VWAP Composites📊 VWAP Composite - Advanced Multi-Period Volume Weighted Average Price Indicator
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🎯 OVERVIEW
VWAP Composite is an advanced volume-weighted average price (VWAP) indicator that goes beyond traditional single-period VWAP calculations by offering composite multi-period analysis and unprecedented customization. This indicator solves a common problem traders face: traditional VWAP resets at arbitrary intervals (session start, day, week), but significant price action and volume accumulation often spans multiple periods. VWAP Composite allows you to anchor VWAP calculations to any timeframe—or combine multiple periods into a single composite VWAP—giving you a true representation of average price weighted by volume across the exact periods that matter to your analysis.
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⚙️ HOW IT WORKS - CALCULATION METHODOLOGY
📌 CORE VWAP CALCULATION
The indicator calculates VWAP using the standard volume-weighted formula:
• Typical Price = (High + Low + Close) / 3
• VWAP = Σ(Typical Price × Volume) / Σ(Volume)
This calculation is performed across user-defined time periods, ensuring each bar's contribution to the average is proportional to its trading volume.
📌 STANDARD DEVIATION BANDS
The indicator calculates volume-weighted standard deviation to measure price dispersion around the VWAP:
• Variance = Σ / Σ(Volume)
• Standard Deviation = √Variance
• Upper Band = VWAP + (StdDev × Multiplier)
• Lower Band = VWAP - (StdDev × Multiplier)
These bands help identify overbought/oversold conditions relative to the volume-weighted mean, with high-volume price excursions having greater impact on band width than low-volume moves.
📌 COMPOSITE PERIOD METHODOLOGY (Auto Mode)
Unlike traditional VWAP that resets at fixed intervals, Auto Mode creates composite VWAPs by combining the current period with N previous periods:
• Period Span = 1: Current period only (standard VWAP behavior)
• Period Span = 2: Current period + 1 previous period combined
• Period Span = 3: Current period + 2 previous periods combined
• And so on...
Example: A 3-period Weekly composite VWAP calculates from the start of 2 weeks ago through the current week's end, creating a single VWAP that represents 21 days of continuous price and volume data. This provides context about where price stands relative to the volume-weighted average over multiple weeks, not just the current week.
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🔧 KEY FEATURES & ORIGINALITY
✅ DUAL OPERATING MODES
1️⃣ MANUAL MODE (5 Independent VWAPs)
Define up to 5 separate VWAP calculations with custom start/end times:
• Perfect for anchoring VWAP to specific events (earnings, Fed announcements, major reversals)
• Each VWAP has independent color settings for lines and deviation band backgrounds
• Individual control over calculation extension and visual extension (explained below)
• Useful for tracking multiple institutional accumulation/distribution zones simultaneously
2️⃣ AUTO MODE (Composite Period VWAP)
Automatically calculates VWAP across combined time periods:
• Supported periods: Daily, Weekly, Monthly, Quarterly, Yearly
• Configurable period span (1-20 periods)
• Always up-to-date, recalculates on each new bar
• Ideal for systematic analysis across consistent timeframes
✅ DUAL EXTENSION SYSTEM (Manual Mode Innovation)
Most VWAP indicators only offer "on/off" for extending calculations. This indicator provides two distinct extension options:
🔹 EXTEND CALCULATION TO CURRENT BAR
When enabled, continues including new bars in the VWAP calculation after the defined end time. The VWAP value updates dynamically as new volume enters the market.
Use case: You anchored VWAP to a major low 3 weeks ago. You want the VWAP to continue evolving with new volume data to track ongoing institutional positioning.
🔹 EXTEND VISUAL LINE ONLY
When enabled (and calculation extension is disabled), projects the "frozen" VWAP value forward as a reference line. The VWAP value remains fixed at what it was at the end time, but the line and deviation bands visually extend to current price.
Use case: You want to see how price is behaving relative to the VWAP that existed at a specific point in time (e.g., "Where is price now vs. the 5-day VWAP that existed at last Friday's close?").
This dual system gives you unprecedented control over whether you're tracking a "living" VWAP that incorporates new data or using historical VWAP levels as static reference points.
✅ CUSTOMIZABLE STANDARD DEVIATION BANDS
• Adjustable multiplier (0.1 to 5.0)
• Independent background colors with opacity control for each VWAP
• Dashed band lines for easy visual distinction from main VWAP
• Bands extend when visual extension is enabled, maintaining zone visibility
✅ COMPREHENSIVE LABELING SYSTEM
Each VWAP displays:
• Current VWAP value
• Upper deviation band value (High)
• Lower deviation band value (Low)
• Extension status indicator (Calc Extended / Visual Extended)
• Color-coded for quick identification
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📖 HOW TO USE THIS INDICATOR
🎯 SCENARIO 1: EVENT-ANCHORED VWAP (Manual Mode)
Use case: A stock gaps down 15% on earnings and you want to track where institutions are positioning during the recovery.
Setup:
1. Switch to Manual Mode
2. Enable VWAP 1
3. Set Start Time to the earnings gap bar
4. Set End Time to current time (or leave far in future)
5. Enable "Extend Calculation to Current Bar"
6. Watch how price respects the VWAP as a dynamic support/resistance
Interpretation:
• Price above VWAP = buyers in control since the event
• Price testing VWAP from above = potential support
• Volume-weighted standard deviation bands show normal price range
• Price outside bands = potential exhaustion/mean reversion setup
🎯 SCENARIO 2: MULTI-WEEK INSTITUTIONAL ACCUMULATION ZONE (Auto Mode)
Use case: You trade swing setups and want to identify where institutions have been accumulating over the past 3 weeks.
Setup:
1. Switch to Auto Mode
2. Select "Weekly" period type
3. Set Period Span to 3
4. Enable standard deviation bands
Interpretation:
• 3-week composite VWAP shows the true average institutional entry
• Price bouncing off VWAP repeatedly = strong support (institutions defending their average)
• Price breaking below VWAP on high volume = potential distribution
• Deviation bands contracting = consolidation; expanding = volatility increase
🎯 SCENARIO 3: COMPARING MULTIPLE TIME HORIZONS (Manual Mode)
Use case: You want to see short-term vs medium-term vs long-term VWAP alignments.
Setup:
1. Switch to Manual Mode
2. VWAP 1: Last 5 trading days (blue)
3. VWAP 2: Last 10 trading days (orange)
4. VWAP 3: Last 20 trading days (purple)
5. Enable "Extend Calculation" for all
6. Set different background colors for visual separation
Interpretation:
• All VWAPs aligned upward = strong trend across all timeframes
• Price between VWAPs = finding equilibrium between different trader timeframes
• Short-term VWAP crossing long-term VWAP = momentum shift
• Price rejecting at higher-timeframe VWAP = that timeframe's traders defending their average
🎯 SCENARIO 4: HISTORICAL VWAP REFERENCE LEVELS (Manual Mode)
Use case: You want to see where the 1-month VWAP was at each month-end as static reference levels.
