[GYTS] VolatilityToolkit LibraryVolatilityToolkit Library
🌸 Part of GoemonYae Trading System (GYTS) 🌸
🌸 --------- INTRODUCTION --------- 🌸
💮 What Does This Library Contain?
VolatilityToolkit provides a comprehensive suite of volatility estimation functions derived from academic research in financial econometrics. Rather than relying on simplistic measures, this library implements range-based estimators that extract maximum information from OHLC data — delivering estimates that are 5–14× more efficient than traditional close-to-close methods.
The library spans the full volatility workflow: estimation, smoothing, and regime detection.
💮 Key Categories
• Range-Based Estimators — Parkinson, Garman-Klass, Rogers-Satchell, Yang-Zhang (academically-grounded variance estimators)
• Classical Measures — Close-to-Close, ATR, Chaikin Volatility (baseline and price-unit measures)
• Smoothing & Post-Processing — Asymmetric EWMA for differential decay rates
• Aggregation & Regime Detection — Multi-horizon blending, MTF aggregation, Volatility Burst Ratio
💮 Originality
To the best of our knowledge, no other TradingView script combines range-based estimators (Parkinson, Garman-Klass, Rogers-Satchell, Yang-Zhang), classical measures, and regime detection tools in a single package. Unlike typical volatility implementations that offer only a single method, this library:
• Implements four academically-grounded range-based estimators with proper mathematical foundations
• Handles drift bias and overnight gaps, issues that plague simpler estimators in trending markets
• Integrates with GYTS FiltersToolkit for advanced smoothing (10 filter types vs. typical SMA-only)
• Provides regime detection tools (Burst Ratio, MTF aggregation) for systematic strategy integration
• Standardises output units for seamless estimator comparison and swapping
🌸 --------- ADDED VALUE --------- 🌸
💮 Academic Rigour
Each estimator implements peer-reviewed methodologies with proper mathematical foundations. The library handles aspects that are easily missed, e.g. drift independence, overnight gap adjustment, and optimal weighting factors. All functions include guards against edge cases (division by zero, negative variance floors, warmup handling).
💮 Statistical Efficiency
Range-based estimators extract more information from the same data. Yang-Zhang achieves up to 14× the efficiency of close-to-close variance, meaning you can achieve the same estimation accuracy with far fewer bars — critical for adapting quickly to changing market conditions.
💮 Flexible Smoothing
All estimators support configurable smoothing via the GYTS FiltersToolkit integration. Choose from 10 filter types to balance responsiveness against noise reduction:
• Ultimate Smoother (2-Pole / 3-Pole) — Near-zero lag; the 3-pole variant is a GYTS design with tunable overshoot
• Super Smoother (2-Pole / 3-Pole) — Excellent noise reduction with minimal lag
• BiQuad — Second-order IIR filter with quality factor control
• ADXvma — Adaptive smoothing based on directional volatility
• MAMA — Cycle-adaptive moving average
• A2RMA — Adaptive autonomous recursive moving average
• SMA / EMA — Classical averages (SMA is default for most estimators)
Using Infinite Impulse Response (IIR) filters (e.g. Super Smoother, Ultimate Smoother) instead of SMA avoids the "drop-off artefact" where volatility readings crash when old spikes exit the window.
💮 Plug-and-Play Integration
Standardised output units (per-bar log-return volatility) make it trivial to swap estimators. The annualize() helper converts to yearly volatility with a single call. All functions work seamlessly with other GYTS components.
🌸 --------- RANGE-BASED ESTIMATORS --------- 🌸
These estimators utilise High, Low, Open, and Close prices to extract significantly more information about the underlying diffusion process than close-only methods.
💮 parkinson()
The Extreme Value Method -- approximately 5× more efficient than close-to-close, requiring about 80% less data for equivalent accuracy. Uses only the High-Low range, making it simple and robust.
• Assumption: Zero drift (random walk). May be biased in strongly trending markets.
• Best for: Quick volatility reads when drift is minimal.
• Parameters: smoothing_length (default 14), filter_type (default SMA), smoothing_factor (default 0.7)
Source: Parkinson, M. (1980). The Extreme Value Method for Estimating the Variance of the Rate of Return. Journal of Business, 53 (1), 61–65. DOI
💮 garman_klass()
Extends Parkinson by incorporating Open and Close prices, achieving approximately 7.4× efficiency over close-to-close. Implements the "practical" analytic estimator (σ̂²₅) which avoids cross-product terms whilst maintaining near-optimal efficiency.
• Assumption: Zero drift, continuous trading (no gaps).
• Best for: Markets with minimal overnight gaps and ranging conditions.
• Parameters: smoothing_length (default 14), filter_type (default SMA), smoothing_factor (default 0.7)
Source: Garman, M.B. & Klass, M.J. (1980). On the Estimation of Security Price Volatilities from Historical Data. Journal of Business, 53 (1), 67–78. DOI
💮 rogers_satchell()
The drift-independent estimator correctly isolates variance even in strongly trending markets where Parkinson and Garman-Klass become significantly biased. Uses the formula: ln(H/C)·ln(H/O) + ln(L/C)·ln(L/O).
• Key advantage: Unbiased regardless of trend direction or magnitude.
• Best for: Trending markets, crypto (24/7 trading with minimal gaps), general-purpose use.
• Parameters: smoothing_length (default 14), filter_type (default SMA), smoothing_factor (default 0.7)
Source: Rogers, L.C.G. & Satchell, S.E. (1991). Estimating Variance from High, Low and Closing Prices. Annals of Applied Probability, 1 (4), 504–512. DOI
💮 yang_zhang()
The minimum-variance composite estimator — both drift-independent AND gap-aware. Combines overnight returns, open-to-close returns, and the Rogers-Satchell component with optimal weighting to minimise estimator variance. Up to 14× more efficient than close-to-close.
• Parameters: lookback (default 14, minimum 2), alpha (default 1.34, optimised for equities).
• Best for: Equity markets with significant overnight gaps, highest-quality volatility estimation.
• Note: Unlike other estimators, Yang-Zhang does not support custom filter types — it uses rolling sample variance internally.
Source: Yang, D. & Zhang, Q. (2000). Drift-Independent Volatility Estimation Based on High, Low, Open, and Close Prices. Journal of Business, 73 (3), 477–491. DOI
🌸 --------- CLASSICAL MEASURES --------- 🌸
💮 close_to_close()
Classical sample variance of logarithmic returns. Provided primarily as a baseline benchmark — it is approximately 5–8× less efficient than range-based estimators, requiring proportionally more data for the same accuracy.
• Parameters: lookback (default 14), filter_type (default SMA), smoothing_factor (default 0.7)
• Use case: Comparison baseline, situations requiring strict methodological consistency with academic literature.
💮 atr()
Average True Range -- measures volatility in price units rather than log-returns. Directly interpretable for stop-loss placement (e.g., "2× ATR trailing stop") and handles gaps naturally via the True Range formula.
• Output: Price units (not comparable across different price levels).
• Parameters: smoothing_length (default 14), filter_type (default SMA), smoothing_factor (default 0.7)
• Best for: Position sizing, trailing stops, any application requiring volatility in currency terms.
Source: Wilder, J.W. (1978). New Concepts in Technical Trading Systems . Trend Research.
💮 chaikin_volatility()
Rate of Change of the smoothed trading range. Unlike level-based measures, Chaikin Volatility shows whether volatility is expanding or contracting relative to recent history.
• Output: Percentage change (oscillates around zero).
• Parameters: length (default 10), roc_length (default 10), filter_type (default EMA), smoothing_factor (default 0.7)
• Interpretation: High values suggest nervous, wide-ranging markets; low values indicate compression.
• Best for: Detecting volatility regime shifts, breakout anticipation.
🌸 --------- SMOOTHING & POST-PROCESSING --------- 🌸
💮 asymmetric_ewma()
Differential smoothing with separate alphas for rising versus falling volatility. Allows volatility to spike quickly (fast reaction to shocks) whilst decaying slowly (stability). Essential for trailing stops that should widen rapidly during turbulence but narrow gradually.
• Parameters: alpha_up (default 0.1), alpha_down (default 0.02).
• Note: Stateful function — call exactly once per bar.
💮 annualize()
Converts per-bar volatility to annualised volatility using the square-root-of-time rule: σ_annual = σ_bar × √(periods_per_year).
• Parameters: vol (series float), periods (default 252 for daily equity bars).
• Common values: 365 (crypto), 52 (weekly), 12 (monthly).
🌸 --------- AGGREGATION & REGIME DETECTION --------- 🌸
💮 weighted_horizon_volatility()
Blends volatility readings across short, medium, and long lookback horizons. Inspired by the Heterogeneous Autoregressive (HAR-RV) model's recognition that market participants operate on different time scales.
• Default horizons: 1-bar (short), 5-bar (medium), 22-bar (long).
• Default weights: 0.5, 0.3, 0.2.
• Note: This is a weighted trailing average, not a forecasting regression. For true HAR-RV forecasting, it would be required to fit regression coefficients.
Inspired by: Corsi, F. (2009). A Simple Approximate Long-Memory Model of Realized Volatility. Journal of Financial Econometrics .
💮 volatility_mtf()
Multi-timeframe aggregation for intraday charts. Combines base volatility with higher-timeframe (Daily, Weekly, Monthly) readings, automatically scaling HTF volatilities down to the current timeframe's magnitude using the square-root-of-time rule.
• Usage: Calculate HTF volatilities via request.security() externally, then pass to this function.
• Behaviour: Returns base volatility unchanged on Daily+ timeframes (MTF aggregation not applicable).
💮 volatility_burst_ratio()
Regime shift detector comparing short-term to long-term volatility.
• Parameters: short_period (default 8), long_period (default 50), filter_type (default Super Smoother 2-Pole), smoothing_factor (default 0.7)
• Interpretation: Ratio > 1.0 indicates expanding volatility; values > 1.5 often precede or accompany explosive breakouts.
