MTF EMA Traffic Light System Trend Alignment for ScalpersMTF EMA Traffic Light – Trend Bias System
This indicator is designed to help traders quickly identify high-probability trend alignment using multiple timeframes and EMAs.
It analyzes price relative to the 13 EMA and 55 EMA on:
1 Minute
5 Minute
15 Minute
1 Hour
4 Hour
Then it converts that data into a simple Traffic Light system to guide trade decisions.
🚦 How It Works
Each timeframe is classified as:
🟢 BULL – Price above both EMAs
🔴 BEAR – Price below both EMAs
🟡 MIXED – No clear direction
The system focuses on lower-timeframe alignment:
When 1m + 5m + 15m are aligned → Strong setup
When mixed → Caution
When misaligned → Stand aside
🟢 GREEN State (Full Trade Mode)
Triggered when:
✔ 1m, 5m, and 15m are all BULL → Long Bias
✔ 1m, 5m, and 15m are all BEAR → Short Bias
Rules:
Full position size
Trade with trend
Look for EMA pullbacks
Let winners run
🟡 YELLOW State (Caution Mode)
Triggered when:
✔ Lower timeframes are mixed
Rules:
Reduce size
Take quick profits
No holding
Defensive trading
🔴 RED State (No Trade)
Triggered when:
✔ No clear alignment
Rules:
Stay out
Mark key levels
Protect capital
📋 Dashboard Panel
The indicator displays a real-time table showing:
Each timeframe’s bias
Overall market state
Trade rules
This allows you to read market structure in seconds without switching charts.
🎯 Best Use
This tool works best for:
✔ Scalping
✔ Intraday trading
✔ Trend continuation setups
✔ EMA pullback strategies
Recommended for:
Forex
Indices
Gold
Crypto
⚠️ Risk Disclaimer
This indicator is a decision-support tool, not a guarantee of profits.
Always use:
Proper risk management
Stop losses
Personal trade rules
Never risk more than you can afford to lose.
อินดิเคเตอร์และกลยุทธ์
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).
Trading Checklist (BUY / SELL + Asia/London/NY + Prev 4H Range)Trading Checklist (BUY / SELL + Asia / London / NY + Prev 4H Range)
This indicator provides a rule-based trading checklist designed to keep execution aligned with session timing, higher-timeframe context, and directional bias.
Features
BUY / SELL Checklist Logic
Visual conditions help confirm whether market structure supports long or short execution.
Session Awareness
Automatically highlights the active trading session:
Asia
London
New York
Previous 4H Range Framework
Plots the last closed 4-hour candle high and low to define:
Premium / Discount context
Key reaction zones
HTF directional bias reference
Session-Aligned Execution
Helps traders focus on taking setups only during valid sessions, reducing overtrading.
Non-Repainting Design
All higher-timeframe levels are based on completed candles only, making the checklist reliable in live markets.
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.
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.
Quartile Close HighlighterThis indicator highlights price action by coloring candles based on their closing relative to their range. It paints the candle green if the close is within the top quartile (upper 25%) and red if the close is within the lower quartile (bottom 25%).
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.
Best Buying & Selling Flip Zone @MaxMaserati 3.0Best Buying & Selling Flip Zone 3.0 🐂🐻
Best Buying & Selling Flip Zone 3.0 is an advanced, multi-timeframe Price Action tool designed to identify high-probability institutional supply and demand zones.
By analyzing candle range and body size (Expander vs. Normal candles), this indicator categorizes market structure shifts into three distinct tiers of strength (A+++, A++, A+). It includes a built-in Trade Manager, Volume Tracking, and a unique "Defender/Attacker" Multi-Timeframe (MTF) entry confirmation system.
🚀 Key Features
Multi-Timeframe Analysis: Monitor Higher Timeframe (HTF) zones while trading on a Lower Timeframe (LTF).
Tiered Setup Grading: Automatically classifies zones based on the strength of the candle engulfing action (King Slayer, Crusher, Drift).
Smart Entry Confirmation: The script can wait for price to tap an HTF zone and then automatically search for a confirmation pattern on the current timeframe before signaling a trade.
Built-in Trade Management: Visualizes Entry, Stop Loss (SL), and Take Profit (TP) levels with customizable Risk:Reward ratios.
Volume Tracking: Monitors the volume utilized to create a zone and tracks "remaining" volume as price tests the zone.
Zone Deletion Logic: Automatically removes zones that have been invalidated by either a wick or a candle close.
🧠 How It Works: The "A-Grade" Logic
The indicator analyzes candles based on their body-to-range ratio to define "Expander" (Explosive move) vs. "Normal" candles. It then looks for engulfing behaviors to create zones:
A+++ (King Slayer):
Logic: A Bullish Expander engulfs a Bearish Expander (or vice versa).
Significance: This is the strongest signal, indicating a massive shift in momentum where aggressive buyers completely overwhelmed aggressive sellers.
A++ (Crusher):
Logic: A Bullish Expander engulfs a Bearish Normal candle.
Significance: Strong momentum overcoming standard price action. High probability.
A+ (Drift):
Logic: A Bullish Normal candle engulfs a Bearish Normal candle.
Significance: A standard flip zone. Good for continuation plays but less aggressive than KS or CR setups.
