Viprasol Elite Advanced Pattern Scanner# 🚀 Viprasol Elite Advanced Pattern Scanner
## Overview
The **Viprasol Elite Advanced Pattern Scanner** is a sophisticated technical analysis tool designed to identify high-probability double bottom (DISCOUNT) and double top (PREMIUM) patterns with unprecedented accuracy. Unlike basic pattern detectors, this elite scanner employs an AI-powered quality scoring system to filter out false signals and highlight only the most reliable trading opportunities.
## 🎯 Key Features
### Advanced Pattern Detection
- **DISCOUNT Patterns** (Double Bottoms): Identifies bullish reversal zones where price may bounce
- **PREMIUM Patterns** (Double Tops): Detects bearish reversal zones where price may decline
- Multi-point validation system (5-point structure)
- Symmetry analysis with customizable tolerance
### 🤖 AI Quality Scoring System
Each pattern receives a quality score (0-100) based on:
- **Symmetry Analysis** (32% weight): How closely the two bottoms/tops match
- **Trend Context** (22% weight): Strength of the preceding trend using ADX
- **Volume Profile** (22% weight): Volume confirmation at key points
- **Pattern Depth** (16% weight): Significance of the pattern's price range
- **Structure Quality** (16% weight): Overall pattern formation quality
Quality Grades:
- ⭐ **ELITE** (88-100): Highest probability setups
- ✨ **VERY STRONG** (77-87): Strong trade opportunities
- ✓ **STRONG** (67-76): Valid patterns with good potential
- ○ **VALID** (65-66): Acceptable patterns meeting minimum criteria
### 🎯 Intelligent Target System
Three target modes per pattern direction:
- **Conservative**: 0.618 Fibonacci extension (safer, closer targets)
- **Balanced**: 1.0 extension (moderate risk/reward)
- **Aggressive**: 1.618 extension (higher risk/reward)
Targets automatically adjust based on pattern quality score.
### 🔧 Advanced Filtering Options
- **Volatility Filter (ATR)**: Excludes patterns during extreme volatility
- **Momentum Filter (ADX)**: Ensures sufficient trend strength
- **Liquidity Filter (Volume)**: Confirms adequate trading volume
### 📊 Pattern Lifecycle Management
- Real-time neckline tracking with extension multiplier
- Pattern invalidation after extended wait period
- Breakout/breakdown confirmation
- Reversal detection (pattern failure scenarios)
- Target achievement tracking
### 🌈 Premium Visual System
- Color-coded quality levels
- Cyber-themed color scheme (Neon Green/Hot Pink/Purple/Cyan)
- Transparent fills for pattern zones
- Dynamic labels with pattern information
- Elite dashboard showing live pattern stats
## 📈 How To Use
### Basic Setup
1. Add indicator to your chart
2. Enable desired patterns (DISCOUNT and/or PREMIUM)
3. Adjust quality threshold (default: 65) - higher = fewer but better signals
4. Set your preferred target mode
### Trading DISCOUNT Patterns (Bullish)
1. Wait for pattern detection (labeled points 1-4)
2. Check quality score on dashboard
3. Entry on breakout above neckline (point 5)
4. Stop loss below the lowest bottom
5. Target shown automatically based on your mode
6. ⚠️ Watch for pattern failure (break below bottoms = SHORT signal)
### Trading PREMIUM Patterns (Bearish)
1. Wait for pattern detection (labeled points 1-4)
2. Check quality score on dashboard
3. Entry on breakdown below neckline (point 5)
4. Stop loss above the highest top
5. Target shown automatically based on your mode
6. ⚠️ Watch for pattern failure (break above tops = LONG signal)
## ⚙️ Input Settings Guide
### 🔍 Detection Engine
- **Left/Right Pivots**: Higher = fewer but cleaner patterns (default: 6/4)
- **Min Pattern Width**: Minimum bars between bottoms/tops (default: 12)
- **Symmetry Tolerance**: Max % difference allowed between levels (default: 1.8%)
- **Extension Multiplier**: How long to wait for breakout (default: 2.2x pattern width)
### ⭐ Quality AI
- **Min Quality Score**: Only show patterns above this score (default: 65)
- **Weight Distribution**: Customize what matters most (symmetry/trend/volume/depth/structure)
### 🔧 Filters
- **Volatility Filter**: Avoid choppy markets (recommended: ON)
- **Momentum Filter**: Ensure trend strength (recommended: ON)
- **Liquidity Filter**: Volume confirmation (recommended: ON)
### 💎 Target System
- Choose target aggression for each pattern type and direction
- Higher quality patterns get adjusted targets automatically
## 🎨 Visual Customization
- Adjust colors for DISCOUNT/PREMIUM patterns
- Set quality-based color coding
- Customize label sizes
- Toggle dashboard visibility and position
- Show/hide historical patterns
## 🚨 Alert System
Set up TradingView alerts for:
- 🚀 **LONG Signals**: DISCOUNT breakout, PREMIUM failure
- 📉 **SHORT Signals**: PREMIUM breakdown, DISCOUNT failure
- ✅ **Target Achievement**: When price hits your target
## 💡 Pro Tips
1. **Higher Timeframes = Better Signals**: Patterns on 4H, Daily, Weekly are more reliable
2. **Quality Over Quantity**: Focus on ELITE and VERY STRONG grades
3. **Combine with Trend**: DISCOUNT in uptrend, PREMIUM in downtrend = best results
4. **Watch Pattern Failures**: Failed patterns often provide strong counter-trend signals
5. **Adjust for Your Style**: Intraday traders use Conservative, swing traders use Aggressive
## 🔒 Pattern Invalidation
Patterns become invalid if:
- No breakout/breakdown within extension period
- Support/resistance levels are broken prematurely
- Pattern shown in faded colors = no longer active
## ⚠️ Risk Disclaimer
This indicator is a tool for technical analysis and does not guarantee profitable trades. Always:
- Use proper risk management
- Combine with other analysis methods
- Never risk more than you can afford to lose
- Past performance does not indicate future results
Phân tích Xu hướng
Harmonic Sniper Trigger [Fisher] - PyraTime**Concept: Precision Momentum**
The Harmonic Sniper Trigger is a custom-tuned implementation of the Fisher Transform, designed specifically to identify sharp market reversals with zero lag. Unlike standard moving averages that react slowly to price changes, the Fisher Transform uses Gaussian probability to convert price into a normal distribution, creating clear, sharp turning points.
This indicator serves as the *Trigger* component of the PyraTime system. While Time Cycles tell you *when* to look, this indicator tells you *what* to do.
Key Features
Visual Signal Markers : Prints clear "B" (Buy) and "S" (Sell) labels on the oscillator pane for instant recognition.
