DEGA RMA | QuantEdgeB🧠 Introducing DEGA RMA (DGR ) by QuantEdgeB
🛠️ Overview
DEGA RMA (DGR) is a precision-engineered trend-following system that merges DEMA, Gaussian kernel smoothing, and ATR-based envelopes into a single, seamless overlay indicator. Its mission: to filter out market noise while accurately capturing directional bias using a layered volatility-sensitive trend core.
DGR excels at identifying valid breakouts, sustained momentum conditions, and trend-defining price behavior without falling into the trap of frequent signal reversals.
🔍 How It Works
1️⃣ Double Exponential Moving Average (DEMA)
The system begins by applying a DEMA to the selected price source. DEMA responds faster than a traditional EMA, making it ideal for capturing transitions in momentum.
2️⃣ Gaussian Filtering
A custom Gaussian kernel is used to smooth the DEMA signal. The Gaussian function applies symmetrical weights, centered around the most recent bar, effectively softening sharp price oscillations while preserving the underlying trend structure.
3️⃣ Recursive Moving Average (RMA) Core
The filtered Gaussian output is then processed through an RMA to generate a stable dynamic baseline. This baseline becomes the foundation for the final trend logic.
4️⃣ ATR-Scaled Breakout Zones
Upper and lower trend envelopes are calculated using a custom ATR filter built on DEMA-smoothed volatility.
• ✅ Long Signal when price closes above the upper envelope
• ❌ Short Signal when price closes below the lower envelope
• ➖ Neutral when inside the band (no signal noise)
✨ Key Features
🔹 Multi-Layer Trend Model
DEMA → Gaussian → RMA creates a signal structure that is both responsive and robust.
🔹 Volatility-Aware Entry System
Adaptive ATR bands adjust in real-time, expanding during high volatility and contracting during calm periods.
🔹 Noise-Reducing Gaussian Kernel
Sigma-adjustable kernel ensures signal smoothness without introducing excessive lag.
🔹 Clean Visual System
Candle coloring and band fills make trend state easy to read and act on at a glance.
⚙️ Custom Settings
• DEMA Source – Input source for trend core (default: close)
• DEMA Length – Length for initial smoothing (default: 30)
• Gaussian Filter Length – Determines smoothing depth (default: 4)
• Gaussian Sigma – Sharpness of Gaussian curve (default: 2.0)
• RMA Length – Core baseline smoothing (default: 12)
• ATR Length – Volatility detection period (default: 40)
• ATR Mult Up/Down – Controls the upper/lower threshold range for signals (default: 1.7)
📌 How to Use
1️⃣ Trend-Following Mode
• Go Long when price closes above the upper ATR band
• Go Short when price closes below the lower ATR band
• Remain neutral otherwise
2️⃣ Breakout Confirmation Tool
DGR’s ATR-based zone logic helps validate price breakouts and filter out false signals that occur inside compressed ranges.
3️⃣ Volatility Monitoring
Watch the ATR envelope width — a narrowing band often precedes expansion and potential directional shifts.
📌 Conclusion
DEGA RMA (DGR) is a thoughtfully constructed trend-following framework that goes beyond basic moving averages. Its Gaussian smoothing, adaptive ATR thresholds, and layered filtering logic provide a versatile solution for traders looking for cleaner signals, less noise, and real-time trend awareness.
Whether you're trading crypto, forex, or equities — DGR adapts to volatility while keeping your chart clean and actionable.
🔹 Summary
• ✅ Advanced Smoothing → DEMA + Gaussian + RMA = ultra-smooth trend core
• ✅ Volatility-Adjusted Zones → ATR envelope scaling removes whipsaws
• ✅ Fully Customizable → Tailor to any asset or timeframe
• ✅ Quant-Inspired Structure → Built for clarity, consistency, and confidence
📌 Disclaimer: Past performance is not indicative of future results. No trading strategy can guarantee success in financial markets.
📌 Strategic Advice: Always backtest, optimize, and align parameters with your trading objectives and risk tolerance before live trading.
지표 및 전략
Gaussian Smooth Trend | QuantEdgeB🧠 Introducing Gaussian Smooth Trend (GST) by QuantEdgeB
🛠️ Overview
Gaussian Smooth Trend (GST) is an advanced volatility-filtered trend-following system that blends multiple smoothing techniques into a single directional bias tool. It is purpose-built to reduce noise, isolate meaningful price shifts, and deliver early trend detection while dynamically adapting to market volatility.
GST leverages the Gaussian filter as its core engine, wrapped in a layered framework of DEMA smoothing, SMMA signal tracking, and standard deviation-based breakout thresholds, producing a powerful toolset for trend confirmation and momentum-based decision-making.
🔍 How It Works
1️⃣ DEMA Smoothing Engine
The indicator begins by calculating a Double Exponential Moving Average (DEMA), which provides a responsive and noise-resistant base input for subsequent filtering.
2️⃣ Gaussian Filter
A custom Gaussian kernel is applied to the DEMA signal, allowing the system to detect smooth momentum shifts while filtering out short-term volatility.
This is especially powerful during low-volume or sideways markets where traditional MAs struggle.
3️⃣ SMMA Layer with Z-Filtering
The filtered Gaussian signal is then passed through a custom Smoothed Moving Average (SMMA). A standard deviation envelope is constructed around this SMMA, dynamically expanding/contracting based on market volatility.
4️⃣ Signal Generation
• ✅ Long Signal: Price closes above Upper SD Band
• ❌ Short Signal: Price closes below Lower SD Band
• ➖ No trade: Price stays within the band → market indecision
✨ Key Features
🔹 Multi-Stage Trend Detection
Combines DEMA → Gaussian Kernel → SMMA → SD Bands for robust signal integrity across market conditions.
🔹 Gaussian Adaptive Filtering
Applies a tunable sigma parameter for the Gaussian kernel, enabling you to fine-tune smoothness vs. responsiveness.
🔹 Volatility-Aware Trend Zones
Price must close outside of dynamic SD envelopes to trigger signals — reducing whipsaws and increasing signal quality.
🔹 Dynamic Color-Coded Visualization
Candle coloring and band fills reflect live trend state, making the chart intuitive and fast to read.
⚙️ Custom Settings
• DEMA Source: Price stream used for smoothing (default: close)
• DEMA Length: Period for initial exponential smoothing (default: 7)
• Gaussian Length / Sigma: Controls smoothing strength of kernel filter
• SMMA Length: Final smoothing layer (default: 12)
• SD Length: Lookback period for standard deviation filtering (default: 30)
• SD Mult Up / Down: Adjusts distance of upper/lower breakout zones (default: 2.5 / 1.8)
• Color Modes: Six distinct color palettes (e.g., Strategy, Warm, Cool)
• Signal Labels: Toggle on/off entry markers ("𝓛𝓸𝓷𝓰", "𝓢𝓱𝓸𝓻𝓽")
📌 Trading Applications
✅ Trend-Following → Enter on confirmed breakouts from Gaussian-smoothed volatility zones
✅ Breakout Validation → Use SD bands to avoid false breakouts during chop
✅ Volatility Compression Monitoring → Narrowing bands often precede large directional moves
✅ Overlay-Based Confirmation → Can complement other QuantEdgeB indicators like K-DMI, BMD, or Z-SMMA
📌 Conclusion
Gaussian Smooth Trend (GST) delivers a precision-built trend model tailored for modern traders who demand both clarity and control. The layered signal architecture, combined with volatility awareness and Gaussian signal enhancement, ensures accurate entries, clean visualizations, and actionable trend structure — all in real-time.
