Logistic Regression ICT FVG🚀 OVERVIEW
Welcome to the Logistic Regression Fair Value Gap (FVG) System — a next-gen trading tool that blends precision gap detection with machine learning intelligence.
Unlike traditional FVG indicators, this one evolves with each bar of price action, scoring and filtering gaps based on real market behavior.
🔧 CORE FEATURES
✨ Smart Gap Detection
Automatically identifies bullish and bearish Fair Value Gaps using volatility-aware candle logic.
📊 Probability-Based Filtering
Uses logistic regression to assign each gap a confidence score (0 to 1), showing only high-probability setups.
🔁 Real-Time Retest Tracking
Continuously watches how price interacts with each gap to determine if it deserves respect.
📈 Multi-Factor Assessment
Evaluates RSI, MACD, and body size at gap formation to build a full context snapshot.
🧠 Self-Learning Engine
The logistic regression model updates on each bar using gradient descent, refining its predictions over time.
📢 Built-In Alerts
Get instant alerts when a gap forms, gets retested, or breaks.
🎨 Custom Display Options
Control the color of bullish/bearish zones, and toggle on/off probability labels for cleaner charts.
🚩 WHAT MAKES IT DIFFERENT
This isn’t just another box-drawing indicator.
While others mark every imbalance, this system thinks before it draws — using statistical modeling to filter out noise and prioritize high-impact zones.
By learning from how price behaves around gaps (not just how they form), it helps you trade only what matters — not what clutters.
⚙️ HOW IT WORKS
1️⃣ Detection
FVGs are identified using ATR-based thresholds and sharp wick imbalances.
2️⃣ Behavior Monitoring
Every gap is tracked — and if respected enough times, it becomes part of the elite training set.
3️⃣ Context Capture
Each new FVG logs RSI, MACD, and body size to provide a feature-rich context for prediction.
4️⃣ Prediction (Logistic Regression)
The model predicts how likely the gap is to be respected and assigns it a probability score.
5️⃣ Classification & Alerts
Gaps above the threshold are plotted with score labels, and alerts trigger for entry/respect/break.
⚙️ CONFIGURATION PANEL
🔧 System Inputs
• Max Retests – How many times a gap must be respected to train the model
• Prediction Threshold – Minimum score to show a gap on the chart
• Learning Rate – Controls how fast the model adapts (default: 0.009)
• Max FVG Lifetime – Expiration duration for unused gaps
• Show Historic Gaps – Show/hide expired or invalidated gaps
🎨 Visual Options
• Bullish/Bearish Colors – Set gap colors to fit your chart style
• Confidence Labels – Show probability scores next to FVGs
• Alert Toggles – Enable alerts for:
– New FVG detected
– FVG respected (entry)
– FVG invalidated (break)
💡 WHY LOGISTIC REGRESSION?
Traditional FVG tools rely on candle shapes.
This system relies on probability — by training on RSI, MACD, and price behavior, it predicts whether a gap will act as a true liquidity zone.
Logistic regression lets the system continuously adapt using new data, making it more accurate the longer it runs.
That means smarter signals, fewer false positives, and a clearer view of where real opportunities lie.
Cari skrip untuk "imbalance"
TJR SEEK AND DESTROYTJR SEEK AND DESTROY – Intraday ICT Trading Tool
Built for day traders, TJR SEEK AND DESTROY combines Smart Money concepts like order blocks, fair value gaps, and liquidity sweeps with structure breaks and daily bias to pinpoint high-probability trades during US market hours (9:30–16:00). Ideal for scalping or intraday strategies on stocks, futures, or forex.
What Makes It Unique?
Unlike standalone ICT indicators, this script integrates:
Order Blocks with volume and range filters for precise support/resistance zones.
Fair Value Gaps (FVG) to spot pre-market price imbalances.
Break of Structure (BOS) and Liquidity Sweeps for trend and reversal signals.
A 1H MA-based Bias to align trades with the day’s direction.
BUY/SELL Labels triggered only when bias, BOS, and sweeps align, reducing noise.
How Does It Work?
Order Blocks: Marks zones with high volume (>1.5x 20-period SMA) and low range (<0.5x ATR20) as teal boxes—potential reversal points.
Fair Value Gap: Compares the prior day’s close to the current open (pre- or post-9:30), shown as a purple line and label (e.g., "FVG: 0.005").
Pivot Point: Calculates (prevHigh + prevLow + prevClose) / 3 from the prior day, plotted as an orange line for equilibrium.
Break of Structure: Detects crossovers of 5-bar highs/lows (gray lines), marked with red triangles.
Liquidity Sweeps: Tracks breaches of the prior day’s high/low (yellow lines), marked with yellow triangles.
Daily Bias: Uses 1H close vs. 20-period MA (blue line) for bullish (green background), bearish (red), or neutral (gray) context.
Signals: BUY (green label) when bias is bullish, price breaks up, and sweeps the prior high; SELL (red label) when bias is bearish, price breaks down, and sweeps the prior low.
How to Use It
Setup: Apply to 1M–15M charts for US session trading (9:30–16:00 EST).
Trading:
Wait for a BUY label after a yellow sweep triangle above the prior day’s high in a green (bullish) background.
Wait for a SELL label after a yellow sweep triangle below the prior day’s low in a red (bearish) background.
Use order blocks (teal boxes) as support/resistance for stop-loss or take-profit.
Markets: Best for SPY, ES futures, or forex pairs with US session volatility.
Underlying Concepts
Order Blocks: High-volume, low-range bars suggest institutional activity.
FVG: Gaps between close and open indicate imbalance to be filled.
BOS & Sweeps: Price breaking key levels signals momentum or stop-hunting.
Bias: 1H MA filters trades by broader trend.
Chart Setup
Displays order blocks (teal boxes), pivot (orange), open (purple), bias (colored background), BOS/sweeps (triangles), and signals (labels). Keep other indicators off for clarity.
Quantum Liquidity Fractal Dynamics (QLFD) v2.1The Quantum Liquidity Fractal Dynamics (QLFD) v2.1 is an advanced multi-dimensional market analysis too l engineered for professional traders seeking to identify high-probability liquidity-driven reversals. Built upon a proprietary Fractal-Liquidity Convergence Model (FLCM), QLFD v2.1 leverages quantum-phase liquidity oscillations and institutional absorption mapping to dynamically assess order flow efficiency within multi-timeframe market structures.
Core Algorithmic Methodology
QLFD v2.1 integrates a Hybridized Recursive Liquidity Matrix (HRLM) with High-Frequency Adaptive EMA Displacement (HFAED) to model non-linear liquidity density clusters. This proprietary framework is further reinforced by a Multi-Layered RSI Vorticity Filter (MLRVF), enhancing the signal integrity by filtering out stochastic noise anomalies.
The EMA-200 Rejection Dynamics, combined with the Vortex RSI Momentum Refraction Index (VRMRI), allow the system to isolate institutional footprint imbalances. By capturing transient liquidity voids and microstructure inefficiencies, QLFD v2.1 enables traders to position themselves ahead of high-probability liquidity sweeps.
Signal Efficiency & Institutional Calibration
While QLFD v2.1 exhibits an exceptionally high accuracy rate in identifying potential reversal vectors, it is imperative for traders to exercise institutional-grade signal filtration. The indicator autonomously detects Phase-Induced False Signal Clusters (PIFSCs), yet discretion remains paramount in avoiding transient liquidity mirages—a common occurrence in markets exhibiting hyper-fractalized liquidity dislocations.
For optimal performance, professional traders must apply a Multi-Stage Confirmation Protocol (MSCP), leveraging additional confluence layers such as:
Order Flow Delta Cohesion (OFDC)
Gamma-Weighted Imbalance Deviation (GWID)
Synthetic Volume Shockwave Ratio (SVSR)
These advanced methodologies ensure that traders engage only with high-probability fractal reversals, filtering out structurally unreliable signals induced by inter-market arbitrage distortions.
Final Thoughts
QLFD v2.1 is not designed for retail-grade signal chasing. It is an institutional-grade analytical framework tailored for professionals who understand the fractal complexity of modern liquidity landscapes. Mastering the art of discretionary filtration—by distinguishing true liquidity-driven reversals from algorithmically-induced decoy impulses—is the key to leveraging this system’s full potential.
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.
Pristine Adaptive Alpha ScreenerThe Pristine Adaptive Alpha Screener allows users to screen for all of the trading signals embedded in our premium suite of TradingView tools🏆
▪ Pristine Value Areas & MGI - enables users to perform comprehensive technical analysis through the lens of the market profile in a fraction of the time!
▪ Pristine Fundamental Analysis - enables users to perform comprehensive fundamental stock analysis in a fraction of the time!
▪ Pristine Volume Analysis - organizes volume, liquidity, and share structure data, allowing users to quickly gauge the relative volume a security is trading on, and whether it is liquid enough to trade
💠 How is this Screener Original?
▪ The screener allows users to screen for breakouts, breakdowns, bullish and bearish trend reversals, and allows users to narrow a universe of stocks based purely on fundamentals, or purely on technicals. One screening tool to support an entire technofundamental workflow!
💠 Signals Overview
Each of the below signals serves one of two purposes:
1) A pivot point to be used as a long or short entry
2) A tool for narrowing a universe of stocks to a shorter list of stocks that have a higher potential for superperformance
▪ HVY(highest volume in a year) -> Featured in Pristine Volume Analysis -> Entry signal
▪ Trend Template -> Inspired by Mark Minervini's famous trend filters -> Tool for narrowing a universe of stocks to a shorter list with a higher potential for superperformance
▪ Rule of 100 -> Metrics from Pristine Fundamental Analysis -> Tool for narrowing a universe of stocks to a shorter list with a higher potential for superperformance
▪ Bullish 80% Rule -> Featured in Pristine Value Areas & MGI -> Long entry signal -> Trend Reversal
▪ Bearish 80% Rule -> Featured in Pristine Value Areas & MGI -> Short entry signal -> Trend Reversal
▪ Break Above VAH -> Featured in Pristine Value Areas & MGI -> Long entry signal -> Trend Continuation
▪ Break Below VAL -> Featured in Pristine Value Areas & MGI -> Short entry signal -> Trend Continuation
💠 Signals Decoded
▪ HVY(highest volume in a year)
Volume is an important metric to track when trading, because abnormally high volume tends to occur when a new trend is kicking off, or when an established trend is hitting a climax. Screen for HVY to quickly curate every stock that meets this condition.
▪ Trend Template
Mark Minervini's gift to the trading world. Via his book "Think and Trade Like a Stock Market Wizard". Stocks tend to make their biggest moves when they are already in uptrends, and the Minervini Trend template provides criteria to assess whether a stock is in a clearly defined uptrend. Filter for trend template stocks using our tool.
▪ Rule of 100
Pristine Capital's gift to the trading world. The rule of 100 filters for stocks that meet the following condition: YoY EPS Growth + YoY Sales Growth >= 100%. Stocks that meet this criteria tend to attract institutional investors, making them strong candidates for swing trading to the long side.
💠 Market Profile Introduction
A Market Profile is a charting technique devised by J. Peter Steidlmayer, a trader at the Chicago Board of Trade (CBOT), in the 1980's. He created it to gain a deeper understanding of market behavior and to analyze the auction process in financial markets. A market profile is used to analyze an auction using price, volume, and time to create a distribution-based view of trading activity. It organizes market data into a bell-curve-like structure, which reveals areas of value, balance, and imbalance.
💠 How is a Value Area Calculated?
A value area is a distribution of 68%-70% of the trading volume over a specific time interval, which represents one standard deviation above and below the point of control, which is the most highly traded level over that period.
The key reference points are as follows:
Value area low (VAL) - The lower boundary of a value area
Value area high (VAH) - The upper boundary of a value area
Point of Control (POC) - The price level at which the highest amount of a trading period's volume occurred
If we take the probability distribution of trading activity and flip it 90 degrees, the result is our Pristine Value Area!
Market Profile is our preferred method of technical analysis at Pristine Capital because it provides an objective and repeatable assessment of whether an asset is being accumulated or distributed by institutional investors. Market Profile levels work remarkably well for identifying areas of interest, because so many institutional trading algorithms have been programmed to use these levels since the 1980's!
The benefits of using Market Profile include better trade location, improved risk management, and enhanced market context. It helps traders differentiate between trending and consolidating markets, identify high-probability trade setups, and adjust their strategies based on whether the market is in balance (consolidation) or imbalance (trending). Unlike traditional indicators that rely on past price movements, Market Profile provides real-time insights into trader behavior, giving an edge to those who can interpret its nuances effectively.
▪ Bullish 80% Rule
If a security opens a period below the value area low , and subsequently closes above it, the bullish 80% rule triggers, turning the value area green. One can trade for a move to the top of the value area, using a close below the value area low as a potential stop!
In the below example, HOOD triggered the bullish 80% rule after it reclaimed the monthly value area!
HOOD proceeded to rally through the monthly value area and beyond in subsequent trading sessions. Finding the first stocks to trigger the bullish 80% rule after a market correction is key for spotting the next market leaders!
▪ Bearish 80% Rule
If a security opens a period above the value area high , and subsequently closes below it, the bearish 80% rule triggers, turning the value area red. One can trade for a move to the bottom of the value area, using a close above the value area high as a potential stop!
ES proceeded to follow through and test the value area low before trending below the weekly value area
▪ Break Above VAH
When a security is inside value, the auction is in balance. When it breaks above a value area, it could be entering a period of upward price discovery. One can trade these breakouts with tight risk control by setting a stop inside the value area! These breakouts can be traded on all chart timeframes depending on the style of the individual trader. Combining multiple timeframes can result in even more effective trading setups.
RBLX broke out from the monthly value area on 4/22/25👇
RBLX proceeded to rally +62.78% in 39 trading sessions following the monthly VAH breakout!
▪ Break Below VAL
When a security is inside value, the auction is in balance. When it breaks below a value area, it could be entering a period of downward price discovery. One can trade these breakdowns with tight risk control by setting a stop inside the value area! These breakouts can be traded on all chart timeframes depending on the style of the individual trader. Combining multiple timeframes can result in even more effective trading setups.
CHWY broke below the monthly value area on 7/20/23👇
CHWY proceeded to decline -53.11% in the following 64 trading sessions following the monthly VAL breakdown!
