Multi-Timeframe ATR MonitorThis indicator displays a table of ATR values across multiple user-defined timeframes (1m, 3m, 5m, 15m, 60m, daily by default) and tracks the session range since 18:00. Customize the timeframes and ATR length via inputs.
Volatility
CME Price Limits (Futures Prop Firm Rule)This indicator shows the CME Price Limit, combined with a safety distance that is used by several futures prop firms. Trading in the highlighted area means a rule violation for many Futures prop firm accounts.
The levels are calculated from the "Settlement as close" closing price of the previous daily candle.
JPMorgan Collar LevelsThis indicator visualizes the current JPMorgan Hedging Collar strategy commonly used by institutional funds like JHEQX. It plots three key levels:
– Short Call strike (upper bound)
– Long Put strike (protection level)
– Short Put strike (cost reduction)
The area between the long put and short call is shaded to represent the active hedging zone. This setup is updated quarterly and can influence SPX market behavior near expiration dates.
Inputs are customizable to reflect the latest collar configuration. Useful for traders tracking institutional hedging flows or analyzing market structure near key option expirations.
RSI-Volume Momentum Signal ScoreRSI-Volume Momentum Signal Score
Description
The RSI-Volume Momentum Signal Score is a predictive technical indicator designed to identify bullish and bearish momentum shifts by combining volume-based momentum with the Relative Strength Index (RSI). It generates a Signal Score derived from:
• The divergence between short-term and long-term volume (Volume Oscillator), and
• RSI positioning relative to a user-defined threshold.
This hybrid approach helps traders detect early signs of price movement based on volume surges and overbought/oversold conditions.
The Signal Score is computed as follows:
Signal Score = Volume Momentum x RSI Divergence Factor
Volume Momentum = tanh ((Volume Oscillator value (vo) – Volume Threshold)/Scaling Factor)
RSI Divergence Factor = ((RSI Threshold – RSI Period)/Scaling Factor)
Or,
Signal Score = tanh((vo - voThreshold) / scalingFactor) * ((rsiThreshold - rsi) / scalingFactor)
The logic of this formula are as follows:
• If Volume Oscillator >= Volume Threshold and RSI <= RSI Threshold: Bullish Signal (+1 x Scaling Factor)
• If Volume Oscillator >= Volume Threshold and RSI >= (100 – RSI Threshold): Bearish Signal (-1 x Scaling Factor)
• Otherwise: Neutral (0)
The tanh function provides the normalization process. It ensures that the final signal score is bounded between -1 and 1, increases sensitivity to early changes in volume patterns based on RSI conditions, and prevent sudden jumps in signals ensuring smooth and continuous signal line.
Input Fields
The input fields allow users to customize the behavior of the indicator based on their trading strategy:
Short-Term Volume MA
- Default: `2`
- Description: The period for the short-term moving average of volume.
- Purpose: Captures short-term volume trends.
Long-Term Volume MA)
- Default: `10`
- Description: The period for the long-term moving average of volume.
- Purpose: Captures long-term volume trends for comparison with the short-term trend.
RSI Period)
- Default: `3`
- Description: The period for calculating the RSI.
- Purpose: Measures the relative strength of price movements over the specified period.
Volume Oscillator Threshold
- Default: `70`
- Description: The threshold for the Volume Oscillator to determine significant volume momentum.
- Purpose: Filters out weak volume signals.
RSI Threshold
- Default: `25`
- Description: The RSI level used to identify overbought or oversold conditions.
- Purpose: Helps detect potential reversals in price momentum.
Signal Scaling Factor
- Default: `10`
- Description: A multiplier for the signal score.
- Purpose: Adjusts the magnitude of the signal score for better visualization.
How To Use It for Trading:
Upcoming Bullish Signal: Signal line turns from Gray to Green or from Green to Gray
Upcoming Bearish Signal: Signal line turns from Gray to Red or from Red to Gray
Note: The price that corresponds to the transition of Signal line from Gray to Green or Red and vise versa is the signal price for upcoming bullish or bearish signal.
The signal score dynamically adjusts based on volume and RSI thresholds, making it adaptable to various market conditions, and this is what makes the indicator unique from other traditional indicators.
