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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. 指标
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. 指标
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.
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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. 指标
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. 指标
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Breaker Block Reversal Engine [forexobroker]Breaker Block Reversal Engine trades the polarity flip of a failed order block: when price closes straight through an order block instead of respecting it, that zone flips polarity and the level that trapped one side now fuels the other. The unique angle is treating the flip as a persistent regime, then timing entries with an EMA reclaim inside the active breaker bias rather than waiting only for the rarer classic retest.
🔶 ALGORITHM
1. Confirm swing structure with symmetric pivots and track the most recent confirmed swing high and low.
2. Detect a structural break: close crosses the last confirmed swing low (down break) or swing high (up break).
3. Find the origin candle by scanning back up to OB Search Lookback bars for the last opposing candle; validate the impulse with Min Impulse x ATR.
4. Polarity flip: a bearish origin OB becomes a BULLISH breaker once the break reference (close or high) closes above its top; a bullish origin OB becomes a BEAR breaker once price closes below its bottom. Confirm-on-close toggle controls strictness.
5. Retest detection uses a Retest Buffer x ATR tolerance band drawn around the flipped breaker.
6. Persistent breaker bias: a flip arms bias +1 / -1; the regime clears when that breaker is retested (one-shot) or the opposite breaker flips.
7. Entry timing: within an active breaker bias, an EMA reclaim (ta.crossover / ta.crossunder of close vs the Entry Reclaim EMA) fires the trade. Retest-Only Mode swaps this for the strict breaker-zone retest.
🔶 SIGNAL LOGIC
- Buy: a bear order block has flipped to a bull breaker (bias +1) and close crosses over the Entry Reclaim EMA (or, in Retest-Only Mode, the bull breaker retest), in session, position not already long, after the cooldown window, only on barstate.isconfirmed; position-lock flips to LONG.
- Sell: a bull order block has flipped to a bear breaker (bias -1) and close crosses under the Entry Reclaim EMA (or the bear breaker retest), in session, position not already short, cooldown elapsed, on barstate.isconfirmed; position-lock flips to SHORT.
Only fires when a breaker has flipped polarity and set directional bias.
🔶 INPUTS
- Structure: pivot length for swing confirmation (default 7), OB search lookback, and ATR length.
- Impulse filter: minimum impulse in ATR multiples to qualify the origin (default 1.0x ATR).
- Retest tolerance: buffer added around the breaker on retest in ATR multiples (default 0.10x ATR).
- Break confirmation: confirm-on-close toggle for the polarity flip (default on).
- Signal Logic: Entry Reclaim EMA length (default 9) and Retest-Only Mode toggle (default off).
- Cooldown: minimum bars between signals (default 6).
- Filters: optional session restriction and one-signal-per-breaker lock (default on).
- Visual: breaker zone boxes, dashboard, 3-layer glow, buy/sell colors, dashboard background.
🔶 ALERTS
BBR Buy, BBR Sell, BBR Any Signal, BBR Down Break, BBR Up Break, BBR Bull Flip, BBR Bear Flip, BBR Bull Retest, BBR Bear Retest, BBR Bull OB Set, BBR Bear OB Set, BBR Structure, BBR Webhook JSON.
🔶 LIMITATIONS
- Requires warm-up: pivots confirm only after Pivot Length bars on each side, so early history yields no structure.
- Defaults are tuned for liquid instruments; the impulse and retest-buffer ATR multiples may need adjustment on thin symbols.
- Retest-Only Mode is intentionally rare; the default EMA-reclaim regime mode trades more frequently.
- Non-repainting by design (pivots confirmed, signals on bar close); the anchoring swing appears Pivot Length bars after it formed.
- A breaker is a probabilistic zone, not a guaranteed reversal; polarity flips can fail in strong trends.
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Adaptive Sharpe Regime [forexobroker]Adaptive Sharpe Regime computes a rolling Sharpe ratio of bar returns and sorts the result into directional regime tiers, then times entries with a reclaim-EMA cross. The unique angle is a Newey-West lag-1 autocovariance correction that stops serial correlation in returns from artificially inflating the Sharpe, making the regime read self-normalising and comparable across instruments and timeframes.
🔶 ALGORITHM
1. Compute per-bar returns (log returns by default, or simple returns) and their rolling mean and variance over the Sharpe window.
2. Estimate the lag-1 autocovariance of demeaned returns over the same window.
3. Apply the Newey-West correction: corrected variance = variance + 2 x autocovariance, then take the square root for the corrected volatility.
4. Sharpe = mean return / corrected volatility (guarded against zero volatility).
5. Sort the Sharpe into five regime tiers using the Strong and Mild thresholds: strong+ (+2), mild+ (+1), flat (0), mild- (-1), strong- (-2). The tier sign is the directional bias.
6. With a bias set, a close crossover (long) or crossunder (short) of the reclaim EMA times the actual entry. Signals respect an optional strong-only filter, a bar cooldown, position-lock, and bar-close confirmation. Non-repainting.
🔶 SIGNAL LOGIC
- Buy: in-session AND tier is tradable AND bias = +1 AND close crosses over the reclaim EMA AND position not already long AND cooldown elapsed AND barstate.isconfirmed (then position locks long).
- Sell: in-session AND tier is tradable AND bias = -1 AND close crosses under the reclaim EMA AND position not already short AND cooldown elapsed AND barstate.isconfirmed (then position locks short).
- Only fires when the Sharpe tier is tradable (mild or stronger; strong-only if "Require Strong Tier" is on).
🔶 INPUTS
- Sharpe group: window length over which returns and Sharpe are measured; default Sharpe Window 40.
- Returns mode: log vs simple returns toggle; default Log Returns on.
- Newey-West: toggle the lag-1 autocovariance variance inflation; default on.