Setup:
1. Switch to Manual Mode
2. VWAP 1: Set to last month's start/end dates
3. VWAP 2: Set to 2 months ago start/end dates
4. VWAP 3: Set to 3 months ago start/end dates
5. Disable "Extend Calculation"
6. Enable "Extend Visual Line Only"
Interpretation:
• Each VWAP represents the volume-weighted average for that complete month
• These become static support/resistance levels
• Price returning to old monthly VWAPs = institutional memory/gap fill behavior
• Useful for identifying longer-term value areas
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🎨 CUSTOMIZATION OPTIONS
GENERAL SETTINGS
• Show/hide labels
• Line style: Solid, Dashed, or Dotted
• Standard deviation multiplier (impacts band width)
• Toggle standard deviation bands on/off
MANUAL MODE (Per VWAP)
• Custom start and end times
• Line color picker
• Background color picker (with transparency control)
• Extend calculation option
• Extend visual option
• Show/hide individual VWAPs
AUTO MODE
• Period type selection (Daily/Weekly/Monthly/Quarterly/Yearly)
• Period span (1-20 periods)
• Line color
• Background color (with transparency control)
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💡 TRADING APPLICATIONS
✓ Mean Reversion: Use deviation bands to identify stretched prices likely to return to VWAP
✓ Trend Confirmation: Price sustained above VWAP = bullish bias; below = bearish bias
✓ Support/Resistance: VWAP often acts as dynamic S/R, especially on higher volume periods
✓ Institutional Positioning: Multi-day/week VWAPs show where large players have established positions
✓ Entry Timing: Wait for pullbacks to VWAP in trending markets
✓ Stop Placement: Use VWAP ± standard deviation as volatility-adjusted stop levels
✓ Breakout Confirmation: Breakouts from consolidation with price reclaiming VWAP = stronger signal
✓ Multi-Timeframe Analysis: Compare short vs long-period VWAPs to gauge momentum alignment
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⚠️ IMPORTANT NOTES
• The indicator redraws on each bar to maintain accurate visual representation (uses `barstate.islast`)
• Maximum lookback is limited to 5000 bars for performance optimization
• Time range calculations work across all timeframes but are most effective on intraday to daily charts
• Standard deviation bands assume volume-weighted distribution; extreme events may violate assumptions
• Auto mode always calculates to current bar; use Manual mode for fixed historical periods
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This indicator is open-source. Feel free to examine the code, learn from it, and adapt it to your needs.
Regressions
Bullmart Discounted Momentum 1.0detection of timeframe-specific patterns on manipulated coins, - screening for over-extension of pronounced directional movements against their usual natural growth-rate boundary
Midnight Lines for Tokyo, London, New Yorkممتاز 👌 إليك **تعريفًا محدثًا وكاملًا للمؤشر باللغتين العربية والإنجليزية**، مع إدراج توضيح دقيق لتعامل المؤشر مع **تغيّر التوقيت الصيفي والشتوي (DST)** في لندن ونيويورك:
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## 🇬🇧 **English Description (with DST behavior)**
**Indicator name:** *Midnight Lines for Tokyo, London, and New York*
**Purpose:**
This indicator automatically draws **vertical lines** on the chart at **midnight (00:00)** for the three major global trading sessions:
* **Tokyo**
* **London**
* **New York**
### 🔹 How it works:
1. The script checks each candle’s time using the built-in TradingView time zone function:
* `"Asia/Tokyo"`
* `"Europe/London"`
* `"America/New_York"`
2. When it detects **00:00** in any of these zones, it draws:
* A **vertical dotted line** that extends from the top to the bottom of the chart.
* A **label** at the top with the session name (e.g., “Tokyo Midnight”).
3. Each session has its own color for clarity:
* **Blue** → Tokyo Midnight
* **Green** → London Midnight
* **Red** → New York Midnight
### 🕒 Automatic Daylight Saving Time (DST) Adjustment:
The indicator automatically adapts to **Daylight Saving Time changes** in both **London** and **New York**:
* When London switches between **GMT and GMT+1**, the midnight line shifts automatically to remain accurate.
* When New York switches between **EST and EDT**, the script also updates accordingly.
* Tokyo does **not** observe DST, so its timing stays constant year-round.
### 🎯 Purpose:
Helps traders visually track the start of each new trading day in the major sessions and analyze:
* Session overlaps (e.g., London–New York overlap)
* Session-based trading strategies
* Price movement behavior at each new day open
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## 🇸🇦 **الوصف بالعربية (مع إدراج تغير التوقيت)**
**اسم المؤشر:** خطوط منتصف الليل لجلسات طوكيو، لندن، ونيويورك
**الهدف:**
يقوم هذا المؤشر تلقائيًا برسم **خطوط عمودية** على الرسم البياني عند **منتصف الليل (00:00)** لكل من الجلسات الثلاث الرئيسية:
* **جلسة طوكيو**
* **جلسة لندن**
* **جلسة نيويورك**
### 🔹 كيفية العمل:
1. يستخدم المؤشر دوال TradingView لحساب الوقت الفعلي لكل مدينة:
* `"Asia/Tokyo"` لطوكيو
* `"Europe/London"` للندن
* `"America/New_York"` لنيويورك
2. عند وصول الساعة إلى **00:00** بتوقيت أي مدينة، يرسم المؤشر:
* **خطًا عموديًا متقطعًا** يمتد من أعلى إلى أسفل الرسم البياني.
* **تسمية (Label)** أعلى الخط باسم الجلسة (مثل “Tokyo Midnight”).
3. كل جلسة لها لون مختلف:
* **أزرق** → منتصف طوكيو
* **أخضر** → منتصف لندن
* **أحمر** → منتصف نيويورك
### 🕒 التعامل مع تغيّر التوقيت الصيفي والشتوي (DST):
يتكيّف المؤشر تلقائيًا مع تغيّر التوقيت في لندن ونيويورك:
* عندما تنتقل لندن بين **التوقيت الشتوي (GMT)** و**التوقيت الصيفي (GMT+1)**، يتحرك الخط تلقائيًا ليبقى في الساعة 00:00 المحلية.
* وعندما تنتقل نيويورك بين **EST** و**EDT**، يتم تعديل الخط كذلك تلقائيًا.
* أما طوكيو فلا تعتمد التوقيت الصيفي، لذا يبقى وقتها ثابتًا دائمًا على الساعة **00:00 JST**.
### 🎯 الفائدة:
يساعد المتداولين على تحديد **بداية كل جلسة تداول رئيسية**، ومراقبة:
* **تداخل الجلسات** مثل لندن ونيويورك
* **تحركات السعر عند بداية اليوم الجديد**
* **استراتيجيات التداول الزمنية حسب الجلسة**
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Smart Levels V8 + Anomaly Detection CombinedTATANKA Smart Levels + Anomaly Detection
A comprehensive analysis tool combining manual level tracking with statistical anomaly detection for identifying high-probability trading opportunities.