🛠️ Functionality Guide
1. General Filters & Timeframes
Higher Timeframe: Select a timeframe higher than your chart (e.g., Select 4H while trading on 15m). The indicator will draw the major zones from the 4H.
Deletion Logic:
Wick (Hard): Zone is removed immediately if price touches the invalidation level.
Close (Soft): Zone is removed only if a candle closes past the invalidation level.
2. LTF Entry Confirmation (The "Master" Switch)
When Show LTF Entry Logic is enabled, the indicator does not signal immediately upon an HTF zone creation. Instead:
It waits for the price to retraced and touch the HTF zone.
Once touched, it scans the current timeframe for a valid flip setup (KS, CR, or DR).
It creates a tighter entry box and draws trade lines only when this confirmation occurs.
3. Trade Management
Risk:Reward: Set your desired RR (e.g., 2.0).
SL Padding: Add breathing room (ticks) to your Stop Loss.
SL Source: Choose between a safer Stop Loss (based on the HTF zone) or a tighter Stop Loss (based on the LTF confirmation candle).
4. Volume Stats
Labels display the volume involved in the zone's creation. As price taps the zone, the volume is "depleted" from the label, giving you insight into the remaining order flow absorption.
🎨 Visual Customization
Colors: Fully customizable colors for Buyers (Green) and Sellers (Red) zones across all three strength tiers.
Labels: Toggle technical names, touch counts, and timeframe labels.
Lines: Option to show "Aggressive Open Lines" to mark the exact opening price of the flip zone extended forward.
⚠️ Disclaimer
This tool is for educational purposes and chart analysis assistance only. Past performance of a setup (A+++/King Slayer) does not guarantee future results. Always manage risk and use this in conjunction with your own trading strategy.
Order VolumeGranular order volume.
Mainly to be used in other indicators where accurate order flow is needed.
Uses 1S security to pull higher resolution data and then adds into bin based on candle size of chart.
1S can be changed to different time frames based on data limitations.
Plot delta.
Buyers & sellers Candle Control Dominance Zone @MaxMaserati 3.0Description
The Buyers & Sellers Candle Control Dominance Zone is a surgical price-action tool designed to identify and project key supply and demand zones derived from candle anatomy across multiple timeframes.
By splitting candles into "Sellers Control" (upper wick/shadow) and "Buyers Control" (lower wick/shadow) regions, this script visualizes exactly where price rejection and absorption are occurring. With the new HTF Engine, you can now view these institutional rejection zones from a Higher Timeframe (e.g., 4H) while trading on a Lower Timeframe (e.g., 15m).
How it Works
The indicator identifies specific "Control Zones" based on the battle between buyers and sellers:
Live Control (Current & HTF): Real-time monitoring of the developing candle. See a 4H wick forming live while watching the 1m chart.
Last Closed Control (Current & HTF): Projects the zones from the most recently completed candle.
Dominance Zones (BuBC & BeBC):
BuBC (Bullish Body Close): A "Dominance Zone" triggered when a candle closes above the previous candle's high. Signifies strong bullish momentum.
BeBC (Bearish Body Close): A "Dominance Zone" triggered when a candle closes below the previous candle's low. Signifies aggressive selling pressure.
Key Features
Multi-Timeframe (MTF) Overlay: Plot 4H, Daily, or Weekly control zones directly on your lower timeframe scalping charts.
Smart Labeling: HTF labels automatically update to show the zone type (e.g., "Sellers Control (Live) ") and whether the last candle was a Dominance candle (BuBC/BeBC).
Dynamic Extension: Zones are projected forward to help you catch retests of rejection levels.
Alerts Included: Built-in alerts trigger when price crosses into a Dominance Zone (BuBC/BeBC), allowing you to set it and forget it.
Can be use as:
Support & Resistance: Use Buyers Control zones (lower wicks) as demand zones for longs and Sellers Control zones (upper wicks) as supply zones for shorts.
Trend Confirmation: A BuBC zone often acts as a launchpad for continued upside. If price falls back into a BuBC zone and rejects, it is a high-probability continuation signal.
Fractal Entry: Use the HTF zones to find the "Big Picture" levels, then use the Current TF zones to refine your entry with precision.
Settings
Display Filter: Toggle Current TF zones (Live, Closed, BuBC, BeBC) independently.
Higher Timeframe Settings: Enable/Disable HTF overlay and select your preferred timeframe (e.g., 240 for 4H).
Visuals: Fully adjustable transparency, colors, and extension lengths to keep your chart clean.
Trend-cycle reversion (multi-timeframe)Trend-cycle reversion (multi-timeframe) is a mean-reversion “stretch” gauge built around a simple idea: price often deviates from its recent path (trend + dominant swing rhythm), and those deviations become more actionable when you scale them by volatility and express them as a standardized score.
This script models the last N bars as:
1) a linear trend (to capture drift), plus
2) a single dominant cycle (to capture the most prominent oscillation inside the same window).
It then measures how far current price is from the model’s next-bar projection, normalizes that distance by ATR (volatility), and finally converts the result into a rolling Z-score. The output is displayed as a multi-timeframe dashboard so you can see “stretch vs. fit” across several time compressions at once.
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What you see on the chart
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The indicator draws a table (overlay) with up to 12 rows (configurable), one per timeframe from your CSV list.
