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Mizan v7.8-S: Pure PSI ObserverDescription: 1. General Overview The Mizan v7.8-S is a specialized high-precision market observer designed to quantify the "Ontological Stability" of financial assets. Unlike traditional indicators that rely solely on price action, this tool projects market data onto a proprietary "PSI Scale" to measure the potential energy and structural integrity of a trend. It operates on the "Pure Justice" (Mizan) theoretical framework, distinguishing between constructive stability and chaotic degradation. 2. Key Features Proprietary PSI Scoring: A unique algorithm that converts market momentum into a standardized stability score (0 - 310,000 Scale). Stability Protocol Visualization: Automatically colors the trend line to indicate the current state of the asset (Green for Stable/Constructive, Red for Unstable/Chaotic). Cyclic Time Markers: Includes deterministic time-cycle markers ("Pulse" and "Reset" points) to identify theoretical inflection points in the market rhythm. Axiom Floor & Peak: Visual references for the theoretical limits of the analyzed asset. 3. How to Use Trend Analysis: Observe the color of the PSI line. A transition from Red to Green indicates that the asset has crossed the critical threshold and entered a stable trend structure. Cycle Timing: Use the geometric markers (Diamonds and Circles) to anticipate potential shifts in market rhythm or exhaustion points based on the Mizan temporal constants. Risk Assessment: The distance of the PSI score from the "Axiom Peak" or "Axiom Floor" provides a perspective on the asset's current potential relative to its theoretical limits. 4. Invite-Only Access This script is a closed-source implementation of a proprietary algorithmic kernel ("Mizan Universal Kernel"). It contains protected logic and experimental constants derived from private research. Access: Access to this indicator is restricted. To request access or learn more about the methodology, please contact me via private message on TradingView. Note: This tool is intended for advanced cycle analysis and experimental observation.
Pine Script® indicator
by muratkavak02
Gold Sniper (Liquidity Sweep)Concept : Stop Hunting the "Smart Money" Way Most traders lose money because they enter exactly where "Smart Money" is looking to trigger Stop Losses. We have all been there: You buy at support, the price dips just below your stop loss, takes you out, and then rockets up without you. Gold Sniper is designed to capitalize on this exact behavior . Instead of buying the support, this script waits for the Liquidity Sweep (the "Stop Hunt"). It identifies when price breaks a key structure level to trap sellers, and signals an entry only when the price reclaims that level with momentum. How It Works (The Logic) This indicator looks for a specific "Perfect Storm" setup using a 4-step confirmation process: Identifies Support (Yellow Dots): It tracks local pivot lows (default 10 bars) to visualize the "Floor" where retail traders likely have their stop losses. Detects the Sweep: It waits for price to drop below these yellow dots. This is the "Trap" phase where liquidity is grabbed. Confirms the Reclaim: It does NOT catch the falling knife. It waits for a candle to close back ABOVE the broken support level. Momentum Check (RSI): It ensures internal strength (RSI) is rising compared to the previous low, confirming that the drop was a trap, not a genuine crash. Visual Features Yellow Dots: Dynamic Support Levels / Pivot Lows. "SWEEP BUY" Label: Signals exactly when the trap is complete and the reclaim has occurred. Red Line (Hard Stop): Automatically draws a Stop Loss level at the lowest point of the sweep candle. How to Use This Strategy Wait for the Setup: Do not trade if price is just drifting. Wait for price to challenge and break the Yellow Dots. The Trigger: Enter immediately on the Close of the candle with the "SWEEP BUY" label. Stop Loss: Place your Hard Stop at the Red Line provided by the indicator. Rule: If price touches the Red Line, the setup has failed (it was a real crash, not a sweep). Exit immediately. Best Timeframes: Optimized for 1-Minute and 5-Minute scalping on Gold (XAUUSD) and Futures, but works on all liquid assets. Settings Pivot Lookback: How many bars back to check for the support floor (Default: 10). RSI Length: Sensitivity of the momentum filter (Default: 14). Disclaimer : This tool is for educational purposes and market analysis only. It identifies high-probability "Liquidity Sweep" setups but does not guarantee future results. Always manage your risk.
