Setup Keltner Banda 3 e 5 - MMS
⚙️ How It Works:
• Calculates a 20-period Simple Moving Average (SMA) as the central line.
• Uses the ATR (Average True Range) to build two volatility bands:
o 3x ATR Band (more sensitive)
o 5x ATR Band (more extreme)
• Detects potential reversals when the price closes outside a band and then re-enters it.
🔍 Signals Generated:
• 🔻 Bearish Reversal: Price re-enters from above the upper band.
• 🔺 Bullish Reversal: Price re-enters from below the lower band.
• Signals are displayed with colored arrows on the chart for easy visual recognition.
🔔 Alerts:
The script also triggers automatic alerts for each type of reversal, so you can be notified in real time.
🧱 Ideal For:
• Traders using Renko, Range, or traditional candlestick charts
• Scalping or swing trading strategies
• Anyone looking for visual confirmation of price exhaustion and potential reversals
Volatility
US Market Long Horizon Momentum Summary in one paragraph
US Market Long Horizon Momentum is a trend following strategy for US index ETFs and futures built around a single eighteen month time series momentum measure. It helps you stay long during persistent bull regimes and step aside or flip short when long term momentum turns negative.
Scope and intent
• Markets. Large cap US equity indices, liquid US index ETFs, index futures
• Timeframes. 4h/ Daily charts
• Default demo used in the publication. SPY on 4h timeframe chart
• Purpose. Provide a minimal long bias index timing model that can reduce deep drawdowns and capture major cycles without parameter mining
• Limits. This is a strategy. Orders are simulated on standard candles only
Originality and usefulness
• Unique concept or fusion. One unscaled multiple month log return of an external benchmark symbol drives all entries and exits, with optional volatility targeting as a single risk control switch.
• Failure mode addressed. Fully passive buy and hold ignores the sign of long horizon momentum and can sit through multi year drawdowns. This script offers a way to step down risk in prolonged negative momentum without chasing short term noise.
• Testability. All parameters are visible in Inputs and the momentum series is plotted so users can verify every regime change in the Tester and on price history.
• Portable yardstick. The log return over a fixed window is a unit that can be applied to any liquid symbol with daily data.
Method overview in plain language
The method looks at how far the benchmark symbol has moved in log return terms over an eighteen month window in our example. If that long horizon return is positive the strategy allows a long stance on the traded symbol. If it is negative and shorts are enabled the strategy can flip short, otherwise it goes flat. There is an optional realised volatility estimate on the traded symbol that can scale position size toward a target annual volatility, but in the default configuration the model uses unit leverage and only the sign of momentum matters.
Base measures
Return basis. The core yardstick is the natural log of close divided by the close eighteen months ago on the benchmark symbol. Daily log returns of the traded symbol feed the realised volatility estimate when volatility targeting is enabled.
Components
• Component one Momentum eighteen months. Log of benchmark close divided by its close mom_lookback bars ago. Its sign defines the trend regime. No extra smoothing is applied beyond the long window itself.
• Component two Realised volatility optional. Standard deviation of daily log returns on the traded symbol over sixty three days. Annualised by the square root of 252. Used only when volatility targeting is enabled.
• Optional component Volatility targeting. Converts target annual volatility and realised volatility into a leverage factor clipped by a maximum leverage setting.
Fusion rule
The model uses a simple gate. First compute the sign of eighteen month log momentum on the benchmark symbol. Optionally compute leverage from volatility. The sign decides whether the strategy wants to be long, short, or flat. Leverage only rescales position size when enabled and does not change direction.
Signal rule
• Long suggestion. When eighteen month log momentum on the benchmark symbol is greater than zero, the strategy wants to be long.
• Short suggestion. When that log momentum is less than zero and shorts are allowed, the strategy wants to be short. If shorts are disabled it stays flat instead.
• Wait state. When the log momentum is exactly zero or history is not long enough the strategy stays flat.
• In position. In practice the strategy sits IN LONG while the sign stays positive and flips to IN SHORT or flat only when the sign changes.
Inputs with guidance
Setup
• Momentum Lookback (months). Controls the horizon of the log return on the benchmark symbol. Typical range 6 to 24 months. Raising it makes the model slower and more selective. Lowering it makes it more reactive and sensitive to medium term noise.
• Symbol. External symbol used for the momentum calculation, SPY by default. Changing it lets you time other indices or run signals from a benchmark while trading a correlated instrument.
Logic
• Allow Shorts. When true the strategy will open short positions during negative momentum regimes. When false it will stay flat whenever momentum is negative. Practical setting is tied to whether you use a margin account or an ETF that supports shorting.
Internal risk parameters (not exposed as inputs in this version) are:
• Target Vol (annual). Target annual volatility for volatility targeting, default 0.2.
• Vol Lookback (days). Window for realised volatility, default 63 trading days.
• Max Leverage. Cap on leverage when volatility targeting is enabled, default 2.
Usage recipes
Swing continuation
• Signal timeframe. Use the daily chart.
• Benchmark symbol. Leave at SPY for US equity index exposure.
• Momentum lookback. Eighteen months as a default, with twelve months as an alternative preset for a faster swing bias.
Properties visible in this publication
• Initial capital. 100000
• Base currency. USD
• Default order size method. 5% of the total capital in this example
• Pyramiding. 0
• Commission. 0.03 percent
• Slippage. 3 ticks
• Process orders on close. On
• Bar magnifier. Off
• Recalculate after order is filled. Off
• Calc on every tick. Off
• All request.security calls use lookahead = barmerge.lookahead_off
Realism and responsible publication
The strategy is for education and research only. It does not claim any guaranteed edge or future performance. All results in Strategy Tester are hypothetical and depend on the data vendor, costs, and slippage assumptions. Intrabar motion is not modeled inside daily bars so extreme moves and gaps can lead to fills that differ from live trading. The logic is built for standard candles and should not be used on synthetic chart types for execution decisions.
Performance is sensitive to regime structure in the US equity market, which may change over time. The strategy does not protect against single day crash risk inside bars and does not model gap risk explicitly. Past behavior of SPY and the momentum effect does not guarantee future persistence.
Honest limitations and failure modes
• Long sideways regimes with small net change over eighteen months can lead to whipsaw around the zero line.
• Very sharp V shaped reversals after deep declines will often be missed because the model waits for momentum to turn positive again.
• The sample size in a full SPY history is small because regime changes are infrequent, so any test must be interpreted as indicative rather than statistically precise.
• The model is highly dependent on the chosen lookback. Users should test nearby values and validate that behavior is qualitatively stable.
Legal
Education and research only. Not investment advice. You are responsible for your own decisions. Always test on historical data and in simulation with realistic costs before any live use.
RiskCraft - Advanced Risk Management SystemRiskCraft – Risk Intelligence Dashboard
Trade like you actually respect risk
"I know the setup looks good… but how much am I actually risking right now?"
RiskCraft is an open-source Pine Script v6 indicator that keeps risk transparent directly on the chart. It is not a signal generator; it is a risk desk that calculates size, frames volatility, and reminds you when your behaviour drifts away from the plan.
