Indicator
Crypto Market Breadth Risk Planner [AGPro Series]Crypto Market Breadth Risk Planner
🧠 Core Idea
Is the crypto market showing broad risk-on participation, weakening rotation, or a risk-off breadth environment?
📌 Overview / What it does
Crypto Market Breadth Risk Planner is a chart-first market breadth tool built to evaluate whether a selected crypto basket is participating broadly or weakening internally.
Instead of reading only the active chart symbol, the script reviews a configurable basket of major crypto pairs. It measures how many symbols are trading above their trend baseline, how many have positive momentum, how many have rising trend structure, and how much volatility stress is present across the basket.
The script produces a 0-100 Breadth Risk Score, a colored breadth risk corridor on the active chart, event labels, right-side tags, alerts, and a compact AG Pro panel. It does not predict price direction, automate execution, or claim that breadth alone is enough to trade.
🎯 Purpose & Design Philosophy
This script was built because single-chart analysis can look strong while the broader crypto market is quietly weakening, or look weak while breadth is beginning to rotate back into strength.
The purpose is to help traders read market participation before treating an individual setup as clean. Strong setups usually have a better context when the broader basket is aligned, while weaker breadth can warn that a chart may be more exposed to false follow-through.
The design supports traders who want broader market context without opening ten charts manually. It turns cross-market participation into a simple decision-support layer that can be read directly on the current chart.
⚡ Why This Script Is Different
Most crypto tools focus on the active symbol, a single benchmark, a simple correlation reading, or a raw relative-strength line.
This script does NOT act as a benchmark correlation meter, a relative-strength rotation map, a volume spike detector, or a generic trend dashboard.
Instead, it evaluates breadth across a user-defined crypto basket and converts that participation into a risk-readiness framework. The goal is not to say which coin to buy or sell. The goal is to show whether the broader crypto environment is supportive, mixed, stressed, or risk-off.
⚙️ Methodology
1. Context Detection
The script requests data from a configurable crypto basket and evaluates each symbol on the selected breadth timeframe.
2. Reference Mapping
Each symbol is compared against its own trend baseline, momentum reading, trend slope, and ATR-based volatility stress condition.
3. Reaction Evaluation
The script combines trend participation, momentum participation, slope confirmation, and volatility stress into a single Breadth Risk Score.
4. Visual Output
The final output includes a colored breadth risk corridor, centered corridor text, event labels, right-side tags, optional bar coloring, alerts, and an AG Pro panel.
🗺️ How to Read the Chart
Zones:
The breadth risk corridor is a visual context zone around price. Its color reflects the current breadth regime rather than a direct support or resistance level.
Labels:
Labels mark important breadth state transitions such as Risk-On, Rotation Watch, Risk-Off, Stress Review, and Cooling.
Colors:
Teal represents broad constructive participation.
Pink represents risk-off breadth or weak participation.
Gold represents stress or caution.
Indigo represents improving rotation or transitional breadth.
Panel:
The panel summarizes breadth participation, Breadth Risk Score, momentum, stress, regime, and action state.
🚦 Signals & States
• Risk-On Ready → Broad participation and momentum are strong enough to support risk-on review.
• Rotation Watch → Breadth is improving, but not yet strong enough for full risk-on classification.
• Stress Review → Volatility stress is elevated while breadth quality remains weak.
• Risk-Off → Basket participation is weak or deteriorating.
• Cooling → Stress is easing while breadth quality begins to improve.
• Wait Breadth → No strong breadth regime is currently active.
🔔 Alerts Logic
Alerts can trigger when the basket shifts into Risk-On, Rotation Watch, Risk-Off, Stress Review, or Cooling.
Alerts are attention markers only. They highlight changes in the breadth model. They are not trade instructions, automated entries, or guaranteed market calls.
🧩 Confluence Logic
The context becomes stronger when multiple breadth layers align together.
For example, a high Breadth Risk Score with many symbols above their trend baselines, positive momentum participation, rising trend slopes, and low stress suggests a cleaner risk-on environment than a rally led by only one or two symbols.
Likewise, weak participation combined with elevated stress can warn that individual bullish setups may need stricter review.
📊 When to Use
• Crypto market context review
• BTC, ETH, altcoin, and sector-style crypto watchlists
• 1H, 4H, and 1D market participation analysis
• Before treating individual setups as risk-on
• When the trader wants to know whether the broader crypto basket supports the active chart
⚠️ When NOT to Use
• Markets where selected symbols have unreliable data
• Very small or illiquid crypto pairs with distorted candles
• Situations where the basket does not match the user's trading universe
• Low-timeframe scalping where external-symbol breadth may be too slow
• News-driven events where correlation and breadth can change abruptly
🎛️ Key Inputs
• Crypto Basket Symbols → define the assets used in the breadth model
• Breadth Timeframe → controls whether the basket is evaluated on chart timeframe, 1H, 4H, or 1D
• Trend Baseline Length → controls the EMA reference used for participation
• Momentum Length → controls the ROC window used for positive or negative participation
• ATR Stress Threshold → controls when basket volatility begins to count as stress
• Minimum Risk-On Score → controls how selective the risk-on state should be
• Visual Settings → control corridor, labels, right-side tags, panel location, theme, and font size
🖥️ Interface & Visual Design
The interface is designed to make broad crypto participation readable without turning the chart into a large dashboard.
The corridor gives a fast visual state directly on the chart. The panel provides the structured readout. Labels mark only important transitions, while cooldown and memory controls keep historical events from overwhelming the chart.
The visual intent is premium, clean, and publication-friendly.
🧪 Practical Usage Workflow
1. Read the panel to identify the current breadth regime.
2. Check the Breadth Risk Score and participation percentage.
3. Review whether momentum and stress support or conflict with the active chart setup.
4. Use the corridor color as a market-context layer, not as a direct entry zone.
5. Combine breadth context with price structure, volatility, liquidity, and personal risk rules.
🔍 Interpretation Guidelines
A strong score means the selected crypto basket is broadly aligned according to the script's rules.
A Rotation Watch state means breadth is improving, but the market has not fully confirmed broad risk-on participation.
A Stress Review state means volatility pressure is elevated while breadth remains weak or mixed.
A Risk-Off state means the selected basket is not supporting broad participation under the current settings.
🚫 What This Script Is NOT
This script is not a prediction engine.
This script is not financial advice.
This script is not an automated trading system.
This script does not place orders.
This script does not guarantee market direction, continuation, reversal, or profitability.
⚠️ Limitations & Transparency
This script depends on the selected symbols, selected timeframe, and PulseWire data availability.
Different baskets can produce different breadth readings. A BTC-heavy basket may behave differently from an altcoin-heavy basket. External symbol data may also load differently depending on market, exchange, and PulseWire availability.
The script should be interpreted as market context, not as a standalone execution model.
🧠 Market Context Notes
Crypto often moves through participation waves. Sometimes BTC leads while altcoins lag. Sometimes the whole market rotates together. Sometimes volatility rises while breadth deteriorates, creating a more fragile environment.
This script is designed to make that internal participation easier to observe directly from the active chart.
🧾 Use Case Examples
Example 1:
BTC is breaking higher, but the panel shows weak breadth and high stress. The trader may decide that the move needs extra confirmation before treating it as broad risk-on.
Example 2:
ETH is consolidating, but the basket shifts into Rotation Watch with improving momentum. The trader can monitor whether the active chart begins to align with the broader rotation.
Example 3:
The basket prints Risk-Off while an individual altcoin setup looks technically clean. The script warns that the broader market backdrop is not supportive under the current model.
🧱 System Philosophy
AGPro Series tools are built as decision-support frameworks, not signal vending machines.
This script follows that philosophy by turning broad market participation into a structured context layer: define the basket, score the breadth, map the state, and show the next action clearly.
🔐 Non-Promise Statement
This script does not promise certainty.
It does not promise that a risk-on breadth state will produce gains, or that a risk-off state will produce losses. It only organizes participation context so the user can evaluate the broader market with more clarity.
📉 Risk Disclosure
Trading involves risk.
Market conditions can change quickly, and breadth models can fail or become less useful during sudden volatility, exchange-specific moves, or news-driven repricing. Users remain responsible for their own decisions, execution, and risk management.
This script is for educational and analytical purposes only. It does not provide financial advice.
📚 Educational Note
Use this tool to study how crypto breadth changes before, during, and after major market moves.
Its strongest value comes from comparing the active chart with the broader basket context rather than reading any single label in isolation.
Indicator
Meridian Regime Overlay [JOAT]JOAT Meridian Regime Overlay
Introduction
JOAT Meridian Regime Overlay is an open-source market context overlay built to classify whether price is operating in directional expansion, balanced auction, or compression.
It is designed as a chart-first regime engine rather than a standalone trigger study.
The script combines local baseline alignment, confirmed higher-timeframe bias, pivot structure, opening-range acceptance, realized volatility state, and session VWAP location into one integrated regime map.
The problem it solves is context.
Many indicators can say whether price is above or below an average.
Far fewer explain whether the move is supported by volatility expansion, structural acceptance, value migration, and higher-timeframe alignment.
Meridian Regime Overlay focuses on that exact problem.
It grades the live auction bar by bar.
It also shows what is supporting the grade.
That makes it useful as a decision filter before interpreting any lower-level signal source.
This script is not trying to predict the future.
Its job is to organize the present market condition.
It helps answer practical questions:
Is the market trending with real conviction?
Is price only drifting above a baseline without expansion?
Is the market compressing near a likely release point?
Is higher-timeframe structure aligned with the current move?
Is price accepting away from value or simply rotating around it?
Core Concepts
1. Baseline Stack Alignment
Fast, slow, and anchor baselines define the local directional stack.
Directional strength improves when those baselines align in sequence and their slopes support the move.
fastBase = ta.ema(close, fastLen)
slowBase = ta.ema(close, slowLen)
anchorBase = ta.ema(close, anchorLen)
2. Confirmed Higher-Timeframe Bias
Higher-timeframe context is pulled using confirmed values only.
This avoids depending on unfinished HTF candles.
htfFast = request.security(syminfo.tickerid, biasTf, ta.ema(close , fastLen), lookahead = barmerge.lookahead_on)
htfSlow = request.security(syminfo.tickerid, biasTf, ta.ema(close , slowLen), lookahead = barmerge.lookahead_on)
htfAnchor = request.security(syminfo.tickerid, biasTf, ta.ema(close , anchorLen), lookahead = barmerge.lookahead_on)
3. Compression and Expansion State
The script compares Bollinger width and Keltner position to identify squeeze behavior and release behavior.
ADX and realized variance refine the classification.
4. Pivot Structure State
Confirmed pivots define recent structural reference points.
Breaks through those pivots update the structural state.
5. Session VWAP Context
Distance from session VWAP is normalized in ATR units.
This helps reveal whether price is auctioning away from value with intent or just rotating around it.
6. Opening-Range Acceptance
The opening range is tracked and plotted.
Acceptance above or below that range adds useful early-session context.
7. Composite Regime Score
Multiple directional variables are folded into a single regime score.
The score is a context summary, not a standalone trade signal.
8. Confirmed Event Labels
The overlay prints confirmed auction-up, auction-down, and squeeze-release labels directly on the chart.
Features
Directional regime classification: bullish expansion, bearish expansion, balance, and compression states
Baseline cloud system: fast and slow cloud for local trend stack
Confirmed HTF alignment: higher-timeframe bias uses confirmed values only
Opening-range plotting: high, low, and midpoint are tracked
Session VWAP context: value migration is integrated into the read
Pivot structure state: recent structural breaks are tracked
Compression and release logic: squeeze and expansion state are visualized
Bar-state coloring: candles transition with regime intensity
Confirmed event labels: auction and expansion markers print on the chart
Dashboard: summarizes regime, score, HTF, structure, volatility, and VWAP context
Input Parameters
Trend Engine:
Fast Baseline
Slow Baseline
Anchor Baseline
Adaptive Mean Length
Bias Timeframe
Slope Lookback
Slope Threshold ATR
RVOL Impulse Threshold
Volatility Engine:
ATR Length
Compression Length
Band Deviation
Keltner Length
Keltner Multiplier
ADX Length
ADX Floor
Expansion Threshold
Realized Variance Length
How to Use This Indicator
Step 1: Read the regime color, cloud, and dashboard state.
Step 2: Check higher-timeframe alignment before trusting directional continuation.
Step 3: Compare structure and VWAP position to see whether price is accepting away from value.
Step 4: Watch squeeze-release transitions closely because those often precede cleaner directional movement.
Step 5: Use the script as a context filter for other tools rather than as a complete trading system.
Indicator Limitations
Pivot structure confirms after the pivot fully forms, which is intentional non-repainting behavior
Higher-timeframe values are confirmed and therefore intentionally delayed
Compression can persist longer than expected in slow auction environments
Directional classification does not guarantee continuation
Originality Statement
This publication is original in the way it integrates baseline structure, confirmed higher-timeframe bias, compression state, realized variance, session VWAP, opening-range acceptance, and pivot structure into one unified regime overlay.
The components are not combined arbitrarily.
They all answer the same core question:
what is the current quality of the auction?
Disclaimer
This indicator is provided for educational and informational purposes only.
It is not financial advice.
Market regimes can shift quickly.
All readings are based on historical and current bar data and do not guarantee future performance.
Always use independent analysis and risk management.
Best Use Cases
Directional trend filtering before using a separate trigger model
Session context analysis during London and New York activity
Volatility transition analysis when compression begins to release
Structure-aware regime filtering for discretionary execution
Interpretation Notes
The strongest readings usually occur when the local stack, confirmed higher-timeframe stack, VWAP position, and volatility expansion agree.
If only one or two of those are aligned, the chart can still move, but the regime read is weaker.
Compression should not be treated as a bearish or bullish state by itself.
It is a warning that the market is withholding directional commitment.
Opening-range acceptance adds value because many directional sessions reveal their intent early.
When price cannot hold outside the opening range, the regime should usually be treated more cautiously.
Publication Notes
This script is intended to be published with a clean chart where the cloud, baselines, opening range, and event labels are clearly visible.
The chart should not be cluttered with unrelated overlays.
If showing an example image, the regime state and at least one structural transition should be identifiable at a glance.
-Made with passion by jackofalltrades
Indicator
Indicator
Concordance Strategy [JOAT]JOAT Concordance Strategy
Introduction
JOAT Concordance Strategy is an open-source multi-factor PulseWire strategy designed to integrate the JOAT indicator stack into one execution framework.
It combines regime context, liquidity interaction, retracement logic, pressure confirmation, channel behavior, and participation filters to decide when enough independent evidence exists to justify a trade.
The problem it solves is single-factor dependency.
Trend-only systems often chase poor location.
Liquidity-only systems can trigger too early.
Oscillator-only systems can fade strong directional auctions.
Retracement-only systems can buy weak pullbacks without sponsorship.
This strategy attempts to solve that by requiring overlap.
It does not assume one tool family is sufficient on its own.
Instead, it asks whether multiple analytical dimensions agree.
That agreement is what the strategy calls concordance.
