Adaptive Regime Momentum [JOAT]Adaptive Regime Momentum
Introduction
The majority of publicly available trend-following strategies rely on one of two entry mechanisms: a moving average crossover, or a price-versus-MA relationship. These are valid starting points, but they share a common weakness — they fire signals based on a single confirmatory condition that can be triggered by brief, low-conviction price moves. A single bar pushing above a moving average while volume is thin and the MA is barely sloping is not the same market condition as a sustained directional move with volume behind it and a clearly sloping MA. Yet a simple strategy would treat both identically.
Adaptive Regime Momentum is a trend-following strategy that requires three independent conditions to align before generating an entry signal. These three layers — MA slope confirmation over multiple consecutive bars, price position relative to the MA, and a volume-based demand filter — must all agree simultaneously. The result is a strategy that generates fewer signals but with higher internal consistency between entry conditions. It is designed for liquid markets on daily or higher timeframes where each component is reliably measurable.
This is an overlay strategy — all visuals are plotted directly on the price chart.
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Strategy Properties
The following default settings are used for all backtests unless modified:
Initial capital: $10,000
Position sizing: 5% of equity per trade
Commission: 0.05% per side
Pyramiding: 0 (only one open position at a time; new signals are ignored while a position is active)
Stop loss: 2.5x ATR below the entry price (long), 2.5x ATR above the entry price (short), calculated from strategy.position_avg_price
Take profit: 4.0x ATR above the entry price (long), 4.0x ATR below the entry price (short), calculated from strategy.position_avg_price
Trail / slope exit: Position is closed early if price crosses to the wrong side of ComboMA ± 1.5x ATR, or if the MA slope reverses direction
The stop and take profit are anchored to strategy.position_avg_price — the actual average fill price of the position — rather than the signal bar's close. This ensures that in backtesting, stop and TP distances are measured from where the trade was actually opened, not from a theoretical signal level.
These are backtesting defaults only. They do not represent a recommendation for live trading position sizing or risk management.
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Core Concepts
Signal 1 — ComboMA Slope Confirmation (Structural Momentum)
The ComboMA is a blend of two moving averages:
ALMA (Arnaud Legoux Moving Average) — a smooth MA with reduced lag, fitting to recent price without overreacting to single bars
ZLMA (Zero-Lag Moving Average) — a lag-compensated MA designed to reduce the delay between price movement and MA response
The two are blended into a single ComboMA value. The slope of this composite is then evaluated not just on the current bar, but across the last N consecutive bars (default: 3). A slope is only confirmed as UP if all of the last 3 bars showed a positive slope. A slope is only confirmed as DOWN if all 3 bars showed a negative slope. A single slope fluctuation — even if the most recent bar shows a positive slope — does not trigger confirmation unless all N bars agree.
This multi-bar slope confirmation is the primary mechanism that distinguishes this strategy from a simple MA-based entry. A one-bar slope flip that immediately reverses is filtered out. Only a sustained slope direction triggers the first condition.
Signal 2 — Price vs. ComboMA (Real-Time Confirmation)
The second condition requires that price is currently on the correct side of the ComboMA:
For a long: close > ComboMA
For a short: close < ComboMA
This condition is evaluated at the current bar, providing real-time confirmation that price is aligned with the structural slope direction. The MA slope could be upward from prior bars, but if price has already pulled back below the MA, the second condition vetoes the entry. Both the historical slope and the current price position must agree.
Signal 3 — Volume RSI (Demand Pressure Validation)
Volume RSI is RSI applied to raw volume over an 8-bar period, then divided by 50. A result above 1.0 (the default threshold) means the Volume RSI is above 50 — indicating that volume activity on recent bars has been relatively elevated compared to the preceding period.
For a long entry: Volume RSI / 50 must exceed the threshold
For a short entry: same condition applies
Volume RSI does not confirm direction — it confirms participation . A move accompanied by above-average volume has more demand/supply backing than a low-volume drift. When volume is below threshold, the third condition is not met and no entry is generated, even if slope and price position align.