Unique Features
Unlike traditional indicators, this indicator combines two different dimensions—volume trends and RSI divergence—for more comprehensive signal generation. The use of tanh() to scale and smooth the signal is a mathematically elegant way to manage signal noise and highlight genuine trends. Traders can tune the scaling factor and thresholds to adapt the indicator for scalping, swing trading, or longer-term investing.
Follow Line Strategy Version 2.5 (React HTF)Follow Line Strategy v2.5 (React HTF) - TradingView Script Usage
This strategy utilizes a "Follow Line" concept based on Bollinger Bands and ATR to identify potential trading opportunities. It includes advanced features like optional working hours filtering, higher timeframe (HTF) trend confirmation, and improved trend-following entry/exit logic. Version 2.5 introduces reactivity to HTF trend changes for more adaptive trading.
Key Features:
Follow Line: The core of the strategy. It dynamically adjusts based on price breakouts beyond Bollinger Bands, using either the low/high or ATR-adjusted levels.
Bollinger Bands: Uses a standard Bollinger Bands setup to identify overbought/oversold conditions.
ATR Filter: Optionally uses the Average True Range (ATR) to adjust the Follow Line offset, providing a more dynamic and volatility-adjusted entry point.
Optional Trading Session Filter: Allows you to restrict trading to specific hours of the day.
Higher Timeframe (HTF) Confirmation: A significant feature that allows you to confirm trade signals with the trend on a higher timeframe. This can help to filter out false signals and improve the overall win rate.
HTF Selection Method: Choose between Auto and Manual HTF selection:
Auto: The script automatically determines the appropriate HTF based on the current chart timeframe (e.g., 1min -> 15min, 5min -> 4h, 1h -> 1D, Daily -> Monthly).
Manual: Allows you to select a specific HTF using the Manual Higher Timeframe input.
Trend-Following Entries/Exits: The strategy aims to enter trades in the direction of the established trend, using the Follow Line to define the trend.
Reactive HTF Trend Changes: v2.5 exits positions not only based on the trade timeframe (TTF) trend changing, but also when the higher timeframe trend reverses against the position. This makes the strategy more responsive to larger market movements.
Alerts: Provides buy and sell alerts for convenient trading signal notifications.
Visualizations: Plots the Follow Line for both the trade timeframe and the higher timeframe (optional), making it easy to understand the strategy's logic.
How to Use:
Add to Chart: Add the "Follow Line Strategy Version 2.5 (React HTF)" script to your TradingView chart.
Configure Settings: Customize the strategy's settings to match your trading style and preferences. Here's a breakdown of the key settings:
Indicator Settings:
ATR Period: The period used to calculate the ATR. A smaller period is more sensitive to recent price changes.
Bollinger Bands Period: The period used for the Bollinger Bands calculation. A longer period results in smoother bands.
Bollinger Bands Deviation: The number of standard deviations from the moving average that the Bollinger Bands are plotted. Higher deviations create wider bands.
Use ATR for Follow Line Offset?: Enable to use ATR to calculate the Follow Line offset. Disable to use the simple high/low.
Show Trade Signals on Chart?: Enable to show BUY/SELL labels on the chart.
Time Filter:
Use Trading Session Filter?: Enable to restrict trading to specific hours of the day.
Trading Session: The trading session to use (e.g., 0930-1600 for regular US stock market hours). Use 0000-2400 for all hours.
Higher Timeframe Confirmation:
Enable HTF Confirmation?: Enable to use the HTF trend to filter trade signals. If enabled, only trades in the direction of the HTF trend will be taken.
HTF Selection Method: Choose between "Auto" and "Manual" HTF selection.
Manual Higher Timeframe: If "Manual" is selected, choose the specific HTF (e.g., 240 for 4 hours, D for daily).
Show HTF Follow Line?: Enable to plot the HTF Follow Line on the chart.
Understanding the Signals:
Buy Signal: The price breaks above the upper Bollinger Band, and the HTF (if enabled) confirms the uptrend.
Sell Signal: The price breaks below the lower Bollinger Band, and the HTF (if enabled) confirms the downtrend.
Exit Long: The trade timeframe trend changes to downtrend or the higher timeframe trend changes to downtrend.
Exit Short: The trade timeframe trend changes to uptrend or the higher timeframe trend changes to uptrend.
