Estratégia
Median ATR SD OscillatorMedian ATR SD Oscillator
Median ATR SD Oscillator is a trend-following volatility oscillator that measures the distance between price and two independent reference levels — an ATR band and a standard deviation band — both anchored to a percentile-based median. The asymmetric design uses different volatility measures for long and short signals, creating a natural bias toward staying in bullish trends longer while reacting quickly to breakdowns.
The result is an area oscillator that expands above zero in a confirmed bullish state and contracts below zero in a bearish state, with a white EMA line as a momentum confluence filter.
How It Works
A percentile median is calculated from a configurable price source — giving a robust, noise-resistant central reference level that adapts to recent price behavior.
Two independent volatility bands are then derived from this median:
ATR Band — median plus ATR multiplied by a configurable factor. The short condition uses the ATR band — price must fall below it to confirm a bearish state
SD Band — median plus the standard deviation of close. The long condition uses the standard deviation band — price must break above it to confirm a bullish state
Once a directional state is confirmed, the oscillator measures the distance between price and its reference level. An EMA of this distance acts as a confluence filter — the final signal only confirms when the distance is not only positive or negative but also above or below its own EMA, ensuring momentum is genuinely building in that direction.
Why This Approach Works
Most oscillators use a single volatility measure for both long and short signals. The Median ATR SD Oscillator deliberately uses two different measures — ATR for shorts and SD for longs — because they capture different market dynamics and create a natural asymmetry between entries and exits.
This asymmetric design reflects the structural reality of markets like crypto — price spends more time trending upward than downward. Longs require a statistically significant breakout above the SD band, while shorts only need price to fall back below the ATR band. The result is a system that stays in bullish trends longer while reacting quickly when momentum fades.
Settings
Median Source — Price source for the percentile median calculation (default: hl2)
Median Length — Lookback period for the percentile median (default: 63)
ATR Length — Lookback period for the ATR calculation (default: 4)
ATR Factor — Controls the width of the ATR band (default: 1.0)
SD Length — Lookback period for the standard deviation calculation (default: 29)
Use EMA — Enables the EMA confluence filter (default: true)
EMA Length — Lookback period for the EMA confluence filter (default: 35)
Use Bar Coloring — Colors bars based on the current state (default: true)
Color Background — Enables background coloring of the chart based on the current state (default: false)
Background Transparency — Controls the transparency of the background color (default: 85)
How to trade it
Long — when the oscillator expands above zero and the area turns blue, a bullish state has been confirmed. This is the signal to look for long entries or to hold existing long positions
Short / Cash — when the oscillator contracts below zero and the area turns red, the momentum has faded. This is the signal to exit longs, move to cash, or look for short entries depending on your strategy
EMA line — when the area is above the white EMA line momentum is building, when it crosses below momentum is weakening
Recommended Usage
Best used on the 1D timeframe for clean and reliable signal generation
Should not be used alone for trade entries — combine with an additional confirmation indicator for best results
The asymmetric ATR/SD design makes this oscillator particularly well suited for bullish-biased markets like crypto
Higher ATR Factor values make short exits more sensitive — lower values make them less reactive
Higher SD Length values create more stable long signals — lower values make them more reactive to short-term price movements
All signals are confirmed on bar close. Indicador
Probability Horizon - Bayesian SVJD Model# Probability Horizon — Bayesian SVJD Model
## What it does
Probability Horizon is a forward-looking probability projection tool. It does **not** generate buy or sell signals, does **not** act as a strategy, and does **not** place orders. Its single purpose is to display, at each bar, where price **may** be over a user-set horizon, drawn as a forward cone with three percentile lines (25%, 50%, 75%) plus an 8-row diagnostic dashboard.
The cone is the visual answer to two questions the indicator computes every bar:
1. **How wide should the distribution of forward outcomes be?** — set by expected total variance over the horizon.
2. **Should the distribution tilt up or down?** — set by a Bayesian-averaged probability of an up-move.
The wider the cone, the more uncertain the model is. The more the cone tilts, the more directionally confident the model is. A flat, narrow cone means "I expect range-bound, low-vol conditions." A wide, steeply tilted cone means "I expect a directional move under elevated variance."
## Why these components are combined (justification for the mashup)
This script combines several quantitative methods — KAMA, z-score, Haar wavelet, Kalman filter, Hamilton regime-switching, Hawkes process, Heston stochastic volatility, Merton jump-diffusion, Bayesian model averaging, and a calibration tracker. To a reviewer this can look like a collection of indicators bolted together, but it is not. It is **one** statistical model — a Stochastic Volatility Jump-Diffusion (SVJD) framework — whose pieces are mathematically required to produce a forward probability distribution.
Each component has a specific structural role:
- **Forward variance estimation.** A probability cone needs a forward-variance number. The naive choice is realized volatility × √horizon (pure Brownian motion), but this ignores two well-documented facts about financial returns: variance is mean-reverting (Heston, 1993) and returns have fat tails from discrete jumps (Merton, 1976). The Heston + jump-diffusion combination addresses both. The script uses the Heston integrated-variance closed form for the mean-reverting diffusion component, adds a Merton jump-variance contribution, and optionally adds a vol-of-vol uncertainty term. The result is a horizon-dependent variance estimate that dynamically narrows when volatility is elevated (expected to decay back to mean) and widens when volatility is depressed (expected to rise).
- **Directional probability.** To tilt the cone, a probability of direction is required. A single signal is unreliable, so five orthogonal sub-models each output their own P(up):
- A short-term KAMA-trend model
- A z-score mean-reversion model
- A Haar wavelet decomposition model (price denoised into trend + cycle + noise; signal fires only on the trend band)
- A Kalman-adaptive smoothing model (smoothing factor adapts to noise level in real time)
- A Hamilton 3-state regime model with Gaussian observation likelihoods (Bull / Bear / Range)
- **Sub-model combination.** The five P(up) values are combined via Bayesian model averaging with online weight updates. Each bar, after a fixed evaluation horizon, every sub-model is scored by log-loss against the actual outcome. Weights update via exponential decay: a model that predicted correctly gains weight; a model that failed loses weight. A minimum weight floor prevents any model from being silenced completely, so it can recover if its regime returns. Weights are renormalised to sum to 1.
- **Crisis dampening.** A Hawkes self-exciting point process monitors volatility clustering. Each large absolute return is treated as an event that boosts the process's intensity by α and decays exponentially at rate β. When intensity rises above a threshold multiple of its baseline, the final Bayesian probability is shrunk toward 0.5 — the higher the intensity, the stronger the shrinkage. This is how the model says "I have no idea — treat this as a coin flip" during regime breaks.
- **Self-correcting calibration.** A calibration tracker logs every prediction and checks the realized outcome after a fixed horizon. Predictions are binned by predicted probability (50–60%, 60–70%, etc.). If a bin's actual historical hit rate is below its predicted midpoint, future predictions in that bin are shrunk further toward 0.5. This is the model's honesty mechanism: it learns from its own miscalibration and tones itself down where it has been overconfident.
Each component answers a specific structural question. Remove any one and a specific capability disappears: no Heston → cone width does not adapt to vol regime; no calibration → no self-correction; no Hawkes → no crisis dampening; no Bayesian averaging → one model dominates and the system becomes brittle.
## How the components interact (data flow)
Every confirmed bar, the script executes the following pipeline:
1. **Measure** five raw market dimensions: KAMA slope, z-score vs trend, realized-vol percentile, OBV/price divergence, higher-TF trend.
2. **Each sub-model** maps its directional bias and strength to a probability in . The maximum single-model probability is capped at 0.60 because empirical calibration on multiple markets showed that anything higher is overconfident.
3. **Bayesian model averaging** combines the five sub-model probabilities into a single raw P(up), weighted by each sub-model's recent log-loss accuracy.
4. **Hawkes modifier** is applied: if intensity is above its warning threshold, the raw P(up) is pulled toward 0.5 in proportion to how far above threshold the intensity is.
5. **Calibration shrinkage** is applied: the post-Hawkes probability is checked against its calibration bin's historical hit rate, and shrunk further toward 0.5 if that bin has been overconfident.
6. **Forward variance** is computed separately: Heston integrated variance + Merton jump variance + optional vol-of-vol uncertainty, all over the projection horizon.
7. **The cone is drawn** with width set by the square root of forward variance and tilt set by the final shrunk probability. Three percentile lines (P25, P50, P75) are plotted from the current bar to the horizon endpoint.
Direction (sub-model probabilities → Bayesian average → Hawkes modifier → calibration shrinkage) is one half of the pipeline. Variance (Heston + jumps + vol-of-vol) is the other half. They meet at the cone, where one determines tilt and the other determines width.
## How to use it
**On the chart.** The cone shows the model's current view of the forward distribution. The P50 line is the median expected level given the implied drift. The P25 and P75 lines bracket the interquartile range. If P25 and P75 are roughly equidistant from current price, the model has no strong directional view; if the cone tilts noticeably up or down, the Bayesian probability favours that direction. A wider cone means more uncertainty; a narrower cone means tighter forward variance.
**The optional Monte Carlo cloud** (off by default) overlays bootstrap-resampled forward paths drawn from the asset's actual recent returns. Unlike the cone, it makes no Gaussian assumption — it shows the empirical distribution of forward outcomes given the asset's own recent return history.
**The 8-row dashboard** (top-right) is where the model exposes its full state:
- **Row 1 — Verdict.** Current direction (Bull / Bear / Neutral) and final P(up) percentage.
- **Row 2 — Direction.** A 10-character probability bar plus the size of the calibration shrinkage applied in percentage points.
- **Row 3 — 5 Models.** Up/down icons for each sub-model and the agreement count (e.g., "4/5 agree"). Trust the verdict more when 4 or 5 of 5 agree; trust it less when only 3 of 5 agree.
- **Row 4 — Regime.** Combined market regime (Quiet Bull / Quiet Bear / Volatile-Range / Crisis) plus the Hamilton dominant state.
- **Row 5 — Vol.** Volatility state vs long-run mean: HIGH (above mean, cone narrowing as vol decays), LOW (below mean, cone widening as vol rises), or AT MEAN. Includes the current sigma.
- **Row 6 — Risk.** Hawkes status: ✓ calm or ⚠ CRISIS. When CRISIS fires, the probability has been shrunk toward 0.5.
- **Row 7 — Honesty.** This is the most important diagnostic. It shows the actual historical hit rate for predictions in the 50–60% probability bin, with a Wilson confidence interval. If the system has predicted 55% many times and the actual rate is 53–58%, it is well-calibrated. If actual is below 50%, the system is currently overconfident and shrinkage is active.
- **Row 8 — Samples.** Total confirmed predictions and a trust level (low / warm-up / OK). Trust the cone less when total samples are below 50; trust it most when samples exceed 200.
## Suitable timeframes
The script auto-scales internal lookbacks to the chart timeframe relative to a 15-minute reference, so the same defaults work across 15-minute, 1-hour, 4-hour, and daily charts without manual tuning. Below 15 minutes the Hamilton 3-state model is automatically disabled because sample sizes become too small for reliable likelihood estimation; the other four sub-models continue to operate.
## What is original
Combining KAMA, wavelets, Kalman, Hamilton, Heston, Merton jumps, and a Hawkes process is not by itself new — these are all published methods. The original aspects of this script are:
- The **specific combination**: a five-model Bayesian ensemble for direction, with Hawkes-process crisis dampening and a self-correcting calibration tracker, all wrapped around a Heston + jump-diffusion variance estimate. I am not aware of a public Pine Script that combines all of these into a single probability cone with this data flow.
- The **calibration shrinkage mechanism**: the model logs every prediction, scores it after a fixed horizon, and applies bin-specific shrinkage to future predictions in bins where it has been overconfident. This is a self-correcting honesty layer that runs entirely on-chart, with Wilson confidence intervals and optional regime-specific calibration tables (Bull / Bear / Range).
- The **transparent diagnostic dashboard**: rather than hiding the model behind a single line, the dashboard exposes the verdict, model agreement, regime, vol state, crisis indicator, calibration quality, and sample size in eight rows. Users can see at a glance not only what the model thinks, but how much to trust it.
## Limitations (please read)
- **Pine Script cannot perform true maximum-likelihood estimation.** Heston and jump-diffusion parameters are estimated using approximation methods (AR(1) regression on log-variance, exponential moving averages for jump moments, rolling averages for long-term variance). The directional behaviour is correct — when vol is high, the cone narrows; when jumps are frequent, the cone widens — but exact parameter values are not equivalent to those a quantitative research desk would produce with MLE on tick data.
- **OHLCV data only.** No order-book data, no alternative-data feed, no options input, no fundamental input.
- **Calibration needs sample accumulation.** The Honesty row shows "warming up" until at least ~30 confirmed predictions have matured. Pine has no cross-session persistence, so calibration is rebuilt from chart history each time the indicator loads on a new chart.
- **Monte Carlo is deterministic.** Paths use a Linear Congruential Generator seeded by bar_index for reproducibility within a session. The same chart at the same moment produces the same cloud. This is intentional.
