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.
Indicators and strategies
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 SMMA | QuantEdgeB📈 Introducing Z-Score SMMA (Z SMMA) by QuantEdgeB
🛠️ Overview
Z SMMA is a momentum-driven oscillator designed to track the standardized deviation of a Smoothed Moving Average (SMMA). By applying Z-score normalization, this tool dynamically adapts to price volatility, enabling traders to detect meaningful directional shifts and trend changes with enhanced clarity.
It serves both as a trend-following and mean-reversion system, identifying opportunities through standardized thresholds while remaining robust across volatile and calm market conditions.
✨ Key Features
🔹 Z-Score Normalization Engine
Applies Z-score to a custom SMMA baseline, allowing traders to compare price action relative to its recent volatility-adjusted mean.
🔹 Dynamic Trend Detection
Generates actionable long/short signals based on customizable Z-thresholds, making it adaptable across different asset classes and timeframes.
🔹 Overbought/Oversold Zones
Highlight reversion and profit-taking zones (default OB: +2 to +4, OS: -2 to -4), great for counter-trend or mean-reversion strategies.
🔹 Visual Reinforcement Tools
Includes candle coloring, gradient fills, and optional ALMA/EMA band overlays to visualize trend regime transitions.
🔍 How It Works
1️⃣ Z-Score SMMA Calculation
The core is a custom Smoothed Moving Average (SMMA) that is normalized by its standard deviation over a lookback period.
Final Formula:
Z = (SMMA - Mean) / StdDev
2️⃣ Signal Generation
• ✅ Long Bias: Z-Score > Long Threshold (default: 0)
• ❌ Short Bias: Z-Score < Short Threshold (default: 0)
3️⃣ Visual Aids
• Candle Color → Shows trend bias
• Band Fills → Highlight trend strength
• Overlays → Optional ALMA/EMA bands for structure analysis
⚙️ Custom Settings
• SMMA Length → Default: 12
• Z-Score Lookback → Default: 30
• Long Threshold → Default: 0
• Short Threshold → Default: 0
• Color Themes → Choose from 6 visual modes
• Extra Plots → Toggle advanced overlays (ALMA, EMA, bands)
• Label Display → Show/hide “𝓛𝓸𝓷𝓰” & “𝓢𝓱𝓸𝓻𝓽” markers
👥 Who Should Use It?
✅ Trend Traders → For early entries with confirmation from Z-score expansion
✅ Quantitative Analysts → Standardized deviation enables comparison across assets
✅ Mean-Reversion Traders → Use OB/OS zones to fade parabolic spikes
✅ Swing & Systematic Traders → Identify momentum shifts with optional ALMA/EMA overlays
📌 Conclusion
Z SMMA offers a smart, adaptive framework for tracking deviation from equilibrium in a quant-friendly format. Whether you're looking to follow trends or catch exhaustion points, Z SMMA provides a clear, standardized view of momentum and price extremes.
🔹 Key Takeaways:
1️⃣ Z-Score standardization ensures dynamic range awareness
2️⃣ SMMA base filters out noise, offering smoother signals
3️⃣ Color-coded visuals support faster reaction and cleaner charts
📌 Disclaimer: Past performance is not indicative of future results. No trading strategy can guarantee success in financial markets.
📌 Strategic Advice: Always backtest, optimize, and align parameters with your trading objectives and risk tolerance before
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.
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.
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.
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.
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.
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.
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!
HILO Interpolation | QuantEdgeB🚀 Introducing HILO Interpolation by QuantEdgeB
🛠️ Overview
HILO Interpolation is a dynamic price-action based signal engine crafted to adapt across trending and ranging conditions. By leveraging percentile-based price band interpolation, it identifies high-confidence breakout and breakdown zones. This indicator is designed to serve both as a momentum trigger in trend phases and as a price-reactive entry system during range-bound consolidation.
By intelligently switching between percentile thresholds and interpolated logic, HILO minimizes noise and whipsaws commonly seen in traditional crossover systems.
✨ Key Features
🔹 Percentile Interpolation Engine
Tracks price breakouts using percentile thresholds, making it adaptable to volatility and asset-specific structure.
🔹 Price-Based Signal Confirmation
Signals are only triggered when price meaningfully crosses through key percentile thresholds (based on historical high/low logic).