Alerts:
The script includes alert conditions for buy and sell signals. To set up alerts, click the "Alerts" button in TradingView and select the desired alert condition from the script. The alert message provides the ticker and interval.
Backtesting and Optimization:
Use TradingView's Strategy Tester to backtest the strategy on different assets and timeframes.
Experiment with different settings to optimize the strategy for your specific trading style and risk tolerance. Pay close attention to the ATR Period, Bollinger Bands settings, and the HTF confirmation options.
Tips and Considerations:
HTF Confirmation: The HTF confirmation can significantly improve the strategy's performance by filtering out false signals. However, it can also reduce the number of trades.
Risk Management: Always use proper risk management techniques, such as stop-loss orders and position sizing, when trading any strategy.
Market Conditions: The strategy may perform differently in different market conditions. It's important to backtest and optimize the strategy for the specific markets you are trading.
Customization: Feel free to modify the script to suit your specific needs. For example, you could add additional filters or entry/exit conditions.
Pyramiding: The pyramiding = 0 setting prevents multiple entries in the same direction, ensuring the strategy doesn't compound losses. You can adjust this value if you prefer to pyramid into winning positions, but be cautious.
Lookahead: The lookahead = barmerge.lookahead_off setting ensures that the HTF data is calculated based on the current bar's closed data, preventing potential future peeking bias.
Trend Determination: The logic for determining the HTF trend and reacting to changes is critical. Carefully review the f_calculateHTFData function and the conditions for exiting positions to ensure you understand how the strategy responds to different market scenarios.
Disclaimer:
This script is for informational and educational purposes only. It is not financial advice, and you should not trade based solely on the signals generated by this script. Always do your own research and consult with a qualified financial advisor before making any trading decisions. The author is not responsible for any losses incurred as a result of using this script.
RSI + ADX + ATR Combo Indicator: RSI + ADX + ATR Combo Filter
This indicator is a confluence filter tool that combines RSI, ADX, and ATR into a single, easy-to-read chart overlay. It is designed to help traders identify low-volatility, non-trending zones with balanced momentum—ideal for strategies that rely on breakouts or reversals.
🔍 Core Components:
RSI (Relative Strength Index)
Standard RSI with custom upper and lower bounds (default: 60 and 40).
Filters out extreme overbought/oversold regions and focuses on price consolidation zones.
ADX (Average Directional Index)
Measures trend strength.
When ADX is below a custom threshold (default: 20), it indicates a weak or range-bound trend.
ATR (Average True Range)
Represents volatility.
Low ATR values (default threshold: 2.5) are used to filter out high-volatility environments, helping refine entries.
🟣 Signal Logic:
A signal is highlighted with a background color when all three conditions are met:
RSI is between lower and upper bounds (e.g., 40 < RSI < 60) ✅
ADX is below the trend threshold (e.g., ADX < 20) ✅
ATR is below the volatility threshold (e.g., ATR < 2.5) ✅
These combined conditions suggest a low-volatility, low-trend strength, and balanced momentum zone—perfect for anticipating breakouts or strong directional moves.
Ultimate MA & PSAR [TARUN]Overview
This indicator combines a customizable Moving Average (MA) and Parabolic SAR (PSAR) to generate precise long and short trade signals. A dashboard displays real-time trade conditions, including signal direction, entry price, stop loss, and PnL tracking.
Key Features
✅ Customizable MA Type & Period – Choose between SMA or EMA with adjustable length.
✅ Adaptive PSAR Settings – Modify start, increment, and max step values to fine-tune stop levels.
✅ Trade Signal Logic – Identifies potential buy (long) and sell (short) opportunities based on:
Price action relative to MA
MA trend direction (rising or falling)
PSAR confirmation
✅ Dynamic Stop Loss Calculation – Uses lowest low/highest high over a specified period for stop loss placement.
✅ Trade State & Reversal Handling – Manages active trades, pending signals, and stop loss exits dynamically.
✅ PnL & Dashboard Table – Displays real-time signal status, entry price, stop loss, and profit/loss (PnL) in an easy-to-read format.