🔹 Summary Highlights
1️⃣ Multi-stage Smoothing — DEMA → Gaussian → SMMA for deep signal integrity
2️⃣ Volatility-Aware Filtering — SD bands prevent false entries
3️⃣ Visual Trend Mapping — Gradient fills + candle coloring for clean charts
4️⃣ Highly Customizable — Adapt to your timeframe, style, and volatility
📌 Disclaimer: Past performance is not indicative of future results. No trading strategy can guarantee success in financial markets.
📌 Strategic Advice: Always backtest, optimize, and align parameters with your trading objectives and risk tolerance before live trading.
Custom NYSE Hourly Intervals (Gris Extra Claro/T)NYSE Custom Hourly Intervals (Background Shading)
Indicator Overview:
This TradingView indicator visually highlights specific hourly intervals during the NYSE trading session (9:30 AM - 4:00 PM ET) using background shading. Its purpose is to help traders easily identify these key periods while analyzing price action.
Features:
Hourly Segmentation: Clearly marks the following hourly blocks within the NYSE session:
9:30 - 10:00 ET
10:00 - 11:00 ET
11:00 - 12:00 ET
12:00 - 13:00 ET
13:00 - 14:00 ET
14:00 - 15:00 ET
15:00 - 16:00 ET
Alternating Background: Uses a subtle, alternating background pattern for visual distinction:
Transparent: Applied during the 9:30-10:00, 11:00-12:00, 13:00-14:00, and 15:00-16:00 intervals (shows your default chart background).
Very Light Gray: Applied during the 10:00-11:00, 12:00-13:00, and 14:00-15:00 intervals.
Timeframe Restriction: The background shading is active only on chart timeframes of 30 minutes or less (e.g., 30m, 15m, 5m, 1m). It will not appear on higher timeframes.
Session Restriction: Shading only occurs during the defined NYSE session hours (9:30 AM - 4:00 PM ET).
Customization: The color and transparency level of the "Very Light Gray" shading can be adjusted in the indicator's settings.
Purpose & Use Case:
This indicator is ideal for intraday traders who want a clean visual guide to track price movement within specific hourly segments of the NYSE trading day, without needing complex overlays.
Z SMMA | QuantEdgeB📈 Introducing Z-Score SMMA (Z SMMA) by QuantEdgeB
🛠️ Overview
Z SMMA is a momentum-driven oscillator designed to track the standardized deviation of a Smoothed Moving Average (SMMA). By applying Z-score normalization, this tool dynamically adapts to price volatility, enabling traders to detect meaningful directional shifts and trend changes with enhanced clarity.
It serves both as a trend-following and mean-reversion system, identifying opportunities through standardized thresholds while remaining robust across volatile and calm market conditions.
✨ Key Features
🔹 Z-Score Normalization Engine
Applies Z-score to a custom SMMA baseline, allowing traders to compare price action relative to its recent volatility-adjusted mean.
🔹 Dynamic Trend Detection
Generates actionable long/short signals based on customizable Z-thresholds, making it adaptable across different asset classes and timeframes.
🔹 Overbought/Oversold Zones
Highlight reversion and profit-taking zones (default OB: +2 to +4, OS: -2 to -4), great for counter-trend or mean-reversion strategies.
🔹 Visual Reinforcement Tools
Includes candle coloring, gradient fills, and optional ALMA/EMA band overlays to visualize trend regime transitions.
🔍 How It Works
1️⃣ Z-Score SMMA Calculation
The core is a custom Smoothed Moving Average (SMMA) that is normalized by its standard deviation over a lookback period.
Final Formula:
Z = (SMMA - Mean) / StdDev
2️⃣ Signal Generation
• ✅ Long Bias: Z-Score > Long Threshold (default: 0)
• ❌ Short Bias: Z-Score < Short Threshold (default: 0)
3️⃣ Visual Aids
• Candle Color → Shows trend bias
• Band Fills → Highlight trend strength
• Overlays → Optional ALMA/EMA bands for structure analysis
⚙️ Custom Settings
• SMMA Length → Default: 12
• Z-Score Lookback → Default: 30
• Long Threshold → Default: 0
• Short Threshold → Default: 0
• Color Themes → Choose from 6 visual modes
• Extra Plots → Toggle advanced overlays (ALMA, EMA, bands)
• Label Display → Show/hide “𝓛𝓸𝓷𝓰” & “𝓢𝓱𝓸𝓻𝓽” markers
👥 Who Should Use It?
✅ Trend Traders → For early entries with confirmation from Z-score expansion
✅ Quantitative Analysts → Standardized deviation enables comparison across assets
✅ Mean-Reversion Traders → Use OB/OS zones to fade parabolic spikes
✅ Swing & Systematic Traders → Identify momentum shifts with optional ALMA/EMA overlays
📌 Conclusion
Z SMMA offers a smart, adaptive framework for tracking deviation from equilibrium in a quant-friendly format. Whether you're looking to follow trends or catch exhaustion points, Z SMMA provides a clear, standardized view of momentum and price extremes.
🔹 Key Takeaways:
1️⃣ Z-Score standardization ensures dynamic range awareness
2️⃣ SMMA base filters out noise, offering smoother signals
3️⃣ Color-coded visuals support faster reaction and cleaner charts
📌 Disclaimer: Past performance is not indicative of future results. No trading strategy can guarantee success in financial markets.
📌 Strategic Advice: Always backtest, optimize, and align parameters with your trading objectives and risk tolerance before
Kernel Weighted DMI | QuantEdgeB📊 Introducing Kernel Weighted DMI (K-DMI) by QuantEdgeB
🛠️ Overview
K-DMI is a next-gen momentum indicator that combines the traditional Directional Movement Index (DMI) with advanced kernel smoothing techniques to produce a highly adaptive, noise-resistant trend signal.
Unlike standard DMI that can be overly reactive or choppy in consolidation phases, K-DMI applies kernel-weighted filtering (Linear, Exponential, or Gaussian) to stabilize directional movement readings and extract a more reliable momentum signal.
✨ Key Features
🔹 Kernel Smoothing Engine
Smooths DMI using your choice of kernel (Linear, Exponential, Gaussian) for flexible noise reduction and clarity.
🔹 Dynamic Trend Signal
Generates real-time long/short trend bias based on signal crossing upper or lower thresholds (defaults: ±1).
🔹 Visual Encoding
Includes directional gradient fills, candle coloring, and momentum-based overlays for instant signal comprehension.
🔹 Multi-Mode Plotting
Optional moving average overlays visualize structure and compression/expansion within price action.
📐 How It Works
1️⃣ Directional Movement Index (DMI)
Calculates the traditional +DI and -DI differential to derive directional bias.
2️⃣ Kernel-Based Smoothing
Applies a custom-weighted average across historical DMI values using one of three smoothing methods:
• Linear → Simple tapering weights
• Exponential → Decay curve for recent emphasis
• Gaussian → Bell-shaped weight for centered precision
3️⃣ Signal Generation
• ✅ Long → Signal > Long Threshold (default: +1)
• ❌ Short → Signal < Short Threshold (default: -1)
Additional overlays signal potential compression zones or trend resumption using gradient and line fills.
⚙️ Custom Settings
• DMI Length: Default = 7
• Kernel Type: Options → Linear, Exponential, Gaussian (Def:Linear)
• Kernel Length: Default = 25
• Long Threshold: Default = 1
• Short Threshold: Default = -1
• Color Mode: Strategy, Solar, Warm, Cool, Classic, Magic
• Show Labels: Optional entry signal labels (Long/Short)
• Enable Extra Plots: Toggle MA overlays and dynamic bands
👥 Who Is It For?