CORE FEATURES:
Manual Level System:
• Plot custom support/resistance levels with automatic inflection point tracking
• Color-coded bull/bear level visualization with adjustable positioning
• Real-time level crossing alerts with directional confirmation
• Multi-timeframe pivot integration for additional context
Anomaly Detection Engine:
• Identifies statistical deviations from price equilibrium
• Generates scored signals based on multiple confirmation factors
• Visual threshold bands show deviation zones
• Horizontal projection lines from anomaly points until broken or session end
Session Management:
• RTH/OVN session filtering with multiple timezone support
• Enhanced scoring during key market hours (opening hour, power hour)
• Option to block signals outside regular trading hours
• Session-specific visual backgrounds for clarity
Signal Intelligence:
• Automated confluence scoring combining price action, volume, and momentum
• Multiple signal types: bounces, breakouts, reversals, exhaustion patterns
• RSI and ADX integration for regime awareness
• Customizable cooldown periods to reduce signal noise
• Quality thresholds to filter low-probability setups
Market Bias Panel:
• Real-time display of current market sentiment
• Shows distance to key levels and signal readiness
• Configurable positioning and visibility options
Visual Customization:
• Adjustable signal sizes, colors, and transparency
• Optional large circles for high-quality setups
• Directional arrows and score labels
• Clean interface with minimal chart clutter
BEST PRACTICES:
• Recommended for 1m-15m timeframes on liquid futures/forex markets
• Paste your key levels at session start or when levels update
• Adjust sensitivity based on asset volatility (lower multipliers = more signals)
• Use signals as reference points within your overall trading plan
• Combine with additional confirmation from order flow or other tools
IMPORTANT NOTES:
• This is an indicator, not an automated strategy
• Signals represent potential opportunities requiring discretionary analysis
• Past performance does not guarantee future results
• Adjust parameters through backtesting on your specific instruments
mean reversion Spread Z-Score Your main "actor" is the Blue Line 🔵 (the Z-Score). It tells you if your spread is "expensive" or "cheap" compared to its average.
The other lines are your action zones.
Here is how to read the signals:
Scenario 1: SELL the Spread (The spread is TOO EXPENSIVE)
• ENTRY Signal: The Blue Line 🔵 moves up and crosses the Red Line 🔴 (at +1.8).
• Meaning: MNQ has become far too expensive compared to MES. The rubber band is stretched too far upwards.
• Your Action (Sell):
• ✅ SELL MNQ
• ✅ BUY MES
• EXIT Signal: The Blue Line 🔵 comes back down and crosses the Dotted Red Line (at +0.5).
• Meaning: The rubber band is back to normal. It's time to take your profits.
• Your Action (Close):
• ✅ BUY BACK your MNQ
• ✅ SELL your MES
Scenario 2: BUY the Spread (The spread is TOO CHEAP)
• ENTRY Signal: The Blue Line 🔵 moves down and crosses the Green Line 🟢 (at -1.8).
• Meaning: MNQ has become far too cheap compared to MES. The rubber band is stretched too far downwards.
• Your Action (Buy):
• ✅ BUY MNQ
• ✅ SELL MES
• EXIT Signal: The Blue Line 🔵 moves back up and crosses the Dotted Green Line (at -0.5).
• Meaning: The rubber band is back to normal. It's time to take your profits.
• Your Action (Close):
• ✅ SELL your MNQ
• ✅ BUY BACK your MES
In summary:
• Blue Line 🔵 touches Red Line 🔴 = Sell the spread.
• Blue Line 🔵 touches Green Line 🟢 = Buy the spread.
Liquidaciones BTCUSDT.PAllows you to manually record liquidation prices for both short and long positions, which are then displayed on the chart:
Orange: Short liquidations
Blue: Long liquidations
Optionally, a specific liquidation price can be highlighted to indicate higher-volume liquidations
Note: All liquidation prices must be entered manually.
Smooth Theil-SenI wanted to build a Theil-Sen estimator that could run on more than one bar and produce smoother output than the standard implementation. Theil-Sen regression is a non-parametric method that calculates the median slope between all pairs of points in your dataset, which makes it extremely robust to outliers. The problem is that median operations produce discrete jumps, especially when you're working with limited sample sizes. Every time the median shifts from one value to another, you get a step change in your regression line, which creates visual choppiness that can be distracting even though the underlying calculations are sound.
The solution I ended up going with was convolving a Gaussian kernel around the center of the sorted lists to get a more continuous median estimate. Instead of just picking the middle value or averaging the two middle values when you have an even sample size, the Gaussian kernel weights the values near the center more heavily and smoothly tapers off as you move away from the median position. This creates a weighted average that behaves like a median in terms of robustness but produces much smoother transitions as new data points arrive and the sorted list shifts.
There are variance tradeoffs with this approach since you're no longer using the pure median, but they're minimal in practice. The kernel weighting stays concentrated enough around the center that you retain most of the outlier resistance that makes Theil-Sen useful in the first place. What you gain is a regression line that updates smoothly instead of jumping discretely, which makes it easier to spot genuine trend changes versus just the statistical noise of median recalculation. The smoothness is particularly noticeable when you're running the estimator over longer lookback periods where the sorted list is large enough that small kernel adjustments have less impact on the overall center of mass.
The Gaussian kernel itself is a bell curve centered on the median position, with a standard deviation you can tune to control how much smoothing you want. Tighter kernels stay closer to the pure median behavior and give you more discrete steps. Wider kernels spread the weighting further from the center and produce smoother output at the cost of slightly reduced outlier resistance. The default settings strike a balance that keeps the estimator robust while removing most of the visual jitter.
Running Theil-Sen on multiple bars means calculating slopes between all pairs of points across your lookback window, sorting those slopes, and then applying the Gaussian kernel to find the weighted center of that sorted distribution. This is computationally more expensive than simple moving averages or even standard linear regression, but Pine Script handles it well enough for reasonable lookback lengths. The benefit is that you get a trend estimate that doesn't get thrown off by individual spikes or anomalies in your price data, which is valuable when working with noisy instruments or during volatile periods where traditional regression lines can swing wildly.
The implementation maintains sorted arrays for both the slope calculations and the final kernel weighting, which keeps everything organized and makes the Gaussian convolution straightforward. The kernel weights are precalculated based on the distance from the center position, then applied as multipliers to the sorted slope values before summing to get the final smoothed median slope. That slope gets combined with an intercept calculation to produce the regression line values you see plotted on the chart.
What this really demonstrates is that you can take classical statistical methods like Theil-Sen and adapt them with signal processing techniques like kernel convolution to get behavior that's more suited to real-time visualization. The pure mathematical definition of a median is discrete by nature, but financial charts benefit from smooth, continuous lines that make it easier to track changes over time. By introducing the Gaussian kernel weighting, you preserve the core robustness of the median-based approach while gaining the visual smoothness of methods that use weighted averages. Whether that smoothness is worth the minor variance tradeoff depends on your use case, but for most charting applications, the improved readability makes it a good compromise.
Spread Trading Z-ScoreIndicator: Z-Score Spread Indicator
Description
The "Z-Score Spread Indicator" is a powerful tool for traders employing mean-reversion strategies on the spread between two financial assets (e.g., futures contracts like MNQ and MES). This indicator calculates and plots the Z-score of the price spread, indicating how far the current spread deviates from its historical mean. It features customizable entry and exit thresholds with adjustable offsets, along with an estimated p-value displayed in a table to assess statistical significance.
Key Features
Asset Selection: Allows users to select two asset symbols (e.g., CME_MINI:MNQ1! and CME_MINI:MES1!) via customizable inputs.
Z-Score Calculation: Computes the Z-score based on the spread’s simple moving average and standard deviation over a user-defined lookback period.
Customizable Thresholds with Offset: Offers adjustable base entry and exit thresholds, with an optional offset to fine-tune trading levels, plotted as horizontal lines.
P-Value Estimation: Provides an approximate p-value to evaluate the statistical significance of the Z-score, displayed in a table anchored to the top-left corner.
Visual Representation: Plots the Z-score with a zero line and threshold lines for intuitive interpretation.
Adjustable Parameters
Asset A Symbol: Symbol for Asset A (default: CME_MINI:MNQ1!).
Asset B Symbol: Symbol for Asset B (default: CME_MINI:MES1!).
