a 200 period average volume and standard deviation. Then it transforms the current bar’s volume into a z score, which shows how many standard deviations the bar stands above normal background activity. Next, it calculates deltaRatio , which measures how large the directional imbalance is relative to total volume. A bar is marked unusual only if both conditions are true: the volume is statistically large enough, and the directional imbalance is meaningful enough. This double filter helps reduce false signals from large but directionless bars. 11) Printing Unusual Volume Bubbles if showUnusual and isUnusual string lblText = (delta > 0 ? "🟢 " : "🔴 ") + str.tostring(math.round(volume)) color lblColor = delta > 0 ? color.new(color.green, 0) : color.new(color.red, 0) label uLbl = label.new(bar_index, delta > 0 ? low : high, text=lblText, style=label.style_none, textcolor=lblColor, yloc=delta > 0 ? yloc.belowbar : yloc.abovebar, size=size.small) When an unusual bar is detected, the script prints a directional marker. If estimated delta is positive, the bubble is shown below price in green. If estimated delta is negative, the bubble is shown above price in red. The displayed text also includes the rounded volume value. This allows the user to quickly see both direction and size of the unusual participation event. So these markers are not random momentum tags. They specifically highlight bars where both participation size and directional imbalance stand out. 12) Rebuilding the Profile on the Last Bar float highestPrice = ta.highest(high, lookback) float lowestPrice = ta.lowest(low, lookback) if barstate.islast and bar_index >= lookback - 1 profile.initBins(highestPrice, lowestPrice, rows) for i = 0 to lookback - 1 profile.addBarVolume(high , low , close , open , volume ) profile.calcValueArea(vaPct) This is the main execution block for the profile. First, the script finds the highest high and lowest low across the chosen lookback window. That defines the total vertical space of the analysis. Then, on the last visible bar, it: initializes the bins, loops through every candle inside the lookback, adds each candle’s weighted volume into the profile, and finally calculates the value area. Running this only on the last bar is efficient because the full profile is a visual structure based on the current lookback window. It does not need to be redrawn historically on every past bar. 13) Scaling and Drawing the Profile Histogram float maxVol = profile.pocVol float allVols = array.new_float() for i = 0 to profile.bins.size() - 1 allVols.push(profile.bins.get(i).totalVol) float avgVol = allVols.avg() float stdVol = allVols.stdev() float clampedMaxVol = math.max(math.min(maxVol, avgVol + (stdVol * 2)), 0.000001) Before drawing the profile, the script prepares a safer scaling reference. Instead of using raw Point of Control volume alone without adjustment, it clamps the maximum drawable scale using the average row volume plus two standard deviations. This helps prevent a single extreme row from making the rest of the profile look too compressed. In practical terms, this means the visual histogram remains readable even when one row is exceptionally dominant. 14) Drawing Buy Side and Sell Side Inside Each Row int buyLen = math.round((b.buyVol / b.totalVol) * (drawVol / clampedMaxVol) * profileWidth) int sellLen = math.round((b.sellVol / b.totalVol) * (drawVol / clampedMaxVol) * profileWidth) bool inVA = b.price <= profile.vah and b.price >= profile.val bool isPocRow = math.abs(b.price - profile.pocPrice) <= profile.binSize * 0.5 int x1 = profileRight int x2 = x1 - buyLen if buyLen > 0 box bB = box.new(x1, topP, x2, botP, border_color=c_border, bgcolor=c_buy) int x3 = x2 int x4 = x3 - sellLen if sellLen > 0 box bS = box.new(x3, topP, x4, botP, border_color=c_border, bgcolor=c_sell) This is the actual profile drawing logic. For each row, the script determines how much of the row’s total activity came from estimated buy volume and how much came from estimated sell volume. It then converts those fractions into horizontal lengths. The buy portion is drawn first, then the sell portion continues from the end of the buy section. This creates a stacked horizontal bar that reveals both total participation and internal directional composition. The row also receives context coloring: rows inside the value area use stronger color treatment, and the Point of Control row can receive a distinct border. So the histogram communicates three layers at once: how much volume was traded there, whether that row sits inside the value area, and how that row’s activity was split between estimated buying and selling pressure. 15) Drawing POC, VAH, and VAL Lines line pocL = line.new(lineStartX, profile.pocPrice, lineEndX, profile.pocPrice, color=col_poc, width=2, style=line.style_solid) profileLines.push(pocL) line vahL = line.new(lineStartX, profile.vah, lineEndX, profile.vah, color=col_vah, width=1, style=line.style_dashed) profileLines.push(vahL) line valL = line.new(lineStartX, profile.val, lineEndX, profile.val, color=col_val, width=1, style=line.style_dashed) profileLines.push(valL) Once the profile is built, the script draws the three most important structural references: Point of Control, Value Area High, and Value Area Low. These lines can behave as compact annotations near the profile or as broader structure references if extension is enabled. This gives the user a quick way to read acceptance and central balance without needing to inspect every row manually. 16) Detecting Liquidity Voids float voidLimit = profile.pocVol * (voidThreshold / 100.0) bool inVoid = false float voidStartPrice = na for i = 0 to profile.bins.size() - 1 PriceBin b = profile.bins.get(i) bool isOutsideVA = b.price > profile.vah or b.price < profile.val This is the beginning of the liquidity void logic. A row is never treated as a void candidate solely because its volume is small. The script first requires that the row be outside the value area. This matters because low volume inside the main acceptance zone does not carry the same meaning as low volume outside it. The script also calculates voidLimit as a percentage of Point of Control volume. That creates a relative participation threshold tied to the strongest row in the profile. 17) Comparing Each Row to Its Neighbors float sumNeighbors = 0.0 int nC = 0 for j = math.max(0, i - 2) to math.min(profile.bins.size() - 1, i + 2) if j != i sumNeighbors += profile.bins.get(j).totalVol nC += 1 float localAvg = nC > 0 ? sumNeighbors / nC : 0.0 bool isGap = b.totalVol < (localAvg * 0.5) bool isLowVol = b.totalVol < voidLimit bool isVoid = isLowVol and isGap and isOutsideVA This is the real filter that defines a liquidity void. The script looks at nearby bins around the current row and calculates a local neighbor average. Then it applies two separate tests: the row must be low relative to the Point of Control threshold, and it must also be weak relative to its nearby neighbors. Only if both are true, and the row is outside the value area, does the script classify it as a void. This is important because it prevents the indicator from marking every low volume row as a void. A valid void must look weak both globally and locally. 18) Grouping Consecutive Void Rows Into Zones if isVoid if not inVoid inVoid := true voidStartPrice := b.price - (profile.binSize / 2) else if inVoid inVoid := false float voidEndPrice = b.price - (profile.binSize / 2) float topCoord = math.max(voidStartPrice, voidEndPrice) float botCoord = math.min(voidStartPrice, voidEndPrice) box vBox = box.new(bar_index - lookback, topCoord, bar_index, botCoord, border_color=na, bgcolor=col_void) Once a void row is detected, the script begins tracking a continuous void run. If the next row is also a void, the zone continues. When the run ends, the script closes the zone and draws a box covering the full void area. This means the indicator does not plot isolated tiny marks for each row. Instead, it groups neighboring weak rows into a cleaner structure that better represents a meaningful liquidity gap. That box is then labeled as a liquidity void, making the zone easy to identify visually.