✅ Trend Traders → Identify sustained directional bias with smoother signal lines
✅ Quant Analysts → Leverage advanced smoothing models to enhance data clarity
✅ Discretionary Swing Traders → Visualize clean breakouts or fades within choppy zones
✅ MA Compression Traders → Use overlay MAs to detect expansion opportunities
📌 Conclusion
Kernel Weighted DMI is the evolution of classic momentum tracking—merging traditional DMI logic with adaptable kernel filters. It provides a refined lens for trend detection, while optional visual overlays support price structure analysis.
🔹 Key Takeaways:
1️⃣ Smoothed and stabilized DMI for reliable trend signal generation
2️⃣ Optional Gaussian/exponential weighting for adaptive responsiveness
3️⃣ Custom gradient fills, dynamic MAs, and candle coloring to support visual clarity
📌 Disclaimer: Past performance is not indicative of future results. No trading strategy can guarantee success in financial markets.
📌 Strategic Advice: Always backtest, optimize, and align parameters with your trading objectives and risk tolerance before live trading.
Normalized DEMA Oscillator SD| QuantEdgeB📊 Introducing Normalized DEMA Oscillator SD (NDOSD) by QuantEdgeB
🛠️ Overview
Normalized DEMA Oscillator SD (NDOSD) is a powerful trend and momentum indicator that blends DEMA-based smoothing with a standard deviation-based normalization engine. The result is an oscillator that adapts to volatility, filters noise, and highlights both trend continuations and reversal zones with exceptional clarity.
It normalizes price momentum within an adaptive SD envelope, allowing comparisons across assets and market conditions. Whether you're a trend trader or mean-reverter, NDOSD provides the insight needed for smarter decision-making.
✨ Key Features
🔹 DEMA-Powered Momentum Core
Utilizes a Double EMA (DEMA) for smoother trend detection with reduced lag.
🔹 Normalized SD Bands
Price momentum is standardized using a dynamic 2× standard deviation range—enabling consistent interpretation across assets and timeframes.
🔹 Overbought/Oversold Detection
Includes clear OB/OS zones with shaded thresholds to identify potential reversals or trend exhaustion areas.
🔹 Visual Trend Feedback
Color-coded oscillator zones, candle coloring, and optional signal labels help traders immediately see trend direction and strength.
📐 How It Works
1️⃣ DEMA Calculation
The core of NDOSD is a smoothed price line using a Double EMA, designed to reduce false signals in choppy markets.
2️⃣ Normalization with SD
The DEMA is normalized within a volatility range using a 2x SD calculation, producing a bounded oscillator from 0–100. This transforms the raw signal into a structured format, allowing for OB/OS detection and trend entry clarity.
3️⃣ Signal Generation
• ✅ Long Signal → Oscillator crosses above the long threshold (default: 55) and price holds above the lower SD boundary.
• ❌ Short Signal → Oscillator drops below short threshold (default: 45), often within upper SD boundary context.
4️⃣ OB/OS Thresholds
• Overbought Zone: Above 100 → Caution / Consider profit-taking.
• Oversold Zone: Below 0 → Watch for accumulation setups.
⚙️ Custom Settings
• Calculation Source: Default = close
• DEMA Period: Default = 30
• Base SMA Period: Default = 20
• Long Threshold: Default = 55
• Short Threshold: Default = 45
• Color Mode: Choose from Strategy, Solar, Warm, Cool, Classic, or Magic
• Signal Labels Toggle: Show/hide Long/Short markers on chart
👥 Ideal For
✅ Trend Followers – Identify breakout continuation zones using oscillator thrust and SD structure
✅ Swing Traders – Catch mid-trend entries or mean reversion setups at OB/OS extremes
✅ Quant/Systemic Traders – Normalize signals for algorithmic integration across assets
✅ Multi-Timeframe Analysts – Easily compare trend health using standardized oscillator ranges
📌 Conclusion
Normalized DEMA Oscillator SD is a sleek and adaptive momentum toolkit that helps traders distinguish true momentum from false noise. With its fusion of DEMA smoothing and SD normalization, it works equally well in trending and range-bound conditions.
🔹 Key Takeaways:
1️⃣ Smoother momentum tracking using DEMA
2️⃣ Cross-asset consistency via SD-based normalization
3️⃣ Versatile for both trend confirmation and reversal identification
📌 Disclaimer: Past performance is not indicative of future results. No trading strategy can guarantee success in financial markets.
📌 Strategic Advice: Always backtest, optimize, and align parameters with your trading objectives and risk tolerance before live trading.
Let me know if you want a strategy script or publish-ready layout for TradingView next!
Median RSI SD| QuantEdgeB📈 Introducing Median RSI SD by QuantEdgeB
🛠️ Overview
Median RSI SD is a hybrid momentum tool that fuses two powerful techniques: Median Price Filtering and RSI-based Momentum. The result? A cleaner, more responsive oscillator designed to reduce noise and increase clarity in trend detection and potential reversals.
By applying the RSI not to raw price but to the percentile-based median, the indicator adapts better to real structural shifts in the market while filtering out temporary price spikes.
✨ Key Features
🔹 Smoothed RSI Momentum
Utilizes a percentile-based median as input to RSI, reducing volatility and enhancing signal reliability.
🔹 Volatility-Weighted SD Zones
Automatically detects overbought/oversold extremes using ±1 standard deviation bands on the median, adapting to current market volatility.
🔹 Trend Signal Overlay
A directional trend signal (Long / Short / Neutral) is derived from the RSI crossing custom thresholds, combined with position relative to SD bands.
🔹 Visual Labeling System
Optional in-chart labels for Long / Short signals and fully color-customizable theme modes.
📊 How It Works
1️⃣ Median RSI Calculation
Instead of using the close price directly, the script first computes a smoothed median via percentile ranking. RSI is then applied to this filtered stream, improving reactivity without overfitting to short-term noise.
2️⃣ Standard Deviation Filtering
Upper and lower SD bands are calculated around the median to identify extreme conditions. A position near the upper SD while RSI is below the short threshold triggers bearish bias. The reverse applies for longs.
3️⃣ Signal Generation
• ✅ Long Signal → RSI crosses above the Long Threshold (default: 65) and price holds above lower SD.
• ❌ Short Signal → RSI crosses below the Short Threshold (default: 45), typically within upper SD range.
4️⃣ Contextual Highlighting
Zone fills on the chart and RSI subgraph indicate Overbought (>75) and Oversold (<25) conditions for added clarity.
⚙️ Custom Settings
• RSI Length → Default: 21
• Median Length → Default: 10
• Long Threshold → Default: 65
• Short Threshold → Default: 45
• Color Mode → Choose from Strategy, Solar, Warm, Cool, Classic, Magic
• Signal Labels Toggle → Optional in-chart long/short labels
👥 Who Should Use It?
✅ Swing & Momentum Traders → Filter entries based on confirmed directional RSI setups.
✅ Range-Bound Traders → Use SD thresholds to spot fakeouts or exhaustion zones.
✅ Intraday Strategists → Enhanced signal clarity makes it usable even on lower timeframes.
✅ System Builders → Combine this signal with price action or confluence layers for smarter rules.
📌 Conclusion
Median RSI SD by QuantEdgeB is more than just a modified oscillator—it's a robust momentum confirmation framework designed for modern volatility. By replacing noisy price feeds with a statistically stable input and layering RSI + SD logic, this tool provides high-clarity signals without sacrificing responsiveness.
🔹 Key Takeaways:
1️⃣ Median-filtered RSI eliminates noise without lag
2️⃣ Standard deviation bands identify exhaustion zones
3️⃣ Reliable for both trend continuation and mean-reversion strategies
📌 Disclaimer: Past performance is not indicative of future results. No trading strategy can guarantee success in financial markets.
