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AMDX TRUE QUARTERS 6h+90m cycles.Этот индикатор предназначен для визуализации концепции Quarterly Theory (SMT / Smart Money Theory) с привязкой к нью-йоркскому времени. Основной цикл дня начинается в 18:00 по Нью-Йорку и длится ровно 24 часа — до 18:00 следующего дня (по местному времени NY, с автоматическим учётом перехода на летнее/зимнее время). Каждый такой 24-часовой SMT-день делится на четыре 6-часовых блока: 18:00–00:00 (Asia Killzone) 00:00–06:00 (London Open) 06:00–12:00 (NY AM) 12:00–18:00 (NY PM) Каждый 6-часовой блок, в свою очередь, разделён на четыре 90-минутных микроцикла. Индикатор рисует: полупрозрачные цветные прямоугольники (боксы) для 6-часовых сессий с разными цветами и соответствующими названиями более светлые оранжевые боксы для 90-минутных циклов внутри каждого 6-часового блока вертикальные пунктирные линии на границах 6-часовых сессий (разные цвета) тонкие точечные линии на границах 90-минутных циклов вертикальную жирную линию на конец SMT-дня (следующие 18:00 NY) текстовые метки с названиями основных сессий над графиком Важные особенности: отображаются только объекты текущего активного SMT-дня (при смене дня в 18:00 NY все предыдущие боксы, линии и метки автоматически удаляются) диапазон по вертикали определяется динамически (максимум high за последние 250 баров минус несколько значений ATR) все временные расчёты производятся в часовом поясе "America/New_York" есть возможность отдельно включать/выключать 6-часовые боксы, 90-минутные боксы, разделительные линии и метки сессий настраивается прозрачность боксов через входные параметры English description of the script This indicator visualizes the Quarterly Theory (SMT / Smart Money Theory) concept anchored to New York time. The main daily cycle starts at 18:00 NY time and lasts exactly 24 hours — until 18:00 the next day (local NY time, automatically handling daylight saving time transitions). Each 24-hour SMT day is divided into four 6-hour blocks: 18:00–00:00 (Asia Killzone) 00:00–06:00 (London Open) 06:00–12:00 (NY AM) 12:00–18:00 (NY PM) Each 6-hour block is further subdivided into four 90-minute micro-cycles. The indicator draws: semi-transparent colored rectangles (boxes) for the 6-hour sessions using different colors and corresponding session names lighter orange boxes for the 90-minute cycles inside each 6-hour block vertical dashed lines at the boundaries of 6-hour sessions (different colors) thin dotted lines at the boundaries of 90-minute cycles a thick vertical line marking the end of the SMT day (next 18:00 NY) text labels with the main session names placed above the chart Key features: only objects belonging to the currently active SMT day are displayed (at the moment the new day starts at 18:00 NY, all previous boxes, lines and labels are automatically removed) vertical range is calculated dynamically (highest high of the last 250 bars minus several ATR values) all time calculations are performed in the "America/New_York" timezone separate toggles are available for 6-hour boxes, 90-minute boxes, session divider lines and session name labels box transparency for both 6-hour and 90-minute rectangles can be adjusted via input parameters
مؤشر Pine Script®
من ‎crypto_daytrade‎
ICT Rejection Block [KTY]ICT Rejection Block Indicator This indicator automatically detects and displays Rejection Blocks based on ICT (Inner Circle Trader) methodology. Rejection Blocks are price zones formed by candles with long wicks, indicating strong buying or selling rejection at that level. Automatic Detection - Identifies candles with significant wick-to-body ratio - Rejection High (Red): Long upper wick showing buying pressure rejected - Rejection Low (Green): Long lower wick showing selling pressure rejected Multi-Timeframe Support - Display rejection blocks from two different timeframes simultaneously (LTF & HTF) - HTF rejection blocks carry more significance 1. Identify rejection blocks on your chart 2. Watch for price reaction when re-entering the rejection zone 3. Combine with Order Block, FVG, or Market Structure for confluence 4. Use rejection block levels as reference for stop-loss placement Pro Tips: - HTF rejection blocks (1H+) are more reliable - Rejection block aligned with OB or FVG increases significance - Multiple rejection blocks at similar levels indicate strong S/R zone LTF: Enable and select lower timeframe HTF: Enable and select higher timeframe Rejection Block Count: Number of rejection blocks to display per type Colors: Customize colors for rejection high and low Show Mitigated Rejection Blocks: Display broken zones in gray Rejection High Detected Rejection Low Detected Rejection High Mitigated Rejection Low Mitigated This indicator is designed for educational purposes. Rejection blocks do not guarantee price reversal. Always combine with proper risk management. If you find this indicator helpful, please leave a like and follow for more ICT-based tools!