Trend Fills : Dynamic Green/Red shading between the signal lines makes it easy to identify trend direction at a glance.
Integrated Alerts: Fully compatible with TradingView alerts, allowing you to be notified the second momentum flips.
How to Use This Indicator
This tool is designed to filter out noise and identify the exact moment a trend reverses.
1. Wait for the Setup: Do not trade every signal. This indicator is most powerful when price is approaching a key support/resistance level or a specific Time Pivot.
2. The Trigger: When the Fisher line crosses the Signal line (changing from Red to Green or vice versa), it confirms that momentum has mathematically shifted.
3. The Execution: Use this crossover as your entry signal *only* if it aligns with your broader market thesis.
Best Practice:
Use this in conjunction with a Time-Cycle indicator (such as the GPM Architecture).
Scenario: Price hits a Vertical Time Line.
Action: Wait for this Fisher indicator to print a "B" or "S".
Result: You enter exactly at the pivot, minimizing drawdown.
Disclaimer: This tool is for technical analysis purposes only. Past performance does not guarantee future results.
Zig Zag & Trendlines with Dynamic Threshold ATRPercentage Zig Zag with Dynamic Threshold
This Pine Script indicator is an advanced Zig Zag tool that identifies and tracks price pivots based on a percentage move required for reversal, offering a clear visual representation of volatility-adjusted trends.
Core Functionality (The Reversal Threshold):
Unlike standard Zig Zag indicators that use a fixed price difference, this indicator calculates the required reversal size (%X) dynamically using the Average True Range (ATR).
It calculates the ATR as a percentage of the current price (ATR%).
The final threshold is this ATR% multiplied by a user-defined factor (default 3x).
This means the reversal threshold is wider during volatile periods and narrower during quiet periods, adapting automatically to market conditions. Users can optionally revert to a fixed percentage if desired.
Trend Extension Lines:
The indicator draws two unique, dynamic trend lines connecting the last two significant Highs and the last two significant Lows. Crucially, these lines do not wait for the entire Zig Zag leg to confirm:
If the price is actively forming a new up-leg, the High Extension Line connects the last confirmed High to the current extreme high of the active move.
The Low Extension Line functions similarly for the downtrend.
This feature allows the user to visualize dynamic support and resistance levels based on the current, active trend structure defined by the percentage threshold.
Order Blocks V5 by GaryIn financial markets, Order Blocks are a powerful Price Action concept representing large-scale buying/selling by institutional investors or major capital at specific price ranges. They often signal potential reversals or trend continuations. However, standard Order Block definitions are often overly broad, generating excessive noise in real-market conditions and leading to misjudgments.
Order Blocks V5 was developed to address this pain point. It integrates complementary technical tools and flexible analysis logic to help you screen high-quality, reliable trading opportunities.
Core Features & Functions:
1. Dual Structure Detection
Swing Order Blocks: Identifies large-scale Order Blocks formed on the primary trend structure based on your custom Swing Length (e.g., 50 bars). These blocks typically indicate significant market turning points.
Internal Order Blocks: Detects smaller-scale Order Blocks within trends using a shorter Internal Structure Length (e.g., 5 bars). This helps capture short-term pullback and reversal opportunities.
2. Complementary Technical Tools for Filtering
Built-in Bollinger Bands (For Reference): The indicator displays Bollinger Bands (customizable length and standard deviation) directly on the chart. While it doesn’t include automatic Bollinger Bands filtering logic, you can use this tool to assess market volatility and overbought/oversold conditions manually. For example, prioritize Internal Order Block signals when price touches or nears the upper/lower bands—adding a discretionary filter to reduce false signals.
Moving Averages for Trend Context: Integrated with 5-period, 10-period, 20-period, 40-period, and 60-period EMAs. Use these to judge the current trend direction:
Focus on bullish Order Block signals when EMAs are in a bullish alignment (uptrend).
Focus on bearish Order Block signals when EMAs are in a bearish alignment (downtrend).
This helps you trade with the trend and filter low-quality counter-trend signals.
3. "Touch & Reversal" Signals
The indicator not only marks Order Blocks but also intelligently monitors price interactions with them. A price retest of an Order Block is inherently noteworthy.
More importantly, when price touches an Order Block and then:
Breaks above the high of the touch bar (for bullish Order Blocks), or
Breaks below the low of the touch bar (for bearish Order Blocks),
The indicator instantly generates prominent labels like B-SOB (Buy - Swing Order Block) or S-IOB (Sell - Internal Order Block) on the chart—signaling a potential reversal confirmed by market action.
4. Highly Customizable
Tailor the indicator to your trading style and instruments with adjustable parameters:
Display colors and transparency for both Order Block types.
Detection lengths for swing and internal structures.
Option to remove Order Block boxes after price breaks.
Sensitivity of Touch & Reversal signals (e.g., max signals per block, minimum bars between touches).
Toggle visibility of Bollinger Bands and individual EMAs.
How to Use Order Blocks V5 to Enhance Your Trading?
1. Identify Trend Direction: Use the built-in EMA system (e.g., 20/40/60 EMA alignment) to determine if the market is in an uptrend, downtrend, or consolidation.
2. Locate Key Zones: Focus on green (bullish) and red (bearish) Order Block boxes automatically drawn on the chart—these are potential support/resistance areas.
3. Apply Discretionary Filtering:
Use Bollinger Bands to gauge volatility: Avoid signals in narrow-range (low-volatility) markets; prioritize signals when price approaches extreme bands.
Combine with trend direction from EMAs to filter 逆势 (counter-trend) signals.
4. Wait for Confirmation:
Conservative Strategy: Enter trades only after price retests an Order Block and triggers a Touch & Reversal signal (e.g., B-... or S-... labels).
Aggressive Strategy: Monitor price when it first touches an Order Block, combining with indicators like RSI or MACD to identify potential entry points.
5. Risk Management: Place stop-loss orders outside the Order Block box to filter false breaks.
Risk Disclaimer: No indicator guarantees 100% win rate. Use this tool as part of your trading system, combine it with other analysis methods, and strictly follow risk management rules.
Download, test it out, and share your feedback in the comments!
EMA Scalp PRO ema cros+heikin-ashi-vol-atr EMA Scalp PRO – indicator is a visual scalping helper designed mainly for crypto pairs on lower timeframes (10–30m). It is NOT an automated trading strategy but a trend and momentum signal tool that helps the user take more disciplined entries.