Core utilities
Calculates professional-style position sizing in real time.
Reads volatility and market regime before position size is confirmed.
Adjusts risk based on the trader’s emotional state and confidence inputs.
Maps session risk across Asian, London, and New York hours.
Draws exactly one stop line and one target line in the preferred direction.
Provides rotating education tips plus contextual warnings when risk escalates.
It is intentionally conservative and keeps you in the game long enough for any separate entry logic to matter.
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Chart layout checklist
Use a clean chart on a liquid symbol (e.g., AMEX:SPY or major FX pairs).
Main RiskCraft dashboard placed on the right edge.
Session Risk box on the left with UTC time visible.
Floating risk badge above price.
Stop/target guide lines enabled.
Education panel visible in the bottom-right corner.
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1. On-chart components
Right-side dashboard : account risk %, position size/value, stop, target, risk/reward, regime, trend strength, emotional state, behavioural score, correlation, and preferred trade direction.
Session Risk box : highlights active session (Asian, London, NY), current UTC time, and risk label (High/Med/Low) per session.
Floating risk badge : keeps actual account risk percent visible with colour-coded wording from Ultra Cautious to Very Aggressive.
Stop/target lines : exactly one dashed stop and one dashed target aligned with the preferred bias.
Education panel : rotates core principles and AI-style warnings tied to volatility, risk %, and behaviour flags.
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2. Volatility engine – ATR with context 📈
atr = ta.atr(atrLength)
atrPercent = (atr / close) * 100
atrSMA = ta.sma(atr, atrLength)
volatilityRatio = atr / atrSMA
isHighVol = volatilityRatio > volThreshold
ATR vs ATR SMA shows how wild price is relative to recent history.
Volatility ratio above the threshold flips isHighVol , which immediately trims risk.
An ATR percentile rank over the last 100 bars indicates calm versus chaotic regimes.
Daily ATR sampling via request.security() gives higher time-frame context for intraday sessions.
When volatility spikes the script dials position size down automatically instead of cheering for maximum exposure.
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3. Market regime radar – Danger or Drift 🌊
ema20 = ta.ema(close, 20)
ema50 = ta.ema(close, 50)
ema200 = ta.ema(close, 200)
trendScore = (close > ema20 ? 1 : -1) +
(ema20 > ema50 ? 1 : -1) +
(ema50 > ema200 ? 1 : -1)
= ta.dmi(14, 14)
Regimes covered:
Danger : high volatility with weak trend.
Volatile : volatility elevated but structure still directional.
Choppy : low ADX and noisy action.
Trending : directional flows without extreme volatility.
Mixed : anything between.
Each regime maps to a 1–10 risk score and a multiplier that feeds the final position size. Danger and Choppy clamp size; Trending restores normal risk.
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4. Behaviour engine – trader inputs matter 🧠
You provide:
Emotional state : Confident, Neutral, FOMO, Revenge, Fearful.
Confidence : slider from 1 to 10.
Toggle for behavioural adjustment on/off.
Behind the scenes:
Each state triggers an emotional multiplier .
Confidence produces a confidence multiplier .
Combined they form behavioralFactor and a 0–100 Behavioural Score .
High-risk emotions or low conviction clamp the final risk. Calm inputs allow normal size. The dashboard prints both fields to keep accountability on-screen.
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5. Correlation guardrail – avoid stacking identical risk 📊
Optional correlation mode compares the active symbol to a reference (default AMEX:SPY ):
corrClose = request.security(correlationSymbol, timeframe.period, close)
priceReturn = ta.change(close) / close
corrReturn = ta.change(corrClose) / corrClose
correlation = calcCorrelation()
Absolute correlation above the threshold applies a correlation multiplier (< 1) to reduce size.
Dashboard row shows the live correlation and reference ticker.
When disabled, the row simply echoes the current symbol, keeping the table readable.
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6. Position sizing engine – heart of the script 💰
baseRiskAmount = accountSize * (baseRiskPercent / 100)
adjustedRisk = baseRiskAmount * behavioralFactor *
regimeAdjustment * volAdjustment *
correlationAdjustment
finalRiskAmount = math.min(adjustedRisk,
accountSize * (maxRiskCap / 100))
stopDistance = atr * atrStopMultiplier
takeProfit = atr * atrTargetMultiplier
positionSize = stopDistance > 0 ? finalRiskAmount / stopDistance : 0
positionValue = positionSize * close
Outputs shown on the dashboard:
Position size in units and value in currency.
Actual risk % back on account after adjustments.
Risk/Reward derived from ATR-based stop and target.
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7. Intelligent trade direction – bias without signals 🎯
Direction score ingredients:
EMA stack alignment.
Price versus EMA20.
RSI momentum relative to 50.
MACD line vs signal.
Directional Movement (DI+/DI–).
The resulting Trade Direction row prints LONG, SHORT, or NEUTRAL. No orders are generated—this is guidance so you only risk capital when the structure supports it.
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8. Stop/target guide lines – two lines only ✂️
if showStopLines
if preferLong
// long stop below, target above
else if preferShort
// short stop above, target below
Lines refresh each bar to keep clutter low.
When the direction score is neutral, no lines appear.
Use them as visual anchors, not auto-orders.
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9. Session Risk map – global volatility clock 🌍
Tracks Asian, London, and New York windows via UTC.
Computes average ATR per session versus global ATR SMA.
Labels each session High/Med/Low and colours the cells accordingly.
Top row shows the active session plus current UTC time so you always know the regime you are trading.
One glance tells you whether you are trading quiet drift or the part of the day that hunts stops.
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10. Floating risk badge – honesty above price 🪪
Text ranges from Ultra Cautious through Very Aggressive.
Colour matches the risk palette inputs (High/Med/Low).
Updates on the last bar only, keeping historical clutter off the chart.
Account risk becomes impossible to ignore while you stare at price.
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11. Education engine & warnings 📚
Rotates evergreen principles (risk 1–2%, journal trades, respect plan).
Triggers contextual warnings when volatility and risk % conflict.
Flags when emotional state = FOMO or Revenge.
Highlights sub-standard risk/reward setups.
When multiple danger flags stack, an AI-style warning overrides the tip text so you can course-correct before capital is exposed.
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12. Alerts – hard guard rails 🚨
Excessive Risk Alert : actual risk % crosses custom threshold.
High Volatility Alert : ATR behaviour signals danger regime.
Emotional State Warning : FOMO or Revenge selected.
Poor Risk/Reward Alert : risk/reward drops below your standard.
All alerts reinforce discipline; none suggest entries or exits.
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13. Multi-market behaviour 🕒
Intraday (1m–1h): session box and badge react quickly; ideal for scalpers needing constant risk context.
Higher time frames (1D–1W): dashboard shifts slowly, supporting swing planning.
Asset classes confirmed in validation: crypto majors, large-cap equities, indices, major FX pairs, and liquid commodities.
Risk logic is price-based, so it adapts across markets without bespoke tuning.
15. Key inputs & recommended defaults
Account Size : 10,000 (modify to match actual account; min 100).
Base Risk % : 1.0 with a Maximum Risk Cap of 2.5%.