RSI Filter
An additional RSI filter is applied to the close:
RSI(14) must be above 50 for long entries
RSI(14) must be below 50 for short entries
This acts as a momentum gating condition — confirming that short-term momentum is consistent with the trade direction before entry is permitted.
Non-Repainting Execution
All entry conditions are gated by barstate.isconfirmed . No signal is generated until the current bar has fully closed. This prevents intra-bar signal flickering and ensures that the backtest accurately represents what would have been traded on confirmed bar closes.
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Exit Logic
The strategy uses a layered exit system combining fixed risk-defined targets with adaptive trend exits:
Fixed exits (via strategy.exit):
Stop loss at 2.5x ATR from entry price
Take profit at 4.0x ATR from entry price
Trail exits (via strategy.close):
Price closes beyond ComboMA ± 1.5x ATR on the wrong side
The ComboMA slope reverses (multi-bar confirmation fails in the opposite direction)
The trail exit allows winning positions to exit earlier if the trend deteriorates before reaching the fixed take profit, while the fixed TP provides a defined maximum target. The stop loss is the unconditional floor regardless of trail conditions.
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ATR Shadow Visual
The chart displays two layers of ATR bands around the ComboMA:
Inner band: ComboMA ± 1x ATR
Outer band: ComboMA ± 2x ATR
These bands give a visual read of how extended price is from the MA relative to recent volatility, and where the trail exit threshold sits (1.5x ATR, between the two bands). They are visual aids only and do not affect strategy logic.
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Performance Table
A table is displayed on the chart showing current strategy metrics:
Net P&L
Open P&L (current unrealized)
Win Rate
Average winning trade
Average losing trade
Maximum drawdown
Total trades
Current position direction
Current MA slope status
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Features
Three-layer entry confirmation: multi-bar MA slope, price vs. MA, and Volume RSI
RSI momentum filter as an additional gating condition
ALMA + ZLMA blend for the ComboMA, reducing lag without sacrificing smoothness
Multi-bar slope confirmation preventing single-bar slope flickers from triggering entries
ATR-based stop and take profit anchored to actual fill price via strategy.position_avg_price
Trail exit on slope reversal or price-vs-MA breach
Non-repainting: all signals confirmed via barstate.isconfirmed
Pyramiding disabled — one position at a time
ATR shadow bands for visual context around the ComboMA
Live performance table with key metrics
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Input Parameters
ALMA / ZLMA settings — length, offset, and sigma for each MA component
Slope Confirm Bars (default 3) — consecutive bars of slope agreement required for confirmation
Volume RSI Length (default 8) — RSI period applied to volume
Volume Threshold (default 1.0) — Volume RSI / 50 minimum for the demand filter
RSI Length (default 14) — RSI period for the momentum filter
ATR Length — period for ATR used in stop, TP, trail, and visual bands
Stop Multiplier (default 2.5) — ATR multiplier for the fixed stop loss
TP Multiplier (default 4.0) — ATR multiplier for the fixed take profit
Trail Multiplier (default 1.5) — ATR multiplier for the trail exit threshold
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How to Use
Apply to daily or higher timeframes on liquid instruments. Volume RSI is most meaningful where volume data is consistent and representative of actual market participation.
Allow the chart to load sufficient historical bars before evaluating backtest results. The ComboMA slope confirmation requires multiple bars of agreement, and early bars in the dataset may not reflect the strategy's typical behavior. Aim for at least several hundred bars of data for meaningful backtest statistics.
Review the performance table while backtesting to understand average win size relative to average loss, drawdown, and total trade count. A strategy with very few trades may show favorable metrics by chance rather than edge — consider whether the trade count is sufficient to draw conclusions.
The default 5% equity position size produces moderate equity curve sensitivity. Smaller sizes will reduce drawdown and return proportionally; larger sizes will amplify both.
Commission is set to 0.05% per side (0.1% round trip) by default. Adjust this to match your actual trading costs. Higher commission rates — especially relevant for frequent-trading timeframes — will reduce net results.
Do not optimize parameters on the same data you use to evaluate performance. Optimization on historical data produces settings tuned to past noise, not future edge.
The trail exit on slope reversal means that strongly trending markets where the MA briefly flattens before resuming may see early exits. This is the tradeoff for using slope as an exit condition.
