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.
Ketidakstabilan
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.
ACD Weekly🔷 ACD Weekly — Discover Weekly Ranges with Market Precision 🔷
Understanding where key levels lie at the beginning of the week can unlock powerful insights into market structure, breakout potential, and directional conviction.
The ACD Weekly indicator is designed to help you identify these levels with clarity and consistency.
🚀 What It Does
The ACD Weekly plots dynamic levels based on Monday’s price action and weekly volatility. These levels are useful for:
Identifying potential breakout and breakdown zones
Understanding market range expansions
Creating clean, repeatable weekly trade setups
Without diving into the formula, this tool uses the weekly opening range and historical volatility to highlight zones where institutional accumulation, distribution, or trending moves are more likely to initiate.
📌 How to Use It
Apply the script to any chart and timeframe – best viewed on intraday timeframes.
Observe the green and red lines that appear every Monday — they mark the reference high/low for the week.
The yellow zones extending above and below act as targets and thresholds — price movement into or beyond these areas can signal trend continuation or potential reversal zones.
Use it in combination with your own system — price action, volume, or momentum tools — to confirm entries/exits.
🧠 Who Is It For?
Intraday and swing traders who want clarity at the start of the week
Traders who use breakout or range strategies
Those who want structure without clutter
⚙️ Features
Customizable opening range multiplier
Choose your higher timeframe (daily/weekly)
Optional real-time or historical plotting with Lookahead mode
Add this to your chart and let the structure guide your setups throughout the week.
Happy trading! 📈
Disclaimer:
This is an educational idea, past performance does not guarantee future success.
All outputs will be your own responsibility.
Regards!
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.
Relative Momentum Deviation | QuantEdgeB📊 Introducing Relative Momentum Deviation (RMD) by QuantEdgeB
🛠️ Overview
Relative Momentum Deviation (RMD) is a precision-crafted momentum-based oscillator that measures relative price deviation through a normalized RSI structure and volatility-weighted SD bands. Unlike standard oscillators, RMD dynamically adapts its thresholds using rolling standard deviation on a DEMA-based foundation, making it uniquely responsive in both trending and ranging environments.
Designed to filter out noise and detect critical breakout zones, RMD is a powerful addition to any quantitative trader’s toolkit. Whether used as a standalone entry/exit signal or confirmation layer, RMD excels at identifying momentum inflection points with statistical confidence.
✨ Key Features
🔹 Normalized RSI-Based Core
RMD calculates momentum using a custom RSI of a DEMA-filtered source, delivering a smooth and responsive signal.
🔹 Volatility-Adaptive SD Thresholds
Dynamic upper and lower thresholds adjust in real-time using standard deviation, reducing false positives during low-volatility phases.
🔹 Dual Confirmation Signal Logic
RMD compares both deviation bands to user-defined thresholds to issue high-confidence trend entries.
🔹 Backtesting Integration & Visual Equity Curve
With built-in support for the QuantEdgeB Backtesting Framework, RMD allows seamless strategy validation.
🔹 Clean Visuals & Label Customization
Includes candle coloring, dynamic overlays, signal labels, and optional trend structure plots.
📊 How It Works
1️⃣ Normalized RSI of a DEMA Source
The heart of RMD lies in a momentum oscillator built from:
• 📌 Source Input → A DEMA of price (default 30)
• 📌 Momentum Foundation → RSI calculated from the DEMA output
• 📌 Smoothing Length → Controls the responsiveness of the base signal (default 14)
This creates a stable momentum oscillator less prone to fake-outs during sudden volatility spikes.
2️⃣ Standard Deviation Filtering Engine
RMD employs volatility-weighted SD bands to define statistically meaningful thresholds:
📌 Formula Breakdown:
• NormUp = RSI - SD
• NormDn = RSI + SD
These boundaries adapt based on recent price dispersion. The upper and lower bands dynamically expand or contract depending on market behavior.
3️⃣ Signal Logic & Triggering Conditions
• ✅ Long Signal → NormUp crosses above the long threshold (default: 65)
• ❌ Short Signal → NormDn drops below the short threshold (default: 50)
This approach means signals only occur during statistically significant deviation from mean momentum, making them less frequent but more robust.
✅ Visual Signal Features
• 🔹 Candle coloring based on signal direction (Long/Short)
• 🔹 Label plots on crossover confirmations
• 🔹 Momentum band plots for discretionary or system-based confirmation
👥 Who Should Use It?