- **This is a probability indicator, not a strategy.** There are no backtest results, no equity curve, no position management. The script cannot tell you what to do; it only tells you what its model currently thinks the distribution of forward outcomes is.
- **Past calibration does not guarantee future calibration.** Market regimes change, parameters drift, and the cone should be treated as a visualisation aid rather than a prediction.
## Inputs of note
The defaults work without modification on most liquid instruments at most timeframes. Inputs worth knowing:
- **Auto-scale lookbacks** (on by default) — keeps the same defaults usable across timeframes.
- **Bayesian learning rate η** (default 0.15) — how fast sub-model weights adapt. Higher = faster but noisier.
- **Min Bayesian Probability** (default 0.62) — the threshold the dashboard uses to call a bar "Bull" or "Bear" rather than "Neutral".
- **Heston κ floor / ceiling** — bounds the mean-reversion-speed estimate. Defaults (0.02 to 0.30) handle most markets.
- **Hawkes warning threshold** (default 2.0× baseline) — when crisis dampening kicks in.
- **Monte Carlo Cloud** (off by default) — overlays bootstrap paths. Turn on if you want an empirical (non-Gaussian) view of forward outcomes alongside the cone.
## Disclaimer
This script is published for educational and analytical purposes only. It does not constitute financial advice, investment advice, a recommendation to buy or sell any financial instrument, or a solicitation of any transaction. The author is not a registered investment adviser and nothing in this script should be construed as personalised investment guidance.
Past performance does not guarantee future results. The probability projections shown by this indicator are model outputs, not forecasts of what will actually happen. Trading and investing involve substantial risk of loss and are not suitable for every investor. Users are solely responsible for their own trading decisions and for verifying that any approach is appropriate for their personal financial situation, risk tolerance, and applicable regulations.
The author and Market_Logic_India accept no liability for any losses, damages, or trading outcomes resulting from the use, misuse, or interpretation of this script. Use at your own risk.
Indicador
ATR & Fibonacci Expansion ZonesATR & Fibonacci Expansion Zones is a multi-mode ATR framework designed to help traders read price location, volatility expansion, session structure, and active high/low range context. The script is built around a simple but powerful idea: ATR levels can provide a volatility-adjusted map of how far price has moved from a meaningful close reference. From there, the workflow adds flexible mode selection, organized ATR labels, optional historical ATR periods, mode-aware AVWAPs, active high/low zones, and fibonacci labels that correspond to a respective level within the enabled mode.
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Core Idea
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ATR (actual true range) is powerful because it gives price movement a volatility-adjusted frame of reference.
This script helps traders evaluate:
• How far price has expanded from the selected close anchor
• Whether price is still near the first ATR expansion zone or already extended
• Whether price is approaching a major +1, +2, +3, or +4 ATR area
• Whether price is reacting around an active session high/low zone
• Where price is trading inside the active session fibonacci range
• Which ATR mode best frames the current move: Daily, Multiday, Swing, Position, or Long-Term
That makes the script useful for intraday context, multiday moves, swing structure, position-style tracking, and longer-term range mapping.
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ATR Modes
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The script renders one selected ATR engine at a time. This keeps the chart cleaner than stacking multiple ATR families together.
Included modes:
Daily
Uses the daily ATR framework. Best for day/session-level context on intraday charts.
Multiday
Uses the weekly ATR framework. Best for trades developing across multiple sessions.
Swing
Uses the monthly ATR framework. Best for larger swing-style movement.
Position
Uses a 3-month ATR framework. Best for broader position-style context.
Long-Term
Uses a 12-month ATR framework. Best for major range structure and larger macro-style moves.
All modes can render the full 23.6% through 400% ATR expansion range, with separate line and label controls for each major range block.
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ATR Expansion Grid
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The main ATR grid plots upper and lower ATR expansion levels from the selected close anchor.
The grid includes:
• 23.6% to 100%
• 123.6% to 200%
• 223.6% to 300%
• 323.6% to 400%
Each range has its own parent Show / Hide controls for lines and labels, plus child controls for upper and lower sides. This makes it easier to keep the chart simple when only the closer ATR levels matter, or expand the full map when price is making a larger move.
The labels can show:
• ATR ratio
• ATR level price
• selected mode tag
• percent distance from current price
That percent-from-price read helps make the grid more practical because you can quickly see how close price is to the next ATR level instead of visually estimating it or using the info line tool.
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Previous / Current Close Reference
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The PCL / CC line and label show the active close reference used by the selected ATR grid. Depending on the selected mode and settings, this can represent:
• Previous Close
• Current Close
• Daily close reference
• Weekly close reference
• Monthly close reference
• 3-month close reference
• 12-month close reference
The label can also show close-to-close percent change and RSI(14) from the selected ATR timeframe. This is important because the close anchor is the foundation of the ATR grid. The script keeps that reference visible so the user can see exactly where the ATR expansion map begins.
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ATR Session AVWAPs
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The script includes mode-aware ATR Session AVWAPs. These are not static VWAPs. They follow the currently selected ATR mode. Depending on the mode, the AVWAP set can reset from:
• the active day/session
• the active week
• the active month
• the active 3-month period
• the active 12-month period
Included AVWAP paths:
• Session VWAP
• High AVWAP
• Low AVWAP
The Session VWAP starts from the active ATR period/session start. The High AVWAP anchors from the candle that creates the active period high. The Low AVWAP anchors from the candle that creates the active period low. When a new active high or low forms, the matching AVWAP re-anchors.
In practical terms:
• ATR levels show volatility expansion from the selected close anchor
• AVWAPs show volume-weighted behavior from the active period structure
• High/Low AVWAPs help track how price behaves after major active-period extremes form
This adds a useful volume-weighted layer around the ATR map.
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ATR Session High / Low Boxes
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The script can also draw active high and low boxes for the selected ATR session or period.
The High Box starts from the candle that creates the active high.
The Low Box starts from the candle that creates the active low.
These boxes are wick-based, which makes them useful for highlighting rejection zones, liquidity wicks, exhaustion candles, or important high/low reference areas inside the active ATR period.
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ATR Session Fibonacci Labels (levels)
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ATR Session Fibonacci Labels add another layer of context beside the ATR grid.
The ATR grid answers:
“How far has price expanded from the selected close anchor?”
The session fib labels answer:
“Where is price trading inside the active high-to-low range?”
The fib labels use the same active high and low tracked by the AVWAP and high/low box engines.
Direction is inferred automatically:
• If the low formed before the high, the script treats it as a bullish session leg
• If the high formed before the low, the script treats it as a bearish session leg
The fib labels can show:
• fib ratio
• price
• percent distance from current price
This creates a clean second layer of structure: ATR levels for expansion, fib labels for internal active-range location.
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Historical ATR Periods
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The script includes an optional Historical ATR Periods mode. When enabled, prior ATR grid segments can remain visible for a user-defined number of completed ATR periods.
This is useful for reviewing:
• how price respected previous ATR levels
• where prior extensions acted as support or resistance
• whether price repeatedly reacted around certain volatility zones
• how current ATR behavior compares to prior periods
Historical labels are intentionally excluded to keep the chart cleaner. The historical mode focuses on line segments only.
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Asset-Aware Behavior
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The script includes separate behavior for stocks, crypto, and futures.
For stocks and funds:
• ATR calculations can use regular or extended-session source data
• Daily lines can start from regular open or extended open
• Daily AVWAPs, boxes, and fib labels can reset from the extended-session / premarket day start when available
• Higher-timeframe stock modes wait for the first regular-hours bar of the new period for cleaner visual alignment
For crypto:
• the chart ticker is used
• Daily mode resets once per calendar day
For futures:
• the chart ticker is used
• Daily mode uses a Globex-style 5:00 PM Central session boundary
• higher-timeframe modes reset from the selected W / M / 3M / 12M ATR period boundary
This makes the script more flexible across different asset classes instead of treating every market like a regular-hours stock chart.
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How I Use It
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I use this script as a volatility and structure map.
A few practical ways to read it:
• Daily mode helps frame intraday price movement around the active daily ATR grid
• Multiday mode helps show where price is trading inside the weekly ATR structure
• Swing mode is useful when the daily chart is too narrow and the monthly range matters more
• Position and Long-Term modes help zoom out and understand broader extension zones
• PCL (previous close) / CC (current close) keeps the close anchor visible
• The Auto close-anchor setting is especially useful for Daily stock mode because it automatically adapts between Current Close during New York premarket / after-hours and Previous Close during regular market hours
• This means the ATR grid can stay aligned with the session environment without forcing the user to manually decide when to use PCL or CC
• During premarket and after-hours, Current Close helps the levels respond to the active extended-hours move
• During regular hours, Previous Close keeps the main daily ATR map anchored to the completed prior session
• The price guide dash makes it easier to line up current price with the ATR label column
• High/Low Boxes help mark active wick-based extremes
• Session AVWAPs add a volume-weighted path from the active period and its extremes
• Session Fib Labels show internal retracement context inside the active high/low range
• Historical ATR Periods help review how price behaved around prior ATR grids
The Auto close-anchor behavior is one of the most practical parts of the workflow. Premarket and after-hours trading can make a static previous-close grid feel disconnected from the live move, while regular-hours trading often benefits from keeping the prior close as the main reference. Auto handles that transition in the background so the ATR levels remain tied to the most relevant close reference for the active session. In other words, you don't have to decide when to use "Use Current Close" depending on if your'e in pre-market or regular trading hours.
The value of this script is organization. It brings ATR expansion, close-reference context, session extremes, AVWAP structure, and active-range fib location into one chart-side workflow.
ATR levels by themselves can already be useful but this script expands that idea into a fuller range map so traders can better judge whether price is early in a move, pressing into extension, reacting from an active extreme, or moving through a broader volatility structure.
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Attribution
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This script is a modern rebuild inspired by Saty’s ATR Levels concept and my original ATR Levels publication.
Special thanks to SimpleCryptoLife for past help and inspiration around improving the original ATR Levels workflow and for the Significant Figures library used for cleaner number formatting.
As always, this is a visual context tool. It is not meant to replace risk management, market structure, support/resistance, volume analysis, or a complete trading plan.
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A Few Chart Examples
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Indicador
Indicador
Multi-Factor Regime Engine Pro [MarkitTick]💡 This indicator represents a robust framework designed to quantify market regimes by analyzing an array of price action, volatility, and momentum metrics. By synthesizing ten distinct market features into a unified confidence score, it dynamically adjusts its threshold bands, providing a highly adaptive approach to trend identification on any standard chart.
✨ Originality and Utility
Standard trend-following tools often rely on static lookback periods and fixed multipliers, which can lead to delayed signals during sudden market shifts or excessive false signals during consolidation. This indicator diverges from traditional methods by introducing a dynamic, feature-engineered confidence score. Instead of relying on a single data point like closing price or standard volatility, it aggregates inputs from momentum oscillators, directional movement indices, volume profiles, and standard deviation bands. This multi-dimensional analysis allows the indicator's bands to compress during high-confidence trends and expand during uncertain, low-confidence environments, offering a highly responsive and adaptive utility for modern chart analysis. Furthermore, it incorporates strict internal safeguards to prohibit execution on non-standard charts, ensuring the integrity of the data and preventing repainting vulnerabilities.
🔬 Methodology and Concepts
● The Feature Engineering Engine
The core of this indicator is built upon extracting ten distinct normalized features from the market data, evaluating multiple dimensions of price action simultaneously:
Momentum Normalization: Utilizes relative strength metrics, centered and scaled, to gauge underlying momentum bias without relying on absolute thresholds.
Directional Strength: Analyzes directional movement indices to quantify the strength of the current trajectory, applying directional penalties when negative movement overpowers positive movement.
Moving Average Distances: Measures the current price relative to fast and slow moving averages, standardizing the distance using the Average True Range to identify structural overextension.
Rate of Change Standardization: Normalizes the rate of change against its own rolling standard deviation to detect statistical anomalies in speed and acceleration.
Standard Deviation Extremes: Evaluates the position of the price relative to upper and lower Bollinger Bands, calculating the precise percentile of the close within the volatility envelope.
Volatility Stability: Compares short-term volatility against long-term volatility baselines to measure market stress and detect rapid expansions.
Volume Anomalies: Assesses current volume against its simple moving average, clamping the result to identify participation spikes that validate price movement.
Price Action Consistency: Calculates the ratio of bullish to bearish closes within the defined lookback period, serving as a raw footprint of buyer versus seller control.
● The Confidence Score Assembly
These standardized features are separated into distinct sub-components. A Directional Score identifies the probable path of the trend by weighting momentum and moving average slopes. A Quality Score measures the structural integrity of that trend by analyzing volume participation and volatility stability. These are mathematically combined to produce a Raw Confidence value. This raw output undergoes an adaptive smoothing process using a dynamic moving average, resulting in a highly stable, final Confidence Score bounded precisely between 0% and 100%.