Indicatore Pine Script®
di UAlgo
1
Adaptive Momentum RibbonWhat It Does Adaptive Momentum Ribbon (AMR) is a directional momentum tool that identifies shifts in price equilibrium and marks them directly on the price chart with entry labels and dynamic invalidation levels. It pairs a robust statistical oscillator with a Keltner Channel envelope to give traders both timing signals and volatility context in a single overlay. How It Works The indicator is built on three independent components working together: 1. Median Absolute Deviation (MAD) Z-Score Instead of using a standard deviation z-score (which is heavily influenced by outlier candles and spike wicks), AMR calculates a rolling median of price, then measures how far price has deviated from that median, normalized by the Median Absolute Deviation. MAD is a statistical measure of dispersion that is approximately 50% more resistant to outliers than standard deviation. The raw MAD value is scaled by 1.4826 to make it directly comparable to standard deviation under normal distribution assumptions. The result is a z-score that reflects genuine shifts in price positioning rather than reacting disproportionately to single volatile bars. 2. Butterworth Low-Pass Filter The raw z-score is then passed through a second-order Butterworth low-pass filter. Unlike cascaded EMAs (which introduce cumulative lag at each stage), the Butterworth filter is designed to have a maximally flat frequency response in the passband. This means it preserves the shape of genuine momentum moves while attenuating high-frequency noise. The filter's two-pole design provides a steeper roll-off than any single-pole smoothing method, delivering cleaner zero-line crossovers with less delay. 3. Keltner Channel Envelope A standard Keltner Channel (moving average +/- ATR multiple) provides volatility context. Unlike Bollinger Bands, which use standard deviation and tend to expand/contract sharply on individual outlier bars, Keltner Channels use Average True Range, producing smoother, more stable bands that account for gap behavior and true intrabar range. Signal Logic A bullish signal (upward label) fires when the filtered z-score crosses above zero, indicating that price has shifted above its rolling median by a statistically meaningful amount after smoothing. A bearish signal (downward label) fires when the filtered z-score crosses below zero. Each signal generates an invalidation level (dashed line), placed at the low (for bullish) or high (for bearish) of the signal bar, offset by a long-term volatility measure (100-period EMA of True Range). If price breaches this level, an X marker appears, indicating the signal's premise has been violated. The invalidation level extends forward in real time until it is either breached or replaced by a new signal in the opposite direction. How to Use It Trend Confirmation: Use the signal labels alongside the Keltner Channel. Bullish signals firing near the lower KC band suggest mean-reversion opportunities. Bullish signals near or above the midline suggest trend continuation. Invalidation as Risk Management: The dashed invalidation line can serve as a reference for stop-loss placement. When the X appears, the statistical basis for the entry no longer holds. Parameter Guidance: Momentum Period (default 20): Controls the lookback for the median and MAD calculation. Shorter periods react faster but produce more signals. Longer periods are smoother but slower. Butterworth Smoothing (default 3): Controls noise filtering. A value of 1 passes the raw z-score through with minimal filtering. Values of 3-5 work well for most timeframes. KC settings: Standard Keltner Channel parameters. The defaults (20 EMA, 14 ATR, 2x multiplier) are widely used and work across most instruments and timeframes. Limitations and Honest Caveats This indicator does not predict future price. It identifies statistical shifts in recent price positioning relative to a rolling median. These shifts may or may not lead to sustained moves. Like all zero-crossing oscillators, it will generate false signals during choppy, range-bound markets. The Keltner Channel can help filter these visually (signals near the midline in a flat channel are lower conviction). The invalidation levels are references, not guaranteed stop levels. Slippage and gaps can cause exits beyond these prices. The MAD z-score assumes sufficient data variation. In extremely low-volatility environments where price barely moves, the MAD can approach zero. A fallback to standard deviation is built in for this edge case, but signals during such periods should be treated with extra caution. No repainting. All signals fire on the confirmed close of the signal bar and reference the prior bar's high/low for level placement. No future data is accessed. Summary AMR combines outlier-robust statistics (MAD z-score), signal-processing-grade smoothing (Butterworth filter), and volatility-adaptive context (Keltner Channel) into a single chart overlay. It is designed for traders who want clean directional signals with built-in invalidation logic, without relying on indicators that overreact to spike wicks or require a separate oscillator pane.
Indicatore Pine Script®
di ZakAlgoTrade
3
Ornstein-Uhlenbeck Mean Reversion Probability Bands [UAlgo]Ornstein-Uhlenbeck Mean Reversion Probability Bands is a statistical mean reversion indicator that models price as a mean reverting process and projects dynamic probability style zones around an estimated equilibrium mean. The script uses a rolling lookback of closing prices, fits an Ornstein-Uhlenbeck inspired parameter set from recent behavior, and then converts that estimate into inner and outer deviation bands around the current mean. The indicator runs directly on price ( overlay=true ) and is built to help traders identify when price is stretched away from its estimated equilibrium. Instead of using a fixed moving average and static standard deviation, the script attempts to infer a mean reverting structure from the data itself. It estimates the long term mean, the speed of reversion, and an equilibrium style dispersion measure, then plots two upside and two downside mean reversion zones. When price pushes into the upper or lower band regions, the script calculates a standardized distance from the estimated mean and displays a probability style label with both the percentage score and the current z score. This gives the user a quick visual read of how statistically extended price is relative to the model. A key strength of this script is that it combines: A rolling Ornstein-Uhlenbeck style parameter estimation Adaptive mean reversion zones Probability style stretch labels at band events A clean overlay presentation with visible upper and lower probability regions Important note: The percentage label in this script is a normal distribution coverage style score derived from the current z score. It is best understood as a probabilistic stretch measure, not a literal exact OU first passage probability. 🔹 Features 🔸 1) Ornstein-Uhlenbeck Inspired Mean Reversion Model The script estimates a mean reverting process from recent closing prices instead of relying only on a moving average. It uses a rolling regression style approach on consecutive price observations, then converts those estimates into Ornstein-Uhlenbeck style parameters. This makes the indicator more model driven than a standard band tool. 🔸 2) Rolling Adaptive Mean Line The central mean line is not a fixed average only. It is the estimated equilibrium level ( mu ) of the fitted process. As the rolling price sample changes, the model updates and the mean shifts with changing market structure. The mean line also changes color depending on whether current price is above or below that estimated equilibrium. 🔸 3) Dual Mean Reversion Zones (Inner and Outer) The script builds two sets of reversion bands around the mean: Inner bands using the inner multiplier Outer bands using the outer multiplier This creates a layered framework where the inner zone marks an early stretch area and the outer zone marks a more extreme statistical extension. 