- Regime tiers: Strong and Mild absolute-Sharpe thresholds; default Strong Sharpe 0.8.
- Mild threshold: lower band for the mild tier; default Mild Sharpe 0.2.
- Volatility reference: ATR length used for the dashboard reference; default ATR Length 14.
- Signal Logic group: entry reclaim EMA, strong-only filter, cooldown; default Entry Reclaim EMA 9.
- Cooldown: minimum bars between signals; default Cooldown Bars 5.
- Filters group: optional session restriction and session window; default Restrict to Session off.
- Visual group: dashboard, 3-layer glow, reclaim EMA, buy/sell colors, dashboard background.
🔶 ALERTS
ASH Buy, ASH Sell, ASH Any Signal, ASH Strong Plus, ASH Strong Minus, ASH Bias Bull, ASH Bias Bear, ASH EMA Up, ASH EMA Down, ASH Sharpe Zero, ASH Tier Change, ASH Armed, ASH Webhook JSON.
🔶 LIMITATIONS
- The Sharpe ratio needs a full window of returns to stabilise, so the first Sharpe Window bars are warm-up and tiers should not be trusted there.
- The Newey-West correction only addresses lag-1 serial correlation; higher-order autocorrelation is not modelled and the corrected variance is clamped to a small positive floor.
- Regime tiers are statistical classifications, not forecasts; a strong tier describes recent risk-adjusted drift, not future returns.
- Default thresholds are tuned for liquid instruments; thin or gappy symbols can produce unstable Sharpe values.
- Entries confirm on bar close, so the reclaim-EMA cross is acknowledged with one-bar lag versus intrabar price.
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TASC 2026.06 One Percent A Week - Adaptive█ OVERVIEW
This script implements a variant of Dion Kurczek's "High-Probability Weekly Trading Strategy For TQQQ" concept, as presented in his article "Part 2: Variations and Community Enhancements" in the June 2026 edition of the TASC Traders' Tips . In the article, the author explores multiple possible revisions to the original strategy. In this publication, we've adapted the final revision presented in the article: the "Adaptive Weekly Momentum Exit" model.
█ BACKGROUND
The One Percent a Week strategy was recently introduced in the March 2026 publication . It utilizes the 3x leverage and volatility of TQQQ to attempt to capture 1% weekly profit from reactive moves made early in the week. The system was designed with simple, strict trading rules to prevent overtrading and control drawdown. Although this was the goal, the system left room for improvements to the base concept.
The original system used the following mechanics:
Enter a trade after a 1% dip from the Monday open.
Set an initial profit target of 1% for the open trade.
If the trade's drawdown is 0.5% or greater, create a short limit order to fill at the entry price.
Close the trade at the end of the week, regardless of profit or loss.
The version discussed in this article overhauls most of these mechanics. It preserves the same premise of striving for 1% average weekly returns, but aims to minimize drawdowns and allow for conditionally higher profit targets by introducing the following improvements:
Entry on Monday open : The strategy enters a trade at the open of each Monday rather than waiting for a 1% dip, increasing market exposure.
Dynamic profit target : The initial profit target for each trade is 7%. Depending on the trade performance during the Monday session, the target increases by 1.1%, decreases to 2.5%, or remains unchanged.
Momentum-weakness exit : If the trade's performance is strong on Monday and begins to weaken on Tuesday, the system places a market order to exit the trade to avoid potential increased losses.
Hard stop-loss : Each trade has a fixed 1.5% stop-loss order to prevent trades from further deterioration.
█ THE RULES
The rules of the "Adaptive Weekly Momentum Exit" model discussed in the article differ significantly from those in the original strategy. The rules for this version are as follows:
Enter a new trade at Monday's open.
Set a fixed stop-loss order at 1.5% below the entry price.
Set an initial take-profit order at 7% above the entry price.
Check the trade's performance at the end of Monday. If the trade is still open and the profit is greater than 0.3%, multiply the take-profit price by 1.011 to allow a potential profit of 8.177%. If the open profit is currently zero or negative, reduce the target to 2.5%.
At the end of Tuesday, check for a momentum-based exit condition. If the return was above a specified threshold on Monday (2% by default) but fails to meet a separate threshold on Tuesday (3% by default), indicating a loss of momentum, place an order to close the position early.
If the position is open at the end of the week, close it immediately.
▌Properties
Position Sizing : Sizing is 10% of equity, the same as in the article.
Commissions : Commission value is set to $0. Most major US brokers charge $0 commission on stocks and ETF trades, including leveraged ETFs such as TQQQ.
Slippage : Slippage is not discussed in the article and likewise has not been included in this strategy.
Note:
This script enables "Recalculate after order is filled" by default, allowing the strategy to update trades and orders on the same bar to match the intended mechanics discussed in the article. Using this setting by default results in a warning in the strategy report because it can cause lookahead bias in some scripts. However, this script does not leak future price data into the past to control its orders or logic.
█ DISPLAY
Similar to the strategy published for Part 1, this script plots lines to visualize the levels that it uses for order execution:
Entry price : The blue line plots the entry price for the position, which is placed at Monday's opening price.
-1.5% Hard stop : The red dashed line is the level at which the 1.5% fixed stop loss is placed.
Profit target : The solid green line shows the profit target.
█ INPUTS
"Monday %" and "Tuesday %" : These thresholds adjust the parameters of the loss-of-momentum exit. An early exit occurs if trade's return percentage as of Monday is greater than the "Monday %" value, and the current percentage on Tuesday is less than the "Tuesday %" value. The default percentages are the specific values referenced in the article.
策略
指标
NEWS [Ordinary Trader]# NEWS
NEWS by Ordinary Trader marks high-impact USD economic news events on your chart — CPI, NFP, FOMC, PCE, GDP, and the rest of the red-folder calendar — both past and upcoming.