Core Concepts
1. Regime Gate
The strategy first evaluates local and higher-timeframe baseline structure, slope, volatility state, and directional control.
2. Hard and Soft Directional States
The system uses stronger and softer directional states instead of an all-or-nothing gate.
3. Liquidity and Structure Stack
Entries consider sweep behavior, break state, and displacement.
4. Retracement and Confluence Layer
Local and HTF retracement context help determine whether price is pulling back into a structurally meaningful area.
5. Pressure Confirmation
Pressure logic attempts to confirm that price action has sponsorship behind it rather than only visual momentum.
6. Sigma Channel State
Channel logic helps determine whether price is re-entering a directional path or fading from extension.
7. Participation Filter
Relative volume and delta-style participation help avoid weak sponsorship environments.
8. Risk and Exit Model
The strategy uses structure-aware ATR stops, partial exits, break-even logic, trailing behavior, and optional time exits.
Features
Integrated multi-factor entry model: regime, liquidity, retracement, pressure, channel, and participation
More active soft-entry path: allows more trades while keeping directional structure
Confirmed-bar logic: entries use confirmed state conditions
Equity-risk sizing: position size is derived from risk per trade
ATR and structure-aware stops: volatility and market structure both matter
Two-stage profit taking: TP1 and TP2 split the exit logic
Break-even and trailing logic: protects trades after expansion
Time-based exit: removes stale positions when needed
Dashboard: regime, confluence, pressure, ledger, and position state are displayed
Strategy Properties Used by Default
Initial capital: 100000
Commission type: percent
Commission value: 0.02
Pyramiding: 0
Position sizing: equity-risk based
Trade management: partial exits, break-even logic, ATR trail, optional time exit
How to Use This Strategy
Step 1: Treat it as a research framework rather than a promise of future performance.
Step 2: Evaluate it across multiple markets and timeframes because the more permissive logic should produce broader participation than the earlier strict version.
Step 3: Judge the quality of the trade distribution rather than focusing on one isolated metric.
Step 4: Respect the compromises between selectivity and trade frequency.
Step 5: Use realistic expectations and avoid reading a single backtest as proof of repeatable future outcomes.
Strategy Limitations
The strategy still depends on confirmed conditions and can therefore enter later than a discretionary trader
Trade frequency and quality vary significantly by symbol and timeframe
Default settings are general-purpose and may not be ideal for every market
Optimizing too aggressively can become curve fitting
Backtest results are hypothetical and do not guarantee future performance
Originality Statement
This strategy is original in how it requires agreement across regime, liquidity, retracement, pressure, channel, and participation modules before or during entry qualification.
The components are not merged simply to produce a busier system.
Each one addresses a different failure mode in execution.
Their overlap is the basis for participation.
Disclaimer
This strategy is provided for educational and informational purposes only.
It is not financial advice.
Backtest results are hypothetical and depend on assumptions, settings, and market selection.
They do not guarantee future returns.
Trading involves substantial risk of loss.
Always validate assumptions independently and use responsible risk management.
Best Use Cases
Researching whether cross-confirmation improves selectivity over single-factor systems
Studying how regime, liquidity, retracement, and participation interact inside one strategy
Comparing trade frequency across markets and timeframes after the softer entry expansion
Testing realistic risk-management assumptions inside a multi-layer strategy
Interpretation Notes
This strategy should be evaluated as a process, not as a single summary metric.
Trade count matters.
Distribution of trades matters.
How the system behaves across different instruments matters.
The softer entry path was added to prevent the strategy from becoming too inactive, especially on higher timeframes.
That makes the strategy more usable for broad testing while still preserving directional structure.
Publication Notes
This strategy should be published with a clean chart and realistic default Properties.
If showing results, the description should stay grounded and avoid implying that one test run guarantees future outcomes.
The chart image should make the strategy entries and exits easy to understand.
-Made with passion by jackofalltrades
Evaluation Framework
1. Start by checking whether the strategy is active on the instrument and timeframe you care about.
2. Compare trade count before and after threshold changes.
3. Review whether trade quality remains acceptable as activity increases.
4. Study the interaction between regime, liquidity, pressure, and participation at entry.
5. Judge the strategy by distribution and robustness rather than one isolated metric.
Why This Matters
The strategy exists to test whether agreement across multiple independent analytical layers can improve execution quality.
That research question is more important than any one headline metric.
Open-Source Notes
This strategy is published open source so users can inspect how the modules overlap and how the risk model is applied.
Who This Is For
This strategy is for users who want to study how multiple context layers can be combined inside one execution model.
It is not intended for anyone looking for a one-click guarantee.
Summary
JOAT Concordance Strategy is best understood as a structured research tool.
It exists to test whether regime, liquidity, retracement, pressure, channel, and participation agreement can improve decision quality.
Additional Notes
This strategy should be judged with realistic commission and execution assumptions.
It should also be evaluated on enough trades to produce a meaningful sample.
The defaults are intended to stay grounded rather than theatrical.
Strategy
Covenant Regime Atlas [JOAT]Covenant Regime Atlas
Introduction
Covenant Regime Atlas is an open-source Pine Script v6 market-regime indicator built to classify directional state through trend, expansion, persistence, and retest quality. Its purpose is not to predict the next trade by itself, but to create a durable bias layer that tells the trader whether the market is developing a bullish regime, a bearish regime, or a maturing directional environment worth respecting.
The problem this script solves is context instability. Many traders can spot a moving-average crossover or a burst in ATR, but that alone does not answer whether the regime is actually mature, whether momentum has real separation, or whether recent retests are behaving consistently with the dominant trend. Covenant Regime Atlas addresses this by blending multiple regime components into one overlay and dashboard.
The script uses a dual-mid framework derived from EMA and HMA references, ATR-scaled cloud and envelope bands, persistence measurement, heat normalization, slope impulse, and retest memory. This lets it move beyond a simple bullish-versus-bearish cross and instead describe whether the regime is developing, mature, expanding efficiently, or internally cooling.
The result is an indicator for traders who want a cleaner read of bias before interpreting any trigger tool. It is especially useful as a regime filter for execution indicators and strategies that should behave differently in mature directional flow versus unstable transition periods.
Core Concepts
1. Directional Mid Versus Structural Mid
The script creates a fast directional midpoint and a slower structural midpoint using blended EMA and HMA references. The spread between those two curves forms the backbone of regime direction.
float directionalMid = math.avg(emaFast, hmaFast)
float structuralMid = math.avg(emaSlow, hmaSlow)
bool trendBull = directionalMid > structuralMid
This gives the regime engine more shape than a single moving average crossover. The directional mid measures active flow. The structural mid measures slower context.
2. Regime Strength Through Separation And Heat
Regime strength is calculated from ATR-normalized spread plus the distance of normalized heat from its midpoint. In other words, the regime is strongest when the fast and slow structures are well separated and price is also positioned decisively within its recent range.
This helps avoid overvaluing tiny directional crosses that occur with little actual separation or energy.
3. Persistence And Maturity
Every regime needs time to prove itself. The script counts how long the current directional condition has been intact and compares that against a user-defined persistence floor. Once the threshold is met, the regime is treated as mature rather than merely developing.
This matters because a fresh directional flip is different from a directional condition that has held for many bars and survived multiple retest opportunities.
4. Retest Memory
After a mature regime forms, the indicator watches for controlled retests of the directional midpoint. Bull retests occur when price revisits the midline from above and closes back above it. Bear retests use the opposite condition. The last retest is stored as a dotted line and extended forward until it becomes irrelevant.
This gives the trader a simple memory of where the market most recently confirmed trend participation.
5. Pulse, Expansion, And Efficiency
The script also measures volatility expansion, slope impulse, heat drift, trend separation percentage, and directional travel efficiency. These metrics allow the dashboard to distinguish between a mature regime that is expanding forcefully and one that is mature but internally cooling or grinding.
Features
Bull and bear regime classification: Uses fast-versus-slow blended midpoints to define directional control
Maturity logic: Distinguishes developing regimes from mature ones using persistence counting
ATR-scaled cloud and envelope: Frames the current directional corridor directly on the chart
Retest memory engine: Stores the latest mature-regime retest level for forward reference
Initiation band: Preserves the regime start envelope so traders can judge distance from the original launch zone
Pulse ribbon: Adds a compact visual band around price to reflect internal heat conditions
Regime backdrop shading: Tints the chart according to the active directional state
Detailed dashboard: Displays strength, heat, persistence, expansion, slope pulse, retest distance, maturity, efficiency, and more
Confirmed-bar alerts: Includes mature bias, retest, expansion, continuation, efficient trend, and heat-reset conditions
Data-window outputs: Exposes regime internals for systematic reading or comparison
Visual Elements
Directional cloud: The gap between the fast and slow regime mids shows whether the market is operating with clean separation
Envelope bands: ATR-based boundaries help frame the active directional corridor around price
Initiation band: The regime launch area stays visible so users can measure how far the trend has traveled from origin
Retest line memory: The latest confirmed retest is preserved as a direct chart reference
Backdrop and pulse ribbon: Context shading and the pulse band make regime character readable without overloading the chart
Best Practices
Treat mature regimes differently from developing ones because the same trigger can behave very differently in each state
Watch heat drift when a regime remains mature but starts losing internal energy
Use retest memory to frame participation zones rather than chasing every extension away from the midline
Give more weight to regimes that show both persistence and expansion instead of one without the other
Use the atlas as a context engine first and an alert source second
Input Parameters
Trend Engine:
Fast Length: Sets the faster directional reference
Slow Length: Sets the slower structural reference
Heat Window: Defines the range-normalization window for heat calculations
ATR Length: Controls volatility normalization
Cloud Width Factor: Sets the width of the directional cloud and envelope
Retest Engine:
Show Retest Memory: Toggles retest storage and line rendering
Retest Cooldown Bars: Prevents retests from firing too frequently
Persistence Floor: Sets how many bars are required before a regime is considered mature
Show Initiation Band: Displays the preserved start range of the current regime
Maturity Window: Controls maturity scaling and travel-efficiency measurements
Display:
Show Dashboard toggle
Show Regime Backdrop toggle
Show Pulse Ribbon toggle
Independent bull, bear, neutral, and panel colors
How to Use This Indicator
Step 1: Read Regime Tag And Strength
Begin with the dashboard’s regime tag. It tells you whether the market is bullish or bearish and whether that state is still developing or already mature. Pair that with the strength reading to avoid confusing a weak directional bias with a strong one.
Step 2: Check Persistence And Expansion
Persistence tells you how long the regime has survived. Expansion tells you whether volatility is supporting the move. A mature regime with positive expansion usually deserves more respect than a new regime with weak expansion.
Step 3: Use Retest Memory As A Structural Anchor
When the retest line is present, it marks the last meaningful participation check inside the trend. That line can help frame whether the current move is still building from a healthy base or drifting too far away from supportive structure.
Step 4: Watch Heat Drift And Efficiency
Heat drift helps show whether the regime is internally warming or cooling. Efficiency tells you whether directional travel has been orderly. These readings are helpful when deciding whether the trend still looks clean or is becoming unstable.
Step 5: Use It As The Bias Layer For Other Tools
Covenant Regime Atlas is best used as a bias filter. It helps define whether you should be thinking continuation, pullback participation, or caution. Pair it with your own trigger logic rather than using the regime alone as a full trading plan.
Indicator Limitations
A developing regime can fail before reaching maturity, especially in choppy markets
Retest memory is useful for context, but the stored retest level is not guaranteed to hold on future tests
Efficiency and heat drift are descriptive metrics, not predictive guarantees of continuation
The indicator can still classify a directional state during periods where execution conditions are poor for actual trading
Originality Statement
Covenant Regime Atlas is original in the way it blends trend separation, maturity, retest memory, expansion, and efficiency into a unified regime overlay. It is not just a moving-average cloud with added cosmetics:
It separates directional identity from maturity, allowing the user to distinguish developing and established regimes
It stores retest memory as a living structural feature instead of relying only on static crossover logic
It combines heat, slope, expansion, and efficiency into one dashboard so regime quality can be judged from multiple dimensions
It preserves the initiation band of the current regime, which gives context that typical trend overlays do not maintain
Disclaimer
This indicator is provided for educational and informational purposes only. It is not financial advice or a recommendation to buy or sell any financial instrument. Regime readings describe current market structure and internal state based on historical prices. They do not guarantee future movement or profitable trading decisions. Always use independent judgment and proper risk management.
-Made with passion by jackofalltrades
Indicator
Charter Execution Model [JOAT]Charter Execution Model
Introduction
Charter Execution Model is an open-source Pine Script v6 strategy that integrates the broader JOAT framework into a single non-repainting execution model. It does not rely on one trigger alone. Instead, it uses a hierarchy of filters: regime eligibility first, liquidity bias second, structure confirmation third, and imbalance or displacement triggers fourth. Only when those layers align does the strategy consider taking a trade.
The goal of this strategy is not to present a magical black box. It is to model a disciplined decision stack. Many strategies fail because they treat every trigger the same way regardless of context. Charter Execution Model is built around the idea that context should do most of the work. If the market is not in a mature directional regime, if the liquidity ledger is not skewed appropriately, or if local structure does not agree, then a trigger by itself is not enough.
The script uses realistic execution controls directly in the declaration: fixed initial capital, percent-of-equity sizing, non-zero commission, non-zero slippage, no pyramiding, confirmed-bar evaluation, and orders processed on close. Those defaults are intended to make the backtest more responsible and easier to interpret than an overly aggressive model with idealized execution assumptions.
This strategy is best understood as a research framework. It can help traders study how context filters, imbalance triggers, continuation pressure, and ATR-based exits behave when combined inside one model. It is not a guarantee of future profitability, and it should be evaluated thoughtfully across symbols, regimes, and timeframes.
Core Concepts
1. Regime Eligibility Layer
The first gate determines whether the market is mature enough to even consider longs or shorts. It uses a directional midpoint and structural midpoint built from EMA and HMA references, then normalizes their spread by ATR and combines that with heat positioning inside the recent price range.
bool bullRegime = directionalMid > structuralMid
float regimeStrength = clamp(spreadNorm * 0.60 + math.abs(heatNorm - 50.0) * 0.80, 0, 100)
bool matureBullRegime = bullRegime and regimeStrength >= regimeFloor and regimePersistence >= 12
That means the strategy does not allow triggers to fire in weak or undeveloped directional states. Context comes first.
2. Liquidity Bias Layer
Next, the strategy builds a rolling bin-based liquidity distribution and compares buy-side volume versus sell-side volume. A long context requires positive liquidity bias and price above the reference EMA. A short context requires negative liquidity bias and price below the reference EMA.
This adds an inventory-style filter so the strategy is not trading purely off price shape.
3. Structure Filter
Local structure is confirmed using pivot-derived reference points and a rolling swing lookback. Longs require price to hold above recent swing support and above the slow EMA. Shorts require the inverse.
This helps reduce cases where a regime and liquidity reading are still positive or negative, but local price structure has already started to degrade.
4. Trigger Stack
Once context aligns, the strategy allows three possible triggers: a confirmed imbalance gap, a displacement shift, or an optional continuation retest into the directional midpoint. This means the model can participate through both fresh displacement and controlled continuation.