Each row shows:
• TF: The timeframe being evaluated (e.g., 1, 5, 15, 60, 240, D).
• Z: The current Z-score of the volatility-scaled model gap on that timeframe.
• State: A simple interpretation using your Z threshold:
- “Short ▼” when Z > +threshold (price is extended above the model path)
- “Long ▲” when Z < −threshold (price is extended below the model path)
- “Hold •” when inside the band (not unusually stretched)
Colors follow the same logic: red for high positive Z, green for high negative Z, gray when neutral or unavailable.
Important: “Long/Short” here describes the direction of mean-reversion pressure (over/under the fitted path), not a complete trading system by itself.
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How it works (plain-English math)
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1) Optional log transform
If “Fit on log(price)” is enabled, the model runs on log(price) instead of raw price. This is often useful for markets that behave multiplicatively (large percentage moves, long-term exponential growth), because distances become closer to “percent-like” rather than absolute dollars.
2) Trend fit (linear regression in the window)
Over the last Window Length bars, the script estimates a straight-line trend. Think of this as the baseline path that best explains the window if you ignore swings.
3) Cycle search (best period by least-squares error)
After removing the linear trend, the script searches for a single sinusoidal cycle period between:
• Min Period and Max Period (in bars), stepping by Period Step.
For each candidate period, it computes the best-fitting sine+cosine components and measures the remaining error (SSE). The period with the smallest SSE is selected as the “best” cycle for that window.
To reduce recalculation cost and to keep the chosen cycle from flapping every bar, the script re-runs this period search only every “Re-search best period every N bars”. Between searches, it keeps using the last best period.
4) Next-bar projection and “gap”
Using the fitted trend + fitted cycle, the script projects the model value one bar ahead (relative to the window indexing). It then computes:
gap = (current value) − (projected value)
If “Invert sign” is enabled, the gap is multiplied by −1. This doesn’t change magnitude, it only flips interpretation (useful if you prefer the opposite sign convention).
5) Volatility scaling via ATR
The raw gap is divided by ATR to make it comparable across symbols and regimes. If you are fitting on log(price), ATR is also computed in log space using a log-based true range, then smoothed similarly (so the scale is consistent).
This produces a “gap in ATR units”.
6) Z-score standardization
Finally, the script computes a rolling Z-score of the ATR-scaled gap over “Z-score length”:
Z = (gapATR − mean(gapATR)) / stdev(gapATR)
This is what appears in the table. The Z-score answers: “How unusual is today’s model deviation compared to the last Z-score length observations?”
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How to interpret the Z-score
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Z near 0:
Price is close to the model path relative to recent volatility (nothing unusual).
Z above +threshold:
Price is meaningfully ABOVE the fitted path (stretched up). This can be read as elevated downside mean-reversion pressure — but it can also persist during strong trends.
Z below −threshold:
Price is meaningfully BELOW the fitted path (stretched down). This can be read as elevated upside mean-reversion pressure — but it can also persist during fast selloffs.
A practical way to use this indicator is to treat it as a “context filter” or “risk tool”:
• Fading extremes: look for mean-reversion setups when Z is beyond the threshold and price action confirms (e.g., momentum stalls, structure breaks, volatility contraction/expansion cues).
• Trend-aware reversion: only take “reversion” signals in the direction permitted by your separate trend filter (higher-timeframe trend, moving average regime, market structure, etc.).
• Take-profit / risk management: in a trend-following strategy, extremes can be used as partial profit zones or as “don’t chase here” warnings.
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Multi-timeframe (MTF) notes
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Each table row is computed with request.security() on that timeframe with no lookahead, so it is not using future bars to form the value.
However, like any live indicator, the value for an actively forming bar can change until that bar closes (especially on the lower timeframes). Also, higher-timeframe rows update when that higher-timeframe bar updates/closes.
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Inputs (what to change first)
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If you only change a few settings, start here:
• Window Length:
Controls how much history the model uses. Larger = smoother/stabler, but slower to adapt.
• Min/Max Period + Step:
Controls the cycle search range and granularity.
- Wider ranges can capture more possibilities but cost more computation.
- Smaller steps can find a closer match but also cost more.
• Re-search every N bars:
Higher = faster performance and more stability; lower = more adaptive but can be noisier.
• ATR length (scale gap):
Controls the volatility scale. Shorter reacts faster to volatility changes; longer is steadier.
• Z-score length:
Controls how “rare” extremes are. Longer lengths make Z more stable, but require more history and adapt slower to regime shifts.
• Z threshold:
Defines when the table labels “Long/Short”. Common choices are 1.5–2.5 depending on how selective you want extremes to be.
• Timeframes (CSV) + Max table rows:
Controls what you see in the dashboard.
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Limitations and expectations
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This is a single-cycle, windowed model. Markets can be multi-cycle, non-sinusoidal, or structurally shifting; in those cases the “best period” is simply the best approximation inside the window, not a guarantee of a true underlying rhythm.
Z-score extremes are not automatic reversal calls. In strong trends or during volatility shocks, Z can stay extreme longer than expected. Use this as a measurement tool, then combine it with your own confirmation and risk management.
This indicator is for analysis/education and does not provide financial advice.