🔸 4) Probability Style Stretch Labels When price crosses into the upper or lower band regions, the script calculates a z score based on current distance from the estimated mean and converts it into a percentage style probability score. The label shows: A directional marker The probability style percentage The current z score This gives the user both a visual event trigger and a numeric measure of extension. 🔸 5) Visual Zone Based Design The indicator uses filled upper and lower zones rather than emphasizing the band lines themselves. This creates a cleaner chart display where the mean line stays visible and the stretch regions are highlighted as colored areas above and below it. This makes the indicator easy to read during fast chart scanning. 🔸 6) Configurable Lookback, Time Step, and Band Width Users can customize: The rolling lookback period used for model estimation The time step parameter ( dt ) used in OU conversion The inner band multiplier The outer band multiplier This makes the script adaptable to different timeframes, instruments, and preferred sensitivity levels. 🔸 7) Built In Estimation Safeguards The parameter estimation logic includes fallback protections. If the inferred model parameters are unstable or unrealistic, the script falls back to simpler sample statistics. This helps prevent unusable outputs during difficult market regimes or low quality fits. 🔸 8) Directional Touch Event Logic The script tracks both upper side and lower side band interaction: Upper side events can signal statistically stretched bullish price movement Lower side events can signal statistically stretched bearish price movement Labels are only created on crossing events, which helps reduce repeated prints while price remains outside the band. 🔹 Calculations 1) Rolling Price Queue Management The script stores recent closing prices in an array with a fixed maximum length: price_array.update_queue(close, length_input) The queue update method behaves differently depending on bar state: On a new bar, it pushes the latest value On an updating live bar, it overwrites the last stored value This keeps the rolling sample aligned with the current chart state without duplicating the active bar. 2) Fallback Mean and Dispersion Estimates Before attempting the OU style fit, the script calculates simple fallback values: float fallback_mu = src_array.avg() float fallback_sigma = src_array.stdev() These act as safety defaults if the regression based OU estimate is not reliable. Important note: In this script, fallback_sigma is a simple sample standard deviation of price levels, not return volatility. 3) AR(1) Style Regression on Consecutive Prices The model estimation is built from consecutive price pairs: x = price y = price The script computes: Mean of x Mean of y Covariance between x and y Variance of x Then it estimates: float b = sum_cov / sum_var_x This creates an AR(1) style coefficient that is later translated into OU style parameters. 4) Conversion from AR(1) Form to OU Style Parameters If the estimated b is within a valid range: if b > 0.05 and b < 0.95 the script computes: float a = mean_y - b * mean_x float mu_exact = a / (1.0 - b) float theta_exact = -math.log(b) / dt Interpretation: mu_exact is the estimated long run mean. theta_exact is the implied mean reversion speed. The conversion assumes the AR(1) relation is a discrete time representation of a mean reverting process. 5) Residual Variance and Equilibrium Dispersion The script next measures residual error from the AR(1) fit: float err = y_i - (a + b * x_i) float var_err = sum_err_sq / (n - 1) Then it converts that residual variance into an equilibrium variance estimate: float var_eq = var_err / (1.0 - b * b) Finally: float calc_sigma = math.sqrt(var_eq) Important implementation note: The variable named sigma in this script is used as an equilibrium style standard deviation around the mean, not as the continuous time OU diffusion coefficient from the SDE form. 6) Stability Filter for the Estimated Sigma Even if the AR(1) fit is mathematically valid, the script only accepts the calculated sigma when it is reasonably close to the fallback sample standard deviation: if calc_sigma < fallback_sigma * 1.5 and calc_sigma > fallback_sigma * 0.5 If this test fails, the script keeps the fallback values instead. This helps avoid unstable band widths caused by bad short term fits. 7) Final Parameter Output The estimation method returns: OU_Params.new(theta, mu, sigma_eq) Where: theta is the estimated reversion speed mu is the estimated equilibrium mean sigma_eq is the accepted equilibrium dispersion measure These parameters are then used to build the bands. 8) Band Construction The script computes four band levels around the estimated mean: float up_out = mean_val + (dev_val * mult_outer) float up_in = mean_val + (dev_val * mult_inner) float dn_in = mean_val - (dev_val * mult_inner) float dn_out = mean_val - (dev_val * mult_outer) Interpretation: Inner bands represent a milder deviation from the mean. Outer bands represent a more extreme deviation from the mean. 9) Mean and Zone Visualization The mean line is explicitly plotted: p_mean = plot(ou_bands.mean, color=color_mean, linewidth=2, title="Mean") The inner and outer band plots are also created, but their colors are fully transparent: color color_inner_up = color.new(#ffb74d, 100) color color_outer_up = color.new(#ef5350, 100) ... This means the visible structure mainly comes from the zone fills: fill(p_ui, p_uo, ...) fill(p_li, p_lo, ...) So the user sees clean upper and lower probability zones rather than several bright boundary lines. 10) Touch and Crossing Logic The script first checks whether price is currently inside a stretch area: bool touch_upper = close >= ou_bands.upper_inner bool touch_lower = close <= ou_bands.lower_inner Then it checks for fresh crossings: bool cross_up_in = ta.crossover(close, ou_bands.upper_inner) bool cross_up_out = ta.crossover(close, ou_bands.upper_outer) bool cross_dn_in = ta.crossunder(close, ou_bands.lower_inner) bool cross_dn_out = ta.crossunder(close, ou_bands.lower_outer) Labels are only created when price is touching the region and a fresh crossing occurs. This avoids creating labels on every bar that remains outside the band. 11) Z Score Calculation When an event occurs, the script calculates the standardized distance from the mean: float current_z_score = dev_val != 0 ? math.abs(close - mean_val) / dev_val : 0.0 Interpretation: A z score of 1 means price is one equilibrium standard deviation away from the estimated mean. Higher values indicate a more statistically stretched condition. 12) Probability Style Score Calculation The script converts the z score into a percentage style score using an approximation of the error function: float x = math.abs(z_score) / math.sqrt(2.0) ... float prob = erf_approx * 100.0 Because erf(|z| / sqrt(2)) corresponds to the probability mass within plus or minus that z distance under a normal distribution, the output behaves like a confidence or coverage score. Important note: This is not a direct OU mean reversion probability in the strict stochastic process sense. It is a normal distribution style stretch score based on the current z distance. 13) Upper Event Label Logic When price crosses into the upper band region: if (touch_upper and cross_up_in) or (touch_upper and cross_up_out) the script prints a bearish styled label above the bar: "▼ %" + str.tostring(probability, "#.##") + " (Z:" + str.tostring(current_z_score, "#.##") + ")" This reflects the idea that price is statistically extended above the mean and may be vulnerable to reversion. 14) Lower Event Label Logic When price crosses into the lower band region: if (touch_lower and cross_dn_in) or (touch_lower and cross_dn_out) the script prints a bullish styled label below the bar: "▲ %" + str.tostring(probability, "#.##") + " (Z:" + str.tostring(current_z_score, "#.##") + ")" This reflects the idea that price is statistically extended below the mean and may be vulnerable to reversion. 15) Role of the Time Step Input The dt_input parameter affects the conversion from the AR(1) coefficient into the OU reversion speed: float theta_exact = -math.log(b) / dt A larger dt lowers the inferred theta for the same b . A smaller dt raises the inferred theta for the same b .
