Gradient Trend Filter [ChartPrime]The Gradient Trend Filter is a dynamic trend analysis tool that combines a noise-filtered trend detection system with a color-gradient cloud. It provides traders with a visual representation of trend strength, momentum shifts, and potential reversals.
⯁ KEY FEATURES
Trend Noise Filtering
Uses an advanced smoothing function to filter market noise and produce a more reliable trend representation.
// Noise filter function
noise_filter(src, length) =>
alpha = 2 / (length + 1)
nf_1 = 0.0
nf_2 = 0.0
nf_3 = 0.0
nf_1 := (alpha * src) + ((1 - alpha) * nz(nf_1 ))
nf_2 := (alpha * nf_1) + ((1 - alpha) * nz(nf_2 ))
nf_3 := (alpha * nf_2) + ((1 - alpha) * nz(nf_3 ))
nf_3 // Final output with three-stage smoothing
Color-Based Trend Visualization
The mid-line changes color based on trend direction—green for uptrends and red for downtrends—making it easy to identify trends at a glance.
Orange diamond markers appear when a trend shift is confirmed, providing actionable signals for traders.
Gradient Color Trend Cloud
A cloud around the base trend line that dynamically changes color, often signaling trend shifts ahead of the main trend line.
When in a downtrend, if the cloud starts turning green, it suggests weakening bearish momentum or an upcoming bullish reversal. Conversely, when in an uptrend, a red cloud indicates potential trend weakening or a bearish reversal.
Multi-Layered Trend Bands
The cloud consists of multiple bands, offering a range of support and resistance zones that traders can use for confluence in decision-making.
⯁ HOW TO USE
Identify Trend Strength & Reversals
Use the mid-line and cloud color changes to assess the strength of a trend and spot early signs of reversals.
Monitor Momentum Shifts
Watch for gradient cloud color shifts before the trend line changes color, as this can indicate early weakening or strengthening of momentum.
Act on Trend Shift Markers
Use the orange diamonds as confirmation of trend shifts and potential trade entry or exit points.
Utilize Cloud Bands as Support/Resistance
The outer bands of the cloud act as dynamic support and resistance, helping traders refine their stop-loss and take-profit placements.
⯁ CONCLUSION
The Gradient Trend Filter is an advanced trend detection tool designed for traders looking to anticipate trend shifts with greater precision. By integrating a noise-filtered trend line with a gradient-based trend cloud, this indicator enhances traders' ability to navigate market trends effectively.
Indikator dan strategi
Intrabar Volume Distribution [BigBeluga]Intrabar Volume Distribution is an advanced volume and order flow indicator that visualizes the buy and sell volume distribution within each candlestick.
🔔 Before Use:
Turn off the background color of your candles for clear visibility.
Overlay the indicator on the top layout to ensure accurate alignment with the price chart.
🔵 Key Features:
Inside Bar Volume Visualization:
Each candlestick is divided into two columns:
Left column displays the sell % volume amount.
Right column displays the buy % volume amount.
Provides a clear representation of buyer-seller activity within individual bars.
Percentage Volume Labels:
Labels above each bar show the percentage share of sell and buy volume relative to the total (100%).
Quickly assess market sentiment and volume imbalances.
Point of Control (POC) Levels:
Orange dashed lines mark the POC inside each bar, indicating the price level with the highest traded volume.
Helps identify key liquidity zones within individual candlesticks.
Multi-Timeframe Volume Analysis:
The indicator automatically uses a timeframe 20-30 times lower than the current one to gather detailed volume data.
For each higher timeframe candle, it collects 20-30 bars of lower timeframe data for precise volume mapping.
Each bar is divided into 100 volume bins to capture detailed volume distribution across the price range.
Bins are filled based on the aggregated volume from the lower timeframe data.
Lookback Period:
Allows traders to select how many bars to display with delta and volume information.
The beginning of the selected lookback period is marked with a gray line and label for quick reference.
Indicator displays up to 80 bars back
🔵 Usage:
Order Flow Analysis: Monitor buy/sell volume distribution to spot potential reversals or continuations.
Liquidity Identification: Use POC levels to locate areas of strong market interest and potential support/resistance.
Volume Imbalance Detection: Pay attention to percentage labels for quick recognition of buyer or seller dominance.
Scalping & Intraday Trading: Ideal for traders seeking real-time insight into order flow and volume behavior.
Historical Analysis: Adjust the lookback period to analyze past price action and volume activity.
Intrabar Volume Distribution is a powerful tool for traders aiming to gain deeper insight into market sentiment through detailed volume analysis, allowing for more informed trading decisions based on real-time order flow dynamics.
SuperTrend + Relative Volume (Kernel Optimized)Introducing our new KDE Optimized Supertrend + Relative Volume Indicator!
This innovative indicator combines the power of the Supertrend indicator along with Relative Volume. It utilizes the Kernel Density Estimation (KDE) to estimate the probability of a candlestick marking a significant trend break or reversal.
❓How to Interpret the KDE %:
The KDE % is a crucial metric that reflects the likelihood that the current candlestick represents a true break in the SuperTrend line, supported by an increase in relative volume. It estimates the probability of a trend shift or continuation based on historical SuperTrend breaks and volume patterns:
Low KDE %: A lower probability that the current break is significant. Price action is less likely to reverse, and the trend may continue.
Moderate KDE - High KDE %: An increased possibility that a trend reversal or consolidation could occur. Traders should start watching for confirmation signals.
📌How Does It Work?
The SuperTrend indicator uses the Average True Range (ATR) to determine the direction of the trend and identifies when the price crosses the SuperTrend line, signaling a potential trend reversal. Here's how the KDE Optimized SuperTrend Indicator works:
SuperTrend Calculation: The SuperTrend indicator is calculated, and when the price breaks above (bullish) or below (bearish) the SuperTrend line, it is logged as a significant event.
Relative Volume: For each break in the SuperTrend line, we calculate the relative volume (current volume vs. the average volume over a defined period). High relative volume can suggest stronger confirmation of the trend break.