Z-Score Lookback: Lookback period for Z-score calculation (default: 40, minimum 2).
Base Entry Threshold: Threshold for entry signals (default: 1.8, adjustable with a step of 0.1).
Base Exit Threshold: Threshold for exit signals (default: 0.5, adjustable with a step of 0.1).
Threshold Offset (+/-): Offset to adjust entry and exit thresholds symmetrically (default: 0.0, range -5.0 to 5.0, step 0.1).
Usage
Add the indicator to your chart via the "Indicators" tab.
Customize the parameters based on your preferred assets and trading strategy (lookback period, thresholds, offset).
Observe the Z-score plot and threshold lines (red for short entry, green for long entry, orange dotted for exits) to identify potential trade setups.
Check the p-value table in the top-left corner to assess the statistical significance of the current Z-score.
Use this data to inform mean-reversion trading decisions, ideally in conjunction with other indicators.
Notes
A Z-score above the entry threshold (positive) or below the negative entry threshold suggests a potential short or long entry, respectively. Exits are signaled when the Z-score crosses the exit thresholds.
The p-value is an approximation based on the normal distribution; a value below 0.05 typically indicates statistical significance, but further validation is recommended.
The indicator uses a simple spread (Asset A - Asset B) without volatility adjustments; consider pairing it with a lots calculator for hedging.
Limitations
The p-value is an approximation and may not reflect advanced statistical tests (e.g., ADF) due to Pine Script constraints.
No automatic trading signals are generated; it provides data for manual analysis.
Author
Developed by grogusama, October 15, 2025, 07:29 PM CEST.
VBE Pro - Advanced Volatility Bands with Zero Lag & PredictionVBE Pro: Zero-Lag Predictive Bands
A next-gen volatility envelope that blends zero-lag smoothing with forward-looking volatility models (EWMA/GARCH/HAR/ML) to keep bands tight in calm markets, responsive in shocks, and adaptive across regimes.
What it does
Builds volatility from multiple methods (ATR, StDev, Parkinson, Garman-Klass, Rogers-Satchell, Yang-Zhang).
Projects near-term vol with your choice of predictor, then blends it via a weight slider.
Applies zero-lag smoothing (ZLEMA/ZLMA/DEMA/TEMA/HMA/JMA/Ehlers/Kalman/T3) to cut delay without over-shoot.
Auto-adapts band width by regime (high/low/normal) and can expand dynamically with price acceleration.
Optional displacement to align with your execution style.
On-chart
Upper/Lower zero-lag bands with optional fill.
Middle line (ZL-smoothed source).
Regime-tinted background (High/Low).
Displacement marker (if used).
Compact top-right info table: current vs predicted vol, regime, squeeze, multiplier, methods, ZL gain, est. lag reduction.
Signals & Alerts
Break↑ / Break↓ when price crosses the bands.
Vol↑ / Vol↓ expansion/contraction sequences.
“Squeeze” when band width compresses vs its ZL average.
“ZL” marker when significant zero-lag is active.
Prediction divergence ⚠ when projected vol deviates > threshold.
Built-in alertconditions for all of the above.
Quick start
Method: ATR or Hybrid for robustness.
Smoothing: ZLEMA, length 5–8, ZL gain 2–3 (push higher only if you accept more projection).
Bands: Multiplier 2.0, Adaptive on, Dynamic off to start.
Prediction: EWMA, weight 0.25–0.35. Move to GARCH in mean-reverty tapes; HAR-RV for mixed regimes.
Regime lookback: 50.
Liquidity Index with Advanced Statistical NormalizationLiquidity Index with Advanced Statistical Normalization
An open-source TradingView indicator for analyzing global liquidity cycles using robust statistical methods
Overview
This Pine Script indicator combines multiple macroeconomic data sources to construct a composite liquidity index that tracks global financial conditions. It employs advanced statistical techniques typically found in quantitative finance research, adapted for real-time charting.
Key Features
📊 Multi-Source Data Integration
- Federal Reserve Components: Fed Funds Rate, Reverse Repo (RRP), Treasury General Account (TGA)
- PBOC Components: China M2 Money Stock adjusted by CNY/USD exchange rate
- Volatility Index: MOVE Index (bond market volatility)
🔬 Advanced Statistical Methods
1. Theil-Sen Estimator: Robust trend detection resistant to outliers
2. Triple Normalization:
- Z-score normalization
- MAD (Median Absolute Deviation) normalization
- Quantile normalization via inverse normal CDF
3. Multi-Timeframe Analysis: Short (8-bar) and long (34-bar) windows with blended composite
📈 Signal Processing
- Log-transformation for non-linear relationships
- Smoothing via customizable SMA
- Composite signal averaging across normalization methods
Why This Approach?
Traditional liquidity indicators often suffer from:
- Sensitivity to outliers in economic data
- Assumption of normal distributions
- Single-timeframe bias
This script addresses these issues by:
- Using median-based robust statistics (Theil-Sen, MAD)
- Applying multiple normalization techniques
- Blending short and long-term perspectives
Customization Options
short_length // Short window (default: 8)
long_length // Long window (default: 34)
show_short // Display short composite
show_long // Display long composite
show_blended // Display blended signal
smoothing_length // SMA smoothing period (default: 10)
How to Use
1. Liquidity Expansion (positive values): Risk-on environment, favorable for asset prices
2. Liquidity Contraction (negative values): Risk-off environment, potential market stress
3. Divergences: Compare indicator direction vs. price action for early warnings
Potential Improvements
Community members are encouraged to enhance:
- Additional data sources (ECB balance sheet, BOJ operations, etc.)
- Alternative normalization methods (robust scaling, rank transformation)
- Machine learning integration (LSTM forecasting, regime detection)
- Alert conditions for liquidity inflection points
- Volatility-adjusted weighting schemes
Technical Notes
- Uses request.security() for multi-symbol data fetching
- All calculations handle missing data via nz() functions
- Median-based statistics computed via array operations
- Custom inverse CDF approximation (no external libraries required)
Contributing
This is a foundation for liquidity analysis. Potential extensions:
- LLM Integration: Use language models to parse Fed/PBOC meeting minutes and adjust weights dynamically
- Sentiment Layer: Incorporate crypto funding rates or options skew
- Adaptive Parameters: Auto-tune window lengths based on market regime
- Cross-Asset Validation: Backtest signals against BTC, equities, bonds
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License: Open source - modify and redistribute freelyDisclaimer: For educational purposes only. Not financial advice.
Litecoin Rainbow Chart by Crypto LamaThis script adapts the popular Bitcoin Rainbow chart to Litecoin to visualize Litecoin's long-term price trend on a logarithmic scale.
It highlights potential buying or caution zones based on a power law growth model tied to Litecoin's halving cycles.
What it does:
The indicator overlays 23 colored bands from purple/blue (undervalued) to orange/red (overvalued) around a power law trend line.
It supports forward projections by extending the chart with user-defined future bars.
How it works:
The core trend uses a power law formula: P(t) = 10^(0.5 + 4.34 * log10(h + 1)), where h represents time in halving cycles.
23 colored bands are constructed by applying multipliers with a decaying factor that narrows over time.
To put it simple, it is the same power law trendline shifted up or down 23 times.
For projections, it adds future bars to the timeline and recalculates the trend and bands accordingly.
How to use it:
Apply the indicator to a Litecoin chart (VANTAGE:LTCUSD for best results).