How It Works
1.Buy (Long) Condition:
MA is rising
Price is above the MA
PSAR is below price
2.Sell (Short) Condition:
MA is falling
Price is below the MA
PSAR is above price
3.Stop Loss Handling:
For long trades → stop loss is set at the lowest low of the last X candles
For short trades → stop loss is set at the highest high of the last X candles
4.Trade Execution & PnL Calculation:
If a valid long/short setup is detected, a pending signal is placed.
On the next bullish (for long) or bearish (for short) candle, the trade is confirmed.
Real-time PnL updates help track trade performance.
Customization Options
🔹 Moving Average: SMA or EMA, adjustable period
🔹 PSAR Settings: Start, Increment, Maximum step values
🔹 Stop Loss Lookback: Choose how many candles to consider for stop loss placement
🔹 Dashboard Positioning: Select preferred display location (top/bottom, left/right)
🔹 Trade Signal Selection: Enable/Disable Long and Short signals individually
How to Use
Add the indicator to your chart.
Customize the MA & PSAR settings according to your trading strategy.
Follow the dashboard signals for trade setups.
Use stop loss levels to manage risk effectively.
Disclaimer
⚠️ This indicator is for educational purposes only and does not constitute financial advice. Always perform proper risk management and backtesting before using it in live trading.
ATR Amplitude RatioATR Amplitude Ratio
The ATR Amplitude Ratio indicator measures price volatility by comparing the current candle's amplitude (high-low range) to the Average True Range (ATR). This helps traders identify when price movement exceeds typical volatility thresholds, potentially signaling unusual market activity.
Key Features:
Displays the ratio between current candle height and ATR as color-coded histogram bars
Customizable ATR calculation with multiple smoothing methods (SMA, EMA, RMA, WMA)
Visual reference lines at 1x, 2x, 3x, 4x, and 5x ATR levels
Dynamic color coding based on volatility intensity (5 customizable threshold colors)
Real-time display of current ratio and ATR values
How to Use:
Volatility Assessment: Quickly identify if price action is within normal volatility ranges or exhibiting unusual movement
Breakout Confirmation: Higher ratios can confirm genuine breakouts versus false moves
Entry/Exit Timing: Consider entries when volatility returns to normal ranges after spikes
Risk Management: Adjust position sizing based on current volatility ratios
Settings:
ATR Length: Determines the lookback period for ATR calculation (default: 14)
ATR Smoothing Type: Choose from SMA, EMA, RMA, or WMA methods
Color Thresholds: Customize colors for different volatility ranges
This indicator helps traders make more informed decisions by providing context about current price action relative to recent historical volatility.
Scalping 15min: EMA + MACD + RSI + ATR-based SL/TP📈 Strategy: 15-Minute Scalping — EMA + MACD + RSI + ATR-based SL/TP
This scalping strategy is designed for 15-minute charts and combines trend-following and momentum confirmation with dynamic stop loss and take profit levels based on volatility.
🔧 Indicators Used:
EMA 50 — identifies the main trend
MACD Histogram — confirms momentum direction
RSI (14) — filters overbought/oversold conditions
ATR (14) — dynamically sets SL and TP based on market volatility
📊 Entry Conditions:
Long Entry:
Price is above EMA 50
MACD histogram is positive
RSI is above 50 but below 70
Short Entry:
Price is below EMA 50
MACD histogram is negative
RSI is below 50 but above 30
🛑 Risk Management:
Stop Loss: 1×ATR (user-configurable)
Take Profit: 2×ATR (user-configurable)
These values can be adjusted in the script inputs depending on your risk/reward preference or market conditions.
⚠️ Notes:
Strategy is optimized for scalping fast-moving pairs (e.g. crypto, forex).
Works best in trending markets.
Use backtesting and forward testing before live trading.
14 EMA & RSI Combo with First Buy/SellEMA14 & RSI stratergy - Used as a indication for BUY and Sell based on EMA 14 and RSI. Chk for higher timeframe trend and stick to the entries that are following the trend
Liquidity Volume Panel Liquidity Volume Panel – Precision Tool for Scalpers & Intraday Traders
This panel is designed to help traders quickly identify volume-driven moves, liquidity events, and fair-value zones. It combines classic volume analysis with enhanced tools like RVOL and VWAP deviation bands, making it ideal for scalping, momentum trading, and intraday strategies.
🔍 Included Features:
✅ Relative Volume (RVOL) Indicator
Displays current volume in relation to its 20-period average – excellent for spotting low-activity zones or high-pressure breakouts.