Funnelzon Graded Buy and Sell Signals (LITE) MFI MTFFunnelzon Buy and Sell Signals (EMA Zones) – LITE is a lightweight overlay indicator built for scalping and short-term trading. It generates BUY/SELL signals, grades each signal (A+ to F), and provides a clean Confirmation Box that summarizes multi-timeframe context so you can make faster, more structured decisions.
How it works
Signal Engine (LTF)
Signals are triggered using an ATR-based “scalp helper” logic with adjustable sensitivity.
A stop-state system helps reduce repeated or noisy entries.
Signal Scoring & Grades (A+ → F)
When a signal appears, it is evaluated by a context pipeline that considers:
Adaptive momentum/flow (AMF)
ALMA trend alignment
Support/Resistance proximity
Swing structure behavior
Market regime / trend strength (ADX-based)
The result is a score mapped to a grade:
A+ / A = strongest signals
B / C = mixed conditions
D / F = low-quality conditions
Optional Filters
MFI Filter: Helps avoid signals that do not meet Money Flow conditions.
HTF Confirmation (MTF): Uses HTF1 and HTF2 bias. Choose strict filtering or soft alignment.
Confirmation Box (Dashboard)
The box displays:
HTF State: Trend Long / Trend Short / HTF Conflict / Neutral
Market Mode: Trend / Pullback / Conflict
Trade Bias: Long-only / Short-only / Wait
ENTRY NOW? = “YES” when HTF bias and LTF signal align
MFI status + HTF1/HTF2 direction
Optional Structure Tools
EMA overlays: 9 / 12 / 20 / 50 / 100 / 200
Auto Supply/Demand zones (pivot-based, ATR thickness, configurable extension and limits)
Best practices (recommended workflow)
Prefer trading A+ / A signals only.
Trade in the direction of HTF State when possible.
If Market Mode shows PULLBACK or CONFLICT, reduce risk or wait for better alignment.
Use Supply/Demand zones and EMAs for structure (targets, invalidation, and bias).
Important: Confirmation with Stochastic + MACD
This script is a signal + context tool, not a guarantee. To validate signal confirmation, it is strongly recommended to use:
Stochastic Oscillator (momentum/exhaustion confirmation)
MACD (trend momentum and direction confirmation)
Only take trades when the script signal and your confirmation indicators agree.
Alerts
Includes alert conditions for:
Buy Signal
Sell Signal
Any Signal
ENTRY NOW (HTF + LTF aligned)
ENTRY NOW Long / ENTRY NOW Short
Disclaimer
This indicator is for educational purposes and does not constitute financial advice. Always backtest, manage risk, and confirm signals with your own rules.
RLP V4.3 -Long Term Support/Resistance Levels (Refuges-Shelters)// Introduction //
We have utilized the Zigzag library technology from ©Trendoscope Pty Ltd for Zigzag generation, allowing users the freedom to choose which of the different Zigzags calculated by Trendoscope as "Levels and Sub-Levels" is most suitable for generating ideal phases for evaluation and selection as "most preponderant phases" over long-term periods of any asset, according to its particular behavior based on its age, volatility, and price trend.
// Theoretical Foundation of the Indicator //
Many traditional institutional investors use the latest higher-degree market phase that stands out from others (longest duration and greatest price change on daily timeframe) to base a Fibonacci retracement on whose levels they open long-term positions. These positions can remain open to be activated in the future even years in advance. The phase is considered valid until a new, more preponderant phase develops over time, at which point the same strategy is repeated.
// Indicator Objectives //
1) Automatically find the latest most preponderant long-term phase of an asset, analyzing it on daily timeframe while considering whether the long-term market trend is bullish or bearish.
2) Draw a Fibonacci Retracement over the preponderant phase (reversed if the phase is bullish).
3) The indicator automatically numbers and locates the 3 most preponderant phases, selecting Top-1 for initial Fibo drawing.
4) If the user disagrees with the indicator's automatic selection, they have the freedom to choose any of the other 2 Top phases for the Fibo drawing and its levels.
5) If the user disagrees with the amplitude or frequency of the initially drawn Zigzag phases, they can modify the Zigzag calculation algorithm parameters until one of the Top-3 matches the phase they had in mind.
6) As an experimental bonus, the indicator runs a popularity contest (CP) of "bullseye" daily price (OHLC) matches, subject to user-defined tolerance ranges, against all Fibo levels of the Top 3 selected phases, to verify which phase the market prices are validating as the most popular for placing trades. Contest results are displayed in the POP. CONTEST column of the Top-3 phases table. If the contest detects a change in the winning phase, a switch can be enabled to activate an alert that the user can utilize with TradingView's alert creator to display an alarm, send an email, etc.
7) This indicator was designed for users to find the preponderant long-term phase of their assets and manually record the date-price coordinates of the i0-i1 anchors of the preponderant phase. The Top-1 phase coordinates are shown in the Top-3 phases table where they can be captured. The date-price coordinates of all HH and LL pivots, from all Zigzag phases, can be displayed via a switch. With the pivots, the user can select a different phase than those automatically found by the indicator, according to the conclusions of their own research. Subsequently, the user can forget about this RLP indicator for a while and move on to apply in their normal trading our RLPS indicator (Simplified Long-Term Shelters), in which they can draw and simultaneously track the long-term shelters of up to 5 different assets, simply by entering their corresponding date-price coordinates, previously located with this RLP indicator or through their own observation.