Indicatore Pine Script®
di UAlgo
1
Hawkes Vol-Expansion Detector [UFVG]Financial markets exhibit "volatility clustering" large moves tend to be followed by large moves, and quiet periods by quiet periods. The Hawkes Vol-Expansion Detector is built on the premise that market volume and volatility are self-exciting. When a significant market shock occurs, it temporarily increases the probability of further shocks before eventually decaying back to a baseline level. By tracking volume anomalies and ATR expansions through a mathematical framework known as a Hawkes Process, this indicator visualizes the buildup and decay of market intensity, giving traders a heads-up before prolonged volatility expansions. The Mathematics: How It Works At the core of this indicator is a discretized version of the Hawkes Process. It calculates the current market intensity (λ) at time t based on a constant baseline, the decayed intensity from previous periods, and new market shocks. The exact formula calculated under the hood is: λ_t = μ + (λ_{t-1} - μ) * e^(-β) + α * X_t Here is what these variables represent in the indicator: λ_t (Current Intensity): The total plotted excitation level of the market. μ (Baseline Intensity, mu_eff): The constant, underlying rate of market activity when no shocks are occurring. α (Excitation Jump, alpha_eff): The exact amount the intensity spikes when a new volume shock is detected. β (Decay Rate, beta_eff): The speed at which the excess market excitement fades back to the baseline. X_t (Shock Event): A boolean value (1 or 0) indicating if a volume/price shock occurred on the current candle. Additionally, the indicator utilizes the Branching Ratio (n), defined mathematically as: n = α / β In quantitative modeling, if n < 1, the process is stable (shocks eventually die out). If n >= 1, the process becomes unstable and explodes. The auto-tuned parameters in this script are specifically designed to keep the branching ratio stable while remaining highly responsive to expansions. Volume Shock Detection (X_t) The indicator does not just fire the excitation parameter (α) on every candle. A "Shock Event" (X_t = 1) strictly occurs only when both of the following conditions are met: Volume Z-Score: The current volume must exceed its rolling moving average by a specific standard deviation threshold. Z = (V_t - μ_V) / σ_V ATR Displacement: High volume without price movement is often absorption. To confirm a valid shock, the candle's high-to-low range must exceed a multiplier of the Average True Range (ATR). Dynamic Timeframe Auto-Tuning Different timeframes have different noise profiles. A 5-minute chart has erratic, frequent spikes, while a Daily chart's volume is more sustained. If the "Auto-adjust by Timeframe" setting is active, the script dynamically adjusts μ, α, β, and the Z-score threshold to fit the timeframe: Low Timeframes (<= 5m): Uses a higher Z-score requirement (Z=2.0) to filter out intraday noise, with a faster decay rate. Mid Timeframes (15m - 1H): Uses balanced parameters (Z=1.8). High Timeframes (Daily+): Lowers the Z-score threshold (Z=1.5) but uses a higher baseline and slower decay, reflecting the longer-lasting impact of macro-level daily shocks. How to Read the Chart The indicator plots the λ_t line against a flat baseline (μ). Yellow Diamonds: Mark the exact candles where a volume/ATR shock (X_t = 1) occurred. Teal/Green Zones (Calm): The intensity is near baseline. The market is consolidating or ranging. Orange Zones (Elevated): Multiple shocks are stacking up. The decay rate (β) cannot keep up with the new excitations (α). This is your warning that a trend or volatility expansion is initiating. Red Zones (Expansion): The market is in a full volatility expansion. Excess intensity has crossed the critical threshold. Look for momentum continuation or impending climax depending on price action.
Indicatore Pine Script®
di unfairvaIuegap
Z-Score Predictive Zones [AlgoPoint]Z-Score Predictive Zones This indicator is an advanced evolution of the classic Z-Score (Standard Score) oscillator. Unlike traditional Z-Score indicators that use static overbought/oversold levels (e.g., +2.0 or -2.0), this script utilizes a dynamic memory system to identify where the market statistically tends to reverse based on recent history. It provides a dual-view system: it displays the oscillator in the bottom pane and simultaneously projects those statistical extremes onto the main chart as Predictive Support and Resistance Zones. 1. Underlying Concept & Math The core calculation is based on the Standard Score formula: Z = (Price - Mean) / Standard Deviation - Adaptive Thresholds : The script detects pivot points (peaks and troughs) in the Z-Score history. It stores the most recent reversals (based on user-defined depth) and calculates the average level where the market has historically reversed. - Reverse Engineering (Price Projection) : The script takes these average Z-Score reversal levels and reverse-engineers the formula to find the corresponding price on the main chart: Projected Price = Mean + (Target Z * Standard Deviation) - Noise Reduction : The raw Z-Score is smoothed using a Volume Weighted Moving Average (VWMA) to reduce false signals. 2. Key Features Dynamic Predictive Zones (Main Chart): - Red Zone (Resistance) : Represents the price area where the Z-Score hits its historical average peak. - Green Zone (Support) : Represents the price area where the Z-Score hits its historical average trough. Note: These zones expand and contract based on market volatility (Standard Deviation). Dual Visualization: - Bottom Pane : Shows the Z-Score oscillator with the calculated dynamic steps. - Main Chart : Uses force_overlay=true to draw the corresponding price channels directly on the candles, allowing for real-time price action analysis. - RSI Gradient Coloring : The oscillator line changes color based on RSI (Relative Strength Index) values to provide additional momentum context (Purple/Blue gradients). - Signal Dots : Plots small circles on the main chart when the price enters these extreme statistical zones, indicating a potential mean reversion opportunity. 3. How to Use This indicator is best used for Mean Reversion strategies. - Potential Short : When price enters the Red Zone (Upper Channel) and a red dot appears, the price is statistically overextended relative to its recent volatility profile. - Potential Long : When price enters the Green Zone (Lower Channel) and a green dot appears, the price is statistically undervalued relative to the mean. - Trend Warning : If the price candles close forcefully beyond these zones and the zones begin to widen rapidly, it may indicate a breakout or a strong trend initiation rather than a reversal. 4. Settings - Z-Score Length : The lookback period for the Mean and Standard Deviation. - Lookback Depth : How many past reversal points the script should "remember" to calculate the average zones. - Reversal Thresholds : The minimum Z-Score value required to register a peak or trough as a valid data point. - Visuals : Toggle signal dots on/off.
Indicatore Pine Script®
di Iamalala
Crypto PCA [LuxAlgo]The Crypto PCA indicator provides a sophisticated, multi-asset sentiment gauge by applying Principal Component Analysis (PCA) to a basket of the top 20 cryptocurrencies. By extracting the primary driver of variance across these assets, the tool offers a "market-wide" oscillator that filters out individual coin noise to highlight the dominant trend and sentiment shifts in the crypto space. In modern quantitative finance, PCA is used to reduce dimensionality and identify the underlying factors that move a group of assets. This indicator brings that institutional-grade approach to the retail trader, condensing the price action of Bitcoin, Ethereum, Solana, and 17 other majors into a single, actionable signal. 🔶 USAGE The script serves as a macro-sentiment oscillator, allowing traders to see the "hidden" force driving the crypto market. It is designed to identify when the market is moving in unison and when that collective movement has reached an extreme. 🔹 Identifying Market Regimes The primary use of the PCA line (PC1) is to determine the current market regime. When the oscillator is above the zero line and colored green, it indicates that the majority of the top 20 assets are experiencing positive variance, signaling a broad bullish regime. Conversely, when the line is below zero and colored red, the market is in a collective bearish state. Traders can use this to align their individual trades with the direction of the total market energy. 🔹 Using Snapshot Mode for Situational Analysis While the continuous mode is ideal for long-term trend following, the Snapshot Mode provides a focused view of market dynamics over the most recent lookback window. This mode isolates the current sentiment cycle, allowing traders to see the specific trajectory and "shape" of the latest move without the influence of older historical data. By enabling Snapshot Mode, you can analyze the immediate internal structure of the market. It is particularly useful for identifying whether a recent pump or dump is a coordinated market-wide event or a more fragmented move. This helps in distinguishing between a broad structural shift and a temporary volatility spike. 🔹 Spotting Overextended Sentiment The indicator includes dashed horizontal lines at +2 and -2, representing standard deviation thresholds. Because the assets are standardized before calculation, these levels mark statistical extremes. Overbought Extremes: When the PCA line exceeds +2, the broad market is significantly overextended to the upside. This often precedes a cooling-off period or a mean-reversion event across the entire sector. Oversold Extremes: When the PCA line drops below -2, it suggests a "panic" or exhausted selling state across the basket. This can signal potential bottoming interest or a relief rally. 🔹 Gauging Relative Strength The faint "ghost" lines in the background represent the individual standardized price paths of the 20 included assets. By comparing these to the main PCA line, traders can identify leaders and laggards. An asset line that stays consistently above the PCA line during a rally is exhibiting relative strength, while an asset trailing below the PCA line is underperforming the market average. 🔶 DETAILS The indicator follows a rigorous mathematical pipeline to ensure the data is statistically significant and comparable across assets with different price scales. 🔹 Standardization (Z-Scores) Before performing PCA, every asset must be on the same scale. The script converts the price of all 20 assets into Z-scores based on the user-defined Lookback Period. A Z-score tells us how many standard deviations a price is from its mean. This allows the movement of a high-priced asset like BTC to be mathematically compared to a lower-priced asset like PEPE. 🔹 The Basket & PCA Approximation The indicator includes the following assets: BTC, ETH, BNB, XRP, SOL, TRX, DOGE, ADA, BCH, WBTC, XLM, LTC, HBAR, LINK, AVAX, PEPE, DOT, UNI, NEAR, and ICP. The script uses a correlation-based approximation to find the First Principal Component. It calculates the correlation of each asset to the equally weighted basket and uses these correlations as "loadings" to compute the PC1. This ensures that assets moving in sync with the general market trend are given higher priority in the final oscillator value. 🔹 Why PCA? Most "Crypto Indices" are simply weighted averages. PCA is superior because it identifies the commonality between assets. If 18 coins are moving up and 2 are moving down, PCA gives more weight to the 18 moving together, as they represent the "Principal Component" of the market's current energy. 🔶 SETTINGS 🔹 Main Settings Lookback Period (N): Determines the window used for Z-score standardization and PCA calculation. A shorter period makes the indicator more reactive, while a longer period identifies macro-cycle shifts. Z-Score Smoothing: Applies a Simple Moving Average (SMA) to the standardized asset values before the PCA calculation. This effectively filters out high-frequency noise and produces a smoother principal component line, which is useful for reducing false regime shifts in volatile markets. Enable Snapshot Mode: Switches the visual output from a continuous rolling line to a static view of the PCA over the most recent lookback window. 🔹 Visual Settings Standardized Assets Color: Controls the color and transparency of the 20 individual asset lines. Bull/Bear Colors: Defines the colors used for positive and negative market sentiment. Disclaimer: This indicator is a statistical tool for sentiment analysis and does not constitute financial advice. The PCA approach measures variance and correlation, not guaranteed future direction.