Part of the Ordinary Trader family alongside **EMAs**, **FVGs**, **KEY LEVELS**, **ZONES**, and **STRATEGY (LITE / PRO)**. The methodology calls for standing aside during high-impact news; this indicator makes those events impossible to miss.
## Visuals
Each event produces a **full-height vertical column** at the exact event time, plus a **small triangle marker** in a thin pane below the chart. Hover the marker for event name, day, date, and time in your selected timezone.
~3 months of historical events stay visible. ~1 week of upcoming events floats in the projected area to the right of your latest candle.
## Tiers + Filters
Events auto-classify into 4 tiers, each with its own colour:
- **Tier 1** — CPI, NFP, FOMC Rate Decision, PCE, GDP
- **Tier 2** — FOMC Minutes, Retail Sales, ISM PMI, PPI
- **Tier 3** — ADP, Consumer Confidence, Michigan, JOLTS, Initial Jobless Claims, Durable Goods
- **Uncategorised** — everything else (default OFF)
Hybrid filtering: tier master toggles + per-event toggles.
## Alerts (optional)
Pre-event TradingView alerts fire N minutes before each visible event (default 30). Same filters apply. One-time setup: enable in indicator settings + add a TradingView alert with condition `Any alert() function call`.
## Timezone
10 options (ET / CT / PT / UTC / UK / CET / Tokyo / HK / Singapore / Sydney) for tooltips and alerts. DST automatic.
## Notes
- USD events only; non-USD on the roadmap.
- Built on the Forex Factory data feed maintained by **@toodegrees** via Pine Seeds. The visual layer, tier classification, filter UI, and alerts are original work.
- License: Mozilla Public License 2.0. Free to use. 指标
Put Credit Spread Combined Price Tracker📊 Description
The Put Credit Spread Combined Price Tracker is an advanced options visualization and risk assessment workspace designed specifically for multi-position credit spread sellers. Instead of guessing your proximity to risk, this indicator maps your mathematical break-even thresholds directly onto your live asset candle chart while maintaining a real-time Black-Scholes Options Pricing Matrix Dashboard.
This tool is ideal for tracking individual or nested index options structures (such as SPX, NDX, or RUT) where precision risk boundary management is critical.
________________________________________
⚙️ How to Set Up Your Spreads
1. Add to Chart: Load the indicator on your target ticker (e.g., SPX).
2. Open Settings: Click the gear icon on the indicator name to open the configuration menu.
3. Configure Your Engine:
o Keep Enable Automated Volatility checked to let the script automatically extract a continuous historical volatility proxy, or uncheck it to key in your broker's implied volatility (IV %) reading manually.
4. Input Your Positions (Supports up to 4 Parallel Trades):
o Check the Box to enable a specific spread position.
o Short/Long Put Strike: Key in your exact leg boundaries.
o Premium Received: Enter the net premium collected per spread block (e.g., 5.00 for a $500 credit).
o Contracts Quantity: Enter your position size.
o Start/Exp Date: Key in your precise calendar execution parameters. The indicator automatically bounds the line drawings to your trade's specific lifecycle window.
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📉 Reading the Chart Visuals
• Dashed Break-Even Lines: The script removes clutter by stripping out raw strike lines. Instead, it plots the absolute net financial break-even floor for each position (Short Strike - Premium Collected). If the market candles drop below this dashed line at expiration, the position enters a net loss state.
• Right-End Anchored Labels: Each active position features a dedicated text flag pinned directly to the right margin of your screen, letting you instantly match a specific break-even line to its designated position entry.
________________________________________
🧮 Understanding the Dashboard Table (Bottom-Right)
The tracking matrix evaluates your positions dynamically using a full Black-Scholes Options Pricing model loop on every price tick:
• Strikes (S/L): Displays your structured position boundaries for quick auditing.
• Current Mark: Shows what the option spread is worth right now in the open market before expiry, modeling continuous time decay (\(\theta \)) and underlying spot movements.
• Live Open P&L: Replicates your broker's dashboard. It dynamically updates using (Net Credit - Current Mark Value) * 100 * Qty. Cells turn Green for an open profit and Red for an open loss.
• Position Delta: Displays your aggregate Greeks exposure. Since put credit spreads are directionally bullish, this value will calculate as a net positive number.
• Current IV: Shows the exact annualized volatility proxy percentage fueling your options pricing calculations.
• Days Left: Counts down the exact localized time window remaining before contract expiration.
________________________________________
指标
Helios Volatility Atlas [JOAT]Helios Volatility Atlas
Introduction
Helios Volatility Atlas is an open-source regime and volatility mapping indicator. It combines an adaptive baseline, DMI/ADX trend strength, volatility scoring, VWAP sigma bands, premium/discount structure, squeeze detection, retest zones, right-side level rails, and a top-right dashboard.
The problem Helios addresses is market context. A trend signal means something different in compression than it does during expansion. A retest means something different near discount than it does at an outer volatility channel. Helios brings those layers into one chart so users can interpret price action with regime, volatility, and location in view.
Core Concepts
1. Adaptive Baseline
The baseline blends an EMA-style trend component with an efficiency-ratio adaptive component. When price moves efficiently, the adaptive side reacts more. During choppy movement, the baseline becomes slower and more stable.
2. Volatility Score
ATR is compared against its own moving average and standard deviation. The result is normalized into a 0-100 volatility score so the dashboard can distinguish low, moderate, and high volatility states.
3. Institutional Channels
Helios draws a channel around a mean using ATR-based distances. Inner channels describe normal volatility, while outer pockets mark more extended price areas where reversion or continuation context may matter.
4. Squeeze and Expansion State
The squeeze model compares a Bollinger-style width against a Keltner-style width. When compression releases, the chart highlights a transition from low-energy conditions into expansion.
