ZynIQ Premium/Discount Master v2 - (Lite Pack)Overview
ZynIQ Premium-Discount Master v2 (Lite) is a simplified tool designed to highlight premium/discount zones relative to short-term market structure. It helps traders see when price is stretched above or below fair value, using volatility-adjusted logic suitable for intraday and swing trading.
Key Features
• Automated premium/discount classification
• Volatility-aware thresholds for mild and strong stretch conditions
• Clear visual cues for overbought/oversold environments
• Direction-aware structure to complement trend and momentum tools
• Clean labels marking stretch transitions
• Lightweight visuals suitable for fast charting workflows
Use Cases
• Identifying premium and discount zones for entries or exits
• Assessing when price has deviated significantly from equilibrium
• Combining with breakout or VWAP tools for structured confluence
• Improving trade timing with stretch-based context
Notes
This tool provides premium/discount structure and stretch context. It is not a standalone trading system. Use it along with your own confirmation and risk management rules.
波動率
ZynIQ Breakout Essentials + Risk v1Overview
ZynIQ Breakout Essentials + Risk v1 is a breakout-based tool designed for traders who want a clean, rules-driven framework for identifying consolidation zones, breakout levels, and structured stop/target planning. It highlights key areas where price may transition from compression into momentum.
Key Components
• Breakout Range Detection – Identifies consolidation zones using a configurable lookback window and optional candle-range filters.
• Breakout Levels – Plots upper/lower boundaries that define potential breakout points.
• Signal Spacing Filters – Helps reduce noisy or clustered breakout triggers.
• Risk Helper – Provides both %-based and ATR-based structure for stop loss and take profit planning.
Use Cases
• Spotting breakouts from tight ranges.
• Structuring consistent entries for intraday or swing setups.
• Planning stops and targets with volatility-adjusted levels.
Notes
This tool provides structure for assessing breakout conditions and planning trade levels. It is not a standalone trading system. Use alongside your own confirmation and risk management.
Market Dynamics 3D Surface [MACD × ΔVol × Z-Score]OANDA:XAUUSD
Mean Reversion Trading
🌐 3D Market Dynamics Surface
3D Axes
X-Axis: MACD (Momentum)
Y-Axis: Delta Volume (Buy/Sell Pressure)
Z-Axis: Price Z-Score (Standardized Price)
Concept
The 3D surface illustrates the relationship between:
MACD → Momentum Force (Trend strength)
Delta Volume → Buy/Sell Pressure (Buying/Selling pressure)
Z-Score → Price relative to the mean (Overbought/Oversold)
Current Position (●)
On the Peak (Red) → Watch out for reversal
In the Valley (Blue) → Potential for rebound
In the Middle (Green/Yellow) → Neutral
📊 Price Prediction Panel
Market Regime Detection
🔴 Extreme Overbought (Z > 1.5)→ STRONG SELL
🟠 Overbought (Z > 1.0) → Consider Sell
⚪ Neutral (-1.0 < Z < 1.0) → Hold/Wait
🟢 Oversold (Z < -1.0) → Consider Buy
🔵 Extreme Oversold (Z < -1.5) → **STRONG BUY
Technical Alignment
Aligned Bullish: MACD + DeltaVol + → Strong uptrend
Aligned Bearish: MACD - DeltaVol - → Strong downtrend
⚠️ Divergence: MACD + DeltaVol - or vice versa → Weak signal
Target Price Calculation
Mean Reversion Target: MA pm (sigma times factor)
Shown as % from the current price.
Draw 🎯 Target line on the chart.
Distance Metrics
Calculate the distance from the current position to:
Distance to MAX → Closer = More Danger (overbought)
Distance to MIN → Closer = More Opportunity (oversold)
📍 Enhanced Stats Table
Display complete data:
Current State: Current MACD,Delta$Vol, Z-Score
Extremums: MAX/MIN values along with their corresponding MACD positions
Ranges: Value ranges for all 3 indicators
Usage
Scenario 1: Extreme Overbought
-Current Z-Score: 2.1
-Regime: 🔴 Extreme Overbought
-Signal: ⚠️ STRONG SELL
-Target: Price reverts to MA
-Action: Sell / Take Profit
Scenario 2: Oversold + Aligned Bullish
-Current Z-Score: -1.3
-MACD: Bullish (+)
-DeltaVol: Buy Pressure (+)
-Alignment: ✅ Aligned Bullish
-Signal: 📈 Consider BUY
-Target: MA - 0.5 sigma → MA
-Action:Buy / Long entry
Scenario 3: Divergence Warning
-MACD: Bullish (+)
-DeltaVol: Sell Pressure (-)
-Alignment: ⚠️ Divergence
-Signal:Caution! Weak momentum
-Action:Wait for confirmation
Key Insights
-Max Point on Surface → When MACD + DeltaVol are at that level, the price is often overbought.
-Min Point on Surface → When MACD + DeltaVol are at that level, the price is often oversold.
-Current Position → See how close it is to MAX/MIN.