Core logic:
• Core signals when EMA 9 crosses EMA 21 (bullish or bearish crossover)
• Higher–timeframe trend filter with EMA 144 and optional EMA 200
• Momentum filter with RSI
• Liquidity/volume filter using Volume SMA with a dynamic multiplier
• Directional filter using Heikin Ashi trend (bull / bear)
• Consolidation detection with ATR, EMA distance and ADX, plus a separate breakout condition
• Cooldown bars after each signal to reduce overtrading and noise
The script plots:
• Long / Short signals with labels directly on the chart
• Exit signals when EMA 9 makes a reverse crossover against EMA 21
• An information table (mode, trend, market state, ATR ratio, RSI, volume, etc.) to quickly assess current market conditions
Important:
• This indicator is strictly for educational and informational purposes.
• It does NOT provide financial or investment advice.
• The user must apply their own risk management (position sizing, SL/TP) and always test the tool on historical data or in paper trading before using it in live markets.
Tomie Tèo EMA 9 / 21EMA 9 / 21 Crossover momentum Signal. If retest happens after Crossover show obvious correlation with crossover => Enter
價漲量增 + 力度 + 艾爾德 精簡版這是一套結合三大核心邏輯的多維強勢趨勢偵測系統：
PUVU 價漲量增：確認價格突破是否具備真實量能。
Strength 力度指標：整合 ROC、RSI 斜率、MACD 動能三項數據，轉換為 0–100 的標準化強度分數。
Elder Impulse System：以視覺化 K 棒顏色呈現趨勢動能變化。
此外，本工具加入 Trend Bias 趨勢偏向濾網、極端反手模式、精準信號三角形與可視化面板，
可用於判斷市場是否具備持續性動能、突破是否可信、反轉是否具備條件。
本指標適用於：
趨勢交易
波段突破
盤整突破偵測
高勝率強勢區辨識
多品種分析（加密貨幣、外匯、指數、股票）
此版本可用於觀察趨勢方向、尋找可能的交易機會與賣出時機。
For English users:
This script provides trend analysis, volume confirmation, strength scoring, and impulse-based visualization to assist traders in identifying potential breakouts and market conditions.
Bookmap Style Aggressor Bubbles
This indicator is designed to emulate the visual aesthetic of professional Order Flow software (such as Bookmap) directly within TradingView. It replaces the traditional candlestick view with a clean "Microstructure" Step Line and highlights significant volume events using dynamic "Aggressor Bubbles."
This tool is perfect for traders who practice Order Flow analysis, Scalping, or VSA (Volume Spread Analysis) and want to visualize the relative intensity of buyers and sellers without the noise of traditional wicks and bodies.
1. How it Works
Since TradingView Pine Script operates on OHLCV (Level 1) data, this indicator uses a heuristic model to approximate Order Flow dynamics:
Aggressor Bubbles (Volume Spikes):
The script calculates a Relative Volume (RVOL) metric by comparing the current bar's volume against a 50-period Simple Moving Average (SMA).
If the current volume exceeds a user-defined threshold (e.g., 2.0x the average), a bubble is plotted.
Size: The bubble size scales dynamically based on how massive the volume spike is (Small, Normal, Large, Huge).
Direction (Color): The aggressor side is approximated using the price action of the bar. If Close >= Open, it is treated as Buy Aggression (Green). If Close < Open, it is treated as Sell Aggression (Red).
Microstructure Price Line:
Standard candles can obscure the immediate path of price. This indicator includes a Step Line option that plots the closing price. This mimics the "Last Price" feed seen in DOM-based software, allowing you to see exactly where price held or broke.
2. Features
Smart Filtering: Filters out low-volume noise. You only see bubbles when "Whales" or significant liquidity changes occur.
Visual Customization: Fully adjustable colors for Buy/Sell bubbles and the price line.
Alert System: Includes a built-in alert that triggers whenever a significant Aggressor Bubble appears, allowing you to be notified of high-activity moments instantly.
Clean Aesthetic: Optimized for Dark Mode/Black backgrounds.
3. How to Use
Chart Setup (Important): For the best experience, hide your standard candles. Go to Chart Settings > Symbol and uncheck Body, Borders, and Wick.
Settings: Set your background to Black.
Interpretation:
Breakouts: Look for large bubbles pushing price through a key level. This indicates strong momentum.
Absorptions: Look for large bubbles appearing at the top/bottom of a range without price follow-through. This often suggests a reversal (Passive limit orders absorbing the aggressive market orders).
4. Technical Disclosure & Limitations
Please note that TradingView Pine Script provides access to OHLCV (History) data, not historical Tick-by-Tick or Level 2 (Depth of Market) data. Therefore, this indicator is a simulation. The "Aggressor" side is derived from bar direction, and the bubbles represent executed volume per bar, not individual tick clusters. It is intended for visual analysis and identifying high-volume nodes relative to recent history.
Renko ScalperWhat it is-
A lightweight Renko Scalper that combines Renko brick direction with an internal EMA trend filter and MACD confirmation to signal high-probability short-term entries. EMAs are used internally (hidden from the chart) so the visual remains uncluttered.
Signals-
Buy arrow: Renko direction turns bullish AND EMA trend up AND MACD histogram positive.
Sell arrow: Renko direction turns bearish AND EMA trend down AND MACD histogram negative.
Consecutive same-direction signals are suppressed (only one arrow per direction until opposite signal).
Visuals-
Buy / Sell arrows (large) above/below bars.
Chart background tints green/red after the respective signal for easy glance recognition.
Inputs:-
Renko Box Size (points)
EMA Fast / EMA Slow
MACD fast/slow/signal lengths
How to use-
Add to chart
Use smaller Renko box sizes for scalping, larger for swing-like entries.
Confirm signal with price action and volume—this indicator is a signal generator, not a full automated system.
Use alerts (built in) to receive Buy / Sell arrow notifications.
Alerts-
Buy Arrow — buySignal
Sell Arrow — sellSignal
Buy Background / Sell Background — background-color state alerts
Recommended settings-
Timeframes: 1m–15m for scalping, 5m for balanced intraday.
Symbols: liquid futures/currency pairs/major crypto.
Disclaimer
This script is educational and not financial advice. Backtest and forward test on a demo account before live use. Past performance is not indicative of future results. Use proper risk management.
Traffic Lights - BETA ZONESTraffic Lights - BETA ZONES
Overview
The Traffic Light indicator is a simple, visual tool designed to help traders gauge market bias, trend strength, and momentum at a glance. It displays three rows of colored dots (like a traffic light) in a separate pane below your chart:
• Green: Bullish signal (go/buy bias).
• Red: Bearish signal (stop/sell bias).
• Orange: Neutral or caution (mixed/uncertain conditions).