5. Retest Zones and Rails
Confirmed retests can create projected zones. Right-side rails label important live references such as baseline, VWAP, channels, premium/discount levels, and previous day levels.
Features
Adaptive baseline: Blends trend-following and efficiency-aware smoothing
Volatility score: Converts ATR expansion/compression into a dashboard value
Trend cloud: Fills the space between baseline and VWAP
VWAP sigma bands: Shows price stretch around VWAP
Premium/discount map: Uses a rolling anchor range with equilibrium, discount, and premium levels
Squeeze detection: Highlights compression and release states
Retest zones: Projects confirmed bullish and bearish retest areas
Right-side rails: Labels baseline, VWAP, channels, premium/discount, and day levels
Theme presets: Includes Helios, Consensus Aqua, Sentinel Neon, and Cobalt Risk color families
Top-right dashboard: Shows regime, bias, volatility, squeeze, channel, VWAP sigma, price state, and key levels
Input Parameters
Core Engine:
Adaptive Baseline Length
Efficiency Lookback
ATR Length
ADX / DMI Length
Volatility Window
Volatility System:
Channel Memory
Base Channel ATR Multiple
Outer Channel ATR Multiple
Squeeze Length
Squeeze BB and KC multipliers
Display:
Show Trend Cloud
Show VWAP Sigma Bands
Show Retest Zones
Show Right-Side Level Rails
Theme Preset
Dashboard Text Size
How to Use This Indicator
Step 1: Read the regime
The dashboard classifies the current state as bullish, bearish, expansion, or range.
Step 2: Check volatility
Use the volatility score and squeeze row to decide whether the market is compressed or expanding.
Step 3: Compare price to VWAP and channels
VWAP sigma and ATR channels show whether price is near value, stretched, or reverting from an outer area.
Step 4: Use retest zones as context
Retest zones mark recent confirmed reactions. They are contextual levels, not automatic entries.
Indicator Limitations
Adaptive baselines can lag during sudden reversals
Squeeze release does not define direction by itself
Retest zones can fail in strong momentum conditions
Higher volatility symbols may require adjusted ATR/channel settings
Originality Statement
Helios Volatility Atlas combines adaptive trend state, DMI/ADX confirmation, ATR volatility scoring, VWAP sigma distance, squeeze detection, premium/discount location, projected retest zones, and right-edge rails into a single open-source Pine v6 regime atlas. The components are designed to work as one context engine, not as a loose mashup.
Disclaimer
This script is for educational and informational use only. It is not financial advice and does not predict future price direction. Use independent analysis and risk management.
-Made with passion by jackofalltrades
指标
OTHERS/USDT.D Z-Score | Astral Vision OTHERS/USDT.D Z-Score | Astral Vision 🌠💠
This indicator constructs a ratio between the OTHERS market cap dominance and USDT dominance, then applies Z-Score normalization in log space to measure how statistically extreme the current positioning of altcoin capital relative to stablecoin dry powder is compared to its own history.
OTHERS dominance tracks the combined market cap share of all cryptocurrencies excluding Bitcoin, Ethereum, and the top few large caps, making it a direct proxy for altcoin season conditions. USDT dominance measures the share of total crypto market cap held in Tether, which rises when capital exits risk assets into stablecoins and falls when stablecoin capital is deployed into crypto assets.
Calculation ⚙️
The ratio is computed as OTHERS dominance divided by USDT dominance on a daily timeframe. A high ratio means altcoin capital is large relative to stablecoin reserves, indicating that available dry powder has been deployed and the altcoin market is extended. A low ratio means stablecoin dominance is high relative to altcoin exposure, indicating that capital has retreated to safety and dry powder is accumulating.
The natural logarithm of this ratio is then taken before all subsequent calculations. Working in log space normalizes the exponential growth of both series across different market cycles, ensuring that a ratio change from 2 to 4 is treated as equivalent in magnitude to a change from 4 to 8, which is the correct treatment for proportional dominance relationships.
A simple moving average and standard deviation are computed over the configurable Z-Score lookback window, producing the Z-Score as: (log ratio minus mean) / standard deviation. This expresses the current ratio in units of standard deviations above or below its own historical average, making readings directly comparable across cycles regardless of the absolute dominance levels involved.
In Trend mode, an EMA of configurable length is applied to the Z-Score itself, functioning as a signal line. When the Z-Score is above its EMA, the momentum of the ratio is positive and altcoin conditions are improving relative to stablecoin reserves; when below, the momentum is negative.
Plots 📊
Z-Score oscillator colored by mode and regime
Two upper threshold lines marking statistically elevated altcoin exposure (Extremes mode)
Two lower threshold lines marking statistically depressed altcoin exposure (Extremes mode)
Fill highlights when Z-Score enters the outer extreme zones
Zero baseline
EMA signal line (Trend mode)
Background color on the price chart when Z-Score enters the outer extreme zones (Extremes mode)
Candle coloring on the price chart reflecting current regime in both modes
Inputs 🎛️
Visualization: Extremes or Trend
Z-Score Lookback: normalization window for mean and standard deviation
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
EMA Length: smoothing period for the trend signal line (Trend mode)
Colors 🎨
5 Astral Vision presets + custom override. Default: Futura.
Purpose 🎯
Standard altcoin season indicators use fixed dominance thresholds or simple moving average crossovers on raw dominance values, which are not statistically normalized and behave differently across cycles as the total crypto market cap grows. This indicator normalizes the ratio in log space over a long rolling window, making a Z-Score of +2.0 in 2021 and a Z-Score of +2.0 in 2024 carry equivalent statistical weight despite the absolute dominance percentages being different. The dual threshold system further separates moderate elevated conditions from statistically severe ones, allowing a more nuanced reading than a single overbought/oversold line can provide.