• Best for: Filtering entries (e.g., "only enter if volatility is expanding"), dynamic risk adjustment, breakout confirmation.
🌸 --------- PRACTICAL USAGE NOTES --------- 🌸
💮 Choosing an Estimator
• Trending equities with gaps: yang_zhang() — handles both drift and overnight gaps optimally.
• Crypto (24/7 trading): rogers_satchell() — drift-independent without the lag of Yang-Zhang's multi-period window.
• Ranging markets: garman_klass() or parkinson() — simpler, no drift adjustment needed.
• Price-based stops: atr() — output in price units, directly usable for stop distances.
• Regime detection: Combine any estimator with volatility_burst_ratio().
💮 Output Units
All range-based estimators output per-bar volatility in log-return units (standard deviation). To convert to annualised percentage volatility (the convention in options and risk management), use:
vol_annual = annualize(yang_zhang(14), 252) // For daily bars
vol_percent = vol_annual * 100 // Express as percentage
💮 Smoothing Selection
The library integrates with FiltersToolkit for flexible smoothing. General guidance:
• SMA: Classical, statistically valid, but suffers from "drop-off" artefacts when spikes exit the window.
• Super Smoother / Ultimate Smoother / BiQuad: Natural decay, reduced lag — preferred for trading applications.
• MAMA / ADXvma / A2RMA: Adaptive smoothing, sometimes interesting for highly dynamic environments.
💮 Edge Cases and Limitations
• Flat candles: Guards prevent log(0) errors, but single-tick bars produce near-zero variance readings.
• Illiquid assets: Discretisation bias causes underestimation when ticks-per-bar is small. Use higher timeframes for more reliable estimates.
• Yang-Zhang minimum: Requires lookback ≥ 2 (enforced internally). Cannot produce instantaneous readings.
• Drift in Parkinson/GK: These estimators overestimate variance in trending conditions — switch to Rogers-Satchell or Yang-Zhang.
Note: This library is actively maintained. Suggestions for additional estimators or improvements are welcome.
אינדיקטורים ואסטרטגיות
Smart Money Flow Oscillator [MarkitTick]💡This script introduces a sophisticated method for analyzing market liquidity and institutional order flow. Unlike traditional volume indicators that treat all market activity equally, the Smart Money Flow Oscillator (SMFO) employs a Logic Flow Architecture (LFA) to filter out market noise and "churn," focusing exclusively on high-impact, high-efficiency price movements. By synthesizing price action, volume, and relative efficiency, this tool aims to visualize the accumulation and distribution activities that are often attributed to "smart money" participants.
✨ Originality and Utility
Standard indicators like On-Balance Volume (OBV) or Money Flow Index (MFI) often suffer from noise because they aggregate volume based simply on the close price relative to the previous close, regardless of the quality of the move. This script differentiates itself by introducing an "Efficiency Multiplier" and a "Momentum Threshold." It only registers volume flow when a price move is considered statistically significant and structurally efficient. This creates a cleaner signal that highlights genuine supply and demand imbalances while ignoring indecisive trading ranges. It combines the trend-following nature of cumulative delta with the mean-reverting insights of an In/Out ratio, offering a dual-mode perspective on market dynamics.
🔬 Methodology
The underlying calculation of the SMFO relies on several distinct quantitative layers:
• Efficiency Analysis
The script calculates a "Relative Efficiency" ratio for every candle. This compares the current price displacement (body size) per unit of volume against the historical average.
If price moves significantly with relatively low volume, or proportional volume, it is deemed "efficient."
If significant volume occurs with little price movement (churn/absorption), the efficiency score drops.
This score is clamped between a user-defined minimum and maximum (Efficiency Cap) to prevent outliers from distorting the data.
• Momentum Thresholding
Before adding any data to the flow, the script checks if the current price change exceeds a volatility threshold derived from the previous candle's open-close range. This acts as a gatekeeper, ensuring that only "strong" moves contribute to the oscillator.
• Variable Flow Calculation
If a move passes the threshold, the script calculates the flow value by multiplying the Typical Price and Volume (Money Flow) by the calculated Efficiency Multiplier.
Bullish Flow: Strong upward movement adds to the positive delta.
Bearish Flow: Strong downward movement adds to the negative delta.
Neutral: Bars that fail the momentum threshold contribute zero flow, effectively flattening the line during consolidation.
• Calculation Modes
Cumulative Delta Flow (CDF): Sums the flow values over a rolling period. This creates a trend-following oscillator similar to OBV but smoother and more responsive to real momentum.
In/Out Ratio: Calculates the percentage of bullish inflow relative to the total absolute flow over the period. This oscillates between 0 and 100, useful for identifying overextended conditions.
📖 How to Use
Traders can utilize this oscillator to identify trend strength and potential reversals through the following signals:
• Signal Line Crossovers
The indicator plots the main Flow line (colored gradient) and a Signal line (grey).
Bullish (Green Cloud): When the Flow line crosses above the Signal line, it suggests rising buying pressure and efficient upward movement.
Bearish (Red Cloud): When the Flow line crosses below the Signal line, it suggests dominating selling pressure.
• Divergences
The script automatically detects and plots divergences between price and the oscillator:
Regular Divergence (Solid Lines): Suggests a potential trend reversal (e.g., Price makes a Lower Low while Oscillator makes a Higher Low).
Hidden Divergence (Dashed Lines): Suggests a potential trend continuation (e.g., Price makes a Higher Low while Oscillator makes a Lower Low).
"R" labels denote Regular, and "H" labels denote Hidden divergences.
• Dashboard
A dashboard table is displayed on the chart, providing real-time metrics including the current Efficiency Multiplier, Net Flow value, and the active mode status.
• In/Out Ratio Levels
When using the Ratio mode:
Values above 50 indicate net buying pressure.
Values below 50 indicate net selling pressure.
Approaching 70 or 30 can indicate overbought or oversold conditions involving volume exhaustion.
⚙️ Inputs and Settings
Calculation Mode: Choose between "Cumulative Delta Flow" (Trend focus) or "In/Out Ratio" (Oscillator focus).
Auto-Adjust Period: If enabled, automatically sets the lookback period based on the chart timeframe (e.g., 21 for Daily, 52 for Weekly).
Manual Period: The rolling lookback length for calculations if Auto-Adjust is disabled.
Efficiency Length: The period used to calculate the average body and volume for the efficiency baseline.
Eff. Min/Max Cap: Limits the impact of the efficiency multiplier to prevent extreme skewing during anomaly candles.
Momentum Threshold: A factor determining how much price must move relative to the previous candle to be considered a "strong" move.
Show Dashboard/Divergences: Toggles for visual elements.
🔍 Deconstruction of the Underlying Scientific and Academic Framework
This indicator represents a hybrid synthesis of academic Market Microstructure theory and classical technical analysis. It utilizes an advanced algorithm to quantify "Price Impact," leveraging the following theoretical frameworks:
• 1. The Amihud Illiquidity Ratio (2002)
The core logic (calculating body / volume) functions as a dynamic implementation of Yakov Amihud’s Illiquidity Ratio. It measures price displacement per unit of volume. A high efficiency score indicates that "Smart Money" has moved the price significantly with minimal resistance, effectively highlighting liquidity gaps or institutional control.
• 2. Kyle’s Lambda (1985) & Market Depth
Drawing from Albert Kyle’s research on market microstructure, the indicator approximates Kyle's Lambda to measure the elasticity of price in response to order flow. By analyzing the "efficiency" of a move, it identifies asymmetries—specifically where price reacts disproportionately to low volume—signaling potential manipulation or specific Market Maker activity.
• 3. Wyckoff’s Law of Effort vs. Result
From a classical perspective, the algorithm codifies Richard Wyckoff’s "Effort vs. Result" logic. It acts as an oscillator that detects anomalies where "Effort" (Volume) diverges from the "Result" (Price Range), predicting potential reversals.
• 4. Quantitative Advantage: Efficiency-Weighted Volume
Unlike linear indicators such as OBV or Chaikin Money Flow—which treat all volume equally—this indicator (LFA) utilizes Efficiency-Weighted Volume. By applying the efficiency_mult factor, the algorithm filters out market noise and assigns higher weight to volume that drives structural price changes, adopting a modern quantitative approach to flow analysis.
● Disclaimer
All provided scripts and indicators are strictly for educational exploration and must not be interpreted as financial advice or a recommendation to execute trades. I expressly disclaim all liability for any financial losses or damages that may result, directly or indirectly, from the reliance on or application of these tools. Market participation carries inherent risk where past performance never guarantees future returns, leaving all investment decisions and due diligence solely at your own discretion.
MDZ Strategy v4.2 - Multi-factor trend strategyWhat This Strategy Does
MDZ (Momentum Divergence Zones) v4.2 is a trend-following strategy that enters long positions when multiple momentum and trend indicators align. It's designed for swing trading on higher timeframes (2H-4H) and uses ATR-based position management.
The strategy waits for strong trend confirmation before entry, requiring agreement across five different filters. This reduces trade frequency but aims to improve signal quality.
Entry Logic
A long entry triggers when ALL of the following conditions are true:
1. EMA Stack (Trend Structure)
Price > EMA 20 > EMA 50 > EMA 200
This "stacked" alignment indicates a strong established uptrend
2. RSI Filter (Momentum Window)
RSI between 45-75 (default)
Confirms momentum without entering overbought territory
3. ADX Filter (Trend Strength)
ADX > 20 (default)
Ensures the trend has sufficient strength, not a ranging market
4. MACD Confirmation
MACD line above signal line
Histogram increasing (momentum accelerating)
5. Directional Movement
+DI > -DI
Confirms bullish directional pressure
Exit Logic
Positions are managed with ATR-based levels:
ParameterDefaultDescriptionStop Loss2.5 × ATRBelow entry priceTake Profit6.0 × ATRAbove entry priceTrailing Stop2.0 × ATROptional, activates after entry
The default configuration produces a 1:2.4 risk-reward ratio.