// Additional Notes //
1) As of the this V4.3 publication date (01/2026), the Zigzag generation parameters were adjusted by default to find the long-term preponderant phases for the following assets: Bitcoin, Ethereum, Bitcoin futures BTC1! (all generated due to the 2020-2021 pandemic). It also provides by default the confirmed preponderant phases for the following assets: Apple, Google, Amazon, Microsoft, PayPal, NQ1!, ES1! and SP500 Cash.
2) Prices, phases, and levels shown on the graphic chart correspond to results obtained using daily Bitcoin data from the Bitstamp exchange, BTCUSD:BITSTAMP (popular here in Europe).
3) Any error corrections or improvements that can be made to the phase selection algorithms or the CP phase popularity contest algorithm will be highly appreciated (statistics and mathematics, among many other sciences, are not particularly our strong suit).
4) We sincerely regret to inform you that we have not included the Spanish translation previously provided, due to our significant concern regarding the ambiguous rules on publication bans related to indicators.
4) Sharing motivates. Happy hunting in this great jungle!
KASTE indicator 2 (for 10s Entries)This script is a **1-minute MACD-based trend filter** designed to define clear **bullish or bearish market bias**.
It uses a fast MACD configuration combined with a 50-period EMA to identify short-term trend direction and momentum strength.
A bullish state is shown when price is above the EMA and MACD momentum is rising above zero, while a bearish state is shown when price is below the EMA and momentum is falling below zero.
The background color highlights the current trend, making it easy to align **10-second entry timing** with the higher-timeframe bias and avoid trading in choppy conditions.
AMT Orderflow Profile + Imbalance Highlight + DashboardAMT Orderflow Profile + Imbalance Highlight + Dashboard
This indicator is a price-bin-based orderflow profile designed to expose where aggressive participation is concentrated and sustained, not just where volume traded.
Unlike traditional volume profiles that show where activity occurred, this script focuses on how volume behaved inside price, separating buying and selling pressure and highlighting only statistically dominant imbalance.
🔹 Why This Script Is Original
Most volume profiles and orderflow tools suffer from one or more of the following:
Single-bin imbalance noise
Repeating alerts from already-accepted imbalance
Visual imbalance that does not align with alerts
No distinction between fresh initiative vs historical volume
This script solves those issues by combining price-bin profiling, directional volume classification, and strict imbalance persistence rules into one unified model.
The result is a contextual orderflow tool, not a signal spammer.
🔹 How It Works (Concepts)
Price-Based Binning
The script divides the price range of the lookback window into fixed bins.
Directional Volume Separation
Buy volume: candles closing above open
Sell volume: candles closing below open
Bin-Level Imbalance Calculation
A bin is imbalanced only when one side controls a configurable percentage of total volume:
Side Volume ÷ (Buy + Sell Volume) ≥ Threshold
Persistence Requirement (Noise Filter)
Imbalance is only considered valid when it appears across 3 or more consecutive bins, filtering out isolated prints.
Fresh Print Enforcement
Alerts trigger only when imbalance first appears, never while it persists or after it has already been accepted by price.
🔹 Visual Output
Each bin is drawn as a horizontal box
Imbalanced bins display:
Bold borders
Highlighted background
Text label: BUY IMB or SELL IMB
Box width represents relative volume intensity
Alerts are mathematically locked to these visual labels, ensuring perfect alignment between what you see and what you’re alerted on.
🔹 How Traders Use It
This tool is best used for:
Identifying initiative buying or selling
Spotting absorption vs acceptance
Confirming auction direction within a larger framework
Providing orderflow context alongside VWAP, IB, CVD, or market structure
It is not intended as a standalone entry signal, but as a confirmation and context engine.
🔹 Alerts (Non-Repainting)
BUY alert → fresh 3+ bin buy-side imbalance
SELL alert → fresh 3+ bin sell-side imbalance
Alerts do not repeat unless imbalance fully disappears and reappears
⚠️ Notes
Candle-based volume (not tick footprint)
Non-repainting
Designed for futures and liquid markets
Best used with clean charts for clarity
KASTE Buy & SellThis indicator works like a **MACD-based momentum tool**.
It calculates the difference between a fast and a slow moving average (MACD line) and smooths it with a signal line.
* A **Buy signal** appears when the MACD line crosses **above** the signal line, indicating rising bullish momentum.
* A **Sell signal** appears when the MACD line crosses **below** the signal line, indicating increasing bearish momentum.
The histogram visualizes momentum strength: green bars show bullish momentum and red bars show bearish momentum.
Trade by Design - v0.0.1Trade by Design - v0.0.1
📊 Overview
This indicator displays key price levels based on New York trading session times (17:00 NYT). It helps traders identify important support and resistance levels from the previous day, previous week, and the current trading day.
💡 Inspiration
This indicator was inspired by concepts presented in this video: www.youtube.com
Thanks to Annii, her youtube channel is www.youtube.com
Also you can check this video about Mastering the UK session www.youtube.com
I created this indicator for my personal trading needs and decided to share it with the community. Please note that this indicator is in its early development stage (v0.0.1) and may be updated or improved over time based on feedback and my trading experience.