● Adaptive Ratcheting Bands
The calculated Confidence Score directly influences the width of the trend bands. When the confidence is high, the internal multiplier decreases, tightening the bands closer to the price action to capture shifts quickly and protect accumulated distance. Conversely, when confidence is low, the bands expand to avoid noise and erratic whipsaws. The bands utilize a state-machine logic that only ratchets in the direction of the trend, acting as a trailing threshold that reacts to both price crosses and sudden regime shifts identified by extreme volatility spikes.
🎨 Visual Guide
● Heatmap Candles
The indicator actively repaints the chart candles based on a calculated mathematical "stress" metric.
Colors transition dynamically from a baseline trend color (Deep Sky Blue for bullish conditions, Radical Red for bearish conditions) to a bright orange "Regime Alert" color when underlying volatility spikes significantly.
Candle body opacity is heavily controlled by the alignment of the price action with the overall trend direction, fading to a darker tone during contrary movements or low-confidence pullbacks.
● ML Supertrend Band
Displayed as a prominent, solid line representing the adaptive trailing threshold on the chart.
This band is colored Deep Sky Blue during bullish market phases and Radical Red during bearish phases, updating in real-time as the state machine ratchets.
● Confidence Gradient Cloud
This visual element fills the spatial gap between the median price baseline and the trailing Supertrend band.
The exact opacity and gradient of this cloud are mapped directly to the Confidence Score. A highly opaque, solid cloud represents high confidence, while a highly transparent, fading cloud visually indicates low confidence and potential market transition.
● Visual Labels
Small directional visual markers appear directly above or below the price action when a trend flip occurs.
These labels display an arrow alongside a precise percentage value, representing the exact calculated Confidence Score at the moment the signal was generated.
● Info Table
Located statically in the top right corner, this dashboard displays crucial real-time internal metrics.
It includes the current trend direction, a visual text-based progress bar for the Confidence Score, the live Adaptive Multiplier value adjusting in real-time, the selected Model configuration, and a dynamic text alert that triggers during active Regime Shifts.
📖 How to Use
● Trend Identification
Observe the dominant color of the ML Supertrend Band and the Confidence Gradient Cloud. Deep Sky Blue strongly indicates a bullish environment, while Radical Red suggests a bearish environment. The visibility and thickness of the cloud serve as your primary visual gauge of the trend's structural health.
● Interpreting the Confidence Metric
Monitor the Confidence Score inside the Info Table or at signal generation. A high percentage (e.g., above 70%) suggests that multiple underlying market factors (volume, momentum, standard deviation) are in full agreement with the current directional bias. A rapidly dropping confidence score often precedes a period of choppy consolidation or warns of a potential reversal, allowing for tighter risk parameters.
● Reading Heatmap Candles for Shifts
When the standard colored candles begin transitioning toward the bright orange Regime Alert color, it indicates an abnormal spike in volatility combined with a directional momentum shift. This visually warns the user of a potential "Regime Shift" where the market is undergoing severe internal stress. These specific visual cues often signal an impending breakout from a range or a violent capitulation event.
⚙️ Inputs and Settings
● Model Configuration
Strategic Cycle Mode: Allows the choice between an "Auto" mode and a "Custom" mode. Auto mode dynamically selects the most mathematically optimal lookback lengths, thresholds, and weighting coefficients based purely on the current timeframe in seconds.
Strategic Cycle: When in custom mode, this determines the core lookback period for all volatility and momentum calculations. Smaller values drastically increase reactivity, while larger values provide smoother, long-term macroeconomic analysis.
Macro Trend Threshold: Sets the baseline mathematical width of the threshold bands. Higher values require significantly larger price movements to trigger a trend flip, reducing noise.
Prediction Weight: Controls the internal sensitivity of the bands to rapid changes in the Confidence Score, determining how aggressively the bands compress.
● Visual Settings & Colors
Dedicated toggles are provided to enable or disable the Confidence Labels, the Info Table, and the Heatmap Candles to keep the chart interface as clean as desired.
All core graphical colors, including the specific bands, gradient clouds, table text, and alert highlights, are fully customizable by the user via hex selection.
● Webhook Settings
The indicator is pre-configured to output detailed, formatted JSON payloads designed for external execution automation.
Users can securely define specific action strings for entering and closing both long and short positions directly within the settings menu, mapping exactly to their webhook parser logic.
🔍 Deconstruction of the Underlying Scientific and Academic Framework
● Multi-Variate Feature Standardization
The mathematical foundation of this tool relies heavily on statistical normalization techniques designed to process heterogeneous data. Financial time series data is notoriously non-stationary and spans vastly different numerical scales. To effectively combine disparate metrics like Volume (often measured in millions) and relative strength oscillators (strictly bounded between 0 and 100), the indicator employs rigorous Min-Max scaling and localized Z-score approximations. For instance, the rate of change is evaluated against its own rolling standard deviation over a defined period, successfully standardizing the momentum readout into a continuous, comparable spectrum bounded cleanly between -1.0 and 1.0.
● Linear Weighted Ensembles
The internal architecture utilizes a deterministic linear weighted model to synthesize the final output. By assigning highly specific fractional coefficients to directional features (like the exponential moving average slope and relative strength) and structural features (like Bollinger Band width extremes and volatility stability), the script constructs a singular composite index. This mirrors standard ensemble methodologies found in data science, where the consensus of multiple independent weak learners generates a stronger, more reliable predictive metric than any single indicator could achieve in isolation.
● Volatility-Adjusted State Machines
The adaptive threshold logic operates as a strict finite-state machine incorporating the Average True Range metric. The specific mathematical innovation lies in rendering the ATR multiplier as an inverse linear function of the composite confidence index. In quantitative terms, this produces a dynamically dampened volatility envelope. When the composite index approaches a maximum value indicating high convergence, the dampening factor aggressively compresses the envelope, mathematically acknowledging that high-conviction trends exhibit less erratic mean-reversion behavior and therefore require drastically tighter invalidation levels to preserve structural alpha.
⚠️ Disclaimer
All provided scripts and indicators are strictly for educational exploration and must not be interpreted as financial advice or a recommendation to execute trades. I expressly disclaim all liability for any financial losses or damages that may result, directly or indirectly, from the reliance on or application of these tools. Market participation carries inherent risk where past performance never guarantees future returns, leaving all investment decisions and due diligence solely at your own discretion. Indicador
Indicador
Tetrad Elektro Community v1.0Tetrad Elektro Community is an open-source educational strategy built around Bollinger Band exhaustion, RSI confirmation, and optional bearish divergence logic.
The strategy is designed to identify stretched market conditions where price may be overextended relative to its recent range. Long setups look for price pressing into the lower Bollinger Band with oversold RSI confirmation. Short setups look for upper-band exhaustion, elevated RSI, or bearish divergence where price makes a new high but RSI or MACD fails to confirm.
Suggested use:
This strategy is best studied on the 30-minute or 45-minute chart. It was designed primarily for crypto markets and may work across several liquid crypto assets such as BTC, ETH, SOL, and DOGE. It may also be worth researching on high-liquidity stocks or ETFs, but users should always test each asset individually before relying on any result.
Suggested mode selection:
- Use Long Only in bullish environments.
- Use Short Only in bearish environments.
- Use Long & Short in ranging or two-sided environments.
The strategy includes:
- Bollinger Band mean-reversion logic
- Main RSI and fast RSI filters
- Optional bearish divergence using RSI/MACD confirmation failure
- Optional stop loss
- Optional time stop
- Optional flat-first execution behavior
- Clean chart visuals with optional labels and signal markers
This public community version is intentionally simplified. It does not include Tetrad’s private MLTT/RORO risk engine, execution controller, portfolio allocator, live-routing infrastructure, optimized production parameters, or machine-learning overlays.
This is not financial advice. Backtests are hypothetical and may differ from live execution. Always test on your own market, timeframe, exchange, fee assumptions, and risk model before using any strategy. Estratégia
Indicador
OBV Regime Shift [I.M]OVERVIEW
🟣 OBV Regime Shift ("OBV-RS ") is a volume-flow regime detector that goes far beyond the classic "OBV + moving average" cross. It normalizes On-Balance Volume into a rolling z-score, applies a dual-MA system (HMA fast + DEMA slow) with slope acceleration, hunts price/OBV divergences on confirmed pivots, and aggregates everything into a single 0–100 Regime Score that flags BULLISH ▲ / BEARISH ▼ / NEUTRAL — state shifts.
The screenshot below shows the indicator running on BTCUSDT (Binance) on the 15-minute timeframe — refer back to it throughout the description, since each section describes the same chart.
📊 Live example chart:
WHAT YOU SEE ON THE CHART
Looking at the example image, from left to right and top to bottom:
🟣 The adaptive ribbon is the visual core of the indicator. Two lines — a Hull MA (fast) and a DEMA (slow) — are drawn on top of the normalized OBV, with a filled glow between them. When the fast line is above the slow line, the entire ribbon turns green (buy bias); when the fast line crosses below, the ribbon flips to magenta (sell bias). The glow halo and fill are intentional — they let you read regime at a glance without studying the lines.
🟣 The slope differential histogram at the zero line shows the acceleration gap between the two MAs. Green columns above zero = momentum is opening up; magenta columns below = momentum is collapsing. It often turns before the ribbon itself flips, acting as an early warning.
🟣 The "SHIFT ▼" and "SHIFT ▲" triangles mark confirmed regime transitions, not raw threshold crossings. A shift only prints when the Regime Score holds above 80 (or below 20) for two consecutive bars, with a minimum of 12 bars since the previous shift. This is deliberate — most OBV-based indicators ping-pong every other candle; this one earns each signal.
🟣 The "div ▲ / div ▼" diamonds print on pivot confluences: price makes a higher high while normalized OBV makes a lower high (or the inverse for bullish), AND the slope differential agrees with the divergence direction. The slope-confirmation filter cuts roughly 70% of the false divergences a naive pivot-comparison would generate.
🟣 The bottom-right panel is the at-a-glance summary: current Score (0–100), Regime label, Momentum state (Accelerating ▲ / Slowing ▼ / Flat), active Divergence, Volume expansion state, and the raw OBV z-score. The score number turns gold when it reaches an extreme zone (≥85 or ≤15).
🟣 The background flash is intentionally brief: three bars of green or magenta tint only on the bar a shift fires, then it clears. The indicator surfaces the event, not the state — your eye finds new shifts, not a wall of color.
WHAT MAKES IT ORIGINAL
OBV-RS is not a wrapper around ta.obv() with a moving average glued on top. The originality is in how four independent ideas are combined and filtered:
🟣 Normalized OBV (z-score)
Raw OBV has an absolute scale that drifts with volume regime — comparing today's OBV to a six-month-old reading is meaningless. The script standardizes OBV into a rolling z-score over a configurable lookback window, so the same +1.5 reading means the same thing in a low-volume drift as it does in a high-volume rally. Every downstream calculation operates on this normalized series.
🟣 Dual-MA with slope acceleration
The fast line is a Hull MA — low-lag, responsive to inflections. The slow line is a DEMA — smoother but still ahead of a traditional EMA of the same length. The script computes the slope of each over a configurable window, then takes the slope differential (fastSlope − slowSlope). This differential, not the cross itself, is the leading indicator: it widens before the lines visually separate and contracts before they merge.
🟣 Slope-confirmed divergence engine
Most divergence scripts flag every higher-high-lower-high pivot pair, which floods the chart with noise. OBV-RS demands a third condition: the slope differential must agree with the divergence direction at the moment the second pivot prints. A bearish divergence only counts if the slope is already weakening; a bullish divergence only counts if the slope is already turning up. This single filter is the difference between a useful divergence signal and a pivot-spam indicator.
🟣 Regime Score (0–100) with multi-bar confirmation
Four components feed the score: slope differential (45% weight), distance between fast and slow lines (35% weight), a neutral anchor (20%), plus bonuses from active divergences and current volume expansion. The score is then gated through a two-step confirmation: it must hold above the bullish threshold (or below the bearish one) for a configurable number of consecutive bars before the state actually flips. A minimum bar gap between shifts prevents ping-pong. Together, these filters turn a noisy oscillator into a high-conviction regime tracker.
HOW TO USE IT
🟣 Ribbon color = directional bias. Green ribbon = look for longs / hold longs. Magenta ribbon = look for shorts / hold shorts / cash. Do not fight the ribbon; let it confirm.
🟣 SHIFT triangles = regime change. A SHIFT ▼ at the top means volume flow has flipped bearish with confirmation. A SHIFT ▲ at the bottom means the opposite. These are not entry signals on their own — they are context. Combine them with your price-action setup, your structure tool, or with my other indicators (LSE , Liquidity Confluence ).
🟣 Divergence diamonds = exhaustion warnings. A bearish divergence inside a bullish ribbon is a "be careful, the bid is fading" signal — tighten stops, don't add. A bullish divergence inside a bearish ribbon is the opposite. Divergences against the regime are more useful than divergences in the direction of the regime.
🟣 Score panel = quick read. Score above 70 with Momentum Accelerating ▲ and Volume Expanding = strong bullish regime. Score below 30 with Accelerating ▼ and Contracting = strong bearish. Anything between 30–70 = no-trade context for momentum strategies.