💠 Metric Columns
▪ %𝚫 - 1-day percent change in price
▪ YTD %𝚫 - Year-to-date percent change in price
▪ MTD %𝚫 - Month-to-date percent change in price
▪ MAx Moving average extension - ATR % multiple from the 50D SMA -Inspired by Jeff Sun
▪ 52WR - Measures where a security is trading in relation to it’s 52wk high and 52wk low. Readings near 100% indicate close proximity to a 52wk high and readings near 0% indicate close proximity to a 52wk low
▪ Avg $Vol - Average volume (50 candles) * Price
▪ Vol RR - Candle volume/ Avg candle volume
💠 Best Practices
Monday -> Friday Post-market Analysis
1) Begin with a universe of stocks. I use the following linked universe screen as a starting point: www.tradingview.com
2) Screen for the HVY signal -> Add those stocks to a separate flagged (colored) watchlist
3) Screen for the Bullish 80% Rule signal -> Add those stocks to a separate flagged (colored) watchlist
4) Screen for the Break Above VAH Signal -> Add those stocks to a separate flagged (colored) watchlist
5) Screen for the Break Below VAL Signal -> Add those stocks to a separate flagged (colored) watchlist
6) Screen for the Bearish 80% Rule Signal -> Add those stocks to a separate flagged (colored) watchlist
7) Screen for the Bearish 80% Rule Signal -> Add those stocks to a separate flagged (colored) watchlist
8) Screen for the Trend Template Signal -> Add those stocks to a separate flagged (colored) watchlist
9) Toggle through each list and analyze each stock chart using the Supercharts tool in TradingView
10)Record the number of stocks in each list as a way of analyzing market conditions
Weekend Analysis
1) Begin with a universe of stocks. I use the following linked universe screen as a starting point: www.tradingview.com
2) Screen for the Rule of 100 Signal. Use this as a starting point for deeper fundamental and/or thematic and/or technical research
3) Screen for stocks that meet specific performance thresholds, such as YTD %𝚫 > 100% etc
💠 Get Creative
▪Users have the ability to layer signals on top of each other when screening. To do so, filter for a signal, and then filter your new list by another signal! Play around with the screener, and find what works best for you!
Bull/Bear FVG Density RatioThis indicator tracks the directional frequency of Fair Value Gaps (FVGs) over a configurable lookback window, offering a clean, responsive measure of market imbalance.
🔍 What It Does:
Detects bullish and bearish FVGs using a 3-bar displacement logic
Calculates the ratio of FVGs to candles over the last N bars
Plots separate density curves for bullish and bearish FVGs
Includes a threshold line to help identify regime shifts (e.g., drought vs spate)
📈 How to Use:
Use rising density to confirm trend strength or breakout momentum
Watch for crossovers above the threshold to signal active imbalance regimes
Combine with price action or volume overlays for high-confluence setups
⚙️ Inputs:
Lookback Window: Number of candles used to calculate FVG density
Threshold: Visual guide for regime classification (default: 0.2)
This tool is ideal for traders who want to move beyond symptomatic signals and model structural causality. It pairs well with lifecycle scoring, retest velocity, and HTF overlays.
Kalman VWAP Filter [BackQuant]Kalman VWAP Filter
A precision-engineered price estimator that fuses Kalman filtering with the Volume-Weighted Average Price (VWAP) to create a smooth, adaptive representation of fair value. This hybrid model intelligently balances responsiveness and stability, tracking trend shifts with minimal noise while maintaining a statistically grounded link to volume distribution.
If you would like to see my original Kalman Filter, please find it here:
Concept overview
The Kalman VWAP Filter is built on two core ideas from quantitative finance and control theory:
Kalman filtering — a recursive Bayesian estimator used to infer the true underlying state of a noisy system (in this case, fair price).
VWAP anchoring — a dynamic reference that weights price by traded volume, representing where the majority of transactions have occurred.
By merging these concepts, the filter produces a line that behaves like a "smart moving average": smooth when noise is high, fast when markets trend, and self-adjusting based on both market structure and user-defined noise parameters.
How it works
Measurement blend : Combines the chosen Price Source (e.g., close or hlc3) with either a Session VWAP or a Rolling VWAP baseline. The VWAP Weight input controls how much the filter trusts traded volume versus price movement.
Kalman recursion : Each bar updates an internal "state estimate" using the Kalman gain, which determines how much to trust new observations vs. the prior state.
Noise parameters :
Process Noise controls agility — higher values make the filter more responsive but also more volatile.
Measurement Noise controls smoothness — higher values make it steadier but slower to adapt.
Filter order (N) : Defines how many parallel state estimates are used. Larger orders yield smoother output by layering multiple one-dimensional Kalman passes.
Final output : A refined price trajectory that captures VWAP-adjusted fair value while dynamically adjusting to real-time volatility and order flow.
Why this matters
Most smoothing techniques (EMA, SMA, Hull) trade off lag for smoothness. Kalman filtering, however, adaptively rebalances that tradeoff each bar using probabilistic weighting, allowing it to follow market state changes more efficiently. Anchoring it to VWAP integrates microstructure context — capturing where liquidity truly lies rather than only where price moves.
Use cases
Trend tracking : Color-coded candle painting highlights shifts in slope direction, revealing early trend transitions.
Fair value mapping : The line represents a continuously updated equilibrium price between raw price action and VWAP flow.
Adaptive moving average replacement : Outperforms static MAs in variable volatility regimes by self-adjusting smoothness.
Execution & reversion logic : When price diverges from the Kalman VWAP, it may indicate short-term imbalance or overextension relative to volume-adjusted fair value.
Cross-signal framework : Use with standard VWAP or other filters to identify convergence or divergence between liquidity-weighted and state-estimated prices.
Parameter guidance
Process Noise : 0.01–0.05 for swing traders, 0.1–0.2 for intraday scalping.
Measurement Noise : 2–5 for normal use, 8+ for very smooth tracking.
VWAP Weight : 0.2–0.4 balances both price and VWAP influence; 1.0 locks output directly to VWAP dynamics.
Filter Order (N) : 3–5 for reactive short-term filters; 8–10 for smoother institutional-style baselines.
Interpretation
When price > Kalman VWAP and slope is positive → bullish pressure; buyers dominate above fair value.
When price < Kalman VWAP and slope is negative → bearish pressure; sellers dominate below fair value.
Convergence of price and Kalman VWAP often signals equilibrium; strong divergence suggests imbalance.
Crosses between Kalman VWAP and the base VWAP can hint at shifts in short-term vs. long-term liquidity control.
Summary
The Kalman VWAP Filter blends statistical estimation with market microstructure awareness, offering a refined alternative to static smoothing indicators. It adapts in real time to volatility and order flow, helping traders visualize balance, transition, and momentum through a lens of probabilistic fair value rather than simple price averaging.
Smart Money Dynamics Blocks - Pearson MatrixSmart Money Dynamics Blocks — Pearson Matrix
A structural fusion of Prime Number Theory, Pearson Correlation, and Cumulative Delta Geometry.
1. Mathematical Foundation
This indicator is built on the intersection of Prime Number Theory and the Pearson correlation coefficient, creating a structural framework that quantifies how price and time evolve together.
Prime numbers — unique, indivisible, and irregular — are used here as nonlinear time intervals. Each prime length (2, 3, 5, 7, 11…97) represents a regression horizon where correlation is measured between price and time. The result is a multi-scale correlation lattice — a geometric matrix that captures hidden directional strength and temporal bias beyond traditional moving averages.
2. The Pearson Matrix Logic
For every prime interval p, the indicator calculates the linear correlation:
r_p = corr(price, bar_index, p)
Each r_p reflects how closely price and time move together across a prime-defined window. All r_p values are then averaged to create avgR, a single adaptive coefficient summarizing overall structural coherence.
- When avgR > 0.8 → strong positive correlation (labeled R+).
- When avgR < -0.8 → strong negative correlation (labeled R−).
This approach gives a mathematically grounded definition of trend — one that isn’t based on pattern recognition, but on measurable correlation strength.
3. Sequential Prime Slope and Median Pivot
Using the ordered sequence of 25 prime intervals, the model computes sequential slopes between adjacent primes. These slopes represent the rate of change of structure between two prime scales. A robust median aggregator smooths the slopes, producing a clean, stable directional vector.
The system anchors this slope to the 41-bar pivot — the median of the first 25 primes — serving as the geometric midpoint of the prime lattice. The resulting yellow line on the chart is not an ordinary regression line; it’s a dynamic prime-slope function, adapting continuously with correlation feedback.
4. Regression-Style Parallel Bands
Around this prime-slope line, the indicator constructs parallel bands using standard deviation envelopes — conceptually similar to a regression channel but recalculated through the prime–Pearson matrix.
These bands adjust dynamically to:
- Volatility, via standard deviation of residuals.
- Correlation strength, via avgR sign weighting.
Together, they visualize statistical deviation geometry, making it easier to observe symmetry, expansion, and contraction phases of price structure.
5. Volume and Cumulative Delta Peaks
Below the geometric layer, the indicator incorporates a custom lower-timeframe volume feed — by default using 15-second data (custom_tf_input_volume = “15S”). This allows precise delta computation between up-volume and down-volume even on higher timeframe charts.
From this feed, the indicator accumulates delta over a configurable period (default: 100 bars). When cumulative delta reaches a local maximum or minimum, peak and trough markers appear, showing the precise bar where buying or selling pressure statistically peaked.
This combination of geometry and order flow reveals the intersection of market structure and energy — where liquidity pressure expresses itself through mathematical form.
6. Chart Interpretation
The primary chart view represents the live execution of the indicator. It displays the relationship between structural correlation and volume behavior in real time.
Orange “R+” and blue “R−” labels indicate regions of strong positive or negative Pearson correlation across the prime matrix. The yellow median prime-slope line serves as the structural backbone of the indicator, while green and red parallel bands act as dynamic regression boundaries derived from the underlying correlation strength. Peaks and troughs in cumulative delta — displayed as numerical annotations — mark statistically significant shifts in buying and selling pressure.
The secondary visualization (Prime Regression Concept) expands on this by illustrating how regression behavior evolves across prime intervals. Each colored regression fan corresponds to a prime number window (2, 3, 5, 7, …, 97), demonstrating how multiple regression lines would appear if drawn independently. The indicator integrates these into one unified geometric model — eliminating the need to plot tens of regression lines manually. It’s a conceptual tool to help visualize the internal logic: the synthesis of many small-scale regressions into a single coherent structure.
7. Interpretive Insight
This model is not a prediction tool; it’s an instrument of mathematical observation. By translating price dynamics into a prime-structured correlation space, it reveals how coherence unfolds through time — not as a forecast, but as a measurable evolution of structure.
It unifies three analytical domains:
- Prime distribution — defines a nonlinear temporal architecture.
- Pearson correlation — quantifies statistical cohesion.
- Cumulative delta — expresses behavioral imbalance in order flow.
The synthesis creates a geometric analysis of liquidity and time — where structure meets energy, and where the invisible rhythm of market flow becomes measurable.
8. Contribution & Feedback
Share your observations in the comments:
- The time gap and alternation between R+ and R− clusters.
- How different timeframes change delta sensitivity or reveal compression/expansion.
- Prime intervals/clusters that tend to sit near turning points or liquidity shifts.
- How avgR behaves across assets or regimes (trending, ranging, high-vol).
- Notable interactions with the parallel bands (touches, breaks, mean-revert).
Your field notes help others read the model more effectively and compare contexts.
Summary
- Primes define the structure.
- Pearson quantifies coherence.
- Slope median stabilizes geometry.
- Regression bands visualize deviation.
- Cumulative delta locates imbalance.
Together, they construct a framework where mathematics meets market behavior.
PulseGrid Universal Scalper - Adaptive Pulse and Symmetric SpansInstrument agnostic. Works on any symbol and timeframe supported by TradingView.
Message or hit me up in chat for full access .
Purpose and scope
PulseGrid is a short timeframe strategy designed to read intrabar structure and recent path so that entries align with actionable momentum and context. The strategy is private. The description below provides all the information needed to understand how it behaves, how it sizes risk, how to tune it responsibly, and how to evaluate results without making unrealistic claims. The design is instrument agnostic. It runs on any asset class that prints open high low close bars on TradingView. That includes commodities such as Gold and WTI, currencies, crypto, equity indices, and single stocks. Performance will always depend on the symbol’s liquidity, spread, slippage, and session structure, which is why the description focuses on principles and safe parameter ranges instead of hard promises.
What the strategy does at a glance
It builds a composite entry signal named Pulse from five normalized bar features that reflect short term pressure and follow through.
It applies regime guards that keep the strategy inactive when the tape is either too quiet, too bursty, or too directionally random.
It optionally uses a directional filter where a fast and a slow exponential average must agree and their gap must be material relative to recent true range.
When a signal is allowed, risk is sized using symmetric spans that come from nearby untraded price distances above and below the market. The strategy sets a single stop and a single take profit from those spans.
Lines for entry, stop, and take profit are drawn on the chart. A compact on chart table shows trade counts, win rate, average R per trade, and profit factor for all trades, longs only, and shorts only.
This combination yields entries that are reactive but not chaotic, and risk lines that respect the market’s recent path instead of generic pip or point targets.
Why the design is original and useful
The core originality is the union of a composite entry that adapts to volatility and a geometry based risk model. The entry uses five different viewpoints on the same bar space instead of relying on a single technical indicator. The risk model uses spans that come from actual untraded distance rather than fixed multipliers of a generic volatility measure. The result is a framework that is simple to read on a chart and simple to evaluate, yet it avoids the traps of curve fitting to one symbol or one month of data. Because everything is normalized locally, the same logic translates across asset classes with only modest tuning.
The Pulse composite in detail
Pulse is a weighted blend of the following normalized features.
Impulse imbalance. The script sums upward and downward impulses over a short window. An upward impulse is the extension of highs relative to the prior bar. A downward impulse is the extension of lows relative to the prior bar. The net imbalance, scaled by the local range, captures whether extension pressure is building or fading.
Wick and close location. Inside each bar, the distance between the close and the extremes carries information about rejection or acceptance. A bar that closes near the high with relatively heavier lower wick suggests upward acceptance. A bar that closes near the low with heavier upper wick suggests downward acceptance. A weight controls the contribution of wick skew versus close location so that users can favor reversal or momentum behaviour.
Shock touches. Within the recent range window, touches that occur very near the top decile or bottom decile are marked. A short sliding window counts recent shocks. Frequent top shocks in a rising context suggest supply tests. Frequent bottom shocks in a declining context suggest demand tests. The count is normalized by window length.
Breakout ledger. The script compares current extremes to lagged extremes and keeps a simple count of recent upside and downside breakouts. The difference behaves as a short term polarity meter.
Curvature. A simple second difference in closing price acts as a curvature term. It is normalized by the recent maximum of absolute one bar returns so that the value remains bounded and comparable to other terms.