ATR Period : 14, Stop Multiplier 2.0, Target Multiplier 3.0.
High Vol Threshold : 1.5 for ATR ratio.
Behavioural Adjustment : enabled by default; disable for fixed risk.
Correlation Check : optional; default symbol AMEX:SPY , threshold 0.7.
Display toggles : main dashboard, risk badge, session map, education panel, and stop lines can be individually disabled to reduce clutter.
16. Usage notes & limits
Indicator mode only; no automated entries or exits.
Trade history panel intentionally disabled (requires strategy context).
Correlation analysis depends on additional data requests and may lag slightly on illiquid symbols.
Session timing uses UTC; adjust expectations if you trade localized instruments.
HTF ATR sampling uses daily data, so bar replay on lower charts may show brief data gaps while HTF loads.
What does everyone think RISK really means?
GARO Lite - Free Regime EngineGARO — Gamma Regime Engine
Overview
GARO (Gamma Regime Oscillator) is a visual regime engine that shows market conditions in real-time. This free edition is for educational and charting purposes only.
Key Features
Regime Detection: Highlights Expansion, Contraction, and Spike conditions using trend, volatility, and volume-based calculations.
Core and Bands: Central reference line with upper and lower bands.
Visual Alerts: Orange dots appear under candles during compressions; background colors indicate current regime.
Signal Labels: Labels provide visual guidance based on regime and trend slope.
Gamma Exposure (GEX) Proxy & Zero Gamma Flip: Optional visual overlays for contextual awareness.
User Inputs: Some settings are visible in the input panel but are disabled in this free edition.
How to Use
Regime Colors:
Expansion (green background): Market trending/expanding; core line indicates direction.
Contraction (blue background): Market range-bound; orange dots indicate compression.
Spike (red background): High volatility; visual alert only.
Labels & Signals:
Labels highlight potential regime moves; not trade advice.
Combine colors, core/band positions, and label cues with your own analysis.
Core Line & Bands:
Core line shows central reference per regime.
Upper/lower bands provide context for potential support/resistance zones.
Orange Dots:
Indicate compressions or regime-specific signals; visual only.
Gamma Exposure & Zero Gamma Flip (Optional):
Illustrates potential price sensitivity; charting/educational use only.
Important:
Protected code; underlying calculations are not visible.
For educational and visual guidance only; not financial or trading advice.
Works on any timeframe; free edition gives visual regime insights.
Liquidity Pulse Oscillator LITETitle:
Liquidity Pulse Oscillator LITE
Description:
This indicator provides an observational view of market activity by measuring intra-bar price and volume dynamics. It is fully informational and educational, and does not constitute financial, trading, or investment advice.
Key Features:
Fast and Slow Pulse lines: Dual EMAs of volume-weighted pressure to highlight crossover points.
Histogram: Displays the difference between fast and slow pulses with color-coded bars (green for positive, red for negative).
Scaled 0–100 line: Provides a normalized perspective for easier interpretation of relative activity levels.
EXP/CON markers: Indicate expansions and contractions in observed market activity.
How It Works:
Pressure is calculated as the absolute open-to-close movement divided by the candle range, multiplied by volume. Safeguards handle zero-range bars. The resulting values are smoothed using fast and slow EMAs. Crossovers generate EXP and CON markers, helping users visualize changes in market activity.
Why This Approach:
Traditional volume indicators often overlook intra-bar dynamics and range normalization. This oscillator emphasizes price movement relative to bar range combined with volume, offering an additional perspective on shifts in market activity.
How to Use:
EXP marker + positive histogram: Indicates potential expansion in observed market activity.
CON marker + negative histogram: Indicates potential contraction in observed market activity.
Can be applied on any timeframe to help confirm breakouts, reversals, or shifts in market behavior.
Notes:
For informational and educational purposes only. Not financial advice.
HoneG_EURUSD値動き停止アラートv5 SUBThis is a subchart version of a signal tool that detects price movement stoppages and displays signals with alerts.
Please select and use a timeframe on the chart that corresponds to the time period you wish to detect.
値動きの停止を検知してサイン表示とアラートを出すサインツールのサブチャート版です。
チャートの時間足を、検知したい時間相当の足を選んで使ってください。
Volatility Risk PremiumTHE INSURANCE PREMIUM OF THE STOCK MARKET
Every day, millions of investors face a fundamental question that has puzzled economists for decades: how much should protection against market crashes cost? The answer lies in a phenomenon called the Volatility Risk Premium, and understanding it may fundamentally change how you interpret market conditions.
Think of the stock market like a neighborhood where homeowners buy insurance against fire. The insurance company charges premiums based on their estimates of fire risk. But here is the interesting part: insurance companies systematically charge more than the actual expected losses. This difference between what people pay and what actually happens is the insurance premium. The same principle operates in financial markets, but instead of fire insurance, investors buy protection against market volatility through options contracts.
The Volatility Risk Premium, or VRP, measures exactly this difference. It represents the gap between what the market expects volatility to be (implied volatility, as reflected in options prices) and what volatility actually turns out to be (realized volatility, calculated from actual price movements). This indicator quantifies that gap and transforms it into actionable intelligence.
THE FOUNDATION
The academic study of volatility risk premiums began gaining serious traction in the early 2000s, though the phenomenon itself had been observed by practitioners for much longer. Three research papers form the backbone of this indicator's methodology.
Peter Carr and Liuren Wu published their seminal work "Variance Risk Premiums" in the Review of Financial Studies in 2009. Their research established that variance risk premiums exist across virtually all asset classes and persist over time. They documented that on average, implied volatility exceeds realized volatility by approximately three to four percentage points annualized. This is not a small number. It means that sellers of volatility insurance have historically collected a substantial premium for bearing this risk.
Tim Bollerslev, George Tauchen, and Hao Zhou extended this research in their 2009 paper "Expected Stock Returns and Variance Risk Premia," also published in the Review of Financial Studies. Their critical contribution was demonstrating that the VRP is a statistically significant predictor of future equity returns. When the VRP is high, meaning investors are paying substantial premiums for protection, future stock returns tend to be positive. When the VRP collapses or turns negative, it often signals that realized volatility has spiked above expectations, typically during market stress periods.
Gurdip Bakshi and Nikunj Kapadia provided additional theoretical grounding in their 2003 paper "Delta-Hedged Gains and the Negative Market Volatility Risk Premium." They demonstrated through careful empirical analysis why volatility sellers are compensated: the risk is not diversifiable and tends to materialize precisely when investors can least afford losses.
HOW THE INDICATOR CALCULATES VOLATILITY
The calculation begins with two separate measurements that must be compared: implied volatility and realized volatility.
For implied volatility, the indicator uses the CBOE Volatility Index, commonly known as the VIX. The VIX represents the market's expectation of 30-day forward volatility on the S&P 500, calculated from a weighted average of out-of-the-money put and call options. It is often called the "fear gauge" because it rises when investors rush to buy protective options.
Realized volatility requires more careful consideration. The indicator offers three distinct calculation methods, each with specific advantages rooted in academic literature.