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Limitations
Backtest results are calculated on historical data and do not guarantee future performance. Market conditions change, and a strategy that performed well in a particular regime may perform differently as conditions evolve.
The Volume RSI filter requires reliable volume data. This strategy is not recommended for synthetic instruments, CFDs where volume represents contracts rather than underlying market activity, or very short intraday timeframes where volume is fragmented and noisy. On such instruments, the third entry condition may be meaningless or misleading.
The multi-bar slope confirmation requirement means the strategy will miss fast, sharp trend initiations where the MA slope has not yet had N bars to confirm. This is a deliberate tradeoff — reducing false entries at the cost of some late entries on fast moves.
Pyramiding is disabled. The strategy will not add to winning positions. This limits upside during strongly trending markets where additional entries might be beneficial, but it also limits drawdown from compounding positions that subsequently reverse.
ATR-based stops and TPs are fixed at entry. They do not adjust after the trade is open (apart from the trail exit). If volatility expands significantly after entry, a 2.5x ATR stop that was appropriate at entry may become relatively tight.
The performance table reflects cumulative backtest results as of the current bar. Results will vary across different lookback windows and instruments.
Default capital of $10,000 with 5% equity sizing means each trade risks approximately $500 before the stop is hit (assuming stop is the loss floor). This is a backtesting convention — it is not a recommendation for live account sizing.
No strategy produces guaranteed results. The three-layer entry system improves internal signal consistency but cannot eliminate the inherent uncertainty of financial markets.
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Originality Statement
Standard MA-based trend strategies treat a single bar's price-vs-MA relationship as sufficient for entry. ARM's primary differentiation is the multi-bar slope confirmation requirement : the ComboMA slope must be consistently positive (or negative) across N consecutive bars before the first condition is met. A one-bar slope deviation — common during consolidations and brief retracements — does not trigger entry. Only a sustained slope direction qualifies.
The ComboMA itself is a blend of ALMA and ZLMA, combining the smoothness and Gaussian weighting of ALMA with the lag-compensation of ZLMA. Neither is used in isolation because each has a specific weakness: ALMA can lag on sharp moves; ZLMA can be sensitive to noise. The blend leverages the strengths of both while partially offsetting their weaknesses.
The three-layer confirmation architecture — slope duration, price position, and demand validation — requires agreement across genuinely different measurement types: structural momentum over time, current price location, and volume activity. These are not three views of the same quantity. The stop and TP placement using strategy.position_avg_price rather than the signal bar close is a practical accuracy measure: in backtesting, it means risk distances are calculated from the price at which the trade was actually filled, not from where the signal was generated, which can differ from the fill price particularly on gap opens.
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Disclaimer
This strategy is provided for educational and informational purposes only. It does not constitute financial advice, investment advice, or a recommendation to buy or sell any security. Backtested results are hypothetical and do not reflect actual trading. Hypothetical performance results have inherent limitations and do not account for execution slippage, liquidity constraints, or the psychological challenges of live trading. All trading involves risk, including the possible loss of principal. Always conduct your own research and consult a qualified financial professional before making any trading or investment decisions.
-Made with passion by officialjackofalltrades
Strategy
Volatility Squeeze Oscillator [JOAT]Volatility Squeeze Oscillator
Introduction
Volatility does not move randomly. It compresses, coils, and then releases — and the magnitude of the release is frequently proportional to the depth and duration of the compression. This relationship between volatility contraction and subsequent expansion is one of the most durable patterns in market behavior across all asset classes and timeframes. The Volatility Squeeze Oscillator is built to quantify this relationship with precision, using a multi-layered analysis framework that goes well beyond standard squeeze detection.
At its core, the indicator uses an ATR compression ratio engine to measure the difference between a short-term and long-term ATR. When the short-term ATR is smaller than the long-term ATR, volatility is contracting — the market is coiling. When the short-term ATR expands beyond the long-term reference, the coil is releasing. This compression differential is normalized against the high-low range, making the oscillator comparable across different instruments and volatility regimes.