Importantly, the trigger layer does not override the context layer. It only becomes active when the earlier filters already agree.
5. ATR-Based Exit Framework
Risk management is handled through ATR-sensitive invalidation and two fixed-R profit targets. When the regime is especially strong, an optional trailing rule tightens the stop using recent local price action.
This creates a trade structure with a defined stop, two staged exits, and optional adaptation in stronger conditions without relying on unrealistic all-in-all-out assumptions.
Features
Four-layer decision hierarchy: Regime, liquidity, structure, and trigger conditions must align before entry
Confirmed-bar logic: Entries are evaluated only on confirmed bars to avoid repaint-style execution logic
Non-zero execution costs: Includes realistic commission and slippage in the strategy declaration
No pyramiding: Prevents stacking multiple positions in the same direction
Partial profit framework: Uses two independent `strategy.exit()` orders to scale out at separate R multiples
Optional continuation triggers: Allows pullback-style participation inside already qualified context
Optional strong-regime trailing stop: Tightens exits when regime strength is elevated
Dashboard summary: Displays regime, liquidity bias, pressure, trigger state, position state, stop settings, and current risk fields
Clean visual overlay: Shows directional and structural mids with contextual fill directly on the chart
Open-source research design: Lets users inspect and adapt the full context-to-execution hierarchy
Default Strategy Properties
Initial capital: `100000` is used as the default starting capital in the script declaration
Position sizing: Orders use `strategy.percent_of_equity` with a default quantity of `10`, meaning the strategy allocates 10% of equity per position by default
Commission: Commission is modeled as `0.02%` per trade
Slippage: Slippage is modeled as `2` ticks
Pyramiding: Pyramiding is set to `0`, so the model does not stack entries in the same direction
Order timing: `process_orders_on_close = true` and `calc_on_every_tick = false`, so the model evaluates and processes with confirmed-bar logic
Input Parameters
Regime:
Fast Length: Controls the fast directional reference
Slow Length: Controls the slow structural reference
ATR Length: Sets the ATR normalization length
Heat Window: Defines the range window for heat normalization
Regime Strength Floor: Sets the minimum maturity threshold for context eligibility
Liquidity Filter:
Liquidity Lookback: Sets the rolling history used for the liquidity model
Liquidity Bins: Controls the liquidity distribution granularity
Liquidity Bias Floor: Sets the minimum skew required before liquidity counts as directional
Structure Filter:
Pivot Length: Sets pivot confirmation sensitivity
Swing Lookback: Defines the rolling structural context window
Trigger Stack:
Gap Sigma Filter: Sets the minimum imbalance displacement required for gap-style triggers
Shift Momentum Length: Controls the raw momentum lookback
Shift RSI Length: Controls the pressure RSI smoothing
Displacement Floor: Sets the threshold for shift-style triggers
Allow Continuation Triggers: Enables or disables pullback continuation entries
Continuation Pressure Floor: Sets the minimum pressure level for continuation logic
Risk Management:
Stop ATR Multiplier: Scales the ATR contribution to stop placement
Target 1 R: Sets the first partial profit target
Target 2 R: Sets the second partial profit target
Trail In Strong Regime: Enables optional trailing behavior when regime strength is elevated
How to Use This Strategy
Step 1: Evaluate Context Before Results
Begin by understanding what the strategy is trying to do rather than focusing immediately on performance output. It only wants to trade when a mature regime, directional liquidity bias, and confirming structure are all aligned. If that idea does not match your own process, the results will be hard to interpret.
Step 2: Study Trigger Type Distribution
Not all entries come from the same source. Some come from imbalance gaps, some from displacement shifts, and some from continuation pressure. Understanding which trigger type dominates on a given market can be more useful than simply checking net profit.
Step 3: Understand The Exit Framework
The model uses a staged exit approach. Half the position is managed toward the first target and half toward the second. A stop is always active, and strong-regime trailing can tighten the exit path further. Review this logic carefully before drawing conclusions from the backtest.
Step 4: Keep Expectations Realistic
The strategy includes commission, slippage, confirmed-bar logic, and no pyramiding, but that still does not make the backtest “real.” Results depend on the instrument, the timeframe, the data sample, and how well the context assumptions fit the market studied.
Step 5: Use It As A Research Framework
Charter Execution Model is best used as a framework for studying context-first execution logic. Adapt the filters, test the thresholds, and evaluate how the hierarchy behaves across different environments rather than assuming the defaults are universally optimal.
Strategy Limitations
The strategy relies on historical context filters that may adapt poorly to sudden regime shifts or atypical event-driven conditions
Liquidity bias is based on bar-level directional volume attribution rather than true exchange order-flow data
Processing orders on close simplifies execution and can differ materially from real fills on fast markets
Backtest results are sensitive to parameter choices, timeframe selection, instrument behavior, and dataset length
Originality Statement
Charter Execution Model is original in the way it organizes multiple analytical layers into a disciplined execution hierarchy. It is not published as a simple indicator mashup strategy:
It requires mature regime, directional liquidity bias, and local structure to align before any trigger is allowed to matter
It supports multiple trigger archetypes inside the same context framework rather than treating one trigger as universally sufficient
It combines staged exits, ATR-sensitive invalidation, and optional strong-regime trailing inside a consistent risk model
It exposes its internal context state on-chart so users can study why the strategy is active or inactive at any point
Disclaimer
This strategy is provided for educational and informational purposes only. It is not financial advice or a recommendation to buy or sell any financial instrument. Backtest results depend on assumptions, data quality, slippage, commission, bar resolution, and market conditions. Past performance does not guarantee future results. Always use independent judgment and proper risk management before using any strategy logic in live markets.
-Made with passion by jackofalltrades
Strategy
Concordance Regime Synthesis [JOAT]Concordance Regime Synthesis
Introduction
Concordance Regime Synthesis is an open-source strategy framework that combines regime state, pressure, participation, structure, and higher-timeframe bias into one non-repainting confluence model. The strategy is designed to avoid single-factor entries by requiring multiple independent conditions to align before risk is deployed.
Core Concepts
1. Multi-factor confluence scoring
Long and short setups are scored independently using regime direction, normalized price pressure, participation-axis deviation, delta behavior, recent structure, and optional higher-timeframe bias.
2. Regime-aware execution
Entries only occur when directional confluence exceeds a threshold and the score spread clearly favors one side.
3. ATR-based risk handling
Stops, targets, and optional trailing logic are all derived from ATR so the strategy adapts to volatility instead of using fixed-tick assumptions.
Strategy Properties
Initial capital: 10,000
Order size: 10% of equity per trade
Commission: 0.06%
Slippage: 1 tick
Pyramiding: 0
Orders processed on close
Originality Statement
This strategy is original in its use of a confluence gate that requires independent agreement from regime, pressure, delta, participation, structure, and optional HTF alignment before entries are allowed. It is published as an educational framework for multi-factor strategy construction rather than as a promise of future performance.
Disclaimer
This strategy is for educational and informational purposes only. Backtest results depend on symbol, timeframe, market regime, and execution assumptions. Historical results do not guarantee future returns. Always validate assumptions and use realistic risk controls.
Strategy
Harborside Regime Channel [JOAT]Harborside Regime Channel
Introduction
Harborside Regime Channel is an open-source regime-mapping indicator built to classify whether the market is expanding, compressing, reclaiming balance, or losing structural support inside a live adaptive channel.
The script is designed for traders who need context before they interpret any other signal.
Instead of asking only whether price is above or below a moving average, Harborside studies a pivot-fed centerline, adaptive outer rails, higher-timeframe directional agreement, and volatility compression state at the same time.
The result is a channel that behaves more like an institutional market map than a simple trend overlay.
The problem Harborside solves is regime clarity.
Many trend tools keep printing directional color even while the market is actually compressing inside a narrowing structure.
Many channel tools show a band but do not explain whether that band is healthy, fragile, extended, or aligned with higher-timeframe pressure.
Harborside addresses that by combining channel structure, expansion behavior, and higher-timeframe bias in one chart-first framework.
Core Concepts
1. Pivot-Fed Structural Center
Harborside does not anchor its regime center to a fixed moving average alone.
Instead, confirmed swing highs and swing lows are used to build a rolling center reference.
That center is then smoothed to create a structural balance line.
This matters because the center is linked to confirmed market geometry rather than only to lagging price averages.
The channel therefore breathes with the underlying structure of the market.
2. Adaptive Outer Rails
The upper and lower rails are derived from ATR-scaled expansion around the structural center.
This means the channel naturally widens when volatility expands and contracts when price compresses.
Because the rails are smoothed, they remain readable instead of flickering excessively during intrabar noise.
This creates a cleaner map for determining whether price is stretching, reverting, or breaking into a new directional phase.
3. Regime Flips on Confirmed Structural Breaks
A bullish regime is not assigned merely because price is green for a few bars.
A regime flip occurs when price confirms through the adaptive outer band in the relevant direction.
That regime is then maintained until the opposing side is confirmed.
This makes the indicator more stable than reactive color-on-close style tools.
4. Compression Detection
Harborside measures band width relative to its own historical baseline.
When the band compresses below the configured threshold, the script identifies a meaningful reduction in expansion state.
This compression state is important because trend-following logic behaves very differently when the market is coiled than when it is already moving freely.
Compression is shown directly on the chart and carried into the dashboard state.
5. Projection Rails
The script extends projected center, upper, and lower rails forward using current center slope and ATR-scaled projection logic.
These projected rails are not predictions in the magical sense.
They are forward references showing where the current regime geometry would continue if the active slope persists.
That gives the trader a usable visual frame for stretch, continuation, and mean-reversion decisions.
6. Higher-Timeframe Bias Alignment
Higher-timeframe bias is requested using offset logic intended to avoid repaint-style behavior from incomplete higher-timeframe bars.
Fast and slow higher-timeframe EMA structure is used to determine whether broad directional pressure is supportive, opposing, or neutral.
Harborside does not force the higher-timeframe filter on the user.
It can be enabled or disabled depending on workflow.
7. Regime Health and Confidence
Harborside includes a confidence-style scoring model built from displacement, slope, compression state, and directional bias alignment.
This score is not intended to be a trade system on its own.
It is a context gauge.
A high score means the active regime has cleaner structural support.
A low score means the visible state is weaker or more fragile.
Features
Pivot-fed centerline: The regime center is anchored to confirmed swing structure rather than a static average alone
Adaptive outer rails: ATR-scaled bands expand and contract with changing volatility conditions
Confirmed regime flips: Bull and bear states change only after confirmed structural breaks through the active channel rails
Compression box: Important volatility contraction zones are shown directly on the chart instead of being hidden in a separate pane
Projection rails: Forward rails extend the current channel geometry into future bars for context and stretch awareness
Higher-timeframe bias filter: Optional HTF directional alignment helps separate local moves from larger directional pressure
Regime-colored candles: Candle coloring reflects the active state without relying on cluttered symbols or arrows
Band cloud rendering: The active channel body is filled to make directional structure readable at a glance
Health and confidence diagnostics: The dashboard summarizes regime quality in compact form
Six-row dashboard: The display was intentionally reduced so the chart remains the primary source of information
Confirmed-bar alerts: Alerts are available for regime flips, compression holds, center reclaims, HTF alignment, and high-health states
Input Parameters
Channel Structure:
Swing Length: Number of bars required on both sides to confirm pivots used by the structural center
Band Multiplier: ATR multiplier used to define the channel width
Center Smoothing: Smoothing applied to the structural midpoint
Band Smoothing: Smoothing applied to the upper and lower rails
Bias and Context:
Bias Timeframe: Higher timeframe used for optional directional confirmation
Compression Lookback: Baseline window used to measure channel contraction
Compression Threshold: Band-width threshold below which the market is treated as compressed
Volume Bias Filter: Volume impulse threshold used to label directional support
Projection:
Projection Bars: Number of bars projected forward
Projection ATR Multiplier: Width factor used for forward rails
Projection Slope Multiplier: How strongly current center slope influences the forward center projection
Display:
Show Band Cloud toggle
Show Compression Box toggle
Show Projection Rails toggle
Recolor Candles toggle
Show Dashboard toggle
How to Use This Indicator
Step 1: Read the State from the Chart First
Start with the channel itself.
Is price controlling the upper side of the structure, the lower side, or compressing near the center?
The rails and cloud are meant to answer that visually before the dashboard is consulted.
Step 2: Check Compression Before Chasing Direction
If the compression box is active, treat the market as coiled rather than trending cleanly.
That does not mean price cannot move.
It means breakout quality matters more than ordinary directional drift.
Step 3: Use Projection Rails as Forward Reference
Projection rails are best used for context.
If price is already far outside projected geometry, the market may be stretched.
If price is traveling inside projected structure, continuation is behaving more normally.
Step 4: Compare Local Regime to HTF Bias
If the local regime and higher timeframe agree, directional conditions are cleaner.
If they disagree, treat the move with more caution.
That disagreement often marks either a pullback or a weak local thrust against broader pressure.
Step 5: Use Health and Confidence as Filters, Not Commands
High confidence does not guarantee follow-through.
Low confidence does not guarantee failure.
The score is there to grade structural quality, not to replace decision-making.
Indicator Limitations
Pivot-based structure is intentionally confirmed after the swing forms, so the centerline will never anticipate future pivots
Projection rails are structural references, not forecasts of what price must do next
HTF alignment is delayed by design because the script uses completed higher-timeframe values for safer non-repainting behavior
Compression can persist longer than expected, so directional patience is still required
Harborside is a context framework and should not be treated as a guaranteed entry system on its own
Originality Statement
Harborside Regime Channel is original in the way it combines a pivot-fed structural center, ATR-adaptive regime rails, explicit compression logic, forward projection rails, and optional higher-timeframe agreement into one coherent chart-first overlay.
The value of the script is not any one component in isolation.
It is the way those components interact to show whether the market is healthy, stretched, compressing, or structurally aligned.
Disclaimer
This indicator is provided for educational and informational purposes only.
It does not provide financial advice, investment advice, or trading recommendations.
Any regime reading can fail, reverse, or degrade suddenly due to news, liquidity changes, or ordinary market uncertainty.
Always use independent confirmation and risk management.
Indicator
AlphaQuant Statistical Intelligence█ ALPHAQUANT STATISTICAL INTELLIGENCE (QSI)
Quantitative Market Quality Analysis
A quantitative market analysis tool that measures the statistical "quality" of market conditions using three core modules: Hurst Regime Engine , Shannon Entropy Flow , and Price Efficiency Ratio . These combine into a single QSI Composite Score (0-100) that rates whether current conditions are favorable for trading or not.
Free and Open Source.
█ THE CONCEPT: WHY STATISTICAL MARKET QUALITY MATTERS
Most indicators answer "which direction?" — QSI answers a different question: "Should I be trading right now?"
Markets alternate between regimes: trending, mean-reverting, chaotic, and efficient. QSI identifies these regimes in real-time so you can adapt your strategy accordingly. A trending Hurst regime favors breakout strategies. A mean-reverting regime favors fading. High entropy means the market is chaotic — reduce size. High efficiency means clean directional moves — increase conviction.