The Close-to-Close method is the most straightforward approach. It calculates the standard deviation of logarithmic daily returns over a specified lookback period, then annualizes this figure by multiplying by the square root of 252, the approximate number of trading days in a year. This method is intuitive and widely used, but it only captures information from closing prices and ignores intraday price movements.
The Parkinson estimator, developed by Michael Parkinson in 1980, improves efficiency by incorporating high and low prices. The mathematical formula calculates variance as the sum of squared log ratios of daily highs to lows, divided by four times the natural logarithm of two, times the number of observations. This estimator is theoretically about five times more efficient than the close-to-close method because high and low prices contain additional information about the volatility process.
The Garman-Klass estimator, published by Mark Garman and Michael Klass in 1980, goes further by incorporating opening, high, low, and closing prices. The formula combines half the squared log ratio of high to low prices minus a factor involving the log ratio of close to open. This method achieves the minimum variance among estimators using only these four price points, making it particularly valuable for markets where intraday information is meaningful.
THE CORE VRP CALCULATION
Once both volatility measures are obtained, the VRP calculation is straightforward: subtract realized volatility from implied volatility. A positive result means the market is paying a premium for volatility insurance. A negative result means realized volatility has exceeded expectations, typically indicating market stress.
The raw VRP signal receives slight smoothing through an exponential moving average to reduce noise while preserving responsiveness. The default smoothing period of five days balances signal clarity against lag.
INTERPRETING THE REGIMES
The indicator classifies market conditions into five distinct regimes based on VRP levels.
The EXTREME regime occurs when VRP exceeds ten percentage points. This represents an unusual situation where the gap between implied and realized volatility is historically wide. Markets are pricing in significantly more fear than is materializing. Research suggests this often precedes positive equity returns as the premium normalizes.
The HIGH regime, between five and ten percentage points, indicates elevated risk aversion. Investors are paying above-average premiums for protection. This often occurs after market corrections when fear remains elevated but realized volatility has begun subsiding.
The NORMAL regime covers VRP between zero and five percentage points. This represents the long-term average state of markets where implied volatility modestly exceeds realized volatility. The insurance premium is being collected at typical rates.
The LOW regime, between negative two and zero percentage points, suggests either unusual complacency or that realized volatility is catching up to implied volatility. The premium is shrinking, which can precede either calm continuation or increased stress.
The NEGATIVE regime occurs when realized volatility exceeds implied volatility. This is relatively rare and typically indicates active market stress. Options were priced for less volatility than actually occurred, meaning volatility sellers are experiencing losses. Historically, deeply negative VRP readings have often coincided with market bottoms, though timing the reversal remains challenging.
TERM STRUCTURE ANALYSIS
Beyond the basic VRP calculation, sophisticated market participants analyze how volatility behaves across different time horizons. The indicator calculates VRP using both short-term (default ten days) and long-term (default sixty days) realized volatility windows.
Under normal market conditions, short-term realized volatility tends to be lower than long-term realized volatility. This produces what traders call contango in the term structure, analogous to futures markets where later delivery dates trade at premiums. The RV Slope metric quantifies this relationship.
When markets enter stress periods, the term structure often inverts. Short-term realized volatility spikes above long-term realized volatility as markets experience immediate turmoil. This backwardation condition serves as an early warning signal that current volatility is elevated relative to historical norms.
The academic foundation for term structure analysis comes from Scott Mixon's 2007 paper "The Implied Volatility Term Structure" in the Journal of Derivatives, which documented the predictive power of term structure dynamics.
MEAN REVERSION CHARACTERISTICS
One of the most practically useful properties of the VRP is its tendency to mean-revert. Extreme readings, whether high or low, tend to normalize over time. This creates opportunities for systematic trading strategies.
The indicator tracks VRP in statistical terms by calculating its Z-score relative to the trailing one-year distribution. A Z-score above two indicates that current VRP is more than two standard deviations above its mean, a statistically unusual condition. Similarly, a Z-score below negative two indicates VRP is unusually low.
Mean reversion signals trigger when VRP reaches extreme Z-score levels and then shows initial signs of reversal. A buy signal occurs when VRP recovers from oversold conditions (Z-score below negative two and rising), suggesting that the period of elevated realized volatility may be ending. A sell signal occurs when VRP contracts from overbought conditions (Z-score above two and falling), suggesting the fear premium may be excessive and due for normalization.
These signals should not be interpreted as standalone trading recommendations. They indicate probabilistic conditions based on historical patterns. Market context and other factors always matter.
MOMENTUM ANALYSIS
The rate of change in VRP carries its own information content. Rapidly rising VRP suggests fear is building faster than volatility is materializing, often seen in the early stages of corrections before realized volatility catches up. Rapidly falling VRP indicates either calming conditions or rising realized volatility eating into the premium.
The indicator tracks VRP momentum as the difference between current VRP and VRP from a specified number of bars ago. Positive momentum with positive acceleration suggests strengthening risk aversion. Negative momentum with negative acceleration suggests intensifying stress or rapid normalization from elevated levels.
PRACTICAL APPLICATION
For equity investors, the VRP provides context for risk management decisions. High VRP environments historically favor equity exposure because the market is pricing in more pessimism than typically materializes. Low or negative VRP environments suggest either reducing exposure or hedging, as markets may be underpricing risk.
For options traders, understanding VRP is fundamental to strategy selection. Strategies that sell volatility, such as covered calls, cash-secured puts, or iron condors, tend to profit when VRP is elevated and compress toward its mean. Strategies that buy volatility tend to profit when VRP is low and risk materializes.
For systematic traders, VRP provides a regime filter for other strategies. Momentum strategies may benefit from different parameters in high versus low VRP environments. Mean reversion strategies in VRP itself can form the basis of a complete trading system.
LIMITATIONS AND CONSIDERATIONS
No indicator provides perfect foresight, and the VRP is no exception. Several limitations deserve attention.
The VRP measures a relationship between two estimates, each subject to measurement error. The VIX represents expectations that may prove incorrect. Realized volatility calculations depend on the chosen method and lookback period.
Mean reversion tendencies hold over longer time horizons but provide limited guidance for short-term timing. VRP can remain extreme for extended periods, and mean reversion signals can generate losses if the extremity persists or intensifies.
The indicator is calibrated for equity markets, specifically the S&P 500. Application to other asset classes requires recalibration of thresholds and potentially different data sources.
Historical relationships between VRP and subsequent returns, while statistically robust, do not guarantee future performance. Structural changes in markets, options pricing, or investor behavior could alter these dynamics.
STATISTICAL OUTPUTS
The indicator presents comprehensive statistics including current VRP level, implied volatility from VIX, realized volatility from the selected method, current regime classification, number of bars in the current regime, percentile ranking over the lookback period, Z-score relative to recent history, mean VRP over the lookback period, realized volatility term structure slope, VRP momentum, mean reversion signal status, and overall market bias interpretation.
Color coding throughout the indicator provides immediate visual interpretation. Green tones indicate elevated VRP associated with fear and potential opportunity. Red tones indicate compressed or negative VRP associated with complacency or active stress. Neutral tones indicate normal market conditions.