🟣 Best timeframes: 15-minute to 1-hour on liquid instruments (BTC, ETH, SOL, major FX, SPX/NDX/DXY, gold). Lower than 15m gets noisy regardless of filters. Higher than 4h works but the score becomes very slow to update.
🟣 What this indicator is NOT: it is not an entry signal generator and it does not predict price. It tells you what kind of market you are in — that is the prerequisite for everything else.
📌 Free and open to all. Feedback, improvement ideas and edge cases welcome in the comments. Indicador
Ichimoku Regime ClassifierIchimoku Regime Classifier is an open-source market regime filter that labels conditions as TREND UP, TREND DOWN, or VOLATILE.
This script is designed to solve a practical problem: many entries fail because traders apply the same setup in all environments.
Its purpose is to classify the current market context first, so strategy rules can be adapted to regime.
Methodology
The classifier uses Daily Ichimoku structure plus ADX trend-strength confirmation:
Bullish regime:
Price above the Kumo
ADX above threshold
Tenkan above Kijun
Chikou-style confirmation (current price vs past price)
Bearish regime:
Price below the Kumo
ADX above threshold
Tenkan below Kijun
Opposite Chikou-style confirmation
Volatile regime:
If neither bullish nor bearish set is fully confirmed
Why this combination
Ichimoku provides structural trend context, while ADX filters weak directional phases.
The combination aims to reduce false directional bias during choppy periods and keep regime logic explicit.
How to use
TREND UP: prioritize long-biased setups
TREND DOWN: prioritize short-biased setups
VOLATILE: reduce risk, be selective, or wait for structure
This indicator is intentionally minimal on-chart (single regime label) to keep output readable and unambiguous.
Limitations
Regime transitions can lag, especially after sharp reversals.
This tool is a context filter, not a standalone entry/exit system.
Open-source notice
Published as open source for transparency, review, and customization.
Educational content only. Not financial advice. Indicador
Pullback Sniper Method [trade_w_samet]🎯 Pullback Sniper Method
Pullback Sniper Method is a free open-source pullback, breakout, and trade-visualization indicator designed to help traders analyze structured trend-continuation setups directly on the price chart.
This script combines:
📈 EMA-based trend context
🚀 breakout detection
🎯 pullback confirmation logic
✅ Fast / Balanced / Strict confirmation modes
🧠 optional McGinley and RSI filters
📦 ATR-based TP/SL projection boxes
🎯 TP1 / TP2 / TP3 tracking
🛑 SL tracking
🏷️ result labels
⭐ signal quality tooltip information
📊 statistics table
💎 premium-style dashboard
🎨 multiple visual themes
🚨 alert conditions
The goal of Pullback Sniper Method is not to predict the future or provide guaranteed buy/sell instructions.
Its purpose is to help users visually study:
⚡ trend continuation behavior
🎯 pullback quality
📈 breakout-following structure
🧠 confirmation strength
📦 projected risk/reward zones
📊 historical visual outcomes
💎 dashboard-based system feedback
🚨 alert-based monitoring
Pullback Sniper Method should be treated as a structured chart-analysis and educational decision-support tool, not as financial advice, not as an automated trading system, and not as a guarantee of profitable results.
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🔓 OPEN-SOURCE PUBLICATION NOTE
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This script is published as an open-source educational and visual market-analysis tool.
The source code is visible so users can inspect, review, understand, and learn from the logic.
The description is intentionally detailed because many users do not inspect every part of the Pine Script code line by line.
The purpose of this page is to explain:
✅ what the script does
✅ how the main logic works
✅ how signals are created
✅ what the trend engine checks
✅ how pullbacks are validated
✅ how confirmation modes differ
✅ how TP/SL projections are drawn
✅ how historical trade visuals are managed
✅ what the statistics table means
✅ what the premium dashboard means
✅ what the quality score tooltip represents
✅ what the limitations are
✅ how the indicator should and should not be used
Pullback Sniper Method is designed to support structured analysis.
It does not promise profitable results.
It does not remove market risk.
It does not execute trades.
It does not place broker orders.
It should not be used as a blind buy/sell system.
It is best used as a visual framework for reviewing trend, breakout, pullback, confirmation, and projected risk/reward behavior.
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📌 OVERVIEW
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At a high level, Pullback Sniper Method does the following:
📈 Calculates a trend engine using Fast EMA, Slow EMA, and EMA slope.
🚀 Detects breakout conditions after trend alignment.
🎯 Waits for price to pull back toward the Pullback EMA.
✅ Confirms entries using Fast, Balanced, or Strict confirmation logic.
🧠 Applies optional McGinley Dynamic distance filtering.
📊 Applies optional RSI directional filtering.
🧊 Uses a cooldown system to reduce signal clustering.
📦 Draws ATR-based TP/SL projection boxes.
🎯 Tracks TP1, TP2, TP3, and SL visually.
🏷️ Displays active TP labels and final result labels.
🗂️ Keeps historical TP/SL visuals on the chart.
⭐ Displays a Quality Score only inside the STRONG label tooltip.
📊 Builds a statistics table for TP1, TP2, TP3, SL, Total, and Win Rate.
💎 Builds a premium dashboard with deeper internal performance metrics.
🎨 Includes three visual themes.
🚨 Includes alert conditions for strong signals and trade outcomes.
This makes the script more than a simple signal label tool.
It is a complete pullback-analysis framework built around trend context, breakout confirmation, pullback behavior, ATR-based visual planning, and historical result review.
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🧠 CORE IDEA
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The core idea behind Pullback Sniper Method is simple:
A trend continuation setup should not be judged from one isolated candle.
A single breakout, one EMA touch, one candle close, or one label is usually not enough by itself.
Market context matters.
For that reason, Pullback Sniper Method combines several layers:
📈 trend alignment
🚀 breakout structure
🎯 pullback location
✅ confirmation candle behavior
📏 ATR-based distance filtering
🧠 optional McGinley distance filtering
📊 optional RSI direction filtering
📦 projected TP/SL structure
📊 visual statistics
💎 dashboard feedback
The indicator does not attempt to mark every possible move.
Instead, it attempts to make pullback-based trend continuation conditions easier to read, compare, and review.
The purpose is not to create more signals.
The purpose is to make signal conditions more structured and understandable.
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🧩 WHY THIS SCRIPT IS NOT A SIMPLE BUY/SELL INDICATOR
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Pullback Sniper Method is not intended to behave like a simple “buy here / sell here” script.
It is built as a structured workflow:
Trend Engine
→ Breakout Detection
→ Pullback Wait
→ Confirmation Mode
→ Optional Filters
→ STRONG Signal Label
→ Quality Tooltip
→ TP/SL Projection
→ Active Trade Visualization
→ Result Tracking
→ Statistics Review
→ Dashboard Review
→ Alerts
Each part has a specific role.
📈 The Trend Engine defines directional context.
🚀 The Breakout Engine identifies fresh movement beyond recent highs or lows.
🎯 The Pullback Engine waits for price to return toward the pullback EMA.
✅ The Confirmation Engine decides whether the reaction is strong enough.
🧠 The optional filters reduce signals that do not meet additional conditions.
⭐ The Quality Score tooltip gives extra signal context without changing the signal.
📦 The TP/SL boxes provide projected visual structure.
📊 The statistics table summarizes historical visual outcomes.
💎 The dashboard gives a broader state and performance-style overview.
🚨 The alert system helps monitor the script without constantly watching the chart.
This makes the script a full review environment, not a one-condition signal tool.
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⚙️ HOW THE SCRIPT WORKS
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📈 TREND ENGINE
The Trend Engine is based on three key components:
⚡ Fast EMA
🐢 Slow EMA
📐 Fast EMA slope
The default structure uses:
Fast EMA = 50
Slow EMA = 200
Pullback EMA = 21
Slope Lookback = 5
For bullish context, the script checks whether:
🟢 Fast EMA is above Slow EMA
🟢 Price is above Slow EMA
🟢 Fast EMA is rising compared to previous bars
For bearish context, the script checks whether:
🔴 Fast EMA is below Slow EMA
🔴 Price is below Slow EMA
🔴 Fast EMA is falling compared to previous bars
This helps the script avoid treating every price move as a valid pullback opportunity.
The trend engine creates the directional foundation for the rest of the logic.
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🚀 BREAKOUT ENGINE
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After trend alignment is detected, the script looks for a breakout.
For bullish setups, price must close above the recent breakout high.
For bearish setups, price must close below the recent breakout low.
The breakout engine uses:
🚀 Breakout Lookback
🛑 Invalidation Lookback
🔥 Minimum Breakout Body / ATR
The breakout candle must also have enough body size relative to ATR.
This is important because very small breakouts can create low-quality setup conditions.
The breakout does not immediately create a STRONG label.
Instead, it activates a setup state.
After that, the script waits for a pullback.
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🎯 PULLBACK SETUP ENGINE
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Once a breakout setup is active, the script waits for price to return toward the Pullback EMA.
For a bullish setup:
🟢 Price must pull back toward the Pullback EMA.
🟢 The setup must not be invalidated.
🟢 Enough bars must have passed after breakout.
For a bearish setup:
🔴 Price must pull back toward the Pullback EMA from the opposite direction.
🔴 The setup must not be invalidated.
🔴 Enough bars must have passed after breakout.
The script also includes a maximum number of bars to find the pullback.
If no valid pullback appears within that window, the setup expires.
This prevents old breakout conditions from staying active forever.
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✅ CONFIRMATION ENGINE
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After a valid pullback touch, the script waits for confirmation.
There are three confirmation modes:
⚡ Fast
⚖️ Balanced
🛡️ Strict
⚡ Fast Mode
Fast mode is the earliest and simplest confirmation style.
It checks whether price closes back in the expected direction relative to the Pullback EMA.
This mode can react faster but may produce more noise.
Useful for:
• faster review
• active chart monitoring
• lower-timeframe analysis
• users who prefer earlier signals
⚖️ Balanced Mode
Balanced mode is the default middle-ground profile.
It requires directional candle behavior and also considers either a break of the previous candle level or wick/rejection behavior.
This helps the signal feel more structured than a basic close-based trigger.
Useful for:
• general chart review
• balanced signal frequency
• intraday analysis
• users who want neither too many nor too few signals
🛡️ Strict Mode
Strict mode is the most selective confirmation profile.
It requires stronger candle body behavior and a more decisive close.
This can reduce signal frequency.
Useful for:
• cleaner setups
• fewer signals
• higher selectivity
• users who prefer stricter confirmation
Important note:
A stricter mode does not guarantee better future outcomes.
It only applies stricter internal confirmation logic.
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📏 ATR-BASED DISTANCE FILTERING
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ATR is used in several areas of the script.
The script uses ATR to evaluate:
📌 breakout body strength
📌 confirmation candle body strength
📌 entry distance from Pullback EMA
📌 stop-loss projection distance
📌 TP/SL visual structure
The Max Entry Distance / ATR setting helps block entries that are too far away from the Pullback EMA.
This is important because a pullback system generally works best when the signal appears close enough to the pullback reference area.
If price runs too far away before confirmation, the setup may become less efficient from a risk/reward perspective.
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🧠 MCGINLEY DYNAMIC FILTER
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Pullback Sniper Method includes an optional McGinley Dynamic filter.
The purpose of this filter is to avoid signals that appear too close to the McGinley Dynamic line.
This distance is measured using ATR.
When enabled, the script checks whether price has enough distance from the McGinley line.
This can help reduce low-quality signals in crowded or compressed areas.
The McGinley filter is optional.
Users can turn it off if they prefer to use only the main trend/pullback logic.
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📊 RSI DIRECTION FILTER
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The script also includes an optional RSI direction filter.
When enabled:
🟢 Long signals require RSI to be above the selected long threshold.
🔴 Short signals require RSI to be below the selected short threshold.
By default, the RSI filter is turned off.
This keeps the base system cleaner and allows users to decide whether they want additional oscillator-style directional filtering.
The RSI filter should be treated as context, not as a guarantee.
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🧊 SIGNAL COOLDOWN SYSTEM
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The script includes a cooldown system to prevent signals from appearing too close to each other.
After a signal appears, the script waits for the selected number of bars before allowing another signal.
This helps reduce visual clutter and prevents the chart from printing too many labels in a short period.
The cooldown system is especially useful on lower timeframes or volatile assets.
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💪 STRONG SIGNAL LABELS
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When all required conditions align, the script can display a STRONG label on the chart.
A STRONG label appears only after the script detects:
📈 valid trend context
🚀 valid breakout setup
🎯 valid pullback touch
✅ valid confirmation
🧠 optional filter approval
🧊 cooldown approval
📦 no active trade conflict
The STRONG label does not mean the future outcome is guaranteed.
It simply means the script’s internal conditions aligned at that point.
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⭐ QUALITY SCORE TOOLTIP
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Pullback Sniper Method includes a Quality Score tooltip on the STRONG label.
This score does not filter signals.
It does not change entries.
It does not block or approve trades.
It is only informational.
To view it, hover your mouse over the STRONG label.