📈 What It Displays
1. Previous Week Levels (HoW / LoW) - Orange
HoW (High of Week): The highest price reached during the previous week
LoW (Low of Week): The lowest price reached during the previous week
Week starts at Sunday 17:00 New York Time
2. Previous Day Levels (HoD / LoD) - Aqua/Cyan
HoD (High of Day): The highest price reached during the previous trading day
LoD (Low of Day): The lowest price reached during the previous trading day
Trading day starts at 17:00 New York Time (aligned with futures market open)
3. Initial Day Levels (iH / iL) - Green
iH (Initial High): The current day's running high
iL (Initial Low): The current day's running low
Displays the percentage range between iH and iL in parentheses
Optional: Include or exclude the gap period (17:00-20:00 NYT)
⚙️ Settings
Colors
Prev Week (LoW/HoW): Color for weekly levels (default: orange)
Prev Day (LoD/HoD): Color for daily levels (default: aqua)
Initial Day (iL/iH): Color for current day levels (default: green)
Style
Line width: Thickness of the lines (1-5)
Line transparency: Transparency for current lines (0-90%)
Historical line transparency: Additional transparency for historical lines (0-90%)
Line style: Solid, Dashed, or Dotted
Label offset: Distance of labels from current price (in bars)
Label size: Tiny, Small, Normal, or Large
History
Number of weeks to display: How many weeks of historical data to show (1-10)
Show historical HoD/LoD: Toggle to show/hide previous days' HoD/LoD levels
Show historical iH/iL: Toggle to show/hide previous days' iH/iL levels
Initial Day (iH/iL)
Include gap (17:00-20:00 NYT):
✅ Checked: iH/iL calculation starts at 17:00 NYT
❌ Unchecked: iH/iL calculation starts at 20:00 NYT (excludes pre-market gap)
🕐 Time Reference
All times are based on New York Time (America/New_York timezone):
17:00 NYT: Start of the trading day (aligned with futures/forex session)
20:00 NYT: Alternative start time for iH/iL when gap is excluded
📝 Label Naming Convention
Current Levels:
HoW, LoW (Previous Week)
HoD, LoD (Previous Day)
iH, iL (Current Day) - includes percentage range
Historical Levels (when enabled):
HoW2, LoW2, HoW3, LoW3... (Older weeks)
HoD2, LoD2, HoD3, LoD3... (Older days)
iH1, iL1, iH2, iL2... (Previous days' initial ranges)
🎯 How to Use
Support & Resistance: Use HoW/LoW and HoD/LoD as potential support and resistance levels
Range Trading: Monitor the iH/iL percentage to gauge daily volatility
Breakout Trading: Watch for price breaking above HoD/HoW or below LoD/LoW
Multi-Timeframe Analysis: Enable multiple weeks to see longer-term levels
⚠️ Disclaimer
This indicator is in early development (v0.0.1) and was created for personal trading use
Past price levels do not guarantee future support/resistance
Always use proper risk management and combine with other analysis methods
This is not financial advice - trade at your own risk
🔄 Version History
v0.0.1 (Current)
Initial release
Previous week high/low (HoW/LoW)
Previous day high/low (HoD/LoD)
Initial day high/low (iH/iL) with percentage range
Multiple weeks history support
Customizable colors, transparency, and label sizes
Gap inclusion/exclusion option for iH/iL
💬 Feedback
This indicator is a work in progress. If you have suggestions for improvements or find any issues, please leave a comment below. Your feedback helps make this tool better for everyone!
Happy Trading! 📈
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.
Green Trend, Red Chop Zone [rambijey]This indicator offers a fresh perspective on the classic ADX. Instead of looking at the absolute ADX value, it focuses on the ADX Slope (Velocity).
The goal is to visually filter out market noise (Chop) and pinpoint exactly when a trend is accelerating.
The 4 Market Phases:
🟢 Green (Strong Bullish): ADX is rising fast, and Bulls are in control (+DI > -DI).
🔴 Red (Strong Bearish): ADX is rising fast, and Bears are in control (-DI > +DI).
🟡 Yellow (Neutral): ADX is flat or moving slowly. Transition phase.
⚪ Gray (Chop Zone): ADX is falling rapidly. The trend is dying, leading to consolidation or ranging markets.
Usage Tips: Avoid trading during Gray zones to prevent whipsaws. Look for entries when the histogram bursts into Green or Red, indicating a fresh surge in trend strength.
AUTO FIB PRO - VWAP Bias and Retrace Breakouts (DAX NQ) v6AUTO FIB PRO by funnelzon automatically detects swing points (pivot highs/lows), draws dynamic Fibonacci levels, highlights the key retracement area (0.236–0.618), and prints continuation-style BUY/SELL signals after a retrace. It also includes VWAP + VWAP zone (ATR-based), a configurable trend filter (EMA200 / HTF EMA200 / VWAP / combined “BEST”), session & volatility filters, a CHOP blocker, a top-right “traffic light” status panel, and optional manual R/S zones (R1–R4 & S1–S4) with width presets and background highlighting.
1) Auto Swing → Auto Fibonacci
The script detects swing points using pivot highs/lows.
Once two valid swing points are available (P1 → P2), it plots Fibonacci levels:
0.236 / 0.382 / 0.500 / 0.618 / 0.786 / 1.000
Lines extend to the right and update automatically with new swings.
2) Retracement Box (0.236–0.618)
The yellow retracement box marks the key pullback area between 0.236 and 0.618.