ALERT CONDITIONS
The indicator provides alerts for regime transitions, extreme statistical readings, term structure inversions, mean reversion signals, and momentum shifts. These can be configured through the TradingView alert system for real-time monitoring across multiple timeframes.
REFERENCES
Bakshi, G., and Kapadia, N. (2003). Delta-Hedged Gains and the Negative Market Volatility Risk Premium. Review of Financial Studies, 16(2), 527-566.
Bollerslev, T., Tauchen, G., and Zhou, H. (2009). Expected Stock Returns and Variance Risk Premia. Review of Financial Studies, 22(11), 4463-4492.
Carr, P., and Wu, L. (2009). Variance Risk Premiums. Review of Financial Studies, 22(3), 1311-1341.
Garman, M. B., and Klass, M. J. (1980). On the Estimation of Security Price Volatilities from Historical Data. Journal of Business, 53(1), 67-78.
Mixon, S. (2007). The Implied Volatility Term Structure of Stock Index Options. Journal of Empirical Finance, 14(3), 333-354.
Parkinson, M. (1980). The Extreme Value Method for Estimating the Variance of the Rate of Return. Journal of Business, 53(1), 61-65.
Kurtosis with Skew Crossover Focused OscillatorDescription:
This indicator highlights Skewness/Kurtosis crossovers for short-term trading:
Green upward arrows: Skew crosses above Kurtosis → potential long signal.
Red downward arrows: Skew crosses below Kurtosis → potential short signal.
Yellow upward arrows: Extreme negative skew (skew ≤ -1.7) → potential oversold/reversal opportunity.
Oscillator Pane:
Orange = Skewness (smoothed)
Blue = Kurtosis (adjusted, smoothed)
Zero line = visual reference
Usage:
Primarily for 2–5 minute charts, highlighting statistical anomalies and potential short-term reversals that can be used in conjunction with OBV and/or CVD
Arrows signal potential entries based on skew/kurt dynamics.
Potential ideas???????
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Add Supporting Market Context
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Currently, signals are purely based on skew/kurt crossovers. Adding supporting indicators could improve reliability:
Volume / CVD: Identify when crossovers occur with real buying/selling pressure.
Wick Imbalance: Detect forced moves in price structure.
Volatility Regime (Parkinson / ATR): Filter signals during high volatility spikes or compressions.
Experimentation: Try weighting these supporting signals to dynamically confirm or filter skew/kurt crossovers and see if false signals decrease on 2–5 minute charts.
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Dynamic Thresholds & Scaling
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Right now, the extreme skew signal is triggered at a fixed level (skew ≤ -1.7). Future improvements could include:
Adaptive thresholds: Scale extreme skew levels based on recent standard deviation or intraday volatility.
Kurtosis thresholds: Introduce a cutoff for kurtosis to identify “fat-tail” events.
Experimentation: Backtest different adaptive thresholds for both skew and kurt, and see how it affects the precision vs. frequency of signals.
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Multi-Timeframe or Combined Oscillator
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Skew/kurt signals could be combined across multiple intraday timeframes (e.g., 1-min, 3-min, 5-min) to improve confirmation.
Create a composite oscillator that blends short-term and slightly longer-term skew/kurt values to reduce noise.
Experimentation: Compare a single timeframe approach vs multi-timeframe composite, and measure signal reliability and lag.
I'm leaving this open so anyone can experiment with it as this project may be on the backburner, but these are my thoughts so far
ZLBD Lite - Free Version🆓 ZLBD Lite - Smart Bounce Detector (Free Version)
This is the free version of our professional ZLBD Pro indicator.
Designed to help traders identify high-probability reversal zones using Smart Money Concepts.
✨ FREE FEATURES:
• Basic Buy/Sell Signals: Identifies potential reversals.
• Demand & Supply Zones: Automatically draws key support/resistance blocks.
• Anti-Repetition Filter: Reduces signal noise.
• Simple Dashboard: Tracks active zones.
🚫 LIMITATIONS (Lite Version):
❌ No Signal Strength Meter (0-100%)
❌ No Market Trend Detection
❌ No FVG (Fair Value Gaps)
❌ No Session Timing
❌ Limited Zone History
🔓 UNLOCK FULL POWER (ZLBD PRO):
Upgrade to the PRO version to get:
✅ Real-time 0-100% Strength Meter
✅ Advanced Trend Filter (Strong/Medium/Weak)
✅ Full FVG Detection
✅ VIP Telegram Access
✅ Personal Support
🚀 HOW TO UPGRADE:
Contact us on Telegram: @ZlbdPro_Support
💡 HOW TO USE (Lite):
1. Wait for a Green Zone (Demand) to form.
2. Look for a 🟢 BUY triangle.
3. Confirm with your own analysis.
4. Target the next Red Zone (Supply).
👍 Boost & Follow for updates!
VWAP Pullback + BOS + OBV v2 (Crypto Futures 15m)This strategy combines VWAP pullbacks, break-of-structure entries, and OBV confirmation to catch high-quality trend continuation moves on crypto futures. It waits for price to trend above or below the 200 EMA, then pulls back into the VWAP band, signaling a potential reload zone. A trade only triggers when price breaks recent structure in the direction of the trend and OBV shows supportive volume flow. An ATR volatility filter blocks entries during choppy, low-energy periods, and all trades use an ATR stop-loss with fixed reward-to-risk targeting. The result is a cleaner, more disciplined trend-following system designed for 15m–30m BTC/ETH scalping.
QuantMotions - TPR SentinelQuantMotions – TPR Sentinel
The TPR Sentinel Band is a full trade-assistant for discretionary traders.
It combines an adaptive trend engine, directional TPR logic, volume intelligence, ATR-based risk management, a brute-force parameter optimizer, and a modern on-chart UI (entries/TP/SL panel + stats). The goal: fewer fake flips, clearer trend shifts, and visually guided trade management.
1. Core Concept
The Sentinel Line is built from a blend of:
- SMA + EMA
- Midline of highest/lowest high/low (Kijun-style)
- Donchian-style mid close
On top of that, the script calculates a Directional TPR (Time-Price-Ratio):
- Short / medium / long slopes of price
- Normalized by ATR
- Converted into a trend state:
+1 = Uptrend
-1 = Downtrend
0 = Neutral / transition
Hysteresis (Flux) controls how easily the trend flips:
- Higher hysteresis → harder to reverse → fewer fake-outs in chop.
2. Signals, Filters & Volume Intelligence
Signals
- Trend Flip Long: TrendState changes from −1/0 → +1.
- Trend Flip Short: TrendState changes from +1/0 → −1.
Filters
- ADX Filter (optional):
- Only allows trades if ADX is above a chosen threshold.
- Avoids trading in flat, low-energy markets.
R:R Filter:
- Before any signal is accepted, the script checks whether the distance to TP1 is at least the configured Risk:Reward ratio relative to the distance to SL.
- Only if that minimum R:R is reached, a signal becomes valid.
Volume Intelligence & Clouds
- Aggregates up/down volume (optionally across multiple tickers you define).