This indicator combines price action (via EMA positioning), trend direction (via RSI), and momentum expansion (via RSI + MACD histogram) to provide a layered view of the market. When all three rows align as green or red, it generates Buy or Sell labels on the main chart for potential entry signals.
It's non-repainting in its core logic (Row 2 uses delayed RSI comparison to avoid noise), making it reliable for live trading. Best used on trending markets like forex, stocks, or crypto on timeframes from 15M to Daily.
How It Works
The indicator evaluates three independent "rows" of conditions, each represented by a colored dot:
1. Row 1: Price Action Signal (EMA Touch) This row assesses the overall trend bias based on price's position relative to a slow EMA (default: 50-period).
o Green: Price is cleanly above the EMA (bullish bias).
o Red: Price is cleanly below the EMA (bearish bias).
o Orange: Price is "touching" or within a volatility buffer around the EMA (neutral/caution). The "touch zone" is defined by ATR padding, which can be toggled off for a stricter (green/red only) mode.
2. Row 2: Buyers/Sellers Trend (RSI) This row tracks the underlying trend of buyer/seller strength using RSI (default: 14-period on close). To reduce noise and repainting, it uses a delayed comparison (RSI vs. RSI ):
o Green: RSI is rising (buyers gaining strength).
o Red: RSI is falling (sellers gaining strength). No orange here—it's purely directional.
3. Row 3: Buyers/Sellers Signal (RSI + MACD Histogram) This row focuses on momentum expansion, requiring alignment across RSI zones and MACD histogram:
o Green: RSI > 50 (bull zone), MACD hist > 0 (positive), and histogram is expanding upward.
o Red: RSI < 50 (bear zone), MACD hist < 0 (negative), and histogram is expanding downward.
o Orange: Any mismatch (e.g., pullbacks, consolidations, or weak momentum). MACD defaults: Fast=12, Slow=26, Signal=9.
Signals
• Buy Signal: Triggers a "Buy" label below the bar when all three rows turn green for the first time (crossover from non-aligned).
• Sell Signal: Triggers a "Sell" label above the bar when all three rows turn red for the first time. These are conservative signals—use them for trend confirmation or entries in alignment with your strategy. They don't repaint once fired.
Inputs & Customization
All inputs are grouped for easy tweaking:
• Row 1: Price Action Signal
o Slow EMA Length (default: 50): Adjusts the trend baseline.
o EMA Timeframe (default: empty/current): Use a higher timeframe (e.g., "240" for 4H) for multi-timeframe analysis.
o Enable Orange 'Touch' Zone (default: true): Toggle for strict (green/red only) vs. touch mode.
o ATR Length (default: 3): Volatility period for touch padding.
o Touch Padding (ATR mult, default: 0.15): Widens the orange buffer; set to 0 for wick-touch only.
• Row 2: Buyers/Sellers Trend (RSI)
o RSI Length (default: 14): Period for RSI calculation.
o RSI Source (default: close): Change to high/low/open for different sensitivities.
• Row 3: Buyers/Sellers Signal (RSI + MACD hist)
o MACD Fast/Slow/Signal Lengths (defaults: 12/26/9): Standard MACD settings.
Usage Tips
• Trend Trading: Wait for all-green for long entries or all-red for shorts. Use in conjunction with support/resistance.
• Scalping/Intraday: Enable orange touch zone for more nuance in choppy markets; disable for cleaner signals in trends.
• Multi-Timeframe: Set Row 1 EMA to a higher TF for "big picture" bias while keeping others on current.
• Risk Management: Always combine with stop-losses (e.g., below recent lows for buys). Backtest on your asset/timeframe.
• Limitations: In ranging markets, orange dots may dominate—pair with volatility filters like ADX. Not a standalone system; use as a confirmation tool.
If you have feedback or suggestions, drop a comment below! Happy trading 🚦
Gaussian Hidden Markov ModelA Hidden Markov Model (HMM) is a statistical model that assumes an underlying process is a Markov process with unobservable (hidden) states. In the context of financial data analysis, a HMM can be particularly useful because it allows for the modeling of time series data where the state of the market at a given time depends on its state in the previous time period, but these states are not directly observable from the market data. When we say that a state is "unobservable" or "hidden," we mean that the true state of the process generating the observations at any time is not directly visible or measurable. Instead, what is observed is a set of data points that are influenced by these hidden states.
The HMM uses a set of observed data to infer the sequence of hidden states of the model (in our case a model with 3 states and Gaussian emissions). It comprises three main components: the initial probabilities, the state transition probabilities, and the emission probabilities. The initial probabilities describe the likelihood of starting in a particular state. The state transition probabilities describe the likelihood of moving from one state to another, while the emission probabilities (in our case emitted from Gaussian probability density functions, in the image red yellow and green Laplace probability densitty functions) describe the likelihood of the observed data given a particular state.
MODEL FIT
Posterior
By default, the indicator displays the posterior distribution as fitted by training a 3-state Gaussian HMM. The posterior refers to the probability distribution of the hidden states given the observed data. In the case of your Gaussian HMM with three states, the posterior represents the probabilities that the model assigns to each of these three states at each time point, after observing the data. The term "posterior" comes from Bayes' theorem, where it represents the updated belief about the model's states after considering the evidence (the observed data).
In the indicator, the posterior is visualized as the probability of the stock market being in a particular volatility state (high vol, medium vol, low vol) at any given time in the time series. Each day, the probabilities of the three states sum to 1, with the plot showing color-coded bands to reflect these state probabilities over time. It is important to note that the posterior distribution of the model fit tells you about the performance of the model on past data. The model calculates the probabilities of observations for all states by taking into account the relationship between observations and their past and future counterparts in the dataset. This is achieved using the forward-backward algorithm, which enables us to train the HMM.
Conditional Mean
The conditional mean is the expected value of the observed data given the current state of the model. For a Gaussian HMM, this would be the mean of the Gaussian distribution associated with the current state. It’s "conditional" because it depends on the probabilities of the different states the model is in at a given time. This connects back to the posterior probability, which assigns a probability to the model being in a particular state at a given time.
Conditional Standard Deviation Bands
The conditional standard deviation is a measure of the variability of the observed data given the current state of the model. In a Gaussian HMM, each state has its own emission probability, defined by a Gaussian distribution with a specific mean and standard deviation. The standard deviation represents how spread out the data is around the mean for each state. These bands directly relate to the emission probabilities of the HMM, as they describe the likelihood of the observed values given the current state. Narrow bands suggest a lower standard deviation, indicating the model is more confident about the data's expected range when in that state, while wider bands indicate higher uncertainty and variability.