🔹 Visual Trend Encoding
Color-coded candles, dynamic interpolation bands, and optional long/cash labels give clear visual cues for trend and trade direction.
🔹 Dynamic Threshold Switching
Interpolated threshold flips based on where price sits relative to percentile bands—providing adaptive long/short logic.
📊 How It Works
1️⃣ Percentile Zone Definition
HILO defines two key percentiles from the historical high and low:
• Upper Threshold: 75th Percentile of Highs
• Lower Threshold: 50th Percentile of Lows
These are calculated using linear interpolation to ensure smoother transitions across lookback periods.
2️⃣ Adaptive Signal Line
Instead of using static crossovers, HILO dynamically flips its signal based on whether price exceeds the upper threshold or falls below the lower one.
📌 If price > upper → Signal = Short threshold
📌 If price < lower → Signal = Long threshold
📌 If price remains between thresholds → no flip (trend continuation)
3️⃣ Signal Logic
✅ Long Signal → Price exceeds upper bound while lower bound acts as ceiling
❌ Short Signal → Price breaks below lower percentile while upper bound flips
This simple yet powerful mechanism creates early entries while maintaining high signal confidence.
👁 Visual & Custom Features
• 🎨 Multiple Color Modes: Strategy, Solar, Warm, Cool, Classic, Magic
• 🔄 Dynamic Candle & Band Coloring
• 🏷️ Signal Labels: Optional “𝓛𝓸𝓷𝓰” and “𝓢𝓱𝓸𝓻𝓽” tags when trend flips
• 💬 Alerts Ready: Long/Short crossover conditions can trigger alerts instantly
👥 Who Should Use HILO?
✅ Breakout Traders – Catch early trend starts using percentile filters
✅ Swing Traders – Identify directional bias shifts in advance
✅ Range Strategists – Use band confluence zones to play reversions
✅ Quant & Rule-Based Traders – Incorporate percentile logic into broader systems
⚙️ Customization & Default Settings
Percentile Length:(Default 35) Lookback for calculating percentile thresholds
Lookback Period:(Default 4) Lag factor for interpolation responsiveness
Upper % Threshold: (Default 75) Defines breakout zone from historical highs
Lower % Threshold: (Default 50) Defines retest/accumulation zone from historical lows
📌 How to Use HILO in Trading
1️⃣ Trend-Following Strategy
✔ Enter long when price flips above the adaptive support line
✔ Exit or go short when price breaks below the interpolated resistance
✔ Continue position as long as trend color persists
2️⃣ Range-Reversion Strategy
✔ Buy when price tests the lower threshold and no short signal is triggered
✔ Sell or reduce when price hits the upper range boundary
🧠 Why It Works
HILO operates on the principle that historical price structure creates natural probabilistic thresholds. By interpolating between these using percentile logic, the system maintains adaptability to changing market conditions—without the lag of moving averages or the noise of fixed bands.
🔹 Conclusion
HILO Interpolation is a minimalist yet powerful signal engine built for adaptive breakout and reversion detection. Its percentile-based logic offers a novel way to identify structure shifts, giving traders an edge in both trend and range markets.
🔹 Key Takeaways:
1️⃣ Breakout Entry Logic – Uses percentile interpolation instead of static bands
2️⃣ Color-Driven Clarity – Visual clarity via gradient zone overlays
3️⃣ Trend Integrity – Avoids overfitting and responds only to significant price movements
📌 Disclaimer: Past performance is not indicative of future results. No trading strategy can guarantee success in financial markets.
📌 Strategic Advice: Always backtest, optimize, and align parameters with your trading objectives and risk tolerance before live trading.
EMA Crossover (Short Focus with Trailing Stop)This strategy utilizes a combination of Exponential Moving Averages (EMA) and Simple Moving Averages (SMA) to generate entry and exit signals for both long and short positions. The core of the strategy is based on the 13-period EMA (short EMA) crossing the 33-period EMA (long EMA) for entering long trades, while a 13-period EMA crossing the 25-period EMA (mid EMA) generates short trade signals. The 100-period SMA and 200-period SMA serve as additional trend indicators to provide context for the market conditions. The strategy aims to capitalize on trend reversals and momentum shifts in the market.