Pine Script® indicator
by chyg8823
Regular & Dollar Volume (+ projected volume, HVE, bar coloring)Regular & Dollar Volume shows standard or dollar-weighted volume with fast and slow volume averages, projected volume for the live bar, and optional high-volume and percentile spike cues. An optional bar coloring feature reflects direction and volume strength so high-participation moves stand out without clutter. Main features - Dollar volume option with selectable price source (Close, Open, High, Low, HL2, HLC3, OHLC4). - Fast and slow volume averages (SMA or EMA) for quick context. - The fast average reacts quickly to recent volume, while the slow average represents the broader baseline. - Bars are classified based on whether volume is above both averages, below both, or between them. This gives a simple three-state read: unusually strong volume (above both), weak volume (below both), or normal (in between). - Using two averages avoids overreacting to a single spike while still highlighting real regime shifts in participation. - Projected volume on the active bar to estimate end-of-bar volume. - High Volume Ever (HVE) labeling and optional HVE bar coloring . - Optional percentile spike detection with markers, threshold line, and bar highlighting. - Optional candle recoloring to match volume bar colors . - Bar colors reflect both direction (up vs down) and volume strength relative to the two averages. - This helps you spot high-participation moves at a glance and distinguish strong pushes from low-energy drift.
Pine Script® indicator
by shftd
Participation Regime (Volume Context)Most failed trades aren’t caused by bad entries. They’re taken in environments where participation is weak. Price can move without participation. Trends usually don’t survive it. This indicator focuses on how much participation is present, not on predicting direction or generating trades. What it looks at: The tool compares a fast and a slow volume EMA to see whether activity is expanding or fading relative to its own recent history. Based on that, the environment is classified into: LOW participation NORMAL participation HIGH participation This is meant to describe the quality of the environment, not the quality of a setup. How it’s meant to be used Use it as a context and risk filter on top of an existing system. Examples: Reduce size or expectations when participation is weak Allow normal or full risk when participation is strong Be more selective in low-quality environments It does not tell you when to enter or exit. It does not predict price. It does not replace a strategy. What this is not: Not a buy/sell indicator Not a confirmation signal Not a volume spike alert Not designed for scalping or mean-reversion Examples Example 1 — High participation environment Participation expands and trend continuation behaves as expected. Example 2 — Low participation environment Weak participation environments tend to produce noise and false moves. Closing thought Structure decides entries. Participation influences outcomes. This tool exists to help judge when trend continuation is statistically more or less favorable, so risk and expectations can be adjusted accordingly. Notes: Works on any market and timeframe Best used as a higher-timeframe context layer Built for trend-following and swing-based approaches If you read this and think “this tells me when to buy”, this tool is not for you. If you read this and think “this helps me understand when to push risk and when not to”, then it’s doing its job.
Pine Script® indicator
by SystemsOverFeelings
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.
Pine Script® indicator
by EvansYu
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).
Pine Script® indicator
by EdgeTools
STDV Extension Zones from Daily Open - OnlyFlowSTDV Extension Zones from Daily Open This indicator plots standard deviation extension zones based on the current day’s opening price. At the start of each trading day, it calculates the daily standard deviation using a configurable lookback and projects price zones at ±0.5 and ±1.0 standard deviations above and below the daily open. Each zone is displayed as a horizontal band with a center line and a customizable thickness, extending forward throughout the session. Zones automatically reset and lock in place when a new day begins, preserving prior sessions for historical context. The indicator is designed to visually highlight statistically significant price extensions relative to the daily open, helping users quickly identify areas where price may be stretched, balanced, or reacting around volatility-based levels.