KDE Array Calculation: KDE is applied to the break points and relative volume data:
Define the KDE options: Bandwidth, Number of Steps, and Array Range (Array Max - Array Min).
Create a density range array using the defined number of steps, corresponding to potential break points.
Apply a Gaussian kernel function to the break points and volume data to estimate the likelihood of the trend break being significant.
KDE Value and Signal Generation: The KDE array is updated as each break occurs. The KDE % is calculated for the breakout candlestick, representing the likelihood of the trend break being significant. If the KDE value exceeds the defined activation threshold, a darker bullish or bearish arrow is plotted after bar confirmation. If the KDE value falls below the threshold, a more transparent arrow is drawn, indicating a possible but lower probability break.
⚙️Settings:
SuperTrend Settings:
ATR Length: The period over which the Average True Range (ATR) is calculated.
Multiplier: The multiplier applied to the ATR to determine the SuperTrend threshold.
KDE Settings:
Bandwidth: Determines the smoothness of the KDE function and the width of the influence of each break point.
Number of Bins (Steps): Defines the precision of the KDE algorithm, with higher values offering more detailed calculations.
KDE Threshold %: The level at which relative volume is considered significant for confirming a break.
Relative Volume Length: The number of historic candles used in calculating KDE %
OrderBlocks || DeadMoneyThe DeadMoney script automatically identifies and visualizes Bullish and Bearish Order Blocks on the chart.
It is based on swing detection and highlights rectangular zones where increased buyer or seller activity is likely.
When the price breaks through an order block the zone changes color - Breaker Block
Key Parameters
🔹 Swing Lookback – Determines how far back the script searches for swing points.
🔹 Bullish OB & Bearish OB – Defines how many recent bullish and bearish Order Blocks should be displayed on the chart.
Additional Settings
✅ Show OB Info – Displays the last traded volume before the Order Block formation.
✅ Show OB Mid – Highlights the midpoint of a formed Order Block.
✅ Show Candle Info – Displays current trade data and indicator readings.
└ Includes: MFI, RSI, MACD
✅ Use Candle Body
└ Enabled: Uses open/close prices for Order Block calculations, ignoring wicks.
└ Disabled: Uses high/low prices, making Order Block detection more sensitive to price extremes.
⚠ Disclaimer:
This script is designed to assist with technical analysis but does not guarantee success.
Before using it, make sure you understand the principles of technical analysis, as well as how order blocks work. 🚀
25-75 Percentile SuperTrend | Mattes25-75 Percentile SuperTrend | Mattes
Overview
The 25-75 Percentile SuperTrend is an advanced trend-following indicator that enhances the traditional SuperTrend concept by incorporating percentile-based smoothing. Instead of using a simple moving average or median price, this indicator calculates the 25th and 75th percentiles over a user-defined period. These percentiles act as dynamic trend levels, adjusting more responsively to price volatility while reducing noise.
How It’s Calculated
Percentile Smoothing:
The 25th percentile of the selected source (low-end smoothing).
The 75th percentile of the selected source (high-end smoothing).
SuperTrend Logic:
The upper band is set at the 75th percentile + ATR multiplier.
The lower band is set at the 25th percentile - ATR multiplier.
The trend flips when the price crosses above/below these dynamic bands.
Signal Generation :
A bullish trend occurs when price remains above the lower band.
A bearish trend occurs when price remains below the upper band.
Trend shifts are highlighted with colored bars and lines for easy visualization.
How It Differs From Traditional SuperTrend
Uses Percentiles Instead of a Moving Average:
Traditional SuperTrend relies on ATR-based offsets from a moving average.
This version replaces the moving average with percentile smoothing, which adapts better to price behavior.
Better Noise Filtering:
Since percentiles are less sensitive to outliers, this indicator reduces false signals in choppy markets.
More Adaptive to Market Conditions:
The percentile smoothing dynamically adjusts trend detection based on price distribution rather than fixed calculations.
Why It’s Useful
✅ Reduces Whipsaws: Helps minimize false breakouts by using percentile-based bands instead of traditional ATR-only bands.
✅ Works in Different Market Conditions: Effective in both trending and ranging environments due to its adaptive nature.
✅ Enhances Trend Confidence: Provides clearer signals by filtering noise more effectively than standard SuperTrend indicators.
Application Examples
Trend Following: Use it to identify strong upward or downward trends.
Stop-Loss Placement: The upper and lower bands can serve as dynamic stop-loss levels.
Breakout Confirmation: Trend flips can confirm breakout signals from other indicators.
Mean Reversion Strategy Filtering: The 25-75 range helps identify strong versus weak reversals.
Risks & Disclaimers
Not a Standalone Strategy: This indicator should be used with other confirmation tools like volume analysis, momentum oscillators, or support/resistance levels.
False Signals in Sideways Markets: Although it reduces noise, choppy markets can still generate occasional false trend flips.
Market Adaptation Required: The best parameters may vary depending on the asset and timeframe.
This indicator was heavily inspired and influenced by the IRS/viResearch Median SuperTrend, improving upon its concept by transforming its median based calculation into a more responsive & effective counterpart of its former self.
Shoutout to all my Masterclass Brothers and L4 Gs !
EMA + VWAP + RSI + MACD + Current Price - PanelEMA + VWAP + RSI + MACD + Current Price - Panel
9/21 ema cross
8/20 ema cross
Panel that shows bearish or bullish
DCStatCalcs_v0.1DCStatCalcs_v0.1 - Session-Based Statistical Projections
This Pine Script indicator overlays customizable horizontal lines on your chart to visualize a session's opening price and its statistical projections based on historical standard deviation (SD). Designed for traders who want to analyze price behavior within defined time sessions, it calculates and plots the session open price along with optional projection lines at 0.5, 1.0, 1.5, 2.0, and 2.5 standard deviations above and below the open, derived from past session data.
Key Features:
Customizable Sessions: Define your session time (e.g., 0600-1500) and timezone (e.g., America/New_York).
Historical Analysis: Uses a user-specified number of past sessions (default: 20) to compute the standard deviation of price movements relative to the session open.