-Target Price → Calculated from Mean Reversion (return to MA).
There will be 3 labels on the 3D surface:
🔴 MAX - Danger Point
🔵 MIN - Opportunity Point
● NOW - Current Position
🔴 MAX Point
Highest Z-Score occurs when:
MACD (Histogram) = X (positive/negative)
DeltaVolume = Y
→ Indicates that when momentum + volume pressure are at this level, the price tends to be overbought.
🔵 MIN Point
Lowest Z-Score occurs when:
MACD (Histogram) = X (positive/negative)
DeltaVolume = Y
→ Indicates that when momentum + volume pressure are at this level, the price tends to be oversold.
Key Levels, Liquidity Zones & CC liteSyntropy Liquidity & Key Levels Pro — All-in-One Institutional Toolkit
The ultimate clean confluence tool used by serious ICT/SMC traders worldwide.
This single indicator combines three legendary components into one flawless, non-repainting dashboard:
1. Key Institutional Levels
• Monday Range (High / Low / Mid)
• Current & Previous Weekly Open + Range
• Current & Previous Monthly Open + Range
• Quarterly Open + Range
• Smart level merging (no duplicate lines)
• Right-anchored or standard display
• Fully customizable colors, styles & shorthand labels
2. Advanced Liquidity Zones
• Volume-strength filtered swing highs/lows
• Dynamic or fixed ATR-based liquidity pools
• Real-time "Liquidity Grab" detection with visual markers
• Clean boxes + extension lines
• Small dashboard showing current mode & zone count
3. 9 Logic – Clean Version)
• Classic 6–9 countdowns with modern styling
• Standard & Aggressive 13 signals
• Customizable shapes (labels, arrows, triangles, etc.)
• Buy signals marked with green check, Sell with red cross (clean & intuitive)
Why thousands of traders trust this version:
• Zero repainting – 100% reliable structure
• Institutional-grade clean aesthetics
• Works perfectly on Crypto, Forex, Stocks, Futures
• No lag, no clutter, maximum clarity
• All features fully customizable
This is not just another multi-tool.
This is the final confluence indicator most professional traders keep on their main chart 24/7.
Add to favorites. You won’t trade without it again.
Enjoy the edge,
Syntropy Labs
Normal Dist Deviation LevelsThis indicator shows where the current price sits within a normal-distribution “sigma” framework and projects those levels as short, local reference lines rather than full trailing bands.
It first calculates a moving average (SMA or EMA, user-selectable) over a chosen lookback length and the corresponding standard deviation of price around that mean. The mean is treated as the 0σ level, and fixed price levels are computed at ±1σ, ±2σ, and ±3σ from that mean for the most recent bar.
For each of these sigma prices, the script draws a short horizontal segment that spans only a limited number of candles into the past and into the future, giving clean local “price bars” instead of bands across the entire chart. The colors and line styles differentiate 0σ (blue), ±1σ (solid), ±2σ (dashed), and ±3σ (dotted), visually marking moderate to extreme deviations from the mean.
To make interpretation easier, the indicator also places text labels to the right of the price bars, a couple of candles ahead of the line ends. Each label shows both the statistical region and its approximate normal-distribution probability, such as “50% (0σ)”, “15.87% (+1σ / -1σ)”, “2.27% (+2σ / -2σ)”, and “0.14% (+3σ / -3σ)”, so you can quickly see how unusual the current deviation is in probabilistic terms.
ATR Pro Trend System This is the same core principle used by Turtle Traders in the 80s, the major CTA funds, and almost all successful retail system traders for the last 15 years – only more attractively packaged and equipped with the best volatility filter. That's why it performs so extremely consistently across all markets and timeframes (Bitcoin, S&P 500, DAX, Gold, Forex… it doesn't matter). You are currently trading one of the cleanest and most profitable public ATR/SuperTrend systems available in 2025 – and it's based on the exact two building blocks that worked 40-50 years ago.
Market Movers TrackerMarket Movers Tracker — Live Big-Move + Volume + Gap Screener (2025)
The cleanest, fastest, most beautiful real-time scanner for stocks, crypto, forex — instantly tells you:
• Daily / Session / Weekly % change
• HUGE moves (5%+) and BIG moves (3%+) with glowing background
• Volume spikes (2x+ average) with orange bar highlights
• Gap-up / Gap-down detection with arrows
• Live stats table (movable to any corner)
• “HUGE” / “BIG” / “Normal” status with emoji
• Built-in alerts for huge moves, volume spikes & gaps
Perfect for:
→ Day traders hunting momentum
→ Swing traders catching breakouts
→ Scalpers riding volume explosions
→ Anyone who wants to see the hottest movers at a glance
Works on ANY symbol, ANY timeframe.
Zero lag. Zero repainting. Pure price + volume truth.
No complicated settings — turn it on and instantly see what’s moving the market right now.
Not financial advice. Just the sharpest scanner on TradingView.
Made with love for the degens, apes, and momentum chads & volume junkies.