Pulse is smoothed over a fraction of the main signal length. Smoothing removes impulse spikes without destroying the quick reaction that scalpers need. The absolute value of smoothed Pulse can be used with an adaptive gate so that only the top percentile of energy for the recent environment is eligible for entries. A small floor prevents accidental entries during very quiet periods.
Regime guards that keep the strategy selective
Three guards must all pass before any entry can occur.
Auction Balance Factor. This is the proportion of closes that land inside a mid band of the prior bar’s high to low range. High values indicate balanced chop where breakouts tend to fail. Low values indicate directional conditions. The strategy requires ABF to sit below a user chosen maximum.
Dispersion via a Gini style measure on absolute returns. Very low dispersion means bars are small and uniform. Very high dispersion means a few outsized bars dominate and slippage risk can be elevated. The strategy allows the user to require the dispersion measure to remain inside a band that reflects healthy activity.
Binary entropy of direction. Over the core window, the proportion of up closes is used to compute a simple entropy. Values near one indicate coin flip behaviour. Values near zero indicate one sided sequences. The guard requires entropy below a ceiling so that random directionality does not produce noise entries.
An optional directional filter asks that a fast and a slow exponential average agree on direction and that their gap, when divided by an average true range, exceed a threshold. This filter can be enabled on symbols that trend cleanly and disabled when the composite entry is already selective enough.
Risk sizing with symmetric spans
Instead of fixed points or a pure ATR multiplier, the strategy sizes stops and targets from a pair of spans. The upward span reflects recent untraded distance above the market. The downward span reflects recent untraded distance below the market. Each span is floored by a fallback that comes from the maximum of a short simple range average and a standard average true range. A tick based floor prevents microscopic stops on instruments with high tick precision. An asymmetry cap prevents one span from becoming many times larger than the other. For long entries the stop is a multiple of the downward span and the target is a multiple of the upward span. For short entries the stop is a multiple of the upward span and the target is a multiple of the downward span. This creates a risk box that is symmetric by construction yet adaptive to recent voids and gaps.
Execution, ties, and housekeeping
Entries evaluate at bar close. Exits are tested from the next bar forward. If both stop and target are hit within the same bar, the outcome can be resolved in a consistent way that favors the stop or the target according to a single user setting. A short cooldown in bars prevents flip flops. Users can restrict entries to specific sessions such as London and New York. The chart renders entry, stop, and target lines for each trade so that every action is visible. The table in the top right shows trade counts, take profit and stop counts, win rate, average R per trade, and profit factor for the whole set and by direction.
Defaults and responsible backtesting
The default properties in the script use a realistic initial capital and commission value. Users should also set slippage in the strategy properties to reflect their broker and symbol. Small timeframe trading is sensitive to friction and the strategy description does not claim immunity to that reality. The strategy is intended to be tested on a dataset that produces a meaningful sample of trades. A sample in the range of a hundred trades or more is preferred because variance in short samples can be large. On thin symbols or periods with little regular trading, users should either change timeframe, change sessions, or use more selective thresholds so that the sample contains only liquid scenarios.
Universal usage across markets
The strategy is universal by design. It will run and produce lines on any open high low close series on TradingView. The composite entry is made of normalized parts. The regime guards use proportions and bounded measures. The spans use untraded distance and range floors measured in the local price scale. This allows the same logic to function on a currency pair, a commodity, an index future, a stock, or a crypto pair. What changes is calibration.
A safe approach for universal use is as follows.
Start with the default signal length and wick weight.
If the chart prints many weak signals, enable the directional filter and raise the normalized gap threshold slightly.
If the chart is too quiet, lower the adaptive percentile or, with adaptive off, lower the fixed pulse threshold by a small amount.
If stops are too tight in quiet regimes, raise the fallback span multiplier or raise the minimum tick floor in ticks.
If you observe long one sided days, lower the maximum entropy slightly so that entries only occur when directionality is genuine rather than alternating.
Because the logic is bounded and local, these simple steps carry over across symbols. That is why the strategy can be used literally on any asset that you can load on a TradingView chart. The code does not depend on a specific tick size or a specific exchange calendar. It will still remain true that symbols with higher spread or fewer regular trading hours demand stricter thresholds and larger floors.
Suggested parameter ranges for common cases
These ranges are guidelines for one to five minute bars. They are not promises of performance. They reflect the balance between having enough signals to learn from and keeping noise controlled.
Signal length between 18 and 34 for liquid commodities and large capitalization equities.
Wick weight between 0.30 and 0.50 depending on whether you want reversal recognition or close momentum.
Adaptive gate percentile between 85 and 93 when adaptive is enabled. Fixed threshold between 0.10 and 0.18 when adaptive is disabled. Use a non zero floor so very quiet periods still require some energy.
Auction Balance Factor maximum near 0.70 for symbols with clear session bursts. Slightly higher if you prefer to include more balanced prints.
Dispersion band with a lower bound near 0.18 and an upper bound near 0.68 for most session instruments. Tighten the band if you want to skip very bursty days or very flat days.
Entropy maximum near 0.90 so coin flip phases are filtered. Lower the ceiling slightly if the symbol whipsaws frequently.
Stop multiplier near one and take profit multiplier between two and three for a single target approach. Larger target multipliers reduce hit rate and lengthen holding time.
These are safe starting points across commodities, currencies, indices, equities, and crypto. From there, small increments are preferred over dramatic changes.
How to evaluate responsibly
A clean chart and a direct test process help avoid confusion. Use standard candles for signals and exits. If you use a non standard chart type such as Heikin Ashi or Renko, do so only for visualization and not for the strategy’s signal computation, as those chart types can produce unrealistic fills. Turn off other indicators on the published chart unless they are needed to demonstrate a specific property of this strategy. When you post results or discuss outcomes, include the symbol, timeframe, commission and slippage settings, and the session settings used. This makes the context clear and avoids misleading readers.
When you look at results, consider the following.
The distribution of R per trade. A positive average R with a moderate profit factor suggests that exits are sized appropriately for the symbol.
The balance between long and short sides. The HUD table separates the two so you can see if one side carries the edge for that symbol.
The sensitivity to the tie preference. If many bars hit both stop and take profit, the market is chopping inside the risk box and you may need larger floors or stricter regime guards.
The session effect. Session hours matter for many instruments. Align your session filter with where liquidity and volatility concentrate.
Known limitations and honest warnings
PulseGrid is not a guarantee of future profit. It is a systematic way to read short term structure and to size risk in a way that reflects recent path. It assumes that the data feed reflects the exchange reality. It assumes that slippage and spread are non zero and uses explicit commission and user provided slippage to approximate that. It does not place multiple targets. It does not trail stops. It is not a high frequency system and does not attempt to model queue priority or microsecond fills. On illiquid symbols or very short timeframes outside regular hours, signals will be less reliable. Users are responsible for choosing realistic settings and for evaluating whether the symbol’s conditions are suitable.
First use checklist
Load the symbol and timeframe you care about.
If the instrument has clear sessions, turn on the session filter and select realistic London and New York hours or other sessions relevant to the instrument.
Set commission and slippage in the strategy properties to values that match your broker or exchange.
Run the strategy with defaults. Look at the HUD summary and the lines.
Decide whether to enable the directional filter. If you see frequent reversals around the entry line, enable it and raise the normalized gap threshold slightly.
Adjust the adaptive gate. If the chart floods, raise the percentile. If the chart starves, lower it or use a slightly lower fixed threshold.
Adjust the fallback span multiplier and tick floor so that stops are never microscopic.
Review per session performance. If one session underperforms, restrict entries to the better one.
This simple process takes minutes and transfers to any other symbol.
Why this script is private
The source remains private so that the underlying method and its implementation details are not copied or republished. The description here is complete and self contained so that users can understand the purpose, originality, usage, and limitations without needing to inspect the source. Privacy does not change the strategy’s on chart behavior. It only protects the specific coding details.
Guarantee and compliance statements
This description does not contain advertising, solicitations, links, or contact information. It does not make performance promises. It explains how the script is original and how it works. It also warns about limitations and the need for realistic assumptions. The strategy is not investment advice and is not created only for qualified investors. It can be tested and used for educational and research purposes. Users should read TradingView’s documentation on script properties and backtesting. Users should avoid non standard chart types for signal computation because those produce unrealistic results. Users should select realistic account sizes and friction settings. Users should not post claims without showing the settings used.
Closing summary
PulseGrid is a compact framework for short timeframe trading that combines a composite entry built from multiple normalized bar features with a symmetric span model for risk. The entry adapts to volatility. The regime guards keep the strategy inactive when the tape is either too quiet or too erratic. The risk geometry respects recent untraded spans instead of arbitrary distances. The entire design is instrument agnostic. It will run on any symbol that TradingView supports and it will behave consistently across asset classes with modest tuning. Use it with a clean chart, realistic friction, and enough trades to make your evaluation meaningful. Use sessions if the instrument concentrates activity in specific hours. Adjust one control at a time and prefer small increments. The goal is not to find a magic parameter. The goal is to maintain a stable rule set that reads market structure in a way you can trust and audit.
Liquidity Pro Map [ChartPrime]⯁ OVERVIEW
Liquidity Pro Map is a market-structure tool that simulates liquidity distribution by splitting price history into buy-side and sell-side profiles. Using candle volume and the standard deviation of close, the indicator builds two mirrored volume maps on the right-hand side of the chart. It also extends liquidity levels backwards in time until they are crossed by price, allowing you to see which zones remain untouched and where liquidity is most likely resting. Cumulative skew lines and highlighted POC levels give additional clarity on imbalance between buyers and sellers.
⯁ KEY FEATURES
Dual Liquidity Profiles: The chart is divided into buy-side (green) and sell-side (red) liquidity profiles, letting you instantly compare both sides of order flow.
Level Extension Logic: Each liquidity level is extended back in time until price crosses it. If not crossed, it persists all the way to the indicator’s lookback period, marking zones that remain “untapped.”
Dynamic Binning with Standard Deviation: The indicator distributes candle volumes into bins using close-price deviation, creating a more realistic liquidity map than static price levels.
priceDeviation = ta.stdev(close, 25) * 2
priceReference = close > open ? low - priceDeviation : high + priceDeviation
Cumulative Volume Skew Lines: Polylines on the right-hand side show the aggregated buy and sell volume profiles, making it easy to spot imbalance.
POC Identification: Highest-volume levels on both sides are marked as POC (Point of Control) , providing key zones of interest.
Clear Color Coding: Gradient shading intensifies with volume concentration—dark teal/green for buy zones, dark pink/red for sell zones.
⯁ HOW IT WORKS (UNDER THE HOOD)
Volume Distribution: Each bar’s volume is assigned to a price bin based on its reference price (close ± standard deviation offset).
Buy vs. Sell Splitting: If bins above last close price, volume is allocated to sell-side liquidity; otherwise, it’s allocated to buy-side liquidity.
Level Extension: Boxes marking liquidity bins extend back until crossed by price. If uncrossed, they anchor all the way to the start of the lookback window.
Cumulative Polylines: As bins are stacked, cumulative buy and sell values form skew polylines plotted at the right edge.
POC Levels: The highest-volume bin on each side is highlighted with labels and arrows, marking where the heaviest liquidity is concentrated.
⯁ USAGE
Use buy/sell profiles to see where liquidity is likely resting. Green shelves suggest potential support zones; red shelves suggest resistance or sell liquidity pools.
Watch untouched extended levels —these often become magnets for price as liquidity is swept.
Track POC levels as primary liquidity targets, where reactions or fakeouts are most common.
Compare cumulative skew lines to judge which side dominates in volume. Heavy buy skew may indicate absorption of sell pressure, and vice versa.
Adjust lookback period to switch between intraday liquidity maps and larger swing-based profiles.
Use separator feature to hide bins borders for better visual clarity.
Use as a confluence tool with OBs, support/resistance, and liquidity sweep setups.
⯁ CONCLUSION
Liquidity Pro Map transforms candle volume into a structured simulation of where liquidity may rest across the chart. By dividing buy vs. sell profiles, extending untouched levels, and marking cumulative skew and POC, it equips traders with a clear visual map of potential liquidity pools. This allows for better anticipation of sweeps, reversals, and areas of high market activity.
DeltaFlow Volume Profile [BigBeluga]🔵 OVERVIEW
The DeltaFlow Volume Profile builds a compact volume profile next to price and enriches every bin with flow context : bullish vs. bearish participation (%), a per-bin Delta % , an optional Delta Heat Map , and a PoC band with the bin’s absolute volume. This lets you see not just where volume clustered, but who (buyers or sellers) dominated inside each price slice.
🔵 CONCEPTS
Binned Volume Profile : Price range over a user-defined LookBack is split into Bins ; each bin aggregates traded volume.
Bull/Bear Split : Within every bin, volume is separated by candle direction into Bull Volume and Bear Volume , then normalized to % of the bin’s displayed size.
Delta % : The difference between Bull % and Bear % for the bin. Positive = buyer dominance; negative = seller dominance.
Delta Heat Map : Bin background shading that scales with both total volume strength and delta bias.
PoC (Point of Control) : The most significant bin gets a PoC band and a label with its absolute volume.
🔵 FEATURES
Profile with Flow : A clean horizontal volume bar per bin plus stacked Bull % and Bear % .
Per-Bin Delta Label : A readable “Δ xx%” tag at the start of each bin shows dominance at a glance.
Delta Heat Map : Optional gradient that intensifies with higher volume and stronger delta.
PoC Highlight : Optional PoC band colored separately, labeled with absolute volume (e.g., “1.23M”).
Configurable Inputs : LookBack, number of Bins (10–100), toggles for Delta, Heat Map, Volume Bars, and PoC color.
Readable Colors : Separate inputs for bullish (volume +) and bearish (volume –) hues.
🔵 HOW TO USE
Set the window : Choose LookBack and Bins to balance detail vs. performance (more bins = finer resolution).
Enable “Volume Bars” to display the bull/bear split as two stacked percent bars inside each bin.
High Bull % near support → constructive demand.
High Bear % near resistance → active supply.
Use Δ labels (toggle “Delta”) to quickly spot bins with clear buyer/seller control; combine with price position for confluence.
Turn on Delta Heat Map to prioritize areas with both large volume and strong imbalance.
Watch the PoC : The PoC band marks the most traded (and often magnet) level; its label shows absolute size for context.
Trade ideas :
Breakout continuation when Δ stays positive across consecutive upper bins.
Reversion risk when price enters a large bearish-Δ cluster below.
Manage risk around the PoC; reactions there can be sharp.