Projection Lines: Displays toggleable lines at multiple SD levels with adjustable styles, colors, and widths for easy visualization.
Flexible Display: Extend lines beyond the current bar with an offset setting, and adjust label sizes for clarity.
Real-Time Updates: Lines dynamically extend as the session progresses, keeping projections relevant to the current bar.
How It Works:
At the start of each user-defined session, the indicator records the opening price and calculates the SD based on price deviations from the open across historical sessions. It then plots the open price line and, if enabled, projection lines at the specified SD intervals. These lines help traders identify potential support, resistance, or volatility zones based on statistical norms.
Use Case:
Ideal for day traders or analysts working with intraday charts to gauge price ranges and volatility within specific trading sessions, such as market opens or key economic hours.
Published under the Mozilla Public License 2.0. Created by dc_77.
EMA 8/13/21 Golden TriangleDescription:
The "EMA Golden Triangle" indicator is a powerful tool for identifying potential trend reversals and continuations, based on the principles of Exponential Moving Averages (EMAs) and the revered Fibonacci sequence. This indicator plots three key EMAs:
EMA 8 (Red): A fast-moving average, highly responsive to recent price changes.
EMA 13 (Orange): A medium-term average, balancing responsiveness and stability.
EMA 21 (Yellow): A slower-moving average, representing the longer-term trend.
Fibonacci and Trading:
EMA lengths (8, 13, 21) are derived from the Fibonacci sequence, often observed in financial markets. Traders use Fibonacci numbers and ratios to identify potential support/resistance, retracements, and extensions. Using these Fibonacci numbers for the EMAs aims to align the indicator with potential market turning points.
How to Use the Indicator:
The indicator identifies "Golden Triangle" formations:
Bullish Golden Triangle: EMA 8 > EMA 13 > EMA 21, and EMA 8 and EMA 13 crossover EMA 21. Signals a potential uptrend. A green triangle appears below the bar, with a light green background.
Bearish Golden Triangle: EMA 8 < EMA 13 < EMA 21, and EMA 8 and EMA 13 cross under EMA 21. Signals a potential downtrend. A red triangle appears above the bar, with a light red background.
This indicator can be particularly useful on lower timeframes for scalping and day trading, where quick reactions to price changes are crucial. On these timeframes:
Lower Timeframes (1-min, 5-min, 15-min):
More Signals: More frequent Golden Triangles, but also more false signals.
Price Action: Confirming candlestick patterns.
Support/Resistance: Avoid significant levels that could invalidate the signal.
Volume: Higher volume strengthens the signal.
Other Indicators: Use RSI, MACD, or Supertrend for confirmation.
Tight Stop Losses: Essential due to potential whipsaws.
Backtest Thoroughly: Test with historical data before live trading.
Alerts:
Built-in alerts for bullish and bearish formations.
Disclaimer:
No indicator is perfect. Past performance doesn't guarantee future results. Use proper risk management and integrate this indicator into a broader strategy.
VIDYA For-Loop | QuantEdgeB Introducing VIDYA For-Loop by QuantEdgeB
Overview
The VIDYA For-Loop indicator by QuantEdgeB is a dynamic trend-following tool that leverages Variable Index Dynamic Average (VIDYA) along with a rolling loop function to assess trend strength and direction. By utilizing adaptive smoothing and a recursive loop for threshold evaluation, this indicator provides a more responsive and robust signal framework for traders.
______
Key Components & Features
📌 VIDYA (Variable Index Dynamic Average)
- Adaptive Moving Average that adjusts its responsiveness based on market volatility.
- Uses a dynamic smoothing constant based on standard deviations.
- Allows for better trend detection compared to static moving averages.
📌 Loop Function (Rolling Calculation)
- A for-loop algorithm continuously compares VIDYA values over a defined lookback range.
- Measures the number of times price trends higher or lower within the rolling window.
- Generates a momentum-based score that helps quantify trend persistence.
📌 Trend Signal Calculation
- A long signal is triggered when the loop score exceeds the upper threshold.
- A short signal is triggered when the loop score falls below the lower threshold.
- The result is a clear directional bias that adapts to changing market conditions.
______
How It Works in Trading
✅ Detects Trend Strength – By measuring cumulative movements within a window.
✅ Filters Noise – Uses adaptive smoothing to avoid whipsaws.
✅ Dynamic Thresholds – Enables customized entry & exit conditions.
✅ Color-Coded Candles – Provides visual clarity for traders.
______
Visual Representation
Trend Signals:
🔵 Blue Candles – Strong Uptrend
🔴 Red Candles – Strong Downtrend
Thresholds:
📈 Long Threshold – Upper bound for bullish confirmation.
📉 Short Threshold – Lower bound for bearish confirmation.
Labels & Annotations (Optional):
✅ Long & Short Labels can be turned on or off for trade signal clarity.
📊 Display of entry & exit points based on loop calculations.
______
Settings:
VIDYA Length: 2 → Number of bars for VIDYA calculation.
SD Length: 5 → Standard deviation length for VIDYA calculation.
Source: Close → Defines the input data source (Close price).
Start Loop: 1 → Initial lookback period for the loop function.
End Loop: 60 → Maximum lookback range for trend scoring.
Long Threshold: 40 → Upper bound for a long signal.
Short Threshold: 10 → Lower bound for a short signal.
Extra Plots: True → Enables additional moving averages for visualization.
______
Conclusion
The VIDYA For-Loop by QuantEdgeB is a next-gen adaptive trend filter that combines dynamic smoothing with recursive trend evaluation, making it an invaluable tool for traders seeking precision and consistency in their strategies.
🔹 Who should use VIDYA For Loop :
📊 Trend-Following Traders – Helps identify sustained trends.
⚡ Momentum Traders – Captures strong price swings.
🚀 Algorithmic & Systematic Trading – Ideal for automated entries & exits.
🔹 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.