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. 指标
Bitcoin vs VUG/VTV Rolling Ratio Analysis | Astral Vision Bitcoin vs VUG/VTV Rolling Ratio Analysis | Astral Vision 🌠💠
This indicator measures the relative momentum of Bitcoin against the VUG/VTV ratio, which is the price of Vanguard's Growth ETF divided by Vanguard's Value ETF. This ratio is one of the most direct and liquid expressions of the growth-versus-value rotation dynamic in institutional equity markets: when VUG outperforms VTV the ratio rises, signaling that markets are rewarding high-multiple, long-duration assets; when VTV outperforms, the ratio falls, signaling a rotation toward defensive, cash-flow-heavy assets typically associated with tightening liquidity or rising rates.
Bitcoin is itself a long-duration, high-beta asset with structural similarities to growth equities: it tends to expand during the same liquidity-driven environments that favor VUG over VTV, and contract during the same risk-off environments that favor VTV over VUG. Tracking the VUG/VTV ratio alongside Bitcoin's own momentum reveals whether the macro environment is confirming or diverging from Bitcoin's price action, which historically has been a more reliable framework for identifying regime shifts than price-only signals.
The indicator operates in three modes.
Average Valuation mode averages the normalized momentum of Bitcoin and the VUG/VTV ratio into a single composite score, comparing it against configurable overbought and oversold percentile thresholds to identify when both are simultaneously extended or depressed.
Average Trend mode compares the composite score against its own midpoint to determine whether aggregate momentum is in its upper or lower historical half.
Double Signal mode plots Bitcoin and the VUG/VTV ratio momentum separately on the same normalized scale, making divergences between the two directly visible.
This is a cross-asset macro momentum and rotation indicator suited for swing trading and position trading on daily timeframes , functioning as a regime filter that connects Bitcoin's price action to one of the most closely watched equity style rotation signals in institutional markets.
How it differs from standard relative strength tools
A standard BTC/VUG ratio chart divides prices directly, producing a series with no statistical normalization and no connection to VTV. This indicator uses percentage returns normalized over a rolling window, making readings statistically consistent across different periods, and compares Bitcoin not against a single equity ETF but against the growth-value spread, which is a second-order signal capturing the macro liquidity regime rather than a single sector's performance. The three-mode architecture further distinguishes it from a simple ratio overlay by providing composite valuation, trend, and component-level views within a single indicator.
Plots 📊
Averaged normalized momentum oscillator with overbought and oversold threshold lines (Average Valuation mode)
Averaged normalized momentum with fill against the 50 midline (Average Trend mode)
Bitcoin and VUG/VTV normalized momentum plotted separately (Double Signal mode)
Overbought and oversold threshold lines (Average Valuation and Double Signal modes)
50 midline reference
Background color on the price chart when average enters extreme zones (Average Valuation mode)
Candle coloring on the price chart reflecting current regime in all three modes
Inputs 🎛️
Mode: Average Valuation, Average Trend, or Double Signal
Normalization Period: rolling window for the min-max normalization of returns
Lookback Period: return calculation window for both Bitcoin and the VUG/VTV ratio
Overbought Threshold: upper percentile level for extreme zone detection
Oversold Threshold: lower percentile level for extreme zone detection
Transparency: opacity of the background color highlight
Colors 🎨
5 Astral Vision presets + custom override. Default: Paradiso.
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. 指标
Tactical Atlas [DAFE]Tactical Atlas : A Multi-Dimensional Regime & Phase Space Visualizer
This is not an indicator. This is a tactical map of the market's current state. It synthesizes multiple dimensions of data—trend, momentum, volatility, and stress—into a single, observable point in a four-quadrant phase space, providing an unparalleled, at-a-glance view of the market's true character.
█ CHAPTER 1: THE PHILOSOPHY - FROM CLUTTER TO CLARITY
The modern trader is drowning in data. We stack our charts with RSI, MACD, ADX, and Bollinger Bands, creating a cluttered screen of conflicting signals. We are then forced to perform a complex, subjective mental synthesis to answer the most fundamental question: " What is the market actually doing right now? " Is it a healthy trend, an exhausted trend, a low-volatility compression, or a nascent recovery? Answering this requires juggling multiple concepts simultaneously.
The Tactical Atlas was engineered to solve this problem of information overload. It is a powerful data visualization engine built on a simple premise: a picture is worth a thousand indicators. It takes a suite of underlying metrics that quantify the market's key characteristics and maps them into a single, intuitive, two-dimensional "phase space." The position and trajectory of a single point within this space provides a complete, synthesized, and immediate understanding of the market's regime.
This tool is not about adding another line to your chart; it's about replacing a dozen. It is a system for achieving profound contextual awareness, allowing you to see the market's state, its evolution over time, and its alignment across multiple timeframes, all within a single, elegant visual interface.
█ CHAPTER 2: THE CORE INNOVATIONS - ORIGINALITY & USEFULNESS
The Tactical Atlas is not a "mashup" of existing indicators. It is a novel framework for data synthesis and visualization, built upon a suite of proprietary and established analytical concepts.
The Phase Space Architecture: This is the core innovation. Instead of displaying multiple oscillators in separate panes, the Atlas maps different dimensions of market data onto the X and Y axes of a scatter plot. The result is a single "point" whose coordinates (x, y) represent the synthesized state of the market. Its position and movement provide a rich, holistic view that is impossible to achieve with standard indicators.
Three Distinct Analytical Modes: The Atlas is not one map, but three. You can instantly switch between different "lenses" to view the market's state from different perspectives, each answering a different critical question:
Phase Space: Maps Trend Strength (X-axis) vs. Momentum (Y-axis). Answers: "Is this a strong trend with momentum, or is it an exhausted trend?"