📌 Strategic Advice: Always backtest, optimize, and align parameters with your trading objectives and risk tolerance before live trading.
Linear % ST | QuantEdgeB🚀 Introducing Linear Percentile SuperTrend (Linear % ST) by QuantEdgeB
🛠️ Overview
Linear % SuperTrend (Linear % ST) by QuantEdgeB is a hybrid trend-following indicator that combines Linear Regression, Percentile Filters, and Volatility-Based SuperTrend Logic into one dynamic tool. This system is designed to identify trend shifts early while filtering out noise during choppy market conditions.
By utilizing percentile-based median smoothing and customized ATR multipliers, this tool captures both breakout momentum and pullback opportunities with precision.
✨ Key Features
🔹 Percentile-Based Median Filtering
Removes outliers and normalizes price movement for cleaner trend detection using the 50th percentile (median) of recent price action.
🔹 Linear Regression Smoothing
A smoothed baseline is computed with Linear Regression to detect the underlying trend while minimizing lag.
🔹 SuperTrend Structure with Adaptive Bands
The indicator implements an enhanced SuperTrend engine with custom ATR bands that adapt to trend direction. Bands tighten or loosen based on volatility and trend strength.
🔹 Dynamic Long/Short Conditions
Long and short signals are derived from the relationship between price and the SuperTrend threshold zones, clearly showing trend direction with optional "Long"/"Short" labels on the chart.
🔹 Multiple Visual Themes
Select from 6 built-in color palettes including Strategy, Solar, Warm, Cool, Classic, and Magic to match your personal style or strategy layout.
📊 How It Works
1️⃣ Percentile Filtering
The source price (default: close) is filtered using a nearest-rank 50th percentile over a custom lookback. This normalizes data to reflect the central tendency and removes noisy extremes.
2️⃣ Linear Regression Trend Base
A Linear Regression Moving Average (LSMA) is applied to the filtered median, forming the core trend line. This dynamic trendline provides a low-lag yet smooth view of market direction.
3️⃣ SuperTrend Engine
ATR is applied with custom multipliers (different for long and short) to create dynamic bands. The bands react to price movement and only shift direction after confirmation, preventing false flips.
4️⃣ Trend Signal Logic
• When price stays above the dynamic lower band → Bullish trend
• When price breaks below the upper band → Bearish trend
• Trend direction remains stable until violated by price.
⚙️ Custom Settings
• Percentile Length → Lookback for percentile smoothing (default: 35)
• LSMA Length → Determines the base trend via linear regression (default: 24)
• ATR Length → ATR period used in dynamic bands (default: 14)
• Long Multiplier → ATR multiplier for bullish thresholds (default: 0.8)
• Short Multiplier → ATR multiplier for bearish thresholds (default: 1.9)
✅ How to Use
1️⃣ Trend-Following Strategy
✔️ Go Long when price breaks above the lower ATR band, initiating an upward trend
✔️ Go Short when price falls below the upper ATR band, confirming bearish conditions
✔️ Remain in trend direction until the SuperTrend flips
2️⃣ Visual Confirmation
✔️ Use bar coloring and the dynamic bands to stay aligned with trend direction
✔️ Optional Long/Short labels highlight key signal flips
👥 Who Should Use Linear % ST?
✅ Swing & Position Traders → To ride trends confidently
✅ Trend Followers → As a primary directional filter
✅ Breakout Traders → For clean signal generation post-range break
✅ Quant/Systematic Traders → Integrate clean trend logic into algorithmic setups
📌 Conclusion
Linear % ST by QuantEdgeB blends percentile smoothing with linear regression and volatility bands to deliver a powerful, adaptive trend-following engine. Whether you're a discretionary trader seeking cleaner entries or a systems-based trader building logic for automation, Linear % ST offers clarity, adaptability, and precision in trend detection.
🔹 Key Takeaways:
1️⃣ Percentile + Regression = Noise-Reduced Core Trend
2️⃣ ATR-Based SuperTrend = Reliable Breakout Confirmation
3️⃣ Flexible Parameters + Color Modes = Custom Fit for Any Strategy
📈 Use it to spot emerging trends, filter false signals, and stay confidently aligned with market momentum.
📌 Disclaimer: Past performance is not indicative of future results. No trading strategy can guarantee success in financial markets.
📌 Strategic Advice: Always backtest, optimize, and align parameters with your trading objectives and risk tolerance before live trading.
Quantile DEMA Trend | QuantEdgeB🚀 Introducing Quantile DEMA Trend (QDT) by QuantEdgeB
🛠️ Overview
Quantile DEMA Trend (QDT) is an advanced trend-following and momentum detection indicator designed to capture price trends with superior accuracy. Combining DEMA (Double Exponential Moving Average) with SuperTrend and Quantile Filtering, QDT identifies strong trends while maintaining the ability to adapt to various market conditions.
Unlike traditional trend indicators, QDT uses percentile filtering to adjust for volatility and provides dynamic thresholds, ensuring consistent signal performance across different assets and timeframes.
✨ Key Features
🔹 Trend Following with Adaptive Sensitivity
The DEMA component ensures quicker responses to price changes while reducing lag, offering a real-time reflection of market momentum.
🔹 Volatility-Adjusted Filtering
The SuperTrend logic incorporates quantile percentile filters and ATR (Average True Range) multipliers, allowing QDT to adapt to fluctuating market volatility.
🔹 Clear Signal Generation
QDT generates clear Long and Short signals using percentile thresholds, effectively identifying trend changes and market reversals.
🔹 Customizable Visual & Signal Settings
With multiple color modes and customizable settings, you can easily align the QDT indicator with your trading strategy, whether you're focused on trend-following or volatility adjustments.
📊 How It Works
1️⃣ DEMA Calculation
DEMA is used to reduce lag compared to traditional moving averages. It is calculated by applying a Double Exponential Moving Average to price data. This smoother trend-following mechanism ensures responsiveness to market movements without introducing excessive noise.
2️⃣ SuperTrend with Percentile Filtering
The SuperTrend component adapts the trend-following signal by incorporating quantile percentile filters. It identifies dynamic support and resistance levels based on historical price data:
• Upper Band: Calculated using the 75th percentile + ATR (adjusted with multiplier)
• Lower Band: Calculated using the 25th percentile - ATR (adjusted with multiplier)
These dynamic bands adjust to market conditions, filtering out noise while identifying the true direction.
3️⃣ Signal Generation
• Long Signal: Triggered when price crosses below the SuperTrend Lower Band
• Short Signal: Triggered when price crosses above the SuperTrend Upper Band
The indicator provides signals with corresponding trend direction based on these crossovers.
👁 Visual & Custom Features
• 🎨 Multiple Color Modes: Choose from "Strategy", "Solar", "Warm", "Cool", "Classic", and "Magic" color palettes to match your charting style.
• 🏷️ Long/Short Signal Labels: Optional labels for visual cueing when a long or short trend is triggered.
• 📉 Bar Color Customization: Bar colors dynamically adjust based on trend direction to visually distinguish the market bias.
👥 Who Should Use QDT?
✅ Trend Followers: Use QDT as a dynamic tool to confirm trends and capture profits in trending markets.
✅ Swing Traders: Use QDT to time entries based on confirmed breakouts or breakdowns.
✅ Volatility Traders: Identify market exhaustion or expansion points, especially during volatile periods.
✅ Systematic & Quant Traders: Integrate QDT into algorithmic strategies to enhance market detection with adaptive filtering.