Full Logic (Indicator)Hey Guys, After about 20 Hours of tweaking and using AI assistance with Coding Nomenclature, I have transferred over my personal Strategy into a Indicator. I have only used this on NQ and Crude Oil. Had the system back test this strategy and it was profitable. Now my recommendation is to use this on the 5 Min, during Volatility. THIS IS FOR TREND TRADERS. For super scalping, refer to my other indicator. I promise you this will make life much easier. If you want one home run or two decent 10 candle trades, use this indicator.
DCSessionStatsOHLC_v1.0DCSessionStatsOHLC_v1.0
© dc_77 | Pine Script™ v6 | Licensed under Mozilla Public License 2.0
This indicator overlays customizable session-based OHLC (Open, High, Low, Close) statistics on your TradingView chart. It tracks price action within user-defined sessions, calculates average manipulation and distribution levels based on historical data, and visually projects these levels with lines and labels. Additionally, it provides a session count table to monitor bullish and bearish sessions.
Key Features:
Session Customization: Define session time (e.g., "0000-1600") and time zone (e.g., UTC, America/New_York). Analyze up to 20 historical sessions.
Anchor Line: Displays a vertical line at session start with customizable style, color, and optional label.
Session Open Line: Plots a horizontal line at the session’s opening price with adjustable appearance and label.
Manipulation Levels: Calculates and projects average price extensions (high/low relative to open) for manipulative moves, shown as horizontal lines with labels.
Distribution Levels: Displays average price ranges (high/low beyond open) for distribution phases, with customizable lines and labels.
Visual Flexibility: Adjust line styles (solid, dashed, dotted), colors, widths, label sizes, and projection offsets (bars beyond session start).
Session Stats Table: Optional table showing counts of bullish (close > open) and bearish (close < open) sessions, with configurable position and size.
How It Works:
Tracks OHLC data within each session and identifies session start/end based on the specified time range.
Computes averages for manipulation (e.g., low below open in bullish sessions) and distribution (e.g., high above open) levels from past sessions.
Projects these levels forward as horizontal lines, extending them by a user-defined offset for easy reference.
Updates a table with real-time bullish/bearish session counts.
Use Case:
Ideal for traders analyzing intraday or custom session behavior, identifying key price levels, and gauging market sentiment over time.
Toggle individual elements on/off and fine-tune visuals to suit your trading style.
Astro R4.0Regarding the code that has a significant impact on Pine Community and many feel helped by it, this is the code that I ported from VBA to PineScript which comes from simontelescopium owner of astroexcel dot wordpress dot com and "astrofnc" by Keith Burnett, previously I used it personally but I forgot to give a citation to those who are entitled to them both so that when I shared it for community use and it has been shared by brother @BarefootJoey with the additions made by him personally, there was no citation for them.
Apologies for my negligence because I am only human.
Hopefully with this script it can help the community to see the potential for implementation in the trading world as a significant variable.
Finally, I publish this script as a reference to find out astronomical charts presented in table form to make it easier to visualize and debug as long as the input.timestamp() allow it.
Future updates for optimization using library of brother @BarefootJoey
Thank you.
Trader Goldentrader ////El indicador Tendencia y Estructura está diseñado para ayudar a los traders a identificar la dirección dominante del mercado y sus cambios estructurales en distintas temporalidades. Se basa en la detección automática de máximos y mínimos relevantes, permitiendo visualizar la estructura del precio y anticipar posibles quiebres de tendencia.
SkyTrendBands (ATR + CCI) - Upper & Lower Band Shading🚀 ATR/CCI Trend Bands – Adaptive Trend & Volatility Zones
🔹 Overview
The ATR/CCI Trend Bands indicator is a trend-following and volatility-based tool designed to help traders identify trend direction, strength, and potential reversals. It combines Average True Range (ATR) for dynamic price bands and Commodity Channel Index (CCI) to filter trends, ensuring traders only focus on high-probability setups.
Unlike static support and resistance levels, these bands dynamically expand and contract based on market volatility, making them highly effective for adapting to changing market conditions.
🔹 🔍 How It Works
✅ ATR-Based Trend Bands – The upper and lower bands are calculated using an ATR multiplier, which expands in high-volatility conditions and contracts in low-volatility conditions. These bands act as adaptive support and resistance zones.
✅ CCI Trend Filter – The CCI value determines whether the trend is bullish or bearish.
If CCI is above 0 → The trendline follows the highest price movement within the ATR bands, indicating an uptrend.
If CCI is below 0 → The trendline follows the lowest price movement within the ATR bands, marking a downtrend.
✅ Dynamic Trendline Coloring
Blue Trendline = Uptrend (CCI ≥ 0)
Red Trendline = Downtrend (CCI < 0)
✅ Shaded Support & Resistance Zones
Red Upper Bands (Resistance) → Indicates potential selling pressure.
Blue Lower Bands (Support) → Indicates potential buying interest.
🔹 📈 How to Use This Indicator?
🔸 Trend Trading – Use the trendline to ride trends with confidence. When the price stays above the trendline (blue), stay bullish; when it's below (red), favor bearish positions.
🔸 Breakout Confirmation – If the price breaks above the upper band, it may signal a strong bullish breakout. Conversely, a break below the lower band could indicate a bearish breakdown.
🔸 Reversal Trading – Look for price exhaustion in the shaded resistance (red) and support (blue) zones. If the price repeatedly fails to break through the bands, a reversal may be forming.
🔹 🎯 Why Use This Indicator?
✅ Eliminates false breakouts by combining ATR & CCI
✅ Works on all timeframes and markets
✅ Perfect for trend traders, breakout traders, and mean-reversion setups
✅ Customizable inputs for different trading styles
🚀 Upgrade your trading with ATR/CCI Trend Bands today!
G-VIDYA | QuantEdgeBIntroducing G-VIDYA by QuantEdgeB
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🔹 Overview
The G-VIDYA | QuantEdgeB is a dynamic trend-following indicator that enhances market trend detection using Gaussian smoothing and an adaptive Variable Index Dynamic Average (VIDYA). It is designed to reduce noise, improve responsiveness, and adapt to volatility, making it a powerful tool for traders looking to capture long-term trends efficiently.