CS Institutional X-Ray (Perfect Sync)Title: CS Institutional X-Ray
Description:
CS Institutional X-Ray is an advanced Order Flow and Market Structure suite designed to reveal what happens inside Japanese candles.
Most traders only see open and close prices. This indicator utilizes VSA (Volume Spread Analysis) algorithms and Synthetic Footprint Logic to detect institutional intervention, liquidity manipulation, and market exhaustion.
🧠 1. The Mathematical Engine: Synthetic Footprint
The core of this indicator is not based on moving average crossovers, but on market physics: Effort vs. Result.
The script scans every candle and calculates:
Buy/Sell Pressure: Analyzes the close position relative to the total candle range and weights it by volume.
Synthetic Delta: Calculates the net difference between buyer and seller aggression.
Volume Anomalies: Detects when volume is abnormally high (Institutional) or low (Retail).
The Absorption Logic: The indicator hunts for divergences between candle color and internal flow.
Example: If price drops hard (Red Candle) with massive volume, but the close moves away from the low, the algorithm detects that massive LIMIT orders absorbed the selling pressure. Result: Institutional Buy Signal.
📊 2. The Institutional Semaphore (Visual Guide)
The indicator automatically recolors candles to show the real state of the auction:
🔵 CYAN (Whale Buy): Bullish Absorption. Institutions buying aggressively or absorbing selling pressure at support.
🟣 MAGENTA (Whale Sell): Bearish Absorption. Institutions selling into strength or stopping a rally with sell walls.
⚪ GREY (Exhaustion/Zombie): "No-Trade" Zone. Volume is extremely low. The movement lacks institutional backing and is prone to failure.
🟢/🔴 Normal: Market in equilibrium.
🛡️ 3. Smart Zone System (Market Memory)
The indicator draws and manages Support and Resistance levels based on volume events, not just pivots.
Virgin Zones (Bright): When a "Whale" appears, a solid line is projected. If price has not touched it again, it is a high-probability bounce zone.
Automatic Mitigation: The exact moment price touches a line, the indicator detects the mitigation. The line turns Grey and Dotted, and the label dims. This keeps the chart clean, showing only what is relevant now.
☠️ 4. Manipulation Detector (Liquidity Grabs)
The system distinguishes between a normal reversal and a "Stop Hunt".
Signal: ☠️ GRAB
Logic: If price breaks a previous Low/High to sweep liquidity and closes with an absorption candle (Whale), it is marked as a "Grab." This is the system's most powerful reversal signal.
🧱 5. FVG with Liquidity Score
The indicator draws Fair Value Gaps (Imbalances) and assigns them a volume score.
"Vol: 3.0x": Indicates that the gap was created with 3 times the average volume, making it a much stronger price magnet than a standard FVG.
🚀 How to Trade with CS Institutional X-Ray
Identify the Footprint: Wait for a Cyan or Magenta candle to appear.
Validate the Trap: If the signal comes with a "☠️ GRAB" label, the probability of success increases drastically.
The Retest (Entry): Do not chase price. Place a Limit order on the generated Zone Line or at the edge of the FVG.
Management: Use opposite zones or mitigated zones (grey) as Take Profit targets.
Included Settings:
Fully configurable Alerts for Whales, Grabs, and Retests.
Total customization of colors and styles.
Average True Range (ATR)Strategy Name: ATR Trend-Following System with Volatility Filter & Dynamic Risk Management
Short Name: ATR Pro Trend System
Current Version: 2025 Edition (fully tested and optimized)Core ConceptA clean, robust, and highly profitable trend-following strategy that only trades when three strict conditions are met simultaneously:Clear trend direction (price above/below EMA 50)
Confirmed trend strength and trailing stop (SuperTrend)
Sufficient market volatility (current ATR(14) > its 50-period average)
This combination ensures the strategy stays out of choppy, low-volatility ranges and only enters during high-probability, trending moves with real momentum.Key Features & ComponentsComponent
Function
Default Settings
EMA 50
Primary trend filter
50-period exponential
SuperTrend
Dynamic trailing stop + secondary trend confirmation
Period 10, Multiplier 3.0
ATR(14) with RMA
True volatility measurement (Wilder’s original method)
Length 14
50-period SMA of ATR
Volatility filter – only trade when current ATR > average ATR
Length 50
Background coloring
Visual position status: light green = long, light red = short, white = flat
–
Entry markers
Green/red triangles at the exact entry bar
–
Dynamic position sizing
Fixed-fractional risk: exactly 1% of equity per trade
1.00% risk
Stop distance
2.5 × ATR(14) – fully adaptive to current volatility
Multiplier 2.5
Entry RulesLong: Close > EMA 50 AND SuperTrend bullish AND ATR(14) > SMA(ATR,50)
Short: Close < EMA 50 AND SuperTrend bearish AND ATR(14) > SMA(ATR,50)
Exit RulesPosition is closed automatically when SuperTrend flips direction (acts as volatility-adjusted trailing stop).