Presets
The strategy includes optimized presets based on historical testing:
PresetTimeframeNotes1H Standard1 HourMore frequent signals2H Low DD2 HourConservative settings3H Optimized3 HourBalanced approach4H Swing4 HourWider stops for swing tradesCustomAnyFull manual control
Select "Custom" to adjust all parameters manually.
Inputs Explained
EMAs
Fast EMA (20): Short-term trend
Slow EMA (50): Medium-term trend
Trend EMA (200): Long-term trend filter
RSI
Length: Lookback period (default 14)
Min/Max: Entry window to avoid extremes
ADX
Min ADX: Minimum trend strength threshold
Risk
Stop Loss ATR: Multiplier for stop distance
Take Profit ATR: Multiplier for target distance
Trail ATR: Trailing stop distance (if enabled)
Session (Optional)
Filter entries by time of day
Recommended OFF for 3H+ timeframes
What's Displayed
Info Panel (Top Right)
Current preset
Trend status (Strong/Wait)
ADX, RSI, MACD readings
Position status
Risk-reward ratio
Stats Panel (Top Left)
Net P&L %
Total trades
Win rate
Profit factor
Maximum drawdown
Chart
EMA lines (20 blue, 50 orange, 200 purple)
Green background during strong uptrend
Triangle markers on entry signals
Important Notes
⚠️ This is a long-only strategy. It does not take short positions.
⚠️ Historical results do not guarantee future performance. Backtests show what would have happened in the past under specific conditions. Markets change, and any strategy can experience drawdowns or extended losing periods.
⚠️ Risk management is your responsibility. The default settings risk 100% of equity per trade for backtesting purposes. In live trading, appropriate position sizing based on your risk tolerance is essential.
⚠️ Slippage and commissions matter. The backtest includes 0.02% commission and 1 tick slippage, but actual execution costs vary by broker and market conditions.
Best Practices
Test on your specific market — Results vary significantly across different instruments
Use appropriate position sizing — Never risk more than you can afford to lose
Combine with your own analysis — No indicator replaces understanding market context
Paper trade first — Validate the strategy matches your trading style before risking capital
Alerts
Two alerts are available:
MDZ Long Entry: Fires when all entry conditions are met
Uptrend Started: Fires when EMA stack first aligns bullish
Methodology
This strategy is based on the principle that trend continuation has better odds than reversal when multiple timeframe momentum indicators agree. By requiring five independent confirmations, it filters out weak setups at the cost of fewer total signals.
The ATR-based exits adapt to current volatility rather than using fixed pip/point targets, which helps the strategy adjust to different market conditions.
Questions? Leave a comment below.
CVD Absorption & Distribution Pro v3 (With Logit Regression)CVD Absorption & Distribution Pro v3 - Complete Guide
Introduction and Overview
The CVD Absorption and Distribution Pro v3 is an advanced trading indicator designed for TradingView that reveals hidden market dynamics invisible on standard price charts. This tool analyzes the battle between buyers and sellers at the micro level, identifying when large institutional players are quietly accumulating or distributing positions while price remains deceptively stable.
Traditional volume indicators fail traders because they treat all volume the same way. They cannot distinguish between aggressive buying and aggressive selling. More importantly, they cannot reveal when significant selling pressure is being absorbed by hidden buyers, or when strong buying pressure is being quietly distributed by large sellers. This information asymmetry has historically given institutional traders a massive advantage over retail participants.
This indicator solves that problem by implementing Cumulative Volume Delta analysis combined with machine learning prediction models, hidden liquidity detection, and comprehensive statistical validation. The result is a professional-grade analytical tool that was previously available only on expensive specialized platforms, now accessible to the entire TradingView community.
What is Cumulative Volume Delta
Cumulative Volume Delta, commonly known as CVD, is a method of categorizing trading volume based on whether it represents buying or selling pressure. The concept is straightforward. When price ticks upward from one moment to the next, the volume associated with that price movement is classified as buying volume. When price ticks downward, that volume is classified as selling volume. The difference between total buying volume and total selling volume over a given period is the delta.
A positive delta indicates that buyers were more aggressive during that period. A negative delta indicates sellers were more aggressive. By tracking this delta cumulatively over time, traders can see the underlying pressure that may not be immediately visible in price action alone.
However, raw CVD analysis has limitations. The real trading edge emerges when we compare what the CVD suggested should happen to price versus what actually happened. When there is significant selling pressure but price fails to decline, something interesting is occurring. Someone is absorbing all that selling. This is where the concepts of absorption and distribution become critically important.
Core Functionality Explained
The indicator operates by accessing one-second bar data from TradingView, the finest granularity available on the platform. This micro-level data is then grouped into clusters, which are user-configurable time blocks. The default setting creates clusters of sixty one-second bars, effectively creating one-minute analysis blocks. However, traders can adjust this to create clusters representing anywhere from a few seconds to several minutes depending on their trading style.
For each one-second bar within a cluster, the script must determine whether to classify the volume as buying or selling. This classification happens based on whether price moved up or down compared to the previous bar. But what happens when price does not change at all? The indicator provides three methods to handle this situation.
The first method, called Last Direction, assigns unchanged volume to whichever direction occurred most recently. If the previous tick was an uptick, the unchanged volume is counted as buying. This approach assumes market momentum tends to persist at very short timeframes.
The second method, called Split Fifty-Fifty, divides unchanged volume equally between buying and selling. This conservative approach acknowledges that when price does not move, we genuinely cannot know whether buyers or sellers were responsible.
The third method simply ignores unchanged ticks entirely, excluding them from the CVD calculation. This purist approach ensures only directionally confirmed volume influences the analysis.
Understanding Absorption
Absorption is one of the two primary signals this indicator detects. Absorption occurs when significant selling pressure fails to push price lower. Imagine a scenario where the delta is strongly negative, meaning sellers are aggressively hitting bids and overwhelming buyers. Under normal circumstances, this should drive price down. But if price stays flat or even rises despite this selling pressure, something unusual is happening. A large buyer is absorbing all that selling without allowing price to fall.
This behavior is characteristic of institutional accumulation. Large players who want to build substantial positions cannot simply place massive buy orders because that would move price against them immediately. Instead, they often buy by absorbing selling pressure. They let other participants sell to them at stable prices, quietly accumulating shares without revealing their intentions.
The indicator identifies absorption by first checking whether the CVD magnitude exceeds a calculated threshold based on historical averages. If the CVD is significantly negative and exceeds this threshold, the script then examines what happened to price. If price moved up or stayed flat, this is classified as full absorption. If price moved down but moved less than expected given the selling pressure, this is classified as partial absorption.
The expected price move is calculated based on the relationship between CVD magnitude and typical price movement observed historically. If current CVD is twice the average, the expected price move would be approximately twice the average price move. When actual price movement falls short of this expectation, the shortfall percentage quantifies the absorption.
Understanding Distribution
Distribution is the mirror image of absorption. It occurs when significant buying pressure fails to push price higher. When delta is strongly positive but price stays flat or even declines, someone is distributing shares into that buying pressure. They are selling to eager buyers without allowing price to rise.
This behavior characterizes institutional distribution. Large holders who want to exit substantial positions face the same challenge as accumulators. They cannot simply dump massive sell orders because that would crash the price before they finish selling. Instead, they often sell by distributing into buying pressure, letting other participants buy from them at stable prices while quietly reducing their position.
The indicator identifies distribution using the same logic as absorption but in reverse. Strongly positive CVD that exceeds the threshold combined with flat or declining price signals distribution. Partial distribution is identified when price rises but rises less than the CVD magnitude would suggest.
Hidden Liquidity Detection
Perhaps the most valuable feature of this indicator is its ability to quantify hidden liquidity. Hidden liquidity refers to large orders that are not fully visible in the order book. Institutional traders commonly use iceberg orders, which display only a small portion of the total order size while the rest remains hidden. As the visible portion gets filled, more of the hidden quantity is revealed.
The indicator estimates hidden liquidity by analyzing partial absorption and partial distribution events. When price moves less than expected given the CVD, the difference represents volume that was absorbed by hidden orders. The cumulative hidden buy liquidity and hidden sell liquidity provide insight into institutional activity that remains completely invisible on standard charts.
A high ratio of hidden buy liquidity to hidden sell liquidity suggests institutional accumulation is occurring. Conversely, a high ratio of hidden sell liquidity to hidden buy liquidity suggests institutional distribution. These signals often precede significant price movements as the institutional positioning eventually influences market direction.
The Prediction Model
This indicator goes beyond simple pattern detection by implementing a genuine machine learning model trained on historical data. The model uses logistic regression to predict whether price will move up or down in subsequent clusters based on current market conditions.
The model considers three primary factors. First, it looks at the normalized CVD, which measures current CVD relative to its historical average and variability. Second, it examines net flow, which is the difference between absorption and distribution. Third, it analyzes hidden flow, the difference between hidden buy liquidity and hidden sell liquidity.
During the training process, the model examines historical clusters where price actually moved significantly. It learns the relationship between these three factors and subsequent price direction. Through iterative gradient descent, the model adjusts its coefficients to best fit the historical data.
The output is a probability between zero and one representing the likelihood that the next cluster will see upward price movement. A probability above sixty percent suggests bullish conditions. A probability below forty percent suggests bearish conditions. Values between forty-five and fifty-five percent indicate neutral or uncertain conditions.
Model Validation Metrics
The indicator provides several metrics to help traders assess whether the prediction model is actually useful for the specific instrument they are analyzing. This validation is critically important because not all instruments exhibit predictable CVD-price relationships.
Logistic Accuracy shows the percentage of correct binary predictions across the training window. An accuracy of fifty percent is essentially random, providing no edge. Accuracy above fifty-five percent suggests the model has genuine predictive value.
Sign Agreement Rate measures how often CVD direction matched price direction historically. When CVD is positive and price goes up, or when CVD is negative and price goes down, this counts as agreement. A sign agreement rate significantly above fifty percent indicates that CVD provides useful directional information for this instrument.