By integrating ATR-based filtering, the indicator creates a dynamic support and resistance band around VIDYA, allowing for more accurate trend confirmations. Additionally, traders have the option to enable trade labels for clearer visual signals.
This indicator is well-suited for medium to long-term trend traders, combining mathematical precision with market adaptability for robust trading strategies.
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🚀 Key Features
1. Gaussian Smoothing → Reduces market noise and smoothens price action.
2. VIDYA Adaptive Calculation → Adjusts dynamically based on market volatility.
3. ATR-Based Filtering → Creates a volatility-driven range around VIDYA.
4. Dynamic Trend Confirmation → Identifies bullish and bearish momentum shifts.
5. Trade Labels (Optional) → Can display Long/Cash labels on chart for better clarity.
6. Customizable Color Modes → Offers multiple visual themes for personalized experience.
7. Automated Alerts → Sends buy/sell alerts for crossover trend changes.
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📊 How It Works
1. Gaussian Smoothing is applied to the closing price to remove noise and improve signal clarity.
2. VIDYA Calculation dynamically adjusts to price movements, making it more reactive during high-volatility periods and stable in low-volatility environments.
3. ATR-Based Filtering establishes a dynamic range (Upper & Lower ATR Bands) around VIDYA:
- If price breaks above the upper ATR band, it signals a potential long trend.
- If price breaks below the lower ATR band, it signals a potential short trend.
4. The indicator assigns color-coded candles based on trend direction:
- Bullish Trend → Blue/Green (Uptrend)
- Bearish Trend → Red/Maroon (Downtrend)
5. Labels & Alerts (Optional)
- Users can activate Long/Cash labels to mark buy/sell opportunities.
- Built-in alerts trigger automatic notifications when trend direction changes.
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🎨 Visual Representation
- VIDYA Line → A smooth, trend-following line that dynamically adjusts to market conditions.
- Upper & Lower ATR Bands → Establishes a volatility-based corridor around VIDYA.
- Bar Coloring → Candles change color according to the detected trend.
- Long/Short Labels (Optional) → Displays trade entry/exit signals (can be enabled/disabled).
- Alerts → Generates trade notifications based on trend reversals.
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⚙️ Default Settings
- Gaussian Smoothing
- Length: 4
- Sigma: 2.0
- VIDYA Settings
- VIDYA Length: 46
- Standard Deviation Length: 28
- ATR Settings
- ATR Length: 14
- ATR Multiplier: 1.3
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💡 Who Should Use It?
✅ Trend Traders → Those who rely on medium-to-long-term trends for trading decisions.
✅ Swing Traders → Ideal for traders who want to capture trend reversals and ride momentum.
✅ Quantitative Analysts → Provides statistically driven smoothing and adaptive trend detection.
✅ Risk-Averse Traders → ATR filtering helps manage market volatility effectively.
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Conclusion
The G-VIDYA | QuantEdgeB is an advanced trend-following indicator that combines Gaussian smoothing, adaptive VIDYA filtering, and ATR-based dynamic trend analysis to deliver robust and reliable trade signals.
✅ Key Takeaways
📌 Adaptive & Dynamic: Adjusts to market conditions, making it effective for trend-following strategies.
📌 Noise Reduction: Gaussian smoothing helps filter out short-term fluctuations, improving signal clarity.
📌 Volatility Awareness: ATR-based filtering ensures better handling of market swings and trend reversals.
By blending mathematical precision and quantitative market analysis, G-VIDYA | QuantEdgeB offers a powerful edge in trend trading 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.
ORB Algo & Customizable Daily 10 AM EST Line (Full Height)ORB Algo & Customizable Daily 10 AM EST Line (Full Height)
DynamicMALibrary "DynamicMA"
Dynamic Moving Averages Library
Introduction
The Dynamic Moving Averages Library is a specialized collection of custom built functions designed to calculate moving averages dynamically, beginning from the first available bar. Unlike standard moving averages, which rely on fixed length lookbacks, this library ensures that indicators remain fully functional from the very first data point, making it an essential tool for analysing assets with short time series or limited historical data.
This approach allows traders and developers to build robust indicators that do not require a preset amount of historical data before generating meaningful outputs. It is particularly advantageous for:
Newly listed assets with minimal price history.
High-timeframe trading, where large lookback periods can lead to delayed or missing data.
By eliminating the constraints of fixed lookback periods, this library enables the seamless construction of trend indicators, smoothing functions, and hybrid models that adapt instantly to market conditions.
Comprehensive Set of Custom Moving Averages
The library includes a wide range of custom dynamic moving averages, each designed for specific analytical use cases:
SMA (Simple Moving Average) – The fundamental moving average, dynamically computed.
EMA (Exponential Moving Average) – Adaptive smoothing for better trend tracking.
DEMA (Double Exponential Moving Average) – Faster trend detection with reduced lag.
TEMA (Triple Exponential Moving Average) – Even more responsive than DEMA.
WMA (Weighted Moving Average) – Emphasizes recent price action while reducing noise.
VWMA (Volume Weighted Moving Average) – Accounts for volume to give more weight to high-volume periods.
HMA (Hull Moving Average) – A superior smoothing method with low lag.
SMMA (Smoothed Moving Average) – A hybrid approach between SMA and EMA.
LSMA (Least Squares Moving Average) – Uses linear regression for trend detection.
RMA (Relative Moving Average) – Used in RSI-based calculations for smooth momentum readings.
ALMA (Arnaud Legoux Moving Average) – A Gaussian-weighted MA for superior signal clarity.
Hyperbolic MA (HyperMA) – A mathematically optimized averaging method with dynamic weighting.
Each function dynamically adjusts its calculation length to match the available bar count, ensuring instant functionality on all assets.
Fully Optimized for Pine Script v6
This library is built on Pine Script v6, ensuring compatibility with modern TradingView indicators and scripts. It includes exportable functions for seamless integration into custom indicators, making it easy to develop trend-following models, volatility filters, and adaptive risk-management systems.