🔵 CONCLUSION
DeltaFlow Volume Profile upgrades a classic profile with flow intelligence. The bull/bear split, explicit Δ %, heat-weighted backdrop, and PoC volume label make dominant participation and key price shelves obvious. Use it to filter levels, time entries with imbalance, and validate breakouts or fades with objective volume-flow evidence.
ATAI Volume Pressure Analyzer V 1.0 — Pure Up/DownATAI Volume Pressure Analyzer V 1.0 — Pure Up/Down
Overview
Volume is a foundational tool for understanding the supply–demand balance. Classic charts show only total volume and don’t tell us what portion came from buying (Up) versus selling (Down). The ATAI Volume Pressure Analyzer fills that gap. Built on Pine Script v6, it scans a lower timeframe to estimate Up/Down volume for each host‑timeframe candle, and presents “volume pressure” in a compact HUD table that’s comparable across symbols and timeframes.
1) Architecture & Global Settings
Global Period (P, bars)
A single global input P defines the computation window. All measures—host‑TF volume moving averages and the half‑window segment sums—use this length. Default: 55.
Timeframe Handling
The core of the indicator is estimating Up/Down volume using lower‑timeframe data. You can set a custom lower timeframe, or rely on auto‑selection:
◉ Second charts → 1S
◉ Intraday → 1 minute
◉ Daily → 5 minutes
◉ Otherwise → 60 minutes
Lower TFs give more precise estimates but shorter history; higher TFs approximate buy/sell splits but provide longer history. As a rule of thumb, scan thin symbols at 5–15m, and liquid symbols at 1m.
2) Up/Down Volume & Derived Series
The script uses TradingView’s library function tvta.requestUpAndDownVolume(lowerTf) to obtain three values:
◉ Up volume (buyers)
◉ Down volume (sellers)
◉ Delta (Up − Down)
From these we define:
◉ TF_buy = |Up volume|
◉ TF_sell = |Down volume|
◉ TF_tot = TF_buy + TF_sell
◉ TF_delta = TF_buy − TF_sell
A positive TF_delta indicates buyer dominance; a negative value indicates selling pressure. To smooth noise, simple moving averages of TF_buy and TF_sell are computed over P and used as baselines.
3) Key Performance Indicators (KPIs)
Half‑window segmentation
To track momentum shifts, the P‑bar window is split in half:
◉ C→B: the older half
◉ B→A: the newer half (toward the current bar)
For each half, the script sums buy, sell, and delta. Comparing the two halves reveals strengthening/weakening pressure. Example: if AtoB_delta < CtoB_delta, recent buying pressure has faded.
[ 4) HUD (Table) Display /i]
Colors & Appearance
Two main color inputs define the theme: a primary color and a negative color (used when Δ is negative). The panel background uses a translucent version of the primary color; borders use the solid primary color. Text defaults to the primary color and flips to the negative color when a block’s Δ is negative.
Layout
The HUD is a 4×5 table updated on the last bar of each candle:
◉ Row 1 (Meta): indicator name, P length, lower TF, host TF
◉ Row 2 (Host TF): current ↑Buy, ↓Sell, ΔDelta; plus Σ total and SMA(↑/↓)
◉ Row 3 (Segments): C→B and B→A blocks with ↑/↓/Δ
◉ Rows 4–5: reserved for advanced modules (Wings, α/β, OB/OS, Top
5) Advanced Modules
5.1 Wings
“Wings” visualize volume‑driven movement over C→B (left wing) and B→A (right wing) with top/bottom lines and a filled band. Slopes are ATR‑per‑bar normalized for cross‑symbol/TF comparability and converted to angles (degrees). Coloring mirrors HUD sign logic with a near‑zero threshold (default ~3°):
◉ Both lines rising → blue (bullish)
◉ Both falling → red (bearish)
◉ Mixed/near‑zero → gray
Left wing reflects the origin of the recent move; right wing reflects the current state.
5.2 α / β at Point B
We compute the oriented angle between the two wings at the midpoint B:
β is the bottom‑arc angle; α = 360° − β is the top‑arc angle.
◉ Large α (>180°) or small β (<180°) flags meaningful imbalance.
◉ Intuition: large α suggests potential selling pressure; small β implies fragile support. HUD cells highlight these conditions.
5.3 OB/OS Spike
OverBought/OverSold (OB/OS) labels appear when directional volume spikes align with a 7‑oscillator vote (RSI, Stoch, %R, CCI, MFI, DeMarker, StochRSI).
◉ OB label (red): unusually high sell volume + enough OB votes
◉ OS label (teal): unusually high buy volume + enough OS votes
Minimum votes and sync window are user‑configurable; dotted connectors can link labels to the candle wick.
5.4 Top3 Volume Peaks
Within the P window the script ranks the top three BUY peaks (B1–B3) and top three SELL peaks (S1–S3).
◉ B1 and S1 are drawn as horizontal resistance (at B1 High) and support (at S1 Low) zones with adjustable thickness (ticks/percent/ATR).
◉ The HUD dedicates six cells to show ↑/↓/Δ for each rank, and prints the exact High (B1) and Low (S1) inline in their cells.
6) Reading the HUD — A Quick Checklist
◉ Meta: Confirm P and both timeframes (host & lower).
◉ Host TF block: Compare current ↑/↓/Δ against their SMAs.
◉ Segments: Contrast C→B vs B→A deltas to gauge momentum change.
◉ Wings: Right‑wing color/angle = now; left wing = recent origin.
◉ α / β: Look for α > 180° or β < 180° as imbalance cues.
◉ OB/OS: Note labels, color (red/teal), and the vote count.
◉Top3: Keep B1 (resistance) and S1 (support) on your radar.
Use these together to sketch scenarios and invalidation levels; never rely on a single signal in isolation.
[ 7) Example Highlights (What the table conveys) /i]
◉ Row 1 shows the indicator name, the analysis length P (default 55), and both TFs used for computation and display.
◉ B1 / S1 blocks summarize each side’s peak within the window, with Δ indicating buyer/seller dominance at that peak and inline price (B1 High / S1 Low) for actionable levels.
◉ Angle cells for each wing report the top/bottom line angles vs. the horizontal, reflecting the directional posture.
◉ Ranks B2/B3 and S2/S3 extend context beyond the top peak on each side.
◉ α / β cells quantify the orientation gap at B; changes reflect shifting buyer/seller influence on trend strength.
Together these visuals often reveal whether the “wings” resemble a strong, upward‑tilted arm supported by buyer volume—but always corroborate with your broader toolkit
8) Practical Tips & Tuning
◉ Choose P by market structure. For daily charts, 34–89 bars often works well.
◉ Lower TF choice: Thin symbols → 5–15m; liquid symbols → 1m.
◉ Near‑zero angle: In noisy markets, consider 5–7° instead of 3°.
◉ OB/OS votes: Daily charts often work with 3–4 votes; lower TFs may prefer 4–5.
◉ Zone thickness: Tie B1/S1 zone thickness to ATR so it scales with volatility.
◉ Colors: Feel free to theme the primary/negative colors; keep Δ<0 mapped to the negative color for readability.
Combine with price action: Use this indicator alongside structure, trendlines, and other tools for stronger decisions.
Technical Notes
Pine Script v6.
◉ Up/Down split via TradingView/ta library call requestUpAndDownVolume(lowerTf).
◉ HUD‑first design; drawings for Wings/αβ/OBOS/Top3 align with the same sign/threshold logic used in the table.
Disclaimer: This indicator is provided solely for educational and analytical purposes. It does not constitute financial advice, nor is it a recommendation to buy or sell any security. Always conduct your own research and use multiple tools before making trading decisions.
Buy/Sell Volume VWAP with Liquidity and Price SensitivityBuy/Sell Volume VWAP with Liquidity & Price Sensitivity
A dual-VWAP overlay that separates buy-side vs sell-side pressure using lower-timeframe volume and recent price behavior. It shows two adaptive VWAP lines and a bias cloud to make trend and imbalance easy to see—no params fussing required.
What you’ll see
Buy VWAP (green) and Sell VWAP (red) plotted on the chart
Slope-aware coloring : brighter when that side is improving, darker when easing
Bias cloud: green when Buy > Sell, red when Sell > Buy
Optional last-value bubbles on the price scale for quick readouts
How it works
Looks inside each bar (lower timeframe, e.g., 1-second) to estimate buy vs sell pressure
Blends that pressure with recent price movement to keep the lines responsive but stable
Maintains separate VWAP tracks for buy-side and sell-side and resets daily or at a time you choose
How to use it
Trend & bias: When Buy VWAP stays above Sell VWAP (green cloud), buyers have the upper hand; the opposite (red cloud) favors sellers.
Conviction: A wider gap between the two lines often means a stronger imbalance.
Context: Use alongside structure (higher highs/lows, key levels) for confirmation—this is not a stand-alone signal.
Inputs
Timeframe: Lower-TF sampling (default 1S).
Reset Time: Defaults to 09:30 (session open); set to your market.
Appearance: Two-shade palettes for buy/sell, line width, last-value bubbles, and cloud opacity.
Tips
Works on most symbols and intraday timeframes; lower-TF sampling can be heavier on resources.
If the cloud flips frequently, consider viewing on a slightly higher chart timeframe for cleaner structure.
Disclaimer
For educational use only. Not investment advice. Test on replay/paper before live decisions.
Advanced ICT Theory - A-ICT📊 Advanced ICT Theory (A-ICT): The Institutional Manipulation Detector
Are you tired of being the liquidity? Stop chasing shadows and start tracking the architects of price movement.
This is not another lagging indicator. This is a complete framework for viewing the market through the lens of institutional traders. Advanced ICT Theory (A-ICT) is an all-in-one, military-grade analysis engine designed to decode the complex language of "Smart Money." It automates the core tenets of Inner Circle Trader (ICT) methodology, moving beyond simple patterns to build a dynamic, real-time narrative of market manipulation, liquidity engineering, and institutional order flow.
AIT provides a living blueprint of the market, identifying high-probability zones, tracking structural shifts, and scoring the quality of setups with a sophisticated, multi-factor algorithm. This is your X-ray into the market's true intentions.
🔬 THE CORE ENGINE: DECODING THE THEORY & FORMULAS
A-ICT is built upon a sophisticated, multi-layered logic system that interprets price action as a story of cause and effect. It does not guess; it confirms. Here is the foundational theory that drives the engine:
1. Market Structure: The Blueprint of Trend
The script first establishes a deep understanding of the market's skeleton through multi-level pivot analysis. It uses ta.pivothigh and ta.pivotlow to identify significant swing points.
Internal Structure (iBOS): Minor swings that show the short-term order flow. A break of internal structure is the first whisper of a potential shift.
External Structure (eBOS): Major swing points that define the primary trend. A confirmed break of external structure is a powerful statement of trend continuation. AIT validates this with optional Volume Confirmation (volume > volumeSMA * 1.2) and Candle Confirmation to ensure the break is driven by institutional force, not just a random spike.
Change of Character (CHoCH): This is the earthquake. A CHoCH occurs when a confirmed eBOS happens against the prevailing trend (e.g., a bearish eBOS in a clear uptrend). A-ICT flags this immediately, as it is the strongest signal that the primary trend is under threat of reversal.
2. Liquidity Engineering: The Fuel of the Market
Institutions don't buy into strength; they buy into weakness. They need liquidity. A-ICT maps these liquidity pools with forensic precision:
Buyside & Sellside Liquidity (BSL/SSL): Using ta.highest and ta.lowest, AIT identifies recent highs and lows where clusters of stop-loss orders (liquidity) are resting. These are institutional targets.
Liquidity Sweeps: This is the "manipulation" part of the detector. AIT has a specific formula to detect a sweep: high > bsl and close < bsl . This signifies that institutions pushed price just high enough to trigger buy-stops before aggressively selling—a classic "stop hunt." This event dramatically increases the quality score of subsequent patterns.
3. The Element Lifecycle: From Potential to Power
This is the revolutionary heart of A-ICT. Zones are not static; they have a lifecycle. AIT tracks this with its dynamic classification engine.
Phase 1: PENDING (Yellow): The script identifies a potential zone of interest based on a specific candle formation (a "displacement"). It is marked as "Pending" because its true nature is unknown. It is a question.
Phase 2: CLASSIFICATION: After the zone is created, AIT watches what happens next. The zone's identity is defined by its actions:
ORDER BLOCK (Blue): The highest-grade element. A zone is classified as an Order Block if it directly causes a Break of Structure (BOS) . This is the footprint of institutions entering the market with enough force to validate the new trend direction.
TRAP ZONE (Orange): A zone is classified as a Trap Zone if it is directly involved in a Liquidity Sweep . This indicates the zone was used to engineer liquidity, setting a "trap" for retail traders before a reversal.
REVERSAL / S&R ZONE (Green): If a zone is not powerful enough to cause a BOS or a major sweep, but still serves as a pivot point, it's classified as a general support/resistance or reversal zone.
4. Market Inefficiencies: Gaps in the Matrix
Fair Value Gaps (FVG): AIT detects FVGs—a 3-bar pattern indicating an imbalance—with a strict formula: low > high (for a bullish FVG) and gapSize > atr14 * 0.5. This ensures only significant, volatile gaps are shown. An FVG co-located with an Order Block is a high-confluence setup.
5. Premium & Discount: The Law of Value
Institutions buy at wholesale (Discount) and sell at retail (Premium). AIT uses a pdLookback to define the current dealing range and divides it into three zones: Premium (sell zone), Discount (buy zone), and Equilibrium. An element's quality score is massively boosted if it aligns with this principle (e.g., a bullish Order Block in a Discount zone).
⚙️ THE CONTROL PANEL: A COMPLETE GUIDE TO THE INPUTS MENU
Every setting is a lever, allowing you to tune the AIT engine to your exact specifications. Master these to unlock the script's full potential.
🎯 A-ICT Detection Engine
Min Displacement Candles: Controls the sensitivity of element detection. How it works: It defines the number of subsequent candles that must be "inside" a large parent candle. Best practice: Use 2-3 for a balanced view on most timeframes. A higher number (4-5) will find only major, more significant zones, ideal for swing trading. A lower number (1) is highly sensitive, suitable for scalping.
Mitigation Method: Defines when a zone is considered "used up" or mitigated. How it works: Cross triggers as soon as price touches the zone's boundary. Close requires a candle to fully close beyond it. Best practice: Cross is more responsive for fast-moving markets. Close is more conservative and helps filter out fake-outs caused by wicks, making it safer for confirmations.