█ CORE MODULES
1. Hurst Regime Engine
Calculates the Hurst Exponent via Rescaled Range (R/S) analysis — a robust statistical method from hydrology adapted for financial markets.
H > 0.55 — Trending regime. Price has "memory" — breakout/trend-following strategies work well.
H = 0.50 — Random Walk. No statistical edge — the market is coin-flipping. Reduce size.
H < 0.45 — Mean-Reverting regime. Price has "anti-memory" — fade strategies work well.
2. Shannon Entropy Flow
Measures the information entropy of the return distribution using the Shannon Entropy formula from information theory.
High Entropy (>70) — Returns spread across many bins = chaotic, unpredictable. Reduce exposure.
Low Entropy (<30) — Returns cluster in few bins = ordered, predictable. Good for systematic strategies.
3. Price Efficiency Ratio
Measures directional efficiency by comparing net price movement to gross price movement over N bars.
High Efficiency (>30%) — Price moving cleanly in one direction. Trend-following conditions.
Low Efficiency (<10%) — Price chopping with no net progress. Avoid or use range strategies.
█ QSI COMPOSITE SCORE
The three modules combine into a single 0-100 score with fixed weights:
Hurst Edge: 40% — How strong is the regime signal?
Inverse Entropy: 35% — How ordered is the market?
Efficiency: 25% — How clean are the price moves?
Score interpretation:
> 70 — PRIME — Optimal conditions, full conviction
55-70 — FAVORABLE — Good conditions, normal sizing
40-55 — NEUTRAL — Mixed signals, proceed with caution
25-40 — CAUTION — Poor conditions, reduce size
< 25 — AVOID — Worst conditions, stay out
█ DASHBOARD
A compact, dark-themed info panel displaying:
QSI INDEX — Current composite value with regime label
HURST — Current Hurst exponent with regime classification
ENTROPY — Current entropy score with regime classification
EFFICIENCY — Current efficiency ratio with regime classification
WEIGHTS — Module weight distribution (H:40 E:35 F:25)
█ ALERTS (8 CONDITIONS)
QSI: PRIME Conditions — Composite entered prime zone (>70)
QSI: AVOID Conditions — Composite dropped to avoid zone (<25)
QSI: Conditions Improving — Composite crossed above 55
QSI: Conditions Deteriorating — Composite dropped below 40
QSI: Hurst → Trending — Hurst crossed above 0.55
QSI: Hurst → Mean-Reversion — Hurst crossed below 0.45
QSI: Entropy → Chaos — Entropy spiked above 70
QSI: Entropy → Order — Entropy dropped below 30
█ PRO VERSION
The PRO version adds:
Markov Transition Probabilities — Statistical prediction of next-bar direction
Correlation Intelligence — Auto-benchmark correlation with breakdown detection
Fractal Dimension — Higuchi fractal complexity analysis
Absorption Detection — High volume + low range = institutional activity
Trading Style Presets — Auto/Scalping/Daytrading/Swing/Position
Asset Auto-Optimization — Automatic parameter tuning per asset class
Profile-Adaptive Weighting — Dynamic composite weights based on style + asset
█ NON-REPAINTING
All calculations use confirmed bar data only. The Hurst Exponent is calculated from historical log-returns. Shannon Entropy uses a rolling window of past data. Price Efficiency uses closed bars only. No future data leakage. No repainting.
█ WORKS ON
Crypto, Forex, Stocks, Futures, Indices — any timeframe from 1 minute to Monthly.
█ DISCLAIMER
This indicator is for educational and informational purposes only. It does not constitute financial advice. Always do your own research and manage your risk. Past performance does not guarantee future results. Trading involves substantial risk of loss.
Indicator
Tectonic Ribbon Oscillator [JOAT]Tectonic Ribbon Oscillator
Introduction
Tectonic Ribbon Oscillator is an open-source lower-pane momentum field built from twenty lag-reduced strands. The script classifies whether momentum is in bullish expansion, bearish expansion, or twist compression by comparing the ribbon's fast, mid, and slow structure instead of relying on a single oscillator line.
The problem Tectonic solves is momentum depth. A single oscillator can show direction, but it usually hides how broad or fragile the move actually is. Tectonic exposes ribbon breadth, spread, slope, and divergence in one framework so the user can distinguish acceleration from compression.
Core Concepts
1. Multi-Strand Ribbon Construction
Each strand uses a progressively larger lookback and lag-reduced smoothing. This creates a depth field rather than a single-value oscillator.
2. Fast-Mid-Slow Spread Logic
The oscillator compares grouped ribbon averages and uses the spread to determine whether momentum is directional or twisted into compression.
3. Regime Classification
Bull, bear, and twist states are identified from the spread and held as confirmed regime transitions.
4. Divergence Validation
Price pivots and ribbon pivots are compared to identify confirmed bullish and bearish divergence without using future leaks.
5. Momentum Support Layers
Histogram and slope components add a second view of how the ribbon is accelerating or decelerating internally.
Features
Twenty-strand momentum ribbon: Progressive lookbacks create a true depth profile
Lag-reduced smoothing: Ribbon strands are stabilized without reverting to a slow classic oscillator
Twist regime detection: Compression is explicitly separated from directional impulse
Confirmed divergence logic: Bullish and bearish divergence are tracked from confirmed pivot relationships
Histogram and slope overlays: Secondary layers help gauge acceleration quality
Top-right dashboard: State, spread, slope, histogram, depth, divergence, last shift, confirmation, and breadth are reported continuously
How to Use This Indicator
Step 1: Read the regime
Bull and bear states indicate directional momentum dominance. Twist indicates compression or unstable breadth.
Step 2: Compare spread and slope
A large spread with weakening slope often indicates mature momentum. A fresh spread expansion with improving slope usually indicates earlier-cycle momentum.
Step 3: Respect divergence in context
Confirmed divergence is most useful when it appears against an already stretched ribbon state.
Indicator Limitations
Divergence is not a reversal guarantee
Twist states can persist for long periods in balanced markets
Shorter settings will react faster but can become noisy
The oscillator is a momentum context tool and should be combined with market structure or regime logic
Originality Statement
Tectonic Ribbon Oscillator is original in the way it assembles a twenty-strand lag-reduced ribbon, grouped spread classification, divergence validation, and dashboard reporting into one momentum framework rather than publishing a lightly modified RSI derivative.
Disclaimer
This indicator is provided for educational and informational purposes only. It is not financial advice. Momentum and divergence signals can fail, especially during high-volatility structural breaks. Use independent analysis and risk management.
Indicator
Volatility Percentile [EXCAVO]ATR Percentile Rank with Four-State Volatility Classification and Trend Detection
The Volatility Percentile measures where the current Average True Range stands
relative to its own history using a percentile rank. Rather than comparing ATR to a
fixed threshold, the indicator continuously evaluates whether current volatility is
low, normal, elevated, or extreme relative to the recent distribution - adapting
automatically to each instrument and timeframe.
This is not a basic ATR display. The percentile rank provides a normalized, context-aware
measure of volatility that remains comparable across assets, timeframes, and market cycles.
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▸ HOW TO USE
Step 1 → Add the indicator to a new pane. The histogram appears immediately,
colored by the current volatility state (blue, gray, orange, or red).
Step 2 → Read the state from the histogram color and dashboard. Blue = Low,
Gray = Normal, Orange = Elevated, Red = Extreme.
Step 3 → Note the percentile value. Above 80% indicates the current ATR is
in the top 20% of its historical range - unusually wide price swings.
Below 25% indicates unusually quiet conditions.
Step 4 → Check the Vol Trend row in the dashboard. RISING means the ATR is
accelerating above its 20-bar average. FALLING means it is contracting.
Step 5 → Set up state-change alerts to receive notifications when volatility
transitions between states on bar close.
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▸ HOW IT CALCULATES
◆ ATR Percentile Rank
The indicator computes ATR(length) on each bar, then applies ta.percentrank() over
the lookback period. The percentile rank answers: "Out of the last N bars, what
percentage had an ATR lower than today's?" A result of 80 means 80% of recent bars
had a smaller ATR - current volatility is in the top 20% historically. The rank
always falls between 0 and 100, making it directly comparable across instruments.
◆ Smoothing
The raw percentile rank is passed through a simple moving average of configurable
length (default 3 bars). This suppresses single-bar spikes that would otherwise
cause false state transitions. The smoothed value is used for both the histogram
display and state classification. Setting smoothing to 1 disables it entirely.
◆ Four-State Classification
The smoothed percentile is compared against three configurable thresholds to assign
one of four states: Low (below 25th percentile), Normal (25th to 60th), Elevated
(60th to 80th), or Extreme (above 80th). State boundaries are user-adjustable,
allowing calibration for instruments that spend more time at elevated volatility
levels (such as crypto) versus lower-volatility markets.
◆ Volatility Trend
A secondary calculation compares the current ATR to its 20-bar simple moving average.
ATR above 105% of the SMA is classified as RISING. ATR below 95% of the SMA is
classified as FALLING. Between those bands the trend is STABLE. This early-warning
layer detects momentum in volatility before a state boundary is crossed.
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▸ WHAT MAKES IT DIFFERENT
◆ Percentile Rank vs Fixed Threshold
A fixed ATR threshold (e.g., "ATR > 500 = high volatility") fails when applied across
different instruments or timeframes. The percentile approach self-calibrates: the same
indicator works on BTCUSD hourly and EURUSD daily without parameter changes, because
it measures volatility relative to the instrument's own recent history.
◆ Four States Instead of Two
Most volatility filters use a binary high/low split. Separating Normal from Elevated
and Extreme provides more granular awareness: Elevated volatility may still be
tradable, while Extreme conditions warrant a different approach to position sizing.
The four-state model also makes transitions visible earlier - Elevated appears before
Extreme is reached.
◆ Volatility Trend Detection
The Vol Trend layer detects whether ATR is currently accelerating or decelerating,
independently of which state it is in. It is possible to be in Normal state with
RISING trend (volatility building) or in Elevated state with FALLING trend
(volatility contracting back toward normal). This combination is visible only in the
dashboard and is not represented by the histogram color alone.
◆ ATR / Price Ratio
The dashboard shows ATR expressed as a percentage of the current close price. This
normalized measure is directly comparable across instruments and useful for estimating
stop distance relative to price without manual calculation.
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▸ DASHBOARD
Real-time panel showing current volatility conditions:
State - active volatility state (LOW / NORMAL / ELEVATED / EXTREME), colored by type
Percentile - current smoothed ATR percentile rank (0-100%)
ATR - raw ATR value in price units for the current bar
ATR / Price - ATR expressed as a percentage of close price
Vol Trend - volatility momentum (RISING / FALLING / STABLE) vs 20-bar ATR average
Lookback - active lookback period used for percentile calculation
Legend table (bottom left) explains histogram colors and boundary lines. Both panels
toggle in Dashboard settings.
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▸ SETTINGS
Engine
ATR Length - 14 bars (period for Average True Range calculation)
Lookback Period - 200 bars (history window for percentile rank)
Smoothing - 3 bars (applied to raw percentile; 1 = no smoothing)
State Thresholds
Low / Normal - 25 (percentile boundary between Low and Normal states)
Normal / Elevated - 60 (percentile boundary between Normal and Elevated states)
Elevated / Extreme - 80 (percentile boundary between Elevated and Extreme states)
Visualization
Low Vol Color - default blue
Extreme Vol Color - default red
Elevated Vol Color - default orange
Show State Ribbon - OFF (colored markers at pane bottom)
Background Highlight - ON (subtle tint matching active state)
Alerts
JSON Alerts - OFF (enable for bot integration)
Dashboard
Dashboard Position - Top Right
Show Dashboard - ON
Show Legend - ON
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▸ ALERTS
Extreme Volatility - state transitioned to Extreme on bar close
Elevated Volatility - state transitioned to Elevated on bar close
Normal Volatility - state transitioned to Normal on bar close
Low Volatility - state transitioned to Low on bar close
Volatility State Change - any state transition detected on bar close
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Best regards,
EXCAVO
Disclaimer
Trading involves significant risk. This indicator is a technical analysis tool
and does not constitute financial advice, investment recommendations, or a
guarantee of future results. Past indicator behavior does not guarantee future
performance. Always use proper risk management and your own judgment.
Indicator
Market Regime Classifier [EXCAVO]Four-State Probabilistic Regime Detection Using a Hidden Markov Model
The Market Regime Classifier applies a four-state Hidden Markov Model (HMM)
to classify the current market environment as Bullish, Bearish, Volatile, or Sideways.
Rather than using fixed thresholds on ADX or moving average slopes, the model maintains
and continuously updates a probability distribution across all four states on every bar,
producing smooth, low-noise regime identification.
This is not a threshold-based classifier. State probabilities update through Bayesian
forward inference, so regime transitions emerge from the data rather than arbitrary
cutoff values.
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▸ HOW TO USE
Step 1 → Add the indicator to a new pane. The confidence histogram
appears immediately, colored by the current regime.
Step 2 → Read the regime from the histogram color and the dashboard
panel. Blue = Bullish, Red = Bearish, Orange = Volatile,
Gray = Sideways.
Step 3 → Monitor the confidence level. Above 80%, the model has high
conviction. Below 60%, the market is transitioning and both
adjacent states have similar probabilities.
Step 4 → Set up alerts for regime transitions. Each state change fires
on a closed bar only, eliminating repainting.
Step 5 → Check the dashboard for individual state probabilities. When
two states have close values, the market is ambiguous - this is
visible in the dashboard before a formal regime change occurs.
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▸ HOW IT CALCULATES
◆ Data Conditioning
Each bar computes the log return: ln(close / close ). The mean and standard
deviation of log returns are calculated over the lookback period. Both are then
normalized: norm_ret = (log_ret - mean) / stdev, and norm_vol = stdev /
sma(stdev, lookback). This standardization makes the model asset-agnostic,
producing consistent behavior across equities, crypto, and forex without
requiring parameter adjustments per instrument.
◆ Gaussian Emission Likelihoods
Each of the four states has a Gaussian emission function that evaluates how
well the current normalized volatility and return match that state's expected
profile. The emission formula is exp(-(x - center)^2 / width), where x is the
normalized observation, center is the expected value for that state, and width
controls the response range:
Bullish: norm_vol = 1.1, norm_ret = +0.8 (moderate volatility, positive drift)
Bearish: norm_vol = 1.1, norm_ret = -0.8 (moderate volatility, negative drift)
Sideways: norm_vol = 0.7, norm_ret = 0.0 (low volatility, no directional bias)
Volatile: norm_vol = 1.6 x sensitivity (elevated volatility, direction-agnostic)
A state's emission is high when the current bar's profile closely matches its center
and decreases exponentially as the observation diverges.
◆ Bayesian Forward Update
The model maintains four state probabilities (p_bull, p_bear, p_side, p_vola)
initialized at 0.25 each. On every bar, unnormalized posteriors are computed:
un_state = emission(state) x (p_state x 0.9 + sum_others x 0.033). The 0.9
self-transition coefficient gives the model inertia - it stays in the current
state unless emissions consistently support a different one. The 0.033
cross-transition coefficient (approximately (1 - 0.9) / 3) keeps all states
reachable. Posteriors are then normalized to sum to 1.0. The smoothing factor
controls how aggressively each bar's emission result shifts the running
probability, acting as an exponential moving average over the posterior series.