Adjust the "Future Bars" input to extend projections, usually staying below 600 future bars prevents visual bugs.
Blue/green bands signal potential accumulation zones, as has been demonstrated for Bitcoin, an average price close to these levels will likely prove to be an excellent buying opportunity, while orange/red suggest distribution or caution.
This indicator should be used to visualize the macro long-term trend of Litecoin, and it is not supposed to be used for short-term forecasts as its accuracy decreases.
Originality:
While inspired by Bitcoin's Rainbow Chart, this version is customized for Litecoin by incorporating its unique halving schedule and calibrated power law parameters in the power law formula, offering a tailored tool for LTC-specific analysis.
Note: This is not financial advice. Use it alongside other tools and manage risks appropriately!
Bitcoin Gold Fair Value Model | AlphaNattBitcoin Gold Fair Value Model | AlphaNatt
A quantitative regression-based projection model that estimates Bitcoin’s fair value using gold as a macro-monetary benchmark.
This model, inspired by RJAlpha, applies a lag-adjusted statistical regression between gold and Bitcoin to identify the time-shifted correlation that historically aligns Bitcoin’s market value with gold’s macro trends. It produces a forward-looking projection, statistical confidence intervals, and explanatory metrics that assess the reliability of the relationship.
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🧠 Core Concept
Gold serves as a proxy for global liquidity and real monetary value, often leading risk assets during liquidity expansions and contractions.
Bitcoin’s long-term trend tends to react to these same liquidity cycles, but with a measurable lag.
This indicator models that lag statistically, estimating Bitcoin’s “fair value” as if its price were fully caught up to gold’s recent movements.
The regression captures both directional influence and proportional magnitude through slope and intercept coefficients.
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⚙️ Model Features
Dynamic Lag Regression – Uses a configurable leadDays period to align gold’s prior movements with Bitcoin’s current pricing behavior.
Rolling Sample Window – Continuously recalibrates the regression coefficients using a user-defined lookback length, allowing the model to adapt to new market conditions.
Forward Projection – Extends Bitcoin’s fair value into the future, based on present gold levels and the established lag relationship.
Volatility-Adjusted Confidence Bands – Displays one standard deviation and 95% confidence intervals around the projected path to visualize expected uncertainty.
Model Fitness Metric – Includes an R² score that quantifies the strength and stability of the BTC–Gold relationship within the active window.
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📈 Visualization Breakdown
Cyan Line: Historical gold-driven fair value of Bitcoin.
Magenta Lines: Future fair value projection and confidence bands (offset by leadDays).
Projection Label: Displays the 60-day projected price target.
Statistical Table: Shows live model output including the projected fair value, 1-SD range, 95% confidence interval, and R² score.
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🔧 User Inputs
Show 1 SD Bands? – Toggles visibility of the standard deviation boundaries.
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📊 Interpretation Guide
When Bitcoin trades below its projected fair value, the model suggests it is temporarily undervalued relative to gold’s macro trend.
When Bitcoin trades above its projected fair value, it may be overextended in relation to the model’s equilibrium estimate.
A higher R² implies greater reliability — periods where gold explains a large portion of Bitcoin’s price variance.
Confidence intervals represent uncertainty, not directional certainty; deviation beyond them often implies a structural shift in correlation or market regime.
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⚠️ Disclaimer
This indicator is designed for quantitative research and macro correlation analysis. It does not constitute investment advice, price prediction, or trading signal generation. Always verify assumptions and cross-check results with independent analysis before using in a live environment.
Bitcoin Power Law Corridor + Z-score
This script visualizes the long-term Bitcoin Power Law Corridor, a conceptual model originally discussed by Harold Christopher Burger, and enhances it with a logarithmic Z-Score framework.
The indicator plots Bitcoin’s long-term regression curve together with estimated resistance and support bands based on power-law relationships between price and time since inception.
The added Z-Score expresses the statistical distance between price and the central regression line, using logarithmic scaling:
Z ≈ 0 → price near its long-term fair-value trajectory.
Z ≈ +2 → price near the lower corridor boundary (historically undervalued region).
Z ≈ −2 → price near the upper corridor boundary (historically overheated region).
This indicator is designed for visual and educational purposes only.
It should not be considered financial advice, a predictive model, or a signal provider.
Users should always combine this tool with other forms of technical, fundamental, and sentiment analysis to confirm confluence before making any decision.
EMA+MACD动态0轴主图动态MACD，EMA55作为当前周期动态0轴使用。EMA13作为小4倍周期动态0轴。当前周期DIF线穿越0轴标记+MACD金死叉标记。
The main chart dynamic MACD and EMA55 are used as the dynamic 0-axis for the current cycle. EMA13 is used as the dynamic 0- axis for the smaller 4x cycle. The current cycle's DIF line has crossed the 0-axis, marked with a "+" sign indicating a golden cross on the MACD.
kNN Trend Classifier (RSI, CCI, W%R, ADX, EMA)This Pine Script indicator, "kNN Trend Classifier," is a specialized tool for identifying market trends using a combination of classic technical indicators and a simple machine learning approach. The script is designed for traders who want to blend quantitative analysis with visual chart signals to improve decision-making.
At its core, the indicator utilizes six key features: RSI (14), RSI (9), CCI, Williams %R, ADX, and 9-period EMA. Each of these measures different dimensions of momentum, trend strength, and market positioning. These values are normalized to ensure they are fairly compared across different market conditions. For every bar close, the script stores a historical snapshot of these features and uses a k-Nearest Neighbor (kNN) technique to classify the current trend as bullish or bearish. The kNN algorithm works by calculating the Euclidean distance between the present market state and all stored historical states, then referencing the majority label among the closest neighbors to decide on the market regime.
The script also provides powerful customization. Users can adjust the number of neighbors (k) to make signals more reactive or more stable; increase the training window for a deeper historical perspective; and fine-tune the lookback window for feature normalization. Two optional filters—based on volume percentile and ATR volatility—help to screen out signals in low-liquidity or low-volatility periods, reducing false positives and improving reliability.
To modify the behavior, traders should experiment with the inputs: lower k for faster signal changes, raise k or the training window for smoother signals, and adjust normalization length for different levels of trend sensitivity. Activating the volume and volatility filters is highly recommended during choppy markets. Altogether, this script gives users a robust framework to catch bullish or bearish trends with a blend of proven technical analysis and adaptive pattern recognition.
Optimus Pair Trader — Long/Short Two SymbolsThis indicator implements a Pairs Trading Z-Score model to identify mean-reversion opportunities between two correlated assets. It allows you to select a second symbol in the settings, automatically calculates the hedge ratio (β) using rolling correlation, and computes a Z-score spread to visualize divergence and convergence between the pair.
When the Z-score rises above the +threshold, the spread is considered wide — signaling a Short A / Long B setup. When it falls below the −threshold, the spread is narrow — signaling a Long A / Short B setup. When the Z-score returns within the exit band, the trade is closed.
Features include:
• Dynamic hedge ratio (β) calculation
• Z-score chart with entry/exit bands
• One-time entry/exit signal labels per trade
• Synthetic cumulative P&L performance line
• Info table displaying active pair stats
• Built-in alert conditions for Long, Short, and Exit signals
Ideal for studying statistical arbitrage, market-neutral strategies, and algorithmic pair signals across stocks, ETFs, futures, or crypto.