Weighted Sign Agreement applies the same concept but weights each observation by CVD magnitude. High-magnitude CVD events that correctly predict direction count more than low-magnitude events. This metric reveals whether strong CVD signals are more reliable than weak ones.
If these validation metrics are close to fifty percent, traders should be cautious about relying on the model for that particular instrument. The CVD-price relationship may be too noisy or the market microstructure may not suit this type of analysis.
Bucket Analysis
The indicator performs bucket analysis by segmenting historical data into five groups based on CVD magnitude. The first bucket contains clusters where CVD was very strongly negative, more than twice the average in the negative direction. The second bucket contains moderately negative CVD clusters. The third bucket represents neutral conditions where CVD was within one standard average of zero in either direction. The fourth bucket contains moderately positive CVD, and the fifth bucket contains very strongly positive CVD.
For each bucket, the indicator calculates what percentage of clusters saw price move upward. In a market where CVD has predictive value, we would expect to see low upward percentages in negative CVD buckets and high upward percentages in positive CVD buckets. The spread between the highest and lowest buckets indicates how useful CVD is for predicting direction.
If the bucket analysis shows similar upward percentages across all buckets, the CVD-price relationship is essentially random for that instrument. If the pattern shows the expected gradient from low to high, CVD analysis should provide genuine trading edge.
Strength Tiers
Not all absorption and distribution events are equally significant. The indicator classifies events into three strength tiers based on their magnitude relative to baseline averages.
Normal events occur when CVD is between one and two times the average magnitude. These events happen regularly throughout trading sessions and represent standard market dynamics.
Strong events occur when CVD is between two and three times the average magnitude. These elevated significance events warrant additional attention and may indicate more substantial institutional activity.
Exceptional events occur when CVD exceeds three times the average magnitude. These rare occurrences often precede significant price movements and represent major institutional footprints in the market.
The indicator tracks how many events of each tier occurred during the display period, helping traders identify sessions with unusual institutional activity.
Divergence Detection
The indicator implements sophisticated divergence detection that compares trends in CVD with trends in price over a rolling window of recent clusters. Divergence occurs when these two metrics move in opposite directions or when one moves significantly while the other remains flat.
Bullish divergence manifests in two forms. Hidden accumulation occurs when the CVD trend turns increasingly positive while price remains flat, suggesting buying pressure is building without yet moving price. CVD accumulation occurs when average CVD is positive but average price movement is minimal.
Bearish divergence also manifests in two forms. Hidden distribution occurs when CVD trend turns increasingly negative while price remains stable, suggesting selling pressure is building. CVD distribution occurs when average CVD is negative but price refuses to decline.
Divergence signals are quantified by their strength relative to baseline averages, allowing traders to prioritize the most significant divergences.
Display and Interface
The indicator presents all its analysis through a comprehensive table overlay positioned on the chart. The table is organized into logical sections that can be individually enabled or disabled based on trader preferences.
The Direction Prediction section shows the current signal, probability, and period cluster breakdown between bullish, bearish, and neutral predictions. The Model Performance section displays accuracy metrics and training sample counts.
The CVD Bucket Analysis section shows the five-bucket breakdown with upward percentages for each, along with an interpretation of whether a predictable pattern exists.
The Baselines section displays the calculated averages for CVD and price movement, along with the current threshold being used for event detection.
The Results section shows total absorption and distribution for the display period, the ratio between them, net values, and an overall flow signal interpretation.
The Full versus Partial section breaks down events by type, showing how much activity was full absorption or distribution versus partial events indicating hidden liquidity.
The Hidden Liquidity section displays estimated hidden buy and sell volumes, their ratio, average shortfall percentages, and an iceberg signal interpretation.
The Strength Tiers section shows event counts by tier for both absorption and distribution, highlighting any exceptional events.
The Divergence section indicates whether bullish or bearish divergence is currently present and its strength.
The Statistics section provides cluster counts and event counts for reference.
Configuration Recommendations
For scalping and very short-term trading with holding periods of one to five minutes, traders should use smaller cluster sizes around thirty to sixty seconds, shorter average lengths around two to three hundred clusters, and enable intensity weighting to emphasize high-magnitude events.
For day trading with holding periods of fifteen to sixty minutes, the default settings work well. Cluster size of sixty for one-minute analysis, average length of seven hundred fifty for approximately two trading days of history, and single-day display period provide balanced analysis.
For swing trading with multi-day holding periods, larger cluster sizes of three hundred to six hundred representing five to ten minute blocks reduce noise. Longer average lengths of seven fifty to fifteen hundred clusters capture broader patterns. Multi-day display periods of three to five days reveal accumulation and distribution over meaningful timeframes.
Interpreting Results
When the absorption to distribution ratio exceeds one point five, this suggests bullish underpinnings. Selling pressure is being absorbed, potentially indicating institutional accumulation. Traders should look for confirmation from hidden buy liquidity metrics, model probability favoring upside, and any bullish divergence signals.
When the ratio falls below zero point six seven, this suggests bearish underpinnings. Buying pressure is meeting distribution, potentially indicating institutional selling. Validate with hidden sell liquidity metrics, model probability favoring downside, and any bearish divergence signals.
When the ratio falls between zero point eight and one point two, the market is in relative equilibrium. Traders should wait for the ratio to break out of this neutral range, watch for exceptional tier events that might signal a shift, or wait for divergence to develop.
Regarding model predictions, when accuracy exceeds fifty-eight percent and sign agreement exceeds fifty-five percent, there is a strong predictive relationship. CVD analysis provides genuine edge for this instrument. When accuracy falls between fifty-four and fifty-eight percent or sign agreement falls between fifty-two and fifty-five percent, there is moderate edge. Use signals for confirmation but not as standalone entry triggers. When both metrics fall below their respective thresholds, the relationship is weak or random. Traders should reconsider whether CVD analysis adds value for this particular instrument.
Best Practices
Allow adequate training time before relying on model predictions. The prediction model requires substantial data to train effectively. Ensure at least five hundred clusters have accumulated before trusting model outputs. The indicator displays training sample count for verification.
Always validate model quality before trading based on predictions. A fifty-two percent accuracy is statistically indistinguishable from random chance. Ensure your edge is real by checking all validation metrics.
Context matters tremendously in interpretation. Absorption during an established uptrend suggests continuation strength. Absorption during a downtrend suggests potential reversal. Always interpret signals within the broader market context rather than in isolation.
Combine this indicator with price action analysis. The CVD analysis reveals hidden dynamics but should not be used alone. Combine with support and resistance levels, trend structure analysis, volume profile, and traditional technical patterns for comprehensive market assessment.
Monitor for regime changes over time. Market microstructure can change as participation patterns evolve. Regularly review bucket analysis to ensure the CVD-price relationship remains stable. Significant deterioration in predictive patterns may indicate changing market conditions requiring parameter recalibration.
Value to the Trading Community
This indicator democratizes institutional-grade analysis. Historically, this level of order flow analysis required expensive specialized platforms that cost hundreds or thousands of dollars monthly. By implementing these concepts within TradingView Pine Script, this tool makes professional analysis accessible to all traders regardless of budget.
The indicator serves as an educational framework. Beyond practical trading applications, the visible statistics help traders understand the CVD-price relationship. Bucket analysis teaches probabilistic thinking. Model coefficients reveal which factors matter most. Validation metrics prevent overconfidence in unreliable signals.
The customization depth accommodates diverse trading styles. With over thirty configurable parameters, the indicator adapts to virtually any approach from rapid scalping to patient swing trading.
The transparent methodology builds trust. Unlike black-box commercial solutions where algorithms remain hidden, every calculation is visible in the source code. Traders can verify the logic, understand the assumptions, and modify the approach to suit their specific needs.
Conclusion
The CVD Absorption and Distribution Pro v3 represents a significant advancement in accessible order flow analysis for retail traders. By combining time-tested CVD concepts with modern statistical validation and machine learning techniques, it provides a comprehensive toolkit for understanding the hidden dynamics driving price action.
Its value lies not merely in generating trading signals but in providing the framework to understand why those signals occur and whether they are statistically meaningful for the specific instrument being traded. This combination of actionable intelligence and educational transparency makes it an invaluable addition to any serious trader analytical arsenal.
The indicator rewards those who invest time in understanding its methodology, optimizing its parameters for their specific trading style, and validating its signals against their own market experience. Used thoughtfully, it reveals the institutional footprints that remain invisible on conventional charts. The absorption, distribution, and hidden liquidity patterns it detects often presage significant market movements, giving attentive traders the opportunity to position themselves alongside smart money rather than against it.
PHEN ATLAS - Market Map & Playbook [PhenLabs]📊 PHEN ATLAS 🎂 #50 🎂
Version: PineScript™ v6
📌 Description
The PHEN ATLAS marks a historic milestone as the 50th official release from PhenLabs . This is a critical release you do not want to miss, serving as a comprehensive Market Map and Playbook designed to provide traders with a complete structural overview of price action. By synthesizing Market Structure, Liquidity concepts, and Regime detection, this script solves the problem of "analysis paralysis" by grading price action in real-time. It moves beyond simple indicators by offering a quantified "Playbook" that scores trade setups from 0 to 100, helping traders focus exclusively on high-probability opportunities while automating the complex math of position sizing and risk management.
🚀 Points of Innovation
Proprietary Scoring Engine: Unlike standard indicators, this script assigns a quantitative score (0-100) to every potential trade based on confluence factors like HTF alignment and displacement.
Dynamic Regime Detection: Features an integrated dashboard that classifies the market into specific phases (Expansion, Trend, Range) using ADX and EMA alignment logic.
Smart Liquidity Pools: Automatically identifies and visualizes resting liquidity, tracking when these pools are "swept" to generate high-probability reversal signals.
Integrated Trade Manager: Automates the calculation of Stop Loss, Take Profit (1:2 and 1:3), and Position Size based on account balance and risk percentage directly on the chart.
Multi-Mode Interface: Offers three distinct visual modes—Clean, Pro, and Sniper—allowing users to toggle between deep analysis and clutter-free execution instantly.