- Builds Volume Clouds around the Sentinel Line:
a) Positive intensity → buying pressure (bullish cloud).
b) Negative intensity → selling pressure (bearish cloud).
Optional Volume Direction Filter:
- Long only when volume intensity ≥ 0.
- Short only when volume intensity ≤ 0.
3. Risk, Exits & Trailing Stop
The indicator includes a complete exit framework (for visual/manual trading):
Stop Loss Modes
- ATR Fixed: SL placed at a fixed ATR multiple from the entry.
- Trend Line (Dynamic): SL placed directly on the Sentinel Band (structural stop).
Take Profits
- TP1 – “safe target”:
a) Based on ATR distance.
b) Closes a configurable percentage of the position (e.g., 50%).
- TP2 (optional):
Second fixed target used only when Trailing Stop is OFF.
- Trend Runner Mode (Use TP = OFF):
Ignores fixed TP levels and rides the trend until the trend state flips.
Trailing Stop
- Activates after TP1 is hit (if enabled).
- Moves with price at a configurable ATR distance:
a) Long: trail creeps up under price.
b) Short: trail creeps down above price.
- Visually plotted as a purple trail line, dynamically replacing the original SL as the effective exit point.
Each trade is tracked internally and drawn as a green/red box with PnL labels between entry and exit.
4. UI & Stats
Candle Coloring (TRON Theme)
- Cyan = active uptrend & valid environment.
- Orange = active downtrend & valid environment.
Modern Trade Panel (on last bar)
- Live overlay of:
a) Entry
b) TP1
c) TP2
d) SL or active Trail (with dynamic label text: “SL (ATR)”, “SL (Struct)”, “TRAIL”)
Info label shows:
- Historical win rate in the current direction (Long/Short).
- Distance to SL, TP1, TP2 from current price.
- Box color blends from red → green depending on whether price is closer to SL or TP.
Stats Table (Bottom Right)
- Separate stats for Long and Short trades:
a) Win rate (%)
b) Cumulative PnL
Alerts
- Generates JSON alerts on signals, for example: {"side":"buy","ticker":"XYZ","price":123.45}
Perfect for webhooks, bots, or external automation.
5. Brute Force Optimizer (TPR Lab) – Important Limitations
The built-in Optimizer is a numerical helper, not a full strategy optimizer.
What it does:
- Runs brute-force simulations over a sliding window of historical data.
- Scans user-defined ranges for:
- Best Period (“Best Cycle”)
- Best Hysteresis (“Best Flux”)
Uses an efficiency score (average profit per trade) to rank combinations.
Displays results in the bottom-left TRON panel:
- Best Cycle
- Best Hysteresis
- Efficiency Score
What it does NOT optimize or take into account:
- It does not include your actual minimum R:R filter.
- It does not simulate or optimize your Stop Loss modes.
- It does not simulate Trailing Stops.
- It does not use the ADX filter.
- It does not use the Volume filters or Volume Clouds.
Because of this, the suggested “best” Period and Hysteresis are purely computational recommendations based on a simplified internal model.
In real trading, with your full setup (R:R filter, SL mode, Trailing, ADX, Volume confirmation, personal style), other parameter combinations can be superior to what the Optimizer suggests.
You should treat the Optimizer as:
A starting point or a research tool, not the final truth.
Always validate its suggestions visually, in the context of your full system and risk management.
6. Practical Usage
- Works on FX, indices, crypto, commodities – anything with decent liquidity.
- Scalping → use lower Period values, higher responsiveness.
- Swing → use higher Period values, more stability.
Recommended:
- Keep ADX filter ON to avoid dead markets.
- Use Volume Clouds as directional bias.
- Use the Info Panel and Stats to align with your own R:R and risk rules.
Disclaimer
This script is for educational/analytical purposes only and does not constitute financial advice. It does not execute trades or manage your risk automatically. Always combine it with your own strategy, money management, and independent decision-making.
Use the Info Panel and Stats to align with your own R:R and risk rules.
X FP Imbalancesprovides advanced volume profile analysis by isolating and visualizing market aggression at a granular price level. It is a powerful tool for short-term and intraday traders seeking objective confirmation of supply and demand dynamics, primarily used to identify high-probability reversal or continuation points based on order flow principles.
Key Functionality and Methodology
The indicator operates by transforming standard time-based candle data into a Volume-at-Price footprint, focusing specifically on aggressive market activity.
Granular Aggression Measurement (Delta)
The script dynamically segments the price range into discrete price levels (tickAmount). This granularity is controlled either by a user-defined fixed tick count or automatically adjusted using the Average True Range (ATR) to adapt the box size to current market volatility.
The script uses lower timeframe data (e.g., 1-minute bars) to accurately distribute the total volume into each price level, distinguishing between aggressive buying (Up Volume) and aggressive selling (Down Volume).
The core output is Delta, which is the net difference between aggressive buying and aggressive selling at each price level.
Stacked Imbalance Identification
The indicator identifies an imbalance when the volume from one side (e.g., aggressive buyers) overwhelms the total volume at that level by a user-defined percentage (imbalanceP).
A single price level where the Delta percentage exceeds the threshold is defined as an Imbalance.
The Stacked Imbalance is the primary signal, triggered when the imbalance is detected on a user-defined number of consecutive price levels (stacked) in the same direction (e.g., 3 consecutive levels of aggressive buying). This signals a high-conviction structural break or strong rejection.
Stacked imbalances are visually highlighted and can trigger real-time alerts upon bar close.
Strategic Applications
This indicator is invaluable for traders who integrate order flow concepts into their decision-making process.
One-Sided Stack (Supply/Demand Zone): Aggressive selling (Red Stack) at a high price, followed by price reversal, identifies a Structural Supply Zone (Resistance). The level is where sellers aggressively rejected demand, leaving an untested area of supply.
Overlapping Stacks (Climax Reversal): Consecutive Buy Stacks followed immediately by Sell Stacks in a tight range signals Buyer Exhaustion and an immediate Climax Reversal. The buying power was absorbed and instantly overwhelmed by waiting supply.
Absence of Stack: When price moves sharply through a level without creating any Stacked Imbalances, it suggests an Orderly Move or Liquidity Void. The absence of resistance means the market move is structurally weak and often vulnerable to a retest.
The choice between a Fixed Tick Distance (for micro-pattern precision) and ATR-based sizing (for volatility-adjusted analysis) allows the user to tailor the indicator to specific asset classes and trading styles.
Relative Strength Line by QuantxThe Relative Strength Line compares the price performance of a stock against a benchmark index (e.g., NIFTY, S&P 500, Bank Nifty, etc.).
It does not indicate momentum of the stock itself — it indicates whether the stock is outperforming or underperforming the market.
🔍 How To Read It
RSL Behavior Meaning
RSL moving up Stock is outperforming the benchmark (strong leadership)
RSL moving down Stock is underperforming the benchmark (weakness vs market)
RSL breaking above previous highs Strong institutional demand, leadership candidate
RSL trending sideways Stock is performing similar to the index (no leadership)
📈 Why It Matters
Institutional traders and top-performing strategies focus on stocks showing relative strength BEFORE price breakout.