💎 Fade Core – RSI Pivots Hybrid + Adjustable EMAThis is a beta version that tries to catch extremes in the marker. It uses RSI extremes aligned with distance from 55EMA (reversion to mean).
FVG Zones with Signals█ OVERVIEW
"FVG Zones with Signals" is a technical analysis tool that identifies Fair Value Gaps (FVG) on the chart and draws customizable zones in the form of boxes. It is ideal for traders using price action and market structure strategies, helping to identify potential imbalance zones and trading opportunities based on breakout and exit signals. With flexible size filter settings, box styles, and signal options, the indicator ensures clarity and precision on the chart.
█ CONCEPTS
The indicator is designed to identify potential entry points for trades based on FVG breakouts or retests. For chart clarity, a size filter for FVGs is included, based on a multiplier of the average candle size over a specified period.
Why are FVGs important? FVG zones represent areas of market imbalance, often attracting price back to "fill" the gap. Larger gaps (with a higher size multiplier) have a greater chance of being retested, as they indicate deeper imbalances—leaving more unexecuted orders in those zones, which attracts liquidity. Market makers and institutions often return to these levels to "refresh" liquidity before further moves. However, not every large FVG is retested quickly—in strong trends, smaller imbalances may be ignored, and the location (e.g., near swing highs/lows) is critical for retest probability.
█ FEATURES
- FVG Detection: Identifies bullish and bearish FVGs based on size filters (Candle Size Period and FVG Size Multiplier), with automatic initialization of historical gaps up to 500 candles back.
- Customizable Boxes: Draws FVG boxes with adjustable border colors, background gradients, border styles (solid, dashed, dotted), border widths, and transparency for both the background and the 50% FVG midline.
- Breakout and Exit Signals: Generates "Break" signals (green upward triangle for breakouts above bearish FVG, red downward triangle for breakouts below bullish FVG) and "Exit" signals (circles for exiting the zone), with options to select signal types (Break, Exit, or Both). A break signal causes the box to disappear, leaving a triangle as a trace of the breakout, which may serve as a signal to open a position. Exit signals (circles) may also indicate entry opportunities but require additional confirmation, such as alignment with the main trend.
- Midline: Automatically draws a dashed line at the 50% FVG level with adjustable transparency, aiding in assessing price reactions within the zone.
- Box Limitation: Automatically removes old or inactive FVGs after 500 candles to avoid chart clutter.
- Alerts: Built-in alerts for all signal types, including price and FVG type descriptions.
█ HOW TO USE
Add to Chart: Apply the indicator to your TradingView chart via the Pine Editor or Indicators menu.
Configure Settings:
- FVG Settings: Adjust Candle Size Period (default 20) and FVG Size Multiplier (default 1) to filter out small gaps—higher values generate fewer but more significant FVGs.
- Box Settings: Configure colors and styles for bullish (green) and bearish (red) boxes, including background transparency (default 80) and midline transparency.
- Signal Settings: Select signal types (Break, Exit, or Both) in Signal Type. Breakout signals appear after a candle closes outside the zone, while exit signals appear when exiting an FVG without a full breakout.
- Styling: Customize signal colors (green for buy/up, red for sell/down) and shape sizes.
Interpreting Signals:
- Break Up Signal: A green triangle below the bar indicates a breakout above a bearish FVG, suggesting potential continuation of an uptrend.
- Break Down Signal: A red triangle above the bar indicates a breakout below a bullish FVG, suggesting potential continuation of a downtrend.
- Exit Up/Down Signal: A green/red circle indicates an exit from an FVG without a full breakout, which may signal the end of a correction or preparation for a reversal.
- FVG Zones: If the price returns to an FVG and fills the gap, it may indicate equilibrium; an unfilled gap often leads to a retest.
- Use signals in conjunction with other technical analysis tools for confirmation, such as RSI (to identify overbought/oversold conditions) or MACD (to confirm momentum). Analyze FVGs from higher timeframes—these zones act as stronger imbalance levels and carry greater structural significance.
Exit signals (retests without breakouts) tend to be most effective when traded in line with the current trend.
█ APPLICATIONS
- Price Action Trading: Use FVG zones as dynamic support and resistance levels. In an uptrend, look for buying opportunities in bullish FVGs, where price often tests the gap before continuing. Combining with RSI, MACD, or Fibonacci levels enhances the significance of zones.
- Breakout Strategies: Trade based on breakout signals from FVGs. A buy signal after breaking a bearish FVG may indicate a strong upward impulse, especially when supported by a rising MACD or RSI exiting oversold conditions.
Larger FVG gaps (higher multiplier) have a greater chance of retest, as they indicate deeper imbalances.
█ NOTES
- Test the indicator across different timeframes and markets (stocks, forex, crypto) to optimize size filters for your trading style.
- The indicator initializes historical FVGs up to 500 candles back, which may slow loading on longer charts.
- For best results, use on high-liquidity markets where FVGs are more frequently retested.
- In consolidation zones, the indicator may generate more false signals, so additional confirmation is recommended.
Machine Learning Price Predictor: Ridge AR [Bitwardex]🔹Machine Learning Price Predictor: Ridge AR is a research-oriented indicator demonstrating the use of Regularized AutoRegression (Ridge AR) for short-term price forecasting.
The model combines autoregressive structure with Ridge regularization , providing stability under noisy or volatile market conditions.
The latest version introduces Bull and Bear signals , visually representing the current momentum phase and model direction directly on the chart.
Unlike traditional linear regression, Ridge AR minimizes overfitting, stabilizes coefficient dynamics, and enhances predictive consistency in correlated datasets.
The script plots:
Fit Line — in-sample fitted data;
Forecast Line — out-of-sample projection;
Trend Segments — color-coded bullish/bearish sections;
Bull/Bear Labels 🐂🐻 — dynamic visual signals showing directional bias.
Designed for researchers, students, and developers, this tool helps explore regularized time-series forecasting in Pine Script™.
🧩 Ridge AR Settings
Training Window — number of bars used for model training;
Forecast Horizon — forecast length (bars ahead);
AR Order — number of lags used as features;
Ridge Strength (λ) — regularization coefficient;
Damping Factor — exponential trend decay rate;
Trend Length — period for trend/volatility estimation;
Momentum Weight — strength of the recent move;
Mean Reversion — pullback intensity toward the mean.
🧮 Data Processing
Prefilter:
None — raw close price;
EMA — exponential smoothing;
SuperSmoother — Ehlers filter for noise reduction.
EMA Length, SuperSmoother Length — smoothing parameters.
🖥️ Display Settings
Update Mode:
Lock — static model;
Update Once Reached — rebuild after forecast horizon;
Continuous — update every bar.
Forecast Color — projection line color;
Bullish/Bearish Colors — colors for trend segments.
🐂🐻 Bull/Bear Signal System
The Bull/Bear Signal System adds directional visual cues to highlight local momentum shifts and model-based trend confirmation.
Bull (🐂) — appears when upward momentum is confirmed (momentum > 0) .
Displayed below the bar, colored with Bullish Color.
Bear (🐻) — appears when downward momentum is dominant (momentum < 0) .
Displayed above the bar, colored with Bearish Color.
Signals are generated during model recalculations or when the directional bias changes in Continuous mode.
These visual markers are analytical aids , not trading triggers.
🧠 Core Algorithmic Components
Regularized AutoRegression (Ridge AR):
Solves: (X′X+λI)−1X′y
to derive stable regression coefficients.