Stress Orbit: Maps Volatility Rank (X-axis) vs. Market Stress (Y-axis). Answers: "Is this a calm, low-volatility environment, or a high-stress, fearful one?"
Permission Field: Maps Directional Bias (X-axis) vs. a proprietary Permission Score (Y-axis). Answers: "Does the market have the underlying conditions (permission) to support a sustained directional move?"
The Four Quadrant Regime Model: The phase space is divided into four distinct, named quadrants: RECOVERY, EXPANSION, COMPRESSION, and EXHAUSTION . The position of the point within these quadrants provides an immediate, human-readable classification of the market's character, allowing you to instantly align your strategy with the current environment.
Multi-Timeframe "Ghost" Points: A revolutionary approach to multi-timeframe analysis. The Atlas calculates the state of the market on up to three higher timeframes and projects them as semi-transparent "ghost" points into the same phase space. This allows you to see, at a glance, if the current timeframe's regime is in alignment or conflict with the macro trend, without ever leaving your chart.
The Velocity Vector: The system doesn't just show you where the market is in phase space; it shows you where it's going. A dynamic arrow is drawn from the current point, indicating the direction and magnitude of its recent travel through the space. A point moving rapidly from Compression towards Expansion is a powerful leading signal of an impending breakout.
█ VISUALS - HOW TO READ THE ATLAS
Every visual element is a data-rich component designed for rapid, intuitive interpretation.
The Scatter Plot (The Map)
The Quadrant Fields: The four colored quadrants that define the map. Each is labeled and has a distinct meaning. Your primary goal is to identify which quadrant the market currently occupies.
The Gridlines: These faint lines mark the 25%, 50%, and 75% levels on both the X and Y axes, providing a clear visual reference for the strength of each component. The central intersection is the "neutral" point.
The Trail: A multi-colored line that shows the historical path of the market's state over the last N bars. It allows you to see the journey the market took to arrive at its current position. A smooth, directional trail indicates a healthy, evolving regime. A tangled, chaotic trail indicates indecision.
The Current Point (The Comet): This is you. It is the most important point on the map, representing the market's state on the most recent bar. It is rendered with a multi-layered, pulsating glow to make it the clear focal point. Its color is determined by the quadrant it is in.
The Velocity Vector: An arrow drawn from the current point, indicating its recent direction of travel through the phase space. This is a critical leading indicator, showing you the "momentum of the regime."
The MTF Ghost Points (◆): These are the semi-transparent, diamond-shaped markers. Each ghost represents the position of a higher timeframe (e.g., 1H, 4H, Daily) within the same coordinate space.
Alignment: If the ghost points are clustered near the current point, it signals strong multi-timeframe confluence.
Divergence: If the current point is in the "Expansion" quadrant but the Daily ghost point is deep in the "Exhaustion" quadrant, it's a powerful warning of a potential macro reversal.
DASHBOARD (THE LEGEND)
Current Quadrant: A clear, color-coded readout of the market's current regime.
MTF Quadrants: Displays the regime classification for each of your selected higher timeframes, allowing for quick confirmation of alignment.
Axis Info: Explicitly tells you what the X and Y axes currently represent based on your selected "Visualization Mode."
Confluence & Stress: Provides the numerical score for MTF alignment and the calculated "Stress Score."
Earnings Block: For equity traders, this section automatically pulls and displays the latest earnings data (Actual vs. Estimate) and the resulting surprise percentage.
How To Read Guide: A built-in legend that provides a quick reference for interpreting the various visual elements.
█ THE THREE ATLASES - A DEEP DIVE INTO THE MODES
MODE 1: PHASE SPACE (The Classic)
X-Axis: Trend Strength. A composite score derived from ADX, price position relative to a long-term MA, and the alignment of multiple EMAs. It measures the existence and quality of a directional trend. A value of 100 is a powerful, unambiguous trend.
Y-Axis: Momentum. A composite score derived from RSI, ROC, and Stochastic RSI. It measures the velocity and acceleration of price.
This mode answers: Is this a strong trend backed by momentum (Expansion), or is the momentum dying while the trend is still high (Exhaustion)? Is momentum starting to build from a low-trend base (Recovery)?
MODE 2: STRESS ORBIT (The Risk Gauge)
X-Axis: Volatility Rank. Measures the current ATR relative to its historical range over a long lookback. A value of 100 means volatility is at a multi-month high.
Y-Axis: Market Stress. A proprietary score that blends Volatility Rank with Bollinger Band Width and a penalty for a lack of trend strength. It quantifies "unproductive" or "fearful" volatility.
This mode answers: Is this a low-volatility, calm environment (Compression), or a high-stress, dangerous one (Exhaustion)? Is volatility starting to expand in a productive way (Expansion)?
MODE 3: PERMISSION FIELD (The Go/No-Go)
X-Axis: Directional Bias. A composite score of momentum oscillators, all pointing in the same direction. It measures the short-term directional pressure.
Y-Axis: Permission Score. A proprietary score that asks if the underlying market conditions have "permission" to trend. It rewards low volatility, rewards existing trend strength, and rewards momentum.
This mode answers: We have a strong directional bias, but do we have the low-volatility, high-trend-strength "permission" needed for a successful breakout (Expansion)? Or is the directional bias occurring in a high-volatility, low-trend environment where it's likely to fail (Exhaustion)?
█ DEVELOPMENT PHILOSOPHY
The Tactical Atlas was born from a need to escape the "tyranny of the single indicator." It is a tool for systems thinking. It is built on the belief that a market's true character cannot be understood by looking at any single metric in isolation, but by observing the interplay and relationships between multiple dimensions of data. By transforming this complex, multi-dimensional state into a simple, intuitive visual, we empower traders to make faster, more accurate, and more contextually-aware decisions. This is not just data visualization; it is the visualization of understanding.