The strategy is designed to execute trades swiftly with an emphasis on entering positions when conditions align in real time. For long entries, the strategy initiates a buy when the 13 EMA is greater than the 33 EMA, indicating a bullish trend. For short entries, the 13 EMA crossing below the 33 EMA signals a bearish trend, prompting a short position. Importantly, the code includes built-in exit conditions for both long and short positions. Long positions are exited when the 13 EMA falls below the 33 EMA, while short positions are closed when the 13 EMA crosses above the 25 EMA.
A key feature of the strategy is the use of trailing stops for both long and short positions. This dynamic exit method adjusts the stop level as the market moves in favor of the trade, locking in profits while reducing the risk of losses. The trailing stop for long positions is based on the high price of the current bar, while the trailing stop for short positions is set using the low price, providing more flexibility in managing risk. This trailing stop mechanism helps to capture profits from favorable market moves while ensuring that positions are exited if the market moves against them.
This strategy works best on the 4H timeframe and is optimized for major cryptocurrency pairs. The daily chart allows for the EMAs to provide more reliable signals, as the strategy is designed to capture broader trends rather than short-term market fluctuations. Using it on major crypto pairs increases its effectiveness as these assets tend to have strong and sustained trends, providing better opportunities for the strategy to perform well.
Adaptive Fibonacci Pullback System -FibonacciFluxAdaptive Fibonacci Pullback System (AFPS) - FibonacciFlux
This work is licensed under a Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). Original concepts by FibonacciFlux.
Abstract
The Adaptive Fibonacci Pullback System (AFPS) presents a sophisticated, institutional-grade algorithmic strategy engineered for high-probability trend pullback entries. Developed by FibonacciFlux, AFPS uniquely integrates a proprietary Multi-Fibonacci Supertrend engine (0.618, 1.618, 2.618 ratios) for harmonic volatility assessment, an Adaptive Moving Average (AMA) Channel providing dynamic market context, and a synergistic Multi-Timeframe (MTF) filter suite (RSI, MACD, Volume). This strategy transcends simple indicator combinations through its strict, multi-stage confluence validation logic. Historical simulations suggest that specific MTF filter configurations can yield exceptional performance metrics, potentially achieving Profit Factors exceeding 2.6 , indicative of institutional-level potential, while maintaining controlled risk under realistic trading parameters (managed equity risk, commission, slippage).
4 hourly MTF filtering
1. Introduction: Elevating Pullback Trading with Adaptive Confluence
Traditional pullback strategies often struggle with noise, false signals, and adapting to changing market dynamics. AFPS addresses these challenges by introducing a novel framework grounded in Fibonacci principles and adaptive logic. Instead of relying on static levels or single confirmations, AFPS seeks high-probability pullback entries within established trends by validating signals through a rigorous confluence of:
Harmonic Volatility Context: Understanding the trend's stability and potential turning points using the unique Multi-Fibonacci Supertrend.
Adaptive Market Structure: Assessing the prevailing trend regime via the AMA Channel.
Multi-Dimensional Confirmation: Filtering signals with lower-timeframe Momentum (RSI), Trend Alignment (MACD), and Market Conviction (Volume) using the MTF suite.
The objective is to achieve superior signal quality and adaptability, moving beyond conventional pullback methodologies.
2. Core Methodology: Synergistic Integration
AFPS's effectiveness stems from the engineered synergy between its core components:
2.1. Multi-Fibonacci Supertrend Engine: Utilizes specific Fibonacci ratios (0.618, 1.618, 2.618) applied to ATR, creating a multi-layered volatility envelope potentially resonant with market harmonics. The averaged and EMA-smoothed result (`smoothed_supertrend`) provides a robust, dynamic trend baseline and context filter.
// Key Components: Multi-Fibonacci Supertrend & Smoothing
average_supertrend = (supertrend1 + supertrend2 + supertrend3) / 3
smoothed_supertrend = ta.ema(average_supertrend, st_smooth_length)
2.2. Adaptive Moving Average (AMA) Channel: Provides dynamic market context. The `ama_midline` serves as a key filter in the entry logic, confirming the broader trend bias relative to adaptive price action. Extended Fibonacci levels derived from the channel width offer potential dynamic S/R zones.