🔧 Core Components
Structure Module: Identifies Pivots, Break of Structure (BOS), and Change of Character (CHoCH) to define the current market bias.
Liquidity Engine: Plots liquidity pools at key swing points and detects "Sweeps" where price grabs liquidity before reversing.
Regime Filter: Uses a combination of EMAs (21/50) and ADX to determine if the market is trending or ranging, filtering out low-quality signals.
Setup Validator: Monitors for three specific setup types (Sweep, Snapback, FVG Retest) and triggers alerts only when specific scoring thresholds are met.
🔥 Key Features
Automated detection of High Timeframe (HTF) structure without repainting issues.
Real-time grading of price displacement to validate institutional intent.
Visual Risk/Reward boxes that automatically adjust to the volatility (ATR) of the asset.
Fair Value Gap (FVG) detection with auto-mitigation tracking to clean up the chart.
Customizable alerts for A+ setups, regime changes, and trade invalidations.
Detailed dashboard displaying current Trend, Phase, Bias, and the score of the last setup.
🎨 Visualization
Structure Points: Triangles for BOS and Diamonds for CHoCH events clearly mark trend shifts.
Liquidity Lines: Dotted lines extending from pivots indicate un-swept liquidity pools; these dim automatically when swept.
Setup Signals: Prominent "A+" labels appear on the chart when a setup meets the minimum score threshold defined by the user.
Risk Boxes: Color-coded boxes (Green for Long, Red for Short) show Entry, Stop Loss, and Take Profit levels visually.
Dashboard: A compact table in the bottom right corner provides a "Heads Up Display" of the market state.
📖 Usage Guidelines
Display Mode: Select between 'Clean' for signals only, 'Pro' for full analysis including FVGs and Structure, or 'Sniper' for only high-score setups.
HTF Timeframe: Sets the higher timeframe for structural analysis (Default: 240/4-Hour) to ensure you trade with the dominant trend.
Min Score for A+ Setup: Threshold (0-100) required to trigger a signal (Default: 83); increase this to filter for only the absolute best trades.
Risk %: Defines the percentage of your account you are willing to risk per trade (Default: 1.0%), used for the position size calculation.
Account Balance: Input your current capital (Default: 10,000) to receive accurate unit sizing for every trade setup.
ADX Threshold: Adjusts the sensitivity of the Regime detection filter (Default: 20) to determine when the market is trending versus ranging.
✅ Best Use Cases
Confluence Trading: Use the scoring system to filter discretionary entries, taking trades only when the system scores them above 80.
Prop Firm Trading: Utilize the built-in position size calculator to strictly adhere to risk management rules during evaluations.
Trend Following: Wait for the Regime Dashboard to show "Bullish Expansion" before taking Long "Snapback" entries.
Reversal Trading: Focus on "Sweep Reclaim" setups where price sweeps a liquidity pool and immediately closes back within range.
⚠️ Limitations
This tool is a trend-following and reversal system; it may produce lower scores during undefined, low-volatility chop.
The position size calculator is an estimation based on the entry candle; actual execution slippage is not accounted for.
HTF data relies on closed candles to prevent repainting, which may result in a slight lag during rapid volatility spikes.
💡 What Makes This Unique
Playbook Scoring: Most indicators just give a signal; PHEN ATLAS gives you a "Grade" (e.g., 85/100), allowing you to make informed decisions based on quality, not just frequency.
Context Awareness: The script understands "Market Regime" and creates a context-aware bias, rather than blindly firing signals in a range.
🔬 How It Works
Step 1 - Regime Definition: The script analyzes the 21/50 EMA relationship and ADX to define if the market is in a Trend or Range.
Step 2 - Structure & Liquidity: It maps key pivots and liquidity pools, waiting for a "Sweep" event or a structural break.
Step 3 - Setup Trigger: When a specific pattern occurs (like a Sweep Reclaim), the engine calculates a score based on displacement, volume, and key level alignment.
Step 4 - Execution Logic: If the score > Threshold, the Trade Manager calculates the invalidation point (SL) and projects 2R/3R targets automatically.
🎉 Message From The Team 🎉
2025 was an amazing year. 12 months of building, shipping, and improving together with you. Hitting our 50th indicator release marks one full year of weekly drops , and we couldn't have done it without this community, and of course, BIG thank you to TradingView and it's team.
Thank you for all the feedback, charts, and support. Let's make 2026 even bigger. We can't wait to show you what we've been working on. 🚀
💡 Note
For best results, we recommend using the "Pro" mode during analysis to understand the narrative, and switching to "Sniper" or "Clean" during execution to maintain focus. Always ensure your "Account Balance" input matches your broker balance for accurate risk calculations.
Apex Adaptive Trend Navigator [Pineify]Apex Adaptive Trend Navigator
The Apex Adaptive Trend Navigator is a comprehensive trend-following indicator that combines adaptive moving average technology, dynamic volatility bands, and market structure analysis into a single, cohesive trading tool. Designed for traders who want to identify trend direction with precision while filtering out market noise, this indicator adapts its sensitivity based on real-time market efficiency calculations.
Key Features
Adaptive Moving Average with efficiency-based smoothing factor
Dynamic ATR-based volatility bands that expand and contract with market conditions
Market Structure detection including BOS (Break of Structure) and CHoCH (Change of Character)
Real-time performance dashboard displaying trend status and efficiency metrics
Color-coded cloud visualization for intuitive trend identification
How It Works
The core of this indicator is built on an Adaptive Moving Average that uses a unique efficiency-based calculation method inspired by the Kaufman Adaptive Moving Average (KAMA) and TRAMA concepts. The efficiency ratio measures the directional movement of price relative to total price movement over the lookback period:
Efficiency = |Price Change over N periods| / Sum of |Individual Bar Changes|
This ratio ranges from 0 to 1, where values closer to 1 indicate a strong trending market with minimal noise, and values closer to 0 indicate choppy, sideways conditions. The smoothing factor is then squared to penalize noisy markets more aggressively, causing the adaptive line to flatten during consolidation and respond quickly during strong trends.
The Dynamic Volatility Bands are calculated using the Average True Range (ATR) multiplied by a user-defined factor. These bands create a channel around the adaptive moving average, helping traders visualize the current volatility regime and potential support/resistance zones.
Trading Ideas and Insights
When price stays above the adaptive line with the bullish cloud forming, consider this a confirmation of uptrend strength
The efficiency percentage in the dashboard indicates trend quality - higher values suggest more reliable trends
Watch for price interactions with the upper and lower bands as potential reversal or continuation zones
A flat adaptive line indicates consolidation - wait for a clear directional break before entering trades
How Multiple Indicators Work Together
This indicator integrates three complementary analytical approaches:
The Adaptive Moving Average serves as the trend backbone, providing a dynamic centerline that automatically adjusts to market conditions. Unlike fixed-period moving averages, it reduces lag during trends while minimizing whipsaws during ranging markets.
The ATR Volatility Bands work in conjunction with the adaptive MA to create a volatility envelope. When the adaptive line is trending and price remains within the cloud (between the MA and outer band), this confirms trend strength. Price breaking through the opposite band may signal exhaustion or reversal.
The Market Structure Analysis using swing point detection adds a Smart Money Concepts (SMC) layer. BOS signals indicate trend continuation when price breaks previous swing highs in uptrends or swing lows in downtrends. CHoCH signals warn of potential reversals when the structure shifts against the prevailing trend.
Unique Aspects
The squared efficiency factor creates a non-linear response that dramatically reduces noise sensitivity
Cloud fills only appear on the trend side, providing clear visual distinction between bullish and bearish regimes
The integrated dashboard eliminates the need to switch between multiple indicators for trend assessment
Pivot-based swing detection ensures accurate market structure identification
How to Use
Add the indicator to your chart and adjust the Lookback Period based on your trading timeframe (shorter for scalping, longer for swing trading)
Monitor the cloud color - green clouds indicate bullish conditions, red clouds indicate bearish conditions
Use the efficiency reading in the dashboard to gauge trend reliability before entering positions
Consider entries when price pulls back to the adaptive line during strong trends (high efficiency)
Use the volatility bands as dynamic take-profit or stop-loss reference levels
Customization
Lookback Period : Controls the sensitivity of trend detection and swing point identification (default: 20)
Volatility Multiplier : Adjusts the width of the ATR bands (default: 2.0)
Show Market Structure : Toggle visibility of BOS and CHoCH labels
Show Performance Dashboard : Toggle the trend status table
Color Settings : Customize bullish, bearish, and neutral colors to match your chart theme
Conclusion
The Apex Adaptive Trend Navigator offers traders a sophisticated yet intuitive approach to trend analysis. By combining adaptive smoothing technology with volatility measurement and market structure concepts, it provides multiple layers of confirmation for trading decisions. Whether you are a day trader seeking quick trend identification or a swing trader looking for reliable trend-following signals, this indicator adapts to your market conditions and trading style. The efficiency-based calculations ensure you always know not just the trend direction, but also the quality and reliability of that trend.
Nixxo Custom IchimokuCustom Ichimoku settings for stock market or the crypto universe! Also has the capability to 2x the settings from the indicator settings (preset) so that settings don't have to be changed all the time.
VWAP Bias (STRONG ONLY) + Alerts (Time Window)VWAP Bias + NO TRADE Discipline Label
Clean, execution-focused indicator that removes decision noise.
Shows LONG / SHORT bias based on price vs VWAP, upgraded to STRONG or WEAK using VWAP slope and EMA(9/20) alignment.
A separate NO TRADE label appears when conditions are weak or neutral, enforcing discipline and preventing low-quality entries.
Designed for day trading VWAP pullbacks and momentum, especially on 1m–5m charts.
No oscillators, no clutter — just directional clarity and risk control.
ALPHA FUSION FIX - RSI Extreme Strategy [Webhook Ready]Overview: This indicator is a simplified, high-precision tool focused on RSI Overbought and Oversold extremes (95/5). It was designed for traders who seek exhaustion points in the market with surgical precision.