█ DISCLAIMER AND BEST PRACTICES
THIS IS A CONTEXTUAL TOOL, NOT A SIGNAL GENERATOR: The Tactical Atlas is designed to provide you with a deep understanding of the market regime. Use this understanding to select the appropriate strategy (e.g., trend-following in Expansion, mean-reversion in Compression).
RISK MANAGEMENT IS PARAMOUNT: All trading involves substantial risk.
CONFLUENCE IS KEY: The most powerful insights come from confluence. A move of the current point into the Expansion quadrant is a good sign. That same move, while the higher timeframe "ghost points" are also in or moving towards Expansion, is an A++ signal that a major, multi-timeframe move is underway.
STUDY THE TRAJECTORY: Do not just look at where the point is. Look at the trail. Where did it come from? How fast is it moving? The history and velocity of the point are often more important than its current location.
— Dskyz, Trade with insight. Trade with anticipation. (Don't follow the trend, be the trend) 指标
Daily Bias Liquidity Profiler [MarkitTick]💡 This advanced analytical framework is engineered to decode market structure, track liquidity sweeps, and map volatility profiles on an intraday basis. Built natively for the sophisticated Pine Script version 6 environment, this indicator transcends basic charting by aggregating Previous Day metrics, session-specific liquidity pools, Fair Value Gap (FVG) confluences, and probabilistic bias models into a single, cohesive visual interface. It is designed for quantitative and algorithmic traders who require a deep understanding of market mechanics, offering unparalleled insight into where resting liquidity is likely positioned and how daily volatility is structured based on pure statistical variance.
✨ Originality and Utility
● Comprehensive Architectural Design
Most standard technical indicators focus on a single mathematical transformation, such as moving averages or simple momentum oscillators. The originality of this profiler lies in its multi-faceted approach, unifying advanced price action concepts that typically require multiple separate scripts. By leveraging Pine Script version 6 User-Defined Types (UDTs), the script maintains an incredibly lightweight footprint while calculating complex, interconnected market states without degrading chart performance.
● Algorithmic Session Tracking
The utility of the indicator is profoundly evident in its automated handling of time-based liquidity. Rather than manually drawing boxes around the Asian and London sessions, the script dynamically profiles these periods. It treats their boundaries not as mere historical artifacts, but as active, magnetic liquidity pools that drive future price action.
● Real-Time Bias Computation
This tool introduces a dynamic probability engine that continuously evaluates the likelihood of price sweeping the Previous Day High or Previous Day Low based on current opening momentum and accumulated volatility. This gives traders a statistical edge in determining their daily directional bias without relying on subjective chart patterns.
🔬 Methodology and Concepts
● Daily Range Profiling
At the core of the script's methodology is the Daily Profile engine. It systematically captures the Previous Day High, Previous Day Low, and Previous Day Close. These levels represent the absolute boundaries of yesterday's value area. The script calculates the total range of the previous day to establish a baseline for current-day expectations and statistical deviation limits.
● Session Liquidity Engineering
The script defines distinct macro-economic windows, specifically targeting the Asian and London trading sessions.
Asian Session Consolidation: Often characterized by tight ranges, the Asian session builds resting liquidity above its highs and below its lows. The algorithm tracks these exact price levels dynamically.
London Session Expansion: The script monitors the London open for initial expansion moves that frequently sweep the liquidity accumulated during the Asian session, triggering internal alerts when these specific thresholds are pierced.
● Fair Value Gap (FVG) Confluence
Market imbalances are identified through a precise Fair Value Gap detection algorithm. The script does not just highlight every random gap on the chart; it specifically looks for FVG formations that align with the directional bias and occur in proximity to session sweeps. This creates a high-probability confluence signal, indicating that the market is rapidly moving to rebalance price delivery.
● Advanced Volatility Metrics
Volatility is not measured through standard lagging indicators. Instead, the script utilizes an Average Daily Range (ADR) calculation. It dynamically tracks the percentage of the ADR that has been fulfilled during the current day. By calculating how many bars it typically takes to reach standard deviation milestones of the ADR, the script provides a predictive model for intraday exhaustion.
● Dynamic Bias Scoring Engine
The indicator calculates a running score to determine the daily bias. It awards positive and negative weights based on several factors: the location of the current price relative to the daily open, whether a session liquidity sweep has occurred, the presence of FVG confluences, and the proximity to the Previous Day's extremes. This score is translated into a probability percentage for sweeping either the high or the low.
🎨 Visual Guide
● Liquidity Zones and Range Boxes
Session Boxes: Distinct, shaded rectangular regions drawn over the chart to encapsulate the high and low bounds of the Asian and London sessions. These boxes visually isolate the accumulation phases.
Range Zone Boxes: Projected areas above and below the current price action representing high-probability reversal or expansion targets based on the ADR calculations.
● Structural Lines
Previous Day Boundaries: Solid, distinct horizontal lines marking the exact price levels of the Previous Day High and Previous Day Low.
Midlines: Subtler horizontal lines traversing the center of the calculated range zones to indicate equilibrium levels where price action may stall or pivot.
● Dynamic Labels and Alerts
Sweep Labels: Textual annotations that appear exactly when price pierces a session boundary or previous day extreme, explicitly confirming a liquidity sweep.
Bias State Text: A dedicated label displaying the current statistical bias, updating dynamically as volatility metrics shift throughout the trading day.
📖 How to Use
● Establishing Directional Bias
Begin your analysis by referencing the Bias State metric displayed on the chart. If the script calculates a high probability of sweeping the Previous Day High, prioritize bullish setups. Conversely, a high probability for the Previous Day Low dictates a bearish posture. Do not fight the algorithmic bias without significant contradicting evidence from higher timeframes.