Transition Matrix
The transition matrix in a HMM is a key component that characterizes the model. It's a square matrix representing the probabilities of transitioning from one hidden state to another. Each row of the transition matrix must sum up to 1 since the probabilities of moving from a given state to all possible subsequent states (including staying in the same state) must encompass all possible outcomes.
For example, we can see the following transition probabilities in our model:
Going from state X: to X (0.98), to Y (0.02), to Z (0)
Going from state Y: to X (0.03), to Y (0.96), to Z (0.01)
Going from state Z: to X (0), to Y (0.11), to Z (0.89)
MODEL TEST
When the "Test Out of Sample” option is enabled, the indicator plots models out-of-sample predictions. This is particularly useful for real-time identification of market regimes, ensuring that the model's predictive capability is rigorously tested on unseen data. The indicator displays the out of sample posterior probabilities which are calculated using the forward algorithm. Higher probability for a particular state indicate that the model is predicted a higher likelihood that the market is currently in that state. Evaluating the models performance on unseen data is crucial in understanding how well the model explains data that are not included in its training process.
Hurst Exponent - Detrended Fluctuation AnalysisIn stochastic processes, chaos theory and time series analysis, detrended fluctuation analysis (DFA) is a method for determining the statistical self-affinity of a signal. It is useful for analyzing time series that appear to be long-memory processes and noise.
█ OVERVIEW
We have introduced the concept of Hurst Exponent in our previous open indicator Hurst Exponent (Simple). It is an indicator that measures market state from autocorrelation. However, we apply a more advanced and accurate way to calculate Hurst Exponent rather than simple approximation. Therefore, we recommend using this version of Hurst Exponent over our previous publication going forward. The method we used here is called detrended fluctuation analysis. (For folks that are not interested in the math behind the calculation, feel free to skip to "features" and "how to use" section. However, it is recommended that you read it all to gain a better understanding of the mathematical reasoning).
█ Detrend Fluctuation Analysis
Detrended Fluctuation Analysis was first introduced by by Peng, C.K. (Original Paper) in order to measure the long-range power-law correlations in DNA sequences . DFA measures the scaling-behavior of the second moment-fluctuations, the scaling exponent is a generalization of Hurst exponent.
The traditional way of measuring Hurst exponent is the rescaled range method. However DFA provides the following benefits over the traditional rescaled range method (RS) method:
• Can be applied to non-stationary time series. While asset returns are generally stationary, DFA can measure Hurst more accurately in the instances where they are non-stationary.
• According the the asymptotic distribution value of DFA and RS, the latter usually overestimates Hurst exponent (even after Anis- Llyod correction) resulting in the expected value of RS Hurst being close to 0.54, instead of the 0.5 that it should be. Therefore it's harder to determine the autocorrelation based on the expected value. The expected value is significantly closer to 0.5 making that threshold much more useful, using the DFA method on the Hurst Exponent (HE).
• Lastly, DFA requires lower sample size relative to the RS method. While the RS method generally requires thousands of observations to reduce the variance of HE, DFA only needs a sample size greater than a hundred to accomplish the above mentioned.
█ Calculation
DFA is a modified root-mean-squares (RMS) analysis of a random walk. In short, DFA computes the RMS error of linear fits over progressively larger bins (non-overlapped “boxes” of similar size) of an integrated time series.
Our signal time series is the log returns. First we subtract the mean from the log return to calculate the demeaned returns. Then, we calculate the cumulative sum of demeaned returns resulting in the cumulative sum being mean centered and we can use the DFA method on this. The subtraction of the mean eliminates the “global trend” of the signal. The advantage of applying scaling analysis to the signal profile instead of the signal, allows the original signal to be non-stationary when needed. (For example, this process converts an i.i.d. white noise process into a random walk.)
We slice the cumulative sum into windows of equal space and run linear regression on each window to measure the linear trend. After we conduct each linear regression. We detrend the series by deducting the linear regression line from the cumulative sum in each windows. The fluctuation is the difference between cumulative sum and regression.
We use different windows sizes on the same cumulative sum series. The window sizes scales are log spaced. Eg: powers of 2, 2,4,8,16... This is where the scale free measurements come in, how we measure the fractal nature and self similarity of the time series, as well as how the well smaller scale represent the larger scale.
As the window size decreases, we uses more regression lines to measure the trend. Therefore, the fitness of regression should be better with smaller fluctuation. It allows one to zoom into the “picture” to see the details. The linear regression is like rulers. If you use more rulers to measure the smaller scale details you will get a more precise measurement.
The exponent we are measuring here is to determine the relationship between the window size and fitness of regression (the rate of change). The more complex the time series are the more it will depend on decreasing window sizes (using more linear regression lines to measure). The less complex or the more trend in the time series, it will depend less. The fitness is calculated by the average of root mean square errors (RMS) of regression from each window.
Root mean Square error is calculated by square root of the sum of the difference between cumulative sum and regression. The following chart displays average RMS of different window sizes. As the chart shows, values for smaller window sizes shows more details due to higher complexity of measurements.
The last step is to measure the exponent. In order to measure the power law exponent. We measure the slope on the log-log plot chart. The x axis is the log of the size of windows, the y axis is the log of the average RMS. We run a linear regression through the plotted points. The slope of regression is the exponent. It's easy to see the relationship between RMS and window size on the chart. Larger RMS equals less fitness of the regression. We know the RMS will increase (fitness will decrease) as we increases window size (use less regressions to measure), we focus on the rate of RMS increasing (how fast) as window size increases.
If the slope is < 0.5, It means the rate of of increase in RMS is small when window size increases. Therefore the fit is much better when it's measured by a large number of linear regression lines. So the series is more complex. (Mean reversion, negative autocorrelation).
If the slope is > 0.5, It means the rate of increase in RMS is larger when window sizes increases. Therefore even when window size is large, the larger trend can be measured well by a small number of regression lines. Therefore the series has a trend with positive autocorrelation.
If the slope = 0.5, It means the series follows a random walk.
█ FEATURES
• Sample Size is the lookback period for calculation. Even though DFA requires a lower sample size than RS, a sample size larger > 50 is recommended for accurate measurement.
• When a larger sample size is used (for example = 1000 lookback length), the loading speed may be slower due to a longer calculation. Date Range is used to limit numbers of historical calculation bars. When loading speed is too slow, change the data range "all" into numbers of weeks/days/hours to reduce loading time. (Credit to allanster)
• “show filter” option applies a smoothing moving average to smooth the exponent.
• Log scale is my work around for dynamic log space scaling. Traditionally the smallest log space for bars is power of 2. It requires at least 10 points for an accurate regression, resulting in the minimum lookback to be 1024. I made some changes to round the fractional log space into integer bars requiring the said log space to be less than 2.