⚙️ Customization & Default Settings
- DEMA Length(30): Controls the lookback period for DEMA calculation
- Percentile Length(10): Sets the lookback period for percentile filtering
- ATR Length(14): Defines the length for calculating ATR (used in SuperTrend)
- ATR Multiplier(1.2 ): Multiplier for ATR in SuperTrend calculation
- SuperTrend Length(30):Defines the length for SuperTrend calculations
📌 How to Use QDT in Trading
1️⃣ Trend-Following Strategy
✔ Enter Long positions when QDT signals a bullish breakout (price crosses below the SuperTrend lower band).
✔ Enter Short positions when QDT signals a bearish breakdown (price crosses above the SuperTrend upper band).
✔ Hold positions as long as QDT continues to provide the same direction.
2️⃣ Reversal Strategy
✔ Take profits when price reaches extreme levels (upper or lower percentile zones) that may indicate trend exhaustion or reversion.
3️⃣ Volatility-Driven Entries
✔ Use the percentile filtering to enter positions based on mean-reversion logic or breakout setups in volatile markets.
🧠 Why It Works
QDT combines the DEMA’s quick response to price changes with SuperTrend's volatility-adjusted thresholds, ensuring a responsive and adaptive indicator. The use of percentile filters and ATR multipliers helps adjust to varying market conditions, making QDT suitable for both trending and range-bound environments.
🔹 Conclusion
The Quantile DEMA Trend (QDT) by QuantEdgeB is a powerful, adaptive trend-following and momentum detection system. By integrating DEMA, SuperTrend, and quantile percentile filtering, it provides accurate and timely signals while adjusting to market volatility. Whether you are a trend follower or volatility trader, QDT offers a robust solution to identify high-probability entry and exit points.
🔹 Key Takeaways:
1️⃣ Trend Confirmation – Uses DEMA and SuperTrend for dynamic trend detection
2️⃣ Volatility Filtering – Adjusts to varying market conditions using percentile logic
3️⃣ Clear Signal Generation – Easy-to-read signals and visual cues for strategy implementation
📌 Disclaimer: Past performance is not indicative of future results. No trading strategy can guarantee success in financial markets.
📌 Strategic Advice: Always backtest, optimize, and align parameters with your trading objectives and risk tolerance before live trading.
HILO Interpolation | QuantEdgeB🚀 Introducing HILO Interpolation by QuantEdgeB
🛠️ Overview
HILO Interpolation is a dynamic price-action based signal engine crafted to adapt across trending and ranging conditions. By leveraging percentile-based price band interpolation, it identifies high-confidence breakout and breakdown zones. This indicator is designed to serve both as a momentum trigger in trend phases and as a price-reactive entry system during range-bound consolidation.
By intelligently switching between percentile thresholds and interpolated logic, HILO minimizes noise and whipsaws commonly seen in traditional crossover systems.
✨ Key Features
🔹 Percentile Interpolation Engine
Tracks price breakouts using percentile thresholds, making it adaptable to volatility and asset-specific structure.
🔹 Price-Based Signal Confirmation
Signals are only triggered when price meaningfully crosses through key percentile thresholds (based on historical high/low logic).
🔹 Visual Trend Encoding
Color-coded candles, dynamic interpolation bands, and optional long/cash labels give clear visual cues for trend and trade direction.
🔹 Dynamic Threshold Switching
Interpolated threshold flips based on where price sits relative to percentile bands—providing adaptive long/short logic.
📊 How It Works
1️⃣ Percentile Zone Definition
HILO defines two key percentiles from the historical high and low:
• Upper Threshold: 75th Percentile of Highs
• Lower Threshold: 50th Percentile of Lows
These are calculated using linear interpolation to ensure smoother transitions across lookback periods.
2️⃣ Adaptive Signal Line
Instead of using static crossovers, HILO dynamically flips its signal based on whether price exceeds the upper threshold or falls below the lower one.
📌 If price > upper → Signal = Short threshold
📌 If price < lower → Signal = Long threshold
📌 If price remains between thresholds → no flip (trend continuation)
3️⃣ Signal Logic
✅ Long Signal → Price exceeds upper bound while lower bound acts as ceiling
❌ Short Signal → Price breaks below lower percentile while upper bound flips
This simple yet powerful mechanism creates early entries while maintaining high signal confidence.
👁 Visual & Custom Features
• 🎨 Multiple Color Modes: Strategy, Solar, Warm, Cool, Classic, Magic
• 🔄 Dynamic Candle & Band Coloring
• 🏷️ Signal Labels: Optional “𝓛𝓸𝓷𝓰” and “𝓢𝓱𝓸𝓻𝓽” tags when trend flips
• 💬 Alerts Ready: Long/Short crossover conditions can trigger alerts instantly
👥 Who Should Use HILO?
✅ Breakout Traders – Catch early trend starts using percentile filters
✅ Swing Traders – Identify directional bias shifts in advance
✅ Range Strategists – Use band confluence zones to play reversions
✅ Quant & Rule-Based Traders – Incorporate percentile logic into broader systems
⚙️ Customization & Default Settings
Percentile Length:(Default 35) Lookback for calculating percentile thresholds
Lookback Period:(Default 4) Lag factor for interpolation responsiveness
Upper % Threshold: (Default 75) Defines breakout zone from historical highs
Lower % Threshold: (Default 50) Defines retest/accumulation zone from historical lows
📌 How to Use HILO in Trading
1️⃣ Trend-Following Strategy
✔ Enter long when price flips above the adaptive support line
✔ Exit or go short when price breaks below the interpolated resistance
✔ Continue position as long as trend color persists
2️⃣ Range-Reversion Strategy
✔ Buy when price tests the lower threshold and no short signal is triggered
✔ Sell or reduce when price hits the upper range boundary
🧠 Why It Works
HILO operates on the principle that historical price structure creates natural probabilistic thresholds. By interpolating between these using percentile logic, the system maintains adaptability to changing market conditions—without the lag of moving averages or the noise of fixed bands.
🔹 Conclusion
HILO Interpolation is a minimalist yet powerful signal engine built for adaptive breakout and reversion detection. Its percentile-based logic offers a novel way to identify structure shifts, giving traders an edge in both trend and range markets.
🔹 Key Takeaways:
1️⃣ Breakout Entry Logic – Uses percentile interpolation instead of static bands
2️⃣ Color-Driven Clarity – Visual clarity via gradient zone overlays
3️⃣ Trend Integrity – Avoids overfitting and responds only to significant price movements
📌 Disclaimer: Past performance is not indicative of future results. No trading strategy can guarantee success in financial markets.
📌 Strategic Advice: Always backtest, optimize, and align parameters with your trading objectives and risk tolerance before live trading.
Normalized MACD with RSI & Stoch RSI + SignalsNormalized MACD with RSI & Stoch RSI Indicator
Overview:
This indicator combines three popular momentum indicators (MACD, RSI, and Stochastic RSI) into a single cohesive, normalized view, making it easier for traders to interpret market momentum and potential buy/sell signals. It specifically addresses an important issue—the different scale ranges of indicators—by normalizing MACD values to match the 0–100 scale of RSI and Stochastic RSI.
Here’s a clear and concise description of your updated Pine Script indicator:
⸻
Normalized MACD with RSI & Stoch RSI Indicator
Overview:
This indicator combines three popular momentum indicators (MACD, RSI, and Stochastic RSI) into a single cohesive, normalized view, making it easier for traders to interpret market momentum and potential buy/sell signals. It specifically addresses an important issue—the different scale ranges of indicators—by normalizing MACD values to match the 0–100 scale of RSI and Stochastic RSI.