Three additional analytical layers are stacked on top of the compression engine. A cumulative delta proxy estimates buying versus selling pressure within each bar using range-based calculations — no Level 2 or order flow data required. A volume RSI module measures whether the current volume is elevated relative to its own history, providing a confluence filter that separates high-conviction from low-conviction squeeze releases. And a statistical deviation band system built on a 200-bar lookback marks the historically significant boundaries of the squeeze oscillator's own distribution, so traders can identify not just whether a squeeze is forming, but how extreme it is relative to its own history.
Core Concepts
1. ATR Compression Ratio Engine
The compression ratio is derived from two ATR calculations at different smoothing periods. Both use EMA smoothing rather than RMA (Wilder's method) to produce a more responsive and visually cleaner oscillator. The short-term ATR reflects current volatility conditions. The long-term ATR (calculated at double the base period) establishes the reference level representing the recent historical norm. The difference between these two — long minus short — is the squeeze value: positive when the market is contracting (short ATR below long-term baseline), negative when expanding.
trueRange = ta.tr(true)
atrShort = ta.ema(trueRange, len)
atrLong = ta.ema(atrShort, len * 2)
sqzRaw = atrLong - atrShort
hlRange = ta.highest(high, len) - ta.lowest(low, len)
sqzVal = hlRange > 0 ? sqzRaw / hlRange : 0
Normalizing by the HL range makes the oscillator dimensionless — a squeeze value of 0.3 carries the same meaning whether you are analyzing a $1 stock or a $50,000 Bitcoin contract. The signal line is an EMA of the squeeze value, used to detect the inflection point where the squeeze begins to build (sqzVal crossing above sqzSig) or release (sqzVal crossing below sqzSig).
2. Hyper-Squeeze Detection
A hyper-squeeze occurs when the squeeze value is not merely positive (compressing) but is actively rising for N consecutive bars — indicating an accelerating contraction rather than a stable one. Accelerating compression is particularly significant because it suggests market participants are increasingly reducing their activity, creating a coiled spring effect where the eventual release may be more forceful.
hyperSqz = sqzVal > 0 and ta.rising(sqzVal, hyperLen)
When a hyper-squeeze is active, a violet tint is overlaid on the oscillator background in addition to the regular delta-driven background color. The dashboard updates the hyper squeeze row to ACTIVE status. This dual visual layer makes extended compression phases immediately distinguishable from ordinary positive squeeze readings.
3. Cumulative Delta Proxy
Order flow analysis — understanding whether buyers or sellers are dominant within a given period — typically requires tick-level data or exchange-provided volume breakdown. This indicator constructs a proxy for cumulative delta using bar-level range analysis, making the information accessible without any data feed requirements.
barRange = high - low
bullPress = barRange > 0 ? (close - low) / barRange : 0.5
bearPress = barRange > 0 ? (high - close) / barRange : 0.5
deltaBar = bullPress - bearPress
deltaSma = ta.sma(deltaBar, deltaLen)
deltaPos = deltaSma > 0
A close near the high of the bar implies buyers dominated (bull pressure near 1.0). A close near the low implies sellers dominated (bear pressure near 1.0). The difference, smoothed over a configurable window, produces a normalized delta reading. When delta is positive during a squeeze, the compressed volatility is accumulating with a bullish lean. When negative, with a bearish lean. This directional information is used both in the histogram coloring (alpha derived from delta conviction) and in dashboard output.
4. Volume RSI Confluence
Volume RSI applies the standard RSI momentum formula to the volume series rather than price. This produces a normalized reading of whether current volume is elevated or depressed relative to its recent distribution. A high volume RSI (default threshold: 65) during a squeeze release indicates that the expansion is occurring on above-average participation — a meaningful distinction from low-volume releases that can quickly reverse.
volRsi = ta.rsi(volume, 14)
highVol = volRsi > volThresh
The volume RSI value and status are displayed in the dashboard. Alert conditions include a "high-volume release" alert specifically when both a squeeze release signal and elevated volume RSI occur simultaneously, providing a higher-conviction composite signal.