• For a more accurate calculation a larger "Base Scale" and "Max Scale" should be selected. However, when the sample size is small, a larger value would cause issues. Therefore, a general rule to be followed is: A larger "Base Scale" and "Max Scale" should be selected for a larger the sample size. It is recommended for the user to try and choose a larger scale if increasing the value doesn't cause issues.
The following chart shows the change in value using various scales. As shown, sometimes increasing the value makes the value itself messy and overshoot.
When using the lowest scale (4,2), the value seems stable. When we increase the scale to (8,2), the value is still alright. However, when we increase it to (8,4), it begins to look messy. And when we increase it to (16,4), it starts overshooting. Therefore, (8,2) seems to be optimal for our use.
█ How to Use
Similar to Hurst Exponent (Simple). 0.5 is a level for determine long term memory.
• In the efficient market hypothesis, market follows a random walk and Hurst exponent should be 0.5. When Hurst Exponent is significantly different from 0.5, the market is inefficient.
• When Hurst Exponent is > 0.5. Positive Autocorrelation. Market is Trending. Positive returns tend to be followed by positive returns and vice versa.
• Hurst Exponent is < 0.5. Negative Autocorrelation. Market is Mean reverting. Positive returns trends to follow by negative return and vice versa.
However, we can't really tell if the Hurst exponent value is generated by random chance by only looking at the 0.5 level. Even if we measure a pure random walk, the Hurst Exponent will never be exactly 0.5, it will be close like 0.506 but not equal to 0.5. That's why we need a level to tell us if Hurst Exponent is significant.
So we also computed the 95% confidence interval according to Monte Carlo simulation. The confidence level adjusts itself by sample size. When Hurst Exponent is above the top or below the bottom confidence level, the value of Hurst exponent has statistical significance. The efficient market hypothesis is rejected and market has significant inefficiency.
The state of market is painted in different color as the following chart shows. The users can also tell the state from the table displayed on the right.
An important point is that Hurst Value only represents the market state according to the past value measurement. Which means it only tells you the market state now and in the past. If Hurst Exponent on sample size 100 shows significant trend, it means according to the past 100 bars, the market is trending significantly. It doesn't mean the market will continue to trend. It's not forecasting market state in the future.
However, this is also another way to use it. The market is not always random and it is not always inefficient, the state switches around from time to time. But there's one pattern, when the market stays inefficient for too long, the market participants see this and will try to take advantage of it. Therefore, the inefficiency will be traded away. That's why Hurst exponent won't stay in significant trend or mean reversion too long. When it's significant the market participants see that as well and the market adjusts itself back to normal.
The Hurst Exponent can be used as a mean reverting oscillator itself. In a liquid market, the value tends to return back inside the confidence interval after significant moves(In smaller markets, it could stay inefficient for a long time). So when Hurst Exponent shows significant values, the market has just entered significant trend or mean reversion state. However, when it stays outside of confidence interval for too long, it would suggest the market might be closer to the end of trend or mean reversion instead.
Larger sample size makes the Hurst Exponent Statistics more reliable. Therefore, if the user want to know if long term memory exist in general on the selected ticker, they can use a large sample size and maximize the log scale. Eg: 1024 sample size, scale (16,4).
Following Chart is Bitcoin on Daily timeframe with 1024 lookback. It suggests the market for bitcoin tends to have long term memory in general. It generally has significant trend and is more inefficient at it's early stage.
Chandelier Exit + Pivots + MA + Swing High/LowIt combines four indicators.
For use in the Hero course.
Advanced S&D Engine | ZikZak-Trader30About This Script
This is a fully custom-built Supply & Demand Zone detection engine for TradingView written by ZikZak-Trader30 (Kotdwar, UK). The script identifies potential key supply and demand zones based on market structure and pattern logic widely used by professional traders.
Detected Patterns:
RBR (Rally-Base-Rally, demand)
DBD (Drop-Base-Drop, supply)
RBD (Rally-Base-Drop, supply)
DBR (Drop-Base-Rally, demand)
Features Highlight
Detailed configurable zone filtering (freshness, gap detection, time spent, width, Fibonacci confluence, etc.)
Fair and adjustable scoring system for zone strength
Automatic management/removal of old or retested/violated zones
Optional Fibonacci level confluence and dynamic labeling
Transparency Statement
How It Works:
This script uses well-known price action concepts and compares candles’ movement, consolidation, and breakout patterns to mark S&D zones.
There are no repaints or future leaks: all logic is based entirely on historical and current bars.
Parameters and variables are fully described in the script inputs. The zone scoring and removal logic is also visible in the code for transparency.
IMPORTANT: Usage & Fair-Use Policy
This script is provided for educational and informational purposes only.
It should not be considered as financial advice or a trading signal.
Trading/investing involves risk—always do your own research or consult a financial advisor before making trading decisions.
Past performance or backtest results are not necessarily indicative of future results.
License & Fair Use
The code is original, written by ZikZak-Trader30.
All logic and comments are visible for users to study, adapt, or improve for personal, non-commercial use within TradingView.
You may NOT resell, repackage, or repost this script as your own.
If you fork or publicly remix/adapt the script, please credit "ZikZak-Trader30" and do not remove this disclosure section.
If you use ideas or snippets, kindly reference this script and author.
Absolutely NO plagiarized or resold code is permitted. This script is not for re-sale.
Acknowledgements
This indicator was inspired by years of price action study and usage of public S&D scripts. While the pattern logic is classic in nature, the version and scoring are original.
No proprietary datasets or paid logic from other sources are included.
Minor ideas on zone freshness and Fibonacci blending are common in the TradingView S&D community and have been custom-implemented here.
Swing v 3Swing v.3 Indicator Description
Swing v.3 is an advanced swing analysis indicator with deep liquidity and volume analysis, designed to identify institutional movements and high-probability reversal points:
Key Components:
🎯 Swing Points Detection:
Intelligent detection of swing highs and lows (SH/SL)
Proper sequencing of peaks and valleys (prevents duplicates)
Identifies strong swings (★) based on high volume
Automatic support and resistance level mapping
📊 Delta Volume Analysis:
Calculates buying/selling pressure for each candle
Identifies strong swings based on Delta threshold
Filters by positive buying or negative selling pressure
Displays detailed liquidity ratios (buy/sell volumes)
⚡ Displacement Candles:
Detects powerful momentum candles with rapid price movement
Multiple conditions: large body, small wicks, high volume
ATR filter to measure strength relative to volatility
Color-codes candles by strength rating
🔍 Wave Analysis:
Tracks waves between swing points
Calculates cumulative buy/sell volume per wave
Detects bullish/bearish divergence patterns
Alerts for fake breakouts and strong accumulation
📊 Live Dashboard:
Real-time statistics for swings and liquidity
Measures price proximity to support/resistance levels
Current Delta information and active wave data
Proximity alerts for nearby key levels
⚙️ Additional Features:
Color-codes candles for strong swing points
Multiple filters for precision (Delta, volume, ATR)
Detailed tooltips for each marker
Flexible color and display settings
The indicator helps traders identify strong reversal points, institutional liquidity zones, and high-momentum candles for more accurate trading decisions.