// Key Component: AMA Midline
ama_midline = (ama_high_band + ama_low_band) / 2
2.3. Multi-Timeframe (MTF) Filter Suite: An optional but powerful validation layer (RSI, MACD, Volume) assessed on a lower timeframe. Acts as a **validation cascade** – signals must pass all enabled filters simultaneously.
2.4. High-Confluence Entry Logic: The core innovation. A pullback entry requires a specific sequence and validation:
Price interaction with `average_supertrend` and recovery above/below `smoothed_supertrend`.
Price confirmation relative to the `ama_midline`.
Simultaneous validation by all enabled MTF filters.
// Simplified Long Entry Logic Example (incorporates key elements)
long_entry_condition = enable_long_positions and
(low < average_supertrend and close > smoothed_supertrend) and // Pullback & Recovery
(close > ama_midline and close > ama_midline) and // AMA Confirmation
(rsi_filter_long_ok and macd_filter_long_ok and volume_filter_ok) // MTF Validation
This strict, multi-stage confluence significantly elevates signal quality compared to simpler pullback approaches.
1hourly filtering
3. Realistic Implementation and Performance Potential
AFPS is designed for practical application, incorporating realistic defaults and highlighting performance potential with crucial context:
3.1. Realistic Default Strategy Settings:
The script includes responsible default parameters:
strategy('Adaptive Fibonacci Pullback System - FibonacciFlux', shorttitle = "AFPS", ...,
initial_capital = 10000, // Accessible capital
default_qty_type = strategy.percent_of_equity, // Equity-based risk
default_qty_value = 4, // Default 4% equity risk per initial trade
commission_type = strategy.commission.percent,
commission_value = 0.03, // Realistic commission
slippage = 2, // Realistic slippage
pyramiding = 2 // Limited pyramiding allowed
)
Note: The default 4% risk (`default_qty_value = 4`) requires careful user assessment and adjustment based on individual risk tolerance.
3.2. Historical Performance Insights & Institutional Potential:
Backtesting provides insights into historical behavior under specific conditions (always specify Asset/Timeframe/Dates when sharing results):
Default Performance Example: With defaults, historical tests might show characteristics like Overall PF ~1.38, Max DD ~1.16%, with potential Long/Short performance variance (e.g., Long PF 1.6+, Short PF < 1).
Optimized MTF Filter Performance: Crucially, historical simulations demonstrate that meticulous configuration of the MTF filters (particularly RSI and potentially others depending on market) can significantly enhance performance. Under specific, optimized MTF filter settings combined with appropriate risk management (e.g., 7.5% risk), historical tests have indicated the potential to achieve **Profit Factors exceeding 2.6**, alongside controlled drawdowns (e.g., ~1.32%). This level of performance, if consistently achievable (which requires ongoing adaptation), aligns with metrics often sought in institutional trading environments.
Disclaimer Reminder: These results are strictly historical simulations. Past performance does not guarantee future results. Achieving high performance requires careful parameter tuning, adaptation to changing markets, and robust risk management.
3.3. Emphasizing Risk Management:
Effective use of AFPS mandates active risk management. Utilize the built-in Stop Loss, Take Profit, and Trailing Stop features. The `pyramiding = 2` setting requires particularly diligent oversight. Do not rely solely on default settings.
4. Conclusion: Advancing Trend Pullback Strategies
The Adaptive Fibonacci Pullback System (AFPS) offers a sophisticated, theoretically grounded, and highly adaptable framework for identifying and executing high-probability trend pullback trades. Its unique blend of Fibonacci resonance, adaptive context, and multi-dimensional MTF filtering represents a significant advancement over conventional methods. While requiring thoughtful implementation and risk management, AFPS provides discerning traders with a powerful tool potentially capable of achieving institutional-level performance characteristics under optimized conditions.
Acknowledgments
Developed by FibonacciFlux. Inspired by principles of Fibonacci analysis, adaptive averaging, and multi-timeframe confirmation techniques explored within the trading community.
Disclaimer
Trading involves substantial risk. AFPS is an analytical tool, not a guarantee of profit. Past performance is not indicative of future results. Market conditions change. Users are solely responsible for their decisions and risk management. Thorough testing is essential. Deploy at your own considered risk.