Indicatore Pine Script®
di LuxAlgo
2
ATR-Based Z-Score (with Signal Line)The ATR-Based Z-Score is an advanced, volatility-normalized oscillator designed to identify extreme price deviations more reliably than the standard Z-Score. By replacing the traditional Standard Deviation with the Average True Range (ATR) in the denominator, this indicator eliminates the "volatility paradox" where rapid price spikes cause standard oscillators to prematurely return to zero, even as the price continues to crash. Why this version is superior In a classic Z-Score calculation: Z = (Price - SMA) / (Standard Deviation) A sudden impulsive price drop causes the Standard Deviation to explode. Because you are dividing by a rapidly increasing number, the Z-Score often "rises" while the price is still falling. The ATR-Based Solution: Z = (Price - SMA) / ATR By using a long-period ATR as the denominator, the volatility measure remains stable and "clean." This ensures that the indicator’s troughs align much more accurately with actual price bottoms, staying in the oversold territory until the momentum truly shifts. Key Features Volatility Cleaning: The ATR-normalization prevents the indicator from "flattening out" during impulsive price movements. Integrated Signal Line: A customizable Moving Average of the Z-Score values helps filter noise and confirms entry/exit points. Independent Periods: You can set the Price MA (responsiveness) and the ATR (volatility baseline) separately to fine-tune the indicator to different timeframes. How to Trade with it 1. Mean Reversion (Buy the Dip / Sell the Rip) Long: Wait for the Z-Score to drop below a significant level (e.g., -10.0). Enter when the Z-Score crosses back above its Signal Line. Short: Wait for the Z-Score to rise above +10.0 and enter when it crosses below the Signal Line. 2. Breakout Trading A strong push of the Z-Score beyond the +/- 7.0 levels can indicate a powerful trend breakout. In this case, the Signal Line crossover serves as an effective Exit Signal, telling you that the initial momentum of the breakout is fading. Summary ✅ This indicator is designed for traders who find standard oscillators too "nervous" during volatile periods. By decoupling price deviation from immediate variance spikes, the ATR-Based Z-Score provides a rock-solid foundation for identifying true market extremes and high-probability reversal points.
Indicatore Pine Script®
di muth96
Volume Weighted LR Z ScoreThis indicator calculates the Volume Weighted Linear Regression Z-Score (VWLRZS). Unlike a standard Z-Score which measures deviation from a static mean, this oscillator measures the statistical distance of price from a dynamic Volume-Weighted Linear Regression Line (Analysis of Residuals). Key Features: 1. **Volatility Decomposition:** The indicator separates volatility based on the 'Estimate Bar Statistics' option. - **Standard Mode (`Estimate Bar Statistics` = OFF):** Calculates standard Regression Residuals using the selected `Source` for both the regression line (baseline) and the signal. - **Decomposition Mode (`Estimate Bar Statistics` = ON):** Uses a hybrid statistical approach: a) **The Model (Baseline):** Uses an estimator to calculate the 'within-bar' mean and fits the Linear Regression through these statistical centers. This creates a stable, trend-following expectation model. b) **The Signal (Observation):** Compares the actual `Source` (e.g., Close) against this regression line. (Result: A Z-Score that measures deviations from the current trend slope rather than a flat average). 2. **Visual Decomposition Logic:** Total Standard Deviation (of Residuals) is the primary metric displayed. Since Standard Deviations are not linearly additive (sqrt(a+b) != sqrt(a)+sqrt(b)), this indicator calculates the *exact* Total Z-Score and partitions the area underneath based on the Variance Ratio. This ensures the displayed total volatility remains mathematically accurate while showing relative composition. 3. **Normalization (Exponential Regression):** Includes an optional 'Normalize' mode. When enabled, the indicator calculates the Linear Regression on logarithmic data. Mathematically, this transforms the baseline into an **Exponential Regression Curve**, making it ideal for analyzing assets with compounding growth characteristics (constant percentage trend). 4. **Full Divergence Suite (Class A, B, C):** The indicator's primary feature is its integrated divergence engine. It automatically detects and plots all three major divergence classes between price and the Z-Score: - Regular (A): Signals potential trend exhaustion and reversals. - Hidden (B): Signals potential trend continuations during pullbacks. - Exaggerated (C): Signals weakness at double tops/bottoms. 5. **Divergence Filtering and Visualization:** - **Price Tolerance Filter:** Divergence detection is enhanced with a percentage-based price tolerance (`pivPrcTol`) to filter out insignificant market noise, leading to more robust signals. - **Persistent Visualization:** Divergence markers are plotted for the entire duration of the signal and are visually anchored to the oscillator level of the confirming pivot. - **Flexible Pivot Algorithms:** Supports various underlying mathematical models for pivot detection provided by the core library 6. **Note on Confirmation (Lag):** Divergence signals rely on a pivot confirmation method to ensure they do not repaint. - The **Start** of a divergence is only detected *after* the confirming pivot is fully formed (a delay based on `Pivot Right Bars`). - The **End** of a divergence is detected either instantly (if the signal is invalidated by price action) or with a delay (when a new, non-divergent pivot is confirmed). 7. **Multi-Timeframe (MTF) Capability:** - **MTF Calculation:** The Z-Score line *itself* can be calculated on a higher timeframe, with standard options to handle gaps (`Fill Gaps`) and prevent repainting (`Wait for...`). - **Limitation:** The Divergence detection engine (`pivDiv`) is designed for the active timeframe. Using it in MTF mode is not recommended as step-data can lead to inaccurate pivot detection. 8. **Integrated Alerts:** Includes a comprehensive set of built-in alerts for the Z-Score crossing the neutral line, the configured Threshold levels, and the start/end of all divergence types. --- **DISCLAIMER** 1. **For Informational/Educational Use Only:** This indicator is provided for informational and educational purposes only. It does not constitute financial, investment, or trading advice, nor is it a recommendation to buy or sell any asset. 2. **Use at Your Own Risk:** All trading decisions you make based on the information or signals generated by this indicator are made solely at your own risk. 3. **No Guarantee of Performance:** Past performance is not an indicator of future results. The author makes no guarantee regarding the accuracy of the signals or future profitability. 4. **No Liability:** The author shall not be held liable for any financial losses or damages incurred directly or indirectly from the use of this indicator. 5. **Signals Are Not Recommendations:** The alerts and visual signals (e.g., crossovers) generated by this tool are not direct recommendations to buy or sell. They are technical observations for your own analysis and consideration.