مؤشر Pine Script®
من ‎Coin-TeacherKim‎
2
Volume Price TrendThis indicator provides an implementation of the Volume Price Trend (VPT) momentum indicator, enhanced with a built-in divergence detection engine. Key Features: 1. **Full Divergence Suite (Class A, B, C):** The primary feature is the integrated divergence engine. It automatically detects and plots all three major types of divergences: - Regular (A): Signals potential trend reversals. - Hidden (B): Signals potential trend continuations. - Exaggerated (C): Signals weakness at double tops/bottoms. 2. **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 VPT level of the confirming pivot. - **Flexible Pivot Algorithms:** Supports various underlying mathematical models for pivot detection provided by the core library 3. **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). 4. **Multi-Timeframe (MTF) Capability:** - **MTF VPT Line:** The VPT 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 **disabled** if a timeframe other than the chart's timeframe is selected. Divergences are only calculated on the active chart timeframe. 5. **Integrated Alerts:** Includes comprehensive alerts that trigger on the *start* and *end* of all divergence types (e.g., "Regular Bullish Started", "Regular Bullish Ended"). --- **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.
مؤشر Pine Script®
من ‎AustrianTradingMachine‎
CCI PKTELUGUTRADERCCI (Commodity Channel Index) is a momentum-based technical indicator used in trading to identify overbought, oversold conditions and possible trend reversals. It was developed by Donald Lambert. CCI shows how far the current price is from its average price over a selected period. Think of it like a rubber band: When price stretches too far from the average, it may snap back. Bullish Divergence Price makes lower low CCI makes higher low 👉 Possible upward reversal Bearish Divergence Price makes higher high CCI makes lower high 👉 Possible downward reversal (You asked about this earlier—this is a strong use of CCI.)
مؤشر Pine Script®
من ‎PK-TELUGUTRADER‎
Bollinger Squeezes (All-in-One)This indicator is a faithful recreation of the popular Bollinger Squeeze studies found in the Sierra Chart platform. It bundles three specific calculation methods (Study IDs 221, 233, and 401) into a single, versatile script. While standard Squeeze indicators on TradingView often use a generalized formula, this script strictly follows the logic documented by Sierra Chart to ensure the exact same visual and numerical output. 📊 Included Modes (Selectable via Settings) You can switch between the following three modes in the settings menu without adding the indicator multiple times: 1. Squeeze 1 (Standard - SC ID 221) The Classic Squeeze: This creates the histogram based on the Momentum of the price relative to the Moving Average. Formula: It utilizes a Linear Regression of the difference between Price and the SMA to smooth the momentum histogram. Visuals: 4-color histogram (Bright/Dark Green for rising/falling positive, Red/Dark Red for negative). 2. Squeeze 2 (Momentum - SC ID 233) The Raw Momentum: This variation calculates momentum more directly. Formula: It uses the raw difference (Price - SMA) without the Linear Regression smoothing found in ID 221. Result: The histogram appears sharper and reacts faster to immediate price changes. 3. Squeeze 3 (Ratio - SC ID 401) The Ratio View: Instead of price momentum, this mode visualizes the "Squeeze Intensity." Formula: It calculates the ratio between the Bollinger Band width and the Keltner Channel width (BB Width / KC Width). Visuals: A solid green histogram representing the ratio. The Squeeze Dots turn red when the ratio drops below the defined threshold (default 1.0), indicating that the Bollinger Bands are completely inside the Keltner Channels. ⚙️ Features & Settings Mode Selection: Easily toggle between the three study types via a dropdown menu. Squeeze Dots: 🔴 Red: Squeeze is ON (Low volatility, potential breakout incoming). 🟢 Green: Squeeze is OFF (Volatility expanded). Full Customization: Length (Standard: 20) Bollinger Band Multiplier (Standard: 2.0) Keltner Channel Multiplier (Standard: 2.0) True Range Option: Toggle between True Range (ATR) or High-Low for Keltner Channels. 💡 How to use Add the script to your chart. Open the settings to choose your preferred calculation method. If you want to replicate the "stacked" view often seen in Sierra Chart (showing all three at once), simply add this indicator to your chart three times and set each one to a different mode. Disclaimer: This script is an independent replication intended for educational and analytical purposes to bridge the gap between Sierra Chart and TradingView. Sierra Chart, best trading software, EVER! With the best datafeet. Denali Exchange Data Feed.