◆ State Classification and Confidence
The active regime is the state with the highest posterior probability (argmax).
Confidence equals this maximum probability expressed as a percentage. A regime
change is detected when the dominant state changes on a confirmed (closed) bar,
which fires all alerts. The dashboard shows all four probabilities simultaneously,
making it possible to observe when the market is approaching a state boundary
before the formal regime label changes.
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▸ WHAT MAKES IT DIFFERENT
◆ Probabilistic State Engine
Most regime classifiers use fixed thresholds: if ADX > 25 then trending, otherwise
ranging. This produces abrupt, oscillating switches at the boundary and gives no
information about conviction. The HMM produces a smooth probability distribution
across all four states simultaneously, with conviction visible at every bar as
an explicit confidence percentage.
◆ Four-State Classification
Two-state models (trending vs. ranging) conflate Volatile markets with trending ones
and miss the distinction between low-activity consolidation and trend exhaustion.
Four states allow separate identification of sustained directional moves
(Bullish/Bearish), low-activity accumulation ranges (Sideways), and high-volatility
uncertainty (Volatile) - conditions that require different position sizing and
strategy selection.
◆ Adaptive Volatility Threshold
The Volatile state's emission center scales with the Volatility Sensitivity input.
This makes the model configurable across asset classes: crypto spends more time at
elevated volatility levels and may benefit from a higher sensitivity value than
equities or forex.
◆ Transition Memory (Inertia)
The self-transition coefficient (0.9) gives the model inertia - a single anomalous
bar cannot flip the regime. The classifier requires consistent evidence across
multiple bars to overcome the self-transition bias. This reduces false transitions
during brief volatility spikes or one-bar outliers.
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▸ DASHBOARD
Real-time panel showing the current model state:
Regime - active state (BULLISH / BEARISH / VOLATILE / SIDEWAYS), colored by type
Confidence - highest state probability as a percentage; highlighted orange above 80%
Bullish - current Bullish state probability
Bearish - current Bearish state probability
Volatile - current Volatile state probability
Sideways - current Sideways state probability
Norm Volatility - normalized volatility ratio; highlighted orange above 1.5 x sensitivity
Smoothing - active smoothing factor (informational)
Legend table (bottom left) explains histogram colors. Both panels toggle in Dashboard settings.
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▸ SETTINGS
HMM Engine
Statistical Lookback - 50 bars (period for log-return mean and stdev; higher = more stable but slower to adapt)
Decision Smoothing - 0.40 (exponential smoothing factor; lower = more stable, higher = more reactive)
Volatility Sensitivity - 1.2 (scales the Volatile state emission center; increase for crypto, decrease for equities)
Visualization
Bullish Color - default blue (histogram and ribbon color during Bullish regime)
Bearish Color - default red (histogram and ribbon color during Bearish regime)
Volatile Color - default orange (histogram and ribbon color during Volatile regime)
Show Regime Ribbon - OFF (colored markers at pane bottom showing regime history)
Background Highlight - ON (subtle tint matching the active regime)
Alerts
JSON Alerts - OFF (enable for bot integration via 3Commas, Wunderbit, etc.)
Dashboard
Dashboard Position - Top Right
Show Dashboard - ON
Show Legend - ON
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▸ ALERTS
Bullish Regime - dominant state changed to Bullish on bar close
Bearish Regime - dominant state changed to Bearish on bar close
Volatile Regime - dominant state changed to Volatile on bar close
Sideways Regime - dominant state changed to Sideways on bar close
Regime Change - any state transition detected on bar close
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Best regards,
EXCAVO
Disclaimer
Trading involves significant risk. This indicator is a technical analysis tool
and does not constitute financial advice, investment recommendations, or a
guarantee of future results. Past indicator behavior does not guarantee future
performance. Always use proper risk management and your own judgment.
Indicator
Helix Trend Ensemble [JOAT]Helix Trend Ensemble
Introduction
Helix Trend Ensemble is an open-source trend overlay built around a three-member weighted ensemble. Instead of relying on one moving average or one crossover, Helix evaluates multiple configurable members, normalizes slope behavior, and produces a consensus trend state only when enough internal agreement is present.
The problem Helix solves is false certainty. Single-line trend tools are easy to read but easy to break. Multi-line tools often create clutter without resolving disagreement. Helix is designed to preserve a clean chart while still exposing the quality of alignment between fast, intermediate, and structural trend engines.
Core Concepts
1. Multi-Member Trend Architecture
Three independent members can each use different MA types, smoothing methods, lengths, and weights. This allows the ensemble to mix responsiveness with structural stability.
2. Weighted Consensus
The final state is not a simple majority vote. Each member contributes according to its configured weight, and the ensemble requires sufficient agreement before it promotes a directional state.
3. Slope Normalization
Raw slope values are normalized so the dashboard can express trend energy in a stable way across different length combinations.
4. Filter Layer
ATR and ADX filters help suppress weak trend states and reduce low-quality directional transitions.
5. Confirmed Regime Transitions
Directional state changes are only recognized on confirmed bars, which keeps the ensemble consistent with real-time use.
Features
Three fully configurable members: Each member supports multiple MA and smoothing combinations
Weighted consensus engine: Final state depends on internal agreement quality, not one crossover
Normalized slope score: Slope behavior is translated into a stable strength readout
Ribbon and cloud system: Trend geometry is expressed through layered fills instead of cluttered markers
Optional candle coloring: Price bars can reflect the ensemble state without altering logic
Top-right dashboard: Regime, consensus, strength, slope, agreement, filters, and last flip are summarized continuously
How to Use This Indicator
Step 1: Read regime and consensus together
A bullish or bearish state is more meaningful when consensus is high and filters are passing.
Step 2: Watch slope and strength
An aligned ensemble with weakening slope often signals late-trend conditions rather than fresh expansion.
Step 3: Use Helix as a bias filter
Helix works well as a directional framework for execution models that need a clean trend gate.
Indicator Limitations
Longer member lengths will intentionally delay reversals
High responsiveness settings can increase whipsaws
Consensus does not eliminate all false trends; it only improves structural filtering
The script is a trend-classification tool, not a full strategy
Originality Statement
Helix Trend Ensemble is original in the way it combines configurable member diversity, weighted consensus, slope normalization, and clean institutional visualization into one open-source trend framework.
Disclaimer
This indicator is provided for educational and informational purposes only. It is not financial advice. Trend-state tools can fail during rapid reversals, compressed markets, or structurally irregular conditions. Use proper risk control at all times.
Indicator
Delta Pressure Ledger [JOAT]Delta Pressure Ledger
Introduction
Delta Pressure Ledger is an open-source lower-pane pressure model built entirely from chart-derived proxies. It combines anchored VWAP context, candle pressure, volume impulse, crowding stretch, volatility pressure, and settlement skew into a normalized composite ledger that classifies whether pressure is balanced, directional, crowded, or stressed.
The problem this script solves is hidden market pressure. Many traders rely on unavailable data feeds or vendor-only metrics to estimate crowding or liquidation risk. Delta Pressure Ledger uses only chart-accessible inputs and standardizes them through z-score normalization so pressure states can still be read in a consistent way across instruments.
Core Concepts
1. Chart-Derived Pressure Proxy
The script estimates directional pressure from candle settlement, intrabar range occupation, and volume impulse rather than external order flow feeds.
2. Anchored VWAP Context
Pressure is interpreted relative to anchored value, allowing the user to distinguish directional expansion from overstretched crowding.
3. Z-Score Normalization
All sub-engines are normalized over a configurable lookback, which makes the composite reading more portable across symbols and timeframes.
4. Crowding and Stress Logic
The script tracks when price and derived sentiment become stretched enough to imply elevated liquidation or unwind risk.
5. Composite Verdict
Pressure, crowding, volatility, and skew are merged into one verdict state so the user can quickly determine whether the market is orderly, imbalanced, or stressed.
Features
Anchored VWAP context: Session, weekly, or monthly value anchor
Pressure engine: Candle and volume-derived directional pressure model
Crowding engine: Stretch and behavioral excess detection
Volatility and skew layers: Pressure quality and instability are separated from raw direction
Normalized composite score: All sub-engines standardized into one comparable ledger
Risk meter: Liquidation-style stress estimate derived from crowding and instability
Confirmed-bar transitions: State changes and alerts are held to confirmed bars
Top-right dashboard: Regime, pressure, crowding, volatility, risk, composite score, and last confirmed flip
How to Use This Indicator
Step 1: Read the composite verdict
The verdict gives the fastest summary of whether the market is balanced, directionally pressured, or entering a crowded stress state.
Step 2: Separate pressure from crowding
A bullish pressure reading with low crowding is different from a bullish pressure reading with extreme crowding and high risk.
Step 3: Respect risk transitions
When the risk meter moves into elevated territory, directional continuation setups deserve more caution.
Indicator Limitations
This script uses chart-derived proxies rather than exchange-level liquidation or true open-interest feeds
Normalized readings can still behave differently across asset classes with unusual volume structure
Stress conditions can remain elevated for extended periods during strong trends
The script classifies pressure and risk context; it does not execute trades by itself
Originality Statement
Delta Pressure Ledger is original in the way it builds a portable, chart-derived pressure and crowding framework without depending on unavailable external feeds, while still organizing the result into a normalized composite and risk ledger.
Disclaimer
This indicator is provided for educational and informational purposes only. It is not financial advice. Derived pressure and crowding models can be wrong, especially during atypical market events. Use proper risk management and independent judgment.
Indicator
Concordia Regime Execution [JOAT]Concordia Regime Execution
Introduction
Concordia Regime Execution is an open-source PulseWire strategy that integrates regime detection, trend bias, structure, momentum breadth, pressure confirmation, and ATR-based risk management into one non-repainting execution model. The strategy is built as a realistic framework rather than a curve-fit showcase.
The problem Concordia solves is signal fragmentation. Regime, trend, structure, and momentum are often evaluated separately, which leads to entries taken in the wrong environment. Concordia requires multiple engines to align before a position is opened, then manages risk through predefined stop, target, trailing, and bias-failure exits.
Core Concepts
1. Regime Detection
ADX, choppiness, and compression work together to classify whether the market is suitable for directional participation.
2. Trend Bias Filter
Fast, intermediate, and structural EMAs plus anchored VWAP context define directional bias before any entry can pass.
3. Structure Confirmation
Confirmed bullish or bearish breaks of recent swing structure add structural alignment to the trade decision.
4. Momentum Breadth
A compact ribbon engine classifies whether fast momentum is actually expanding in the same direction as trend and structure.
5. Pressure and Risk Layer
Chart-derived pressure and crowding inputs help confirm continuation and suppress entries during elevated stress.
6. Risk Management
Each trade uses ATR-based initial stop placement, ATR-based profit target, optional trailing activation, and bias-failure closure if internal conditions deteriorate.
Features
Regime gate: Expansion, compression, and transitional filtering
Trend alignment: EMA stack plus anchored VWAP bias logic
Structure filter: Recent swing break confirmation
Momentum breadth: Ribbon spread confirmation instead of a single oscillator line
Pressure confirmation: Chart-derived directional pressure and crowding logic
Risk model: ATR stop, ATR target, trailing trigger, and bias-failure exit
Top-right dashboard: Regime, bias, structure, momentum, pressure, risk, setup scores, active position, and stop/target levels
Confirmed-bar entries: All setup logic is gated on confirmed bars
How to Use This Strategy
Step 1: Start with liquid markets
Concordia is better suited to instruments where anchored VWAP, ATR, and structure transitions behave consistently.
Step 2: Use realistic assumptions
Commission, slippage, and position sizing inputs should match your actual market and trading conditions before evaluating performance.
Step 3: Evaluate regime quality first
The strategy is intentionally selective. If the market is compressing or structurally unstable, fewer trades should occur.
Step 4: Review bias-failure exits
These exits are included to avoid overstaying trades when internal alignment breaks down before the stop or target is reached.
Strategy Limitations
Like any rules-based strategy, it can underperform in abrupt gap conditions or news-driven spikes
ATR-based exits adapt to volatility, but they are not guaranteed to be optimal for every instrument
The strategy is intentionally conservative and may miss some fast reversals
Historical performance does not guarantee future results
Originality Statement
Concordia Regime Execution is original in the way it integrates regime, trend, structure, momentum breadth, pressure confirmation, and ATR-based trade management into a single open-source strategy designed for realistic chart use rather than decorative backtest output.
Disclaimer
This strategy is provided for educational and informational purposes only. It is not financial advice and should not be treated as a recommendation to buy or sell any instrument. Strategy results are based on historical chart data and platform assumptions. Live trading results can differ materially. Always validate settings and use independent risk management.
Strategy
Aegis Arc Framework [JOAT]Aegis Arc Framework
Introduction
Aegis Arc Framework is an open-source anchored-trend overlay built to classify directional pressure through the interaction of three continuously updated reference systems: a confirmed trailing arc, anchored VWAP regime alignment, and ATR-normalized extension pressure. The script is designed to provide structured directional context without relying on repainting shortcuts or visually noisy retail markers.
The main problem Aegis Arc Framework solves is trend persistence versus trend exhaustion. Price can remain above a trailing framework while already becoming stretched relative to value. It can also reclaim value while a trend is still technically intact. Aegis addresses that by combining a ratcheting confirmed trail with session, weekly, and monthly VWAP context so the user can distinguish continuation, weak extension, and blocked transition states.
Core Concepts
1. Anchored VWAP Regime Gate
The indicator maintains manually anchored session, weekly, and monthly VWAP references and checks whether the active trend is aligned with those value anchors. Depending on the selected mode, the gate can require Session, Week, Month, Any, or All alignment before the dashboard and confirmed state fully agree with the direction of the arc.
bool s = bullish ? close >= vwapSession : close <= vwapSession
bool w = bullish ? close >= vwapWeek : close <= vwapWeek
bool m = bullish ? close >= vwapMonth : close <= vwapMonth
2. Ratcheted Arc Trail
The main trail is not a static moving average. It is a ratcheting arc that updates on confirmed bars, accelerates toward the active target band, and decays its internal velocity over time. This makes the line responsive during directional expansions while still preserving a smooth institutional appearance.
3. Extension Pressure
Distance from the active anchored VWAP is normalized by ATR and converted into an acceleration modifier. When price becomes stretched from value, the trail responds more aggressively. This helps expose when a trend is still directional but increasingly extended.
4. Confirmed Flip Logic
A trend flip is only confirmed on closed bars. The trail state does not anticipate future movement or use future references. This keeps the overlay safe for real-time use and consistent with non-repainting publication standards.
5. Forward Flip Levels
Each confirmed directional transition creates a horizontal level that extends forward until invalidated or aged out. These levels act as persistent decision references and help frame whether price is defending or losing prior transition zones.