5. Statistical Deviation Bands
Rather than using fixed threshold lines at arbitrary values, the oscillator's own distribution is analyzed statistically using a 200-bar lookback. The mean and one and two standard deviation levels of the squeeze value over this window establish dynamically updating bands. These bands are filled with a gradient and rendered at adaptive transparency based on the current Z-score — as the oscillator approaches the 2σ band, the fill becomes more opaque, visually emphasizing extreme readings.
sqzMean = ta.sma(sqzVal, statLen)
sqzStd = ta.stdev(sqzVal, statLen)
band1Up = sqzMean + sqzStd
band2Up = sqzMean + 2 * sqzStd
band1Dn = sqzMean - sqzStd
band2Dn = sqzMean - 2 * sqzStd
zScore = sqzStd > 0 ? (sqzVal - sqzMean) / sqzStd : 0
A squeeze reading above the 2σ upper band is historically anomalous compression — significantly above what has been typical over the prior 200 bars. Such readings often precede the most explosive release moves.
6. Histogram Coloring and Background Rendering
The histogram bar colors encode two simultaneous dimensions. The base color is red when the squeeze is building (sqzVal above sqzSig) and teal when releasing (sqzVal below sqzSig). The alpha channel of each bar is modulated by the absolute value of the delta conviction — high delta conviction produces more saturated colors, while low-conviction delta (price closing near the bar midpoint) produces more transparent bars. The background color is a 93% alpha gradient driven entirely by delta: teal for bullish delta, red for bearish delta, with the hyper-squeeze violet tint layered on top when active.
Features
ATR Compression Ratio Engine: Measures the difference between short-term and long-term EMA-smoothed ATR, normalized by HL range for cross-instrument comparability.
Signal Line: EMA of the squeeze value provides the crossover reference for detecting compression buildup and release initiation.
Hyper-Squeeze Detection: Identifies accelerating compression phases where the squeeze is rising for N consecutive bars simultaneously.
Cumulative Delta Proxy: Bar-range-based buying and selling pressure estimate, smoothed and normalized, requiring no Level 2 data.
Volume RSI Confluence: RSI applied to volume series identifies above-average participation, separating high-conviction releases from low-volume ones.
Statistical Deviation Bands: 200-bar mean and sigma levels with gradient fill and adaptive transparency based on Z-score position.
Delta-Driven Alpha Histogram: Histogram color and opacity encode both squeeze direction and delta conviction simultaneously.
Layered Background Coloring: Delta-based background with hyper-squeeze overlay provides immediate pane-level context without requiring close inspection.
Signal Markers: Circle markers at oscillator bottom on squeeze cross and release cross events.
Seven-Row Dashboard: Real-time status covering state, hyper squeeze, volume RSI, delta bias, Z-score, and squeeze value.
Four Alert Conditions: Squeeze building, release detected, hyper squeeze active, and high-volume release composite signal.
Input Parameters
ATR Settings:
Base Length: Period for short-term ATR EMA and HL range lookback (default: 20)
Hyper-Squeeze Settings:
Hyper Squeeze Consecutive Bars: Number of consecutive rising bars required for hyper-squeeze (default: 3)
Delta Settings:
Delta Smoothing Window: SMA period for the delta bar average (default: 10)
Volume RSI Settings:
Volume RSI Period: RSI lookback applied to volume series (default: 14)
Volume RSI Threshold: Level above which volume is considered elevated (default: 65)
Statistical Bands Settings:
Statistical Lookback: Bar count for mean and standard deviation computation (default: 200)
Show Bands: Toggle deviation band fills (default: true)
Display Settings:
Show Background: Toggle delta and hyper-squeeze background coloring (default: true)
Show Signal Markers: Toggle circle markers at squeeze and release crosses (default: true)
Show Dashboard: Toggle the seven-row information table (default: true)
How to Use This Indicator
Step 1: Monitor the Squeeze State
The primary read from this oscillator is the current state displayed in the dashboard: SQUEEZING, RELAXING, or EXPANDING. Squeezing means the compression ratio is positive and rising — the market is actively coiling. Relaxing means the compression is positive but flattening or declining — the coil is beginning to unwind. Expanding means the oscillator has gone negative — volatility is actively expanding beyond the historical baseline. The transition from SQUEEZING to RELAXING is the early warning signal; the transition to EXPANDING is confirmation that the release has begun.