✅ Momentum Traders → Identify directional bias with low noise
✅ Swing Traders → Confirm turning points with volatility-adjusted deviation
✅ Quantitative Developers → Integrate into backtested strategies with ease
✅ Range-Trading Specialists → Use SD bands to anticipate overextended moves
⚙️ Customization & Default Settings
🔧 Core Inputs:
• Base RSI Length (Default: 14)
• Source Smoothing (DEMA, Default: 7)
• SD Length (Default: 40) → Controls volatility window
• SD Multiplier (Default: 0.7) → Adjusts sensitivity of deviation thresholds
• Signal Thresholds (L/S Default: 65 Long - 50 Short) → Controls breakout trigger levels
• Color Mode Themes → Six color themes included
• Signal Labels Toggle → Optional signal label plotting
• Backtest Table & Equity Curve Options
📊 Backtest Mode
RMD includes an optional backtest table, enabling traders to assess its historical effectiveness before applying it in live trading conditions.
🔹 Backtest Metrics Displayed:
• Equity Max Drawdown → Largest historical loss from peak equity.
• Profit Factor → Ratio of total profits to total losses, measuring system efficiency.
• Sharpe Ratio → Assesses risk-adjusted return performance.
• Sortino Ratio → Focuses on downside risk-adjusted returns.
• Omega Ratio → Evaluates return consistency & performance asymmetry.
• Half Kelly → Optimal position sizing based on risk/reward analysis.
• Total Trades & Win Rate → Assess historical success rate.
📌 Disclaimer:
Backtest results are based on past performance and do not guarantee future success. Always incorporate real-time validation and risk management in live trading.
🚀 Why This Matters?
✅ Strategy Validation → Gain insight into historical trend accuracy.
✅ Customization Insights → See how different settings impact performance.
✅ Risk Awareness → Understand potential drawdowns before deploying capital.
📌 How to Use RMD in Your Strategy
1️⃣ Momentum Breakout Strategy
✔ Go Long when NormUp > L → Indicates strong upward deviation
✔ Go Short when NormDn < S → Indicates sharp downward momentum
✔ Use SD Mult to control sensitivity and smoothness
2️⃣ Volatility Regime Awareness
✔ In low-volatility → Decrease SD multiplier to catch early signals
✔ In high-volatility → Increase SD multiplier to avoid noise
🔍 Bonus: Extra Trend Structure Plots
RMD includes optional ALMA + multi-EMA trend band overlays:
• Use them to confirm momentum alignment
• Great for hybrid strategies (e.g. trend + momentum)
📌 Conclusion
Relative Momentum Deviation (RMD) by QuantEdgeB offers a clean and adaptive approach to momentum trading by combining a normalized RSI structure with volatility-driven breakout zones.
With built-in signal confirmation, smart filtering, and rich backtest capabilities, RMD excels as a dynamic momentum companion for both discretionary and system traders.
🔹 Key Takeaways:
1️⃣ Adaptive Deviation Zones – Responsive to real-time volatility
2️⃣ Normalized RSI Core – Clean, smoothed momentum insight
3️⃣ Backtest + Visual Toolkit – Strategy-friendly and ready to deploy
📌 Trade with Statistical Precision | Powered by QuantEdgeB
🔹 Disclaimer: Past performance is not indicative of future results.
🔹 Strategic Advice: Always backtest, optimize, and align parameters with your trading objectives and risk tolerance before live trading.
HALC SYHALC SY @CK
Heikin Ashi Last Candle shows color of the last closed 30m heikin ashi candle for every new candle on your graph indicating local trend for scalp & short term trading in rder to help u choose right direction in your 1-5m tf trading. Non-repainting & designed for use on any graph type incl HA, Renko and other problematic syntetic as well as any of your own.
Dont recommend as entry signal but strong support to confirm/deny your trade system entry signal. Enjoy!
SEMA JMA | QuantEdgeB
📈 Introducing SEMA JMA by QuantEdgeB
🛠️ Overview
SEMA JMA is a precision-engineered, dual-signal trend indicator that blends Jurik Moving Average (JMA) logic with Double Exponential Moving Average (DEMA) smoothing and normalized statistical filters.
This advanced indicator is built for high-quality trend detection, reducing false signals by confirming momentum through both price-based SD bands and normalized JMA logic. The result is a powerful, noise-filtered tool ideal for directional trading in volatile and ranging environments.
SEMA JMA offers adaptive volatility bands, backtest-ready analytics, and dynamic signal labeling, making it a favorite for traders demanding speed, precision, and strategic clarity.