A stock making new RSL highs even before a price breakout often becomes a top performer in the coming trend.
🧠 Core Trading Edge
You don’t need to predict the market.
Just identify which stocks are being accumulated and leading the market right now — that’s what the Relative Strength Line reveals.
ROMAN INDIThis script creates an on-chart information panel / watermark that summarizes the most important technical and contextual data for the current symbol in one place. It’s designed as a compact trading dashboard overlay, fully configurable from the Inputs menu.
1. General instrument info
The table shows:
Company name + market cap
Market cap is calculated from shares_outstanding_total * close and formatted in M / B / T.
Ticker + timeframe (e.g. AAPL, 1D, AAPL, 1H, etc.).
Sector & industry (when available from syminfo).
You can choose the panel position (Top/Middle/Bottom & Left/Center/Right) and text size/color from the inputs.
2. Volatility & stop-loss (ATR block)
Calculates ATR(14) and the ATR as % of price.
Colors ATR with an emoji:
🔴 = high volatility (above red threshold)
🟡 = medium
🟢 = low
Computes a dynamic stop loss:
Source price can be: Today / Yesterday / 2 Days Ago.
Stop = base price − ATR × user-defined multiplier.
Also calculates the distance from close to stop in percent and marks it:
🟢 if distance > 5%
🟡 if distance > 2%
🔴 otherwise
When price crosses the stop level (or if the stop is very tight and marked 🔴), a label is plotted just ahead of the current bar:
Shows either “SELL” (if close ≤ stop) or the stop price.
3. Moving averages distance row
Calculates SMA 50 / 150 / 200.
Shows a single row:
MA50: +X.XX% | MA150: +Y.YY% | MA200: +Z.ZZ%
Values are the percentage distance between close and each MA (positive/negative).
This row can be toggled on/off via the inputs.
4. Volume analysis
Uses a 20-period average volume as baseline.
Computes:
Absolute volume difference vs. 20-SMA (in K/M units).
Percent difference vs. average.
Adds:
🔴 if current volume < average
🟡 if up to +10% above average
🟢 if more than +10% above average
Detects streaks of rising or falling volume (last 3 bars):
⬆️ / ⬆️⬆️ / ⬆️⬆️⬆️ for 1–3 bars of increasing volume
⬇️ / ⬇️⬇️ / ⬇️⬇️⬇️ for 1–3 bars of decreasing volume
Final row example:
ΔVol: 1.25M (15.32%) 🟢 ⬆️⬆️
5. Earnings countdown
Uses earnings.future_time to detect the next earnings date.
Shows:
Earnings: X days remaining
(only if there is a future earnings date and the option is enabled).
6. RSI (momentum)
Calculates RSI(14).
Displays:
Current RSI value.
Trend arrow vs. previous bar: ⬆️ / ⬇️ / (no arrow).
Emoji color:
🔴 when RSI > 70 (overbought)
🔴 when RSI < 30 (oversold)
🟢 otherwise
Example:
RSI (14): 63.25 🟢 ⬆️
7. CCI (trend strength & short-term swings)
Calculates CCI(14) on hlc3.
Tracks the direction of CCI (up / down / flat) and interprets it:
If CCI is falling:
100 → “Overbought 🔴”
0 to 100 → “Negative Momentum 🟡”
−100 to 0 → “2-4 Days Down 🟠”
< −100 → “Oversold 🔴”
If CCI is rising:
100 → “Overbought 🔴”
0 to 100 → “2-4 Days Up 🟢”
−100 to 0 → “Building Momentum 🟡”
< −100 → “Oversold 🔴”
The row shows value, direction arrow and text interpretation.
Example:
CCI (14): -45.32 🟡 ⬆️ Building Momentum 🟡
8. Market context: VIX & Bitcoin row
Tracks:
VIX (CBOE:VIX)
Bitcoin (BINANCE:BTCUSDT)
If the current chart is directly on one of these symbols, it uses the live close; otherwise it pulls the data via request.security.
Shows last price of VIX and BTC plus trend arrows based on the last 3 closes (up/down streak).
Example:
VIX: 15.23 ⬆️ | BTC: 113,000 ⬇️⬇️
Summary
In short, ROMAN INDICATOR is an overlay info-panel that combines:
Instrument fundamentals (name, sector, industry, market cap)
Volatility & ATR-based stop-loss engine
Distance from major moving averages (50/150/200)
Volume vs. average with streak detection
RSI & CCI with clear emoji-based interpretation
Earnings countdown (days to next report)
Global context via VIX + Bitcoin row
Everything is configurable in the Inputs, making it a convenient single-glance trading dashboard on top of your chart.
NovaNOVA – Momentum & Trend Validation Indicator
NOVA is a custom-built confirmation indicator designed to filter false signals and highlight real momentum shifts with higher precision. It combines trend direction, momentum strength, and volatility behavior into a single, clean visual structure.
Key Features
Trend direction detection based on dynamic price structure
Momentum strength validation for breakout and continuation setups
Volatility-aware signal filtering
Non-repainting logic on closed candles
Works across all timeframes and markets
Compatible with crypto, forex, indices, and stocks
Best Use Case
NOVA performs best when used:
After key support/resistance reactions
During breakout confirmations
With trend-following systems
As a filter to avoid low-quality entries
Important Disclaimer
This indicator is not a financial advice tool. Trading involves significant risk. Past performance does not guarantee future results. Always use proper risk management.
Liquidation Heatmap [Alpha Extract]A sophisticated liquidity zone visualization system that identifies and maps potential liquidation levels based on swing point analysis with volume-weighted intensity measurement and gradient heatmap coloring. Utilizing pivot-based pocket detection and ATR-scaled zone heights, this indicator delivers institutional-grade liquidity mapping with dynamic color intensity reflecting relative liquidity concentration. The system's dual-swing detection architecture combined with configurable weight metrics creates comprehensive liquidation level identification suitable for strategic position planning and market structure analysis.
🔶 Advanced Pivot-Based Pocket Detection
Implements dual swing width analysis to identify potential liquidation zones at pivot highs and lows with configurable lookback periods for comprehensive level coverage. The system detects primary swing points using main pivot width and optional secondary swing detection for increased pocket density, creating layered liquidity maps that capture both major and minor liquidation levels across extended price history.
🔶 Multi-Metric Weight Calculation Engine
Features flexible weight source selection including Volume, Range (high-low spread), and Volume × Range composite metrics for liquidity intensity measurement. The system calculates pocket weights based on market activity at pivot formation, enabling traders to identify which liquidation levels represent higher concentration of potential stops and liquidations with configurable minimum weight thresholds for noise filtering.
🔶 ATR-Based Zone Height Framework
Utilizes Average True Range calculations with percentage-based multipliers to determine pocket vertical dimensions that adapt to market volatility conditions. The system creates ATR-scaled bands above swing highs for short liquidation zones and below swing lows for long liquidation zones, ensuring zone heights remain proportional to current market volatility for accurate level representation.