⸻
Key Components:
① MACD (Normalized):
• The Moving Average Convergence Divergence (MACD) originally has an unlimited numerical range.
• Normalization Method:
• Uses a custom tanh(x) function implemented directly in Pine Script:
\tanh(x) = \frac{e^{x}-e^{-x}}{e^{x}+e^{-x}}
• MACD values are scaled using this method to a range of 0–100, with the neutral line at exactly 50.
• Interpretation:
• Values above 50 indicate bullish momentum.
• Values below 50 indicate bearish momentum.
② RSI (Relative Strength Index):
• Measures market momentum on a 0–100 scale.
• Traditional RSI interpretation:
• Overbought conditions: RSI > 70–80.
• Oversold conditions: RSI < 30–20.
③ Stochastic RSI:
• Combines RSI and Stochastic Oscillator to give short-term, highly sensitive signals.
• Helps identify immediate market extremes:
• Above 80 → Short-term overbought.
• Below 20 → Short-term oversold.
⸻
How the Indicator Works:
• Visualization:
• All three indicators (Normalized MACD, RSI, Stochastic RSI) share the same 0–100 scale.
• Clear visual lines and reference levels:
• Midline at 50 indicates neutral momentum.
• Dashed lines at 20 and 80 clearly mark oversold/overbought zones.
• Trading Signals (Recommended approach):
• Bullish Signal (Potential Buy):
• Normalized MACD crosses above 50.
• RSI below or approaching oversold zone (below 30–20).
• Stochastic RSI below 20, indicating short-term oversold conditions.
• Bearish Signal (Potential Sell):
• Normalized MACD crosses below 50.
• RSI above or approaching overbought zone (above 70–80).
• Stochastic RSI above 80, indicating short-term overbought conditions.
⸻
Why Use This Indicator?
• Harmonized Signals:
Normalization of MACD significantly improves clarity and comparability with RSI and Stochastic RSI, providing a unified momentum picture.
• Intuitive Analysis:
Traders can rapidly and intuitively identify momentum shifts without needing multiple indicator windows.
• Improved Decision-Making:
Clear visual references and signals help reduce subjective interpretation, potentially improving trading outcomes.
⸻
Suggested Usage:
• Combine with traditional support
Z-Score Normalized Volatility IndicesVolatility is one of the most important measures in financial markets, reflecting the extent of variation in asset prices over time. It is commonly viewed as a risk indicator, with higher volatility signifying greater uncertainty and potential for price swings, which can affect investment decisions. Understanding volatility and its dynamics is crucial for risk management and forecasting in both traditional and alternative asset classes.
Z-Score Normalization in Volatility Analysis
The Z-score is a statistical tool that quantifies how many standard deviations a given data point is from the mean of the dataset. It is calculated as:
Z = \frac{X - \mu}{\sigma}
Where X is the value of the data point, \mu is the mean of the dataset, and \sigma is the standard deviation of the dataset. In the context of volatility indices, the Z-score allows for the normalization of these values, enabling their comparison regardless of the original scale. This is particularly useful when analyzing volatility across multiple assets or asset classes.
This script utilizes the Z-score to normalize various volatility indices:
1. VIX (CBOE Volatility Index): A widely used indicator that measures the implied volatility of S&P 500 options. It is considered a barometer of market fear and uncertainty (Whaley, 2000).
2. VIX3M: Represents the 3-month implied volatility of the S&P 500 options, providing insight into medium-term volatility expectations.
3. VIX9D: The implied volatility for a 9-day S&P 500 options contract, which reflects short-term volatility expectations.
4. VVIX: The volatility of the VIX itself, which measures the uncertainty in the expectations of future volatility.
5. VXN: The Nasdaq-100 volatility index, representing implied volatility in the Nasdaq-100 options.
6. RVX: The Russell 2000 volatility index, tracking the implied volatility of options on the Russell 2000 Index.
7. VXD: Volatility for the Dow Jones Industrial Average.
8. MOVE: The implied volatility index for U.S. Treasury bonds, offering insight into expectations for interest rate volatility.
9. BVIX: Volatility of Bitcoin options, a useful indicator for understanding the risk in the cryptocurrency market.
10. GVZ: Volatility index for gold futures, reflecting the risk perception of gold prices.
11. OVX: Measures implied volatility for crude oil futures.
Volatility Clustering and Z-Score
The concept of volatility clustering—where high volatility tends to be followed by more high volatility—is well documented in financial literature. This phenomenon is fundamental in volatility modeling and highlights the persistence of periods of heightened market uncertainty (Bollerslev, 1986).
Moreover, studies by Andersen et al. (2012) explore how implied volatility indices, like the VIX, serve as predictors for future realized volatility, underlining the relationship between expected volatility and actual market behavior. The Z-score normalization process helps in making volatility data comparable across different asset classes, enabling more effective decision-making in volatility-based strategies.
Applications in Trading and Risk Management
By using Z-score normalization, traders can more easily assess deviations from the mean in volatility, helping to identify periods when volatility is unusually high or low. This can be used to adjust risk exposure or to implement volatility-based trading strategies, such as mean reversion strategies. Research suggests that volatility mean-reversion is a reliable pattern that can be exploited for profit (Christensen & Prabhala, 1998).
References:
• Andersen, T. G., Bollerslev, T., Diebold, F. X., & Vega, C. (2012). Realized volatility and correlation dynamics: A long-run approach. Journal of Financial Economics, 104(3), 385-406.
• Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327.
• Christensen, B. J., & Prabhala, N. R. (1998). The relation between implied and realized volatility. Journal of Financial Economics, 50(2), 125-150.
• Whaley, R. E. (2000). Derivatives on market volatility and the VIX index. Journal of Derivatives, 8(1), 71-84.
EMA 21 and SMA 50 Low ConditionsDescription:
This indicator highlights trend zones on a daily chart using the 21-day Exponential Moving Average (EMA) and 50-day Simple Moving Average (SMA). It’s designed to identify bullish conditions with two distinct background colors:
• Green Background: Signals a strong bullish trend. Appears when the low of the candle stays above the 21 EMA for 3 or more consecutive days, with either the 3rd or 4th day closing higher than its open (an “up” day). The green zone persists until a candle closes below the 21 EMA.
• Yellow Background: Indicates a potential support zone. Triggers when the low of the candle remains above the 50 SMA after the green condition ends, suggesting the price is still holding above a longer-term average. The yellow zone lasts until a candle closes below the 50 SMA.
Features:
• Plots the 21 EMA (blue line) and 50 SMA (orange line) for visual reference.
• Uses background colors to mark trend zones, making it easy to spot bullish phases and support levels.
• Optimized for daily timeframes, ideal for swing traders or long-term trend followers.
How to Use:
1. Apply the indicator to a daily chart.
2. Watch for the green background to identify strong bullish momentum (lows holding above the 21 EMA with an up close confirmation).
3. Look for the yellow background as a sign of potential support after the short-term trend weakens (lows above the 50 SMA).
4. Exit zones are triggered by closes below the respective averages (21 EMA for green, 50 SMA for yellow).
Notes:
• Best used on symbols with sufficient historical data to ensure accurate EMA and SMA calculations.
• The indicator prioritizes the green condition over yellow—green will override if both could apply.
Author’s Intent:
Created to help traders visualize sustained bullish trends and key support levels using simple moving average rules. Perfect for confirming uptrends and monitoring pullbacks within a broader bullish context.
Buffett Indicator with Historical Bubbles (Clean)The Buffett Indicator is a trusted macroeconomic gauge that compares the total US stock market capitalization to the nation’s GDP. Popularized by Warren Buffett, this metric highlights periods of overvaluation and undervaluation in the market.