✨ Key Features
🔹 Hybrid JMA + DEMA Core
Combines the ultra-smooth JMA with lag-reducing DEMA for exceptional trend clarity.
🔹 Volatility-Based SD Band Filtering
Uses rolling standard deviation on JMA for adaptive long/short bands that respond to market dynamics.
🔹 Normalized Price Filter Confirmation
A second JMA stream is normalized against price and filtered via SD for added trend confirmation and false signal suppression.
🔹 Backtest Integration & Equity Curve Plotting
Built-in compatibility with QuantEdgeB/BacktestingIndV2, delivering historical metrics, equity visualization, and strategic evaluation.
🔹 Fully Customizable UI
Includes label toggles, signal overlays, visual themes, and backtest table position selection.
📊 How It Works
1️⃣ JMA-DEMA Hybrid Trend Engine
The foundation of SEMA JMA lies in a custom-built JMA engine, enhanced by a DEMA smoothing layer to:
• Minimize lag without losing trend integrity.
• Maintain responsiveness in noisy or low-volume environments.
• Create a central trend structure used by both raw price and normalized filters.
2️⃣ Standard Deviation Band Filtering
SEMA JMA applies a rolling SD filter over the JMA signal. This creates adaptive upper and lower bands:
• Long Signal = Price > Upper Band
• Short Signal = Price < Lower Band
These bands adjust based on price volatility, offering a dynamic alternative to traditional fixed thresholds.
3️⃣ Normalized JMA for Momentum Confirmation
A second JMA-DEMA structure is normalized by dividing by price, then smoothed:
• If the normalized signal rises above -1, it suggests upside pressure.
• If it drops below -1, it signals momentum decay.
Only when both raw and normalized signals agree does the indicator issue a trade trigger.
✅ Signal Logic
📌 Long Signal →
🔹 Price breaks above volatility-adjusted upper SD band
🔹 AND Normalized JMA rises above -1
📌 Short Signal →
🔹 Price breaks below lower SD band
🔹 AND Normalized JMA falls below -1
⚙️ SEMA JMA stays in its active trend state until an opposing signal triggers, enabling tren riding while filtering short lived swings.
👥 Who Should Use It?
✅ Swing & Trend Traders → Ride strong directional moves with reduced whipsaws
✅ Volatility-Adaptive Systems → Filter trades using rolling SD-based thresholds
✅ Quantitative Strategy Builders → Deploy within algo-driven strategies using backtest-ready metrics
✅ Risk-Aware Traders → Use dual confirmation to minimize signal risk
⚙️ Customization & Default Settings
🔧 Core Settings:
• JMA Length (Default: 35) → Defines JMA sensitivity.
• DEMA Length (Default: 20) → Smoothing after JMA to refine structure.
• Normalized JMA Lengths → Control confirmation layer smoothness (default: 1 for short and long).
• Standard Deviation Length (Default: 30) → Determines the volatility lookback.
• SD Weight Factors → Separate values for long (default: 1.0) and short (default: 1.002) bands.
📊 Backtest Mode
SEMA JMA includes an optional backtest table, enabling traders to assess its historical effectiveness before applying it in live trading conditions.
🔹 Backtest Metrics Displayed:
• Equity Max Drawdown → Largest historical loss from peak equity.
• Profit Factor → Ratio of total profits to total losses, measuring system efficiency.
• Sharpe Ratio → Assesses risk-adjusted return performance.
• Sortino Ratio → Focuses on downside risk-adjusted returns.
• Omega Ratio → Evaluates return consistency & performance asymmetry.
• Half Kelly → Optimal position sizing based on risk/reward analysis.
• Total Trades & Win Rate → Assess historical success rate.
📌 Disclaimer:
Backtest results are based on past performance and do not guarantee future success. Always incorporate real-time validation and risk management in live trading.
🚀 Why This Matters?
✅ Strategy Validation → Gain insight into historical trend accuracy.
✅ Customization Insights → See how different settings impact performance.
✅ Risk Awareness → Understand potential drawdowns before deploying capital.
📌 How to Use SEMA JMA
🌀 Trend-Following Strategy
✔ Go Long: When price breaks above SD band and normalized momentum rises
✔ Go Short: When price breaks below SD band and normalized momentum falls
✔ Stay in position: Until signal reversal confirms
⚙️ Volatility-Adaptive Configuration
✔ Tune w1 (Long SD weight) and w2 (Short SD weight) for responsiveness
✔ Increase SD length in noisy markets for smoother bands
📌 Conclusion
SEMA JMA by QuantEdgeB delivers surgical precision trend signals using a dual-layer approach:
• JMA + DEMA core smoothing
• Statistical SD breakout filters
• Normalized confirmation logic
It’s a versatile indicator suited for trend-following, volatility tracking, and system-based signal generation—engineered for clarity, confidence, and adaptability.