مؤشر Pine Script®
من ‎p_zerbst‎
1
Bubble Risk ModelThe question of whether markets can be objectively assessed for overextension has occupied financial researchers for decades. Charles Kindleberger, in his seminal work "Manias, Panics, and Crashes" (1978), documented that speculative bubbles follow remarkably consistent patterns across centuries and asset classes. Yet identifying these patterns in real time remains notoriously difficult. The Bubble Risk Model attempts to address this challenge not by predicting crashes, but by systematically measuring the statistical characteristics that historically precede fragile market conditions. The theoretical foundation draws from two distinct research traditions. The first is the work on regime-switching models pioneered by James Hamilton (1989), who demonstrated that economic time series often exhibit discrete shifts between different behavioral states. The second is the literature on tail risk and market fragility, most notably articulated by Nassim Taleb in "The Black Swan" (2007), which emphasizes that extreme events carry disproportionate importance and that traditional risk measures systematically underestimate their probability. Rather than attempting to build a probabilistic model requiring assumptions about underlying distributions, the Bubble Risk Model operates as a deterministic state-inference system. This distinction matters. Lawrence Rabiner's foundational tutorial on Hidden Markov Models (1989) established the mathematical framework for inferring hidden states from observable data through Bayesian updating. The present model borrows the conceptual architecture of states and transitions but replaces probabilistic inference with rule-based logic. States are not computed through forward-backward algorithms but inferred through deterministic thresholds. This trade-off sacrifices theoretical elegance for practical robustness and interpretability. The measurement framework rests on four empirically grounded components. The first captures trailing twelve-month returns, reflecting the well-documented momentum effect identified by Jegadeesh and Titman (1993), who found that securities with strong past performance tend to continue outperforming over intermediate horizons. The second component measures trend persistence as the proportion of positive daily returns over a quarterly window, drawing on the research by Campbell and Shiller (1988) showing that price trends exhibit serial correlation that deviates from random walk assumptions. The third normalizes the distance between current prices and their long-term moving average by volatility, addressing the cross-sectional comparability problem noted by Fama and French (1992) when analyzing assets with different variance characteristics. The fourth component calculates return efficiency as the ratio of returns to realized volatility, a concept related to the Sharpe ratio but stripped of distributional assumptions that often fail in practice. The aggregation methodology deliberately prioritizes worst-case scenarios. Rather than averaging component scores, the model uses quantile-based aggregation with an explicit tail penalty. This design choice reflects the asymmetric error costs in bubble detection: failing to identify fragility carries greater consequences than occasional false positives. The approach aligns with the precautionary principle advocated by Taleb and colleagues in their work on fragility and antifragility (2012), which argues that systems exposed to tail risks require conservative assessment frameworks. Normalization presents a particular challenge. Raw metrics like year-over-year returns are not directly comparable across asset classes with different volatility profiles. The model addresses this through percentile ranking over multiple historical windows, typically two and five years. This dual-window approach provides regime stability, preventing the normalization from adapting too quickly during extended bull markets where elevated readings become statistically normal. The methodology draws on the concept of lookback bias documented by Lo and MacKinlay (1990), who demonstrated that single-window statistical measures can produce misleading results when market regimes shift. The state machine introduces controlled inertia into the system. Once the model enters a particular state, transitions become progressively more difficult as the state matures. This transition resistance mechanism prevents rapid oscillation near threshold boundaries, a problem that plagues many indicator-based systems. The concept parallels the hysteresis effects described in economic literature by Dixit (1989), where systems exhibit path dependence and resist returning to previous states even when underlying conditions change. Volatility regime detection adds contextual interpretation. Research by Engle (1982) on autoregressive conditional heteroskedasticity established that volatility clusters, with periods of high volatility tending to follow other high-volatility periods. The model scales its maturity thresholds inversely with volatility: in calm markets, states mature slowly and persist longer; in turbulent markets, information decays faster and states become more transient. This adaptive behavior reflects the empirical observation that low-volatility environments often precede significant market dislocations, as documented by Brunnermeier and Pedersen (2009) in their work on liquidity spirals. The confidence metric addresses internal model consistency. When individual components diverge substantially, the overall score becomes less reliable regardless of its absolute level. This approach draws on ensemble methods in machine learning, where disagreement among predictors signals increased uncertainty. Dietterich (2000) provides theoretical justification for this principle, demonstrating that ensemble disagreement correlates with prediction error. Distribution drift detection monitors whether the model's calibration remains valid. By comparing recent score distributions to longer historical baselines, the model can identify when market structure has shifted sufficiently to potentially invalidate its historical percentile rankings. This self-diagnostic capability reflects the concern raised by Andrews (1993) about parameter instability in time series models, where structural breaks can render previously estimated relationships unreliable. The cross-asset analysis extends the framework beyond individual securities. By calculating scores for multiple asset classes simultaneously and measuring their correlation, the model distinguishes between idiosyncratic overextension affecting a single asset and systemic conditions affecting markets broadly. This differentiation matters for portfolio construction, as documented by Longin and Solnik (2001), who found that correlations between international equity markets increase significantly during periods of market stress. Several limitations deserve explicit acknowledgment. The model cannot identify timing. Overextended conditions can persist far longer than rational analysis might suggest, a phenomenon documented by Shiller (2000) in his analysis of speculative episodes. The model provides no mechanism for determining when fragile conditions will resolve. Additionally, the cross-asset analysis lacks lead-lag detection, meaning it cannot distinguish whether assets became overextended simultaneously or sequentially. Finally, the rule-based nature of state inference means the model cannot express graduated probability assessments; states are discrete rather than continuous. The philosophical stance underlying the model is one of epistemic humility. It does not claim to