This tool offers a clean and accurate visualization of the Buffett Indicator, enhanced with historical bubble annotations for key market events:
Dot-com Bubble (2000)
Global Financial Crisis Peak (2007)
COVID-19 Pre-crash Peak (2020)
Post-COVID Bull Market Peak (2021)
Features:
Dynamic Buffett Ratio (%) calculation using Wilshire 5000 Index as the market cap proxy.
Customizable GDP input for accuracy (update quarterly).
Visual thresholds for fair value, undervaluation, and overvaluation zones.
Historical event markers for educational and analytical context.
Optimized to display clearly across all timeframes: Daily, Weekly, Monthly.
How to Use:
Manually update the GDP input as new data is released.
Use this indicator for macro-level market sentiment analysis and valuation tracking.
Combine with other tools and risk management strategies for comprehensive market insights.
Disclaimer:
This indicator is for educational purposes only. It does not constitute financial advice. Always perform your own research and analysis.
Version: 1.0
we ask Allah reconcile and repay
#BuffettIndicator #MarketValuation #MacroAnalysis #BubbleDetector #LongTermInvestor #USMarket #Wilshire5000 #TradingViewScript
Option Contract Size CalculatorOption Contract Size Calculator
This indicator helps you to figure out the ideal number of contracts for your trade and its only used for options day trading.
The indicator needs to fill the input section in order to give you the information table that includes Contract size .
The input section consists of two sections. The first section requires user entry of the delta of the options contract from the broker chain and the stop loss size on the chart.
The second section allows you to enter your account balance and risk per trade
(2% recommended) .
There is also the option for where you wish to display your table like bottom right , bottom left or top right, top left.
special thanks to @Mohamedawke for the open source script this code is based off
Market Clock with Inline HoursThis script displays a powerful, configurable market session clock that shows the open/closed status and trading hours for major global financial markets — including specialized logic for NY Futures (Globex).
🔑 Key Features:
✅ Real-Time Session Status:
Shows whether each selected market is currently OPEN or CLOSED, based on the user’s selected time zone.
✅ NY Futures Weekend Logic:
Built-in logic ensures NY Futures are marked CLOSED:
Friday after 5:00 PM ET
All of Saturday
Sunday until 6:00 PM ET
This reflects the true CME Globex trading schedule.
✅ 12-Hour Format + Timezone Labels:
Session hours are displayed in 12-hour AM/PM format alongside their associated timezone (EST, GMT, JST, etc.) for clarity.
✅ Fully Configurable Markets:
You can choose to display:
NY Market (RTH)
NY Futures (Globex)
London
Tokyo
Frankfurt
And you can easily toggle them on/off in the settings.
✅ Text Size & Position Customization:
Easily control the text size (tiny → huge) and screen position (top/bottom, left/center/right).
✅ Auto Timezone Offset Support:
Select from a list of common time zones (EST, UTC, JST, etc.), or enter your own custom UTC offset for global flexibility.
✅ Compact & Clean Design:
The layout groups each market’s:
Real-time OPEN/CLOSED status
Trading hours
All into a single column, making the layout clean and dashboard-ready.
🧠 Who is this for?
Day traders
Futures traders
Forex traders
Anyone who tracks multiple time zones or global markets
📌 Notes:
Clock updates based on chart timeframe (e.g., every 1m on a 1-minute chart)
Pine Script doesn't support real-time per-second updates, but works well for market status tracking
💬 Feedback Welcome!
This script was designed to be lightweight and user-friendly. Suggestions and improvements are always welcome — feel free to leave a comment or reach out directly.
Prior LevelThe "Prior Level" indicator displays the previous day's key price levels (Open, High, Low, Close) directly on your chart. These reference levels are essential for intraday trading strategies, support/resistance analysis, and breakout identification.
Key features:
- Shows previous session's Open, High, Low and Close values
- Customizable line colors for better visual distinction
- Adjustable line length for cleaner chart appearance
- Optional data table showing exact values
- Simple and lightweight design for easy chart reading
This indicator helps traders identify important price zones from the previous trading session, allowing for more informed trading decisions based on how current price action interacts with these established levels.
The Silver Lining – GSR🍯 This tool converts the Gold/Silver Ratio (GSR) into a precision timing lens for short-term traders operating inside digital silver markets. It reveals structural dominance, trend exhaustion, and regime inflection by comparing the GSR to its smoothed baseline and historical percentile rhythm. On high timeframes (1D+), it reflects macroeconomic sentiment shifts 📈.
🧐 The lower the timeframe, the higher the alpha; the 15m and 1h charts are where you will the hidden pots of gold. For LTF traders, it becomes a hyper-responsive bias filter — especially when paired with volatility-based confirmation systems like SUPeR TReND 2.718, as shown.
🧠 The core logic compares the GSR (gold ÷ silver) against a user-defined moving average (VWMA or EMA). A color-coded fill shifts based on direction: amber when gold leads, teal when silver gains strength. Percentile bands (20th, 50th, 80th) map structural zones — helping traders anchor trades based on confluence, not hype.
📊 In the example chart, four theoretical long trades are shown on the 1h chart, manually drawn on the 15m timeframe. Each begins when the GSR reverses from the 80th percentile or breaks below its MA. The trades occur precisely as silver tested support, with confirmation from SUPeR TReND’s trend shift. Although idealized, these aren’t guesses — they are compression-to-expansion sequences backed by macro relative strength flow. Several yielded gains exceeding 4%.
🏆 Best-case long trades occur when GSR rotates down through the 50th percentile and silver catches a reactive bid. Shorts appear when GSR rises through the upper percentile band while silver fails to hold key intraday levels. The percentile bands function like behavioral tiers:
🥈 Below 20th = Silver Dominance
⚠️ Around 50th = Crossover Area
🥇 Above 80th = Gold Dominance
🥈 Why silver? It’s faster, more emotional, and more manipulated than gold — which paradoxically makes it more tradable on low timeframes. Its range-bound nature is ideal for rinse-and-repeat systems. Because we trade the derivative (XAGUSD), there’s no friction or delivery constraint — just price action, clean and liquid.
⚖️ The underlying strategy isn’t just technical; it’s alchemical. The system begins with short-term trading in digital silver and funnels gains into physical gold — converting volatility into wealth. Over time, this establishes a perpetual motion model: when profits allow, trade silver, extract value, cash out and convert into gold. The account stays active, and the hedge keeps growing.
🔁 The Silver Lining isn’t a signal engine. It’s a structural overlay. It tells you when the market’s invisible bias is shifting — so your tactics stay aligned with macro rhythm.
🌊 Silver moves fast. Gold moves first. The Silver Lining helps you bridge that gap — with clarity, confluence, and edge.
TP/SL Percentage & RR Visual ToolThis tool is designed to help traders visually and statistically assess their trade setup by calculating Stop Loss (SL), Take Profit (TP), and Risk-to-Reward (RR) based on percentage inputs from the current price.
🔧 How It Works:
Uses the current candle’s close price as your entry.
Calculates TP and SL as percentage-based levels (e.g., 1% SL, 1.5% TP).
Displays horizontal lines and labels on the chart for TP and SL (only on the latest candle to reduce clutter).
Shows a compact table in the top-right corner with all key values:
Entry Price
Current Price
TP Price (+%)
SL Price (-%)
TP Distance from current price
RR Ratio (e.g., 1:1.5)
💡 Use Cases:
Quickly validate if a trade setup meets your desired RR profile (e.g., 1:2).
Perfect for scalpers, swing traders, and position traders who rely on structured risk management.