✅ Dynamic Volume Coloring & Spike Detection
Color-coded volume logic highlights normal, strong, and extremely high volume, with visual markers for volume spikes (>200% of average).
✅ VWAP with ±1σ & ±2σ Bands
Industry-standard deviation bands show overbought/oversold conditions and dynamic support/resistance based on volume-weighted pricing.
✅ Background Highlighting
Subtle orange background alerts you when volume surges beyond extreme levels – making liquidity clusters instantly recognizable.
Usage:
Use this panel as a decision-making tool for entries, reversals, or breakouts – especially in fast-moving markets.
Best used on lower timeframes for precision scalping.
P-Motion Trend | QuantEdgeB⚡ Introducing P-Motion Trend (PMT) by QuantEdgeB
🧭 Overview
P-Motion Trend is a refined trend-following framework built for modern market dynamics. It combines DEMA filtering, percentile-based smoothing, and volatility-adjusted envelopes to create a clear, noise-filtered trend map directly on your chart.
This overlay indicator is engineered to detect breakout zones, trend continuation setups, and market regime shifts with maximum clarity and minimum lag.
Whether you're swing trading crypto, managing intraday FX moves, or positioning in equities — P-Motion Trend adapts, aligns, and simplifies.
🧠 Core Logic
1️⃣ DEMA Filtering Core
The input source is processed through a Double EMA to reduce lag while retaining trend sensitivity.
2️⃣ Percentile Median Smoothing
To eliminate short-lived spikes, the DEMA output is passed through a percentile median rank — effectively smoothing without distortion.
3️⃣ Volatility Envelope with EMA Basis
An exponential moving average (EMA) is applied to the smoothed median, and standard deviation bands are wrapped around it:
• ✅ Long Signal → Price closes above the upper band
• ❌ Short Signal → Price closes below the lower band
• ➖ Inside Band = Neutral
These bands expand/contract with market volatility — protecting against false breakouts in quiet regimes and adapting quickly to strong moves.
📊 Visual & Analytical Layers
• 🎯 Bar Coloring: Color-coded candles highlight trend state at a glance.
• 📈 EMA Ribbon Overlay: A dynamic ribbon of EMAs helps confirm internal momentum and detect transitions (trend decay or acceleration).
• 🔹Gradient Fill Zones: Visually communicates squeeze vs. expansion phases based on band width.
⚙️ Custom Settings
• EMA Length – Defines the core trend path (default: 21)
• SD Length – Controls volatility band smoothing (default: 30)
• SD Mult Up/Down – Sets thresholds for breakout confirmation (default: 1.5)
• DEMA Filter Source – Raw input used for trend processing
• DEMA Filter Length – Sets DEMA smoothing (default: 7)
• Median Length – Percentile-based smoothing window (default: 2)
📌 Use Cases
✅ Trend Confirmation
Use PMT to confirm whether the price action is structurally valid for trend continuation. A close above the upper band signals entry alignment.
🛡️ Reversal Guard
Avoid early reversion entries. PMT keeps you in-trend until price truly breaks structure.
🔍 Momentum Visualizer
With multiple EMA bands, the indicator also functions as a momentum envelope to spot divergence between price and smoothed trend flow.
🔚 Conclusion
P-Motion Trend is a hybrid volatility + trend system built with precision smoothing, dynamic filtering, and clean visual output. It balances agility with stability, helping you:
• Filter out price noise
• Enter with structure
• Stay in trades longer
• Exit with confidence
🧩 Summary of Benefits
• 🔹 Lag-minimized trend structure via DEMA core
• 🔹 Real-time volatility band adaptation
• 🔹 Gradient visual feedback on compression/expansion
• 🔹 EMA ribbon assists in phase detection
• 🔹 Suitable for all markets & timeframes
📌 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.
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.
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.
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!
Z-Score Normalized VIX StrategyThis strategy leverages the concept of the Z-score applied to multiple VIX-based volatility indices, specifically designed to capture market reversals based on the normalization of volatility. The strategy takes advantage of VIX-related indicators to measure extreme levels of market fear or greed and adjusts its position accordingly.