identify bubbles definitively or predict their collapse. Instead, it provides a systematic framework for measuring characteristics that have historically been associated with fragile market conditions. The distinction between information and action remains the user's responsibility. States describe current conditions; how to respond to those conditions requires judgment that no quantitative model can provide. Practical guide for traders This section translates the model's outputs into actionable intelligence for both retail traders managing personal portfolios and professional traders operating within institutional frameworks. The interpretation differs not in kind but in scale and consequence. Understanding the score The primary output is a continuous score ranging from zero to one. Lower scores indicate elevated bubble risk; higher scores suggest more sustainable market conditions. This inverse relationship may seem counterintuitive but reflects the model's construction: it measures how extreme current conditions are relative to historical norms, with extremity mapping to fragility. A score above 0.50 generally indicates normal market conditions where standard investment approaches remain appropriate. Scores between 0.30 and 0.50 represent an elevated zone where caution is warranted but not alarm. Scores below 0.30 enter the extreme territory where historical precedent suggests increased fragility. These thresholds are not magical boundaries but represent statistical rarity: a score below 0.30 indicates conditions that occur in roughly the bottom quintile of historical observations. For retail traders, a score in the normal range means continuing with established strategies without modification. In the elevated range, this might mean pausing new position additions while maintaining existing holdings. In the extreme range, retail traders should consider whether their portfolio could withstand a significant drawdown and whether their time horizon permits waiting for recovery. For professional traders, the score integrates into broader risk frameworks: normal conditions permit full risk budgets, elevated conditions might trigger reduced position sizing or tighter stop losses, and extreme conditions could warrant defensive positioning or increased hedging activity. Reading the states The model classifies conditions into three discrete states: Normal, Elevated, and Extreme. These states differ from the continuous score by incorporating persistence and transition resistance. A market can have a score temporarily dipping below 0.30 without triggering an Extreme state if the condition proves transient. The Normal state indicates business as usual. Market conditions fall within historical norms across all measured dimensions. For retail traders, this means standard portfolio management applies. For professional traders, full strategy deployment remains appropriate with normal risk parameters. The Elevated state signals heightened attention. At least one dimension of market behavior has moved outside normal ranges, though not to extreme levels. Retail traders should review portfolio concentration and ensure diversification remains intact. Professional traders might reduce leverage slightly, tighten risk limits, or increase monitoring frequency. The Extreme state represents statistically rare conditions. Multiple dimensions show readings that historically occur infrequently. Retail traders should seriously evaluate whether they can tolerate potential drawdowns and consider reducing exposure to volatile assets. Professional traders should implement defensive protocols, potentially reducing gross exposure, increasing cash allocations, or adding protective positions. Interpreting transitions State transitions carry more information than states themselves. The model tracks whether conditions are entering, persisting in, or exiting particular states. An Entry into Extreme represents the most important signal. It indicates a regime shift from normal or elevated conditions into territory associated with historical fragility. For retail traders, this warrants immediate portfolio review. For professional traders, this typically triggers predefined defensive protocols. Persistence in a state indicates stability. Whether Normal or Extreme, persistence suggests the current regime has become established. For retail traders, persistence in Extreme over extended periods actually reduces immediate concern; the dangerous moment was the entry, not the continuation. For professional traders, persistent Extreme states require maintained vigilance but do not necessarily demand additional action beyond what the initial entry triggered. An Exit from Extreme suggests improving conditions. For retail traders, this might warrant cautious return to normal positioning over time. For professional traders, exits permit gradual normalization of risk budgets, though institutional memory typically counsels slower reentry than the mathematical signal might suggest. Duration and its meaning The model distinguishes between Tactical, Accelerating, and Structural durations in critical zones. Tactical duration (10-39 bars in critical territory) represents short-term overextension. Many Tactical episodes resolve without significant market disruption. Retail traders should note the condition but need not take dramatic action. Professional traders might implement modest hedges or reduce marginal positions. Accelerating indicates Tactical duration combined with actively deteriorating scores. This combination historically precedes more significant corrections. Retail traders should consider lightening positions in their most volatile holdings. Professional traders typically implement more substantial hedges. Structural duration (40+ bars in critical territory) indicates persistent overextension that has become a market feature rather than a temporary condition. Paradoxically, Structural conditions are both more concerning and less immediately actionable than Accelerating conditions. The market has demonstrated ability to sustain extreme readings. Retail traders should maintain heightened awareness but recognize that timing remains impossible. Professional traders often find Structural conditions require strategy adaptation rather than simple defensive positioning. Confidence and what it tells you The Confidence reading indicates internal model consistency. High confidence means all four underlying components agree in their assessment. Low confidence means components diverge significantly. High confidence combined with Extreme state represents the clearest signal. The model is both indicating fragility and agreeing with itself about that assessment. Retail and professional traders alike should treat this combination with maximum seriousness. Low confidence in any state reduces signal reliability. For retail traders, low confidence suggests waiting for clearer conditions before making significant portfolio changes. For professional traders, low confidence warrants increased skepticism about the score and potentially reduced position sizing in either direction. Alignment and model health The Alignment indicator monitors whether the model's calibration remains valid relative to recent market behavior. Good alignment means recent score distributions match longer-term historical patterns. The model's percentile rankings remain meaningful. Both retail and professional traders can interpret scores at face value. Degraded alignment indicates that recent market behavior has shifted somewhat from historical norms. Scores remain interpretable but with reduced precision. Retail traders should apply wider uncertainty bands to their interpretation. Professional