Step 2: Watch for Hyper-Squeeze Conditions
When the dashboard shows HYPER SQUEEZE: ACTIVE and the chart shows the violet tint overlay, the compression is accelerating — each bar the market is coiling tighter. These conditions historically precede more forceful releases. In hyper-squeeze conditions, position sizing on the anticipated breakout can be considered carefully, as the magnitude of the release may be larger than during ordinary squeeze exits.
Step 3: Check Delta Bias for Directional Lean
Before committing to a directional bias, check the delta row in the dashboard. Positive delta (bullish) during a squeeze indicates that even during compression, buyers have been closing bars near the upper portion of their range — a bullish accumulation signature. Negative delta (bearish) suggests the opposite. Delta bias does not guarantee direction, but it provides a useful lean when combined with the squeeze release signal.
Step 4: Require Volume RSI Confluence on Release
Not all squeeze releases produce sustained moves. Low-volume releases frequently reverse within a few bars. The "High-Volume Release" alert fires only when both a release cross and elevated volume RSI (above threshold) occur simultaneously. Waiting for this composite signal before acting on a release — rather than responding to the release cross alone — filters out a meaningful number of false expansion signals in low-participation environments.
Indicator Limitations
The ATR compression ratio measures relative volatility contraction but cannot determine the direction of the eventual breakout. This indicator identifies when a release is likely, not which way price will move. Directional analysis must come from structure, trend, or other contextual tools.
The delta proxy is a bar-level approximation of order flow. It does not access actual tick data, order book data, or trade-level information. In markets with high-frequency activity, the close-to-high/low ratio can systematically misrepresent actual buying and selling pressure.
The statistical deviation bands require 200 bars to be fully seeded. On instruments or timeframes with limited history, or immediately after loading a new chart, the bands may produce unreliable readings until sufficient data is available.
Volume RSI confluence is not applicable to instruments where volume data is unreliable, unavailable, or represents synthetic aggregation (some forex pairs, certain CFDs). In these cases, the volume RSI row should be treated as informational only.
The hyper-squeeze condition measures consecutive rising bars in the squeeze value. This makes it sensitive to the base period setting — shorter periods produce more variable squeeze values, leading to more frequent interruptions of the consecutive count.
This indicator operates entirely on the chart's native timeframe. It does not incorporate multi-timeframe squeeze data — a squeeze on a 15-minute chart may be occurring within the context of a much larger timeframe expansion that this indicator would not reflect.
Originality Statement
The Volatility Squeeze Oscillator is a purpose-built analytical instrument that combines techniques not previously assembled in this specific architecture.
The ATR compression ratio engine — using EMA-smoothed ATR at the base period versus double the base period, normalized by the HL range — is an original squeeze quantification method. It differs from the widely used Lazybear TTM Squeeze (which measures Bollinger Band width versus Keltner Channel width) by operating entirely within the ATR framework with range normalization.
The hyper-squeeze detection via ta.rising() on the already-positive squeeze value identifies accelerating compression as a distinct state separate from ordinary compression, a categorization not found in standard squeeze implementations.
The cumulative delta proxy using bar-range ratios (close minus low divided by range for bull pressure; high minus close divided by range for bear pressure), smoothed and normalized, provides order-flow-inspired information without any data dependency beyond OHLC — an original application of range analysis.
The integration of volume RSI as a confluence gate within the squeeze oscillator framework — not as a separate indicator but as an internal filter with dedicated dashboard output and composite alert conditions — is an original design choice.
The statistical deviation band system applied to the squeeze oscillator's own values (using a 200-bar SMA and StDev of the squeeze value itself) to create adaptive significance thresholds is an original meta-statistical layer not found in comparable oscillators.
Disclaimer
The Volatility Squeeze Oscillator is provided for educational and informational purposes only. It is a technical analysis tool and does not constitute financial advice. Identifying squeeze conditions does not predict the direction or magnitude of subsequent price moves with any certainty. All trading involves risk of loss. Users are solely responsible for their own trading decisions. Please consider your individual risk tolerance and consult a licensed financial professional before engaging in any trading activity.