Key Features:
Pure RSI Logic: Signals are triggered strictly at RSI 95 (Short) and RSI 5 (Long), avoiding market noise.
Automation Ready: Includes a dynamic JSON Webhook integration for automated trading on exchanges like Binance.
Risk Management: Built-in inputs for Margin, Leverage, and Max Positions directly in the UI.
Visual Aids: Includes a Trio of EMAs (28, 80, 200) for trend context.
How to use:
Attach to any chart (Optimized for 15m/1h timeframes).
Configure your Webhook Secret and risk parameters.
Set an alert using "Any alert() function call".
Fair Value Gap [CT TRADERS]What does a Fair Value Gap do?
👉 It marks areas where price moved too fast, creating an imbalance between buyers and sellers.
This usually happens when institutional money enters the market aggressively.
Why is it important?
Because the market tends to revisit these areas to “rebalance” or fill the gap.
When price returns to an FVG, one of these usually happens:
🔼 Bounce (continuation of the move)
🔽 Rejection (price reversal)
Types of Fair Value Gaps
🔹 Bullish FVG
Created during a strong upward move
Acts as a support zone
Price often returns to it before continuing higher
🔹 Bearish FVG
Created during a strong downward move
Acts as a resistance zone
Price often returns to it before continuing lower
How traders use FVGs
✔️ Identify key price zones
✔️ Improve entry precision
✔️ Avoid chasing price
✔️ Understand institutional market behavior
COT Net Positions -TFF, LEGACY, DISAGGREGATED ReportsShow Net Positions, Long, Short for the CoT Reports
US Election Cycle Strategy [Druckenmiller]US Election Cycle Strategy
This indicator allows you to visually backtest and monitor the "US Presidential Election Cycle" theory, famously advocated by legendary investors like Stanley Druckenmiller. The core premise of this strategy is that the stock market tends to demonstrate strong performance in the two years leading up to a US Presidential Election, largely driven by fiscal stimulus, increased government spending, and economic maneuvering aimed at securing re-election.
How it works:
The script algorithmically calculates the exact date of US Presidential Elections (defined as the Tuesday next after the first Monday in November) for every cycle from 1900 to 2040. It creates a theoretical "Buy" signal exactly two years prior to the election and a "Sell" signal on Election Day itself.
Key Features of this Version:
Dynamic Date Calculation: Unlike scripts with hard-coded dates, this version uses a mathematical algorithm to determine the precise election date for any given year, ensuring historical accuracy and future-proofing.
Maximized History: The script automatically utilizes all available historical data provided by your chart. It does not arbitrarily cut off data (e.g., at 1970) unless you specifically choose a different start year in the settings.
Performance Statistics: An integrated dashboard displays key metrics based on the available history, including Average Return, Median Return, and the overall Win Rate of the strategy.
Visual Feedback: The "Entry" point is marked with a dashed line, which automatically colors itself Green (Profit) or Red (Loss) once the cycle is completed, giving you an immediate visual heatmap of historical performance.
Settings:
You can customize the "Start Calculation From Year" to filter the statistics for specific eras (e.g., set it to 2000 to see only modern market behavior). The visual appearance of lines and the statistics table are fully customizable.
Note:
This "strategy" is best applied to major US Indices (such as the S&P 500 or Dow Jones Industrial Average) on a Daily or Weekly timeframe.
Trader Guy WMThis is my very own unique code that allows users to place text at the top of their charts.
example something like your name, a quote or something you want to remember before entering a trade. Be creative. Enjoy.
Day of Week MarkersDay of Week Markers Indicator
Overview
This Pine Script (v6) indicator visually identifies specific days of the week on your chart using colored circle markers. It is designed to help traders quickly recognize the start of a new trading session or keep track of the day of the week based on traditional Thai color schemes.
Main Features:
Traditional Color Coding: Automatically assigns colors to markers based on the day:
- Monday: Yellow
- Tuesday: Pink
- Wednesday: Green
- Thursday: Orange/Amber
- Friday: Blue
Smart Timeframe Logic:
- Intraday Charts: Automatically shows the marker only on the first bar of the day to keep your chart clean.
- Daily/Higher Charts: Shows the marker on every bar corresponding to the selected day.
Customizable Visibility: Easily toggle the visibility of each individual day (Mon-Fri) through the indicator settings.
Flexible Appearance:
- Location: Choose to display markers Above Bar, Below Bar, or On Bar.
- Size: Adjustable marker sizes from Tiny to Large.
Settings:
Day Visibility: Checkboxes to enable or disable markers for specific days.
Marker Location: Dropdown to select where the circle appears relative to the price candle.
Marker Size: Dropdown to adjust the visual scale of the circles.
Regime-Filtered Overbought/Oversold V1 (Ariston)《Regime-Filtered Overbought/Oversold V1（Ariston）》是一个overlay主图型的“状态识别”工具，用超买超卖阈值去捕捉极端动量区间，同时用Regime Filter把同样的超买/超卖拆分成“趋势延伸”与“震荡反转”两类完全不同的交易语境，并将结论直接投射到价格图上。
“Regime-Filtered Overbought/Oversold V1 (Ariston)” is an on-chart overlay state-identification tool. It uses overbought/oversold thresholds to capture extreme momentum conditions, and applies a regime filter to split the same OB/OS readings into two very different trading contexts—trend extension versus range reversal—then projects the result directly onto the price chart.
指标的第一层是 Stochastic 计算，它不是一个频繁给出提示的 oscillators，而是更偏“极端状态报警器”：当一致性进入极端区间，才进入可执行的观察窗口。
Layer one is the Stochastic calculation. It is not designed to fire frequent oscillator prompts; it functions more like an “extreme-state alarm.” Only when the signal aligns and enters an extreme zone does it open an actionable observation window.
第二层是 Regime Filter：这个过滤器的意义在于同一个“超买”在趋势中常常代表“强势延伸的顺势机会”，在震荡中更接近“均值回归的反向机会”，两者不应被同一种颜色、同一种心理预期去处理。
Layer two is the Regime Filter. Its purpose is to reframe the same “overbought” reading: in trends it often represents a continuation-friendly extension opportunity, while in ranges it is closer to a mean-reversion fade setup. These two contexts should not be handled with the same color coding or the same mental model.
你也可以关闭 useRegime，此时指标退化为“震荡风格”的展示（超买=黄，超卖=蓝），保持简单。
You can also disable useRegime, in which case the indicator falls back to a simplified range-style display (Overbought = Yellow, Oversold = Blue).
可视化层面，该指标把状态映射成四种主图背景色（可调透明度）：趋势背景下的超买显示红色（Trend+OB=Red），趋势背景下的超卖显示绿色（Trend+OS=Green）；震荡背景下的超买显示黄色（Range+OB=Yellow），震荡背景下的超卖显示蓝色（Range+OS=Blue）。
Visually, the indicator maps states into four on-chart background colors (with adjustable transparency): Trend+OB is Red, Trend+OS is Green; Range+OB is Yellow, Range+OS is Blue.
这四种颜色本质上是在告诉你“同样是 OB/OS，当前更像 continuation 还是 mean-reversion”，从而在交易执行上自动切换思维框架。
These four colors are effectively telling you: “For the same OB/OS reading, does the current context look more like continuation or mean reversion?”—so you can switch execution mindset accordingly.
在“趋势且极端”的红/绿场景下，指标还会额外绘制分段趋势线（Segment Trendlines），用来给出更贴近价格的动态参考。红色状态（Trend+OB）会在K线下方生成一条红色上行分段线；绿色状态（Trend+OS）会在K线上方生成一条绿色下行分段线。
In “trend and extreme” red/green scenarios, the indicator additionally draws Segment Trendlines as a closer-to-price dynamic reference. In Red state (Trend+OB), it prints a red rising segment line below candles; in Green state (Trend+OS), it prints a green falling segment line above candles.
线条只在 useRegime=true 且趋势过滤达到趋势阈值时启用，且每一段状态结束后都会保留历史，不会回收删除，方便你回看过去的极端区间是如何展开与终结的。
These lines only activate when useRegime = true and the trend filter meets its threshold, and each segment is kept historically after the state ends (no cleanup/deletion), making it easy to review how prior extreme regimes evolved and resolved.
使用上，这个指标更适合作为“仓位管理与情境提示器”而不是机械开平仓信号机。
In practice, this indicator is better used as a “position management and context prompt” rather than a mechanical entry/exit signal engine.
参数方面，你主要会动三组：Stochastic 的 kLen/dLen/阈值决定“极端”的敏感度；ADX 长度与阈值决定趋势/震荡分界；背景透明度与 ATR 偏移决定视觉与线条贴合程度。
Parameter-wise, you will mainly adjust three blocks: Stochastic kLen/dLen/thresholds define extreme sensitivity; ADX length and threshold define the trend/range boundary; background transparency and ATR offset tune visual fit and line proximity.
若你希望信号更少更“干净”，通常提高 ADX 阈值或加大 kLen 会更有效；若你希望更快捕捉极端，降低 kLen 或降低 OB/OS 阈值即可，但要接受噪声上升。Debug 选项会在红/绿状态打点，用于检验状态触发是否符合预期。
If you want fewer, cleaner signals, raising the ADX threshold or increasing kLen tends to be effective. If you want faster extreme detection, reduce kLen or relax OB/OS thresholds—at the cost of more noise. The Debug option prints markers in red/green states to validate triggers against your expectations.
免责声明：该指标输出的是“条件状态与市场语境”，不保证对未来收益率有确定性预测价值；在低流动性品种、跳空频繁品种或极端新闻驱动阶段，Stochastic 与 ADX 的解释力可能下降，建议结合你自己的风险框架与执行规则使用。
Disclaimer: this indicator outputs conditional states and market context; it does not guarantee predictive edge or deterministic future returns. In low-liquidity markets, gap-prone instruments, or extreme news-driven regimes, the explanatory power of Stochastic and ADX may degrade. Use it alongside your own risk framework and execution rules.