وصف مؤشر Swing v.3
Swing v.3 هو مؤشر متقدم لتحليل نقاط التأرجح (السوينق) والزخم السعري مع تحليل عميق للسيولة وحجم التداول:
المكونات الرئيسية:
🎯 نقاط السوينق (Swing Points):
كشف نقاط التأرجح العليا والسفلى (SH/SL) بطريقة ذكية
ترتيب صحيح للقمم والقيعان (يمنع التكرار)
تحديد السوينقات القوية (★) بناءً على حجم التداول العالي
رسم مستويات الدعم والمقاومة تلقائياً
📊 تحليل Delta Volume:
حساب ضغط الشراء/البيع لكل شمعة
تحديد السوينقات القوية بناءً على Delta
فلترة حسب ضغط الشراء الإيجابي أو البيع السلبي
عرض نسب السيولة التفصيلية (شراء/بيع)
⚡ شموع Displacement (الإزاحة السريعة):
كشف الشموع القوية ذات الحركة السريعة
شروط متعددة: جسم كبير، ذيول صغيرة، حجم تداول عالي
فلتر ATR لقياس القوة نسبة للتقلبات
تلوين الشموع حسب قوتها
🔍 تحليل الموجات (Wave Analysis):
تتبع الموجات بين السوينقات
حساب إجمالي حجم الشراء/البيع لكل موجة
كشف التباين الإيجابي/السلبي (Divergence)
تنبيهات الاختراق الوهمي والتجميع القوي
📊 لوحة المعلومات (Dashboard):
عرض إحصائيات حية للسوينقات والسيولة
قياس قرب السعر من مستويات الدعم/المقاومة
معلومات Delta الحالية والموجة النشطة
تنبيهات للمستويات القريبة
⚙️ المميزات الإضافية:
تلوين الشموع للسوينقات القوية
فلاتر متعددة للدقة (Delta، حجم التداول، ATR)
معلومات تفصيلية في Tooltips لكل علامة
إعدادات مرنة للألوان والعرض
Smart Money ProSmart Money Pro V 8.1 is an advanced trading indicator that tracks institutional "smart money" movements using multiple Smart Money Concepts (SMC) techniques:
Market Structure: Identifies Change of Character (CHoCH), Break of Structure (BOS), and Internal/External Market Structure (IDM)
Order Blocks: Detects demand/supply zones including EXT OB, IDM OB, SCOB, and mitigation/breaker blocks
Order Flow: Tracks major and minor order flows with mitigation levels
Fair Value Gaps (FVG): Highlights price inefficiencies and imbalance zones
Liquidity Levels: Maps liquidity sweeps and key pivot levels
Price Structure: Shows OTE (Optimal Trade Entry) zones, PDH/PDL (Previous Day High/Low), equilibrium levels, and swing sweeps
Candle Patterns: Detects Inside and Outside bars
The indicator helps traders identify institutional entry/exit points, liquidity grabs, and high-probability trading zones.
Smart Money Pro V 8.1 هو مؤشر متقدم لتتبع تحركات المؤسسات المالية "الأموال الذكية" باستخدام مفاهيم Smart Money Concepts (SMC):
هيكل السوق: يحدد تغيير الاتجاه (CHoCH)، كسر الهيكل (BOS)، والهيكل الداخلي/الخارجي (IDM)
مناطق الطلب والعرض: يكتشف Order Blocks بأنواعها (EXT OB, IDM OB, SCOB) ومناطق الاختراق والتخفيف
تدفق الأوامر: يتتبع التدفقات الرئيسية والثانوية مع مستويات التخفيف
فجوات القيمة العادلة (FVG): يبرز مناطق عدم الكفاءة السعرية وعدم التوازن
مستويات السيولة: يرسم مصائد السيولة والنقاط المحورية الرئيسية
هيكل السعر: يعرض مناطق OTE (نقاط الدخول المثلى)، أعلى/أدنى سعر سابق (PDH/PDL)، مستويات التوازن، وكسر القمم/القيعان
أنماط الشموع: يكتشف شموع Inside و Outside Bar
Trend Pullback S-MSNRThis Indicator Identify two Major Time Frames for Trend Selection and Pullback.
NY time 10:00 AM to 10:15 AM zone will decide for trend.
NY time 10:30 AM to 11:30 AM zone will Pullback and Follow the Previous Trend.
Use S-MSNR Strategy for these two time Zone.
Quantum Trend MatrixThe Quantum Trend Matrix (QTM) is a comprehensive technical analysis suite designed to solve the problem of market noise by combining Statistical Volatility Structure with Momentum Trend Filtration.
Many traders struggle because they trade momentum signals (like crossovers) without considering the daily structural limits of the market. This script integrates these two concepts into a single "Roadmap" to help traders align their entries with institutional price structure.
🎯 Concept & Methodology (How it Works)
This script is not merely a collection of indicators; it is a logic-based system where components effectively filter one another:
1. Structural Volatility Levels (The "Map")
Unlike standard Support/Resistance which is subjective, QTM calculates objective levels based on the internal logic.
Methodology: The script applies specific percentage-based volatility coefficients (tailored to the asset class, e.g., Indices ,Commodities,etc) to the Price.
* The Green Line (Breakout Level) : Represents the statistical upper volatility limit above which a "Bullish Expansion" is expected to occur.
* The Red Line (Breakdown Level): Represents the statistical lower volatility limit Below which a "Bearish Expansion" is expected to occur.
* Why this is useful: It prevents traders from chasing trends in the "chop zone" (between the lines) and highlights high-probability breakout areas.
2. The Value Zone (Trend Validation)
* Methodology: This utilizes a High-Timeframe moving average ribbon logic (calculated using Daily data).
* Function: It acts as a dynamic trend filter. A breakout signal (Green Line cross) is statistically significant if the Price is also supported by the Value Zone (Blue Ribbon). If the Ribbon is Orange, a bullish breakout is likely a "False Trap".
3. Momentum & Exhaustion Logic
* Crossovers (Circles): Validates short-term trend shifts using smoothed exponential average crossovers.