Indicatore Pine Script®
di AustrianTradingMachine
Volume Weighted Z ScoreThis indicator calculates the Volume Weighted Z-Score (VWZS), a statistical oscillator that measures the number of standard deviations the price is removed from its mean. It combines robust volatility decomposition with advanced divergence detection. Key Features: 1. **Volatility Decomposition:** The indicator separates volatility based on the 'Estimate Bar Statistics' option. - **Standard Mode (`Estimate Bar Statistics` = OFF):** Calculates a simple (Volume-Weighted) Standard Deviation using the selected `Source` for both the baseline and the signal. - **Decomposition Mode (`Estimate Bar Statistics` = ON):** Uses a hybrid statistical approach: a) **The Model (Baseline):** Uses an estimator to calculate the 'within-bar' mean and volatility. This creates a stable, mathematically idealized expectation value (mu). b) **The Signal (Observation):** Compares the actual `Source` (e.g., Close) against this statistical baseline. (Result: A Z-Score that combines a noise-filtered trend baseline with a highly reactive price signal). 2. **Visual Decomposition Logic:** Total Standard Deviation is the primary metric displayed. Since Standard Deviations are not linearly additive (sqrt(a+b) != sqrt(a)+sqrt(b)), this indicator plots the *exact* Total StdDev and partitions the area underneath based on the Variance Ratio. This ensures the displayed total volatility remains mathematically accurate while showing relative composition. 3. **Normalization (Geometric Average):** Includes an optional 'Normalize' mode. When enabled, the indicator uses a Geometric Moving Average (GMA) as its baseline and applies a statistical correction for the log-normal distribution ensuring symmetry between upside and downside movements. 4. **Full Divergence Suite (Class A, B, C):** The indicator's primary feature is its integrated divergence engine. It automatically detects and plots all three major divergence classes between price and the Z-Score: - Regular (A): Signals potential trend exhaustion and reversals. - Hidden (B): Signals potential trend continuations during pullbacks. - Exaggerated (C): Signals weakness at double tops/bottoms. 5. **Divergence Filtering and Visualization:** - **Price Tolerance Filter:** Divergence detection is enhanced with a percentage-based price tolerance (`pivPrcTol`) to filter out insignificant market noise, leading to more robust signals. - **Persistent Visualization:** Divergence markers are plotted for the entire duration of the signal and are visually anchored to the oscillator level of the confirming pivot. - **Flexible Pivot Algorithms:** Supports various underlying mathematical models for pivot detection provided by the core library 6. **Note on Confirmation (Lag):** Divergence signals rely on a pivot confirmation method to ensure they do not repaint. - The **Start** of a divergence is only detected *after* the confirming pivot is fully formed (a delay based on `Pivot Right Bars`). - The **End** of a divergence is detected either instantly (if the signal is invalidated by price action) or with a delay (when a new, non-divergent pivot is confirmed). 7. **Multi-Timeframe (MTF) Capability:** - **MTF Calculation:** The Z-Score line *itself* can be calculated on a higher timeframe, with standard options to handle gaps (`Fill Gaps`) and prevent repainting (`Wait for...`). - **Limitation:** The Divergence detection engine (`pivDiv`) is designed for the active timeframe. Using it in MTF mode is not recommended as step-data can lead to inaccurate pivot detection. 8. **Integrated Alerts:** Includes a comprehensive set of built-in alerts for the Z-Score crossing the neutral line, the configured Threshold levels, and the start/end of all divergence types. --- **DISCLAIMER** 1. **For Informational/Educational Use Only:** This indicator is provided for informational and educational purposes only. It does not constitute financial, investment, or trading advice, nor is it a recommendation to buy or sell any asset. 2. **Use at Your Own Risk:** All trading decisions you make based on the information or signals generated by this indicator are made solely at your own risk. 3. **No Guarantee of Performance:** Past performance is not an indicator of future results. The author makes no guarantee regarding the accuracy of the signals or future profitability. 4. **No Liability:** The author shall not be held liable for any financial losses or damages incurred directly or indirectly from the use of this indicator. 5. **Signals Are Not Recommendations:** The alerts and visual signals (e.g., crossovers) generated by this tool are not direct recommendations to buy or sell. They are technical observations for your own analysis and consideration.
Indicatore Pine Script®
di AustrianTradingMachine
Pair Correlation Master [Macro]The Main Idea Trading represents a constant battle between Systemic Flows (the whole market moving together) and Idiosyncratic Moves (one specific asset moving on its own). This tool allows you to monitor a "basket" of 4 assets simultaneously (e.g., the major USD pairs). It answers the most important question in forex and multi-asset trading: "Is this move happening because the Dollar is weak, or because the Euro is strong?" It separates the "Signal" (the unique move) from the "Noise" (the herd movement). 1. The Chart Lines: The "Race" (Macro Trend) Think of the lines on your chart as a long-distance race. They visualize the performance of all 4 assets over the last 200 candles (adjustable). - Bunched Together: If all lines are moving in the same direction, the market is highly correlated. (e.g., "The Dollar is selling off everywhere"). - Fanning Out: If the lines are spreading apart, specific currencies are outperforming others. - The Zero Line: This is the starting line. --- Above 0: The pair is in a macro uptrend. --- Below 0: The pair is in a macro downtrend. 2. The Dashboard: The "Health Check" (Micro Data) The table in the top right gives you the immediate statistics for right now. - A. The Z-Score (The Rubber Band) This measures how "stretched" price is compared to its normal behavior. - White (< 2.0): Normal trading activity. - Orange (> 2.0): The price is stretching. Warning sign. - Red (> 3.0): Critical Stretch. The rubber band is pulled to its limit. Statistically, a pullback or pause is highly likely. B. The Star (★) The script automatically calculates the average behavior of your group. If one asset is behaving completely differently from the rest, it marks it with a Star (★). - Example: EURUSD, GBPUSD, and NZDUSD are flat, but AUDUSD is rallying hard. AUDUSD gets the ★. This is where the unique opportunity lies. 🎯 Best Uses: 4H & Daily Timeframes This indicator is tuned for "Macro" analysis. It works best on the "4-Hour" and "Daily" charts to filter out intraday noise and capture swing trading moves. - Strategy 1: The "Rubber Band" Snap (Mean Reversion) - Setup: Look for a Z-Score in the RED (> 3.0) on the Daily timeframe. - Action: This indicates an unsustainable move. Look for reversals or exhaustion patterns to trade against the trend back toward the mean. - Strategy 2: The "Lone Wolf" (Trend Following) - Setup: Look for the asset with the Star (★). - Action: If the whole basket is flat (Balanced), but the Star asset is breaking out, that creates a high-quality trend trade because that specific currency has its own catalyst (News/Earnings). - Strategy 3: Systemic Flows (Basket Trading) - Setup: The dashboard footer says "⚠️ SYSTEMIC MOVE." - Action: This means everything is moving together (e.g., a massive USD crash). Don't look for unique setups; just join the trend on the strongest pair. Dashboard Footer Key The bottom of the table summarizes the current state of the market for you: - Balanced / Rangebound: The market is quiet. Good for range trading. - Focus: : Trade this specific pair. It is moving independently. - Systemic Move: The whole basket is moving violently. Trade the momentum. p.s. Suggestion - apply and use on the chart rather than an oscillator.