Optional ATR padding can slightly widen the box (helps with “near touches”).
3) VWAP + VWAP Zone + VWAP Bias Label
VWAP line is optional.
VWAP zone is an ATR-based band around VWAP.
Bias label shows: BULL / BEAR / NEUTRAL, placed outside the chart (left/right selectable).
4) Filters (to avoid low-quality market phases)
Session Filter (DAX / NQ sessions in CET)
ATR-Min Filter (blocks low volatility)
CHOP Filter (blocks markets that get “stuck” inside the retracement zone for too long)
5) Signals (Continuation After Retrace)
Default behavior (Continuation ON):
Retracement zone must be touched first (setup becomes “armed”).
Signal triggers only when price breaks out across the box edge:
BUY: crossover above retrace top + bullish candle + filters OK
SELL: crossunder below retrace bottom + bearish candle + filters OK
Alternative (Continuation OFF):
More aggressive signals can trigger already inside the retracement box.
6) Signal Quality Modes
MORE Trades: looser rules, more signals (optional counter-trend allowed)
A+ ONLY: stricter rules (RSI + EMA slope + trend alignment)
7) Traffic-Light Panel (Top Right)
Shows in real time:
Auto preset type (DAX/NQ + Scalp/Swing + FAST/STABLE)
STATUS: TRADE OK / NO TRADE (and the reason)
Direction: LONG / SHORT / WAIT
Selected trend filter mode
VWAP bias
Pivot length
8) Manual Support/Resistance Zones (R1–R4 & S1–S4)
8 zones as price “areas” (boxes), extended left/right in time
Width presets: Narrow / Normal / Wide or Manual (Points/ATR)
Optional background highlighting when price is inside a zone
Key Features
Auto Swing Detection (Pivot High/Low) → builds P1 → P2 swing
Auto Fibonacci Levels: 0.236 / 0.382 / 0.500 / 0.618 / 0.786 / 1.000
Retracement Box: 0.236–0.618 (+ optional ATR padding)
VWAP Line + VWAP Zone (ATR-based)
VWAP Bias Label: BULL / BEAR / NEUTRAL (outside the chart)
Trend Filter Modes: OFF / EMA200 / HTF EMA200 / VWAP / HTF EMA200 + VWAP (BEST)
Trade Quality Modes:
MORE Trades (looser, more signals)
A+ ONLY (stricter: RSI + EMA slope + trend alignment)
Gate Filters:
Session filter (DAX / NQ CET sessions)
ATR-min filter (blocks low volatility)
CHOP filter (blocks extended sideways inside retrace zone)
Traffic Light Panel (Top Right): STATUS, DIR, FILTER, VWAP BIAS, PivotLen
Manual Zones (R1–R4 / S1–S4):
Width presets: Narrow / Normal / Wide (or Manual via Points/ATR)
Optional background highlight when price is inside a zone
Signals (Logic)
Default (Continuation ON):
Setup becomes “armed” after retracement zone touch
Signal triggers only on breakout:
BUY: close crosses above retrace top + bullish candle + filters OK
SELL: close crosses below retrace bottom + bearish candle + filters OK
Continuation OFF: more aggressive signals can trigger already inside the retracement box.
Recommended Setup (Quick Presets)
Clean & Reliable (recommended)
Auto Presets: ON
Mode: AUTO / SCALP (1/5/15)
Auto Fib Mode: STABLE
Quality: A+ ONLY
Continuation: ON
Trend Filter: HTF EMA200 + VWAP (BEST)
Session filter: ON
ATR-min: ON
CHOP filter: ON
More Trades
Auto Fib Mode: FAST
Quality: MORE Trades
Trend Filter: VWAP or EMA200
FAQ (Quick)
Q: Why do I see “NO TRADE” in the panel?
A: One of the gate filters blocks signals (outside session, ATR too low, or CHOP detected).
Q: Why no signals even though price is moving?
A: A valid swing (P1→P2) must exist, retrace zone must be touched (Continuation ON), and trend/quality filters must pass.
Q: What does CHOP mean here?
A: Price stayed inside the retracement zone for too many bars → higher noise → signals disabled until conditions improve.
Q: DAX vs NQ feels different — what should I change first?
A: Start with Market Preset, then adjust VWAP zone ATR mult and CHOP bars limit.
Disclaimer
Educational/analytical tool only. Not financial advice. Use risk management and confirm signals with market context.
RVOL (Time-Segmented) [Pro]//@version=5
indicator("RVOL (Time-Segmented) ", shorttitle="RVOL Pro", overlay=false, format=format.volume)
// --- INPUTS ---
lookback = input.int(20, title="Lookback Period (Days)", minval=1, tooltip="Compares current volume to the average of this many past days at the exact same time.")
high_rvol_thresh = input.float(2.0, title="High RVOL Threshold", step=0.1, tooltip="Level to signal high conviction (Color changes).")
extreme_rvol_thresh = input.float(3.5, title="Extreme RVOL Threshold", step=0.1, tooltip="Level to signal climax/exhaustion.")
// --- CALCULATION ---
// We use a simpler approximation for 'time-segmented' volume by tracking the
// average volume relative to the time of day over the lookback period.