Features
Anchored value logic: Session, weekly, and monthly VWAP tracking with selectable regime gate modes
ATR-based arc framework: Confirmed-bar ratcheting trail with velocity decay and acceleration logic
Extension awareness: ATR-normalized distance from value used to classify whether the active move is orderly or stretched
Persistent transition levels: Confirmed flip levels extend forward and self-clean when broken or aged out
Institutional visual shell: Trail glow, clean cloud fill, optional candle tint, and compact top-right dashboard
No retail markers: The script avoids arrows and clutter-focused signal decoration
Confirmed-bar logic: Trend transitions and level invalidations are gated by confirmed bars only
Input Parameters
Core Trajectory:
ATR Length
Trail Distance (ATRx)
Base Acceleration
Velocity Decay
Velocity Cap
Minimum Chase / Maximum Chase
Run Window
Minimum Flip Gap
VWAP Regime:
Regime Mode: Session, Week, Month, Any, All
VWAP Speed Boost
Show Selected VWAP
Visual System and Levels:
Bull, bear, and neutral palette controls
Cloud, trail glow, and candle tint toggles
Confirmed flip level styling and retention controls
Dashboard display toggle
How to Use This Indicator
Step 1: Read the active regime
Check whether price is above or below the confirmed trail and whether the dashboard shows value alignment with the selected anchored VWAP mode.
Step 2: Judge extension
Use the extension and acceleration rows in the dashboard to determine whether the move is orderly or increasingly stretched from anchored value.
Step 3: Use flip levels as structure references
When a confirmed transition occurs, the generated forward level becomes a practical decision point for retests, failures, and continuation checks.
Step 4: Combine with execution tools
Aegis is best used as a directional and structural context layer. It can filter other entry models by allowing long setups only when the trail and value regime agree, and short setups only when the opposite condition is present.
Indicator Limitations
Anchored VWAP alignment may temporarily lag during very early reversals, especially when a higher anchor such as the weekly reference is selected
The arc can tighten quickly during extreme volatility because acceleration is intentionally sensitive to extension pressure
Flip levels are contextual structure references, not guaranteed support or resistance
The script classifies directional state and extension; it does not predict future price movement
Originality Statement
Aegis Arc Framework is original in the way it combines a confirmed ratcheting trail, anchored VWAP regime gating, extension-driven acceleration, and managed forward flip levels into one coherent overlay. It is published because the combination is meaningfully different from a standard SuperTrend, a standalone VWAP overlay, or a simple trailing stop line.
Disclaimer
This indicator is provided for educational and informational purposes only. It is not financial advice and does not recommend any specific trade or investment. All calculations are derived from historical and real-time chart data and can produce false signals. Trading involves substantial risk. Always use independent judgment and risk management.
Indicator
Velorum Deviation Corridor [JOAT]Velorum Deviation Corridor
Introduction
Velorum Deviation Corridor is an open-source adaptive price envelope designed to measure directional bias, stretch, and compression around a dynamic baseline. The script does not treat all volatility the same. It allows different baseline engines and different width engines, then converts that information into an overlay corridor that can show trend continuation, overextension, and volatility contraction in one place.
The problem this script solves is that static envelopes often fail when market speed changes. A fixed moving average with a fixed-width band may lag badly during acceleration and overreact during compression. Velorum addresses that by pairing adaptive baseline logic with multiple volatility models, then confirming state shifts only after bars close. The result is a directional overlay that can function as a trend frame, pullback map, and stretch monitor.
Core Concepts
1. Adaptive Baseline Selection
The script allows the user to choose among several baseline models: EMA, Hull, Adaptive KAMA, VIDYA, FRAMA, and Gaussian smoothing. This makes the corridor usable across different styles. Faster baselines react more quickly to rotation. More adaptive baselines try to react quickly in clean trends and slow down in noisy environments.
2. Multi-Model Width Estimation
The width engine can use ATR, standard deviation, Parkinson volatility, efficiency range, or a hybrid model. This matters because volatility can be defined in different ways. ATR captures absolute travel, standard deviation captures dispersion, Parkinson emphasizes high-low structure, and the hybrid approach blends multiple aspects into one corridor width.
widthModel = input.string("Hybrid Volatility", "Width Model",
options = )
3. Compression and Expansion Detection
The script tracks corridor width over a rolling lookback and compares it against a compression percentile. When width contracts into the lower part of its recent range, the script identifies a compression state. When width expands with directional slope and position agreement, the script identifies expansion. This helps distinguish quiet consolidation from meaningful travel.
4. Trend State and Stretch Logic
Trend state is determined by baseline slope, price position relative to the corridor, and confirmation bars. The script also measures stretch so users can see whether price is trading inside the value area of the corridor, near the edge, or outside it. That makes it useful for both continuation logic and reversion-aware caution.
5. Transition Ribbon, Reaction Shelves, and Drift Lanes
The overlay uses outer bands, inner bands, corridor fills, glow layers, and a narrow transition ribbon around the baseline. It also projects on-chart structure when important corridor events occur. Confirmed constructive and defensive shifts can create forward shelf boxes. Confirmed excursions outside the corridor can create upper and lower drift lanes. Compression and expansion transitions can also stamp temporary forward boxes directly on the chart, turning the corridor into a working structure map instead of only a band set.
Features
Six baseline models: EMA, Hull, Adaptive KAMA, VIDYA, FRAMA, and Gaussian
Five width engines: ATR, standard deviation, Parkinson, efficiency range, and hybrid volatility
Compression detection: Width percentile model highlights contraction phases
Trend confirmation bars: Direction changes require confirmation before they are treated as valid
Stretch context: Shows whether price is centered, extended, or outside the corridor
Layered overlay: Baseline, glow, inner bands, outer bands, fills, and transition ribbon
Reaction shelves: Confirmed constructive and defensive shifts can project forward box zones on the chart
Drift lanes: Confirmed closes outside the corridor can stamp directional lane boxes
Compression shelf and expansion release: Corridor state transitions can create temporary forward structure boxes
On-chart labels: Shelf, lane, and release labels appear directly on the price chart
Compact dashboard summary: Trend state, regime, stretch, strength, and confirmed shift in a smaller top-right panel
Confirmed-bar alerts: Lift, fade, compression, and expansion events
Input Parameters
Core Engine:
Source
Baseline Model
Baseline Length
Fast and Slow Components for adaptive models
Trend State:
Trend Confirmation Bars
Slope Lookback
Trend Strength Length
Compression Lookback
Compression Percentile
Width Model:
Width Model
Width Length
Width Multiplier
Elasticity Factor
How to Use This Indicator
Step 1: Identify the Baseline Bias
Start with price relative to the baseline and the dashboard's Trend State row. If price is holding above a rising baseline, the corridor is acting as bullish structure. If price is holding below a falling baseline, the corridor is acting as bearish structure.
Step 2: Check Compression Before Breakouts
Compression phases are useful because directional expansions often begin after width contracts. If the chart is tinted for compression and width percentile is low, watch for a confirmed shift rather than treating every small move as a new trend.
Step 3: Use Inner vs Outer Bands Differently
The inner bands are the working area for pullbacks and value. The outer bands represent more extended travel. When price repeatedly walks an outer band, that is continuation behavior. When price snaps outside and immediately loses follow-through, that is often stretch rather than sustainable expansion.
Step 4: Use Reaction Shelves and Drift Lanes as Forward Reference
When a confirmed constructive or defensive shift occurs, Velorum can project a forward shelf box. When price closes beyond the outer corridor, it can print a drift lane. These structures are intended to mark the part of the chart where continuation behavior should stay organized. If price immediately loses those zones, the move is weakening.
Step 5: Treat Confirmed Shift as the State Change
The confirmed shift output is still the important regime event. Intrabar movement can test both sides of the corridor, but the script only promotes a new state after bar confirmation and only stamps new corridor structures after confirmation.
Indicator Limitations
No single baseline model is best for every market; users may need to select a model appropriate for their instrument and timeframe
Compression does not guarantee breakout direction, only reduced width
A fast corridor can overreact in noisy markets while a slow corridor can lag during sharp reversals
Stretch beyond the outer band can persist longer than expected in strong trends
Reaction shelves and drift lanes are contextual structure tools, not guaranteed support or resistance
Originality Statement
Velorum Deviation Corridor is original in the way it separates the baseline problem from the width problem and lets those two adaptive layers interact in one confirmed-state overlay. The script is not simply a renamed moving average envelope. It combines multiple smoothing families, multiple volatility families, width percentile compression logic, stretch-state interpretation, transition-ribbon state framing, and event-driven forward shelf and lane boxes into one cohesive corridor framework.
Disclaimer
This script is provided for educational and informational purposes only. It is not financial advice. Corridor behavior is based on historical price action and can lag, compress, or expand unpredictably during unusual market conditions. Always evaluate signals in context and use appropriate risk controls.
Indicator
Crownmark Allocation Engine [JOAT]Crownmark Allocation Engine
Introduction
Crownmark Allocation Engine is an open-source PulseWire strategy that integrates regime classification, adaptive trend corridors, auction-value context, pressure confirmation, divergence suppression, and ATR-based trade management into one non-repainting framework. Its purpose is not to maximize signal count. Its purpose is to require multiple independent layers to agree before risk is deployed.
The problem this strategy solves is overcommitting to one analytical dimension. Trend alone can be late. Pressure alone can be noisy. Divergence alone can fire too early. Auction context alone does not create an entry. Crownmark combines those ideas so that entries occur only when broader regime, local structure, value position, and internal participation are aligned.
Core Concepts
1. Regime Filter First
The strategy begins with a composite regime score built from fast/slow EMA displacement, ADX strength via `ta.dmi()`, efficiency, volatility expansion, and trend slope. If the regime is not sufficiently directional, the strategy does not permit an entry.
2. Adaptive Corridor Pullback Entry
Once regime bias is directional, price must also align with an adaptive KAMA-based corridor. Longs require bullish corridor structure and a pullback that remains constructive relative to the baseline and inner band. Shorts require the mirror condition on the downside.
3. Auction and VWAP Context
Price must be positioned acceptably relative to a rolling value area and rolling VWAP. This is intended to keep entries from triggering in structurally poor locations when the rest of the model is favorable.
4. Pressure Confirmation and Divergence Guard
The strategy uses an effort-versus-result style pressure model to require directional initiative at the moment of entry. It also suppresses entries when recent confirmed divergence argues against the trade direction.
5. Managed Exits
Open positions use ATR-based stop loss and take profit levels, plus a trailing logic anchored to the adaptive baseline. Positions can also be closed by context failure, by opposite signal, or by maximum bar duration in trade.
Features
Composite regime filter: Trend, ADX, volatility, slope, and efficiency must support the direction
Adaptive corridor entries: Uses a KAMA baseline with hybrid ATR/stdev width
Auction location filter: Requires acceptable relation to rolling value area and VWAP
Pressure confirmation: Requires directional effort-versus-result support
Divergence suppression: Recent opposing divergence can block new entries
ATR-based risk management: Stop, target, and trailing logic
Context exits: Positions can flatten when regime or pressure collapses
Dashboard summary: Regime, corridor, auction, pressure, divergence, exposure, and regime score
Default Strategy Properties
Initial Capital: 100000
Order Size: 10% of equity
Pyramiding: 0
Commission: 0.01%
Order Processing: on close
calc_on_every_tick: false
Input Parameters
Regime Engine:
Fast/Slow regime lengths
ADX length
Volatility length
Efficiency length
Bull and bear regime thresholds
Trend Corridor:
Baseline length
KAMA fast and slow parameters
Width length
Width multiplier
Pullback tolerance
Auction Context:
Auction lookback
Value area width
Auction acceptance toggle
VWAP alignment toggle
Risk Management:
ATR length
Stop ATR multiple
Target ATR multiple
Trail ATR multiple
Maximum bars in trade
Flatten on opposite signal toggle
How to Use This Strategy
Step 1: Verify the Market Type
If the regime score is near balance, the strategy is intentionally selective. Crownmark is designed for directional conditions more than rotational ones.
Step 2: Let the Entry Layers Stack
An entry requires regime, corridor, auction, pressure, and divergence conditions to agree. If one layer is missing, the strategy waits.
Step 3: Respect the Exit Logic
The strategy uses both price-based and context-based exits. A position can close because the stop or target was reached, but it can also close because the original trade thesis has weakened.
Step 4: Review Results in Context
Do not evaluate the strategy by win rate alone. Regime selectivity, trade duration, average excursion, commission sensitivity, and the instrument being tested all matter.
Strategy Limitations
This strategy uses bar-close logic and does not replicate intrabar execution behavior
The rolling auction model is a simplified proxy and not a full market profile engine
Divergence suppression can skip trades that later work, by design
Different assets and timeframes may require threshold tuning to remain realistic
Originality Statement
Crownmark Allocation Engine is original in how it combines regime scoring, corridor structure, auction-value location, pressure confirmation, and divergence suppression into one coordinated execution model. The strategy is not a basic trend-following template with cosmetic additions. Each layer has a distinct analytical role, and entries are only allowed when those layers support one another.
Disclaimer
This strategy is provided for educational and informational purposes only. It is not financial advice and does not imply future performance. Backtest results depend heavily on instrument, timeframe, execution assumptions, and historical conditions. Use realistic expectations, confirm settings carefully, and apply sound risk management.
Strategy
Segmented Pressure Bands [JOAT]Segmented Pressure Bands
Introduction
Segmented Pressure Bands (SPB) is an open-source, institutional-grade regression channel system that computes a linear best-fit line and deviation bands from scratch using manual Ordinary Least Squares (OLS) mathematics — no built-in regression functions used. The channel operates in distinct segments: it builds over a dynamic lookback window, freezes all parameters at a minimum length threshold, extrapolates forward using the frozen slope and intercept, and resets automatically when price closes beyond the outer deviation band. Gradient linefill layers between the basis and outer bands communicate channel pressure visually. A volume regime tint adjusts visual weight based on relative volume activity, and ATR-based TP/SL visualization is drawn on each breakout reset.
The core problem SPB solves is that standard regression channels repaint continuously as new bars add to the calculation window, making historical channel boundaries unreliable for reference. SPB's freeze-and-extrapolate architecture locks the regression parameters at a fixed point in time, then projects the channel forward. Price that deviates far enough from that projection triggers a segment reset — the channel is redrawn from the breakout point. This creates a clear, non-repainting record of each regression segment and the breakout that ended it.
Core Concepts
1. Manual OLS Linear Regression
The regression is computed using the standard Ordinary Least Squares normal equations applied to the source series over the active lookback window:
float denom = float(length) * sumX2 - sumX * sumX
slope := (float(length) * sumXY - sumX * sumY) / denom
intercept := (sumY - slope * sumX) / float(length)
RMSE (root mean square error) is calculated as the deviation of the source from the fitted line, providing the basis for band width. All accumulator variables (sumX, sumY, sumXY, sumX2) are computed in a per-bar loop, giving full control over the calculation window without relying on built-in functions that may change behavior across versions.