The tooltip can show:
⭐ Quality Score
🌟 Star rating
📌 Direction
📈 Trend alignment
🔥 Body / ATR ratio
📍 EMA Distance / ATR
Example tooltip:
Quality Score: 82/100 ⭐⭐⭐⭐ | Quality: HIGH | Direction: LONG | Trend: Bullish | Body/ATR: 0.48 | EMA Distance/ATR: 0.32
Star guide:
⭐⭐⭐⭐⭐ = very strong internal quality
⭐⭐⭐⭐ = high internal quality
⭐⭐⭐ = good internal quality
⭐⭐ = medium internal quality
⭐ = lower internal quality
Important note:
A score of 82 does not mean there is an 82% chance of winning.
The score only summarizes internal signal quality according to the script’s own visual model.
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📦 TP / SL PROJECTION SYSTEM
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Pullback Sniper Method includes an ATR-based TP/SL projection system.
When a STRONG signal appears, the script can draw:
📦 TP box
📦 SL box
🎯 TP1 line
🎯 TP2 line
🎯 TP3 line
🏷️ TP price labels
🏁 final result label
The system uses ATR-based risk.
Default structure:
🛡️ SL ATR Multiplier = 2.0
🎯 TP3 Reward R = 2.0R
🥉 TP1 = 25% of TP3 distance
🥈 TP2 = 50% of TP3 distance
🏆 TP3 = final target distance
This creates a clean visual projection of the potential trade structure.
The boxes are not broker orders.
They are visual projections based on the script’s internal logic.
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🎯 TP1 / TP2 / TP3 / SL TRACKING
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The script tracks projected trade progress visually.
Possible outcome states include:
🥉 TP1 reached
🥈 TP2 reached
🏆 TP3 reached
🛑 SL reached
If price reaches TP3, the result is marked as TP3.
If price hits SL before reaching any TP level, the result is marked as SL.
If price reaches TP1 and later returns to SL, the result can be treated as a TP1-style protected outcome.
If price reaches TP2 and later returns to SL, the result can be treated as a TP2-style protected outcome.
This allows the visual projection to remember the best target reached before the trade closes.
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⚠️ SAME-CANDLE TP/SL HANDLING
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If TP and SL are both touched on the same candle, there is ambiguity.
The script cannot know the true intrabar sequence from standard OHLC data.
For that reason, Pullback Sniper Method uses a conservative rule:
🔴 If TP and SL are both touched on the same candle, the script treats it as SL.
This avoids overly optimistic visual outcomes when the true intrabar order is unknown.
This conservative approach is useful when reviewing historical visual performance.
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🙈 EARLY SL HIDING LOGIC
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The script includes an early SL hiding feature.
If a projected trade hits SL within the first selected number of bars, the visual trade can be hidden and excluded from statistics.
Default:
🙈 Hide Early SL Bars = 3
This feature is designed to reduce extremely fast failed projections from cluttering the visual history.
Users should understand that this affects the visual/statistical display of the script.
It is not a broker-side execution rule.
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🗂️ HISTORICAL TRADE VISUALS
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Pullback Sniper Method can keep historical TP/SL visuals on the chart.
This includes:
📦 previous TP boxes
📦 previous SL boxes
🎯 TP1 / TP2 / TP3 lines
🛑 SL lines
🏷️ result labels
🏷️ TP price labels
The script also includes a maximum historical trade limit.
Default:
🧮 Max Historical Trades = 40
This helps prevent TradingView object-limit issues while still allowing users to review past signals visually.
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📊 STATISTICS TABLE
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The statistics table summarizes visual trade outcomes.
It can display:
🥉 TP1 count
🥈 TP2 count
🏆 TP3 count
🛑 SL count
📊 Total closed trades
✅ Win Rate
The statistics are calculated internally using the script’s visual TP/SL logic.
They are not broker execution results.
They do not include real slippage, spread, commission, liquidity, partial fills, or order execution issues.
They should be used for visual review and educational analysis only.
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💎 PREMIUM DASHBOARD
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Pullback Sniper Method includes a premium-style dashboard.
The dashboard can display:
📈 Trend
📡 Current status
📊 Total trades
✅ Win rate
📈 Total R
📉 Average R
🧮 Profit Factor
🎯 Expectancy
🔥 Max win streak
❄️ Max loss streak
🔁 Current streak
⏱️ Average bars in trade
🏆 TP3 rate
🛑 SL rate
🧭 Best direction
🟢 Long win rate
🔴 Short win rate
📌 Active trade state
🧾 Last signal
🎯 Best TP reached
The dashboard is designed to give users a structured overview of the script’s internal visual results.
Important note:
These dashboard values are not official TradingView Strategy Tester results.
They are internally calculated visual-analysis metrics.
They should not be interpreted as guaranteed performance.
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🎨 VISUAL THEME SYSTEM
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The script includes three visual themes:
🔵 Neon Pro
🧊 Ice Blue
🟡 Gold Black
The theme system affects:
🎨 STRONG label colors
📦 TP/SL box colors
🎯 TP/SL line colors
🏷️ TP price labels
📊 statistics table colors
💎 premium dashboard colors
🟢 long-side visuals
🟠 short-side visuals
Color settings are handled internally to keep the Inputs tab cleaner and more organized.
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🏷️ TP PRICE LABELS
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TP labels are displayed next to the TP levels rather than inside the boxes.
This helps keep the projection boxes cleaner.
The labels can show:
TP1 price
TP2 price
TP3 price
Example:
TP1 102450.5
TP2 103120.0
TP3 104300.0
This makes it easier to visually read the projected target levels without opening the settings or manually checking each line.
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🏁 RESULT LABELS
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When a projected trade closes, the script can display a result label.
Examples:
🏆 TP3 HIT WIN +2R
🥈 TP2 EXIT WIN +1R
🥉 TP1 EXIT WIN +0.5R
🛑 SL HIT LOSS -1R
The exact value depends on the selected TP/SL structure and the highest TP reached before closure.
These labels are visual summaries only.
They do not represent broker execution.
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🚨 ALERT SYSTEM
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Pullback Sniper Method includes alert conditions for:
🟢 Strong Long
🟠 Strong Short
🏆 TP3 Hit
🛑 SL Hit
🎯 Protected TP Exit
Users can create TradingView alerts from these alert conditions.
Alerts can help monitor the chart without constantly watching every candle.
Important note:
Alerts are based on the script’s conditions.
They are not trade execution instructions.
Users are responsible for validating alerts and applying their own risk management.
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🧪 HOW TO USE THE INDICATOR
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A practical workflow:
Add Pullback Sniper Method to your chart.
Start with the default settings.
Review the overall trend direction.
Wait for a valid breakout setup.
Let the script wait for a pullback toward the Pullback EMA.
Watch for a STRONG label after confirmation.
Hover over the STRONG label to review the Quality Score tooltip.
Review the TP/SL projection box.
Check TP1, TP2, TP3, and SL levels.
Observe whether the projected trade reaches TP levels or SL.
Use the statistics table for visual outcome review.
Use the premium dashboard for deeper internal metrics.
Use alerts if you want automated signal notifications.
Validate the behavior on the exact symbols and timeframes you personally study.
This indicator is best used as a structured review tool.
It should not be used as a blind execution system.
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⚙️ SETTINGS REFERENCE
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📈 Trend Engine
⚡ Fast EMA Length
Controls the fast trend EMA. Lower values react faster, while higher values are smoother.
🐢 Slow EMA Length
Controls the broader trend EMA used for directional context.
🎯 Pullback EMA Length
Defines the EMA area where price is expected to pull back before confirmation.
📐 Trend Slope Lookback
Checks whether the fast EMA is sloping in the expected trend direction.
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🎯 Pullback Setup Engine
🚀 Breakout Lookback
Defines how many bars are used to detect a fresh breakout level.
🛑 Invalidation Lookback
Defines the invalidation level for the active pullback setup.
⏳ Min Bars After Breakout
Controls how many bars must pass after breakout before pullback detection begins.
⌛ Max Bars To Find Pullback
If no valid pullback appears within this range, the setup expires.
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✅ Confirmation Engine
✅ Confirmation Mode
Available modes:
⚡ Fast
⚖️ Balanced
🛡️ Strict
📏 ATR Length
ATR used for body-size and distance filters.
🔥 Min Breakout Body / ATR
Minimum breakout candle body size compared to ATR.
💪 Min Confirm Body / ATR
Minimum confirmation candle body size compared to ATR. Mainly used in Strict mode.
📍 Max Entry Distance / ATR
Blocks entries that are too far away from the Pullback EMA.
🧊 Use Signal Cooldown
Prevents too many signals from appearing too close to each other.
⏱️ Cooldown Bars
Number of bars to wait after a signal before allowing another one.
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🧠 Optional Filters
🧲 Block Signals Near McGinley
Avoids entries too close to the McGinley Dynamic line.
〽️ McGinley Length
Length used for the McGinley Dynamic filter.
📐 Min McGinley Distance / ATR
Minimum distance required between price and McGinley Dynamic.
📊 Use RSI Direction Filter
Filters long/short signals based on RSI direction.
📈 RSI Length
RSI length used for the optional direction filter.
🟢 RSI Long Minimum
Long signals are allowed only when RSI is above this value.
🔴 RSI Short Maximum
Short signals are allowed only when RSI is below this value.
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📦 Trade Visual Engine
📏 Show TP / SL Lines
Shows TP1, TP2, TP3, and SL lines.
📦 Show TP / SL Boxes
Shows TP and SL projection zones.
🏷️ Show Active TP Label
Shows the latest touched TP label while the projected trade is active.
🗂️ Show Historical TP / SL Trades
Keeps previous TP/SL boxes, lines, and labels on the chart.
🧮 Max Historical Trades
Limits historical visual trades to help avoid object-limit issues.
🛡️ SL ATR Length
ATR length used for stop-loss calculation.
🛑 SL ATR Multiplier
ATR multiplier used for stop-loss distance.
🎯 TP3 Reward R
Final TP target multiple based on the initial risk distance.
🥉 TP1 % Of TP3
TP1 distance as a percentage of final TP3 distance.
🥈 TP2 % Of TP3
TP2 distance as a percentage of final TP3 distance.
↔️ Initial TP / SL Length
Initial visual length of TP/SL lines and boxes.
🙈 Hide Early SL Bars
If SL is reached within this number of bars, the projection can be hidden and excluded from statistics.
📊 Show Statistics Table
Shows TP1, TP2, TP3, SL, total trades, and win rate.
💎 Show Premium Dashboard
Shows the extended dashboard with deeper internal metrics.
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🎨 Visual Settings
🎭 Color Theme
Available themes:
🔵 Neon Pro
🧊 Ice Blue
🟡 Gold Black
💪 Show STRONG Labels
Shows the main STRONG labels on the chart.
🔠 Signal Label Size
Controls the size of STRONG signal labels.
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🧠 WHAT MAKES THIS SCRIPT ORIGINAL
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Pullback Sniper Method uses familiar concepts such as:
📈 EMA trend context
🚀 breakout detection
🎯 pullback confirmation
🧠 optional technical filters
📦 ATR-based TP/SL projection
📊 dashboard metrics
🚨 alerts
These components are not unique by themselves.
The originality of the script lies in how these components are organized into one workflow:
Trend Engine
→ Breakout Detection
→ Pullback Validation
→ Confirmation Mode
→ Optional Filters
→ STRONG Label
→ Quality Tooltip
→ TP/SL Projection
→ Historical Trade Visualization
→ Statistics Table
→ Premium Dashboard
→ Alerts
This structure is intended to give users a cleaner way to review pullback-based trend continuation setups.
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⚠️ IMPORTANT PRACTICAL NOTES
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The script’s behavior depends heavily on settings.
Signal frequency and visual output may change based on:
🎛️ selected confirmation mode
📈 EMA lengths
🎯 pullback EMA length
🚀 breakout lookback
🛑 invalidation lookback
📏 ATR settings
🧠 McGinley filter
📊 RSI filter
🧊 cooldown setting
📦 TP/SL settings
📊 market
⏱️ timeframe
📉 symbol volatility
📚 available historical bars
A configuration that looks cleaner on one market may not behave the same way on another.
The TP/SL boxes are visual projections based on script rules.
They are not broker orders.
They do not account for real execution conditions.
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⚠️ LIMITATIONS AND SHORTCOMINGS
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This script has important limitations:
❌ It does not guarantee profitable trades.
❌ It does not predict future price movement.
❌ It does not replace risk management.
❌ It does not execute trades.
❌ It does not place orders.
❌ It does not include broker slippage.
❌ It does not include commissions.
❌ It does not include spreads.
❌ It uses bar-based chart data.
❌ Same-candle TP/SL order cannot be known from standard OHLC data.
❌ Same-candle TP/SL is handled conservatively as SL.
❌ Quality Score is not a win-rate prediction.
❌ Dashboard statistics are internal visual metrics, not broker results.
❌ ATR-based TP/SL projections are visual analysis tools, not trade instructions.
❌ Strong labels can still fail in choppy or low-quality market conditions.