Pine Script® indicator
by ofderk
EMA Slope Filter (ATR Threshold) + Supertrend WindowEMA Slope Filter (ATR Threshold) + Supertrend Window This indicator highlights “trade-allowed” segments based on a mechanical EMA slope condition. It compares the current EMA value (user-defined length) to the EMA value N bars ago (user-defined lookback). A direction is allowed only if the EMA change exceeds an ATR-based threshold: ATR multiplier × ATR(length). What it shows on the price chart Green segments (background / EMA color / optional dots): long bias allowed. Red segments: short bias allowed. Neutral (gray/no background): filter not satisfied. Start markers L / S labels appear at the start of a new allowed segment. Optional Supertrend delay: start labels can be delayed by X bars after a Supertrend direction switch (Supertrend ATR length and factor are configurable inside the script). “STOP” wave marker Define a Supertrend-based search window (e.g., bars 3…20 after a switch). If the EMA slope filter never aligns with the Supertrend direction within that window, the script prints a STOP label on bar (max+1) to indicate the current wave is considered non-tradable (do not search for entries until the next Supertrend switch). Extras Key values (EMA diff, ATR, threshold, diff/ATR, bars since ST switch) are available in the Data Window for quick inspection.
Pine Script® indicator
by Fantom2020
UM Multi MA type, Directional Colors + Flip LabelsSummary UM Multi MA is a multi–moving average trend overlay supporting SMA, EMA, WMA, HMA, KAMA, DEMA, and TEMA. Each MA is colored by slope direction, displays clean right-side Flip prices, and optionally adds price↔MA fills, bar/candle coloring, and alerts for MA direction changes. ⸻ Description This indicator plots up to five independently configurable moving averages directly on the price chart. Each MA is colored green when rising and red when falling, based on its current slope. On the last bar only, an optional right-side label displays the MA’s projected Flip price calculation: • If the MA is currently green (rising), the label is green and shows “Flip red @ ” • If the MA is currently red (falling), the label is red and shows “Flip green @ ” The script also supports optional price↔MA fills, optional bar/candle coloring driven by any selected MA, and alerts when MA slope direction changes. ⸻ Features • Up to five moving averages (MA1–MA5), each with independent Enable, Length, and Type settings • Supported MA types: SMA, EMA, WMA, HMA, DEMA, TEMA, KAMA • Directional MA coloring (green rising / red falling) • Right-side labels (last bar only), indicator at what price MA will flip color MA# TYPE LEN Flip red/green @ target price • Optional price↔MA fill (user-selectable MA) • Green fill when price > selected MA and MA is rising • Red fill when price < selected MA and MA is falling • Optional bar/candle coloring driven by any selected MA • Alerts: • Dropdown alertconditions (visible in the TradingView alert menu) • Optional dynamic alert() messages that include MA type and length ⸻ Default Values • Source: Close • MA1: Enabled, EMA 8, Right-side label ON • MA2: Enabled, EMA 21, Right-side label OFF • MA3: Enabled, EMA 50, Right-side label OFF • MA4: Enabled, EMA 100, Right-side label OFF • MA5: Enabled, EMA 200, Right-side label OFF • Label offset: 10 bars • Price↔MA fill: OFF • Fill MA: MA1 • Fill transparency: 90 • Candle coloring: OFF • Color bars using: MA1 • Bar transparency: 0 • Alerts: • Dropdown alertconditions ON • Dynamic alert() messages OFF • MA1 Bull/Bear alerts enabled by default • MA2–MA5 alerts disabled by default ⸻ Suggested Uses • Trend Regime Filtering Use MA200 or MA100 to define bull vs bear regimes, then MA8 or MA21 for trade timing. • Flip Target Awareness Use the right-side Flip label as a quick visual reference for where MA slope direction is projected to change. • Alignment Confirmation Enable fills and/or candle coloring using your “decision MA” (commonly MA21 or MA50) to maintain consistency. • Alerting Workflow Use dropdown alertconditions for standard alerts. Enable dynamic alerts only if you want messages that include MA type and length (alert type: Any alert() function call). • KAMA for Chop Reduction Try KAMA on MA21 or MA50 to reduce noise while staying responsive in trends. • Faster MA Options (DEMA / TEMA) Use DEMA or TEMA on MA8 or MA21 for earlier flips, understanding they are more sensitive in sideways markets. • Volatility Expansion Awareness Watch for periods where multiple MAs compress tightly; these often precede volatility or price expansion. • Trade Execution Ideas Red → green transitions may be used for entries or add-ons. Green → red transitions may be used for exits or risk reduction. • Multi-Timeframe Analysis The author commonly uses Daily and 6-Hour timeframes together. • MA Stretching Across Timeframes If you like an 8-period MA on the Daily, try its longer equivalent on lower timeframes (for example, ~55 on the Hourly). • Indicator Stacking Designed to pair well with momentum, volatility, and market-structure indicators.