Why Use Dynamic Moving Averages?
Traditional moving averages suffer from a common limitation: they require a fixed historical window to generate meaningful values. This poses several problems:
New Assets Have No Historical Data - If an asset has only been trading for a short period, traditional moving averages may not be able to generate valid signals.
High Timeframes Require Massive Lookbacks - On 1W or 1M charts, a 200-period SMA would require 200 weeks or months of data, making it unusable on newer assets.
Delayed Signal Initialization - Standard indicators often take dozens of bars to stabilize, reducing effectiveness when trading new trends.
The Dynamic Moving Averages Library eliminates these issues by ensuring that every function:
Starts calculation from bar one, using available data instead of waiting for a lookback period.
Adapts dynamically across timeframes, making it equally effective on low or high timeframes.
Allows smoother, more responsive trend tracking, particularly useful for volatile or low-liquidity assets.
This flexibility makes it indispensable for custom script developers, quantitative analysts, and discretionary traders looking to build more adaptive and resilient indicators.
Final Summary
The Dynamic Moving Averages Library is a versatile and powerful set of functions designed to overcome the limitations of fixed-lookback indicators. By dynamically adjusting the calculation length from the first bar, this library ensures that moving averages remain fully functional across all timeframes and asset types, making it an essential tool for traders and developers alike.
With built-in adaptability, low-lag smoothing, and support for multiple moving average types, this library unlocks new possibilities for quantitative trading and strategy development - especially for assets with short price histories or those traded on higher timeframes.
For traders looking to enhance signal reliability, minimize lag, and build adaptable trading systems, the Dynamic Moving Averages Library provides an efficient and flexible solution.
SMA(sourceData, maxLength)
Dynamic SMA
Parameters:
sourceData (float)
maxLength (int)
EMA(src, length)
Dynamic EMA
Parameters:
src (float)
length (int)
DEMA(src, length)
Dynamic DEMA
Parameters:
src (float)
length (int)
TEMA(src, length)
Dynamic TEMA
Parameters:
src (float)
length (int)
WMA(src, length)
Dynamic WMA
Parameters:
src (float)
length (int)
HMA(src, length)
Dynamic HMA
Parameters:
src (float)
length (int)
VWMA(src, volsrc, length)
Dynamic VWMA
Parameters:
src (float)
volsrc (float)
length (int)
SMMA(src, length)
Dynamic SMMA
Parameters:
src (float)
length (int)
LSMA(src, length, offset)
Dynamic LSMA
Parameters:
src (float)
length (int)
offset (int)
RMA(src, length)
Dynamic RMA
Parameters:
src (float)
length (int)
ALMA(src, length, offset_sigma, sigma)
Dynamic ALMA
Parameters:
src (float)
length (int)
offset_sigma (float)
sigma (float)
HyperMA(src, length)
Dynamic HyperbolicMA
Parameters:
src (float)
length (int)
CZ INDICATORS premium structure - Higher High / Lower loweng
The best indicator on the Higher High - Lower low system.
This script identifies Orderblocks, Breakerblocks and Range using higher order pivots and priceaction logic.
I tried to reduce the number of blocks to make the chart cleaner, for this purpose I use only second order pivots for both MSB lines and supply/demand boxes, I also tried to filter out shifts in MS and false breakouts.
Green arrows show our lows, red arrows show our highs. This is done in order to clearly and clearly understand the current trend.
Also added order block, and breaker block.
Any box has GRAY color until it gets tested.
After successful test box gets colors:
RED for Supply
GREEN for Demand
BLUE for any Breakerblocks
For cleaner chart and script speed all broken boxes deletes from chart.
It gives comparatively clean chart on any TF, even on extra small (5m, 3m, 1m).
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Лучший индикатор по системе Higher High - Lower Low.
Этот скрипт определяет ордерные блоки, брейкерные блоки и диапазоны, используя развороты высшего порядка и логику ценовых действий.
Я попытался уменьшить количество блоков, чтобы сделать график чище, для этого я использую только развороты второго порядка для линий MSB и блоков спроса/предложения, я также попытался отфильтровать сдвиги в MS и ложные прорывы.
Зеленые стрелки показывают наши минимумы, красные - максимумы. Это сделано для того, чтобы четко и ясно понимать текущий тренд.
Также добавлен блок ордеров и блок пробоев.
Любой блок имеет серый цвет до тех пор, пока он не будет протестирован.
После успешного тестирования блок приобретает цвет:
КРАСНЫЙ для предложения
ЗЕЛЕНЫЙ для спроса
СИНИЙ для любых блоков прерывателей.
Для более чистого графика и скорости работы скрипта все сломанные блоки удаляются с графика.
Это дает сравнительно чистый график на любом ТФ, даже на сверхмалых (5м, 3м, 1м).
TEMA OBOS Strategy【Pakun】TEMA OBOS Strategy
Overview
This strategy combines a trend-following approach using the Triple Exponential Moving Average (TEMA) with Overbought/Oversold (OBOS) indicator filtering.
By utilizing TEMA crossovers to determine trend direction and OBOS as a filter, it aims to improve entry precision.
This strategy can be applied to markets such as Forex, Stocks, and Crypto, and is particularly designed for mid-term timeframes (5-minute to 1-hour charts).
Strategy Objectives
Identify trend direction using TEMA
Use OBOS to filter out overbought/oversold conditions
Implement ATR-based dynamic risk management
Key Features
1. Trend Analysis Using TEMA
Uses crossover of short-term EMA (ema3) and long-term EMA (ema4) to determine entries.
ema4 acts as the primary trend filter.
2. Overbought/Oversold (OBOS) Filtering
Long Entry Condition: up > down (bullish trend confirmed)
Short Entry Condition: up < down (bearish trend confirmed)
Reduces unnecessary trades by filtering extreme market conditions.
3. ATR-Based Take Profit (TP) & Stop Loss (SL)
Adjustable ATR multiplier for TP/SL
Default settings:
TP = ATR × 5
SL = ATR × 2
Fully customizable risk parameters.