Indicatore Pine Script®
di siLve_R88
ZScore SemiConductoresZ-Score of Semiconductor Sector Volume This custom Pine Script indicator applies a Z-Score calculation to the aggregated trading volume of leading semiconductor companies. The goal is to highlight statistical extremes in sector activity that may signal unusual market behavior. 🔧 How it works - Fixed ticker list: NVDA, AVGO, TSM, AMD, ASML, MU, ARM, ON, TXN, QCOM, INTC. - Aggregate volume: The script sums the trading volume of all tickers in the list for the selected timeframe. - Z-Score calculation: - Moving average and standard deviation are computed over a configurable window (default = 50 bars). - Formula: Z= (Current Volume - Mean) / Standard Deviation Visualization: - Z-Score plotted in green. - Reference lines at 0, ±1σ, ±2σ. - Labels (triangles) mark critical signals when Z > +2 or Z < -2. 📈 Why it matters - Detects abnormal surges or drops in sector-wide volume. - Highlights potential euphoria (+2σ) or panic (-2σ) moments. - Useful as a filter for trading strategies or as a sector-level alert system. ⚠️ Disclaimer: This script is for educational purposes only and not financial advice
Indicatore Pine Script®
di JuanDeNogoya
Z-Fusion Oscillator | Lyro RSThe Z-Fusion Oscillator converts five momentum indicators into Z-scores and blends them into one normalized signal that adapts across markets. By combining normalization, smoothing, and divergence detection, users can easily identify when momentum is accelerating, weakening, reversing, or entering extreme zones 🔶 USAGE The Z-Fusion Oscillator is designed to give traders a unified reading of market momentum—removing the noise of comparing tools that normally run on different scales. By transforming RSI, MACD histogram, Stochastic, Momentum, and Rate of Change into Z-scores, this tool standardizes all inputs, making trend strength and shifts easier to interpret. A dual-line system (fast Z-fusion line + slower baseline) highlights turning points, while overbought/oversold bands and “X-marks” help traders spot exhaustion and potential reversals. 🔹 Unified Momentum Structure The indicator’s core strength comes from combining five Z-scored signals into one average. Which makes momentum behavior more consistent across assets, reduces false extremes, and highlights true shifts in trend conviction. 🔹 Divergence Detection The tool includes fully integrated divergence detection: Regular Bullish Divergence: Price makes a lower low while Z-Fusion forms a higher low. Regular Bearish Divergence: Price makes a higher high while Z-Fusion forms a lower high Bullish and bearish divergences are marked directly on the oscillator with labels and colored pivot connections, making hidden momentum shifts obvious. 🔹 Visual Extremes Two sets of upper and lower Z-score thresholds help identify: Extreme overbought surges Extreme oversold drops Reversal zones Potential exhaustion conditions Background coloring reinforces when the oscillator moves beyond major levels, helping traders quickly assess momentum pressure. 🔹 Detecting Momentum Anomalies Z-scores allow the oscillator to highlight when market momentum behaves abnormally relative to its own recent history. For example: The oscillator reaching +1 or –1 after an extended trend may indicate a climax. A sharp Z-score reversal within an extreme zone can signal a trend exhaustion or a corrective move. Divergences often appear earlier due to normalization smoothing out indicator noise. This makes the Z-Fusion Oscillator particularly useful for spotting subtle shifts in trend direction that traditional indicators may miss. 🔶 DETAILS 🔹 Composite Z-Score Framework Each momentum tool is smoothed, normalized, and transformed: RSI → EMA-smoothed, Z-scored MACD histogram → Z-scored Stochastic → EMA + SMA smoothing, then Z-scored Momentum → EMA-smoothed, Z-scored Rate of Change → EMA-smoothed, Z-scored These are averaged into one composite Z-score to provide a consistent reading across assets and market conditions. 🔹 Fusion Trend Lines Two lines serve as the core signal: Fast Line (savg) – reacts quicker to trend changes Slow Line (savg2) – acts as a baseline filter Crossovers between these lines highlight momentum shifts, while their color reflects trend bias. 🔹 Overbought/Oversold Zones Two upper and two lower Z-score thresholds define “zones”: Upper zones highlight overheated momentum or potential bearish reversals Lower zones highlight depressed momentum or potential bullish reversals Filled regions and background colors help visually confirm extreme conditions. 🔹 Pivot-Based Divergence Engine The script includes filtered pivot detection with customizable look-backs and range limits to ensure divergences are meaningful, not noise-driven. 🔶 SETTINGS 🔹 Indicator Settings Source — Price series used for all calculations. Z-Score Length — Lookback period for Z-score normalization. Z-Score MA Length — Smoothing length for the fusion signal lines. Overbought/Oversold Levels — Four customizable threshold lines. Color Palette — Choose from preset themes or define custom colors. 🔹 RSI Length — RSI calculation period. EMA Smoothing Length — Smooths RSI before Z-score conversion. 🔹 MACD Fast Length — Fast EMA length. Slow Length — Slow EMA length. Signal Line Length — MACD signal smoothing. 🔹 Stochastic %K Length — Main stochastic length. EMA Smoothing — Smooths %K for stability. %D Length — Smoothing for the signal line. 🔹 Momentum Length — Momentum lookback. EMA Smoothing — Smooths momentum before Z-scoring. 🔹 Rate of Change Length — ROC lookback. EMA Smoothing — Smooths ROC values. 🔹 Divergence Enable/Disable Divergence Detection — Toggle divergence engine. Pivot Left/Right Lookback — Defines pivot detection sensitivity. Detection Range Limits — Controls allowable range for divergence. Bull/Bear Colors & Styling — Customize divergence visualization. 🔶 SUMMARY The Z-Fusion Oscillator combines multiple momentum signatures into a single normalized signal, enabling traders to: Identify reversals early Detect momentum exhaustion Spot bullish and bearish divergences Track overbought/oversold conditions Visualize trend strength with clarity Whether you're a swing trader, intraday analyst, or trend-reversal hunter, the Z-Fusion Oscillator provides a powerful and adaptive way to read momentum.