Min Element Size (ATR): A crucial noise filter. How it works: It requires a detected zone to be at least this multiple of the Average True Range (ATR). Best practice: Keep this around 0.5. If you see too many tiny, irrelevant zones, increase this value to 0.8 or 1.0. If you feel the script is missing smaller but valid zones, decrease it to 0.3.
Age Threshold & Pending Timeout: These manage visual clutter. How they work: Age Threshold removes old, mitigated elements after a set number of bars. Pending Timeout removes a "Pending" element if it isn't classified within a certain window. Best practice: The default settings are optimized. If your chart feels cluttered, reduce the Age Threshold. If pending zones disappear too quickly, increase the Pending Timeout.
Min Quality Threshold: Your primary visual filter. How it works: It hides all elements (boxes, lines, labels) that do not meet this minimum quality score (0-100). Best practice: Start with the default 30. To see only A- or B-grade setups, increase this to 60 or 70 for an exceptionally clean, high-probability view.
🏗️ Market Structure
Lookbacks (Internal, External, Major): These define the sensitivity of the trend analysis. How they work: They set the number of bars to the left and right for pivot detection. Best practice: Use smaller values for Internal (e.g., 3) to see minor structure and larger values for External (e.g., 10-15) to map the main trend. For a macro, long-term view, increase the Major Swing Lookback.
Require Volume/Candle Confirmation: Toggles for quality control on BOS/CHoCH signals. Best practice: It is highly recommended to keep these enabled. Disabling them will result in more structure signals, but many will be false alarms. They are your filter against market noise.
... (Continue this detailed breakdown for every single input group: Display Configuration, Zones Style, Levels Appearance, Colors, Dashboards, MTF, Liquidity, Premium/Discount, Sessions, and IPDA).
📊 THE INTELLIGENCE DASHBOARDS: YOUR COMMAND CENTER
The dashboards synthesize all the complex analysis into a simple, actionable intelligence briefing.
Main Dashboard (Bottom Right)
ICT Metrics & Breakdown: This is your statistical overview. Total Elements shows how much structure the script is tracking. High Quality instantly tells you if there are any A/B grade setups nearby. Unmitigated vs. Mitigated shows the balance of fresh opportunities versus resolved price action. The breakdown by Order Blocks, Trap Zones, etc., gives you a quick read on the market's recent character.
Structure & Market Context: This is your core bias. Order Flow tells you the current script-determined trend. Last BOS shows you the most recent structural event. CHoCH Active is a critical warning. HTF Bias shows if you are aligned with the higher timeframe—the checkmark (✓) for alignment is one of the most important confluence factors.
Smart Money Flow: A volume-based sentiment gauge. Net Flow shows the raw buying vs. selling pressure, while the Bias provides an interpretation (e.g., "STRONG BULLISH FLOW").
Key Guide (Large Dashboard only): A built-in legend so you never have to guess. It defines every pattern, structure type, and special level visually.
📖 Narrative Dashboard (Bottom Left)
This is the "story" of the market, updated in real-time. It's designed to build your trading thesis.
Recent Elements Table: A live list of the most recent, high-quality setups. It displays the Type , its Narrative Role (e.g., "Bullish OB caused BOS"), its raw Quality percentage, and its final Trade Score grade. This is your at-a-glance opportunity scanner.
Market Narrative Section: This is the soul of A-ICT. It combines all data points into a human-readable story:
📍 Current Phase: Tells you if you are in a high-volatility Killzone or a consolidation phase like the Asian Range.
🎯 Bias & Alignment: Your primary direction, with a clear indicator of HTF alignment or conflict.
🔗 Events: A causal sequence of recent events, like "💧 Sell-side liquidity swept →
📊 Bullish BOS → 🎯 Active Order Block".
🎯 Next Expectation: The script's logical conclusion. It provides a specific, forward-looking hypothesis, such as "📉 Pullback expected to bullish OB at 1.2345 before continuation up."
🎨 READING THE BATTLEFIELD: A VISUAL INTERPRETATION GUIDE
Every color and line is a piece of information. Learn to read them together to see the full picture.
The Core Zones (Boxes):
Blue Box (Order Block): Highest probability zone for trend continuation. Look for entries here.
Orange Box (Trap Zone): A manipulation footprint. Expect a potential reversal after price interacts with this zone.
Green Box (Reversal/S&R): A standard pivot area. A good reference point but requires more confluence.
Purple Box (FVG): A market imbalance. Acts as a magnet for price. An FVG inside an Order Block is an A+ confluence.
The Structural Lines:
Green/Red Line (eBOS): Confirms the trend direction. A break above the green line is bullish; a break below the red line is bearish.
Thick Orange Line (CHoCH): WARNING. The previous trend is now in question. The market character has changed.
Blue/Red Lines (BSL/SSL): Liquidity targets. Expect price to gravitate towards these lines. A dotted line with a checkmark (✓) means the liquidity has been "swept" or "purged."
How to Synthesize: The magic is in the confluence. A perfect setup might look like this: Price sweeps below a red SSL line , enters a green Discount Zone during the NY Killzone , and forms a blue Order Block which then causes a green eBOS . This sequence, visible at a glance, is the story of a high-probability long setup.
🔧 THE ARCHITECT'S VISION: THE DEVELOPMENT JOURNEY
A-ICT was forged from the frustration of using lagging indicators in a market that is forward-looking. Traditional tools are reactive; they tell you what happened. The vision for A-ICT was to create a proactive engine that could anticipate institutional behavior by understanding their objectives: liquidity and efficiency. The development process was centered on creating a "lifecycle" for price patterns—the idea that a zone's true meaning is only revealed by its consequence. This led to the post-breakout classification system and the narrative-building engine. It's designed not just to show you patterns, but to tell you their story.
⚠️ RISK DISCLAIMER & BEST PRACTICES
Advanced ICT Theory (A-ICT) is a professional-grade analytical tool and does not provide financial advice or direct buy/sell signals. Its analysis is based on historical price action and probabilities. All forms of trading involve substantial risk. Past performance is not indicative of future results. Always use this tool as part of a comprehensive trading plan that includes your own analysis and a robust risk management strategy. Do not trade based on this indicator alone.
観の目つよく、見の目よわく
"Kan no me tsuyoku, ken no me yowaku"
— Miyamoto Musashi, The Book of Five Rings
English: "Perceive that which cannot be seen with the eye."
— Dskyz, Trade with insight. Trade with anticipation.
Pristine Value Areas & MGIThe Pristine Value Areas indicator enables users to perform comprehensive technical analysis through the lens of the market profile in a fraction of the time! 🏆
A Market Profile is a charting technique devised by J. Peter Steidlmayer, a trader at the Chicago Board of Trade (CBOT), in the 1980's. He created it to gain a deeper understanding of market behavior and to analyze the auction process in financial markets. A market profile is used to analyze an auction using price, volume, and time to create a distribution-based view of trading activity. It organizes market data into a bell-curve-like structure, which reveals areas of value, balance, and imbalance.
💠 How is a Value Area Calculated?
A value area is a distribution of 68%-70% of the trading volume over a specific time interval, which represents one standard deviation above and below the point of control, which is the most highly traded level over that period.
The key reference points are as follows:
Value area low (VAL) - The lower boundary of a value area
Value area high (VAH) - The upper boundary of a value area
Point of Control (POC) - The price level at which the highest amount of a trading period's volume occurred
If we take the probability distribution of trading activity and flip it 90 degrees, the result is our Pristine Value Area!
Market Profile is our preferred method of technical analysis at Pristine Capital because it provides an objective and repeatable assessment of whether an asset is being accumulated or distributed by institutional investors. Market Profile levels work remarkably well for identifying areas of interest, because so many institutional trading algorithms have been programmed to use these levels since the 1980's!
The benefits of using Market Profile include better trade location, improved risk management, and enhanced market context. It helps traders differentiate between trending and consolidating markets, identify high-probability trade setups, and adjust their strategies based on whether the market is in balance (consolidation) or imbalance (trending). Unlike traditional indicators that rely on past price movements, Market Profile provides real-time insights into trader behavior, giving an edge to those who can interpret its nuances effectively.
Virgin Point of Control (VPOC) - A point of control from a previous time period that has not yet been revisited in subsequent periods. VPOCs are great for identifying prior supply or demand zones.
Below is a great example of price reversing lower after taking out an upside VPOC
💠 Are all POCs Created Equal?
If POCs are used to gauge supply & demand zones at key levels, then a POC with higher volume should be viewed as more significant than a POC that traded lower volume, right? We created Golden POCs as a tool to identify high volume POCs on all timeframes.
Golden POC (GPOC) - A POC that traded the highest volume compared to prior POCs (proprietary to Pristine Capital)
We calculate value areas for the following time intervals based on the user selected timeframe:
5 Minute and 15 Minute Timeframes -> Daily Value Area
The daily value area paints the distribution of the PRIOR session's trading activity. The "d" in the label references for VAHd, POCd and VALd is a visual cue that value area shown is daily.
1 Hour Timeframe -> Weekly Value Area
The weekly value area paints the distribution of the PRIOR week's trading activity. The "w" in the label references for VAHw, POCw and VALw is a visual cue that value area shown is weekly.
1 Day Timeframe -> Monthly Value Area
The monthly value area paints the distribution of the PRIOR month's trading activity. The "m" in the label references for VAHm, POCm and VALm is a visual cue that value area shown is monthly.
1 Week Timeframe -> Yearly Value Area
The yearly value area paints the distribution of the PRIOR year's trading activity. The "y" in the label references for VAHy, POCy and VALy is a visual cue that value area shown is yearly.
💠 What is a developing value area?
The developing value area provides insight into the upcoming value area while it is still forming! It appears when 80% of the way through the current value area. As the end of a trading period approaches, it can make sense to start trading off the developing value area. When the time period flips, the developing value area becomes the active value area!
💠 Value Areas Trading Setups
Two popular market profile concepts are the bullish and bearish 80% rules. The concept is that there is an 80% probability that the market will traverse the entire relevant value area.
Bullish 80% Rule - If a security opens a period below the value area low , and subsequently closes above it, the bullish 80% rule triggers, turning the value area green. One can trade for a move to the top of the value area, using a close below the value area low as a potential stop!
In the below example, HOOD triggered the bullish 80% rule after it reclaimed the monthly value area!
HOOD proceeded to rally through the monthly value area and beyond in subsequent trading sessions. Finding the first stocks to trigger the bullish 80% rule after a market correction is key for spotting the next market leaders!
Bearish 80% Rule - If a security opens a period above the value area high , and subsequently closes below it, the bearish 80% rule triggers, turning the value area red. One can trade for a move to the bottom of the value area, using a close above the value area high as a potential stop!
ES proceeded to follow through and test the value area low before trending below the weekly value area
Value Area Breakouts - When a security is inside of value, the auction is in balance. When it breaks out from a value area, it could be entering a period of price discovery. One can trade these breaks out of value with tight risk control by setting a stop inside the value area! These breakouts can be traded on all chart timeframes depending on the timeframe of the individual trader. Combining multiple timeframes can result in even more effective trading setups.
RBLX broke out from the monthly value area on 4/22/25👇
RBLX proceeded to rally +62.78% in 39 trading sessions following the monthly VAH breakout!
💠 Market Generated Information to Improve Your Situational Awareness!
In addition to the value areas, we've also included stat tables with useful market generated information. The stats displayed vary based on the timeframe the user has up on their screen. This incentivizes traders to check the chart on multiple timeframes before taking a trade!
Metrics Grouped By Use Case
Performance
▪ YTD α - YTD Alpha (α) measures the risk-adjusted, excess return of a security over its user defined benchmark, on a year-to-date basis.
▪ MTD α - MTD Alpha (α) measures the risk-adjusted, excess return of a security over its user defined benchmark, on a month-to-date basis.
▪ WTD α - WTD Alpha (α) measures the risk-adjusted, excess return of a security over its user defined benchmark, on a week-to-date basis.
▪ YTD %Δ - Year-to-date percent change in price
▪ MTD %Δ - Month-to-date percent change in price
▪ WTD %Δ - Week-to-date percent change in price
Volatility
▪ ATR % - The Average True Range (ATR) expressed as a percentage of an asset's price.
▪ Beta - Measures the price volatility of a security compared to the S&P 500 over the prior 5 years (since inception if 5 years of data is not available)
Risk Analysis
▪ LODx - Low-of-day extension - ATR % multiple from the low of day (measures how extended a stock is from its low of day)
▪ MAx - Moving average extension - ATR % multiple from the user-defined moving average (measures how extended a security is from its moving average). Default moving average = 50D SMA
Why does MAx matter?
MAx measures the number of ATR % multiples a security is trading away from a key moving average. The default moving average length is 50 days.
MAx can be used to identify mean reversion trades . When a security trends strongly in one direction and moves significantly above or below its moving average, the price often tends to revert back toward the average.
Example, if the ATR % of the security is 5%, and the stock is trading 50% higher than the 50D SMA, the MAx would be 50%/5% = 10. A user might opt to take a countertrend trade when the MAx exceeds a predetermined level.
The MAx can also be useful when trading breakouts above or below the key moving average of your choosing. The lower the MAx, the tighter stop loss one can take if trading against that level.
Identifying an extreme price extension using MAx 👇
Price mean reverted immediately following the high MAx 👇
💠 Trend Analysis
The Trend Analysis section consists of short-term and long-term stage analysis data as well as the value area timeframe and price in relation to the value area.
Stage Analysis
▪ ST ⇅ - Short-term stage analysis indicator
▪ LT ⇅ - Long-term stage analysis indicator
Short-term and long-term stage analysis data is provided in the two rightmost columns of each table. The columns are labeled ST ⇅ and LT ⇅.
Why is Stage Analysis important? Popularized by Stan Weinstein, stage analysis is a trend following system that classifies assets into four stages based on price-trend analysis.
The problem? The interpretation of stage analysis is highly subjective. Based on the methodology provided in Stan Weinstein’s books, five different traders could look at the same chart, and come to different conclusions as to which stage the security is in!
We solved for this by creating our own methodology for classifying stocks into stages using moving averages. This indicator automates that analysis, and produces short-term and long-term trend signals based on user-defined key moving averages. You won’t find this in any textbook or course, because it’s completely unique to the Pristine trading methodology.
Our indicator calculates a short-term trend signal using two moving averages; a fast moving average, and a slow moving average. We default to the 10D EMA as the fast moving average & the 20D SMA as the slow moving average. A trend signal is generated based on where price is currently trading with respect to the fast moving average and the slow moving average. We use the signal to guide shorter-term swing trades.
In general, we want to take long trades in stocks with strengthening trends, and short trades in stocks with weakening trends. The user is free to change the moving averages based on their own short-term timeframe. Every trader is unique!