Pine Script® indicator
by UnderwearMillionaire
Last 4H Range + Fibs + Bias Last Closed 4H Range + Fibs + Bias This indicator displays the last fully closed 4-hour (4H) candle range and projects it forward as a higher-timeframe framework for intraday trading. Features Last Closed 4H Range Box Plots the high and low of the most recent completed 4H candle (non-repainting). 4H Fibonacci Levels Automatically draws key internal levels (25%, 50% EQ, 75%, 61.8%, 78.6%). 4H Bias Detection Bias is determined using the 4H close relative to the 50% equilibrium: Above EQ → Bullish Bias Below EQ → Bearish Bias Bias Flip Alerts Alerts trigger only when the 4H candle closes and bias changes. Execution-Friendly Design No candle colouring. Clean structure for use on lower timeframes.
Pine Script® indicator
by stevenbel23
Market Structure BOS - Session Based (5m, NY Time) This indicator visualizes market structure using a strict, rule-based Break of Structure (BOS) logic, calculated on the 5-minute timeframe and evaluated in New York time. The script detects swing Highs and Lows based on candle direction (bullish → bearish for Highs, bearish → bullish for Lows). From each validated structure point, a horizontal level is drawn at the true price extreme (wick included). Once created, structure levels never repaint or move. A Break of Structure is confirmed only when a candle CLOSES beyond the most recent valid structure level: - Bullish BOS: close above the latest High - Bearish BOS: close below the latest Low The indicator is trend-aware: once a bullish or bearish BOS is confirmed, only BOS signals in the same direction are shown until the trend changes. This prevents duplicate or redundant structure breaks during trends. Session logic is fully integrated and based on New York time: - Asia - London (with pre-open range) - New York (with pre-open range) Structure levels and BOS logic can optionally reset at the end of each New York trading day, keeping the chart clean and session-relevant. The indicator is designed for traders who focus on intraday price action, market structure, and session-based behavior without visual clutter. No labels, alerts, or signals are plotted — only clean structure levels.
Pine Script® indicator
by Nicolasios
Ease of MovementThis indicator provides an implementation of the Ease of Movement (EOM) indicator, enhanced with a built-in divergence detection engine. The EOM highlights the relationship between volume and price change. High positive values indicate that the price is increasing with low resistance (ease), while low negative values indicate the price is dropping with ease. 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 (e.g., price rising but "ease" of movement is diminishing). - 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 EOM level of the confirming pivot. - **Flexible Pivot Algorithms:** Supports various underlying mathematical models for pivot detection provided by the core library 3. **Customizable Signal Line:** Includes an optional moving average of the EOM, which serves as a signal line. The type of MA (`Signal Smoothing`) and its length can be customized. This signal line can also be optionally volume-weighted (`Volume weighted`). 4. **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). 5. **Multi-Timeframe (MTF) Capability:** - **MTF EOM & Signal Lines:** The EOM and its signal line 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. 6. **Integrated Alerts:** Includes comprehensive alerts for: - The *start* and *end* of all divergence types. - The EOM crossing its signal line. - The EOM crossing the zero line. --- **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.
Pine Script® indicator
by AustrianTradingMachine
ElectZA MACD Range Momentum Filter**ElectZA MACD Range Momentum Filter (EZ_RangeMACD)** is a clean MACD-style momentum tool that helps you avoid choppy, low-volatility periods. It uses **ATR compression** to detect when price is likely ranging (and visually shades those zones), then **filters signals** so buy/sell triggers only appear when the market is *not* in a range. You get a color-coded histogram (gray in ranges, green/red in trends), classic MACD + signal lines, and optional crossover/crossunder markers to highlight higher-quality momentum shifts. **Disclaimer:** This indicator/script is provided for **educational and informational purposes only** and does **not** constitute financial, investment, or trading advice. Trading and investing involve **significant risk**, and you may lose some or all of your capital. Past performance is **not** indicative of future results. Always do your own research, test strategies on a demo account, and consider seeking advice from a qualified financial professional. By using this script, you agree that you are solely responsible for any trading decisions and outcomes.