traders might reduce position sizing slightly or require additional confirmation before acting. Poor alignment signals significant distribution shift. The model may be comparing current conditions to an increasingly irrelevant historical baseline. Retail traders should rely more heavily on other information sources during Poor alignment periods. Professional traders typically reduce model weight in their decision frameworks until alignment recovers. Volatility regime context The volatility regime provides essential context for score interpretation. Low volatility combined with Extreme state creates maximum concern. Research consistently shows that low-volatility environments can precede significant market dislocations. The market's apparent calm masks underlying fragility. Retail traders should recognize that low volatility does not mean low risk; it often means compressed risk premiums that will eventually normalize, potentially violently. Professional traders typically maintain or increase defensive positioning despite the market's calm appearance. High volatility combined with Extreme state is actually less immediately concerning than low volatility. The market has already acknowledged stress; risk premiums have expanded; potential sellers may have already sold. Retail traders should resist the urge to panic sell during high-volatility extremes, as much of the adjustment may have already occurred. Professional traders recognize that high-volatility extremes often represent better entry points than low-volatility extremes. Normal volatility requires no regime adjustment to interpretation. Scores mean what they appear to mean. Cross-asset analysis When enabled, the model calculates scores for multiple asset classes simultaneously, enabling systemic versus idiosyncratic risk assessment. Systemic risk (multiple assets in Extreme with high correlation) indicates market-wide fragility. Diversification benefits are reduced precisely when most needed. Retail traders should recognize that their portfolio's apparent diversification may not protect them during systemic events. Professional traders implement cross-asset hedges and consider tail-risk protection. Broad risk (multiple assets in Extreme with low correlation) suggests widespread but potentially unrelated overextension. Diversification may still provide some protection. Retail traders can take modest comfort in genuine diversification. Professional traders analyze which assets might offer relative value. Isolated risk (single asset in Extreme while others remain Normal) indicates asset-specific rather than market-wide conditions. Retail traders holding the affected asset should evaluate their position specifically. Professional traders may find relative value opportunities going long unaffected assets against the extended one. Scattered risk represents a few assets showing elevation without clear pattern. This typically warrants monitoring rather than action for both retail and professional traders. Parameter guidance The Short Percentile parameter (default 504 bars, approximately two years) controls the shorter normalization window. Increasing this value makes the model more conservative, requiring more extreme readings to flag concern. Retail traders should generally leave this at default. Professional traders might increase it for assets with shorter reliable history. The Long Percentile parameter (default 1260 bars, approximately five years) controls the longer normalization window. This provides regime stability. Again, default settings suit most applications. The Critical Threshold (default 0.30) determines where the Extreme state boundary lies. Lowering this value makes the model less sensitive, flagging fewer Extreme conditions. Raising it increases sensitivity. Retail traders seeking fewer false alarms might lower this to 0.25. Professional traders seeking earlier warning might raise it to 0.35. The Structural Duration parameter (default 40 bars) determines when Tactical conditions become Structural. Shorter values provide earlier Structural classification. Longer values require more persistence before reclassification. The State Maturity and Transition Resistance parameters control how readily the model changes states. Higher values create more stable states with fewer transitions. Lower values create more responsive but potentially noisier state changes. Default settings balance responsiveness against stability. The Adaptive Smoothing parameters control how the model filters noise. In extreme zones, longer smoothing periods reduce whipsaws but increase lag. In normal zones, shorter periods maintain responsiveness. Most traders should leave these at defaults. What the model cannot do The model cannot predict when overextended conditions will resolve. Markets can remain irrational longer than any trader can remain solvent, as the saying goes. Extended Extreme readings may persist for months or even years before any correction materializes. The model cannot distinguish between healthy bull markets and dangerous bubbles in their early stages. Both initially appear as strong returns and positive momentum. The model begins flagging concern only when statistical extremity develops, which may occur well into an advance. The model cannot account for fundamental changes in market structure. If a new paradigm genuinely justifies higher valuations (rare but not impossible), the model will continue flagging extremity against historical norms that may no longer apply. The Alignment indicator provides partial protection against this failure mode but cannot eliminate it. The model cannot replace judgment. It provides systematic measurement of conditions that have historically preceded fragility. Whether and how to act on that measurement remains entirely the trader's responsibility. Retail traders must still evaluate their personal circumstances, time horizons, and risk tolerance. Professional traders must still integrate model output with fundamental analysis, portfolio constraints, and client mandates. References Andrews, D.W.K. (1993). Tests for Parameter Instability and Structural Change with Unknown Change Point. Econometrica, 61(4). Brunnermeier, M.K., & Pedersen, L.H. (2009). Market Liquidity and Funding Liquidity. Review of Financial Studies, 22(6). Campbell, J.Y., & Shiller, R.J. (1988). Stock Prices, Earnings, and Expected Dividends. Journal of Finance, 43(3). Dietterich, T.G. (2000). Ensemble Methods in Machine Learning. Multiple Classifier Systems. Dixit, A. (1989). Entry and Exit Decisions under Uncertainty. Journal of Political Economy, 97(3). Engle, R.F. (1982). Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica, 50(4). Fama, E.F., & French, K.R. (1992). The Cross-Section of Expected Stock Returns. Journal of Finance, 47(2). Hamilton, J.D. (1989). A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica, 57(2). Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. Journal of Finance, 48(1). Kindleberger, C.P. (1978). Manias, Panics, and Crashes: A History of Financial Crises. Basic Books. Lo, A.W., & MacKinlay, A.C. (1990). Data-Snooping Biases in Tests of Financial Asset Pricing Models. Review of Financial Studies, 3(3). Longin, F., & Solnik, B. (2001). Extreme Correlation of International Equity Markets. Journal of Finance, 56(2). Rabiner, L.R. (1989). A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Proceedings of the IEEE, 77(2). Shiller, R.J. (2000). Irrational Exuberance. Princeton University Press. Taleb, N.N. (2007). The Black Swan: The Impact of the Highly Improbable. Random House. Taleb, N.N., & Douady, R. (2012). Mathematical Definition, Mapping, and Detection of (Anti)Fragility. Quantitative Finance, 13(11).