The same process is applied to calculate the long-term trend signal. We default to the 50D SMA as our fast moving average, and the 200D SMA as the slow moving average for the LT ⇅ signal calculation, but users can change these to fit their own unique trading style.
What is Stage 1?
Stage 1 identifies stocks that transitioned from downtrends, into bottoming bases.
Stage 1A - Bottom Signal: Marks the first day a security shows initial signs of recovery after a downtrend, with early indications of strength emerging.👇
Stage 1B - Bottoming Process: Identifies the ongoing phase where the security continues to stabilize and strengthen, confirming the base-building process after the initial signal.👇
Stage 1R - Failed Uptrend: Detects when a security that had entered an early uptrend loses momentum and slips back into a bottoming phase, signaling a failed breakout.👇
What is Stage 2?
Stage 2 identifies stocks that transitioned from bottoming bases to uptrends.
Stage 2A - Breakout: Marks the first day a security decisively breaks out, signaling the start of a new uptrend.👇
Stage 2B - Uptrend: Identifies when the security continues to trade in an established uptrend following the initial breakout, with momentum building but not yet showing full strength.👇
Stage 2C - Strong Uptrend: Detects when the uptrend strengthens further, with the security displaying clear signs of accelerating strength and buying pressure.👇
Stage 2R - Failed Breakdown: Detects when a security that had recently entered a corrective phase reverses course and reclaims its upward trajectory, moving back into an uptrend.👇
What is Stage 3?
Stage 3 identifies stocks that transitioned from uptrends to topping bases.
Stage 3A - Top Signal: Marks the first day a security shows initial signs of weakness after an uptrend, indicating the start of a potential topping phase.👇
Stage 3B - Topping Process: Identifies the period following the initial signal when the security continues to show signs of distribution and potential trend exhaustion.👇
Stage 3R - Failed Breakdown: Detects when a security that had entered a deeper corrective phase reverses upward, recovering enough strength to re-enter the topping phase.👇
What is Stage 4?
Stage 4 identifies stocks that transitioned from topping bases to downtrends.
Stage 4A - Breakdown: Marks the first day a security decisively breaks below key support levels, signaling the start of a new downward trend.👇
Stage 4B - Downtrend: Identifies when the security continues to trend lower following the initial breakdown, with sustained bearish momentum, though not yet fully entrenched.👇
Stage 4C - Strong Downtrend: Detects when the downtrend intensifies, with the security displaying clear signs of accelerating weakness and selling pressure.👇
Stage 4R - Failed Bottom: Detects when a security that had begun to show early signs of bottoming reverses course and resumes its decline, falling back into a downtrend.👇
Stage N/A - Recent IPO: Applies to stocks that recently IPO’ed and don’t have enough data to calculate all necessary moving averages.
Value Area
In Trend Analysis, the value area information is helpful to gauge price in relation to the value area.
▪ VA(y) - Categorizes the security based on the relation of price to the yearly value area
▪ VA(m) - Categorizes the security based on the relation of price to the monthly value area
▪ VA(w) - Categorizes the security based on the relation of price to the weekly value area
Value area states:
▪ ABOVE = Price above the value area high
▪ BELOW = Price below the value area low
▪ INSIDE = Price inside the value area
▪ Bull 80% = Bullish 80% rule in effect
▪ Bear 80% rule = Bearish 80% rule in effect
For example, in the chart above, VA(m) - ABOVE indicates a monthly value area and price is above the VAH.
💠 What Makes This Indicator Unique
There are many value area indicators, however...
Value Area
▪ Golden POC (GPOC) - This is a proprietary concept.
▪ Unique Label Customization
Pristine value areas often comprehensive and unique label customizations. Styles include options to display any combination of the following on your labels:
• Price levels associated with market profile levels
• % distance of market profile levels from security price
• ATR% extension of market profile levels from security price
Multi-Timeframe Analysis
Based on the chart timeframe, unique market generated information is shown to facilitate multi-timeframe analysis.
▪ Weekly Timeframe
On the weekly timeframe the focus is the bigger picture and the metrics reflect this perspective. Performance data includes YTD Alpha and YTD percent change in price. Volatility is measured using ATR % and the industry standard beta. Trend analysis for this higher timeframe include the 52-week range, which measures where a security is trading in relation to its 52wk high and 52wk low. Also included is the where price is in relation to yearly value area.
▪ Daily Timeframe
As one drills down to the daily timeframe, the performance metrics include MTD alpha and MTD percent change in price.
Risk analysis includes the low-of-day extension (LODx), which is the ATR % multiple from the low of the day, to measures how extended a stock is from its low of day. In addition, the moving average extension (MAx) is the ATR % multiple from the user-defined moving average, measures how extended a security is from its
moving average. The default moving average is the 50D SMA, however this can be customized in Settings.
Trend Analysis on the daily timeframe includes the Pristine Capital methodology for classifying stocks into stages using moving averages. Both short-term and long-term stage analysis data is included. Finally, price in relation to monthly value area is shown.
▪ Hourly Timeframe
An the hourly timeframe, performance metrics include WTD alpha and WTD percent change in price. Trend analysis includes the daily closing range (DCR) and price in relation to weekly value area.
💠 Settings and Preferences
💠 Acknowledgements
We'd like to thank @dgtrd, a TradingView Pine Wizard, for his insight on the finer details when working with volume profiles.
FVG Zones (Remove Filled) + AlertA powerful TradingView indicator that automatically identifies Fair Value Gap (FVG) zones, removes them once price “fills” the gap, and sends you crystal-clear alerts specifying Bull or Bear zones—so you never miss a market imbalance.
🔍 Key Features
Automatic FVG Detection
Spots three-candle imbalance patterns (low > high for Bull, high < low for Bear) and draws colored boxes on your chart.
Auto-Remove Filled Zones
Once price enters a gap, the corresponding box is deleted—keeping your chart clutter-free.
Dedicated Alerts
Two separate alert conditions with constant messages:
“Price filled Bull FVG zone”
“Price filled Bear FVG zone”
On-Chart Labels
Enable debugging to display Bull FVG or Bear FVG tags above the triggering candle.
Performance-Tuned
Supports up to 500 active zones without slowing down your chart.
⚙️ Inputs & Customization
Show Alert Labels (Boolean) – Toggle on-chart text labels.
Max Boxes Count (Integer) – Control the maximum number of zones displayed.
StockLeave Signal BarThe indicator identifies potential trade entries by highlighting expansion and reversal bars. These are defined by individual bar characteristics and refined by contextual factors such as price position relative to structural boundaries. The purpose is to locate bars that could indicate potential market initiation.
Expansion Bars
The expansion captures bars that breakout from a period of reduced volatility. These often initiate directional movement and are recognized using a two-part definition:
Range Expansion The current bar’s range must exceed the average range. This ensures the move is comparatively large and stands out from recent behavior.
Range Compression The bars before the expansion must be below a threshold of the average range. This confirms a low-volatility lead-up, strengthening the likelihood that the expansion has significance.
This script applies additional filters. A local breakout ensures price breaks the previous bar’s high or low. A strong close confirms directional intent by requiring the close near the bar’s extreme. Mean proximity checks that expansion starts near the mean price using a dynamic buffer relative to bar size. A directional filter blocks signals during extended directional runs. Consecutive suppression prevents multiple expansions to show in succession.
Reversal Bars
Reversal setups aim to identify potential turning points after price has reached a zone of imbalance or extension. These bars typically exhibit long tails and occur near structural boundaries such as the outer Keltner bands. Their design favors short-term price rejection and potential reversal.
Tail Dominance The wick must be at least twice the body and make up a significant portion of the bar’s total range, signaling strong rejection rather than indecision.
Close Location The close should be near the opposite end of the wick, near the low for bearish signals and near the high for bullish, confirming pressure in the reversal direction.
This script applies additional filters. Local extreme ensures the bar marks a local turning point to confirm reversals occur after extension, not within structure. Boundary proximity requires the bar to appear near the outer envelope, aligning bearish signals with the upper band and bullish with the lower, indicating price has reached an area of likely imbalance.
This section also incorporate snapback reversals, designed to capture failed extensions beyond structural boundaries. Unlike single-bar rejections, snapbacks use a two-bar sequence: a strong impulse bar that closes outside the envelope, followed by a reversal bar that closes back inside.
Alert Configuration
The Signal Bars indicator includes an alert function with two built-in conditions to help reduce screen time and focus attention when predefined conditions are met.
Expansion: Alerts when a bar meets all conditions for a valid expansion.
Reversal: Alerts when a bar meets the criteria for a pin bar or snapback reversal.
These are built into the indicator with the alertcondition() function and can be turned on whenever the indicator is applied to a chart. Each alert includes a default message that uses dynamic placeholders; {{ticker}} for the symbol and {{interval}} for the timeframe.
Create a new alert and select the condition “StockLeave Signal Bars.”
Then select from the two options: Expansion and Reversal.
For expansions, select “once per bar” to capture developing momentum.
For reversals, use “once per bar close” to confirm rejection setups.
Apply alerts across multiple timeframes to improve coverage. Lower timeframes are better suited for fast-moving markets, while higher timeframes work well in slower or more selective environments. This process only needs to be done once. The created alerts can then be toggled on or off from the Alerts panel as preferred, without requiring reconfiguration.
Applied Discretion
The indicator functions on fixed logic, but interpretation always takes precedence. Consider price action, structure, volatility, and broader market context. Most signals will not lead to trades; while many may appear in a session, only a select few will align with context and warrant execution based on discretion.
ZenAlgo - AvengerThe ZenAlgo - Avenger indicator provides a multi-layered view of market behavior by combining volume delta analytics, trend-following EMAs, average price comparison, and price-volume profiling into a unified overlay. It is designed to visually assist traders in identifying areas of interest, momentum shifts, and potential reversals using cumulative data from both spot and perpetual markets.
Volume Delta Calculation
This indicator computes delta as the difference between estimated buy and sell volumes using volume data from multiple centralized exchanges. It distinguishes between spot and perpetual volumes, combining them into total volume.
To estimate buying and selling volume from raw volume data, candle structure is broken down into body and wicks. The body is interpreted as the core directional movement (buy/sell), while the wicks are treated as uncertain or counteraction. This segmentation helps infer the likely share of buying and selling within each bar.
The delta is calculated per bar and then aggregated over a lookback period (default 14 bars) to generate a cumulative delta. This approach provides a smoothed value of volume pressure trends over time.
A moving average is applied to the delta values (using selectable MA types like EMA or SMA) to define signal crossovers and suppress noise.
Delta Visualization
To contextualize delta within price action, the delta is scaled dynamically (by ATR or user-defined value) and plotted as a band around the closing price. Positive delta expands upward from price, negative delta downward. This provides a visual overlay that reflects net market pressure in context with price movement.
In cases of extreme delta (threshold set at 80% of recent maximum), the indicator marks spike bars using symbols to indicate significant directional pressure.
Identification of Noteworthy Conditions
The indicator highlights points on the chart where specific conditions are met based on the interaction between volume delta and its moving average. These conditions may align with moments of market pressure imbalance and directional movement, but they are not to be interpreted as trade signals in isolation.
Instead, these chart markers serve as visual flags for potential interest. They are intended to draw the user’s attention to scenarios where:
The delta crosses above or below its moving average, suggesting a potential shift in volume pressure.
The cumulative delta supports the direction of this crossover.
Optional filters can further restrict these markings to periods where:
The short-term trend (as inferred from EMA slope) supports the direction.
Volume is elevated relative to a recent average.
A user-defined cooldown period prevents multiple markings within short succession to avoid clutter.
It is essential to underscore that these markers do not constitute buy or sell advice . Their role is diagnostic , helping the trader to identify potential moments of interest which should be analyzed in conjunction with broader context, such as trend structure, price action, support/resistance levels, or external market data.
EMA Structure
Six EMAs with fixed lengths (13 to 56) are plotted and colored dynamically based on the most recent crossover between the fastest and slowest (EMA1 and EMA6). These EMAs help visualize short- to mid-term trends. The crossover itself is marked with symbols, with vertical offset based on ATR to maintain chart readability.
Average Line (AVG)
The indicator also calculates an average price based on a fixed window (100 bars). This is not a standard moving average but rather a raw average of recent prices stored in a circular buffer. The average is plotted, and its relative distance to the current price is labeled as a percentage. This feature serves as a simplified representation of fair value or mean reversion anchor.
EMA6 vs AVG Cross
Another layer of point of interest detection involves EMA6 crossing the AVG line. This crossover is only considered valid if EMA6 shows slope consistency in the crossing direction. These events are marked using symbols and offset vertically to avoid overlapping price action.
Divergence Detection
The script detects both regular and hidden divergences between price and delta:
Regular divergences are defined when price makes a higher high or lower low, while delta fails to confirm (makes a lower high or higher low).
Hidden divergences occur when price retraces (lower high or higher low), but delta moves against this retracement, indicating underlying strength or weakness.
Divergence points are labeled with "R" (regular) or "H" (hidden) and appear at local pivot highs or lows. The number of visible divergence labels can be limited for chart clarity.
POC and nPOC Calculations
The script includes a simplified volume profile implementation, calculating:
POC (Point of Control): the price level with the highest volume for the given period.
nPOC (non-tested POC): historical POCs that have not yet been revisited by price.
Price levels are bucketed into rows (user-defined), and volume per bucket is tracked to identify the POC. Upon a new period (e.g., day, week), a horizontal POC line is drawn. Once tested by price, the line’s appearance changes (color fades, label shrinks), helping users distinguish between untouched and touched levels.
Limits are enforced on the number of retained POCs and their maximum distance from current bars to optimize performance and chart readability.
Exchange Aggregation
Volume data is aggregated across major exchanges. This ensures that the delta calculation captures a broader market picture beyond a single venue, reducing exchange-specific noise.
How to Interpret Values
Delta Band: Wide bands indicate strong directional imbalance. Narrow bands suggest indecision or low volume.
EMA Crossover Symbols: Appear on directional shifts in moving averages. Multiple EMAs reinforcing the same slope typically indicate stronger trend.
AVG Line: Represents average price over recent history. Large deviations can indicate overextension or potential mean reversion.
Divergences: Regular ones may point to weakening momentum; hidden ones can suggest continuation despite corrective price action.
POC / nPOC: Key volume-based support/resistance levels. Untested nPOCs can act as magnets for price retests.
How to Best Use This Indicator
Use in conjunction with trend context (e.g., higher timeframe EMAs) to avoid counter-trend indications.