2. Channel Freeze and Extrapolation
When the lookback window reaches the minimum length threshold, the slope, intercept, and RMSE are locked into freeze variables. From that point forward, the x-coordinate passed to the regression formula is the number of bars elapsed since the freeze bar, allowing the channel to project forward without recalculating:
float xCur = -float(bar_index - freezeBar)
basis := frozenIcpt + frozenSlope * xCur
This extrapolation means the bands continue to move with the slope direction, but their relative spacing (the RMSE deviation) remains constant from the freeze point.
3. Segment Reset on Breakout
When a candle closes beyond the outer upper or lower band, the current segment is terminated. The channel redraws from the current bar using the fresh source data from that point forward. Old linefill objects are explicitly deleted before new ones are created to stay within Pine Script's object limits.
4. Gradient Linefills and Volume Regime Tint
N intermediate lines are drawn between the basis and each outer band, filled progressively with increasing transparency from the inner region to the outer edge. This creates a gradient pressure visualization — tighter fills near the basis signal equilibrium, wider fills near the outer band signal stretch. When the volume regime ratio (short-term MA / long-term MA) is elevated above the high threshold, line widths increase and fill opacity deepens to communicate high-activity conditions visually.
Features
Manual OLS Regression: Slope, intercept, and RMSE computed entirely from first principles — no built-in regression functions
Freeze and Extrapolate Architecture: Regression parameters locked at minimum length; channel projected forward along the locked slope
Automatic Segment Reset: Outer band close-beyond triggers segment restart — prior segment preserved as a historical record
RMSE Deviation Bands: Upper and lower bands placed at configurable RMSE multiples from the basis line
Gradient Linefill Layers: N intermediate lines fill the channel space with a visual pressure gradient — configurable step count
Volume Regime Tint: Relative volume ratio (short/long MA) adjusts visual weight — elevated volume deepens channel fills and thickens lines
ATR TP/SL Visualization: On each breakout reset, ATR-based take profit and stop loss boxes drawn from the breakout close
Channel Direction Color: Downward slope (bullish context — price above a declining regression) renders in teal; upward slope (bearish context) renders in rose
Non-Repainting Basis: Freeze architecture ensures historical segment boundaries do not move after they are drawn
Configurable Source: Basis line source is selectable (close, hl2, hlc3, ohlc4, etc.)
Dashboard (Top Right): Current slope, RMSE, volume regime label, band multiplier, and active segment bar count
Near-Band Warning Dots: Subtle circle markers appear on the chart when price is within 12% of either channel edge — early warning that price is approaching a band extreme before a breakout occurs
Distance-to-Nearest-Band in Dashboard: Current distance from price to the nearest band displayed as a percentage of channel width — provides a precise quantitative read of how stretched or compressed the current position is within the segment
Live Regression Slope in Dashboard: Live regression slope value shown in the dashboard — communicates the current directional angle of the frozen channel projection in real time
Breakout Win/Loss Tracking: Outcome of every breakout trade tracked against ATR-based TP/SL levels — total breakout trade count and cumulative win rate displayed in the dashboard
Expanded Dashboard (7 Rows): Dashboard expanded to 7 rows — now includes distance-to-band percentage, live slope, and breakout win rate alongside existing regime and segment data
Input Parameters
Regression Settings:
Source: Price input for regression calculation (default: close)
Lookback Length: Maximum bar window for OLS computation (default: 50)
Min Length to Freeze: Bar count at which slope/intercept are locked (default: 20)
Band Multiplier: RMSE multiple for outer band placement (default: 2.0)
Gradient Settings:
Gradient Steps: Number of intermediate fill lines between basis and outer band (default: 5)
Volume Regime:
Short Vol MA: Short-term volume moving average length (default: 10)
Long Vol MA: Long-term volume moving average length (default: 40)
High Vol Threshold: Vol ratio above which volume tint activates (default: 1.5)
ATR / Risk:
ATR Length: Period for ATR calculation (default: 14)
ATR SL Multiplier: Stop loss distance on breakout (default: 1.5)
Reward:Risk Ratio: Take profit multiple of stop distance (default: 3.0)
How to Use This Indicator
Step 1: Read the Channel Direction
A teal channel indicates a downward-sloping regression — price is above a declining trend line, suggesting bullish pressure within the distribution. A rose channel indicates an upward-sloping regression — price is below a rising channel ceiling, suggesting bearish pressure. The gradient fills communicate how far price has deviated from the basis within that segment.
Step 2: Trade Within the Channel
Price compressing toward the basis from an outer band (thin fill region narrowing) suggests mean reversion is underway. Price expanding toward the outer band (fills widening) suggests momentum continuation. The outer band itself acts as a stretch boundary — closes beyond it trigger a new segment.
Step 3: React to Breakout Resets
When a segment resets, the breakout bar is the reference point for directional bias. The ATR TP/SL boxes visualize the immediate risk/reward from that close. The new channel building from the breakout will establish the next directional context.
Step 4: Monitor Volume Context
Elevated volume regime (shown in dashboard) at a channel boundary gives more conviction to breakout or reversal signals. Low-volume channel touches carry less institutional weight.
Indicator Limitations
The OLS calculation runs a loop over the lookback window on every bar. On very long lookback lengths with high chart data density, this may increase script execution time — keep lookback below 200 for best performance
The freeze architecture means the channel projection can diverge significantly from price if the instrument trends strongly after the freeze point. Segment resets bring the channel back to current price, but wide outer bands may delay that reset on low-volatility instruments
Gradient linefills are subject to Pine Script's 50-linefill object limit. SPB manages this with explicit deletion on each segment reset. If the gradient steps setting is set very high (above 10), this limit may be approached in active markets
ATR TP/SL boxes on breakout are drawn from the breakout close. They do not adjust for gaps, overnight moves, or instrument-specific spread — manual adjustment of the ATR multiplier may be needed for highly volatile instruments
Volume regime calculation uses simple moving averages of volume. On instruments where volume data is synthetic or unavailable, the regime indicator will not reflect true market activity
Originality Statement
SPB implements a regression channel with a freeze-extrapolate-reset lifecycle that produces stable, non-repainting historical segment boundaries. This design is original for the following reasons:
Computing OLS slope, intercept, and RMSE from scratch using raw accumulator mathematics — rather than using ta.linreg() or similar built-ins — gives full control over the calculation window, source, and update behavior, and avoids implicit look-ahead that some built-in functions can introduce
The freeze-and-extrapolate architecture is distinct from standard rolling regression, where every new bar shifts the entire historical channel. Once frozen, SPB's channel parameters are immutable — historical band boundaries drawn in past segments are permanent reference levels
The gradient linefill layer system communicates statistical deviation pressure visually across the full channel width, rather than drawing only a basis and outer band with no information about the space between them
The integration of a volume regime tint directly into the regression channel visualization — adjusting visual weight based on relative volume — provides immediate context for whether current channel position is occurring during active or quiet market conditions
Disclaimer
This indicator is provided for educational and informational purposes only. It is not financial advice or a recommendation to buy or sell any financial instrument. Trading involves substantial risk of loss. Regression channels and statistical deviation bands are mathematical constructs applied to historical data — they do not predict future price behavior. Breakout signals at band extremes do not guarantee continuation in any direction. Always apply proper risk management. The author is not responsible for any trading losses resulting from the use of this indicator.
-Made with passion by jackofalltrades
Indicator
Trend Strength Meter [AGPro Series]Trend Strength Meter
⚡ OVERVIEW
Trend Strength Meter is a multi-factor composite oscillator that quantifies how strong a directional trend actually is, on a single 0-to-100 score. It merges five independent trend dimensions (ADX, slope angle, momentum ROC, moving-average alignment, and pullback depth) into one weighted reading, then classifies the market into three clear states: Strong Trend (80+), Mild Trend (50-80), and Weak / Range (<50). The goal is to give a trader the answer to one of the most common daily questions in technical analysis: "Is the trend strong enough to act on right now, or is it fading?"
The indicator is built for discretionary and systematic traders who want a single, normalized number instead of reading half a dozen separate trend tools. It is scale-invariant (ATR-normalized) and works across timeframes and instruments.
🎯 UNIQUE EDGE
Trend-strength tools usually give a single-factor reading (for example, ADX alone) which can be misleading. ADX can be high during range contractions, slopes can spike during noise, MA stacks can be aligned while price sits deep in pullback. This indicator fuses all five dimensions so that no single factor can dominate the score without confirmation from the others.
Three design choices set it apart:
1. Weighted multi-factor composite. Every factor is independently normalized to 0-100, then combined with user-adjustable weights that auto-normalize. A Strong reading therefore requires broad agreement across independent trend dimensions, not just one signal firing.
2. Dominant Factor readout. The information panel shows which of the five factors is contributing most to the current score, so the trader understands why the score is where it is. A score of 84 driven by ADX and a score of 84 driven by Alignment are structurally different markets, and the readout makes that visible.
3. Historical context built in. The panel exposes Historical Max Score over a configurable lookback window, and Strong-Trend Duration (how many bars the score has held above the Strong threshold). Both of these help gauge trend maturity and exhaustion risk.
📊 METHODOLOGY
The composite score is built from five independently scored factors, each normalized to 0-100:
• Factor 1 — ADX. Directional movement strength from the standard DMI/ADX system, linearly mapped so that ADX = 60 maps to a score of 100.
• Factor 2 — Slope Angle. The slope of an EMA over its length window, normalized by ATR to be scale-invariant, then converted to a 0-100 score via an arctangent curve. High slope in either direction yields a high score.
• Factor 3 — Momentum ROC. Rate of Change normalized by ATR and converted to a bounded 0-100 value. Captures impulse magnitude independent of price scale.
• Factor 4 — MA Alignment. Stacked EMA alignment across Fast / Mid / Slow timeframes, plus price position relative to the fast MA. Full bullish or bearish stack yields 100; partial stacks are scored proportionally (70, 40, or 15).
• Factor 5 — Pullback Depth. Distance from the nearest recent extreme (highest high or lowest low over lookback) measured in ATR units. Shallow pullback = strong trend = high score.
Each factor is multiplied by its weight, summed, and divided by total weight to produce the final 0-100 score. All five weights are independently adjustable and auto-normalize, so changing one weight does not force manual rebalancing of the others.
Directional bias (Bull / Bear / Range) is determined by the combination of DMI crossover state and close-vs-mid-MA position. State color shifts between strong bull green, strong bear magenta, neutral yellow, and weak gray based on score plus direction.
🔥 VISUAL SYSTEM
Six coordinated visual elements deliver the information without clutter:
• Score histogram on the sub-panel, colored per bar by that bar's own score level (green for Strong, yellow for Mild, gray for Weak / Range). Each historical bar shows its true state at the time, not the current state.
• Horizontal reference lines at the Strong (green) and Mild (yellow) thresholds on the sub-panel.
• Historical Max step line in indigo accent, showing the highest score reached within the lookback window, so past trend peaks are immediately visible.
• Mini Gauge on the right edge of the sub-panel. A compact vertical meter split into two halves: left half shows the fixed 0-100 zone reference (Weak / Mild / Strong), right half fills up to the current score, with a bold needle line marking the exact level.
• Price badge floating above the last candle, showing "TSM " so the reading is visible without needing to look at the panel.
• Information panel on the price chart with seven rows: Score, State, Direction, Dominant Factor, Historical Max, and Strong Bars duration.
🧭 SIGNALS AND ALERTS
Three built-in alerts:
• Strong Bull Entry — Score crosses above the Strong threshold while directional bias is Bull.
• Strong Bear Entry — Score crosses above the Strong threshold while directional bias is Bear.
• Trend Fade — Score drops below the Mild threshold, indicating the trend is weakening.
All alerts fire once per bar close, so there is no intra-bar repainting.
🧮 KEY INPUTS
Core Settings
• ADX Length, Slope MA Length, Momentum ROC Length, MA Alignment Fast / Mid / Slow, Pullback ATR Length
• Score Smoothing (EMA applied to score for optional overlay line)
• Strong Threshold (default 80), Mild Threshold (default 50), Historical Lookback (default 100 bars)
Factor Weights (auto-normalized)
• ADX 30, Slope 20, Momentum 20, Alignment 20, Pullback 10
Visual
• Badge toggle, Background tint toggle, Reference lines toggle, Mini Gauge toggle, Historical Max line toggle, Smoothed Score line toggle, Label size, Badge ATR offset
Panel
• Show / hide, Position (6 anchors), Theme (Dark / Light), Font size
📈 HOW TO USE
1. Add the indicator to any chart and any timeframe. Defaults are calibrated for 4H / Daily; for lower timeframes consider reducing the ADX and ROC lengths.
2. Use the 0-100 score as a regime filter. Many trend-following setups perform better when the score is above 50, and breakout / continuation setups perform best when the score is crossing above 80 with a clear Bull or Bear direction.
3. Watch the Dominant Factor. A score of 85 driven primarily by Momentum may fade fast; the same score driven by Alignment tends to be more structural.
4. Use Historical Max and Strong Bars to gauge maturity. A Strong-Bars reading of 30+ on a daily chart often signals late-cycle conditions where continuation risk increases and fresh entries need tighter risk management.
5. Combine with structure tools (support / resistance, order blocks, market-structure tools) for entries. This indicator is designed to answer "how strong is the trend," not "where do I enter."
⚠️ LIMITATIONS AND TRANSPARENCY
• This is an indicator, not a trading strategy. It does not produce buy / sell recommendations and it does not backtest trade outcomes.
• The score is a lagging composite built from historical price data. It does not predict future price movement.
• During sharp regime transitions (news events, gap opens), the score can change rapidly from one bar to the next. This is by design, not a bug.
• Factor weights are user-adjustable. Defaults are a reasonable starting point but may need tuning per instrument / timeframe.
• The Pullback factor assumes trending behavior. In tight consolidations it can read misleadingly high, which is why the Dominant Factor readout exists as a cross-check.
• No-repaint: all calculations are based on confirmed (closed) bar data; alerts fire on bar close only.
📌 RISK DISCLOSURE
Trading carries substantial risk. This indicator is an analytical tool for research and study purposes and does not constitute financial advice, a trading signal, or a recommendation to buy or sell any instrument. Past behavior of any indicator is not indicative of future results. Users are solely responsible for their trading decisions and should conduct their own due diligence and risk management.
Indicator
Volatility Regime Cycle [AGPro Series]Volatility Regime Cycle
🌀 Overview
Volatility Regime Cycle classifies every bar on your chart into one of four distinct volatility phases: Contraction, Expansion, Climax, and Reset. Unlike traditional trend or regime indicators that focus on price direction, this tool maps the cyclical behavior of volatility itself — helping traders recognize whether the market is coiling, releasing, climaxing, or resetting. Each phase is detected through a multi-factor confluence engine and displayed with gradient background shading, transition markers, and S/R-style climax reaction zones. The framework is asset- and timeframe-agnostic: it adapts to crypto, forex, indices, stocks, and commodities on any timeframe.
💎 Unique Edge
Most volatility tools present a single metric (ATR, Bollinger Width, VIX proxy). Volatility Regime Cycle differs in structure and intent:
🔸 Phase-based classification, not just a reading — every bar is assigned to a named regime with a trader-actionable bias.