Money ManagementRisk per trade: exactly 1% of current account equity
Position size is recalculated on every new entry based on current ATR
Automatically scales up in strong trends, scales down in low-volatility regimes
Performance Highlights (2015–Nov 2025, real backtests)CAGR: 22–50% depending on market
Max Drawdown: 18–28%
Profit Factor: 1.89–2.44
Win Rate: 57–62%
Average holding time: 10–25 days (daily timeframe)
Best Markets & TimeframesExcellent on: Bitcoin, S&P 500, Nasdaq-100, DAX, Gold, major Forex pairs
Recommended timeframes: 4H, Daily, Weekly (Daily is the sweet spot)
[CT] ATR Ratio MTFThis indicator is an enhanced, multi-timeframe version of the original “ATR ratio” by RafaelZioni. Huge thanks to RafaelZioni for the core concept and base logic. The script still combines an ATR-based ratio (Z-score style reading of where price sits within its recent ATR envelope) with an ATR Supertrend, but expands it into a more flexible trade-decision and visual context tool.
The ATR ratio is normalized so you can quickly see when price is pressing into extended bullish or bearish territory, while the Supertrend defines directional bias and a dynamic support-resistance trail. You can choose any higher timeframe in the settings, allowing you to run the ATR ratio and Supertrend from a larger anchor timeframe while trading on a lower chart.
Upgrades include a full Pine Script v6 rewrite, multi-timeframe support for both the ATR ratio and Supertrend, user-controlled colors for the Supertrend in bull and bear modes, and optional bar coloring so price bars automatically reflect Supertrend direction. Entry, pyramiding and take-profit logic from the original script are preserved, giving you a familiar framework with more control over timeframe, visuals and trend bias.
This indicator is designed to give you a clean directional framework that blends volatility, trend, and timing into one view. The ATR ratio side of the script shows you where price sits inside a recent ATR-based envelope. When the ATR ratio pushes up and sustains above the bullish threshold, it signals that price is trading in an extended, momentum-driven zone relative to recent volatility. When it drops and holds below the bearish threshold, it shows the opposite: sellers have pushed price down into an extended bearish zone. The optional background coloring simply makes these bullish and bearish environments easier to see at a glance.
On top of that, the Supertrend and bar colors tell you what side of the market to favor. The Supertrend is calculated from ATR on whatever timeframe you choose in the settings. If you set the MTF input to a higher timeframe, the Supertrend and ATR ratio become your higher time frame bias while you trade on a lower chart. When price is above the MTF Supertrend, the line uses your bullish color and, if bar coloring is enabled, candles adopt your bullish bar color. That is your “long only” environment: you generally look for buys when price is above the Supertrend and the ATR ratio is either turning up from neutral or already in a bullish zone. When price is below the MTF Supertrend, the line uses your bearish color and candles can shift to your bearish bar color; that is where you focus on shorts, especially when the ATR ratio is rolling over or holding in the bearish zone.
The built-in long and short conditions are meant as signal prompts, not rigid rules. Long signals fire when the ATR ratio crosses up through a positive level while the Supertrend is bullish. Short signals fire when the ATR ratio crosses down through a negative level while the Supertrend is bearish. The script tracks how many longs or shorts have been taken in sequence (pyramiding) and will only allow a new signal up to the limit you set, so you can control how aggressively you stack positions in a trend. The take-profit logic then watches the percentage move from your last entry and flags “TP” when that move has reached your take-profit percent, helping you standardize exits instead of eyeballing them bar by bar.
In practice you typically start by choosing your anchor timeframe for the MTF setting, for example a 1-hour or 4-hour Supertrend and ATR ratio while watching a 5-minute or 15-minute chart. You then use the Supertrend direction and bar colors as your bias filter, only taking signals in the direction of the trend, and you use the ATR ratio behavior to judge whether you are entering into strength, fading an extreme, or trading inside a neutral consolidation. Over time this gives you a consistent way to answer three questions on every chart: which side am I allowed to trade, how extended is price within its recent volatility, and where are my structured entries and exits based on that framework.
Bitcoin Power-Law Bands + Quantile OscillatorDescription
This indicator visualizes a set of statistically derived Power-Law bands for the Bitcoin price.
The model is based on a log–log regression of the Bitcoin price over time and a weighted quantile regression that captures the distributional structure of the price across several long-term quantiles.
It provides a historical context for where the price currently lies relative to these mathematically estimated zones.
This indicator does not perform any new model fitting; it only displays the pre-computed band structure derived from the full historical dataset.
How the model works
This indicator is based on a statistical Power-Law model of the Bitcoin price.
A long-term trend was estimated using a log–log OLS regression, and the deviations from this trend were analyzed through a rolling multi-year volatility measure.
The inverse of this volatility served as the weight for several quantile regression fits, producing robust long-term bands at multiple distribution levels (0.1%, 15%, 50%, 85%, 95%, 99.9%).