🔹 Key Takeaways:
1️⃣ Multi-Filter Trend Logic – JMA + DEMA + Normalized filtering for high-confidence signals
2️⃣ SD-Based Volatility Control – Reduces noise, avoids ATR limitations
3️⃣ Quant-Ready System – Includes full backtesting
📌 Master your market edge with precision – SEMA JMA | QuantEdgeB
🔹 Disclaimer: Past performance is not indicative of future results.
🔹 Strategic Advice: Always backtest, optimize, and align parameters with your trading objectives and risk tolerance 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
Open Range Candle [TradeWithRon]This Open Range Break indicator is a tool designed to help traders identify and visualize key price levels using the Opening Range Breakout (ORB) strategy. This indicator dynamically plots critical levels such as the high, low, and middle of a predefined range, along with Fibonacci retracement levels for further analysis. It also features several customization options to fit various trading styles.
Key Features:
Session Setup: Allows the user to set the time offset in GMT - or + to adjust the ORB session to their local time zone.
The default ORB session is set at 9:45 AM but can be adjusted based on user preferences.
Warning: Only supports 5-minute and 15-minute timeframes.
Visual Customization:
Line Styles: Users can choose from Solid, Dotted, or Dashed lines to represent key price levels.
Color Adjustments: Customizable colors for the high, middle, and low levels of the range, as well as Fibonacci levels and vertical lines.
Labeling Options: The labels can be customized in terms of size and color, helping to keep the chart clean and clear.
Fibonacci Retracement Levels: Fibonacci retracement levels are automatically drawn between the high and low of the range. Users can toggle these on or off and customize the offset to suit different trading instruments.
Time-Based Visuals: A vertical line is drawn at the start of the ORB session, providing a clear visual marker of where the breakout starts. This is useful for pinpointing key trade setups.
The indicator supports both 5-minute and 15-minute timeframes.
EMA Integration: The user can enable an Exponential Moving Average (EMA) on any chosen timeframe with adjustable parameters such as the length and color, providing additional trend context.
Dynamic Labeling: The indicator labels the high, middle, and low points of the ORB with custom text. These labels are updated in real-time as new data becomes available.
Limit on Lines and Labels: The indicator allows for a limit on the number of lines and labels drawn to maintain a clean chart, preventing unnecessary clutter as more ORB levels are plotted.
Daily Bias Information: The indicator assesses the daily trend bias (bullish or bearish) based on the relationship between the open and close prices for the last three daily candles, providing context for the current trading session.
Countdown Timer: The remaining time until the end of the current session is displayed in a countdown format, which helps traders to time their entries and exits more precisely.
How To Use:,
- Set the Timeframe to 15 minutes.
- Adjust the Time Zone Offset if needed, based on your local time zone.
- Enable the Show ORB feature for the first 15-minute candle to be drawn as the opening range. - The indicator will automatically mark the high, middle, and low points of the range.
Identify Breakout Points:
Bullish Breakout: If the price breaks above the high of the 15-minute opening range, this indicates a potential bullish breakout. The indicator will plot a vertical line marking the breakout point for further confirmation.
Bearish Breakout: If the price breaks below the low of the 15-minute opening range, this signals a potential bearish breakout. Again, the indicator will plot the breakout point with a vertical line for easy identification.
🔶 Disclaimer
Use with Caution: This indicator is provided for educational and informational purposes only and should not be considered as financial advice. Users should exercise caution and perform their own analysis before making trading decisions based on the indicator's signals.
Not Financial Advice: The information provided by this indicator does not constitute financial advice, and the creator (Tradewithron) shall not be held responsible for any trading losses incurred as a result of using this indicator.
Backtesting Recommended: Traders are encouraged to backtest the indicator thoroughly on historical data before using it in live trading to assess its performance and suitability for their trading strategies.
Risk Management: Trading involves inherent risks, and users should implement proper risk management strategies, including but not limited to stop-loss orders and position sizing, to mitigate potential losses.
No Guarantees: The accuracy and reliability of the indicator's signals cannot be guaranteed, as they are based on historical price data and past performance may not be indicative of future
AlphaSignal | MindMarketAlphaSignal — Smart Indicator for Precise Entries
What does AlphaSignal do?