* Mean Reversion (Diamonds): Uses an integrated Oscillator Momentum logic to detect over-extended price action. A Diamond signal warns that the price has deviated too far from the mean (VWAP) and trend continuation is risky.
🛠️ Practical Application
This script is designed for a top-down decision process:
1. Wait for Structure: For Trending Moves do not trade inside the Pivot (Blue) to Breakout (Green/Red) range. This is the "Noise" zone.
2. Confirm the Breakout: Wait for a candle to CLOSE outside the Green or Red volatility levels or to take Support/Resistance from Red/Green Levels respectively.
3. Check the "Value Zone": Ensure the background ribbon color matches the breakout direction (Blue for Long, Orange for Short).
4. Monitor Health: Use the bottom-right panel (displaying RSI, ADX, and DI metrics) to ensure trend strength is sufficient to sustain the move.
⚠️ Disclaimer & Risk Disclosure
* Logic Disclosure: While the specific volatility coefficients and smoothing lengths are proprietary, this script relies on standard technical analysis concepts including Moving Averages, RSI, ADX, and Percentage-based levels relative to the Price.
* No Guarantee: Technical analysis is probabilistic, not predictive. Past performance does not guarantee future results.
* Risk Management: Always use Stop Losses. This tool is an aid for analysis, not a replacement for risk management.
🔒 Access Information
This is a proprietary Invite-Only script.
*(Note: Do not ask for access in the comments below. Please refer to the author's signature or profile for more information).*
BankNifty Aggregate Weighted OBVDescription-
This indicator calculates the aggregate On Balance Volume (OBV) of the entire Bank Nifty Index by analyzing its 12 individual constituents rather than the index futures volume.
Why is this different?
Standard OBV on the Bank Nifty Index usually analyzes the volume of the Index Futures or the raw index volume (which can be inaccurate or derivative-heavy). This script queries the real-time volume and price action of the 12 specific banks that make up the index (HDFC, ICICI, SBI, Axis, Kotak, etc.).
How it works-
Weighted Calculation:- It calculates the Net Flow (Volume * Weightage) for every single bank for the current bar.
Aggregation:- It sums the Net Flow of all 12 banks to create a "Total Sector Flow."
Accumulation:- It generates the OBV line based on this aggregated sector flow.
Normalization:- Unlike simple summation scripts, this calculates flow per bar before accumulating, ensuring that stocks with longer trading histories do not skew the data.
Features:
Customizable Weights:- Users can adjust the weightage of each bank if NSE rebalances the index.
Toggle Constituents:- You can turn specific banks on/off to see their impact.
Signal Line:- Includes an SMA/EMA signal line to help identify volume trend reversals.
Trend Coloring:- The fill color changes (Green/Red) based on the OBV's position relative to the signal line.
How to use:
Trend Confirmation: If Bank Nifty price is rising but this Weighted OBV is falling, it indicates a divergence and potential weakness in the move (lack of institutional participation).
Breakouts: Use the Signal Line crossover to validate breakout moves.
Alper-EMAAlper-EMA
Description:
This indicator allows you to display 5 customizable EMAs (Exponential Moving Averages) on a single chart. Each EMA can be configured independently with length, color, visibility, and calculation timeframe.
Features:
5 fully customizable EMAs
Set individual length and color for each EMA
Toggle visibility for each EMA
Multi-timeframe calculation: e.g., display EMA300 calculated on a 30-minute timeframe while viewing a 1-minute chart
Labels display EMA period and timeframe for clarity
Adjustable label size: tiny / small / normal / large
Clear and readable plot lines
Use Cases:
Monitor multiple timeframe EMAs simultaneously
Analyze trend and support/resistance levels
Track EMA crossovers for strategy development
Note:
This indicator is suitable for both short-term (scalping) and medium-to-long term analysis. The multi-timeframe feature allows you to see different EMA perspectives on a single chart quickly.
DTR Volume TrendDTR Volume Trend is a volume-based oscillator designed to measure trend strength, momentum shifts, and mean-reversion opportunities using volume-weighted price data. The indicator analyzes recent volume profiles, VWAP deviation, and smoothed signals to create a responsive oscillator that adapts to market conditions.
Key Features:
- Volume-weighted oscillator based on VWAP and volume distribution.
- Mean reversion mode to detect when price deviates strongly from its volume-weighted average.
- Adaptive midline that adjusts automatically to recent oscillator behavior.
- Bull and bear zones that highlight potential exhaustion or reversal areas.
- Fast and slow signal lines to show momentum changes through crossovers.
- Optional bar coloring to highlight bullish or bearish conditions on the chart.
How to Use:
- When the oscillator is above the midline, momentum tends to be bullish.
- When it is below the midline, momentum tends to be bearish.
- Upper zones may indicate overbought or exhaustion levels.
- Lower zones may indicate oversold or accumulation levels.
- Crossovers between fast and slow signals can highlight early trend or momentum shifts.
Best For:
- Trend confirmation
- Mean-reversion strategies
- Identifying momentum changes
- Spotting volume-driven extremes
KVS-Ultimate FVG & iFVG System [MTF + Distance Filter]Description: This indicator identifies Fair Value Gaps (FVG) and Inversion FVGs (iFVG) across multiple timeframes (MTF) with an advanced visualization system. Unlike standard FVG indicators, this script solves the "chart clutter" problem with a unique Distance Filter and offers a customizable Split Label System.
Key Features:
1. Unique Distance Filter (Clean Screen Mode):
When enabled, the script only shows the closest FVGs to the current price within a user-defined limit.
Keeps your chart clean while focusing on relevant price action levels.
2. Split Label System (Tabular Design):
Completely customizable label positioning, sizing, and coloring.
Separate controls for Normal FVGs and iFVGs.
Smart Label Logic: If you hide the FVG box, its label automatically hides. If an FVG breaks and becomes an iFVG (or fades), the label logic switches automatically to the iFVG settings.
3. Strict Mode Filtering:
Enabled: Checks if the candle closing price effectively breaks the previous structure (High/Low of the 1st candle), ensuring high-quality gaps.
Disabled: Detects all gaps between wicks (Standard calculation).
4. Multi-Timeframe (MTF) Support:
Monitor FVGs from up to 5 different timeframes simultaneously on a single chart.
5. Dynamic Interaction:
Choose how the script reacts when an FVG is broken: Turn it into an iFVG (Inversion) or simply fade the color (Ghost/Fade mode).
How to Use:
Use the "Distance Filter" checkbox in settings to clean up old/far blocks.
Adjust "TF1" to "TF5" to set up your multi-timeframe analysis.
Customize the Label Panel to align text perfectly with your chart style.
Disclaimer: This tool is for educational purposes and support for technical analysis.