These quantile curves represent the historical valuation zones of the Bitcoin price.
All final regression coefficients are fixed and embedded into the Pine script, which reconstructs the bands directly on the chart.
The extension of the bands into the future is based solely on the mathematical form of each curve and does not use any future market data.
What the indicator displays
• Six Power-Law quantile bands (0.1%, 15%, 50%, 85%, 95%, 99.9%) displayed as stacked colored zones
• Future-offset projection curves (mathematical extrapolation of the fitted Power-Laws, not based on future prices)
• Quantile Oscillator: A normalized representation of where the current price lies relative to the quantile structure.
How to use it
This indicator is not a timing tool.
It provides a structural, long-term statistical context for the Bitcoin price, showing:
• how extreme a current valuation is relative to long-term history
• where the price sits within the Power-Law quantile spectrum
• long-term distribution zones derived from the quantile regressions
• a volatility-weighted representation of historical deviations
It may be useful for long-term cycle studies or valuation comparisons, but there is no guarantee that this historical relationship will persist.
Important notes
• This indicator does not repaint.
• All projections are non-predictive mathematical extrapolations.
• This script is designed only for the symbol: INDEX:BTCUSD
• It does not provide trading signals, recommendations, or financial advice.
Why closed-source?
The underlying regression model, weighting logic, and quantile estimations were produced externally using Python and constitute the core intellectual component of the study. The Pine version contains only the pre-calculated parameters and the visualization logic.
Bottom Up - Reverso ProReverso Pro by Bottom Up - Excess is the signal. Reversion is the edge.
Reverso is a mean reverting indicator that identifies market excesses and signals reversals for highly probable retracements to an average value.
Reverso's algorithm is extremely precise because it also takes into account the historical volatility of the instrument and constantly recalibrates itself dynamically without repainting.
This tool is suitable for mean-reversion traders who want to study EMA reactions, understand market trends, and refine entry/exit strategies based on price-memory dynamics.
Why Reverso Pro is different (This isn’t just another indicator)
Zero repainting – What you see is what you get. No tricks, no redraws, ever.
Dynamically adapts to the historical volatility of the instrument — works the same on Forex, stocks, indices, or some random crypto.
Constant real-time recalibration — adjusts instantly to volatility regime changes.
Fully adjustable sensitivity — From machine-gun signals for brutal scalping to only the most extreme deviations for monster-probability swing trades.
Native multi-timeframe control — Choose the timeframe used for signal calculation (5 min, 1H, daily, or custom). Reverso bends to your style.
When a Reverso signal fires:
Price has reached a statistically extreme deviation from its historical memory.
The probability of a snapback to the mean is at its peak.
It’s time to go counter-trend with the lowest risk and the highest reward possible.
Customization Options
You can use it on any timeframe and instrument.
You can customize also the timeframe over which the signals are processed to suit very fast scalping trading or to intercept slower and longer movements for swing trading.
The sensitivity of the indicator can also be customized to emit multiple signals or identify only the most extreme levels of deviation from the mean.
Add to chart. Turn on alerts. Happy trading!
Bottom Up - The Ecosystem Designed for Traders
bottomup.finance
VIX vs VIX1Y SpreadSpread Calculation: Shows VIX1Y minus VIX
Positive = longer-term vol higher (normal contango)
Negative = near-term vol elevated (inverted term structure)
Can help identify longer term risk pricing of equity assets.
NQ-VIX Expected Move LevelsNQ -VIX Daily Price Bands
This indicator plots dynamic intraday price bands for NQ futures based on real-time volatility levels measured by the VIX (CBOE Volatility Index). The bands evolve throughout the trading day, providing volatility-adjusted price targets.
Formulas:
Upper Band = Daily Open + (NQ Price × VIX ÷ √252 ÷ 100)
Lower Band = Daily Open - (NQ Price × VIX ÷ √252 ÷ 100)
The calculation uses the square root of 252 (trading days per year) to convert annualized VIX volatility into an expected daily move, then scales it as a percentage adjustment from the current day's open.
Features:
Real-time band calculation that updates throughout the trading session
Upper band (green) extends from the current day's open
Lower band (red) contracts from the current day's open
Inner upper band (green) at 50% of expected move
Inner lower band (red) at 50% of expected move
Middle Inner upper band (green) at 80% of expected move
Middle Inner lower band (red) at 80% of expected move
Information table displaying:
Current NQ price and VIX level
Daily Open
Expected move
NQ-VIX Expected Move LTF LevelsNQ -VIX LTF Price Bands
This indicator plots dynamic intraday price bands for NQ futures based on real-time volatility levels measured by the VIX (CBOE Volatility Index). The bands evolve throughout the trading day, providing volatility-adjusted price targets.
Formulas:
Upper Band = (Input TF Open) + (NQ Price × VIX x √(Input TF ÷ (23h in min) ) ÷ 100
Lower Band = Daily Open - (NQ Price × VIX x √(Input TF ÷ (23h in min) ) ÷ 100
The calculation uses the square root of Input TF ÷ (23h in min) to convert annualized VIX volatility into an expected TF move, then scales it as a percentage adjustment from the current TF input's open.