AlphaSignal looks for moments when the price moves too far from its average, volume spikes, and overall market activity increases. When these things line up, it gives you a clean, high-quality trading signal — either to buy or sell.
How it works :
Activity & Volume Detection
It monitors for sudden bursts in trading volume and volatility — clear signs that something important is happening in the market.
Price Deviation with Nadaraya-Watson Envelope
The indicator uses a smooth curve (called the Nadaraya-Watson estimate) to track the average price. When price drifts too far from this curve, it might be ready to snap back. That’s where AlphaSignal starts paying attention.
Signal Rating System
Each potential trade gets a score based on:
Market activity
Volume deviation
How far price is from the NW envelope
(Optionally) Trend strength and momentum via ADX, RSI, MACD
Only if the total score is high enough — a signal is fired.
Advanced Filters (Optional)
Want more confirmation? Turn on ADX, RSI, and MACD checks to avoid weak setups during choppy, low-trend periods.
Cooldown Logic
To avoid overtrading, AlphaSignal waits a set number of bars between signals — you can customize this.
Trading Suggestions (Signal Panel)
AlphaSignal gives you real-time trading guidance with a simple suggestion box:
BUY NOW / SELL NOW
All conditions are met, rating is strong — take action.
PREPARE BUY / PREPARE SELL
No full confirmation yet, but the price is very close to key levels (within 1.5% of the NW envelope). Get ready — a signal might appear soon.
AWAIT BUY / AWAIT SELL
The market is leaning toward a buy or sell, but price isn’t in a good spot yet. Be patient and watch for better positioning.
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.
Automated Lot Size Calculator // © Laurent3372
The "Automated Lot Size Calculator" is a sophisticated tool for traders who want to calculate the ideal position size based on their capital, risk, and the asset pair they wish to trade. Here is a detailed description of its features:
1. Language Selector
You can select the interface language (French, English, Spanish, German, or Italian). This makes the tool accessible and understandable to an international audience.
2. User Settings for Risk Calculation
The risk percentage per trade is configurable. The entered percentage is divided by 100 to obtain a fraction (for example, 1% becomes 0.01).
3. Selection of Equity in USD or EUR
The user chooses whether their equity is in US dollars or euros. Based on this choice, the calculation is based on the appropriate value.
A field for entering equity is available for both currencies, with a default initial amount. 4. Stop Loss in Pips
The stop loss can be entered in decimal places (such as 2.8 pips), allowing for high precision in risk calculations.
5. Interface Color Customization
You can configure the text and background colors for headers, values, and other visual elements, allowing you to customize the display.
6. Display Table Position and Size
You can choose the table location (top right, top left, bottom right, bottom left) as well as the display size (extra small, small, normal, large, extra large).
7. Asset Pair Detection and Pip Value
The code automatically detects the financial instrument (currency, crypto, precious metal) and adjusts the pip value according to the asset's characteristics. For example:
For JPY pairs, the pip is 0.01.
For cryptocurrencies, the pip is adjusted to 0.01.
For precious metals such as gold and silver, specific adjustments are also made.
8. Retrieving real-time exchange rates
The script uses the request.security function to retrieve real-time exchange rates for currencies or cryptocurrencies.
The code automatically adapts according to the trading pair and retrieves the appropriate rate (e.g., EUR/GBP, BTC/USD).
9. Calculating the risk amount in USD or EUR
The risk is calculated based on the selected capital (USD or EUR).
If the capital is in euros, it is converted to USD to simplify lot calculations.
10. Calculating position sizes in standard lots
The formula for calculating position sizes varies depending on the asset:
EUR/GBP is calculated with a specific adjustment.
Precious metals and cryptocurrencies have their own adapted formulas.
Exotic currencies incorporate a special conversion factor, taking into account pairs with more than two decimal places. 11. Lot Type Definition
The lot type is automatically adjusted according to the asset: "Micro Lot", "Standard Lot", or "Exotic Lot".
12. Results Display with Dynamic Translation
The results (currency, equity, risk, lot type, and size) are displayed in real time and automatically translated into the selected language.
The left column contains the parameters, and the right column displays the corresponding values.
13. Dynamically Creating the Results Table
The table is dynamically created using the specified position and size options. It contains all essential information, such as currency, equity, risk, and position size in lots.
Conclusion:
This script allows traders to automatically calculate their ideal position size by taking into account the currency, desired risk, and asset-specific parameters (such as cryptocurrencies and metals). Thanks to its customization options and automatic translations, it is suitable for global use, regardless of user profile.
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!
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.