1. Overview of the Z-Score Methodology
The Z-score is a statistical measure that describes the position of a value relative to the mean of a distribution in terms of standard deviations. In this strategy, the Z-score is calculated for various volatility indices to assess how far their values are from their historical averages, thus normalizing volatility levels. The Z-score is calculated as follows:
Z = \frac{X - \mu}{\sigma}
Where:
• X is the current value of the volatility index.
• \mu is the mean of the index over a specified period.
• \sigma is the standard deviation of the index over the same period.
This measure tells us how many standard deviations the current value of the index is away from its average, indicating whether the market is experiencing unusually high or low volatility (fear or calm).
2. VIX Indices Used in the Strategy
The strategy utilizes four commonly referenced volatility indices:
• VIX (CBOE Volatility Index): Measures the market’s expectations of 30-day volatility based on S&P 500 options.
• VIX3M (3-Month VIX): Reflects expectations of volatility over the next three months.
• VIX9D (9-Day VIX): Reflects shorter-term volatility expectations.
• VVIX (VIX of VIX): Measures the volatility of the VIX itself, indicating the level of uncertainty in the volatility index.
These indices provide a comprehensive view of the current volatility landscape across different time horizons.
3. Strategy Logic
The strategy follows a long entry condition and an exit condition based on the combined Z-score of the selected volatility indices:
• Long Entry Condition: The strategy enters a long position when the combined Z-score of the selected VIX indices falls below a user-defined threshold, indicating an abnormally low level of volatility (suggesting a potential market bottom and a bullish reversal). The threshold is set as a negative value (e.g., -1), where a more negative Z-score implies greater deviation below the mean.
• Exit Condition: The strategy exits the long position when the combined Z-score exceeds the threshold (i.e., when the market volatility increases above the threshold, indicating a shift in market sentiment and reduced likelihood of continued upward momentum).
4. User Inputs
• Z-Score Lookback Period: The user can adjust the lookback period for calculating the Z-score (e.g., 6 periods).
• Z-Score Threshold: A customizable threshold value to define when the market has reached an extreme volatility level, triggering entries and exits.
The strategy also allows users to select which VIX indices to use, with checkboxes to enable or disable each index in the calculation of the combined Z-score.
5. Trade Execution Parameters
• Initial Capital: The strategy assumes an initial capital of $20,000.
• Pyramiding: The strategy does not allow pyramiding (multiple positions in the same direction).
• Commission and Slippage: The commission is set at $0.05 per contract, and slippage is set at 1 tick.
6. Statistical Basis of the Z-Score Approach
The Z-score methodology is a standard technique in statistics and finance, commonly used in risk management and for identifying outliers or unusual events. According to Dumas, Fleming, and Whaley (1998), volatility indices like the VIX serve as a useful proxy for market sentiment, particularly during periods of high uncertainty. By calculating the Z-score, we normalize volatility and quantify the degree to which the current volatility deviates from historical norms, allowing for systematic entry and exit based on these deviations.
7. Implications of the Strategy
This strategy aims to exploit market conditions where volatility has deviated significantly from its historical mean. When the Z-score falls below the threshold, it suggests that the market has become excessively calm, potentially indicating an overreaction to past market events. Entering long positions under such conditions could capture market reversals as fear subsides and volatility normalizes. Conversely, when the Z-score rises above the threshold, it signals increased volatility, which could be indicative of a bearish shift in the market, prompting an exit from the position.
By applying this Z-score normalized approach, the strategy seeks to achieve more consistent entry and exit points by reducing reliance on subjective interpretation of market conditions.
8. Scientific Sources
• Dumas, B., Fleming, J., & Whaley, R. (1998). “Implied Volatility Functions: Empirical Tests”. The Journal of Finance, 53(6), 2059-2106. This paper discusses the use of volatility indices and their empirical behavior, providing context for volatility-based strategies.
• Black, F., & Scholes, M. (1973). “The Pricing of Options and Corporate Liabilities”. Journal of Political Economy, 81(3), 637-654. The original Black-Scholes model, which forms the basis for many volatility-related strategies.
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.
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.
Reversal Trading Bot Strategy[BullByte]Overview :
The indicator Reversal Trading Bot Strategy is crafted to capture potential market reversal points by combining momentum, volatility, and trend alignment filters. It uses a blend of technical indicators to identify both bullish and bearish reversal setups, ensuring that multiple market conditions are met before entering a trade.