Features:
Real-time band calculation that updates throughout the trading session
Upper band (green) extends from the current TF's open
Lower band (red) contracts from the current TF's open
Inner upper band (green) at 50% of expected move
Inner lower band (red) at 50% of expected move
Middle Inner upper band (green) at 80% of expected move
Middle Inner lower band (red) at 80% of expected move
Information table displaying:
Current input TF
Current NQ price and VIX level
Current input TF Open
Expected move
ES-VIX Expected Move LTF LevelsES-VIX LTF Price Bands
This indicator plots dynamic intraday price bands for ES futures based on real-time volatility levels measured by the VIX (CBOE Volatility Index). The bands evolve throughout the trading day, providing volatility-adjusted price targets.
Formulas:
Upper Band = (Input TF Open) + (ES Price × VIX x √(Input TF ÷ (23h in min) ) ÷ 100
Lower Band = Daily Open - (ES Price × VIX x √(Input TF ÷ (23h in min) ) ÷ 100
The calculation uses the square root of Input TF ÷ (23h in min) to convert annualized VIX volatility into an expected TF move, then scales it as a percentage adjustment from the current TF input's open.
Features:
Real-time band calculation that updates throughout the trading session
Upper band (green) extends from the current TF's open
Lower band (red) contracts from the current TF's open
Inner upper band (green) at 50% of expected move
Inner lower band (red) at 50% of expected move
Middle Inner upper band (green) at 80% of expected move
Middle Inner lower band (red) at 80% of expected move
Information table displaying:
Current input TF
Current ES price and VIX level
Current input TF Open
Expected move
Santhosh Time Block HighlighterI have created an indicator to differentiate market trend/momentum in different time zone during trading day. This will help us to understand the market pattern to avoid entering trade during consolidation/distribution. Its helps to measure the volatility and market sentiment
ATR + infoIt shows the ATR, the stop loss and stop profit levels, and the amount to invest based on capital and the risk you are willing to take.
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.
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.
Fast Autocorrelation Estimator█ Overview:
The Fast ACF and PACF Estimation indicator efficiently calculates the autocorrelation function (ACF) and partial autocorrelation function (PACF) using an online implementation. It helps traders identify patterns and relationships in financial time series data, enabling them to optimize their trading strategies and make better-informed decisions in the markets.
█ Concepts:
Autocorrelation, also known as serial correlation, is the correlation of a signal with a delayed copy of itself as a function of delay.
This indicator displays autocorrelation based on lag number. The autocorrelation is not displayed based over time on the x-axis. It's based on the lag number which ranges from 1 to 30. The calculations can be done with "Log Returns", "Absolute Log Returns" or "Original Source" (the price of the asset displayed on the chart).
When calculating autocorrelation, the resulting value will range from +1 to -1, in line with the traditional correlation statistic. An autocorrelation of +1 represents a perfect correlation (an increase seen in one time series leads to a proportionate increase in the other time series). An autocorrelation of -1, on the other hand, represents a perfect inverse correlation (an increase seen in one time series results in a proportionate decrease in the other time series). Lag number indicates which historical data point is autocorrelated. For example, if lag 3 shows significant autocorrelation, it means current data is influenced by the data three bars ago.
The Fast Online Estimation of ACF and PACF Indicator is a powerful tool for analyzing the linear relationship between a time series and its lagged values in TradingView. The indicator implements an online estimation of the Autocorrelation Function (ACF) and the Partial Autocorrelation Function (PACF) up to 30 lags, providing a real-time assessment of the underlying dependencies in your time series data. The Autocorrelation Function (ACF) measures the linear relationship between a time series and its lagged values, capturing both direct and indirect dependencies. The Partial Autocorrelation Function (PACF) isolates the direct dependency between the time series and a specific lag while removing the effect of any indirect dependencies.
This distinction is crucial in understanding the underlying relationships in time series data and making more informed decisions based on those relationships. For example, let's consider a time series with three variables: A, B, and C. Suppose that A has a direct relationship with B, B has a direct relationship with C, but A and C do not have a direct relationship. The ACF between A and C will capture the indirect relationship between them through B, while the PACF will show no significant relationship between A and C, as it accounts for the indirect dependency through B. Meaning that when ACF is significant at for lag 5, the dependency detected could be caused by an observation that came in between, and PACF accounts for that. This indicator leverages the Fast Moments algorithm to efficiently calculate autocorrelations, making it ideal for analyzing large datasets or real-time data streams. By using the Fast Moments algorithm, the indicator can quickly update ACF and PACF values as new data points arrive, reducing the computational load and ensuring timely analysis. The PACF is derived from the ACF using the Durbin-Levinson algorithm, which helps in isolating the direct dependency between a time series and its lagged values, excluding the influence of other intermediate lags.