4. Customizable Parameters
TEMA Length (for trend calculation)
OBOS Length (for overbought/oversold detection)
Take Profit Multiplier
Stop Loss Multiplier
EMA Display (Enable/Disable TEMA lines)
Bar Color Change (Enable/Disable candle coloring)
Trading Rules
Long Entry (Buy Entry)
ema3 crosses above ema4 (Golden Cross)
OBOS indicator confirms up > down (bullish trend)
Execute a buy position
Short Entry (Sell Entry)
ema3 crosses below ema4 (Death Cross)
OBOS indicator confirms up < down (bearish trend)
Execute a sell position
Take Profit (TP)
Entry Price + (ATR × TP Multiplier) (Default: 5)
Stop Loss (SL)
Entry Price - (ATR × SL Multiplier) (Default: 2)
TP/SL settings are fully customizable to fine-tune risk management.
Risk Management Parameters
This strategy emphasizes proper position sizing and risk control to balance risk and return.
Trading Parameters & Considerations
Initial Account Balance: $7,000 (adjustable)
Base Currency: USD
Order Size: 10,000 USD
Pyramiding: 1
Trading Fees: $0.94 per trade
Long Position Margin: 50%
Short Position Margin: 50%
Total Trades (M5 Timeframe): 128
Deep Test Results (2024/11/01 - 2025/02/24)BTCUSD-5M
Total P&L：+1638.20USD
Max equity drawdown：694.78USD
Total trades：128
Profitable trades:44.53
Profit factor:1.45
These settings aim to protect capital while maintaining a balanced risk-reward approach.
Visual Support
TEMA Lines (Three EMAs)
Trend direction is indicated by color changes (Blue/Orange)
ema3 (short-term) and ema4 (long-term) crossover signals potential entries
OBOS Histogram
Green → Strong buying pressure
Red → Strong selling pressure
Blue → Possible trend reversal
Entry & Exit Markers
Blue Arrow → Long Entry Signal
Red Arrow → Short Entry Signal
Take Profit / Stop Loss levels displayed
Strategy Improvements & Uniqueness
This strategy is based on indicators developed by "l_lonthoff" and "jdmonto0", but has been significantly optimized for better entry accuracy, visual clarity, and risk management.
Enhanced Trend Identification with TEMA
Detects early trend reversals using ema3 & ema4 crossover
Reduces market noise for a smoother trend-following approach
Improved OBOS Filtering
Prevents excessive trading
Reduces unnecessary risk exposure
Dynamic Risk Management with ATR-Based TP/SL
Not a fixed value → TP/SL adjusts to market volatility
Fully customizable ATR multiplier settings
(Default: TP = ATR × 5, SL = ATR × 2)
Summary
The TEMA + OBOS Strategy is a simple yet powerful trading method that integrates trend analysis and oscillators.
TEMA for trend identification
OBOS for noise reduction & overbought/oversold filtering
ATR-based TP/SL settings for dynamic risk management
Before using this strategy, ensure thorough backtesting and demo trading to fine-tune parameters according to your trading style.
3x Supertrend + EMA200 Signal Buy/Sell [nsen]The indicator uses signals from three Supertrend lines to determine whether to trade Buy or Sell, with the assistance of a moving average for bias.
Buy/Sell signals are generated when the conditions are met:
A Buy signal is triggered when all three Supertrend lines indicate a bullish trend and are above the EMA.
A Sell signal is triggered when all three Supertrend lines indicate a bearish trend and are below the EMA.
Indicator ใช้สัญญาณจาก Supertrend ทั้งหมด 3 เส้น โดยใช้ในการกำหนดว่าจะเลือกเทรด Buy หรือ Sell โดยการใช้ moveing average เข้ามาช่วยในการ bias
แสดงสัญญาณ Buy/Sell เมื่อเข้าเงื่อนไข
- Supertrend ทั้ง 3 เส้นเป็นสัญญาณ Bullish และอยู่เหนือเส้น EMA จะเปิดสัญญาณ Buy
- Supertrend ทั้ง 3 เส้นเป็นสัญญาณ Bearish และอยู่ใต้เส้น EMA จะเปิดสัญญาณ Sell
Themp Strategy [SMC+CRT]SMC Indicator
** Indicator is not signal for trade but I will let you know where u should trade**
***you need to know the SMC strategy
***you need to know the CRT strategy
Indicator Object
1. Market Stucture SMC
2. Liquidity sweep zone
3. Fair value gap
4. IDM (inducement)
5. OrderBlock
This is my stratgy setup
Step
1. Analysis The market direction on TF 4H with SMC stucture
2. mark the Key Level of TF 4H (OB,FVG,Swing H/L,LIQ)
3. wait price coming to Key level and wait for Liquidity Sweep
4. use CRT Strategy to confirm the reversal at Key level just wait for the A and M candle at TF 1H
5. go to TF15M and looking for the Orderblock Inside the CRT 1H around M candle then pending the order wait for the price retest the orderblock
[GYTS] FiltersToolkit LibraryFiltersToolkit Library
🌸 Part of GoemonYae Trading System (GYTS) 🌸
🌸 --------- 1. INTRODUCTION --------- 🌸
💮 What Does This Library Contain?
This library is a curated collection of high-performance digital signal processing (DSP) filters and auxiliary functions designed specifically for financial time series analysis. It includes a shortlist of our favourite and best performing filters — each rigorously tested and selected for their responsiveness, minimal lag and robustness in diverse market conditions. These tools form an integral part of the GoemonYae Trading System (GYTS), chosen for their unique characteristics in handling market data.
The library contains two main categories:
1. Smoothing filters (low-pass filters and moving averages) for e.g. denoising, trend following
2. Detrending tools (high-pass and band-pass filters, known as "oscillators") for e.g. mean reversion
This collection is finely tuned for practical trading applications and is therefore not meant to be exhaustive. However, will continue to expand as we discover and validate new filtering techniques. I welcome collaboration and suggestions for novel approaches.