EMA Crossover 9/21 📈 **EMA Crossover Signal Line**
Created by **Ahmet AKSOY (tugeday)**
This indicator visualizes the crossover between two EMAs using a single dynamic-colored line.
✅ **How it works:**
- The script calculates two EMAs: a short-period EMA and a long-period EMA.
- Only the **short EMA line** is displayed on the chart.
- When the short EMA **crosses above** the long EMA (bullish crossover), the line color turns **green**.
- When the short EMA **crosses below** the long EMA (bearish crossover), the line color turns **red**.
- The line color remains based on the last crossover signal.
🎛️ **Customizable Inputs:**
- Short EMA period (default: 9)
- Long EMA period (default: 21)
All EMA periods can be adjusted from the settings panel, allowing traders to fine-tune the indicator to match their strategy.
Simple, clean, and effective.
Developed by **Ahmet AKSOY (tugeday)** — enjoy and trade smart!
Multi-Oscillator Adaptive Kernel | OpusMulti-Oscillator Adaptive Kernel | Opus 📈
Technical Indicator Overview
The Multi-Oscillator Adaptive Kernel (MOAK) | Opus is a sophisticated technical analysis tool 🛠️ that combines multiple oscillators—RSI, Stochastic, MFI, and CCI—through advanced kernel-based smoothing algorithms.
Designed to filter market noise and deliver clear trend signals, this indicator provides traders with a comprehensive, adaptive view of momentum across various timeframes.
Key Features 🌟
Oscillator Fusion : Integrates normalized readings from RSI, Stochastic, MFI, and CCI for a holistic momentum assessment.
Advanced Kernel Smoothing : Offers three kernel types—Exponential, Linear, and Gaussian—to refine signals and reduce noise.
Customizable Sensitivity : Allows adjustment of lookback periods, kernel length, and sensitivity for tailored responsiveness.
Clear Visual Signals : Displays trend shifts with a color-coded signal line (cyan for bullish, magenta for bearish) and gradient fills for trend intensity.
Overbought/Oversold Zones : Uses a zero line to highlight momentum extremes, with gradient layers indicating signal strength.
Adaptive Signal Design : Dynamically adjusts to market conditions, enhancing reliability across trends and ranges.
Usage Guidelines 📋
Bullish Momentum (Cyan) : Enter long positions when the signal line rises above the zero line, indicating upward momentum.
Bearish Momentum (Magenta) : Consider shorts or exits when the signal line falls below the zero line, signaling downward momentum.
Trend Shifts : Monitor the signal line’s direction and gradient intensity to anticipate momentum changes .
Overbought/Oversold Conditions : Watch for extreme values relative to the zero line to identify potential reversals or pullbacks.
Customizable Settings ⚙️
Oscillator Selection : Enable/disable RSI, Stochastic, MFI, or CCI, and adjust their lookback periods (default: 14 for RSI/Stochastic/MFI, 20 for CCI).
Kernel Type : Choose Exponential (trend-focused), Linear (balanced), or Gaussian (range-friendly) smoothing (default: Exponential) .
Kernel Length : Set the smoothing period (default: 25) for the kernel function.
Sensitivity : Fine-tune responsiveness (default: 1.5) to market changes.
Display Options : Toggle bar coloring and customize gradient layers for a personalized visual experience .
Applications 🌍
The Multi-Oscillator Adaptive Kernel | Opus is a versatile tool for traders seeking noise-filtered momentum signals. Its fusion of multiple oscillators, adaptive kernel smoothing, and gradient visualization makes it ideal for trend-following, counter-trend strategies, and multi-timeframe analysis, offering actionable insights in diverse market conditions .
Technical Methodology (Bonus Section) 🔍
1. Oscillator Fusion : Normalizes and combines RSI, Stochastic, MFI, and CCI values, scaling them around a zero baseline.
2. Kernel Smoothing : Applies Exponential, Linear, or Gaussian kernel weighting to smooth the combined oscillator data, with a secondary smoothing layer for trend confirmation.
3. Visualization : Plots a main signal line with gradient fills (10 layers per side) to reflect trend strength, alongside bar coloring for trend direction.