🔶 Dynamic Gradient Heatmap Visualization
Implements sophisticated color gradient system that maps pocket weights to intensity scales, creating intuitive visual representation of relative liquidity concentration. The system applies power-law transformation with configurable contrast adjustment to enhance differentiation between weak and strong liquidity pockets, using cyan-to-blue gradients for long liquidations and yellow-to-orange for short liquidations.
🔶 Intelligent Pocket State Management
Features advanced pocket tracking system that monitors price interaction with liquidation zones and updates pocket states dynamically. The system detects when price trades through pocket midpoints, marking them as "hit" with optional preservation or removal, and manages pocket extension for untouched levels with configurable forward projection to maintain visibility of approaching liquidity zones.
🔶 Real-Time Liquidity Scale Display
Provides gradient legend showing min-max range of pocket weights with 24-segment color bar for instant liquidity intensity reference. The system positions the scale at chart edge with volume-formatted labels, enabling traders to quickly assess relative strength of visible liquidation pockets without numerical clutter on the main chart area.
🔶 Touched Pocket Border System
Implements visual confirmation of executed liquidations through border highlighting when price trades through pocket zones. The system applies configurable transparency to touched pocket borders with inverted slider logic (lower values fade borders, higher values emphasize them), providing clear historical record of liquidated levels while maintaining focus on active untouched pockets.
🔶 Dual-Swing Density Enhancement
Features optional secondary swing width parameter that creates additional pocket layer with tighter pivot detection for increased liquidation level density. The system runs parallel pivot detection at both primary and secondary swing widths, populating chart with comprehensive liquidity mapping that captures both major swing liquidations and intermediate level clusters.
🔶 Adaptive Pocket Extension Framework
Utilizes intelligent time-based extension that projects untouched pockets forward by configurable bar count, maintaining visibility as price approaches potential liquidation zones. The system freezes touched pocket right edges at hit timestamps while extending active pockets dynamically, creating clear distinction between historical liquidations and forward-projected active levels.
🔶 Weight-Based Label Integration
Provides floating labels on untouched pockets displaying volume-formatted weight values with dynamic positioning that follows pocket extension. The system automatically manages label lifecycle, creating labels for new pockets, updating positions as pockets extend, and removing labels when pockets are touched, ensuring clean chart presentation with relevant liquidity information.
🔶 Performance Optimization Framework
Implements efficient array management with automatic clean-up of old pockets beyond lookback period and optimized box/label deletion to maintain smooth performance. The system includes configurable maximum object counts (500 boxes, 50 labels, 100 lines) with intelligent removal of oldest elements when limits are approached, ensuring consistent operation across extended timeframes.
This indicator delivers sophisticated liquidity zone analysis through pivot-based detection and volume-weighted intensity measurement with intuitive heatmap visualization. Unlike simple support/resistance indicators, the Liquidation Heatmap combines swing point identification with market activity metrics to identify where concentrated liquidations are likely to occur, while the gradient color system instantly communicates relative liquidity strength. The system's dual-swing architecture, configurable weight metrics, ATR-adaptive zone heights, and intelligent state management make it essential for traders seeking strategic position planning around institutional liquidity levels across cryptocurrency, forex, and futures markets. The visual heatmap approach enables instant identification of high-probability reversal zones where cascading liquidations may trigger significant price reactions.
Pre-Market Confirmed Momentum – FULL WATCHLIST 2025**Pre-Market Confirmed Momentum – High-Conviction Gap Scanner (2025)**
Scans 94 high-liquidity NASDAQ/NYSE stocks (NVDA, TSLA, COIN, AMD, SOFI, ASTS, CIFR, etc.) for strong pre-market gap-ups that are confirmed by both elevated volume and broad-market strength.
**Entry triggers only when ALL are true at 09:29 ET:**
- ≥ +1.5% gap from previous regular close
- Pre-market volume ≥ 2.5× the 20-day average
- QQQ pre-market ≥ +0.5% (market filter)
Back-tested June 2024 – Dec 2025:
68 signals → **+1.96% average intraday return** → **75% win rate** after 1.5% hard stop.
Features large on-chart labels, triangle markers, and dynamic `alert()` messages with exact gap % and volume multiple. Works on 1-min or 5-min charts with extended hours enabled – perfect for day traders hunting clean, high-probability momentum entries at the open.
Ready for watchlist scanning and real-time alerts. Enjoy the edge! 🚀
HoneG_EURUSD値動き停止アラートv5This is a signal tool that detects price movement stoppages and displays signals with alerts.
Please select the time frame on the chart that corresponds to the time period you wish to detect.
値動きの停止を検知してサイン表示とアラートを出すサインツールです。
チャートの時間足を、検知したい時間相当の足を選んで使ってください。
HoneG_EURUSD値動き停止アラートv5This is a signal tool that detects price movement stoppages and displays signals with alerts.
Please select the time frame on the chart that corresponds to the time period you wish to detect.
値動きの停止を検知してサイン表示とアラートを出すサインツールです。
チャートの時間足を、検知したい時間相当の足を選んで使ってください。
Chaos Volatility Breakout (ATR + Breakout)-VMThis indicator is a volatility-based breakout trading tool inspired by principles from Chaos Theory, where small changes in momentum during high-energy market conditions can lead to large price movements.
Instead of predicting the market, it focuses on identifying “high-probability expansion zones”—moments when the market is under stress (high volatility) and price is breaking out of a recent range.
ADX + ATR% Zonas (Overlay - Azul si ambos, si no Naranja)OVERLAY
ADX
ATR
Pintado de Zonas para Entradas Seguras
Trendslinger CVDCVD - Cumulative Volume Delta
Cumulative Volume Delta (CVD) tracks the running total of buying versus selling pressure by analyzing volume distribution within each price bar. This indicator visualizes order flow dynamics to help identify accumulation, distribution, and potential trend reversals.
How It Works
CVD calculates the "delta" (difference between buying and selling volume) for each bar and accumulates it over time. Two calculation methods are available:
Close Position: Estimates buy/sell volume based on where price closes within the bar's range. A close near the high suggests more buying pressure; a close near the low suggests more selling pressure.
Polarity: Simple method where green candles count as buy volume and red candles count as sell volume.
Key Features
Multiple Display Types: View CVD as candlesticks, line, histogram, area, or columns
Flexible Reset Options: Reset CVD hourly, daily, or weekly for cleaner intraday analysis
Divergence Detection: Automatically identifies bullish and bearish divergences between price and CVD
Session Tracking: Optional high/low reference lines for the current session
Smoothing Options: Apply SMA, EMA, WMA, or RMA smoothing to reduce noise
Info Table: Real-time display of current CVD value, bar delta, and session extremes
Built-in Alerts: Zero line crosses, divergences, and new session highs/lows
How To Use
Trend Confirmation: Rising CVD confirms bullish price action; falling CVD confirms bearish moves
Divergences: Price making new highs while CVD makes lower highs signals weakening buying pressure (bearish). Price making new lows while CVD makes higher lows signals weakening selling pressure (bullish)
Zero Line: CVD crossing above zero suggests buyers taking control; crossing below suggests sellers dominating
Hourly Resets: Useful for scalping and intraday trading to track momentum within each hour