Pine Script® indicator
by ElectZA
TradeAxis Trendlines - Full RangeOverview TradeAxis Trendlines is an overlay indicator that automatically builds and maintains diagonal support/resistance trendlines from confirmed swing pivots, ranks candidates to reduce clutter, and provides optional breakout-based risk framing (TP/SL boxes) using structural stops. This script is built as a single workflow: Identify structurally valid trendlines Reduce clutter by ranking/filters Monitor/visualize breakouts with clear risk framing (disabled in Analysis Mode and on non-standard chart types) How the trendlines are detected and filtered 1) Confirmed pivot engine (non-instant pivots) Trendline anchors come from confirmed pivot highs/lows using user-defined Left/Right pivot strength. Because pivots require Right bars to confirm, lines are not drawn at the turning candle and will appear only after confirmation. 2) Candidate generation + structural validation The script tests pivot-to-pivot vectors and rejects candidates that fail structural criteria, including: Minimum line length (bars between anchors) Slope filtering with two modes: Absolute slope bounds (price-per-bar) ATR-relative slope bounds (thresholds scaled by ATR) Body-intersection rejection: candidates are filtered out if candle bodies repeatedly cut through the line beyond a tolerance Opposite-side invalidation gate: candidates can be rejected/disabled when price closes (or evaluates by Mid-body/Body mode) beyond the “wrong side” of the line, to avoid keeping lines that are already invalidated by structure 3) Touch counting + scoring (clutter control) Valid candidates are ranked using a weighted score that prioritizes: Number of valid touches Recency of the last touch Line span By default, the script plots both the primary and secondary (“2nd best”) support and resistance lines; you can disable the secondary set if you prefer a cleaner chart. 4) Dynamic cleanup behavior Trendlines are continuously refreshed as new pivots confirm. Lines that are decisively broken and then reclaimed can be removed to prevent stale structure from lingering on the chart. Optional modules A) Safety lines (structural stop references) When enabled, the script calculates additional diagonal “safety” lines from a separate pivot stream and selects the best safety reference near the active structure. These safety lines are used as structural candidates for Stop Loss placement in the breakout framing module. B) Higher-timeframe (HTF) overlays When enabled, the script runs its trendline detection logic on a user-selected higher timeframe using `request.security()` with lookahead disabled, and overlays the HTF support/resistance onto the current chart. HTF lines are plotted using time-based coordinates and can update as HTF bars confirm. C) Breakout + Risk/Reward visualization (optional) When enabled (and on standard charts), the script can flag breakouts and draw a risk/reward box: Breakout trigger: candle-body confirmation through the trendline plus a user-selected ATR-based buffer. Buffer Mode can be set to ATR (buffer = ATR × multiplier) or None (no buffer). Optional filters: Wick filter (rejects candles with excessive upper/lower wick percentage) Time windows (inputs are labeled in UTC+4) with optional overnight restrictions and specific block windows Minimum breakout body size (ticks) Stop Loss / Take Profit framing Stop selection is structural-first. The script prioritizes the active safety line (when available), otherwise it falls back to recent swing structure (recent swing high/low candidates) and the best available structural reference. Entries can be skipped if risk constraints are violated, including: Min SL Size (ticks) Max Allowed SL (×ATR) Take Profit is projected from the actual stop distance using the selected Risk/Reward Target. Important notes about the position tools This is a visualization/alerting aid. It does not place trades. TP/SL hit detection is bar-based (OHLC). If both TP and SL are within the same candle range, the script cannot know which occurred first. On non-standard chart types, position tools and entry/exit alerts are disabled. D) Analysis Mode When Analysis Mode is enabled, the script disables the breakout/risk framing logic and focuses on technical trendlines (plus structural alerts). Alerts Alert conditions are available for: Touch Support/Resistance (Primary, Secondary, or HTF) New Support/Resistance line detected Long/Short position tool placed (when enabled on standard charts) A combined “Any Event” condition
Pine Script® indicator
by murids4d4t