All under a Creative Commons Attribution-Non Commercial 4.0 International License (CC BY-NC 4.0) © 2025 Opus Capital.
Tetris with Auto-PlayThis indicator is implemented in Pine Script™ v6 and serves as a demonstration of TradingView's capabilities. The core concept is to simulate a classic Tetris game by creating a grid-based environment and managing game state entirely within Pine Script.
Key Technical Aspects:
Grid Representation:
The script defines a custom grid structure using a user-defined type that holds the grid’s dimensions and a one-dimensional array to simulate a two-dimensional board. This structure is used to track occupied cells, clear full rows, and determine stack height.
Piece Management:
A second custom type is used to represent the state of a tetromino piece, including its type, rotation, and position. The code includes functions to calculate the block offsets for each tetromino based on its rotation state.
Collision Detection and Piece Locking:
Dedicated functions check for collisions against the grid borders and existing blocks. When a collision is detected during a downward move, the piece is locked into the grid, and any complete lines are cleared.
AIgo-Driven Placement:
The script incorporates a simple heuristic to determine the best placement for the next tetromino. It simulates different rotations and horizontal positions, evaluating each based on aggregated column height, cleared lines, holes, and bumpiness. This decision-making process is encapsulated in an AI-like function that returns the optimal rotation and placement.
Rendering Using Tables:
The visual representation is managed via TradingView’s table objects. The game board is rendered with a bordered layout, while a separate preview table displays the next piece and the current score. Each cell is updated with text and background colors that correspond to the state of the game.
Execution Flow and Timing:
The main execution loop handles real-time updates by dropping pieces at set intervals and checking for game-over conditions. The code leverages persistent variables and time comparisons to control game speed and manage transitions between piece drops.
Executing:
Add the indicator to the chart
It starts playing itself till game over
There are no parameters to change in this version but the grid in the code directly
p.s. Sadly we have no interactive buttons in the current pinescript versions to play ourself, but its about the possibilitys what we could do ;-)
Maybe in a future version there is more possible, if i find time to enhance and expand the idea
Have fun :-)
MA Smoothed RSI For Loop | OpusMA Smoothed RSI For Loop | Opus
Technical Indicator Overview
The MA Smoothed RSI For Loop | Opus is an innovative technical tool 🛠️ that combines traditional RSI with multiple moving average smoothing options and a unique for-loop calculation method. This indicator provides enhanced trend detection and momentum analysis by applying customizable moving averages to the RSI calculation and implementing a weighted comparison system across multiple timeframes.
Key Features 🌟
• Multi-MA Smoothing Options ✅: Supports 8 different moving average types (EMA, SMA, WMA, VWMA, HMA, RMA, DEMA, or None) for RSI smoothing 📊
• Weighted For-Loop Analysis 🔄: Implements a sophisticated loop calculation that compares current values against historical data with optional weighting ⚡
• Dynamic Color Signals 🎨: Features customizable color themes (Synthwave, Origins, Outrun, Lush, Eighties, Sapphire, Scarlet Blues) for enhanced visual interpretation 🎯
• Comprehensive Dashboard 📊: Includes a detailed information panel with signal strength, trend direction, and duration metrics 📈
Usage Guidelines 📋
• Bullish Signal (Above Upper Threshold) ✅: Enter long positions when the indicator crosses above the upper threshold (default 40) 🚀
• Bearish Signal (Below Lower Threshold) ❌: Consider short positions when the indicator falls below the lower threshold (default -40) 🛑
• Trend Confirmation : Use the weighted for-loop calculation to confirm trend strength and direction 💪
• Signal Stars : Watch for decorative signal markers that appear at trend changes ⭐
Customizable Settings ⚙️
RSI Settings :
• Length : Adjust the RSI calculation period (default: 14) 🔧
• Source : Select price source for RSI calculation (default: close) 📊
Moving Average Options :
• Type : Choose from 8 different MA types for smoothing 📈
• Smooth Length : Set the period for MA smoothing (default: 3) ⚙️
For Loop Parameters :
• Range : Define the comparison range (default: 1 to 50) 🔄
• Weighted Calculation: Toggle weighted vs. unweighted comparison 🎚️
Threshold Settings :
• Upper Threshold: Adjust bullish signal level (default: 40) 📈
• Lower Threshold: Adjust bearish signal level (default: -40) 📉
Applications 🌍
The MA Smoothed RSI For Loop | Opus is particularly effective for:
• Trend identification and confirmation across multiple timeframes
• Momentum analysis with reduced noise through MA smoothing
• Entry and exit signal generation with customizable sensitivity
• Market regime detection through the weighted for-loop calculation
Technical Methodology (Bonus Section) 🔍
1. RSI Calculation :
• Computes traditional RSI using the specified price source and length
• Applies the selected moving average type to smooth the RSI values
2. For-Loop Analysis :
• Compares current values against historical data within the specified range
• Implements optional weighting to give more importance to recent price action
3. Signal Generation :
• Generates signals based on threshold crossings
• Calculates trend strength and duration for additional confirmation
The indicator includes built-in alerts for both long and short signals, a customizable dashboard with three display styles (Minimal, Standard, Full), decorative elements like pulse effects and signal stars for enhanced visualization, and supports multiple color themes for different visual preferences.