مؤشر Pine Script®
من ‎EdgeTools‎
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.
مؤشر Pine Script®
من ‎AustrianTradingMachine‎
SMA Multi-Sync Granville & MTF CounterSMA Multi-Sync Granville & MTF Counter Overview This indicator is an environmental awareness tool that identifies when and to what level moving averages (SMAs) across multiple time frames align in the same direction, visualizing the timing and freshness of the trend. Its greatest feature is that it does not simply determine synchronization; rather, it precisely distinguishes the time frame upon which synchronization is completed using the number of stars (★). Key Features 1. Calculation of "Stars" Based on Confirmed Time Frame Trigger The number of stars displayed upon synchronization completion indicates the signal's "temporal weight." ★ (1): Synchronization is completed upon confirmation of the displayed time frame. ★★ (2): Synchronization is completed upon confirmation of the next higher time frame (e.g., 15 minutes). ★★★ (3): Synchronization is completed upon confirmation of the next higher time frame (e.g., 1 hour). The more higher the time frame is confirmed, the more powerful the trend reversal or regression it acts as. 2. MTF Sync Panel The table on the right side of the screen displays the price position (background) and MA direction (text) for each level (displayed to daily) in real time. By watching the background and text colors match, you can understand the accumulation of energy before a star appears. 3. Cross Counter The number of bars elapsed from the synchronization starting point (MA crossover, etc.) to the current bar is displayed numerically in the lower right corner. The closer to "0" the number, the more likely it is the beginning of a trend, while the higher the number, the more likely it is the end of the trend (expiration date). Usability of Input Settings Min Stars (1-5) This sets the signal cutoff. Setting it to "2" eliminates noise caused by the displayed bar being confirmed and narrows down to only the moment when the higher bar is confirmed (★2 or higher). Cancel Alert if MA Slope Same If the MA of the displayed time frame is already leaning in the same direction (leading), the confirmation (★1) on that time frame will be considered "not an initial move" and excluded. 5m TF: Use 30m SMA When using 5-minute time frames, this physically changes the ★2 trigger from the confirmation on the 15-minute chart to the confirmation on the 30-minute chart. This is effective when targeting milestones on larger time frames. *If you have any questions about how to use this, please ask in the comments. SMA Multi-Sync Granville & MTF Counter 概要 本インジケーターは、複数の時間足の移動平均線（SMA）が「いつ、どの階層まで同じ方向に揃ったか」を特定し、そのトレンドの**「確定タイミング」と「鮮度」**を可視化する環境認識ツールです。 最大の特徴は、単なる同調判定ではなく、**「どの時間足の確定（Close）によって同期が完成したか」**を星（★）の数で厳密に区別する点にあります。 主な機能 1. 確定足トリガーによる「星」の算出 同期が完成した瞬間に表示される星の数は、そのシグナルの「時間的な重み」を示します。 ★（1つ）：表示足の確定により同期が完成。 ★★（2つ）：1つ上の上位足（15分等）の確定により同期が完成。 ★★★（3つ）：2つ上の上位足（1時間等）の確定により同期が完成。 上位の足が確定する節目ほど、より強力なトレンドの転換・回帰として機能します。 2. MTF同期パネル 画面右側のテーブルで、各階層（表示足〜日足）の「価格の位置（背景）」と「MAの向き（文字）」をリアルタイムに表示します。 背景色と文字色が一致していく過程を見ることで、星が出る前の**「エネルギーの蓄積」**を把握できます。 3. クロスカウンター 同期の起点（MAクロス等）から、現在の足まで何本経過したかを右下に数値で表示します。 「0」に近いほど初動であり、数値が大きくなるほどトレンドの終盤（賞味期限切れ）である可能性を論理的に示唆します。 インプット設定の使い勝手 Min Stars (1-5) シグナルの足切り設定です。「2」に設定すれば、表示足の確定によるノイズを排除し、**上位足の確定が伴った瞬間（★2以上）**のみに絞り込めます。 Cancel Alert if MA Slope Same 表示足のMAがすでに同方向へ傾いている（先行している）場合、その足での確定（★1）を「初動ではない」とみなして除外します。 5m TF: Use 30m SMA 5分足運用時、★2のトリガーを「15分足」から「30分足」の確定に物理的に変更します。より大きな時間軸の節目を狙う場合に有効です。 ※使い方が不明なところはコメントで聞いてください。
مؤشر Pine Script®
من ‎hmp5below‎
Balance Zone ProjectorOVERVIEW Projects balance zones above and below up to 3 anchor zones. Each zone represents a 2x, 4x, 8x... multiple of the original anchor height, helping you identify key price levels for entries, exits, and targets. HOW TO USE 1. Add the indicator to your chart 2. Click to set Anchor 1 High (top of your zone) 3. Click to set Anchor 1 Low (bottom of your zone) 4. Zones automatically project above and below MULTIPLE ANCHORS Enable Anchor 2 and Anchor 3 in settings to track multiple zones at different time periods. Each anchor has its own: - High/Low prices - Bars Back (where to start drawing) - Bars Forward (zone width) ZONE GROUPS Zones are colored by group for easy identification: - Group 1: Zones 1-2 (nearest to anchor) - Group 2: Zones 3-6 - Group 3: Zones 7-14 - Group 4: Zones 15-30 CUSTOMIZATION - Enable/disable up or down projections - Adjust colors and transparency per zone group - Show/hide zone labels and midlines - Customize label text templates SETTINGS All anchors share the same visual settings (colors, labels, midlines) for consistency. Individual anchor timing is controlled per-anchor. Based on the Balance Zone Engine concept for Sierra Chart.