BMM hybrid 2026 Auto Trading BMM 2026 – Hybrid Trading Strategy
BMM 2026 Hybrid Strategy is a precision-based TradingView strategy designed to adapt to different trading styles while maintaining high-probability trade entries. By combining trend direction, momentum confirmation, and market structure logic, the strategy delivers consistent results across multiple markets.
With a historical win rate exceeding 70% under optimal conditions, BMM 2026 focuses on quality over quantity, helping traders avoid overtrading and emotional decision-making.
🔹 Key Features
✅ Hybrid logic combining trend + momentum + confirmation
✅ Over 70% win rate when used with recommended settings
✅ Works on Forex, Indices, and Synthetic markets
✅ Clear Buy & Sell signals
✅ Built-in risk-to-reward structure
✅ Designed for both manual and automated execution
✅ Optimized for scalping, day trading, and swing trading
🔹 Choose the Best Timeframe for Your Trading Style
The BMM 2026 Hybrid Strategy allows you to select the timeframe that best matches your trading personality:
Scalpers: 1M – 5M
Fast entries, quick exits, high session accuracy
Day Traders: 15M – 30M
Balanced trades with strong intraday trends
Swing Traders: 1H – 4H
Fewer trades, higher conviction, larger targets
⚠️ For best results, align lower timeframes with the higher timeframe trend.
🔹 How It Works
The strategy identifies:
Primary trend direction
Momentum alignment
High-probability entry zones
Confirmation before execution
Trades are only triggered when multiple conditions agree, filtering out low-quality setups and improving overall accuracy.
🔹 Risk Management
Always risk 1–2% per trade
Follow the recommended timeframe & market combinations
Avoid trading during low-liquidity sessions
Best performance during London & New York sessions
🔹 Who This Strategy Is For
Traders seeking consistent, rule-based entries
Beginners who want clear signals
Advanced traders looking for a hybrid confirmation system
Traders planning to automate with alerts or APIs
⚠️ Disclaimer
Trading involves risk. Past performance does not guarantee future results. Always backtest and forward-test before trading live.
MACD Matrix: Angle & SettlementThis indicator is a comprehensive Multi-Timeframe (MTF) Dashboard designed for technical traders who rely on MACD not just for crossovers, but for Momentum Angle and Settlement (Hooks).
Instead of cluttering your screen with 5 different MACD charts, this Matrix calculates the math in the background and presents a clean "Heads-Up Display" of the MACD state across your specific timeframes (Default: 3m, 15m, 1h, 4h, 16h).
The Concept: "Angle Settlement"
Standard MACD indicators only show you when a cross happens. By then, the move is often halfway over. This script focuses on the Angle (Slope) of the MACD line to predict turns before they happen:
Steep Angle: Momentum is accelerating. (Strong Trend)
Settling Angle: The slope is flattening out. The MACD line is "hooking." (Reversal/Cross Imminent)
Dashboard Columns Explained
TF (Timeframe): Auto-formats your settings into readable text (e.g., "240" becomes "4h").
Zone:
> 0 (Green): MACD is above the Zero Line (Bullish Trend context).
< 0 (Red): MACD is below the Zero Line (Bearish Trend context).
Cross:
PCO (Green): Positive Crossover (MACD > Signal).
NCO (Red): Negative Crossover (MACD < Signal).
Deg (°):
The calculated mathematical angle of the MACD line.
Positive (+): Momentum is rising.
Negative (-): Momentum is falling.
State (The Strategy):
STEEP (Bright Color): The angle is increasing. Do not trade against this momentum.
SETTLE (Dim Color): The angle is decreasing compared to the previous bar. The momentum is "cooling off," often signaling a "Hook" or an upcoming crossover.
Settings & Customization
Custom Timeframes: You can freely change TF-1, TF-2, etc., in the settings. The table labels will auto-update (e.g., if you change 4h to 1D, the table will display "1D").
MACD Lengths: Fully customizable (Default 12, 26, 9).
Angle Sensitivity: A multiplier to calibrate the "Degrees" to your specific asset class (Crypto, Forex, or Indices). If angles look too small, increase this value.
IFM 2.0only for pips college
IFM (Inner Force Model) is a price-action based trading model that focuses on who controls the market internally—buyers or sellers—before the big move happens.
It’s not an indicator.
It’s a market behavior framework used to read institutional intent.
🔍 What IFM Really Means
IFM studies the internal strength (force) inside price by analyzing:
Liquidity grabs
Market structure shifts
Displacement (strong candles)
Premium / Discount positioning
The goal is simple:
👉 Enter where smart money has already committed
Red Bull Wings [JOAT]RED BULL WINGS - Bullish-Only Institutional Overlay
Introduction and Purpose
RED BULL WINGS is an open-source overlay indicator that combines five distinct bullish detection methods into a single composite scoring system. The core problem this indicator solves is that individual bullish signals (patterns, volume, zones, trendlines) often disagree or fire in isolation. A bullish engulfing pattern means little if volume is weak and price is far from support. Traders need confluence across multiple dimensions to identify high-probability setups.
This indicator addresses that by scoring each bullish component separately, then combining them into a weighted WINGS score (0-100) that reflects overall bullish conviction. When multiple components align, the score rises; when they disagree, the score stays low.
Why These Five Modules Work Together
Each module measures a different aspect of bullish market structure:
1. Module A - Bullish Candlestick Engine - Detects classic reversal patterns (engulfing, marubozu, hammer, 3-bar cluster). These patterns identify WHERE buyers are stepping in.
2. Module B - PVSRA Volume Climax - Measures spread x volume to detect institutional participation. This tells you WHETHER smart money is involved.
3. Module C - Demand Zone Detection - Identifies and tracks order block zones where buyers previously overwhelmed sellers. This shows you WHERE institutional support exists.
4. Module D - Trendline Channel - Builds dynamic support/resistance from pivot points. This reveals the STRUCTURE of the current trend.
5. Module E - Ichimoku Assist - Optional filter using Tenkan/Kijun cross, cloud position, and Chikou confirmation. This provides TREND PERMISSION context.
The combination works because:
Patterns alone can fail without volume confirmation
Volume alone means nothing without price structure context
Zones alone are static without pattern/volume triggers
Trendlines alone miss the micro-level entry timing
When 3+ modules agree, the probability of a valid bullish setup increases significantly
How the Calculations Work
Module A - Pattern Detection:
Bullish Engulfing - Current bullish bar completely engulfs prior bearish bar:
bool engulfingCond = isBullish() and
isBearish() and
open <= close and
close >= open and
bodySize() > bodySize()
Marubozu - Strong body with minimal wicks (body >= 1.8x average, wick ratio < 20%):
float wickRatio = candleRange() > 0 ? (upperWick() + lowerWick()) / candleRange() : 0
bool marubozuCond = isBullish() and
bodySize() >= bodySizeAvg * i_maruMult and
wickRatio < i_wickRatioMax
Hammer - Long lower wick (>= 2.5x body), close in upper third, volume confirmation:
bool hammerWick = lowerWick() >= i_hammerWickMult * bodySize()
bool hammerClose = close >= low + (candleRange() * 0.66)
bool hammerVol = volume >= i_pvsraRisingMult * volAvg
3-Bar Cluster - Three consecutive bullish closes with increasing prices and volume spike:
bool threeBarBullish = isBullish() and isBullish() and isBullish()
bool increasingCloses = close > close and close > close
bool volSpike3Bar = volume >= i_pvsraRisingMult * volAvg or
volume >= i_pvsraRisingMult * volAvg
Module B - PVSRA Volume Analysis:
Uses spread x volume to detect climax conditions:
float spreadVol = candleRange() * volume
float maxSpreadVol = ta.highest(spreadVol, ADJ_PVSRA_LOOKBACK)
bool volClimax = volume >= i_pvsraClimaxMult * volAvg or spreadVol >= maxSpreadVol
bool volRising = volume >= i_pvsraRisingMult * volAvg and volume < i_pvsraClimaxMult * volAvg
Volume only scores when the candle is bullish, preventing false signals on bearish volume spikes.
Module C - Demand Zone Detection:
Identifies zones using a two-candle structure:
// Small bearish candle A followed by larger bullish candle B
bool candleA_bearish = isBearish()
bool candleB_bullish = isBullish()
bool newZoneCond = candleA_bearish and candleB_bullish and
candleB_size >= i_zoneSizeMult * candleA_size
Zones are drawn as rectangles and tracked for retests. Score increases when price is near or inside an active zone, with bonus points for rejection candles.
Module D - Trendline Channel:
Builds dynamic channel from confirmed pivot points:
float ph = ta.pivothigh(high, i_pivotLeft, i_pivotRight)
float pl = ta.pivotlow(low, i_pivotLeft, i_pivotRight)
Pivots are stored and connected to form upper/lower channel lines. The indicator detects breakouts when price closes beyond the channel with volume confirmation.
Module E - Ichimoku Assist:
Standard Ichimoku calculations with bullish scoring:
float tenkan = (ta.highest(high, i_tenkanLen) + ta.lowest(low, i_tenkanLen)) / 2
float kijun = (ta.highest(high, i_kijunLen) + ta.lowest(low, i_kijunLen)) / 2
bool tkCross = ta.crossover(tenkan, kijun)
bool priceAboveCloud = close > cloudTop
bool chikouAbovePrice = chikou > close
Module F - WINGS Composite Score:
All module scores are combined using adjustable weights:
float WINGS_score = 100 * (nW_pattern * S_pattern +
nW_volume * S_vol +
nW_zone * S_zone +
nW_trend * S_trend +
nW_ichi * S_ichi)
Default weights: Pattern 30%, Volume 25%, Zone 20%, Trend 15%, Ichimoku 10%.