-Made with passion by officialjackofalltrades
Indicator
Liquidity Zone Harvester [JOAT]Liquidity Zone Harvester
Introduction
Institutional order flow leaves footprints in market structure. When a large buyer or seller places a significant order, the execution of that order creates an imbalance between supply and demand at a specific price level — and markets frequently return to these levels to test whether the original interest remains. These price areas are commonly referred to as order blocks or liquidity zones, and they form one of the core concepts in institutional and Smart Money trading methodology.
The Liquidity Zone Harvester is an automated order block detection and management system that identifies these zones using statistically validated momentum signals rather than arbitrary manual placement. Instead of drawing boxes wherever a trader's eye thinks supply or demand may exist, this indicator uses Z-score cumulative impulse detection to identify when directional momentum has reached statistically significant levels — and only then marks the most recent opposing-close candle as the source order block. Volume quality gates ensure that only high-participation impulses create zones, filtering out low-conviction moves that are less likely to represent genuine institutional activity.
What sets this indicator apart from standard order block tools is what happens after zone creation. Every active zone is tracked through a dual-mechanism aging system. The Bayesian exponential decay model progressively reduces zone visual intensity over time with a configurable half-life, providing a continuous probability signal about zone freshness. Simultaneously, a Kaplan-Meier survival analysis engine — borrowed from medical statistics — estimates the probability that a given zone will survive future price tests, based on the historical survival rates of all previously observed zones in the training window. Each zone displays both its current age and its estimated survival probability directly on the chart, turning static boxes into dynamically updated probability estimates.
Core Concepts
1. Z-Score Cumulative Impulse Detection
Zone creation is triggered only when directional momentum reaches a statistically defined threshold. The system accumulates a running streak of directional closes — when consecutive bars close higher than their open, the bull accumulator grows; when consecutive bars close lower, the bear accumulator grows. The streak resets when direction reverses. This cumulative streak is then normalized against its own rolling mean and standard deviation, producing a Z-score that measures how unusual the current momentum streak is relative to recent history.
cumBull := close > open ? nz(cumBull ) + (close - open) : 0
cumBear := close < open ? nz(cumBear ) + (open - close) : 0
zBull = (cumBull - ta.sma(cumBull, zLen)) / ta.stdev(cumBull, zLen)
zBear = (cumBear - ta.sma(cumBear, zLen)) / ta.stdev(cumBear, zLen)
bullEvent = ta.crossover(zBull, zThresh) and barstate.isconfirmed and volOK
bearEvent = ta.crossover(zBear, zThresh) and barstate.isconfirmed and volOK
When a bullEvent fires (bull Z-score crosses the threshold with volume confirmation), the system looks backward to find the most recent down-close candle — the last bar where sellers were dominant before the impulse began. This becomes the demand zone. Similarly, a bearEvent marks the most recent up-close candle as the supply zone.
2. Volume Quality Gate
Not all Z-score impulses are created equal. An impulse that occurs on abnormally low volume represents weak conviction — possibly a thin-market price drift rather than genuine institutional momentum. The volume gate applies RSI to the volume series to normalize it against its own history. Only when volume RSI exceeds the configurable threshold is the volOK condition true, enabling zone creation.
volRsi = ta.rsi(volume, 14)
volOK = volRsi > volThresh
This filter meaningfully reduces the number of zones created during low-participation conditions such as pre-market sessions, lunch hours, or holiday-period trading — precisely the times when order block levels are least likely to represent significant institutional interest.