Indicatore Pine Script®
di LyroRS
2
Uptrick: Dynamic Z-Score DivergenceIntroduction Uptrick: Dynamic Z-Score Divergence is an oscillator that combines multiple momentum sources within a Z-Score framework, allowing for the detection of statistically significant mean-reversion setups, directional shifts, and divergence signals. It integrates a multi-source normalized oscillator, a slope-based signal engine, structured divergence logic, a slope-adaptive EMA with dynamic bands, and a modular bar coloring system. This script is designed to help traders identify statistically stretched conditions, evolving trend dynamics, and classical divergence behavior using a unified statistical approach. Overview At its core, this script calculates the Z-Score of three momentum sources—RSI, Stochastic RSI, and MACD—using a user-defined lookback period. These are averaged and smoothed to form the main oscillator line. This normalized oscillator reflects how far short-term momentum deviates from its mean, highlighting statistically extreme areas. Signals are triggered when the oscillator reverses slope within defined inner zones, indicating a shift in direction while the signal remains in a statistically stretched state. These mean-reversion flips (referred to as TP signals) help identify turning points when price momentum begins to revert from extended zones. In addition, the script includes a divergence detection engine that compares oscillator pivot points with price pivot points. It confirms regular bullish and bearish divergence by validating spacing between pivots and visualizes both the oscillator-side and chart-side divergences clearly. A dynamic trend overlay system is included using a Slope Adaptive EMA (SA-EMA). This trend line becomes more responsive when Z-Score deviation increases, allowing the trend line to adapt to market conditions. It is paired with ATR-based bands that are slope-sensitive and selectively visible—offering context for dynamic support and resistance. The script includes configurable bar coloring logic, allowing users to color candles based on oscillator slope, last confirmed divergence, or the most recent signal of any type. A full alert system is also built-in for key signals. Originality The script is based on the well-known concept of Z-Score valuation, which is a standard statistical method for identifying how far a signal deviates from its mean. This foundation—normalizing momentum values such as RSI or MACD to measure relative strength or weakness—is not unique to this script and is widely used in quantitative analysis. What makes this implementation original is how it expands the Z-Score foundation into a fully featured, signal-producing system. First, it introduces a multi-source composite oscillator by combining three momentum inputs—RSI, Stochastic RSI, and MACD—into a unified Z-Score stream. Second, it builds on that stream with a directional slope logic that identifies turning points inside statistical zones. The most distinctive additions are the layered features placed on top of this normalized oscillator: A structured divergence detection engine that compares oscillator pivots with price pivots to validate regular bullish and bearish divergence using precise spacing and timing filters. A fully integrated slope-adaptive EMA overlay, where the smoothing dynamically adjusts based on real-time Z-Score movement of RSI, allowing the trend line to become more reactive during high-momentum environments and slower during consolidation. ATR-based dynamic bands that adapt to slope direction and offer real-time visual zones for support and resistance within trend structures. These features are not typically found in standard Z-Score indicators and collectively provide a unique approach that bridges statistical normalization, structure detection, and adaptive trend modeling within one script. Features Z-Score-based oscillator combining RSI, StochRSI, and MACD Configurable smoothing for stable composite signal output Buy/Sell TP signals based on slope flips in defined zones Background highlighting for extreme outer bands Inner and outer zones with fill logic for statistical context Pivot-based divergence detection (regular bullish/bearish) Divergence markers on oscillator and price chart Slope-Adaptive EMA (SA-EMA) with real-time adaptivity based on RSI Z-Score ATR-based upper and lower bands around the SA-EMA, visibility tied to slope direction Configurable bar coloring (oscillator slope, divergence, or most recent signal) Alerts for TP signals and confirmed divergences Optional fixed Y-axis scaling for consistent oscillator view The full setup mode can be seen below: Input Parameters General Settings Full Setup: Enables rendering of the full visual system (lines, bands, signals) Z-Score Lookback: Lookback period for normalization (mean and standard deviation) Main Line Smoothing: EMA length applied to the averaged Z-Score Slope Detection Index: Used to calculate directional flips for signal logic Enable Background Highlighting: Enables visual region coloring in overbought/oversold areas Force Visible Y-Axis Scale: Forces max/min bounds for a consistent oscillator range Divergence Settings Enable Divergence Detection: Toggles divergence logic Pivot Lookback Left / Right: Defines the structure of oscillator pivot points Minimum / Maximum Bars Between Pivots: Controls the allowed spacing range for divergence validation Bar Coloring Settings Bar Coloring Mode: ➜ Line Color: Colors bars based on oscillator slope ➜ Latest Confirmed Signal: Colors bars based on the most recent confirmed divergence ➜ Any Latest Signal: Colors based on the most recent signal (TP or divergence) SA-EMA Settings RSI Length: RSI period used to determine adaptivity Z-Score Length: Lookback for normalizing RSI in adaptive logic Base EMA Length: Base length for smoothing before adaptivity Adaptivity Intensity: Scales the smoothing responsiveness based on RSI deviation Slope Index: Determines slope direction for coloring and band logic Band ATR Length / Band Multiplier: Controls the width and responsiveness of the trend-following bands Alerts The script includes the following alert conditions: Buy Signal (TP reversal detected in oversold zone) Sell Signal (TP reversal detected in overbought zone) Confirmed Bullish Divergence (oscillator HL, price LL) Confirmed Bearish Divergence (oscillator LH, price HH) These alerts allow integration into automation systems or signal monitoring setups. Summary Uptrick: Dynamic Z-Score Divergence is a statistically grounded trading indicator that merges normalized multi-momentum analysis with real-time slope logic, divergence detection, and adaptive trend overlays. It helps traders identify mean-reversion conditions, divergence structures, and evolving trend zones using a modular system of statistical and structural tools. Its alert system, layered visuals, and flexible input design make it suitable for discretionary traders seeking to combine quantitative momentum logic with structural pattern recognition. Disclaimer This script is for educational and informational purposes only. No indicator can guarantee future performance, and trading involves risk. Always use risk management and test strategies in a simulated environment before deploying with live capital.
Indicatore Pine Script®
di Uptrick
14
Fear & Greed Oscillator - Risk SentimentThe Fear & Greed Oscillator – Risk Sentiment is a macro-driven sentiment indicator inspired by the popular Fear & Greed Index , but rebuilt from the ground up using real, market-based economic data and statistical normalization. While the traditional Fear & Greed Index uses components like volatility, volume, and social media trends to estimate sentiment, this version is powered by the Copper/Gold ratio — a historically respected gauge of macroeconomic confidence and risk appetite. 📈 Expansion vs. Contraction Theory At the heart of this oscillator is a simple macroeconomic insight: 🟢 Copper performs well during periods of economic expansion and risk-on behavior (industrials, construction, manufacturing growth). 🔴 Gold performs well during periods of economic contraction , as a classic risk-off, capital-preserving asset. By tracking the ratio of Copper to Gold prices over time and converting it into a Z-score , this tool shows when macro sentiment is statistically stretched toward greed or fear — based on how unusually strong one side of the ratio is relative to its historical average. ⚙️ How It Works The script takes two user-defined tickers (default: Copper and Gold) and calculates their ratio. It then applies Z-score normalization over a user-defined period (default: 200 bars). A color gradient line is plotted: 🔴 Z < -2 = Extreme Fear 🟣 -2 to 0 = Mild Fear to Neutral 🔵 0 to 2 = Neutral to Greed 🟢 Z > 2 = Extreme Greed Visual guides at ±1, ±2, ±3 standard deviations give immediate context. Includes alert conditions when the Z-score crosses above +2 (Greed) or below -2 (Fear). 🔔 Alerts “Z-Score has entered the Greed Zone ” when Z > 2 “Z-Score has entered the Fear Zone ” when Z < -2 These are designed to help catch macro sentiment extremes before or during large shifts in market behavior. ⚠️ Disclaimer This indicator is a macro sentiment tool, not a direct trading signal. While the Copper/Gold ratio often reflects economic risk trends, correlation with risk assets (like Bitcoin or equities) is not guaranteed and may vary by cycle. Always use this indicator in conjunction with other tools and contextual analysis.
Indicatore Pine Script®
di RWCS_LTD