Treat delta spikes as caution zones—especially if they occur at known support/resistance.
Watch for divergences as early warning signs before price reverses.
Use POC/nPOC as target levels, especially if aligned with delta signals.
Apply volume and trend filters to reduce noise on shorter timeframes.
Added Value
Multi-exchange volume aggregation makes the delta calculation more robust.
Real-time cumulative delta overlaid directly on the price chart provides immediate context.
Points of interest on chart are conservative and filterable, intended to reduce false positives.
The combination of delta, trend-following EMAs, fair value line, and volume profile data is rarely found in one overlay script.
POC/nPOC visualization based on real traded volume helps identify high-interest zones for future price interaction.
Why Is It Worth Paying For
While free alternatives may provide partial insights (e.g., basic delta or single EMA crossovers), this indicator integrates multiple domains—delta, divergence, average price, trend overlays, and profile levels—into a coherent, optimized chart tool. The value lies not just in having these tools, but in how they are synchronized and visualized.
Furthermore, sourcing and synchronizing volume data from multiple exchanges for delta estimation is not straightforward in Pine Script and adds to the indicator's complexity and utility.
Disclaimers and Limitations
Delta estimation is based on candle structure and assumes wick/body distribution reflects buyer/seller activity, which may not always be precise.
Multi-exchange volume data relies on availability via TradingView’s request.security() function; if exchange data is missing or delayed, results may be incomplete.
Divergences do not guarantee reversals—should be used as part of a broader analysis framework.
On illiquid instruments or exotic pairs, the value of delta and volume-based analytics may be reduced due to unreliable volume.
Forever Model [Pro+] (Sniper)Introduction
Forever Model (Sniper) is a clean, structured framework for visualizing internal liquidity to external liquidity rotations. It identifies shifts in market delivery by combining internal liquidity zones (Fair Value Gaps), divergence between correlated markets (Smart Money Technique), and lower timeframe orderflow changes (Orderblocks).
Designed for repeatability, the model helps analysts build confidence through familiarity, not complexity.
Rather than attempting to forecast direction, this model focuses on recognizing recurring patterns in delivery behavior across Timeframes. It presents a structured visual logic that can support manual analysis, with the aid of alerts that prompt analysts to investigate and validate potential price rotations.
The model is non-repainting, thoughtfully built to highlight past rotations once formed. It offers flexibility across assets and Timeframes, adapting to analysts' preferences while remaining consistent in its components.
Description
The model is organized into a three-part sequence. These three conditions form the visual foundation of the model. All parameters can be customized to match your preferred timeframe, session, and market:
Internal Range Liquidity Tag (IRL)
Price must interact with a defined internal inefficiency—typically a Fair Value Gap (FVG), which is an area between a three candle structure where price moves rapidly, leaving an imbalance that may later be revisited to be filled for efficiency.
Smart Money Technique Divergence Detected (SMT)
SMT transpires as a crack in correlation between two assets – this divergence is used to indicate potential shifts in price delivery.
SMT can be observed between two correlated assets, where one makes a lower low while the other holds a higher low (or conversely, one makes a higher high while the other forms a lower high).
Similarly, SMT can also occur between inverse correlated assets, where one makes a lower low while the other holds a lower high (or conversely, one makes a higher high while the other forms a higher low).
Change in State of Delivery (CISD)
After SMT occurs, the model identifies a CISD—a strong close engulfing the body of a previous directional candle that sweeps a short-term high or low. This suggests that price may be shifting from one delivery regime to another. The candle is labelled as an Orderblock (OB) candidate, with optional projected measures for better range of opportunity estimation.
Key Features
Model History Control
Controls how many past model formations appear on the chart, with a maximum of 40. Analysts may use shorter history for live charting or increase the count when studying past performance or recurring conditions.
When History is equal to 0, it will only show only live models in development, or nothing if no models are currently active.
Note: historical invalidated rotations are visualized through small markers, and may not display the model's components unless reviewed in Replay Mode. This mechanism keeps the chart clean, and allows the analyst to focus on the confirmed rotations.
Directional Bias Filter
Filters whether the model shows formations in only one direction or both. For example, selecting “Bullish” displays only internal range zones and divergence setups that meet criteria for upside movement. This feature is crucial for allowing analysts to align with higher Timeframe bias or studying unidirectional structures.
SMT Pair Input
The model compares your active chart with a second asset to detect SMT Divergence. You may manually enter a symbol (e.g., ES1!, BTCUSD, NZDUSD) or use Automatic SMT Pair Detection , which selects the most relevant correlated market. Inverse SMT inverts the logic, useful for negatively correlated pairs (e.g., gold vs dollar).
For example, although the Automatic SMT Pair Detection for CME_MINI:NQ1! is CME_MINI:ES1! , one may decide to use a leading stock in the NASDAQ such as NASDAQ:NVDA :
Timeframe Alignment
Defines which higher Timeframe the IRL is drawn from, and which lower Timeframe is used to evaluate the Model's conditions. These Timeframe Alignments can be selected individually to only showcase a specific combination of IRL and LTF Conditions; for a more dynamic approach, the "Automatic" option adjusts these pairings based on the current chart Timeframe. By selecting the "Custom" option, analysts can define and monitor their own preferred Timeframe Alignment.
Example: 5m Conditions ➞ 1H IRL vs. 4H Conditions ➞ Weekly IRL
Fair Value Gap (FVG) Visualization
Fair Value Gaps are areas where price moved quickly between two candles without overlap—these areas represent the IRL of the model, and are often revisited before continuing. Optionally, the analyst can decide to showcase the Consequent Encroachment (CE), the midpoint where price begins to fill the imbalance. Further, the analyst can maintain a cleaner chart by only showing FVG where SMT occurs, substantially limiting the number of drawings on the chart.
SMT Visualization
Draws visual lines connecting SMT points between the HTF reference points of the current chart's asset, and the SMT Pair asset. Helps analysts confirm divergence location and relationship at a glance, especially when reviewing multiple correlated pairs.
Liquidity Sweep Visualization
Most recent short-term liquidity swept, which resulted in a CISD. Marking this liquidity pool—a high or low that has been taken out—provides context and can give additional insight to evaluate the current market rotation.
Orderblock + Projections (OB)
When a CISD is recognized, an OB candidate is plotted. Projections from the OB can be displayed at customizable distances, serving as measurements for better range of opportunity estimation.
External Range Liquidity (ERL)
External Range Liquidity refers to price levels that sit beyond internal structures—typically local highs or lows that may be revisited after a retracement, for continuation.
Session Filters + Timezone Control
Define up to four time blocks (e.g., London Open, NY AM, PM session, Asia) for when the model is active. Timezones are fully customizable, supporting global use cases and precise filtering of formations to sessions with expected volume or cleaner structure.
Information Table
A compact, floating panel is available to display key model parameters in real time: Timeframe Alignment, Bias Direction, Active SMT Pair, Time Filter Conditions, Date.
This feature provides immediate context under which the model is operating — especially useful during active chart review or multi-pair monitoring. The table can be repositioned, resized, or disabled entirely depending on visual preference.
Model Markers & Backtest Support
The model includes a visual marker system to support chart review and backtesting. These overlays provide reference points for past structure, showcasing the following:
Reaching an OB Projection after revisiting the OB
Reaching the External Range Liquidity after revisiting the OB
Reaching an OB Projection without revisiting the OB
Reaching the External Range Liquidity without revisiting the OB
Invalidating the detected OB
Fully Automated Framework: all these components, when put together in the Forever Model ($niper), yield a clean and simple approach to studying and observing market rotations, empowering analysts in seeing the market through $niper's point of view. Each component is customizable to the analyst's liking to match their unique visual and technical preferences.
Usage Guidance:
Add Forever Model ($niper) to your TradingView chart.
Select your preferred SMT Pair, Timeframe Alignments, Model Style, and Time Filters.
Automate your analysis process with Forever Model (Sniper) and leverage it into your existing strategies to fine-tune your view through Sniper's point of view.
Terms and Conditions
Our charting tools are products provided for informational and educational purposes only and do not constitute financial, investment, or trading advice. Our charting tools are not designed to predict market movements or provide specific recommendations. Users should be aware that past performance is not indicative of future results and should not be relied upon for making financial decisions. By using our charting tools, the purchaser agrees that the seller and the creator are not responsible for any decisions made based on the information provided by these charting tools. The purchaser assumes full responsibility and liability for any actions taken and the consequences thereof, including any loss of money or investments that may occur as a result of using these products. Hence, by purchasing these charting tools, the customer accepts and acknowledges that the seller and the creator are not liable nor responsible for any unwanted outcome that arises from the development, the sale, or the use of these products. Finally, the purchaser indemnifies the seller from any and all liability. If the purchaser was invited through the Friends and Family Program, they acknowledge that the provided discount code only applies to the first initial purchase of the Toodegrees Premium Suite subscription. The purchaser is therefore responsible for cancelling – or requesting to cancel – their subscription in the event that they do not wish to continue using the product at full retail price. If the purchaser no longer wishes to use the products, they must unsubscribe from the membership service, if applicable. We hold no reimbursement, refund, or chargeback policy. Once these Terms and Conditions are accepted by the Customer, before purchase, no reimbursements, refunds or chargebacks will be provided under any circumstances.
By continuing to use these charting tools, the user acknowledges and agrees to the Terms and Conditions outlined in this legal disclaimer.
Skrip berbayar
SCE Price Action SuiteThis is an indicator designed to use past market data to mark key price action levels as well as provide a different kind of insight. There are 8 different features in the script that users can turn on and off. This description will go in depth on all 8 with chart examples.
#1 Absorption Zones
I defined Absorption Zones as follows.
//----------------------------------------------
//---------------Absorption---------------------
//----------------------------------------------
box absorptionBox = na
absorptionBar = ta.highest(bodySize, absorptionLkb)
bsab = ta.barssince(bool(ta.change(absorptionBar)))
if bsab == 0 and upBar and showAbsorption
absorptionBox := box.new(left = bar_index - 1, top = close, right = bar_index + az_strcuture, bottom = open, border_color = color.rgb(0, 80, 75), border_width = boxLineSize, bgcolor = color.rgb(0, 80, 75))
absorptionBox
else if bsab == 0 and downBar and showAbsorption
absorptionBox := box.new(left = bar_index - 1, top = close, right = bar_index + az_strcuture, bottom = open, border_color = color.rgb(105, 15, 15), border_width = boxLineSize, bgcolor = color.rgb(105, 15, 15))
absorptionBox
What this means is that absorption bars are defined as the bars with the largest bodies over a selected lookback period. Those large bodies represent areas where price may react. I was inspired by the concept of a Fair Value Gap for this concept. In that body price may enter to be a point of support or resistance, market participants get “absorbed” in the area so price can continue in whichever direction.
#2 Candle Wick Theory/Strategy
I defined Candle Wick Theory/Strategy as follows.
//----------------------------------------------
//---------------Candle Wick--------------------
//----------------------------------------------
highWick = upBar ? high - close : downBar ? high - open : na
lowWick = upBar ? open - low : downBar ? close - low : na
upWick = upBar ? close + highWick : downBar ? open + highWick : na
downWick = upBar ? open - lowWick : downBar ? close - lowWick : na
downDelivery = upBar and downBar and high > upWick and highWick > lowWick and totalSize > totalSize and barstate.isconfirmed and session.ismarket
upDelivery = downBar and upBar and low < downWick and highWick < lowWick and totalSize > totalSize and barstate.isconfirmed and session.ismarket
line lG = na
line lE = na
line lR = na
bodyMidpoint = math.abs(body) / 2
upWickMidpoint = math.abs(upWickSize) / 2
downWickkMidpoint = math.abs(downWickSize) / 2
if upDelivery and showCdTheory
cpE = chart.point.new(time, bar_index - 1, downWickkMidpoint)
cpE2 = chart.point.new(time, bar_index + bl, downWickkMidpoint)
cpG = chart.point.new(time, bar_index + bl, downWickkMidpoint * (1 + tp))
cpR = chart.point.new(time, bar_index + bl, downWickkMidpoint * (1 - sl))
cpG1 = chart.point.new(time, bar_index - 1, downWickkMidpoint * (1 + tp))
cpR1 = chart.point.new(time, bar_index - 1, downWickkMidpoint * (1 - sl))
lG := line.new(cpG1, cpG, xloc.bar_index, extend.none, color.green, line.style_solid, 1)
lE := line.new(cpE, cpE2, xloc.bar_index, extend.none, color.white, line.style_solid, 1)
lR := line.new(cpR1, cpR, xloc.bar_index, extend.none, color.red, line.style_solid, 1)
lR
else if downDelivery and showCdTheory
cpE = chart.point.new(time, bar_index - 1, upWickMidpoint)
cpE2 = chart.point.new(time, bar_index + bl, upWickMidpoint)
cpG = chart.point.new(time, bar_index + bl, upWickMidpoint * (1 - tp))
cpR = chart.point.new(time, bar_index + bl, upWickMidpoint * (1 + sl))
cpG1 = chart.point.new(time, bar_index - 1, upWickMidpoint * (1 - tp))
cpR1 = chart.point.new(time, bar_index - 1, upWickMidpoint * (1 + sl))
lG := line.new(cpG1, cpG, xloc.bar_index, extend.none, color.green, line.style_solid, 1)
lE := line.new(cpE, cpE2, xloc.bar_index, extend.none, color.white, line.style_solid, 1)
lR := line.new(cpR1, cpR, xloc.bar_index, extend.none, color.red, line.style_solid, 1)
lR
First I get the size of the wicks for the top and bottoms of the candles. This depends on if the bar is red or green. If the bar is green the wick is the high minus the close, if red the high minus the open, and so on. Next, the script defines the upper and lower bounds of the wicks for further comparison. If the candle is green, it's the open price minus the bottom wick. If the candle is red, it's the close price minus the bottom wick, and so on. Next we have the condition for when this strategy is present.
Down delivery:
Occurs when the previous candle is green, the current candle is red, and:
The high of the current candle is above the upper wick of the previous candle.
The size of the current candle's top wick is greater than its bottom wick.
The total size of the previous candle is greater than the total size of the current candle.
The current bar is confirmed (barstate.isconfirmed).
The session is during market hours (session.ismarket).
Up delivery:
Occurs when the previous candle is red, the current candle is green, and:
The low of the current candle is below the lower wick of the previous candle.
The size of the current candle's bottom wick is greater than its top wick.
The total size of the previous candle is greater than the total size of the current candle.
The current bar is confirmed.
The session is during market hours
Then risk is plotted from the percentage that users can input from an ideal entry spot.