All under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0) © 2025 Opus Capital 💼.
SMC Structures and FVGThe provided Pine Script code is an indicator for trading platforms, specifically designed to identify and visualize key trading concepts such as Fair Value Gaps (FVG) and market structures. Here is a detailed description of its functionality:
### Overall Purpose
This indicator aims to assist traders in analyzing market dynamics by highlighting important price levels and areas of interest. It combines the identification of FVGs and market structures to provide a comprehensive view of the market.
### Key Features and Functionalities
#### 1. Input Parameters
- **Fair Value Gap (FVG) Settings**:
- `isFvgToShow`: A boolean input to determine whether to display FVGs on the chart.
- `bullishFvgColor` and `bearishFvgColor`: Define the colors for bullish and bearish FVGs respectively.
- `mitigatedFvgColor`: Specifies the color for mitigated FVGs.
- `fvgHistoryNbr`: Determines the number of FVGs to display on the chart (default set to 10).
- `isMitigatedFvgToReduce`: A boolean input to decide whether to reduce the size of mitigated FVGs.
- **Market Structure Settings**:
- `isStructBodyCandleBreak`: A boolean input to indicate whether to consider the candle body for structure breaks.
- `isCurrentStructToShow`: A boolean input to control the display of the current market structure.
- `bullishBosColor` and `bearishBosColor`: Set the colors for bullish and bearish Break of Structure (BOS) lines (both set to green).
- `bosLineStyleOption` and `bosLineWidth`: Define the style and width (set to 2) of BOS lines.
- `bullishChochColor` and `bearishChochColor`: Determine the colors for bullish and bearish Change of Character (CHoCH) lines.
- `chochLineStyleOption` and `chochLineWidth`: Specify the style and width (set to 2) of CHoCH lines.
- `currentStructColor`, `currentStructLineStyleOption`, and `currentStructLineWidth`: Set the color, style, and width (set to 2) of the current market structure lines.
- `structHistoryNbr`: Defines the number of structure breaks to display on the chart.
#### 2. Fair Value Gap (FVG) Detection and Visualization
- **FVG Identification**:
- The code identifies bullish FVGs when the high of the third - previous bar is less than the low of the previous bar (`isBullishFVG`).
- Bearish FVGs are identified when the low of the third - previous bar is greater than the high of the previous bar (`isBearishFVG`).
- **FVG Drawing**:
- For each identified FVG, a box is drawn on the chart with the appropriate color based on its bullish or bearish nature.
- Labels are added to the FVG boxes.
- The code also handles the removal of FVGs that have been mitigated (i.e., when the price crosses the FVG range).
- Mitigated FVGs are colored differently, and an alert can be triggered when an FVG is mitigated.
#### 3. Market Structure Analysis
- **Structure Identification**:
- The code identifies the highest and lowest price levels within a lookback period (default 10 bars).
- It tracks the start index of the current high and low structures.
- Structure breaks are detected based on whether the price crosses the previous high or low structure levels, considering the candle body if `isStructBodyCandleBreak` is set to true.
- **Line Drawing**:
- When a structure break occurs, either a BOS or CHoCH line is drawn on the chart depending on the market direction.
- The lines are drawn with the specified color, style, and width.