Signal Thresholds
WATCH (30-49) - Interesting bullish context forming, not yet actionable
MOMENTUM (50-74) - Strong bullish conditions, multiple modules agreeing
LIFT-OFF (75+) - High-confidence bullish confluence across most modules
WINGS Badge (Dashboard)
The right-side panel displays:
WINGS Score - Current composite score (0-100)
Pattern - Active pattern name and strength, or neutral placeholder
Volume - Normal / Rising / CLIMAX status
Zone - ACTIVE if price is near a demand zone
Trend - Channel position or BREAK status
Ichimoku - OFF / Weak / Bullish / STRONG
Status - Overall signal level (Neutral / WATCH / MOMENTUM / LIFT-OFF)
Input Parameters
Module Toggles:
Enable Bullish Patterns (true) - Toggle pattern detection
Enable PVSRA Volume (true) - Toggle volume analysis
Enable Order Blocks (true) - Toggle demand zone detection
Enable Trendlines (true) - Toggle pivot channel
Enable Ichimoku Assist (false) - Toggle Ichimoku filter (off by default for performance)
Enable Visual Effects (false) - Toggle labels, trails, and visual elements
LIVE MODE (false) - Enable intrabar signals (WARNING: signals may repaint)
Pattern Engine:
Pattern Lookback (5) - Bars for body size averaging
Marubozu Body Multiplier (1.8) - Minimum body size vs average
Hammer Wick Multiplier (2.5) - Minimum lower wick vs body
Max Wick Ratio (0.2) - Maximum wick percentage for marubozu
Volume / PVSRA:
PVSRA Lookback (10) - Period for volume averaging
Climax Multiplier (2.0) - Volume threshold for climax detection
Rising Volume Multiplier (1.5) - Volume threshold for rising detection
Order Blocks:
Zone Size Multiplier (2.0) - Minimum bullish candle size vs bearish
Zone Extend Bars (200) - How far zones project forward
Max Zones (12) - Maximum active zones displayed
Remove Zone on Close Below (true) - Delete broken zones
Trendlines:
Pivot Left/Right Bars (3/3) - Pivot detection sensitivity
Min Slope % (0.25) - Minimum trendline angle
Max Trendlines (5) - Maximum pivot points stored
Trendline Projection Bars (60) - Forward projection distance
Ichimoku:
Tenkan Length (9) - Conversion line period
Kijun Length (26) - Base line period
Senkou B Length (52) - Leading span B period
Displacement (26) - Cloud displacement
WINGS Score:
Weight: Pattern (0.30) - Pattern contribution to score
Weight: Volume (0.25) - Volume contribution to score
Weight: Zone (0.20) - Zone contribution to score
Weight: Trend (0.15) - Trendline contribution to score
Weight: Ichimoku (0.10) - Ichimoku contribution to score
Lift-Off Threshold (75) - Score required for LIFT-OFF signal
Momentum Watch Threshold (50) - Score required for MOMENTUM signal
Visuals:
Signal Cooldown (8) - Minimum bars between labels
Show WINGS Score Badge (true) - Toggle dashboard
Show Wing Combos (true) - Show DOUBLE/MEGA WINGS streaks
Red Background Wash (true) - Tint chart background
Show Lift-Off Trails (false) - Toggle golden trail visuals
How to Use This Indicator
For Bullish Entry Identification:
1. Monitor the WINGS badge for score changes
2. Wait for MOMENTUM (50+) or LIFT-OFF (75+) signals
3. Check which modules are contributing (Pattern + Volume + Zone = stronger)
4. Use demand zones and trendlines as structural reference for entries
For Confluence Confirmation:
1. Use alongside your existing analysis
2. LIFT-OFF signals indicate multiple bullish factors aligning
3. Low scores (< 30) suggest weak bullish context even if one factor looks good
For Zone-Based Trading:
1. Watch for price approaching active demand zones
2. Look for pattern + volume confirmation at zone retests
3. Zone score increases with successful retests
For Trendline Analysis:
1. Monitor the pivot-based channel for trend structure
2. Breakouts with volume confirmation trigger TREND BREAK alerts
3. Price inside channel with bullish patterns = trend continuation setup
1M and lower timeframes:
Alerts Available
LIFT-OFF - High-confidence bullish confluence
MOMENTUM - Strong bullish conditions
Zone Retest - Bullish rejection from demand zone
Trendline Break - Breakout with volume confirmation
Individual patterns (Engulfing, Marubozu, Hammer, 3-Bar Cluster)
Volume Climax - Institutional volume spike
DOUBLE WINGS / MEGA WINGS - Consecutive lift-off signals
Repainting Behavior
By default, the indicator uses confirmed bars only (barstate.isconfirmed), meaning signals appear after the bar closes and do not repaint. However:
LIVE MODE - When enabled, signals can appear intrabar but may disappear if conditions change before bar close. A warning label displays when LIVE MODE is active.
Trendlines - Pivot detection requires lookback bars, so the most recent trendline segments may adjust as new pivots confirm. This is inherent to pivot-based analysis.
Demand Zones - Zones are created on confirmed bars and do not repaint, but they can be removed if price closes below the zone bottom (configurable).
Live Mode with 'Enable Visual Effect' turned off in settings:
Limitations
This is a bullish-only indicator. It does not detect bearish setups or provide short signals.
The WINGS score is a confluence measure, not a prediction. High scores indicate favorable conditions, not guaranteed outcomes.
Pattern detection uses simplified logic. Not all candlestick nuances are captured.
Volume analysis requires reliable volume data. Results may vary on instruments with inconsistent volume reporting.
Ichimoku calculations add processing overhead. Disable if not needed.
Demand zones are based on a specific two-candle structure. Other valid zones may not be detected.
Trendlines use linear regression between pivots. Curved or complex channels are not supported.
Timeframe Recommendations
15m-1H: More frequent signals, useful for intraday analysis. Higher noise.
4H-Daily: Best balance of signal quality and frequency for swing trading.
Weekly: Fewer but more significant signals for position trading.
Adjust lookback periods and thresholds based on your timeframe. Shorter timeframes may benefit from shorter lookbacks.
Open-Source and Disclaimer
This script is published as open-source under the Mozilla Public License 2.0 for educational purposes. The source code is fully visible and can be studied to understand how each module works.
This indicator does not constitute financial advice. The WINGS score and signals do not guarantee profitable trades. Past performance does not guarantee future results. Always use proper risk management, position sizing, and stop-losses. Test thoroughly on your preferred instruments and timeframes before using in live trading.
- Made with passion by officialjackofalltrades
Standard Deviation Channel with SignalsStandard Deviation Channel with Signals
This Pine Script is a **Standard Deviation Channel (or Linear Regression Channel) indicator** designed for TradingView. It automatically draws a channel around price action based on statistical deviation from a central linear regression trendline.
Here is a breakdown of its key features:
* **Trend Identification:** It calculates a linear regression line (the "mean" price) over a user-defined length (default 128 bars) to show the current trend direction.
* **Volatility Bands:** It plots parallel upper and lower bands at specific standard deviations (e.g., ±1 and ±2 deviations) from the center line. These act as dynamic support and resistance levels.
* **Actionable Signals:** It generates **"BUY"** signals when the price crosses below the lower deviation band (suggesting the asset is oversold) and **"SELL"** signals when the price crosses above the upper deviation band (suggesting it is overbought). This logic is based on a Mean Reversion strategy.
* **Historical & Live Visualization:** Unlike standard versions that only show the channel for the *current* moment, this script plots the historical path of the bands so you can backtest visual signals, while also projecting the live channel forward for real-time analysis.
TTP Checklist By AwaisFxThis is a dynamic, multi-row trading checklist designed to help traders track key criteria and calculate a trade score directly on the chart. It combines customizable table rows, price-based automated checkboxes, and sweep detection logic for high-timeframe (HTF) blocks.
Automated rows
HTF OC - This will toggle true if you put the price of your original consolidation that you will be targetting
HTF POI - This will toggle true if price is within the POI range (High - low)
(xx)m Sweep - This will toggle true if price sweeps the prior xx cycle - *xx will be the value that is selected (10/30/90)
TOI - This will toggle true if price is within the Time of Interest
Sessions and Killzones [Tradeuminati]Tradeuminati – Sessions & Killzones is a New York local time based session toolkit designed for traders who want clean, objective session structure on their chart: session boundaries, killzones, session highs/lows, and previous day levels plus a live “liquidity taken” checklist.
Key Features
1) Sessions (New York Time)
London Session (0:00 – 6:00 NY)
- Vertical start/end lines
- Live session High and Low tracking during the session
- High/Low levels extend until 16:00 NY
- Labels: Ls - H and Ls - L
- Option to display only the current day
Asia Session (Previous Day, 18:00 – 00:00 NY)
- Vertical start/end lines for the previous day session
- Live session High and Low tracking
- High/Low levels extend into the next day until 16:00 NY
- Labels: As - H and As - L
- Option to display only the current day
2) Killzones (New York Time)
London Killzone: 2:00 – 5:00 NY
- Optional DAX-only mode: If enabled, DAX uses 3:00 – 5:00 NY (DAX opening), while other assets remain 2:00 – 5:00 NY
New York Killzone (auto-adjust by asset type)
- Indices: 9:30 – 11:00 NY
- Other assets (FX / Commodities / Crypto): 7:00 – 10:00 NY
New York PM Killzone: 14:00 – 15:00 NY (all assets)
ll killzone lines are placed from the start of the NY day, so you can see upcoming killzones in advance (not only after candles appear).
3) Previous Day High / Low (PDH / PDL)
- Automatically calculates the full previous NY day range (00:00 – 23:59 NY)
- Plots PDH and PDL into the current day
- Labels: PDH and PDL
4) Live “Liquidity Taken” Table
- A compact table in the bottom-left shows whether price has:
- swept Asia High / Asia Low
- swept London High / London Low
- taken PDH / PDL
A green checkmark appears instantly once a level is broken.
Customization
Fully adjustable colors, widths, and line styles for:
- Session vertical lines
- Session high/low lines
- Killzones
- PDH/PDL
Adjustable label size
Day filtering options (current day only)
-----
Disclaimer
This indicator is for educational and technical analysis purposes only. It does not constitute financial or investment advice. Trading involves risk.