#3 Candle Size Theory
I defined Candle Size Theory as follows.
//----------------------------------------------
//---------------Candle displacement------------
//----------------------------------------------
line lECD = na
notableDown = bodySize > bodySize * candle_size_sensitivity and downBar and session.ismarket and barstate.isconfirmed
notableUp = bodySize > bodySize * candle_size_sensitivity and upBar and session.ismarket and barstate.isconfirmed
if notableUp and showCdSizeTheory
cpE = chart.point.new(time, bar_index - 1, close)
cpE2 = chart.point.new(time, bar_index + bl_strcuture, close)
lECD := line.new(cpE, cpE2, xloc.bar_index, extend.none, color.rgb(0, 80, 75), line.style_solid, 3)
lECD
else if notableDown and showCdSizeTheory
cpE = chart.point.new(time, bar_index - 1, close)
cpE2 = chart.point.new(time, bar_index + bl_strcuture, close)
lECD := line.new(cpE, cpE2, xloc.bar_index, extend.none, color.rgb(105, 15, 15), line.style_solid, 3)
lECD
This plots candles that are “notable” or out of the ordinary. Candles that are larger than the last by a value users get to specify. These candles' highs or lows, if they are green or red, act as levels for support or resistance.
#4 Candle Structure Theory
I defined Candle Structure Theory as follows.
//----------------------------------------------
//---------------Structure----------------------
//----------------------------------------------
breakDownStructure = low < low and low < low and high > high and upBar and downBar and upBar and downBar and session.ismarket and barstate.isconfirmed
breakUpStructure = low > low and low > low and high < high and downBar and upBar and downBar and upBar and session.ismarket and barstate.isconfirmed
if breakUpStructure and showStructureTheory
cpE = chart.point.new(time, bar_index - 1, close)
cpE2 = chart.point.new(time, bar_index + bl_strcuture, close)
lE := line.new(cpE, cpE2, xloc.bar_index, extend.none, color.teal, line.style_solid, 3)
lE
else if breakDownStructure and showStructureTheory
cpE = chart.point.new(time, bar_index - 1, open)
cpE2 = chart.point.new(time, bar_index + bl_strcuture, open)
lE := line.new(cpE, cpE2, xloc.bar_index, extend.none, color.red, line.style_solid, 3)
lE
It is a series of candles to create a notable event. 2 lower lows in a row, a lower high, then green bar, red bar, green bar is a structure for a breakdown. 2 higher lows in a row, a higher high, red bar, green bar, red bar for a break up.
#5 Candle Swing Structure Theory
I defined Candle Swing Structure Theory as follows.
//----------------------------------------------
//---------------Swing Structure----------------
//----------------------------------------------
line htb = na
line ltb = na
if totalSize * swing_struct_sense < totalSize and upBar and downBar and high > high and showSwingSturcture and session.ismarket and barstate.isconfirmed
cpS = chart.point.new(time, bar_index - 1, high)
cpE = chart.point.new(time, bar_index + bl_strcuture, high)
htb := line.new(cpS, cpE, xloc.bar_index, color = color.red, style = line.style_dashed)
htb
else if totalSize * swing_struct_sense < totalSize and downBar and upBar and low > low and showSwingSturcture and session.ismarket and barstate.isconfirmed
cpS = chart.point.new(time, bar_index - 1, low)
cpE = chart.point.new(time, bar_index + bl_strcuture, low)
ltb := line.new(cpS, cpE, xloc.bar_index, color = color.teal, style = line.style_dashed)
ltb
A bearish swing structure is defined as the last candle’s total size, times a scalar that the user can input, is less than the current candles. Like a size imbalance. The last bar must be green and this one red. The last high should also be less than this high. For a bullish swing structure the same size imbalance must be present, but we need a red bar then a green bar, and the last low higher than the current low.
#6 Fractal Boxes
I define the Fractal Boxes as follows
//----------------------------------------------
//---------------Fractal Boxes------------------
//----------------------------------------------
box b = na
int indexx = na
if bar_index % (n * 2) == 0 and session.ismarket and showBoxes
b := box.new(left = bar_index, top = topBox, right = bar_index + n, bottom = bottomBox, border_color = color.rgb(105, 15, 15), border_width = boxLineSize, bgcolor = na)
indexx := bar_index + 1
indexx
The idea of this strategy is that the market is fractal. It is considered impossible to be able to tell apart two different time frames from just the chart. So inside the chart there are many many breakouts and breakdowns happening as price bounces around. The boxes are there to give you the view from your timeframe if the market is in a range from a time frame that would be higher than it. Like if we are inside what a larger time frame candle’s range. If we break out or down from this, we might be able to trade it. Users can specify a lookback period and the box is that period’s, as an interval, high and low. I say as an interval because it is plotted every n * 2 bars. So we get a box, price moves, then a new box.
#7 Potential Move Width
I define the Potential Move Width as follows
//----------------------------------------------
//---------------Move width---------------------
//----------------------------------------------
velocity = V(n)
line lC = na
line l = na
line l2 = na
line l3 = na
line l4 = na
line l5 = na
line l6 = na
line l7 = na
line l8 = na
line lGFractal = na
line lRFractal = na
cp2 = chart.point.new(time, bar_index + n, close + velocity)
cp3 = chart.point.new(time, bar_index + n, close - velocity)
cp4 = chart.point.new(time, bar_index + n, close + velocity * 5)
cp5 = chart.point.new(time, bar_index + n, close - velocity * 5)
cp6 = chart.point.new(time, bar_index + n, close + velocity * 10)
cp7 = chart.point.new(time, bar_index + n, close - velocity * 10)
cp8 = chart.point.new(time, bar_index + n, close + velocity * 15)
cp9 = chart.point.new(time, bar_index + n, close - velocity * 15)
cpG = chart.point.new(time, bar_index + n, close + R)
cpR = chart.point.new(time, bar_index + n, close - R)
if ((bar_index + n) * 2 - bar_index) % n == 0 and session.ismarket and barstate.isconfirmed and showPredictionWidtn
cp = chart.point.new(time, bar_index, close)
cpG1 = chart.point.new(time, bar_index, close + R)
cpR1 = chart.point.new(time, bar_index, close - R)
l := line.new(cp, cp2, xloc.bar_index, extend.none, color.aqua, line.style_solid, 1)
l2 := line.new(cp, cp3, xloc.bar_index, extend.none, color.aqua, line.style_solid, 1)
l3 := line.new(cp, cp4, xloc.bar_index, extend.none, color.red, line.style_solid, 1)
l4 := line.new(cp, cp5, xloc.bar_index, extend.none, color.red, line.style_solid, 1)
l5 := line.new(cp, cp6, xloc.bar_index, extend.none, color.teal, line.style_solid, 1)
l6 := line.new(cp, cp7, xloc.bar_index, extend.none, color.teal, line.style_solid, 1)
l7 := line.new(cp, cp8, xloc.bar_index, extend.none, color.blue, line.style_solid, 1)
l8 := line.new(cp, cp9, xloc.bar_index, extend.none, color.blue, line.style_solid, 1)
l8
By using the past n bar’s velocity, or directional speed, every n * 2 bars. I can use it to scale the close value and get an estimate for how wide the next moves might be.
#8 Linear regression
//----------------------------------------------
//---------------Linear Regression--------------
//----------------------------------------------
lr = showLR ? ta.linreg(close, n, 0) : na
plot(lr, 'Linear Regression', color.blue)
I used TradingView’s built in linear regression to not reinvent the wheel. This is present to see past market strength of weakness from a different perspective.
User input
Users can control a lot about this script. For the strategy based plots you can enter what you want the risk to be in percentages. So the default 0.01 is 1%. You can also control how far forward the line goes.
Look back at where it is needed as well as line width for the Fractal Boxes are controllable. Also users can check on and off what they would like to see on the charts.
No indicator is 100% reliable, do not follow this one blindly. I encourage traders to make their own decisions and not trade solely based on technical indicators. I encourage constructive criticism in the comments below. Thank you.
Fibonacci Snap Tool [TradersPro]
OVERVIEW
The Fibonacci Snap tool automatically snaps to the swing high and swing low of the price data shown on the chart display. Fibonacci retracement levels can be used for entry, exit, or as a confirmation of trend continuation.
If the swing high on the chart comes before the swing low, the price is in a downtrend.If the swing high comes after the swing low, the price is in an uptrend.
We call the 23.60% Fibonacci level the momentum zone of the trend. Price in a solid trend, either up or down, will typically hold the 23.60% Fibonacci level as support (demand) in an uptrend or resistance (supply) in a downtrend.
Deeper Fibonacci levels of 38.20%, 50.00%, and 61.80% are corrective supply/demand zones. As price moves against the found trend, it can move into this range block we call the corrective zone.
Fibonacci retracement levels are used to identify potential supply/demand areas where price could reverse or consolidate. These levels are based on key ratios derived from the Fibonacci sequence, and we only use the core 23.60%, 38.20%, 50.00%, and 61.80% ratios.
CONCEPTS
Price action moves in trend cycles, these retracement levels help traders measure proportional relationships between the high/low swings in the price trend.
When a price trend is moving against the trend, traders can find opportunities to trade with the current trend at key Fibonacci levels. Fibonacci levels can be used to anticipate where price might find supply/demand imbalance and continue moving in the trend direction.
Traders apply the indicator by selecting a window of price they want to analyze in the chart display, and the Fibonacci Snap tool will snap to the high and low of the visible price display.
The Intent and Use of This Tool
The 23.60% level acts as a momentum or continuation of trend. The 38.20% to 61.80% range are corrective zones of the trend.
The 61.80% level, also known as the golden ratio (Google the term “Golden Ratio”; it's fun), can often represent the location of supply/demand imbalance.
In an uptrend, it can represent the area of no more selling supply, and the balance can shift to buying demand. In a downtrend, it can represent the area of no more buying demand and the balance can shift to selling supply.
When used with the Momentum Zones indicator, these two tools create a powerful combination for traders to find, implement, and manage trades.
Double FVG-BPR [QuantVue]The Double FVG BPR Indicator is a versatile tool that helps traders identify potential support and resistance levels through the concept of balanced price ranges.
A Balanced Price Range (BPR) is a zone on a price chart where the market has found equilibrium after a period of price imbalance.
It is identified by detecting a Fair Value Gap (FVG) in one direction, followed by an overlapping Fair Value Gap in the opposite direction.
Components of a Balanced Price Range
Fair Value Gap (FVG): A FVG occurs when there is a rapid price movement, creating a gap in the price chart where minimal trading occurs. This gap represents an imbalance between supply and demand.
Bullish FVG: A bullish FVG is identified when the low of a candle is higher than the high of a candle two periods ago, and the close of the previous candle is higher than the high of that same period.
Bearish FVG: A bearish FVG is identified when the high of a candle is lower than the low of a candle two periods ago, and the close of the previous candle is lower than the low of that same period.
Overlapping Fair Value Gap: For a BPR to be formed, an initial FVG must be followed by an overlapping FVG in the opposite direction. This creates a balanced zone where the price has moved up (or down) quickly and then moved down (or up) with similar intensity, suggesting a temporary equilibrium.
The area between the high and low points of these overlapping FVGs forms the BPR. This zone represents a temporary market equilibrium where supply and demand have balanced out after a period of significant price movement in both directions.
How to Use
Support and Resistance Levels: The upper and lower boundaries of the BPR act as dynamic support and resistance levels. Traders can use these levels to place buy and sell orders, anticipating that the price may find support or face resistance within these zones.
Trend Reversal and Continuation: The BPR can signal potential trend reversals or continuations.
If the price moves back into the BPR after a breakout, it may indicate a reversal. Conversely, if the price breaks out of the BPR with strong momentum, it may signal a trend continuation.
MTF Fair Value Gap Indicator ULTRAFVG Fair Value Gap Indicator
FVG's commonly known as Fair Value Gaps are mostly in use for forex trading, however it’s been widely used in price action trading, even on regular large cap stocks. Think of it as an imbalance area where the price of the stock may actually be under/over valued due to many orders being injected in a short amount of time, ie . a gap caused by an impulse created by the speed of the price movement. In essence, the FVG can become a kind of magnet drawing the price back to that level to attempt to balance out the orders (when? we don't know). Please do research to understand the concept of FVG's.
You can look for an opportunity as price approaches the FVG for entry either long/short because after all, it is an "Area of Interest" so the price will either bounce or blow through the area. No indicator works 100% of the time so take in context as just another indicator. It tends work on larger time frames best.
IMPORTANT TV RELATED LIMITATIONS: You should take the time to understand the following. A MAXIMUM of 500 boxes and labels are allowed, thus if you elect to display many different time frames of FVGs and/or select to not auto delete old Daily FVGs, the oldest FVGs will be deleted and not be seen. Additionally if you are on a smaller chart time frame (1 min), you may not see older FVGs such as Daily ones that occurred and still exist from long ago. This is due to TV limitation of 20,000 candles of history in each chart timeframe. Example: A 1 minute chart supports approximately 14 days worth of data so looking for Daily FVGs would only go back that far, whereas if your chart was set to 5 minutes you'd be able to see 5 times as many, ie . 60 days worth of Daily FVG's. Obviously setting your chart and looking for Daily FVG's would support up to 20,000 days worth.
The Indicator Provides many different features:
*Creation of FVG's for all hours or just during market hours. Currently you can enable FVG’s for the following timeframes: Current chart timeframe, 5Min, 10Min, 15Min, 1Hr, 4Hr, 8Hr, Daily, Weekly, Monthly.
*Text label displays overlaying FVG bands including creation timestamps.
* Bands reflecting FVG's in action (created/deleted) for the current chart time frame, 15min, 1hr, 4hr, 8hr and daily time frames. The FVG's will be overlayed on the chart if enabled.
*Mitigation Action - Normal - When FVG is balanced out by price action, the FVG will disappear. Dynamic - The FVG band will decrease as the price movement eats into it thus only showing the remaining imbalance. None - For those that wish to retain FVG's even if they were mitigated. Half - FVG’s disappear when the price intrudes 50% of the overall FVG band zone.
*Mitigation Type - The elimination or balancing of the FVG is caused by either the candle wick or body passing completely through the FVG.
*Maximum FVGs - A maximum number of FVGs are created for each different enabled time frame (be aware setting a large number could impact system performance).
*All FVG band colors can be customized by the user.
* All FVG bands auto extend to the right.
* Intrusion Alerts - Trading View alerts are supported. You can use the indicator settings to enable an alert if the price intrudes into the FVG zone by a certain percentage. This is not related to mitigation or removal of the FVG, just a warning that price has reached the area of interest.