🔸 Multi-factor confluence scoring — five independent volatility inputs (ATR level, BB Width level, BB/KC squeeze, volume z-score, ATR rate-of-change) vote on the active phase. No single factor can dominate.
🔸 Winsorized normalization — outlier events (single extreme bars) do not compress the scale and hide current readings, a common flaw in simple percentile-based tools.
🔸 Climax Reaction Zones — each Climax event is preserved as an S/R-style rectangle with mid-pivot line, creating a memory of past volatility exhaustion levels that often act as future reaction areas.
🔸 Cycle-aware analytics — tracks historical phase durations and estimates current cycle progress based on rolling averages of past phases of the same type.
🔸 Phase-specific Trader Bias — panel translates the current regime into a plain-language bias (Breakout-watch, Momentum-favor, Reversal-risk, Cooldown).
This is not a Wyckoff phase tool, an Elliott counter, or a Dow-theory classifier. It is a pure volatility-cycle mapper, engineered from the ground up to stand apart from both classical cycle indicators and single-metric volatility meters.
🧠 Methodology
The engine runs three layers:
🔹 Factor Layer
• ATR Level — 14-period ATR, winsorized min-max normalized (5%-95% range) over a configurable lookback window.
• BB Width Level — Bollinger Band width as percent of basis, normalized identically.
• Squeeze State — true when Bollinger Bands are contained inside Keltner Channels (classic volatility compression).
• Volume Z-Score — standardized volume relative to its rolling mean and standard deviation.
• ATR Rate-of-Change — momentum of volatility itself.
🔹 Scoring Layer
Each of the four phases has its own scoring formula that weights the five factors differently. For each bar, all four phase scores are calculated in parallel, and the phase with the highest score is the candidate regime for that bar.
🔹 Confirmation Layer
To suppress whipsaw, the candidate phase must persist for a configurable number of bars (default 3) before replacing the active phase. A minimum phase duration lock additionally prevents rapid flips. Climax events include a de-duplication cooldown so that clustered climax bars produce a single marker rather than a cluster of overlapping labels.
Phase transitions are classified as major (Contraction→Expansion breakouts and Climax entries) or minor (all other routine changes). Only major transitions receive labels; minor changes are shown as subtle dotted lines to keep the chart clean.
🔔 Signals & Alerts
The script exposes alerts for every phase transition as well as two high-value composite events:
🔸 Any Phase Transition — fires whenever the active phase changes.
🔸 Entered Contraction / Expansion / Climax / Reset — fires for specific phase entries.
🔸 Contraction → Expansion (Breakout) — coil release event; of interest to breakout traders.
🔸 Climax Entry (Exhaustion Warning) — volatility peak event; of interest to mean-reversion and risk-management traders.
All alerts fire only on confirmed bar close to prevent intra-bar flip-flop.
⚙️ Key Inputs
🔹 Engine Settings — normalization lookback, ATR length, Bollinger/Keltner length and multipliers, volume z-score length, ATR rate-of-change length, confirmation bars, minimum phase duration.
🔹 Phase Thresholds — low volatility level, high volatility level, climax volatility gate, climax volume z-score threshold, climax de-dup cooldown.
🔹 Visuals — toggles for background shading, major transition labels, minor transition lines, volatility ribbon, current phase label.
🔹 S/R Zones — climax zones toggle, breakout zones toggle, maximum active zones, zone initial length, zone range lookback.
🔹 Panel — show/hide, location, Dark/Light theme, font size.
🔹 Label Sizing — font size for on-chart labels.
🔹 Alerts — per-event toggles.
📘 How to Use
🔸 Breakout traders: watch for Contraction phase on the panel with Trader Bias showing Breakout-watch. When the phase transitions to Expansion, a coil release is underway and a Breakout label is printed. Optional Breakout Zones can be enabled to preserve the breakout level as a retest reference.
🔸 Momentum / trend traders: ride the Expansion phase while Trader Bias reads Momentum-favor. Phase Duration and Cycle Progress on the panel give a sense of where the current momentum leg sits relative to historical averages.
🔸 Mean-reversion / exhaustion traders: a Climax label with Trader Bias Reversal-risk highlights volatility exhaustion. The Climax Reaction Zone drawn at each climax often behaves as a future reaction level and can be used as confluence with other reversal tools.
🔸 Risk managers: the Reset phase with Trader Bias Cooldown typically signals reduced market conviction and can be used to scale down position size until a new Contraction builds up.
🔸 Multi-timeframe reading: run the script on the higher timeframe for regime context and on the lower timeframe for entry timing.
Hover the panel header to see a statistics tooltip with average durations of each phase over the last completed cycles.
⚠️ Limitations & Transparency
🔹 The script does not predict future prices or issue buy/sell recommendations. It is a classification and context tool.
🔹 Phase detection is inherently lagging because it requires the confirmation window and minimum duration lock. This is a deliberate design choice to suppress whipsaw at the cost of some responsiveness.
🔹 Normalization uses a rolling lookback window; the first lookback bars after loading the script may show compressed or unstable readings while the window fills.
🔹 Cycle Progress is an estimate based on historical phase averages and may exceed 100% when the current phase runs longer than past cycles.
🔹 Climax and Reset scores rely partly on volume; on instruments with unreliable or missing volume feeds, volume-dependent factors will contribute less.
🔹 All visual elements are cosmetic and toggleable; they do not alter the underlying phase logic.
🛡️ Risk Disclosure
This indicator is a technical analysis tool. It is not a trading system, not a signal service, not financial advice, and not a guarantee of future results. Trading involves substantial risk of loss. Past market behavior does not predict future market behavior. Users are solely responsible for their own trading decisions, risk management, and position sizing. Always test any tool on your preferred instruments and timeframes with appropriate historical review before using it as part of a live decision-making process. Indicator
Trend Quality [AGPro Series]Trend Quality
Trend Quality fuses three independent regime dimensions — ADX directional strength, Kaufman Efficiency Ratio, and ATR-normalized EMA slope — into a single 0–100 composite Trend Quality Score. A hysteresis + confirmation + cooldown gate turns that score into a stable TREND / CHOP regime, enhanced with HTF confirmation, lifecycle phases, score velocity, breakout grading, directional dominance, and a full adaptive on-chart quality window. The goal is simple: replace noisy "is this a trend?" guessing with a transparent, multi-dimensional, low-lag quality reading you can read in one glance.
🎯 OVERVIEW
Most trend filters fail at the same thing — they tell you a trend exists, but not whether that trend is clean, accelerating, fading, or already exhausted. Trend Quality answers the harder question. Every bar is scored on three independent dimensions that each measure a different physical property of price movement:
• ADX — directional strength (how strongly one side dominates)
• Kaufman Efficiency Ratio (ER) — path efficiency (how little wasted motion)
• ATR-normalized EMA slope — normalized trend velocity (how fast, relative to volatility)
These three signals are combined into one 0–100 Trend Quality Score. A hysteresis band + confirmation bars + cooldown filter convert that score into a stable TREND / CHOP regime — no single-bar flipping, no false recovery wicks. On top of the core regime, the indicator layers Score Velocity, Lifecycle phases (Emerging → Confirmed → Exhausting), Breakout Quality grading (A / B / C), directional dominance, and a visual Quality Window that tracks the active trend zone and projects it forward.
💎 UNIQUE EDGE
What separates Trend Quality from a standard ADX filter, an EMA slope indicator, or a generic regime meter:
• Tri-factor fusion (not a single metric) — ADX alone misses path quality; ER alone misses direction; slope alone misses choppy-but-strong moves. Weighted fusion (45% ADX, 35% ER, 20% Slope) neutralizes each component's blind spot.
• Stable regime, not a flickering line — the TREND / CHOP state passes through a 3-layer filter: hysteresis band around the threshold, N confirmation bars, and a cooldown window after every transition. The result is a regime reading that holds through pullbacks without flipping.
• Score Velocity Engine — a second-derivative layer that watches how fast the score itself is changing. Surges flag momentum ignition; collapses flag quality breakdown before price confirms it. A bearish divergence detector fires when price makes new highs while quality is fading.
• Lifecycle phases — inside every TREND regime, the script distinguishes Emerging (young, fresh, accelerating), Confirmed (mature, stable, above buffer), and Exhausting (score rolling over from a peak). This lets you see whether you are entering early, running mid-trend, or catching the end.
• Breakout Quality Badge (A / B / C) — every CHOP → TREND transition receives a graded badge based on composite score plus velocity bonus. Grade A breakouts are rare and have an optional dedicated alert.
• HTF confirmation with Auto-HTF mapping — the same engine runs on a higher timeframe. When LTF is trending but HTF is not, the regime is marked BLOCKED (not forced to CHOP) so you retain full transparency about why the regime is gated.
• Adaptive Quality Window — a live rectangular zone that tracks the full trend's high/low from its start bar, projects forward, shows ceiling/floor projection labels, and preserves historical windows with directional color coding (green for up-trends, pink for down-trends, amber for HTF-blocked trends).
🧪 METHODOLOGY
Core composite score (every bar, LTF):
Score = 100 × (0.45 × ADX_norm + 0.35 × ER_norm + 0.20 × Slope_norm)
• ADX_norm = min(ADX / 50, 1)
• ER_norm = |close − close | / (SMA(|Δclose|, N) × N)
• Slope_norm = min(|EMA − EMA | / ATR × 10, 1)
Regime gating:
• Hysteresis: +3 above threshold to enter TREND, −3 below to enter CHOP
• Confirmation: N consecutive bars above/below the hysteresis band
• Cooldown: N bars after every regime flip where no new flip is allowed
MTF confirmation (optional, default ON):
The same core function is called via request.security on the HTF (Auto: 30m→4H, 4H→Daily, Daily→Weekly, Weekly→Monthly in Strict mode). When LTF=TREND but HTF=CHOP, the regime is tagged BLOCKED — a transparent third state that is neither forced-CHOP nor accepted-TREND.
Lifecycle logic:
• Emerging: TREND is young (bars since start ≤ Emerging Bars) OR score slope ≥ 0 and score below buffer
• Confirmed: score ≥ threshold + Confirmed Buffer AND HTF passes (optional)
• Exhausting: score slope < 0 AND pullback from peak ≥ Exhaustion Pullback
Score velocity:
velocity = score − score (default 5-bar look-back)
Breakout quality grading:
bqScore = score + velocity_bonus (bonus: +15 if vel>15, +7 if vel>5, else 0)
A ≥ 82, B ≥ 67, C < 67
🔔 SIGNALS & ALERTS
The script exposes 12 alert conditions — all moderator-safe, educational, non-solicitating:
• CHOP → TREND / TREND → CHOP regime flips with LTF+HTF context
• Strong Trend composite conviction threshold
• HTF Blocked / HTF Unblocked third-state transparency events
• Emerging / Confirmed / Exhausting Trend lifecycle phase changes
• Velocity Surge / Velocity Collapse second-derivative extremes
• Grade-A Breakout rare high-conviction breakouts
• Bearish Divergence price up, quality down warning
On-chart visual events (also filterable via inputs):
• Breakout Quality badge (A / B / C) at every CHOP → TREND
• Bearish divergence ⚠ marker at trend peaks where quality fades
• State tag near backbone: EMERGING / CONFIRMED / EXHAUSTING / HTF BLOCKED
• Quality Window label: ACTIVE + phase
• Projection labels on the right edge: QUALITY CEILING / TREND FLOOR
All badge and warning labels are gated with an 8-bar cooldown so the chart stays clean even on repeated intra-swing triggers.
⚙️ KEY INPUTS
Core engine:
• ADX Length (14) — directional strength look-back
• Efficiency Length (20) — ER path-efficiency window
• Slope EMA Length (50) — trend backbone reference
• ATR Length (14) — volatility normalization
• TREND Threshold (55) — composite score level to enter TREND
• Confirmation Bars (1) — bars of persistence before flipping
• Strong Trend Offset (15) — extra score above threshold for STRONG tag
MTF:
• HTF Confirmation (ON) — enable/disable HTF gate
• Auto HTF (ON, Strict) — smart HTF mapping per chart TF
• Manual HTF (240) — override timeframe
Stability:
• Change Cooldown Bars (2) — lock-out window after any regime flip
Lifecycle:
• Emerging Phase Bars (4) — max trend age to stay Emerging
• Confirmed Buffer (8.0) — score must clear threshold+buffer
• Exhaustion Pullback (4.0) — peak-to-current drop to flag Exhausting
Visual Overlay:
• Backbone + Glow + Zone + State Candles + Quality Window + Historical Windows + Projection Box + Guides + Midline (all toggleable)
Panel, Theme, Layout, Help rows, Alerts, Score Velocity Engine, Breakout Quality Badge, Divergence Detector — every layer has its own input group and can be shown/hidden independently.
📘 HOW TO USE
Read-in-one-glance panel (standard AGPro format):
• Blue header row: script title
• Line 2: REGIME / LIFECYCLE · Score N/100 · Velocity state
• Line 3: LTF regime · Direction · Directional Dominance
• Line 4: HTF regime · MTF PASS/BLOCKED · Active Window state · Streak
Quick playbook:
1. CHOP on LTF → wait. No setup, no commitment.
2. CHOP → TREND transition with Grade A badge + HTF PASS → highest-conviction regime start.
3. CONFIRMED phase with DOM HIGH and rising score → the middle of the trend, usually the cleanest section.
4. Velocity COLLAPSE or EXHAUSTING phase with bearish divergence ⚠ → quality is deteriorating; reduce exposure or tighten stops.
5. HTF BLOCKED amber window → LTF trend exists but higher timeframe disagrees; treat as lower-conviction and be aware of mean-reversion risk.
The indicator does NOT issue buy/sell signals, does NOT define entry/exit prices, and is NOT a strategy. It is a regime-quality reading — a context layer you pair with your own trade management.
⚠️ LIMITATIONS & TRANSPARENCY
• Trend-quality indicators are inherently trend-following. In low-volatility ranges the score can stay above threshold on minor moves; in very fast markets the score can lag by 1–3 bars while the filters stabilize.
• HTF confirmation introduces a natural HTF delay. This is intentional (it removes noise) but means the HTF gate may lift several LTF bars after price has already moved.
• All composite signals rely on look-backs (ADX 14, ER 20, Slope EMA 50, ATR 14). On very short intraday timeframes with low bar counts these need calibration.
• Lifecycle phases are structural readings, not predictions. EXHAUSTING means the score is rolling over — not that price must reverse.
• Past performance of any visual regime does not imply future performance. Charts showing clean historical windows are illustrative of the indicator's logic, not trading results.
• No repainting on historical bars. The HTF call uses lookahead_off and barmerge.gaps_off. Score and regime values on closed bars are final.
🛡️ RISK DISCLOSURE
This script is published as an educational and analytical tool. It does not provide financial advice, does not generate trade signals of any kind, and must not be used as a standalone decision system. Markets involve substantial risk of loss. Past behavior of any market regime, indicator output, or historical visual window is no guarantee of future results. Always combine any indicator with independent risk management, position sizing, a tested plan, and — where appropriate — the guidance of a licensed professional. You are solely responsible for any trading decisions you make. Indicator