Core Components :
Technical Indicators Used :
RSI (Relative Strength Index) :
Purpose : Detects divergence conditions by comparing recent lows/highs in price with the RSI.
Parameter : Length of 8.
Bollinger Bands (BB) :
Purpose : Measures volatility and identifies price levels that are statistically extreme.
Parameter : Length of 20 and a 2-standard deviation multiplier.
ADX (Average Directional Index) & DMI (Directional Movement Index) :
Purpose : Quantifies the strength of the trend. The ADX threshold is set at 20, and additional filters check for the alignment of the directional indicators (DI+ and DI–).
ATR (Average True Range) :
Purpose : Provides a volatility measure used to set stop levels and determine risk through trailing stops.
Volume SMA (Simple Moving Average of Volume ):
Purpose : Helps confirm strength by comparing the current volume against a 20-period average, with an optional filter to ensure volume is at least twice the SMA.
User-Defined Toggle Filters :
Volume Filter : Confirms that the volume is above average (or twice the SMA) before taking trades.
ADX Trend Alignment Filter : Checks that the ADX’s directional indicators support the trade direction.
BB Close Confirmation : Optionally refines the entry by requiring price to be beyond the upper or lower Bollinger Band rather than just above or below.
RSI Divergence Exit : Allows the script to close positions if RSI divergence is detected.
BB Mean Reversion Exit : Closes positions if the price reverts to the Bollinger Bands’ middle line.
Risk/Reward Filter : Ensures that the potential reward is at least twice the risk by comparing the distance to the Bollinger Band with the ATR.
Candle Movement Filter : Optional filter to require a minimum percentage move in the candle to confirm momentum.
ADX Trend Exit : Closes positions if the ADX falls below the threshold and the directional indicators reverse.
Entry Conditions :
Bullish Entry :
RSI Divergence : Checks if the current close is lower than a previous low while the RSI is above the previous low, suggesting bullish divergence.
Bollinger Confirmation : Requires that the price is above the lower (or upper if confirmation is toggled) Bollinger Band.
Volume & Trend Filters : Combines volume condition, ADX strength, and an optional candle momentum condition.
Risk/Reward Check : Validates that the trade meets a favorable risk-to-reward ratio.
Bearish Entry :
Uses a mirror logic of the bullish entry by checking for bearish divergence, ensuring the price is below the appropriate Bollinger level, and confirming volume, trend strength, candle pattern, and risk/reward criteria.
Trade Execution and Exit Strateg y:
Trade Execution :
Upon meeting the entry conditions, the strategy initiates a long or short position.
Stop Loss & Trailing Stops :
A stop-loss is dynamically set using the ATR value, and trailing stops are implemented as a percentage of the close price.
Exit Conditions :
Additional exit filters can trigger early closures based on RSI divergence, mean reversion (via the middle Bollinger Band), or a weakening trend as signaled by ADX falling below its threshold.
This multi-layered exit strategy is designed to lock in gains or minimize losses if the market begins to reverse unexpectedly.
How the Strategy Works in Different Market Conditions :
Trending Markets :
The ADX filter ensures that trades are only taken when the trend is strong. When the market is trending, the directional movement indicators help confirm the momentum, making the reversal signal more reliable.
Ranging Markets :
In choppy markets, the Bollinger Bands expand and contract, while the RSI divergence can highlight potential turning points. The optional filters can be adjusted to avoid false signals in low-volume or low-volatility conditions.
Volatility Management :
With ATR-based stop-losses and a risk/reward filter, the strategy adapts to current market volatility, ensuring that risk is managed consistently.
Recommendation on using this Strategy with a Trading Bot :
This strategy is well-suited for high-frequency trading (HFT) due to its ability to quickly identify reversal setups and execute trades dynamically with automated stop-loss and trailing exits. By integrating this script with a TradingView webhook-based bot or an API-driven execution system, traders can automate trade entries and exits in real-time, reducing manual execution delays and capitalizing on fast market movements.
Disclaimer :
This script is provided for educational and informational purposes only. It is not intended as investment advice. Trading involves significant risk, and you should always conduct your own research and analysis before making any trading decisions. The author is not responsible for any losses incurred while using this script.
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.