Combine with your entry signal strategy to visualize targets and stops without manual calculations.
⚙️ Inputs:
Stop Loss % – Sets how far your SL is from the entry.
Take Profit % – Sets how far your TP is from the entry.
QT NY Session High/LowShows Asia & London High/Low which are key liquidity points price will react to.
You can also adjust the NY AM 6am - 12pm EST range to divide the time frames into 4 quarters
It delivers NY AM true open and the true day open
It gives you previous day high & previous day low
2013-2025 EclipsesIndicator Description: 2013-2025 Eclipses
This Pine Script (version 5) indicator overlays solar and lunar eclipse events on a TradingView chart, covering the period from 2013 to 2025. It is designed for traders and astrology enthusiasts who wish to visualize these significant astronomical events alongside price action, potentially identifying correlations with market movements or key turning points.
Features:
Eclipses:
Visualization: Displayed as a semi-transparent aqua background highlight across the chart.
Data: Includes 48 specific eclipse dates (both solar and lunar) from April 25, 2013, to September 21, 2025.
Purpose: Highlights dates of eclipses, which are often considered powerful astrological events associated with sudden changes, revelations, or significant shifts in energy and market sentiment.
Technical Details:
Overlay: The indicator is set to overlay=true, ensuring it displays directly on the price chart rather than in a separate pane.
Date Matching: Utilizes a helper function is_date(y, m, d) to determine if the current chart date matches any of the predefined eclipse dates, using TradingView's year, month, and dayofmonth variables.
Visualization Method:
bgcolor: Applies a light aqua background (using color.new(color.aqua, 85)) on the specific dates of eclipses. The transparency level of 85 allows price action to remain visible through the highlight.
Time Range: Spans from April 2013 to September 2025, covering a 12+ year period of eclipse events.
Usage:
Add the script to your TradingView chart to see eclipse dates highlighted with an aqua background on your chosen symbol and timeframe.
The background highlight appears only on the exact dates of eclipses, making it easy to spot these events amidst price data.
Ideal for those incorporating astrological analysis into trading or studying the potential impact of eclipses on financial markets.
Notes:
The script uses a single-line definition for eclipse_dates to ensure compatibility with Pine Script v5 syntax and avoid line continuation errors.
The aqua color matches the original circle-based visualization, with transparency adjustable via the color.new(color.aqua, 85) parameter (0 = fully opaque, 100 = fully transparent).
Works best on daily or higher timeframes for clear visibility of individual eclipse dates, though it functions on any TradingView-supported timeframe.
Eclipse dates should be cross-checked with astronomical sources for critical applications, as the script relies on the provided data accuracy.
Purpose:
This indicator provides a straightforward way to track eclipses over a 12-year period, offering a visual representation of these potent celestial events. By using a background highlight instead of markers, it maintains chart clarity while emphasizing the specific days when eclipses occur, potentially aiding in the analysis of their influence on market behavior or personal trading strategies.
2013-2025 Moon Phases & Mercury RetrogradesIndicator Description: 2013-2025 Moon Phases & Mercury Retrogrades
This Pine Script (version 5) indicator overlays key astrological events on a TradingView chart, specifically tracking full moons, new moons, and Mercury retrograde periods from 2013 to 2025. It is designed to help traders and astrology enthusiasts visualize these celestial events alongside price action, potentially identifying correlations or patterns.
Features:
New Moons:
Visualization: Plotted as small white circles above the price bars.
Data: Includes 156 specific new moon dates from January 11, 2013, to December 20, 2025.
Purpose: Marks the start of the lunar cycle, often associated with new beginnings or shifts in energy.
Full Moons:
Visualization: Plotted as small orange circles above the price bars.
Data: Includes 157 specific full moon dates from January 27, 2013, to December 15, 2025.
Purpose: Highlights the peak of the lunar cycle, often linked to heightened emotions or market volatility in astrological analysis.
Mercury Retrogrades:
Visualization: Displayed as a light red background highlight across the chart.
Data: Covers 39 Mercury retrograde periods, with precise start and end timestamps from February 23, 2013, to November 29, 2025.
Purpose: Indicates periods traditionally associated with communication issues, delays, or reversals, which some traders monitor for potential market impacts.
Technical Details:
Overlay: The indicator is set to overlay=true, meaning it displays directly on the price chart rather than in a separate pane.
Date Matching: Uses a helper function is_date(y, m, d) to check if the current chart date matches any of the predefined event dates, leveraging TradingView's year, month, and dayofmonth variables.
Visualization Methods:
plotshape: Used for new moons (white circles) and full moons (orange circles), positioned above bars for clear visibility.
bgcolor: Used for Mercury retrograde periods, applying a semi-transparent red highlight (transparency level 85) to the background during active retrograde periods.
Time Range: Spans from January 2013 to December 2025, providing a comprehensive 13-year view of these astrological events.
Usage:
Add the script to your TradingView chart to see new moons, full moons, and Mercury retrograde periods overlaid on your chosen symbol and timeframe.
The white and orange circles appear on specific dates, while the red background highlights extend across the duration of each Mercury retrograde period.
Useful for traders incorporating astrology into their analysis or anyone interested in tracking these celestial events alongside financial data.
Notes:
The script assumes accurate date data as provided; users should verify dates against astronomical sources if precision is critical.
The transparency of the Mercury retrograde background can be adjusted by modifying the value in color.new(color.red, 85) (0 = fully opaque, 100 = fully transparent).
Best viewed on daily or higher timeframes for clarity, though it works on any timeframe supported by TradingView.
This indicator provides a visual tool to explore the potential influence of lunar phases and Mercury retrograde periods on market behavior, blending astrology with technical analysis in a clear, customizable format.
Chonky ATR Levels 2.0Show ATR based high/low projections.
Choose a custom ATR calculation in the indicator's settings.
The default is a 20day RMA based ATR.
----------How projections are calculated----------
To project the ATR High, the ATR value is added to the low of the current candle that matches the ATR's timeframe.
To project the ATR Low, the ATR value is subtracted from the high of the current candle that matches the ATR's timeframe.
Example:
If a 20day RMA ATR is used:
- the ATR High will be the current day's low + the ATR value.
- the ATR Low will be the current day's high - the ATR value.
*However*, if the price action exceeds either ATR projection, the opposite ATR level will be fixed to the extreme of the period.
See the AUDUSD screenshot above for an example.
The ATR Low was exceeded, so the ATR High projection is capped at the high of day.
If the ATR High is exceeded, the ATR Low would be capped at the low of day.
Rachas ATR AssistHey Traders!
This indicator is a simple, it uses Average True Range (ATR) data from the daily chart and the current timeframe to estimate potential range and volatility.
This indicator compares the daily ATR to the current daily wick range (from low to high), helping you gauge how much "room" might be left for price movement within the day. Alongside that, it shows the ATR over the last 14 candles and 5 candles on your current chart for intraday volatility awareness—ideal for setting stops, targets, or position sizing.
Gauge Daily Potential Movement:
The "Day Range Difference" cell shows how much of the expected daily range (based on ATR) is still unfilled. If the market has moved less than the average, there's still potential for expansion. If it's close to or above the ATR, expect a slowdown or reversal.
Position Sizing & Stop Losses:
Use the 14-period ATR and 5-period ATR on your current timeframe to understand recent volatility. This helps in choosing logical stop loss levels and adjusting position sizes based on market conditions.
Volatility Awareness:
Knowing the average daily range and how much of it has been used lets you avoid entering trades too late in the move or placing stops in overly tight spots.
Table Position & Font:
You can adjust the table location (top/bottom left/right) and font size to best fit your chart layout.