█ How to Use the Indicator:
Interpreting autocorrelation values can provide valuable insights into the market behavior and potential trading strategies.
When applying autocorrelation to log returns, and a specific lag shows a high positive autocorrelation, it suggests that the time series tends to move in the same direction over that lag period. In this case, a trader might consider using a momentum-based strategy to capitalize on the continuation of the current trend. On the other hand, if a specific lag shows a high negative autocorrelation, it indicates that the time series tends to reverse its direction over that lag period. In this situation, a trader might consider using a mean-reversion strategy to take advantage of the expected reversal in the market.
ACF of log returns:
Absolute returns are often used to as a measure of volatility. There is usually significant positive autocorrelation in absolute returns. We will often see an exponential decay of autocorrelation in volatility. This means that current volatility is dependent on historical volatility and the effect slowly dies off as the lag increases. This effect shows the property of "volatility clustering". Which means large changes tend to be followed by large changes, of either sign, and small changes tend to be followed by small changes.
ACF of absolute log returns:
Autocorrelation in price is always significantly positive and has an exponential decay. This predictably positive and relatively large value makes the autocorrelation of price (not returns) generally less useful.
ACF of price:
█ Significance:
The significance of a correlation metric tells us whether we should pay attention to it. In this script, we use 95% confidence interval bands that adjust to the size of the sample. If the observed correlation at a specific lag falls within the confidence interval, we consider it not significant and the data to be random or IID (identically and independently distributed). This means that we can't confidently say that the correlation reflects a real relationship, rather than just random chance. However, if the correlation is outside of the confidence interval, we can state with 95% confidence that there is an association between the lagged values. In other words, the correlation is likely to reflect a meaningful relationship between the variables, rather than a coincidence. A significant difference in either ACF or PACF can provide insights into the underlying structure of the time series data and suggest potential strategies for traders. By understanding these complex patterns, traders can better tailor their strategies to capitalize on the observed dependencies in the data, which can lead to improved decision-making in the financial markets.
Significant ACF but not significant PACF: This might indicate the presence of a moving average (MA) component in the time series. A moving average component is a pattern where the current value of the time series is influenced by a weighted average of past values. In this case, the ACF would show significant correlations over several lags, while the PACF would show significance only at the first few lags and then quickly decay.
Significant PACF but not significant ACF: This might indicate the presence of an autoregressive (AR) component in the time series. An autoregressive component is a pattern where the current value of the time series is influenced by a linear combination of past values at specific lags.
Often we find both significant ACF and PACF, in that scenario simply and AR or MA model might not be sufficient and a more complex model such as ARMA or ARIMA can be used.
█ Features:
Source selection: User can choose either 'Log Returns' , 'Absolute Returns' or 'Original Source' for the input data.
Autocorrelation Selection: User can choose either 'ACF' or 'PACF' for the plot selection.
Plot Selection: User can choose either 'Autocorrelarrogram' or 'Historical Autocorrelation' for plotting the historical autocorrelation at a specified lag.
Max Lag: User can select the maximum number of lags to plot.
Precision: User can set the number of decimal points to display in the plot.
Expected Move BandsExpected move is the amount that an asset is predicted to increase or decrease from its current price, based on the current levels of volatility.
In this model, we assume asset price follows a log-normal distribution and the log return follows a normal distribution.
Note: Normal distribution is just an assumption, it's not the real distribution of return
Settings:
"Estimation Period Selection" is for selecting the period we want to construct the prediction interval.
For "Current Bar", the interval is calculated based on the data of the previous bar close. Therefore changes in the current price will have little effect on the range. What current bar means is that the estimated range is for when this bar close. E.g., If the Timeframe on 4 hours and 1 hour has passed, the interval is for how much time this bar has left, in this case, 3 hours.
For "Future Bars", the interval is calculated based on the current close. Therefore the range will be very much affected by the change in the current price. If the current price moves up, the range will also move up, vice versa. Future Bars is estimating the range for the period at least one bar ahead.
There are also other source selections based on high low.
Time setting is used when "Future Bars" is chosen for the period. The value in time means how many bars ahead of the current bar the range is estimating. When time = 1, it means the interval is constructing for 1 bar head. E.g., If the timeframe is on 4 hours, then it's estimating the next 4 hours range no matter how much time has passed in the current bar.
Note: It's probably better to use "probability cone" for visual presentation when time > 1
Volatility Models :
Sample SD: traditional sample standard deviation, most commonly used, use (n-1) period to adjust the bias
Parkinson: Uses High/ Low to estimate volatility, assumes continuous no gap, zero mean no drift, 5 times more efficient than Close to Close
Garman Klass: Uses OHLC volatility, zero drift, no jumps, about 7 times more efficient
Yangzhang Garman Klass Extension: Added jump calculation in Garman Klass, has the same value as Garman Klass on markets with no gaps.
about 8 x efficient
Rogers: Uses OHLC, Assume non-zero mean volatility, handles drift, does not handle jump 8 x efficient
EWMA: Exponentially Weighted Volatility. Weight recently volatility more, more reactive volatility better in taking account of volatility autocorrelation and cluster.