مؤشر Pine Script®
من ‎ianhay78‎
SMT Quarterly Theory - REAL 6H+90M cyclesEnglish description of the script This indicator visualizes the Quarterly Theory (SMT / Smart Money Theory) concept anchored to New York time. The main daily cycle starts at 18:00 NY time and lasts exactly 24 hours — until 18:00 the next day (local NY time, automatically handling daylight saving time transitions). Each 24-hour SMT day is divided into four 6-hour blocks: 18:00–00:00 (Asia Killzone) 00:00–06:00 (London Open) 06:00–12:00 (NY AM) 12:00–18:00 (NY PM) Each 6-hour block is further subdivided into four 90-minute micro-cycles. The indicator draws: semi-transparent colored rectangles (boxes) for the 6-hour sessions using different colors and corresponding session names lighter orange boxes for the 90-minute cycles inside each 6-hour block vertical dashed lines at the boundaries of 6-hour sessions (different colors) thin dotted lines at the boundaries of 90-minute cycles a thick vertical line marking the end of the SMT day (next 18:00 NY) text labels with the main session names placed above the chart Key features: only objects belonging to the currently active SMT day are displayed (at the moment the new day starts at 18:00 NY, all previous boxes, lines and labels are automatically removed) vertical range is calculated dynamically (highest high of the last 250 bars minus several ATR values) all time calculations are performed in the "America/New_York" timezone separate toggles are available for 6-hour boxes, 90-minute boxes, session divider lines and session name labels box transparency for both 6-hour and 90-minute rectangles can be adjusted via input parameters Этот индикатор предназначен для визуализации концепции Quarterly Theory (SMT / Smart Money Theory) с привязкой к нью-йоркскому времени. Основной цикл дня начинается в 18:00 по Нью-Йорку и длится ровно 24 часа — до 18:00 следующего дня (по местному времени NY, с автоматическим учётом перехода на летнее/зимнее время). Каждый такой 24-часовой SMT-день делится на четыре 6-часовых блока: 18:00–00:00 (Asia Killzone) 00:00–06:00 (London Open) 06:00–12:00 (NY AM) 12:00–18:00 (NY PM) Каждый 6-часовой блок, в свою очередь, разделён на четыре 90-минутных микроцикла. Индикатор рисует: полупрозрачные цветные прямоугольники (боксы) для 6-часовых сессий с разными цветами и соответствующими названиями более светлые оранжевые боксы для 90-минутных циклов внутри каждого 6-часового блока вертикальные пунктирные линии на границах 6-часовых сессий (разные цвета) тонкие точечные линии на границах 90-минутных циклов вертикальную жирную линию на конец SMT-дня (следующие 18:00 NY) текстовые метки с названиями основных сессий над графиком Важные особенности: отображаются только объекты текущего активного SMT-дня (при смене дня в 18:00 NY все предыдущие боксы, линии и метки автоматически удаляются) диапазон по вертикали определяется динамически (максимум high за последние 250 баров минус несколько значений ATR) все временные расчёты производятся в часовом поясе "America/New_York" есть возможность отдельно включать/выключать 6-часовые боксы, 90-минутные боксы, разделительные линии и метки сессий настраивается прозрачность боксов через входные параметры
مؤشر Pine Script®
من ‎crypto_daytrade‎
Moving Average Exponential//@version=6 indicator(title="Moving Average Exponential", shorttitle="EMA", overlay=true, timeframe="", timeframe_gaps=true) len = input.int(9, minval=1, title="Length") src = input(close, title="Source") offset = input.int(title="Offset", defval=0, minval=-500, maxval=500, display = display.data_window) out = ta.ema(src, len) plot(out, title="EMA", color=color.blue, offset=offset) // Smoothing MA inputs GRP = "Smoothing" TT_BB = "Only applies when 'SMA + Bollinger Bands' is selected. Determines the distance between the SMA and the bands." maTypeInput = input.string("None", "Type", options = , group = GRP, display = display.data_window) var isBB = maTypeInput == "SMA + Bollinger Bands" maLengthInput = input.int(14, "Length", group = GRP, display = display.data_window, active = maTypeInput != "None") bbMultInput = input.float(2.0, "BB StdDev", minval = 0.001, maxval = 50, step = 0.5, tooltip = TT_BB, group = GRP, display = display.data_window, active = isBB) var enableMA = maTypeInput != "None" // Smoothing MA Calculation ma(source, length, MAtype) => switch MAtype "SMA" => ta.sma(source, length) "SMA + Bollinger Bands" => ta.sma(source, length) "EMA" => ta.ema(source, length) "SMMA (RMA)" => ta.rma(source, length) "WMA" => ta.wma(source, length) "VWMA" => ta.vwma(source, length) // Smoothing MA plots smoothingMA = enableMA ? ma(out, maLengthInput, maTypeInput) : na smoothingStDev = isBB ? ta.stdev(out, maLengthInput) * bbMultInput : na plot(smoothingMA, "EMA-based MA", color=color.yellow, display = enableMA ? display.all : display.none, editable = enableMA) bbUpperBand = plot(smoothingMA + smoothingStDev, title = "Upper Bollinger Band", color=color.green, display = isBB ? display.all : display.none, editable = isBB) bbLowerBand = plot(smoothingMA - smoothingStDev, title = "Lower Bollinger Band", color=color.green, display = isBB ? display.all : display.none, editable = isBB) fill(bbUpperBand, bbLowerBand, color= isBB ? color.new(color.green, 90) : na, title="Bollinger Bands Background Fill", display = isBB ? display.all : display.none, editable = isBB)
مؤشر Pine Script®
من ‎serkandanki‎
1
Regression ChannelAn enhanced version of TradingView's Linear Regression Channel that displays multiple upper and lower deviation channels with support for both linear and exponential regression models. Getting Started & Usage This indicator overlays a regression channel with up to 4 customizable standard deviation levels above and below the regression line. By default, it uses linear regression, but you can switch to an exponential regression model for curved price trends. For detailed explanations of the statistical concepts and additional usage examples, please visit the documentation .
مؤشر Pine Script®
من ‎ZenithClown‎
1
EvansThis is a simple math problem: If your risk-reward ratio is 1:3. Even if you lose 3 out of 4 trades (a win rate of only 25%), as long as you hit one big win, you'll still break even. That extra bit of win rate is your pure profit. 📊 How to use it with LuxAlgo? This script is your "skeleton," and LuxAlgo is your "muscle." Hearing the green/red alarm: This means your system has detected a DEMA 9/20 crossover. Confirm with the chart: If LuxAlgo also shows a dark blue right-pointing arrow at this time, it represents a strong momentum 1:3 opportunity. If the price is currently in the 0.618 Discount Zone, you must hold this trade. Hearing the yellow alarm: This is a reminder that the trend has changed. If you are already in profit but haven't reached a 1:3 ratio, you can consider manually reducing your position by half and then moving your stop loss to the entry point (Break Even), allowing the remaining profits to run without risk.
مؤشر Pine Script®
من ‎EvansYu‎
ICT Supply & Demand [KTY]ICT Supply & Demand Indicator This indicator automatically detects and displays Supply and Demand zones based on swing highs and lows. Supply and Demand zones are horizontal support/resistance areas where price previously showed strong buying or selling pressure. Automatic Detection - Supply Zone (Red): Formed at swing highs where selling pressure was strong - Demand Zone (Green): Formed at swing lows where buying pressure was strong - Zones are automatically removed when price breaks through Dynamic Extension - Zones extend automatically as new bars form - Clear visual labels showing SUPPLY and DEMAND 1. Identify Supply and Demand zones on your chart 2. Watch for price reaction when re-entering the zone 3. Combine with Order Block, FVG, or Market Structure for confluence 4. Use zones as reference for take-profit or stop-loss targets Pro Tips: - Zones that align with OB or FVG have higher significance - Multiple touches on a zone increase chance of breakout - Fresh (untested) zones tend to have stronger reactions Show Supply & Demand Zones: Toggle zone display on/off Supply Zone Color: Customize supply zone color Demand Zone Color: Customize demand zone color Label Color: Customize text color Supply Zone Detected Demand Zone Detected Supply Zone Broken Demand Zone Broken This indicator is designed for educational purposes. Supply and Demand zones do not guarantee price reversal. Always combine with proper risk management. If you find this indicator helpful, please leave a like and follow for more ICT-based tools!
مؤشر Pine Script®
من ‎Coin-TeacherKim‎