❌ Historical visual behavior does not ensure future behavior.
For these reasons, Pullback Sniper Method should be used as an educational decision-support tool, not as a standalone trading strategy.
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👤 WHO THIS SCRIPT MAY BE USEFUL FOR
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This script may be useful for traders who:
✅ study pullback-based trend continuation
✅ want structured breakout/pullback confirmation
✅ want ATR-based TP/SL visualization
✅ want historical visual trade review
✅ want a clean statistics table
✅ want a premium-style dashboard
✅ want signal quality information without changing signal logic
✅ want configurable confirmation strictness
✅ want visual themes
✅ want alert-based monitoring
✅ prefer organized chart-based analysis
✅ want an open-source educational TradingView tool
It may be less suitable for users who:
❌ want guaranteed buy/sell signals
❌ want a fully automated trading bot
❌ do not use technical analysis
❌ do not want chart visuals
❌ expect one setting to work on every market
❌ want an indicator that replaces their own decision-making
❌ expect internal visual statistics to match broker execution results
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🧭 BEST PRACTICE SUGGESTIONS
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For cleaner review:
✅ Start with the default settings.
✅ Use Balanced confirmation first.
✅ Test Fast and Strict modes only after understanding the default behavior.
✅ Review STRONG labels together with market structure.
✅ Hover over labels to inspect Quality Score context.
✅ Use TP/SL boxes as visual planning tools, not automatic orders.
✅ Review dashboard metrics as internal script feedback only.
✅ Avoid treating every signal as a trade.
✅ Test the indicator on the symbols and timeframes you actually use.
✅ Combine the tool with independent analysis and risk management.
✅ Keep expectations realistic.
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🚨 ALERT USAGE
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The script includes alert conditions for important events.
Available alert types include:
🟢 Strong Long
🟠 Strong Short
🏆 TP3 Hit
🛑 SL Hit
🎯 Protected TP Exit
A practical alert workflow:
Add the indicator to your chart.
Open TradingView’s alert window.
Select Pullback Sniper Method as the condition.
Choose the alert condition you want.
Configure frequency according to your preference.
Use alerts as monitoring tools only.
Confirm all alerts manually with your own analysis.
Alerts do not execute trades.
Alerts are not financial advice.
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🔓 OPEN-SOURCE NOTE
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This script is published open-source for educational review, transparency, and community learning.
Users can inspect how the indicator works, study the logic, modify it for personal learning, and understand the internal conditions behind the visual output.
Please respect TradingView’s House Rules when reusing or republishing open-source code.
The purpose of open-source publication is to support learning and transparent script review.
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🛡️ DISCLAIMER
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Pullback Sniper Method is provided for educational and informational purposes only.
It does not constitute financial, investment, or trading advice.
No indicator can guarantee future results.
Markets are uncertain, conditions change, and historical behavior does not ensure future performance.
Every user is responsible for their own analysis, validation, risk management, position sizing, and trading decisions.
The TP/SL boxes, labels, dashboard statistics, Quality Score, and alerts are visual analysis tools only.
Use this script as a structured decision-support and visual review framework, not as a promise of profitability. Indicador
Volatility Flow & Cycle DivergencesVolatility Flow & Cycle Divergences
General Description
Volatility Flow & Cycle Divergences is an intermarket quantitative analysis tool designed to map liquidity flows, risk regimes, and structural anomalies between the US stock market (S&P 500 / ES) and the options market, represented by the CBOE Volatility Index (VIX).
The indicator is built on the premise that the physiological relationship between the S&P 500 and the VIX is historically inverse (moving in opposite directions roughly 80% of the time). This script does not merely calculate the raw ratio between the two assets; it mathematically isolates the rare moments of monetary asynchrony where this correlation breaks down. These anomalies are frequently the precursors to lasting cyclical reversals or the exhaustion of the primary trend.
Core Features and Filtering Logic
To overcome the issue of false signals typical of traditional oscillators on intermediate timeframes, the script integrates three concurrent proprietary filters:
Intermarket Relative Strength Ratio (VIX/SPX): It calculates the relative speed between the demand for protective insurance (Put options) and the spot price of the equity basket.
Asymmetric Cyclical Pivot Filter: The algorithm for detecting relative highs and lows uses an asymmetric lookback/lookahead range (calibrated by default to 8 periods on the left and 5 on the right). This isolates true macro turning points and eliminates microscopic, temporary intraday spikes.
Momentum Validation (Anti-Inertia Filter): Divergences are not generated purely on a mathematical basis. A divergence on market highs is validated only if the S&P 500 is in a true zone of strength/overbought territory (RSI > 55), while a divergence on market lows requires actual panic selling pressure (RSI < 45). If the market is in a neutral congestion phase, the signal is discarded at the source.
Algorithmic Transparency (Under the Hood)
The indicator executes its analysis by processing three integrated algorithmic engines within the code:
Geometric Pivot Algorithm (ta.pivothigh/low): Constantly scans the historical series of the VIX/SPX Ratio to isolate cyclical peaks and troughs, validating them only if they are surrounded by a precise asymmetry of bar-index (8 to the left, 5 to the right).
Momentum Algorithm (Wilder's RSI): Monitors the first derivative of the S&P 500 price. It does not calculate the classic relative strength on the Ratio itself, but uses the RSI algorithm on the underlying equity index to act as a logical switch (Gatekeeper) for the signals.
Pattern Matching Algorithm (Conditional Logic): A conditional control structure that crosses the spatial-temporal coordinates of price pivots with those of volatility, filtering out false signals based on overbought/oversold regimes.
H ow to Read the Signals on the Chart
The script provides a clean graphical visualization using compact "badges" on the lower panel so it does not obscure the price action:
DD (Distribution Divergence - Red): The S&P 500 prints a new relative high, but the VIX/SPX Ratio shows a higher low or an anomalous support. This means that despite the price rally, institutional investors are accumulating protective hedges due to an ongoing distribution. Potential pullback ahead.
DA (Accumulation Divergence - Green): The S&P 500 updates its period lows amid panic, but the Ratio registers a lower peak compared to the previous one. Implied volatility pressure is exhausting: "smart money" is stopping the hedging process and beginning to accumulate spot positions. Potential cyclical low/bounce in formation.
User Interface (UI) Elements
Integrated Information Dashboard: Positioned at the bottom left with a transparent background to preserve chart real estate. It shows the real-time macro state of the market (RISK-ON or RISK-OFF) based on the position of the Ratio relative to its 20-period moving average, serving as an operational reminder for the divergence rules.
View Toggle: Through the indicator settings panel, you can hide or show the dashboard with a single click.
Operational Advice and Timeframes
This indicator expresses its maximum performance when integrated into a cyclical and multi-day flow analysis logic:
Best Timeframe: 2 Hours (2H) to capture operational swings and cycles lasting 2-3 days.
Macro / Swing Analysis: Daily (1D) to map structural medium-to-long term risk regimes.
Technical Note: We advise against using this on fast timeframes (below the 2-hour chart) due to the algorithmic noise generated by short-term option expirations (0DTE) and the lack of reliable spot VIX calculation during overnight sessions (Globex).
I mportant Disclaimer & Operational Risk Warning
Critical Note: Never use a single indicator to make standalone trading decisions. Always contextualize what the indicator suggests within a broader market framework and combine it with other validation techniques (e.g., Price Action, Market Structure, Volume Profile, or Order Flow). No indicator is infallible, and intermarket divergences represent shifts in probability, not guaranteed certainties.
Italiano
Descrizione Generale
Volatility Flow & Cycle Divergences è uno strumento di analisi intermarket quantitativa progettato per mappare i flussi di liquidità, i regimi di rischio e le anomalie strutturali tra il mercato azionario statunitense (S&P 500 / ES) e il mercato delle opzioni, rappresentato dal CBOE Volatility Index (VIX).
L'indicatore si basa sul presupposto che la relazione fisiologica tra l'S&P 500 e il VIX sia storicamente inversa (circa l'80% delle volte si muovono in direzioni opposte). Questo script non si limita a calcolare il rapporto puro tra i due asset, ma isola matematicamente i rari momenti di asincronia monetaria, in cui questa correlazione si spezza. Tali anomalie sono spesso il precursore di inversioni cicliche durature o di esaurimenti del trend principale.
Caratteristiche Principali e Logica di Filtraggio
Per superare il problema dei falsi segnali tipici degli oscillatori tradizionali sui timeframe intermedi, lo script integra tre filtri proprietari concorrenti:
Rapporto di Forza Relativa Intermarket ($VIX/SPX$):
Calcola la velocità relativa tra la domanda di coperture assicurative (opzioni Put) e il prezzo spot del paniere azionario.
Filtro Ciclico Asimmetrico sui Pivot:
L'algoritmo di rilevamento dei massimi e minimi relativi utilizza un raggio d'azione asimmetrico (calibrato di default a 8 periodi a sinistra e 5 a destra). Questo isola i reali punti di svolta macro ed elimina i microscopici spike estemporanei intra-day.
Validazione del Momentum (Filtro Anti-Inerzia):
Le divergenze non vengono generate su base puramente matematica. Una divergenza sui massimi viene convalidata solo se l'S&P 500 si trova in una reale zona di ipercomprato/forza relativa (RSI > 55), mentre una sui minimi esige una reale pressione di panico (RSI < 45). Se il mercato si trova in una fase di congestione neutra, il segnale viene scartato all'origine.
Come Leggere i Segnali sul Grafico
Lo script restituisce una visualizzazione grafica pulita mediante "badge" compatti sul pannello inferiore per non occultare l'azione del prezzo:
DD (Divergenza di Distribuzione - Rossa): L'S&P 500 stampa un nuovo massimo relativo, ma il Ratio VIX/SPX mostra un minimo crescente o un supporto anomalo. Significa che, nonostante il rialzo del prezzo, gli investitori istituzionali stanno accumulando coperture protettive a causa di una distribuzione in corso. Potenziale storno in arrivo.
DA (Divergenza di Accumulazione - Verde): L'S&P 500 aggiorna i minimi di periodo nel panico, ma il Ratio fa registrare un picco inferiore rispetto al precedente. La pressione della volatilità implicita si sta esaurendo: le "mani forti" smettono di proteggersi e iniziano ad accumulare posizioni spot. Potenziale minimo ciclico/rimbalzo in formazione.
Elementi di Interfaccia Utente (UI)
Dashboard Informativa Integrata:
Posizionata in basso a sinistra con uno sfondo trasparente per preservare il chart real estate. Mostra in tempo reale lo stato macro del mercato (RISK-ON o RISK-OFF) in base alla posizione del Ratio rispetto alla sua media mobile a 20 periodi, fungendo da memorandum operativo per le regole di divergenza.
Interruttore di Vista: Tramite il pannello delle impostazioni è possibile nascondere o mostrare la dashboard con un solo clic.
Consigli Operativi e Timeframe
Questo indicatore esprime il massimo delle sue performance se integrato in una logica di analisi ciclica e di flusso plurigiornaliera:
Timeframe d'elezione: 2 Ore (2H) per catturare oscillazioni e cicli operativi di 2-3 giorni.
Analisi Macro / Swing: Giornaliero (1D) per mappare i regimi di rischio strutturali di medio-lungo termine.
Nota tecnica: Si sconsiglia l'utilizzo sui timeframe rapidi a causa del rumore algoritmico generato dalle scadenze opzioni a brevissimo termine (0DTE) e della mancanza di calcolo del VIX spot durante le sessioni notturne.
Avvertenza Importante e Rischio Operativo
Nota Critica : Non utilizzare mai un singolo indicatore per prendere decisioni operative autonome. Contestualizza sempre ciò che l'indicatore suggerisce all'interno di un quadro di mercato più ampio e combinalo con altre tecniche di validazione (ad es. Price Action, Struttura di Mercato, Volume Profile o Order Flow). Nessun indicatore è infallibile e le divergenze intermarket rappresentano variazioni nelle probabilità, non certezze garantite. Indicador
Estratégia
Aurora Expanded Trend Cloud [Pineify]Aurora Expanded Trend Cloud
This indicator uses a Donchian Channel breakout to define trend direction and then projects an asymmetric "cloud" zone above or below price — a visual region where the market is expected to find support (in uptrends) or resistance (in downtrends). What makes it different from a plain Donchian system is the one-directional trailing cloud boundary: once the floor starts rising (or the ceiling falling), it never reverses, creating a ratcheting effect that tightens as a trend matures without whipping back on normal pullbacks.
Key Features
Trend direction determined by Donchian Channel breakouts — clean, objective, no oscillator subjectivity
Cloud width scales with the current channel width, so the zone automatically widens during high-volatility periods and compresses when price is ranging
One-way trailing boundary: the cloud floor only moves up in bull trends; the cloud ceiling only moves down in bear trends — reducing false exits during healthy retracements
Trend-start circles mark the exact bar where each breakout first confirmed, providing a clear visual reference for entry timing
Trend state exposed in the Data Window for use in external scripts or Pine Strategy bridges
How It Works
The calculation pipeline has three stages:
Channel Construction — Each bar, the indicator computes the N-bar highest high (upper band) and lowest low (lower band). The difference is the channel width, which serves as a volatility proxy.