YangZhang: Uses OHLC, combines Rogers and Garmand Klass, handles both drift and jump, 14 times efficient, alpha is the constant to weight rogers volatility to minimize variance.
Median absolute deviation: It's a more direct way of measuring volatility. It measures volatility without using Standard deviation. The MAD used here is adjusted to be an unbiased estimator.
Volatility Period is the sample size for variance estimation. A longer period makes the estimation range more stable less reactive to recent price. Distribution is more significant on a larger sample size. A short period makes the range more responsive to recent price. Might be better for high volatility clusters.
Standard deviations:
Standard Deviation One shows the estimated range where the closing price will be about 68% of the time.
Standard Deviation two shows the estimated range where the closing price will be about 95% of the time.
Standard Deviation three shows the estimated range where the closing price will be about 99.7% of the time.
Note: All these probabilities are based on the normal distribution assumption for returns. It's the estimated probability, not the actual probability.
Manually Entered Standard Deviation shows the range of any entered standard deviation. The probability of that range will be presented on the panel.
People usually assume the mean of returns to be zero. To be more accurate, we can consider the drift in price from calculating the geometric mean of returns. Drift happens in the long run, so short lookback periods are not recommended. Assuming zero mean is recommended when time is not greater than 1.
When we are estimating the future range for time > 1, we typically assume constant volatility and the returns to be independent and identically distributed. We scale the volatility in term of time to get future range. However, when there's autocorrelation in returns( when returns are not independent), the assumption fails to take account of this effect. Volatility scaled with autocorrelation is required when returns are not iid. We use an AR(1) model to scale the first-order autocorrelation to adjust the effect. Returns typically don't have significant autocorrelation. Adjustment for autocorrelation is not usually needed. A long length is recommended in Autocorrelation calculation.
Note: The significance of autocorrelation can be checked on an ACF indicator.
ACF
The multimeframe option enables people to use higher period expected move on the lower time frame. People should only use time frame higher than the current time frame for the input. An error warning will appear when input Tf is lower. The input format is multiplier * time unit. E.g. : 1D
Unit: M for months, W for Weeks, D for Days, integers with no unit for minutes (E.g. 240 = 240 minutes). S for Seconds.
Smoothing option is using a filter to smooth out the range. The filter used here is John Ehler's supersmoother. It's an advance smoothing technique that gets rid of aliasing noise. It affects is similar to a simple moving average with half the lookback length but smoother and has less lag.
Note: The range here after smooth no long represent the probability
Panel positions can be adjusted in the settings.
X position adjusts the horizontal position of the panel. Higher X moves panel to the right and lower X moves panel to the left.
Y position adjusts the vertical position of the panel. Higher Y moves panel up and lower Y moves panel down.
Step line display changes the style of the bands from line to step line. Step line is recommended because it gets rid of the directional bias of slope of expected move when displaying the bands.
Warnings:
People should not blindly trust the probability. They should be aware of the risk evolves by using the normal distribution assumption. The real return has skewness and high kurtosis. While skewness is not very significant, the high kurtosis should be noticed. The Real returns have much fatter tails than the normal distribution, which also makes the peak higher. This property makes the tail ranges such as range more than 2SD highly underestimate the actual range and the body such as 1 SD slightly overestimate the actual range. For ranges more than 2SD, people shouldn't trust them. They should beware of extreme events in the tails.
Different volatility models provide different properties if people are interested in the accuracy and the fit of expected move, they can try expected move occurrence indicator. (The result also demonstrate the previous point about the drawback of using normal distribution assumption).
Expected move Occurrence Test
The prediction interval is only for the closing price, not wicks. It only estimates the probability of the price closing at this level, not in between. E.g., If 1 SD range is 100 - 200, the price can go to 80 or 230 intrabar, but if the bar close within 100 - 200 in the end. It's still considered a 68% one standard deviation move.
ES-VIX Expected Move - Open basedES-VIX Daily Price Bands
This indicator plots dynamic intraday price bands for ES futures based on real-time volatility levels measured by the VIX (CBOE Volatility Index). The bands evolve throughout the trading day, providing volatility-adjusted price targets.
Formulas:
Upper Band = Daily Open + (ES Price × VIX ÷ √252 ÷ 100)
Lower Band = Daily Open - (ES Price × VIX ÷ √252 ÷ 100)
The calculation uses the square root of 252 (trading days per year) to convert annualized VIX volatility into an expected daily move, then scales it as a percentage adjustment from the current day's open.
Features:
Real-time band calculation that updates throughout the trading session
Upper band (green) extends from the current day's open
Lower band (red) contracts from the current day's open
Inner upper band (green) at 50% of expected move
Inner lower band (red) at 50% of expected move
Middle Inner upper band (green) at 80% of expected move
Middle Inner lower band (red) at 80% of expected move
Information table displaying:
Current ES price and VIX level
Daily Open
Expected move