● Executing the Sweep and Reverse
Monitor the Asian and London session boxes. A prime setup occurs when price aggressively breaks outside a session box and immediately faces strong rejection. This false breakout is the trigger for a mean-reversion trade targeting the opposite side of the session range. Look for the script's sweep labels to confirm the level has been compromised.
● Filtering with Volatility
Consult the Volatility Metrics before entering a trade. If the current daily range has already fulfilled a high percentage of the Average Daily Range (ADR), the probability of further directional expansion diminishes. In such cases, avoid breakout trades and look for exhaustion reversals at the projected Range Zone extremes.
● Utilizing FVG Confluence
When a sweep occurs, wait for the algorithm to highlight a valid Fair Value Gap in the opposite direction of the sweep. Enter the market on the retracement into this FVG, placing stop losses just beyond the sweep extreme for optimal risk-to-reward ratios.
⚙️ Inputs and Settings
● Time and Session Configuration
Asia Session Hours: Allows the user to precisely define the start and end times of the Asian session based on their specific exchange and timezone.
London Session Hours: Configurable inputs to match the precise opening and closing dynamics of the European market.
● Volatility Parameters
ADR Lookback Length: The historical window (number of days) used to calculate the Average Daily Range. A shorter lookback makes the indicator more responsive to recent volatility spikes, while a longer lookback provides a smoother, more stable expected range.
● Visual Toggles
Show Session Boxes: A boolean toggle to enable or disable the shaded background for trading sessions, allowing for a cleaner chart if only the boundary lines are desired.
Show Sweep Labels: Allows users to turn off the text annotations for liquidity sweeps to reduce visual clutter during highly volatile, choppy market conditions.
🔍 Deconstruction of the Underlying Scientific and Academic Framework
● Auction Market Theory and Liquidity
The foundational logic of this script is deeply rooted in Auction Market Theory. Financial markets operate as a continuous dual-auction process, seeking areas of high liquidity to facilitate trade execution for large-scale participants. The script mathematically models this by isolating session highs and lows, recognizing them as high-density zones for stop-loss orders and breakout triggers.
● Statistical Variance and Range Forecasting
The Volatility Metrics engine relies on historical variance. By computing the Average Daily Range over a predefined dataset, the script applies a simplified standard deviation model to predict the expected boundaries of the current day. This creates a probabilistic bell curve of expected price distribution, where the extremes of the ADR represent the tails of the distribution curve, indicating areas of high mean-reversion probability.
● Microstructural Order Flow Imbalances
The Fair Value Gap (FVG) detection logic is an algorithmic representation of order flow imbalance. In academic market microstructure, when price moves with extreme velocity, it creates a void in the bid-ask spread where only one side of the market was effectively matched. The script mathematically identifies these structural inefficiencies, utilizing them as high-probability zones for future price retracements, as the market naturally seeks to re-auction these inefficiently traded areas.
● Probabilistic Modeling
The bias engine utilizes a rudimentary form of multi-factor linear weighting. By assigning specific values to isolated market events (e.g., crossing the open price, sweeping a specific session), the model computes a composite score. This deterministic approach strips away emotional trading by replacing it with a quantifiable metric that guides directional expectations.
⚠️ 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. 指标
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Fractional CUSUM Regime Filter [Jamallo]🔹 Intro
The Fractional CUSUM Regime Filter is a quantitative trend and volatility channel indicator designed to identify structural market regimes with high stability. Unlike traditional filters that suffer from lag or whipsaw signals, this indicator applies a dual-layered preprocessor combining Lopez de Prado’s Fixed-Window Fractional Differentiation (FFD) with a classic Cumulative Sum (CUSUM) statistical trigger.
🔹 Break down
Fractional Differentiation (FFD) : Stationarizes pricing input while retaining long-term historical memory (controlled by the "d" parameter).
Classic CUSUM Filter : Accumulates deviations from a rolling baseline relative to current volatility. When cumulative deviation exceeds the threshold multiplier (h), a new regime change is triggered, and the baseline steps dynamically to the new price level.
Vol-Adjusted Percentile Bands : Linear interpolation percentiles of price deviation relative to the stepped baseline. The distances are "locked" and only update on CUSUM regime triggers to prevent wobbly bands.
🔹 Visual Guide: Indicator Anatomy
Here we have the structure of the indicator, including the CUSUM baseline, the 68% inner percentile band, and the 95% outer percentile band.
How to use: Mean Reversion (Pullbacks to the Mean)
The channel boundaries represent statistical extremes. When the price is pushed outside the bands, it is mathematically overextended and highly likely to revert back to the CUSUM baseline.
Long Setup (Pullbacks in Bullish Trend): During a green CUSUM uptrend, watch for price to pull back to the lower bands ("micro pullback") and enter as it heads back up to the baseline mean.
Short Setup (Pullbacks in Bearish Trend): During a red CUSUM downtrend, watch for price to rally into the upper bands ("micro pullback") and enter as it reverts down to the baseline mean.
🔹 How to use: Trend Following & Risk Management
The indicator is designed to capture sustained macro trends while providing clear risk parameters.
Entering on Breakthroughs : Enter when the BUY/SELL signal flags appear (indicating a new CUSUM regime shift).
Stop Loss Placement : Place stop loss orders just below the opposite outer band or below the stepped baseline.
Letting Winners Run : Ride the trend as long as the CUSUM baseline maintains its colored regime state (Green for Long, Red for Short).
🔹 Settings Parameters
d (0.01 - 0.99) : Differentiation order. Lower values retain more historical memory; higher values approach first-difference.
CUSUM Drift & Threshold : Controls baseline sensitivity to regime changes.
Percentile Lookback & Targets : Set the statistical width of the inner and outer boundaries.
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