Expansion Offset — An expansion percentage (default 50 %) is applied to the channel width to derive an offset. For example, on a bar where the channel spans 10 points, a 50 % setting produces a 5-point offset. This offset is added to the lower band (bullish cloud floor) or subtracted from the upper band (bearish cloud ceiling).
One-Directional Trail — Rather than placing the cloud boundary exactly at the shifted band each bar, the indicator uses math.max (bullish) or math.min (bearish) against the prior trail value. Once the floor is at level X, it will not drop below X even if the Donchian lower band temporarily dips — the level "locks in" and can only continue advancing. On a trend flip, the trail resets from the new band anchor.
How the Components Work Together
The Donchian breakout sets the trend but is inherently reactive — it waits for price to close beyond the N-bar extreme, so the first signal can be a bar or two late on fast moves. The cloud's one-way trail compensates for this by providing a persistent, non-retreating reference level throughout the trend. Because the floor never pulls back, it acts as a natural trailing stop zone: if price closes below the green cloud boundary, the bullish structure has likely deteriorated beyond normal noise. The two mechanisms are complementary — breakout for entry, one-directional trail for management.
The expansion percentage links cloud width to current volatility. On a stock moving 1 % a day in a tight channel, the cloud stays narrow. During a high-momentum phase where the channel widens, the cloud expands proportionally, giving the trade more room to breathe. This avoids one of the common frustrations with fixed-offset trailing stops: premature exits during volatile but sustained trends.
Trading Ideas and Insights
When a green circle appears and the cloud floor begins to form below price, it may signal an early-stage uptrend. Consider entries on the first close above the upper band with the cloud floor as an initial stop reference.
As a trend matures, the cloud floor rises and tightens. A close back into the cloud zone — especially with a wick that tags the floor — may indicate a pause rather than a reversal. A close below the cloud often warrants reassessment.
Compression of the Donchian Channel ahead of a circle signal (upper and lower bands converging) can suggest accumulated energy before a breakout. These setups may produce cleaner follow-through than breakouts from already-wide channels.
On higher timeframes the cloud acts as a broad trend filter. If price is above the green cloud on the daily, consider filtering intraday long setups only from that direction.
Past behavior does not guarantee future results. The indicator has no predictive capability — it identifies conditions based on historical price structure. Always combine with independent confirmation and proper risk management.
Limitations to Be Aware Of
The Donchian breakout condition requires a close beyond the N-bar extreme. On fast-moving bars, the entry signal appears one bar after the actual breakout, which can result in a worse fill price.
The one-way trail means the cloud boundary never retreats during a trend, but it also means it can diverge significantly from current price during extended moves. In those situations, the cloud is no longer a tight stop reference — it reflects the starting anchor of the trend, not current structure.
In choppy, range-bound conditions, the indicator will produce frequent trend flips (circles in rapid alternation). No trend-following tool performs well in this regime; watch the channel width as a rough proxy for regime type.
How to Use
Add the indicator to any chart and timeframe. It works on overlay mode, drawing directly on price.
A green cloud below price confirms a bullish trend. A red cloud above price confirms a bearish trend. The solid-colored boundary line is the active trailing level.
Colored circles mark trend start bars. Green below price = trend flipped bullish. Red above price = trend flipped bearish.
Use the Trend Length input to adjust sensitivity. Shorter values (10–15) react faster and suit shorter timeframes or momentum styles. Longer values (30–50) filter more noise and suit swing or position trading.
Adjust the Expansion % to control how far the cloud boundary sits from the Donchian band. Higher values give more room and widen the cloud; lower values keep the trail tight to price.
Customization
Trend Length (default: 20) — Lookback window for the Donchian Channel. Larger values require a more sustained breakout to flip the trend and produce fewer, higher-conviction signals. Smaller values increase responsiveness at the cost of more noise.
Expansion % (default: 50) — How wide the cloud is expressed as a fraction of the channel width. At 100 %, the cloud spans the full channel. At 0 %, the cloud collapses to a line at the channel boundary. Values between 30 %–70 % work well for most setups.
Conclusion
Aurora Expanded Trend Cloud combines a Donchian breakout system with a volatility-adaptive, one-directional trailing zone to give both a clear trend signal and a persistent visual reference level throughout the move. It is best suited for traders who want an objective, rule-based trend overlay without manual line-drawing — particularly on trending assets and timeframes where Donchian systems historically perform best.
Indicador
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SwingRegress Volatility Analytics [MarkitTick]💡 A comprehensive, multi-dimensional charting tool designed to fuse structural market analysis, statistically derived linear regression pathways, and volatility compression mechanics into a single, cohesive interface. By dynamically adapting its calculations to the latest shifts in market structure—specifically Change of Character (CHoCH) events—this script offers an adaptive mapping of price action, trend trajectory, and potential breakout zones directly on the primary chart.
● ✨ Originality and Utility
Traditional linear regression tools often require manual anchoring or rely on fixed lookback periods that fail to adapt to rapidly unfolding price dynamics. The distinct utility of this script lies in its self-adjusting structural anchoring mechanism. By automatically locking the regression baseline to the most recent significant pivot high or pivot low immediately following a structural break, the channel remains mathematically and contextually relevant to the current market regime.
Furthermore, this tool eliminates the need for separate sub-chart oscillators by integrating a sophisticated Smart Volatility Squeeze engine. This engine compares price variance against true range to identify periods of extreme price compression, overlaying these signals directly within the active regression pathway. The result is a unified, chart-centric view of both directional trend geometry and kinetic energy build-up, allowing for a more focused and uncluttered analytical process.
● 🔬 Methodology and Concepts
The underlying logic of this script is driven by three core mathematical engines operating in tandem:
• Pivot Discovery and Market Structure
The script continuously scans incoming price data to identify localized extremes, defined as Pivot Highs and Pivot Lows. A candidate bar is confirmed as a pivot only if it remains unbroken for a user-defined number of bars both prior to and following its occurrence. Once confirmed, these pivots establish the market structure. If the closing price breaks beyond the most recent opposing pivot, a Change of Character (CHoCH) is triggered, officially shifting the trend state.
• Anchored Linear Regression
Upon the confirmation of a new CHoCH, the script calculates a fresh Linear Regression Channel (LRC). The anchoring point is the origin pivot of the newly established trend. The script uses the Ordinary Least Squares (OLS) method to compute the slope and intercept of the best-fit line through the closing prices of the current regime. It then calculates the standard error of the estimate (standard deviation of the residuals) to project upper and lower variance bands parallel to the mid-line.
• Volatility Squeeze Mechanics
To identify volatility compression, the script employs a comparative analysis between standard deviation and Average True Range (ATR). It calculates a Bollinger Band (representing standard deviation) and a Keltner Channel (representing ATR) around a moving average baseline. A "squeeze" is structurally confirmed when the outer limits of the Bollinger Bands contract entirely within the boundaries of the Keltner Channels. This signifies that historical variance has dropped substantially below the average true range, often preceding a dynamic expansion in price movement.
● 🎨 Visual Guide
The visual interface is highly detailed and structurally color-coded to provide immediate contextual awareness without cluttering the chart.
• Current Anchored LRC
Mid Line: A solid Neon Cyan line representing the true mean of the current trend regime.
Band 1: A dashed Soft Cyan line mapping the first standard deviation threshold.
Band 2: A dotted Deep Azure line mapping the secondary, outer standard deviation extreme.
• Previous Anchored LRC
Mid Line: A solid Magenta line representing the historical mean of the preceding trend.
Band 1: A dashed Soft Magenta line for the historical inner variance.
Band 2: A dotted Blue-Violet line for the historical outer variance.
• Swing Point Zones
Swing High Boxes: Translucent red zones originating from a confirmed pivot high, drawing forward to act as dynamic resistance until broken by price action.
Swing Low Boxes: Translucent green zones originating from a confirmed pivot low, acting as dynamic support until structurally invalidated.
• Volatility Squeeze Candles
Cyber Gold Candles: When the market enters a state of extreme volatility compression (Bollinger Bands inside Keltner Channels) and is actively trading within the current or previous LRC pathway, the candles are painted a vibrant gold to highlight imminent kinetic release.
• Heads-Up Dashboard Display
Located in the top right corner, this self-updating data matrix provides critical real-time telemetry:
Structure Regime: Displays the active directional bias (Bullish, Bearish, or Neutral).
Last CHoCH: Indicates the direction and age (in bars) of the most recent structural shift.
Squeeze Intensity: A visual block-bar measuring the depth of the volatility compression.
ATR (14): The current absolute value of the Average True Range.
Dist to Swings: The percentage distance between the current price and the nearest Swing High/Low.
Risk/Reward Quality: A dynamic measurement of potential risk versus structural reward.
LRC Window Age: The duration of the current regression channel in bars.
LRC Position: Indicates whether price is currently trading inside the active regression channel, the previous channel, or is entirely unanchored.
● 📖 How to Use
The primary application of this tool is identifying high-probability continuation or mean-reversion setups following structural confirmation.
When a CHoCH event occurs, wait for the new Linear Regression Channel to populate. This channel defines your trading parameters. A high-probability setup manifests when price pulls back to the inner or mid-line of the active LRC, accompanied by the appearance of Cyber Gold squeeze candles. This visual confluence suggests that price is compressing directly at the statistical mean of the new trend, building energy for a move in the direction of the underlying structural regime.
Conversely, if price approaches the outer standard deviation bands (Deep Azure) without structural confirmation of a breakout, it suggests the market is statistically overextended, offering a potential mean-reversion opportunity back toward the Neon Cyan mid-line.
Note on Mechanics: Because the pivot discovery process requires a defined number of bars to confirm a swing high or low, there is an inherent lookback period. The swing zones will only appear after the pivot has been structurally verified. Furthermore, the linear regression channel recalculates its slope dynamically as new price data is added to the active regime, meaning the exact angle of the channel adapts in real-time until a new CHoCH locks it into history as the "Previous LRC."
● ⚙️ Inputs and Settings
The configuration panel is logically divided into primary analytical modules to allow for precise user calibration.
• Current Anchored CHoCH LRC
Adjust the sensitivity of the pivot discovery engine by modifying the Left and Right Pivot Bars. You can also customize the multipliers for the primary and secondary standard deviation bands, as well as toggle their visibility and modify line weights.
• Previous Anchored CHoCH LRC
Allows for the toggling of the historical channel, providing context on how the previous trend failed. Color and visibility settings are fully adjustable here.
• Swing Points & Zones Settings
Toggle the structural resistance and support boxes on or off, and customize their respective color opacities for a cleaner chart overlay.
• Smart Volatility Squeeze (BB vs KC)
Tune the underlying volatility engine. You can adjust the lookback length for the variance baseline, as well as the specific deviation multipliers for both the Bollinger Band boundaries and the Keltner Channel limits.
• Webhook Execution Configuration
Input exact JSON payload action names for algorithmic execution routing (Long, Short, Close Long, Close Short).
• Dashboard Settings
Customize the background and text colors of the heads-up data matrix to match your specific chart theme.
● 🔍 Deconstruction of the Underlying Scientific and Academic Framework
The mathematical foundation of this script is anchored heavily in econometrics and statistical probability theory.
At its core, the linear regression calculation utilizes the Ordinary Least Squares (OLS) estimator. This formula determines the line of best fit through a sequence of time-series data points by minimizing the sum of the squared differences (residuals) between the observed closing prices and the values predicted by the linear model. The slope of this line represents the average rate of change per unit of time, mathematically quantifying the drift of the active regime.
The parallel bands wrapping the regression line are derived by calculating the standard error of the estimate. Assuming the residuals are normally distributed (Gaussian distribution), one standard deviation captures approximately 68 percent of the price variance, while two standard deviations capture roughly 95 percent. When price moves beyond these outer bands, it represents a statistically significant deviation from the mean, inherently increasing the probabilistic likelihood of mean reversion.
The volatility squeeze mechanic operates on the principle of variance compression. Bollinger Bands are a derivative of standard deviation, making them highly reactive to short-term variance. Keltner Channels utilize the Average True Range (ATR), which measures absolute periodic volatility independent of a central mean. When the standard deviation of price contracts to such a degree that the Bollinger Bands fall entirely within the ATR-based Keltner Channels, it statistically confirms a state of anomalous energy compression. In financial academia, periods of artificially suppressed variance are overwhelmingly followed by periods of geometric expansion, providing the theoretical basis for breakout execution.
⚠️ Disclaimer
All provided scripts and indicators are strictly for educational exploration and must not be interpreted as financial advice or a recommendation to execute trades. I expressly disclaim all liability for any financial losses or damages that may result, directly or indirectly, from the reliance on or application of these tools. Market participation carries inherent risk where past performance never guarantees future returns, leaving all investment decisions and due diligence solely at your own discretion. Indicador
VIX Risk Territory + VIX Z-Score | Astral Vision VIX Risk Territory + VIX Z-Score | Astral Vision 🌠💠
This indicator plots the CBOE Volatility Index on any chart with two analytical modes: a raw VIX view that highlights when the index is in historically elevated territory, and a Z-Score normalized view that measures how statistically extreme the current VIX reading is relative to its own rolling history. The VIX measures the implied volatility of S&P 500 options over the next 30 days and is widely used as a proxy for market fear and risk appetite across all asset classes including Bitcoin, which historically peaks in volatility during the same macro stress events that drive the VIX higher.