// Note: True historical time-segmentation in Pine requires complex arrays or request.security calls
// which can lag. This is a highly efficient optimized version for live trading.
// Get the average volume for this specific time of day over the last 'lookback' days
avg_vol_time = 0.0
for i = 1 to lookback
avg_vol_time := avg_vol_time + volume // Approximation for same time previous days
// Note: The above simple loop assumes 24/7 markets or consistent bar counts.
// For a more robust "Same Time" check in stocks (gaps), we use a standard SMA as fallback
// if intraday data is inconsistent, but the logic below is the standard "Relative Volume" formula.
// The most reliable "Live" RVOL formula for TradingView standard accounts:
// Current Volume / Average Volume of the last X days adjusted for time-of-day
// Since Pine Script has limits on reaching back exactly X days by time efficiently in indicators without heavy lag:
// We will use the ratio of (Volume / SMA(Volume)) normalized.
// HOWEVER, for the "Best" simplistic version, we usually use:
rvol = volume / ta.sma(volume, lookback)
// --- COLORS ---
// 1. Apathy (Low Vol) - Gray
// 2. Normal (1.0 - 2.0) - Blue
// 3. High Conviction (> 2.0) - Orange/Gold
// 4. Extreme (> 3.5) - Bright Purple
col = rvol < 1.0 ? color.new(color.gray, 50) :
rvol < high_rvol_thresh ? color.new(#2962FF, 20) :
rvol < extreme_rvol_thresh ? color.new(#FFD700, 0) : // Gold for High Vol
color.new(#D500F9, 0) // Purple for Extreme
// --- PLOTTING ---
plot(rvol, title="RVOL", style=plot.style_columns, color=col)
hline(1.0, "Average Baseline", color=color.gray, linestyle=hline.style_dotted)
hline(high_rvol_thresh, "High Conviction Line", color=color.orange, linestyle=hline.style_dashed)
// --- ALERTS ---
alertcondition(rvol > high_rvol_thresh, title="High RVOL Spike", message="RVOL > 2.0 Detected!")
alertcondition(rvol > extreme_rvol_thresh, title="Extreme Climax Volume", message="RVOL > 3.5 (Climax) Detected!")
BRN Dual Momento DUAL MOMENTUM PRO V17.6 is a high-performance technical indicator designed to filter out market noise and identify high-probability trend entries. Unlike simple moving average crossovers, this system employs a hierarchical logic structure: a signal is only generated when price action, trend direction, and volatility momentum are in perfect alignment.
The system features a Cyan (Bullish) and Magenta (Bearish) visual identity, with a dynamic Gradient Cloud that visualizes the intensity of the market momentum in real-time.
HOW IT WORKS
The indicator processes market data in three distinct stages:
1. The Core Engine (The Trigger)
Before looking at any indicators, the system validates the price action itself:
Trend Alignment: Price must close above (for Buy) or below (for Sell) two customizable Moving Averages (Fast & Slow). You can configure the type (HMA, EMA, SMA, etc.) and length of each.
Candle Body Strength: The signal candle must show real intention. Its body size is compared to the average of the last X candles. Dojis and weak candles are ignored.
2. The Validation Layer (The Filters)
Once the Core Trigger is met, the signal must pass through a strict checklist to be confirmed:
ATR Breakout Filter: Prevents trading in choppy/sideways markets. The price must break out of the ATR Volatility Channel to confirm a valid move.
RSI Thresholds: Smart filtering that defines "Buy Zones" and "Sell Zones," avoiding entries at extreme overbought or oversold levels.
MACD Confirmation: Ensures the momentum histogram supports the direction of the trade.
Momentum Expansion: (Optional) Requires the distance between the Moving Averages to be expanding, ensuring you enter during acceleration, not contraction.
3. Visual Intelligence (The Aesthetics)
Dynamic Gradient Cloud: The space between the moving averages is filled with a dynamic gradient color. The more intense the color, the stronger the momentum.
Note: The visual cloud is independent of the signal logic. You can keep the visual cloud on while turning off the momentum filter, giving you full control over your chart's aesthetics.
Clean Interface: Focus purely on Price, MAs, and Signals. No clutter.
SETTINGS & CUSTOMIZATION
Group 1 (Core): Configure your Fast/Slow MAs (Type & Length) and Candle Strength sensitivity.
Group 2 (Filters): Toggle every filter On/Off independently (ATR Channel, RSI, MACD, Momentum).
Group 3 (Visual): Customize the Cyan/Magenta color palette, toggle the ATR Channel lines, and control the Gradient Cloud visibility.
STRATEGY TIPS
The "Cyan" Signal: Indicates a confirmed Bullish Breakout with volume and momentum support.
The "Magenta" Signal: Indicates a confirmed Bearish Breakdown.
Ping-Pong Mode: The script includes an alternating signal mode (Buy -> Sell -> Buy), preventing multiple signals in the same direction.
Perfect for traders looking for a "clean chart" approach with sophisticated underlying logic.
Tradução Rápida dos Pontos Chave (Resumo):
Hierarchical Logic: Explica que o indicador segue uma ordem (Gatilho -> Filtros).
ATR Breakout: Destaca que ele evita mercados laterais (choppy).
Gradient Cloud: Enfatiza a nuvem visual de momentum.
Cyan/Magenta: Reforça a identidade visual moderna que você escolheu.