🌸 ——— 2. ADDED VALUE ——— 🌸
💮 Unified syntax and comprehensive documentation
The FiltersToolkit Library brings together a wide array of valuable filters under a unified, intuitive syntax. Each function is thoroughly documented, with clear explanations and academic sources that underline the mathematical rigour behind the methods. This level of documentation not only facilitates integration into trading strategies but also helps underlying the underlying concepts and rationale.
💮 Optimised performance and readability
The code prioritizes computational efficiency while maintaining readability. Key optimizations include:
- Minimizing redundant calculations in recursive filters
- Smart coefficient caching
- Efficient state management
- Vectorized operations where applicable
💮 Enhanced functionality and flexibility
Some filters in this library introduce extended functionality beyond the original publications. For instance, the MESA Adaptive Moving Average (MAMA) and Ehlers’ Combined Bandpass Filter incorporate multiple variations found in the literature, thereby providing traders with flexible tools that can be fine-tuned to different market conditions.
🌸 ——— 3. THE FILTERS ——— 🌸
💮 Hilbert Transform Function
This function implements the Hilbert Transform as utilised by John Ehlers. It converts a real-valued time series into its analytic signal, enabling the extraction of instantaneous phase and frequency information—an essential step in adaptive filtering.
Source: John Ehlers - "Rocket Science for Traders" (2001), "TASC 2001 V. 19:9", "Cybernetic Analysis for Stocks and Futures" (2004)
💮 Homodyne Discriminator
By leveraging the Hilbert Transform, this function computes the dominant cycle period through a Homodyne Discriminator. It extracts the in-phase and quadrature components of the signal, facilitating a robust estimation of the underlying cycle characteristics.
Source: John Ehlers - "Rocket Science for Traders" (2001), "TASC 2001 V. 19:9", "Cybernetic Analysis for Stocks and Futures" (2004)
💮 MESA Adaptive Moving Average (MAMA)
An advanced dual-stage adaptive moving average, this function outputs both the MAMA and its companion FAMA. It combines adaptive alpha computation with elements from Kaufman’s Adaptive Moving Average (KAMA) to provide a responsive and reliable trend indicator.
Source: John Ehlers - "Rocket Science for Traders" (2001), "TASC 2001 V. 19:9", "Cybernetic Analysis for Stocks and Futures" (2004)
💮 BiQuad Filters
A family of second-order recursive filters offering exceptional control over frequency response:
- High-pass filter for detrending
- Low-pass filter for smooth trend following
- Band-pass filter for cycle isolation
The quality factor (Q) parameter allows fine-tuning of the resonance characteristics, making these filters highly adaptable to different market conditions.
Source: Robert Bristow-Johnson's Audio EQ Cookbook, implemented by @The_Peaceful_Lizard
💮 Relative Vigor Index (RVI)
This filter evaluates the strength of a trend by comparing the closing price to the trading range. Operating similarly to a band-pass filter, the RVI provides insights into market momentum and potential reversals.
Source: John Ehlers – “Cybernetic Analysis for Stocks and Futures” (2004)
💮 Cyber Cycle
The Cyber Cycle filter emphasises market cycles by smoothing out noise and highlighting the dominant cyclical behaviour. It is particularly useful for detecting trend reversals and cyclical patterns in the price data.
Source: John Ehlers – “Cybernetic Analysis for Stocks and Futures” (2004)
💮 Butterworth High Pass Filter
Inspired by the classical Butterworth design, this filter achieves a maximally flat magnitude response in the passband while effectively removing low-frequency trends. Its design minimises phase distortion, which is vital for accurate signal interpretation.
Source: John Ehlers – “Cybernetic Analysis for Stocks and Futures” (2004)
💮 2-Pole SuperSmoother
Employing a two-pole design, the SuperSmoother filter reduces high-frequency noise with minimal lag. It is engineered to preserve trend integrity while offering a smooth output even in noisy market conditions.
Source: John Ehlers – “Cybernetic Analysis for Stocks and Futures” (2004)
💮 3-Pole SuperSmoother
An extension of the 2-pole design, the 3-pole SuperSmoother further attenuates high-frequency noise. Its additional pole delivers enhanced smoothing at the cost of slightly increased lag.
Source: John Ehlers – “Cybernetic Analysis for Stocks and Futures” (2004)
💮 Adaptive Directional Volatility Moving Average (ADXVma)
This adaptive moving average adjusts its smoothing factor based on directional volatility. By combining true range and directional movement measurements, it remains exceptionally flat during ranging markets and responsive during directional moves.
Source: Various implementations across platforms, unified and optimized
💮 Ehlers Combined Bandpass Filter with Automated Gain Control (AGC)
This sophisticated filter merges a highpass pre-processing stage with a bandpass filter. An integrated Automated Gain Control normalises the output to a consistent range, while offering both regular and truncated recursive formulations to manage lag.
Source: John F. Ehlers – “Truncated Indicators” (2020), “Cycle Analytics for Traders” (2013)
💮 Voss Predictive Filter
A forward-looking filter that predicts future values of a band-limited signal in real time. By utilising multiple time-delayed feedback terms, it provides anticipatory coupling and delivers a short-term predictive signal.
Source: John Ehlers - "A Peek Into The Future" (TASC 2019-08)
💮 Adaptive Autonomous Recursive Moving Average (A2RMA)
This filter dynamically adjusts its smoothing through an adaptive mechanism based on an efficiency ratio and a dynamic threshold. A double application of an adaptive moving average ensures both responsiveness and stability in volatile and ranging markets alike. Very flat response when properly tuned.
Source: @alexgrover (2019)
💮 Ultimate Smoother (2-Pole)
The Ultimate Smoother filter is engineered to achieve near-zero lag in its passband by subtracting a high-pass response from an all-pass response. This creates a filter that maintains signal fidelity at low frequencies while effectively filtering higher frequencies at the expense of slight overshooting.
Source: John Ehlers - TASC 2024-04 "The Ultimate Smoother"
Note: This library is actively maintained and enhanced. Suggestions for additional filters or improvements are welcome through the usual channels. The source code contains a list of tested filters that did not make it into the curated collection.