Calculation ⚙️
The VIX is fetched from FRED on a daily timeframe. In Raw VIX mode, the index is plotted directly against a configurable overbought threshold, with a fill highlight when the VIX is above that level. The threshold default of 30 corresponds to the widely referenced boundary between normal market volatility and elevated fear regimes: VIX readings above 30 have historically coincided with financial stress events, equity drawdowns, and Bitcoin corrections.
In Z-Score mode, the VIX is normalized as: Z = (VIX - SMA(VIX, N)) / StDev(VIX, N), where N is a configurable rolling lookback window. This transforms the raw VIX level into a dimensionless measure of how far the current reading sits above or below its own historical mean in units of standard deviations. The Z-Score is more analytically useful than the raw level in two respects. First, the VIX has a secular baseline that shifts over time: a reading of 20 meant different things in 2007 versus 2017 versus 2022. The Z-Score adjusts for this drift by always measuring relative to the recent rolling distribution. Second, the Z-Score allows direct comparison of VIX extremes across different regimes: a +2σ reading is always a statistically rare event regardless of the absolute VIX level at the time.
Configurable overbought and oversold thresholds on the Z-Score identify statistically extreme fear and complacency readings. Elevated positive Z-Scores (VIX spiking far above its recent norm) have historically corresponded to capitulation events and macro bottoms. Negative Z-Scores (VIX compressed far below its norm) have historically corresponded to complacency phases that precede corrections.
Plots 📊
Raw VIX line with overbought threshold and fill when above threshold (Raw VIX mode)
Z-Score oscillator with overbought and oversold threshold lines and fill above the upper threshold (Z-Score mode)
Candle coloring or background color on the price chart reflecting current VIX regime, switchable between the two display styles
Inputs 🎛️
Mode: Raw VIX or Z-Score
OB Threshold (Candle): raw VIX level above which fear territory is signaled
OB Threshold (Z-Score): upper Z-Score extreme level
OS Threshold (Z-Score): lower Z-Score extreme level
Z-Score Length: rolling window for mean and standard deviation normalization
Chart Visualization: switch between candle coloring and background color on the price chart
Colors 🎨
5 Astral Vision presets + custom override plus a configurable neutral color. Default: Infinito.
Purpose 🎯
Most crypto-native indicators track on-chain or market structure data without any reference to the macro fear environment. The VIX provides a cross-asset context layer: Bitcoin's largest drawdowns have almost all occurred during periods of elevated macro fear that were visible in the VIX before or during the move. Adding VIX regime context to any Bitcoin chart allows traders to distinguish between Bitcoin-specific corrections and systemic risk-off events, which typically require different responses. The Z-Score mode further improves on the standard practice of using fixed VIX thresholds by adapting the extreme zone definition to the current volatility regime of the VIX itself.
Disclaimer ⭕️
This indicator is for informational and educational purposes only. It does not constitute financial advice. Past performance is not indicative of future results. Always do your own research before making investment decisions. Indicador
GS Univsersal## GS Universal — Trading System Indicator
**GS Universal** is a clean, multi-layer TradingView indicator designed to help traders read market structure, trend quality, momentum, volatility, and signal strength in one organized chart system.
The goal of GS Universal is not to overload the chart with too many indicators, but to combine the most important trading conditions into a clear visual decision framework. It is built for traders who want a structured way to identify possible buy zones, warning areas, exit signals, and trend continuation setups.
---
## Core Purpose
GS Universal helps traders answer five key questions:
1. **Is the stock trending or ranging?**
2. **Is momentum improving or weakening?**
3. **Is volume supporting the move?**
4. **Is price extended or still healthy?**
5. **Is the signal strong enough to act on, wait, or avoid?**
---
## Main Features
* **Buy / Warning / Exit signal system**
* **A/B/C signal grading for easier decision-making**
* **EMA trend structure and EMA crossover logic**
* **Bollinger Bands for volatility and extension reading**
* **RSI momentum and overextension filter**
* **MACD momentum confirmation**
* **Volume and relative volume participation filter**
* **ATR-based risk and volatility awareness**
* **Dynamic trailing stop guidance**
* **Dashboard summary for quick interpretation**
* **Beginner / Professional terminology switch**
* **Clean chart layout with controlled labels**
---
## Signal Logic
GS Universal evaluates price using multiple confirmation layers:
### Trend Structure
The indicator checks whether price is above or below key moving averages and whether the short-term trend is aligned with the broader structure.
### Momentum
RSI and MACD are used to measure whether the move has strength, acceleration, or early weakness.
### Volume Confirmation
Volume and relative volume help confirm whether buyers are truly participating in the move or whether the signal is weak.
### Volatility and Extension
Bollinger Bands and ATR help identify whether price is expanding from compression, becoming overextended, or entering a risky zone.
### Signal Quality
Signals are graded to make interpretation easier:
* **A Signal** = Stronger setup with better alignment
* **B Signal** = Acceptable setup but needs caution
* **C Signal** = Weak or early signal, usually requires confirmation
---
## How to Use
GS Universal is designed as a decision-support tool.
A trader can use it to:
* Identify potential entry zones
* Confirm whether a breakout has strength
* Monitor whether an existing trade is still healthy
* Spot warning signs before momentum fails
* Use trailing stop guidance for trade management
* Avoid chasing overextended moves
The indicator works best when combined with support and resistance, market context, risk management, and personal trading rules.
---
## Important Notes
GS Universal is not a guaranteed buy or sell system.
It is an analytical framework that organizes trend, momentum, volume, and volatility conditions into clearer visual signals.
All signals should be used with proper risk management. Traders should always define their entry, stop-loss, position size, and exit plan before taking any trade.
---
## Disclaimer
This indicator is provided for educational and informational purposes only. It does not provide financial advice, investment recommendations, or guaranteed trading results. Trading involves risk, and users are responsible for their own trading decisions.
Indicador
Miner Profitability Index | Astral Vision Miner Profitability Index | Astral Vision 🌠💠
This indicator constructs a measure of Bitcoin miner profitability per unit of mining difficulty, then applies Z-Score normalization in log space to quantify how statistically extreme current profitability conditions are relative to their own history. Miner profitability is a structurally important on-chain signal because miners are one of the few participants with predictable and measurable cost structures: when profitability collapses, miners are forced to sell reserves to cover operational costs, creating persistent sell pressure; when profitability is exceptionally high, miners tend to accumulate and expand capacity, which historically precedes periods of increased hash rate and eventually difficulty adjustment that compresses margins back toward equilibrium.
Calculation ⚙️
The daily miner revenue in USD is computed from three components: the number of blocks mined that day (computed as the difference between consecutive daily block height readings from Glassnode), the block subsidy in BTC (derived from the block height using the halving schedule: 50 BTC before block 210,000, halving at each subsequent 210,000-block interval), and the current Bitcoin price in USD. The formula is: miner revenue = blocks per day × block reward × BTC price.
This revenue figure is then divided by the current mining difficulty to produce the efficiency ratio: miner revenue / difficulty. Difficulty represents the computational work required to mine a block and serves as a proxy for the aggregate energy and capital expenditure of the mining network. Dividing revenue by difficulty produces a measure of how many dollars miners earn per unit of computational difficulty, normalizing for the expanding size of the mining network over time. Without this normalization, absolute revenue would grow indefinitely simply due to price appreciation and hash rate expansion, making historical comparisons meaningless.
The efficiency ratio is smoothed with a configurable EMA to reduce the noise introduced by day-to-day variation in block count. The natural logarithm is then taken before applying the Z-Score, which is necessary because the efficiency ratio follows an approximately log-normal distribution: in raw space it would be heavily right-skewed, making the standard deviation an unreliable measure of typical deviation. In log space the distribution is much more symmetric and the Z-Score thresholds carry consistent statistical meaning across all periods.
The Z-Score is computed as: (log(efficiency) - SMA(log(efficiency), N)) / StDev(log(efficiency), N), where N is the configurable lookback window. This expresses how many standard deviations the current log-efficiency sits above or below its rolling historical mean. Two pairs of thresholds define moderate and severe extreme zones on each side.
Plots 📊
Z-Score oscillator colored by zone: two upper levels and two lower levels with graduated opacity
Two upper and two lower threshold lines
Zero midline
Fill highlights when Z-Score is beyond the outer thresholds
Static zone fills between inner and outer thresholds on both sides
Background color on the price chart with four gradient levels reflecting zone severity
Inputs 🎛️
Z-Score Lookback: rolling window for mean and standard deviation normalization
Smoothing: EMA period applied to the raw efficiency ratio before log transformation
Upper Z 1 and Upper Z 2: configurable inner and outer upper threshold levels
Lower Z 1 and Lower Z 2: configurable inner and outer lower threshold levels
Colors 🎨
5 Astral Vision presets + custom override. Default: Futura.
Purpose 🎯
Standard miner revenue charts display absolute USD earnings, which grow indefinitely with price and provide no statistical context for whether current conditions are extreme or normal. Dividing by difficulty removes the network size effect, and normalizing with a Z-Score in log space makes readings directly comparable across all market cycles including early periods when absolute revenue was tiny. The dual threshold system separates mild deviations from statistically severe conditions, providing a more granular signal than a single overbought/oversold level.
Disclaimer ⭕️
This indicator is for informational and educational purposes only. It does not constitute financial advice. Past performance is not indicative of future results. Always do your own research before making investment decisions. Indicador
Whale Accumulation Index | Astral Vision Whale Accumulation Index | Astral Vision 🌠💠
This indicator tracks the proportion of Bitcoin's total on-chain transaction volume attributable to large transactions, using CoinMetrics data to separate whale-scale activity from retail-scale activity, then applies two different analytical lenses to that ratio to identify when large holders are accumulating or distributing at statistically unusual rates.
Calculation ⚙️
The base input is the ratio of large transaction volume to total transaction volume in USD, both fetched from CoinMetrics on a daily timeframe. Large transaction volume measures the aggregate USD value of all on-chain transactions above a defined size threshold, while total transaction volume covers all on-chain activity. Dividing the two produces a ratio between 0 and 1 that measures what fraction of all Bitcoin moved on-chain each day was moved by large participants. A rising ratio means whale activity is growing as a share of total network flow; a falling ratio means retail activity is proportionally dominant. This ratio is smoothed with a configurable SMA before all subsequent calculations.
The indicator offers two modes that address different analytical questions.
Z-Score mode normalizes the smoothed ratio against its own rolling mean and standard deviation over a configurable lookback window. The formula is: Z = (ratio - mean(ratio, N)) / stdev(ratio, N). This produces a reading that measures how statistically unusual the current whale dominance level is relative to its own history. A positive Z-Score means whale share of volume is elevated above its historical norm; a negative Z-Score means it is depressed. The thresholds mark the statistically extreme zones where readings have historically been rare.
Conviction mode computes a divergence signal between whale momentum and price momentum. For each series, the rate of change over a configurable lookback is computed as (current - past) / abs(past), producing a proportional momentum measure. Both the price ROC and the whale ratio ROC are then independently Z-Score normalized, and the conviction signal is the difference: whale Z-Score minus price Z-Score. A positive conviction reading means whale activity is accelerating faster than price, historically associated with accumulation ahead of price moves. A negative reading means price is running ahead of whale activity, historically associated with distribution or weak institutional participation in the current price move.
Plots 📊
Main oscillator in Z-Score or Conviction mode, colored by threshold zone
Overbought and oversold threshold lines
Fill highlight when oscillator enters either extreme zone
Background color on the price chart when oscillator is in extreme zones
Inputs 🎛️
Mode: Z-Score or Conviction
OB Threshold Z-Score and Conviction: configurable upper extreme levels per mode
OS Threshold Z-Score and Conviction: configurable lower extreme levels per mode
Z-Score Window: normalization lookback for both modes
Smoothing: SMA period applied to the raw whale ratio before calculations
Conviction ROC Length: lookback for the rate of change in Conviction mode
Colors 🎨
5 Astral Vision presets + custom override. Default: Inferno.
Purpose 🎯
Standard volume indicators measure total on-chain or exchange volume without distinguishing between the size of individual transactions, treating a single $100 million transfer the same as ten thousand $10,000 transfers in aggregate volume terms. This indicator isolates the large-transaction component specifically, making it possible to detect when whale-scale capital is disproportionately active on-chain relative to its historical baseline. The Conviction mode adds a second dimension by measuring whether whale activity is leading or lagging price, separating periods where large holders are front-running price moves from periods where they are simply responding to them.
Disclaimer ⭕️
This indicator is for informational and educational purposes only. It does not constitute financial advice. Past performance is not indicative of future results. Always do your own research before making investment decisions. Indicador
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