- Labels are added to the lines to indicate whether they are BOS or CHoCH lines.
- The current market structure is also displayed on the chart with the defined color, style, and width.
#### 4. Alerts
- **Alert Conditions**:
- Alerts are set for BOS and CHoCH events. When a BOS or CHoCH occurs, an alert is triggered with the corresponding title and message.
In summary, this indicator provides traders with a visual representation of FVGs and market structures, along with alerts for key events, helping them to make more informed trading decisions.
TP/SL Percentage & RR Visual Tool📈 TP/SL Percentage & RR Visual Tool with UT Bot & EMA Suite
This all-in-one visual trading tool combines powerful precision with clean, dynamic overlays designed to help traders make smarter decisions in real time.
✅ Features:
TP/SL Lines + Zones
Visualize up to 3 Take Profit targets and your Stop Loss level on the chart. Zones are shaded for quick risk/reward perception.
Customizable Risk/Reward
Adjust TP/SL percentages and see real-time percentage distance from price, plus live R:R ratio.
Floating Table Display
A sleek, movable table shows:
Entry & Current Price
TP & SL values
% distance to TP
R:R Ratio
Current Session + Next Session time
Session Awareness
Automatically detects and displays whether you're in the Asian, London, or New York session. Highlights upcoming session transitions.
UT Bot Alerts Integration
Get clear Buy/Sell signals using trailing stop logic powered by ATR and optional Heikin Ashi candles.
Built-in EMAs (20, 50, 100, 200)
Customize and plot key moving averages directly on the chart with color-coded lines.
🔧 Customization Options:
TP/SL levels
Line style, color & thickness
EMA lengths
Table placement
Session timing logic
UT Bot sensitivity (ATR & key multiplier)
💡 Ideal For:
Scalping & intraday trading
Swing traders who use clear entry/exit zones
Anyone who wants clean R:R visuals and precision-based decisions
Log Regression Oscillator Channel [BigBeluga]
This unique overlay tool blends logarithmic trend analysis with dynamic oscillator behavior. It projects RSI, MFI, or Stochastic lines directly into a log regression channel on the price chart — offering an intuitive way to detect overbought/oversold momentum within the broader price structure.
🔵Key Features:
Logarithmic Regression Channel:
➣ Draws a trend-based channel using logarithmic regression, adapting to price growth curvature over time.
➣ Features upper, lower, and optional midline boundaries to visualize trend flow and range extremes.
Oscillator Overlay (RSI / MFI / Stochastic):
➣ Projects your chosen oscillator inside the channel using dynamic polylines.
➣ Allows switching between RSI, Money Flow Index, or Stochastic for versatile momentum insight.
Threshold-Based Scaling:
➣ The top and bottom of the channel represent traditional oscillator thresholds (e.g., RSI 70/30).
➣ Users can modify the scale in settings to customize what "overbought" or "oversold" means visually.
Signal Line Integration:
➣ Adds a yellow moving average (signal line) for smoother confirmation of oscillator turns.
➣ Helps identify divergence, momentum shifts, and fakeouts with better clarity.
Live Oscillator Readout:
➣ Displays the real-time oscillator value at the right edge of the chart.
➣ Ensures traders stay aware of current momentum levels without switching panels.
🔵Usage:
Momentum Context:
➣ When the oscillator touches the upper regression band, it may signal local overbought pressure.
➣ Touching the lower band may indicate oversold conditions within the current log trend.
Divergence Detection:
➣ Use the oscillator’s behavior relative to the channel slope to spot divergence from price.
➣ For example, RSI rising inside a falling channel can flag early trend shifts.
Trend-Sensitive Entries:
➣ Combine oscillator signals with log channel direction to filter trades in trend alignment.
➣ Signal line crossovers inside the channel act as early warning for momentum turns.
The Log Regression Oscillator Channel transforms how traders view classic momentum tools. By embedding oscillators into a logarithmic trend structure, it offers unmatched clarity on momentum positioning relative to price expansion. Ideal for swing traders, mean-reverters, or trend followers looking to sharpen entries and exits with style.
NEW Non-Directional Market StrategyFinal New Non Directional Trading Strategy! which can be used for all markets , the candles will turn grey during the choppy conditions.