Matrix and Pseudoinverse Operations — implemented natively in Pine Script™.
Prefiltering (EMA / Ehlers SuperSmoother) — stabilizes noisy data.
Forecast Dynamics — integrates damping, momentum, and mean reversion.
Trend Visualization — color-coded bullish/bearish line segments.
Bull/Bear Signal Engine — visualizes real-time impulse direction.
📊 Applications
Academic and educational purposes;
Demonstration of Ridge Regression and AR models;
Analysis of bull/bear market phase transitions;
Visualization of time-series dependencies.
⚠️ Disclaimer
This script is provided for educational and research purposes only.
It does not provide trading or investment advice.
The author assumes no liability for financial losses resulting from its use.
Use responsibly and at your own risk.
LSVR - Liquidity Sweep & Volume ReversalLSVR condenses a pro workflow into one visual overlay: Higher-Timeframe (HTF) Trend → Liquidity Sweep & Reclaim → Volume Confirmation. A signal only prints when all three gates align at bar close, and the chart shows everything you need—trend context, the sweep “trap” candle, and a projected Entry/SL/TP based on your chosen R multiple.
How it works
HTF Trend Filter: Projects a smoothed KAMA/EMA from a higher timeframe to the chart using a safe, lookahead-off request. Long signals are considered only above the HTF line; shorts only below.
Liquidity Sweep & Reclaim: Finds confirmed swing highs/lows, then detects an ATR-scaled overshoot through that swing followed by a reclaim (close back inside a configurable % of the bar range).
Volume Confirmation: Requires either a volume spike over Volume SMA × multiplier or optional OBV divergence. No participation = no signal.
Score: Each setup is scored: trend (0/1) + overshoot strength (0..1.5) + conviction (0/1). Signals fire only when the score ≥ Min Signal Score.
What you see
HTF Ribbon (subtle green/red backdrop) for bias.
Sweep Box on the signal candle (green = long, red = short).
Signal markers (“L” / “S”) with a small score label.
Projected lines that persist until the next signal: Entry (close), Stop (beyond swept swing), Target (R multiple).
Heatmap that intensifies when the score crosses your threshold.
Dashboard (top-right): HTF direction, Volume×SMA, current Score, gate pass status.
Tooltip on the last bar with quick stats.
Quick start
Apply to any liquid symbol and set HTF to ~3–6× your chart timeframe (e.g., 15m chart → 1H–4H).
Trade with the HTF trend: take L signals above the HTF line and S signals below it.
Entry = signal bar close, SL = beyond the swept swing, TP = your Projected Take-Profit (R).
Tighten or loosen selectivity with Min Signal Score, Reclaim %, Overshoot (ATR×), and Cooldown.
Recommended presets
Choppy/crypto 15m: minScore 1.25, reclaimPct 0.60–0.65, overshootATR 1.0–1.2, useOBVDiv=false, cooldown 8.
FX 5m / session trend: minScore 1.0–1.1, reclaimPct 0.50–0.55, overshootATR 0.8–1.0, useOBVDiv=true, cooldown 5.
Indices 1m (RTH): minScore 1.2, reclaimPct 0.55–0.60, useOBVDiv=false, cooldown 10.
Non-repainting by design
HTF values use lookahead_off with realtime offset.
Swings are confirmed pivots (no “forming” pivots).
Signals print at bar close only.
Notes
OBV divergence can add sensitivity on liquid markets; keep it off for stricter filtering.
Use Cooldown to avoid clustered sweeps.
This is an overlay/analysis tool, not financial advice. Test settings in Replay/Paper Trading before using live.
Stella Edge｜SuperFundedStella Edge — Quick Guide
What it is
Stella Edge is a higher-timeframe (HTF) EMA + ATR channel with adaptive zones, a volatility hazard filter, and clean entry/exit cues. It projects an HTF EMA with ATR bands, paints a calm-to-active “aurora” background using normalized ATR, and marks:
・Long cue when price crosses up into/through the lower band (potential buy zone).
・Short cue when price crosses down into/through the upper band (potential sell zone).
・Take-profit star when price crosses back through the HTF EMA against your active direction.
・Skull marker on extreme volatility bars (ATR & Volume spikes) to warn of unstable conditions.
Why this is not a simple mashup
・HTF regime first: Instead of reacting to local noise, entries are contextualized by an HTF EMA±ATR envelope (request.security) that frames price with structural zones (upper = supply bias, lower = demand bias).
・Risk-aware gating: A dual-threshold volatility filter flags bars where true range and volume spike far above their baselines—conditions that often degrade signal quality.
・Signal hygiene: Cross checks use band values from the prior bar to reduce duplicate/ambiguous triggers when HTF data updates, yielding cleaner, fewer, higher-quality icons.
・Visual cognition: The aurora background blends two night tones by the percent-rank of HTF ATR, so your eye immediately senses regime intensity without reading numbers.
How it works (concise)
1. Pull HTF EMA(len) and HTF ATR(len) via request.security.
2. Compute upper/lower bands = EMA ± ATR×multiplier (projected continuously).
3. Aurora mode: Normalize HTF ATR by 200-bar percent-rank and map it to a calm→active gradient for background.
4. Signals
・Long when close crosses up the lowerBand .
・Short when close crosses down the upperBand .
・Track tradeDirection and print a ⭐️ when price crosses the HTF EMA against the current direction (TP cue).
5. Volatility hazard (optional): Flag bars where
・TR ≥ ATR(avg, N) × multiplier and
・Volume ≥ SMA(volume, M) × multiplier.
These get a 💀 label so you can avoid forced entries/exits during disorderly bursts.
Parameters (UI mapping)
Higher-Timeframe & Core
・Higher TF for EMA/ATR: HTF used by request.security (e.g., 60).
・EMA Length (HTF): HTF EMA period.
・ATR Length (HTF): HTF ATR period.
・ATR Multiplier: Band width.
・Aurora mode: Toggle dynamic background (ATR-based gradient).
Volatility Filter (Volatility Filter group)
・Enable Extreme Volatility Filter?: On/off.
・ATR Period / ATR Spike Multiplier: Bar is “extreme” if TR ≥ ATR×multiplier.
・Volume MA Period / Volume Spike Multiplier: “Extreme” also requires Volume ≥ SMA×multiplier.
Signal Settings
・Long Arrow Color / Short Arrow Color: Icon colors for long/short cues.
Practical usage
・Plan around the HTF envelope:
・Below lower band → price may be stretched into demand zone (look for long cue & reaction).
・Above upper band → stretched into supply zone (look for short cue & reaction).
・Confirmation: Treat arrows as triggers, not commands. Favor entry when you also see reaction candles (rejection wicks, engulfings) or micro-structure alignment.
・Exit discipline: The ⭐️ on EMA cross-back is a simple, mechanical TP. You can scale out earlier using fixed R-multiples or prior swing levels.
・Hazard bars: Avoid initiating on 💀 bars; widen stops or step aside until volatility normalizes.
・Clutter control: If zones feel too reactive, raise HTF (e.g., 120/240) or increase ATR Length/Multiplier for broader, slower bands.
Repainting & HTF notes
・HTF series from request.security are final only when the HTF bar closes. Using upperBand /lowerBand for crosses helps reduce duplicate/early prints, but intrabar behavior on the current HTF bar can still evolve. Evaluate on closed bars for strict confirmation.
Best markets & timeframes
・Pairs/indices/crypto where trend–pullback cycles are common.
・Start with entry TF = your usual trading TF (e.g., 5m–1h) and HTF = 3–12× that TF (e.g., 60/120/240).
・For BTC/Gold or newsy assets, prefer higher HTF and the volatility filter ON.
Disclaimer
This tool identifies zones and timing cues; it does not guarantee outcomes. News shocks and liquidity gaps can invalidate any setup. Always size positions prudently and trade at your own risk.
SuperFunded invite-only
To obtain access, please DM me on TradingView or use the link in my profile.
Stella Edge — クイックガイド（日本語）
概要
Stella Edgeは、上位足EMA±ATRバンドで相場をフレーミングし、アダプティブな買い/売りゾーン、極端なボラティリティ警告、そしてシンプルなエントリー/利確キューを提供するインジです。
・ロング：価格が Lower Bandを上抜けたタイミングで矢印。
・ショート：価格が Upper Bandを下抜けたタイミングで矢印。
・利確⭐️：建玉方向に対して価格が HTF EMA を逆行クロスしたら表示。
・💀警告：ATRと出来高が同時スパイクした「危険」バーを明示。
・背景はHTF ATRのパーセントランクで静→動にグラデーションする「オーロラ」表現。
独自性・新規性
・上位足の構造を先に定義（EMA±ATR）→そこへ戻る/抜ける動きだけを狙うため、ノイズを減らした文脈型の判断が可能。
・二重スパイク条件（TR×ATR基準＋出来高×SMA基準）で、荒れ相場のエントリー回避を支援。
・シグナルの重複・不安定を抑制、見やすい最小限のアイコンに整理。
・視覚設計としてATRの相対的な強度を背景で可視化し、一目で局面認識。
使い方のヒント
・ゾーンは押し目/戻り目の候補。矢印はトリガーとして扱い、ローソクの反応（ピンバー/包み足など）で確認してから入る。
・⭐️は機械的TPの目安。スケールアウトやR倍数での利確も併用可。
・💀が出た足での新規は原則回避。HTFを上げるとゾーンはより鈍感＝落ち着いた絵に。
・HTF更新の注意：上位足バー確定までは値が変化し得ます。確定足ベースで検証するのが安全。
免責
本ツールは反発や到達を保証しません。イベントや流動性によって機能しないことがあります。資金管理のもと自己責任でご利用ください。
SuperFunded 招待専用スクリプト
このスクリプトはSuperFundedの参加者専用です。アクセスをご希望の方は、SuperFundedにご登録のメールアドレスから partner@superfunded.com 宛に、TradingViewの登録名をご送信ください。
Normalized Portfolio TrackerThis script lets you create, visualize, and track a custom portfolio of up to 15 assets directly on TradingView.
It calculates a synthetic "portfolio index" by combining multiple tickers with user-defined weights, automatically normalizing them so the total allocation always equals 100%.
All assets are scaled to a common starting point, allowing you to compare your portfolio’s performance versus any benchmark like SPY, QQQ, or BTC.
🚀 Goal
This script helps traders and investors:
• Understand the combined performance of their portfolio.
• Normalize diverse assets into a single synthetic chart .
• Make portfolio-level insights without relying on external spreadsheets.
🎯 Use Cases
• Backtest your portfolio allocations directly on the chart.
• Compare your portfolio vs. benchmarks like SPY, QQQ, BTC.
• Track thematic baskets (commodities, EV supply chain, regional ETFs).
• Visualize how each component contributes to overall performance.
📊 Features
• Weighted Portfolio Performance : Combines selected assets into a synthetic value series.
• Base Price Alignment : Each asset is normalized to its starting price at the chosen date.
• Dynamic Portfolio Table : Displays symbols, normalized weights (%), equivalent shares (based on each asset’s start price, sums to 100 shares), and a total row that always sums to 100%.
• Multi-Asset Support : Works with stocks, ETFs, indices, crypto, or any TradingView-compatible symbol.
⚙️ Configuration
Flexible Portfolio Setup
• Add up to 15 assets with custom weight inputs.
• You can enter any arbitrary numbers (e.g. 30, 15, 55).
• The script automatically normalizes all weights so the total allocation always equals 100%.
Start Date Selection
• Choose any custom start date to normalize all assets.
• The portfolio value is then scaled relative to the main chart symbol, so you can directly compare portfolio performance against benchmarks like SPY or QQQ.
Chart Styles
• Candlestick chart
• Heikin Ashi chart
• Line chart
Custom Display
• Adjustable colors and line widths
• Optionally display asset list, normalized weights, and equivalent shares
⚙️ How It Works
• Fetch OHLC data for each asset.
• Normalizes weights internally so totals = 100%.
• Stores each asset’s base price at the selected start date.
• Calculates equivalent “shares” for each allocation.
• Builds a synthetic portfolio value series by summing weighted contributions.
• Renders as Candlestick, Heikin Ashi, or Line chart.
• Adds a portfolio info table for clarity.
⚠️ Notes
• This script is for visualization only . It does not place trades or auto-rebalance.
• Weight inputs are automatically normalized, so you don’t need to enter exact percentages.
Regression Channel (ShareScope-style, parallel)What it does
Replicates ShareScope’s Trend of displayed data look: a single straight linear-regression line (dashed) across a chosen window with parallel, constant-width bands above and below, plus optional shading.
Use it to see the overall trend gradient for a period and a statistically sized channel based on the fit’s residual error.
How it works (math, short)
Computes an OLS regression once over the analysis window.
Residual standard error s is derived from SSE and degrees of freedom (n−2).
Band half-width is constant across the window:
Mean CI (narrower): half = z * s / √n
Prediction (wider): half = z * s * √(1 + 1/n)
Three straight, parallel lines are drawn from the regression endpoints; midline is dashed.
This is intentionally not a tapered CI (which widens at the ends). It matches the visual behaviour of ShareScope’s shaded trend line channel.
Inputs
Source – Price series (Close, High, Low, HL2, etc.).
Use last N bars / N (bars) – Rolling window length.
From / To (date mode) – Alternative fixed date window.
Confidence (%) – 90 / 95 / 99 / Custom (uses z≈t).
Custom Z (t) – Override the quantile if desired.
Prediction bands – Use wider prediction envelope instead of mean CI.
Shade region + colors / opacity / line width.
Usage
To mimic ShareScope exactly, pick the same date span (use date mode) and set Confidence 99%.
Choose Prediction OFF for a tighter “confidence” look; ON for a wider, more permissive channel.
If ShareScope used High as source, set Source = High here as well.
Notes & limitations
TradingView does not expose the visible viewport to Pine. The script cannot auto-read “displayed data.” Use last N bars or date range.
Bands are parallel by design. Prices may close outside; the channel does not bend.
Window capped at 5,000 bars for performance. No alerts are emitted.
Differences vs TV’s native tools
Linear Regression (drawing) – manual object; no statistical sizing or shading.
Linear Regression Channel (indicator) – uses price standard deviations around the regression; width is a user stdev multiple.
This script – uses residual error of the OLS fit and a z/t quantile to size a statistically meaningful parallel channel.
Changelog
r3.1 – Guard fix (no return at top level), minor refactor, stable line updates.
r3 – Switched to single-fit OLS with parallel constant-width bands (ShareScope look).
(Earlier experimental builds r1–r2.2 implemented rolling/tapered CI; superseded.)
Disclaimer: Educational use only. Not investment advice.