3. Order Block Zone Construction
When a signal event is confirmed, the most recent opposing candle is identified using ta.valuewhen(). For a bullEvent, the system finds the most recent bar where close was less than open (a down candle) — its high and low define the demand zone boundaries. For a bearEvent, it finds the most recent up candle — its high and low define the supply zone boundaries. A box object is created spanning from that historical bar to the current bar, with height defined by the candle's actual high-low range.
lastDnHigh = ta.valuewhen(close < open, high, 0)
lastDnLow = ta.valuewhen(close < open, low, 0)
lastDnBar = ta.valuewhen(close < open, bar_index, 0)
if bullEvent
newBox = box.new(lastDnBar, lastDnHigh, bar_index, lastDnLow, ...)
bullBoxes.push(newBox)
4. Overlap Prevention (f_no_overlap)
To avoid cluttering the chart with redundant zones that occupy the same price territory, an overlap check function evaluates whether a proposed new zone overlaps with any existing zone of the same type. The function iterates over all existing bull or bear boxes and compares the new zone's top and bottom against each existing box's top and bottom. A guard condition (nBull > 0) prevents the iteration from running on an empty array, which would cause an index -1 crash.
f_no_overlap(newTop, newBot, boxes) =>
noOverlap = true
if boxes.size() > 0
for i = 0 to boxes.size() - 1
b = boxes.get(i)
if newTop >= box.get_bottom(b) and newBot <= box.get_top(b)
noOverlap := false
noOverlap
5. Bayesian Exponential Decay
Each zone's visual transparency is driven by an exponential decay function that represents the diminishing probability of zone relevance over time. The half-life parameter (default: 75 bars) defines how quickly a zone fades. At age 0, the zone is fully opaque. At age 75 bars, the zone is at 50% opacity. At age 150 bars, 25% opacity. This continuous decay — rather than a binary active/expired switch — provides an analog probability signal directly encoded in the zone's visual intensity.
decayFactor = math.exp(-0.693 * age / halfLife)
zoneAlpha = math.round(decayFactor * 200)
box.set_bgcolor(b, color.new(zoneColor, 255 - zoneAlpha))
6. Kaplan-Meier Survival Analysis
The Kaplan-Meier estimator is a nonparametric statistical method originally developed to measure survival probabilities in clinical trial data. In this indicator, "survival" is defined as a liquidity zone remaining unmitigated (not breached by a closing price on two separate occasions). Each time a zone is mitigated, it is recorded as a "death event" at its current age. Zones that expire by age limit without mitigation are recorded as "censored events" — incomplete observations. The KM formula multiplies survival probabilities across all observed events up to a given age.
// For each completed event (death at age t_i with n_i at-risk zones):
S_t := S_t * (1.0 - d_i / n_i)
// Product over all event times <= query age
For each active zone, the indicator queries the KM estimate at the zone's current age and displays the result as a percentage label. A zone at age 40 showing "Age 40 | 72%" means that historically, 72% of zones survived to at least 40 bars without being mitigated — giving traders a quantitative assessment of how likely the zone is to hold on the next test.
Features
Z-Score Cumulative Impulse: Statistical momentum threshold using normalized cumulative directional streaks to gate zone creation.
Volume Quality Gate: Volume RSI filter ensures only high-participation impulses create zones.
Precise Order Block Identification: Most recent opposing candle (last down-close for bull event, last up-close for bear event) defines zone boundaries.
Overlap Prevention: f_no_overlap function checks all existing zones before creating a new one, preventing chart clutter from redundant levels.
Bayesian Exponential Decay: Zone opacity decays over time with configurable half-life, encoding freshness as a visual probability signal.
Kaplan-Meier Survival Analysis: Medical-statistics survival estimator applied to zone longevity, displayed as a percentage probability label on each active zone.
Dynamic Zone Extension: Box right edge extends to the current bar on every update, keeping zones visually connected to the present.
Mitigation Tracking: Zones that are closed through twice are flagged as mitigated and removed, with the event recorded for KM analysis.
Seven-Row Dashboard: Active demand count, active supply count, bull Z, bear Z, volume RSI, KM training size, and signal status.
Two Alert Conditions: Zone created alert and zone rejection (price tests and bounces back) alert.
Input Parameters
Z-Score Settings:
Z Lookback: Rolling window for Z-score normalization (default: 50)
Z Threshold: Sigma level required to trigger an impulse event (default: 2.0)
Volume Gate Settings:
Volume RSI Period: RSI lookback for volume normalization (default: 14)
Volume RSI Threshold: Minimum volume RSI for zone creation eligibility (default: 55)
Zone Management Settings:
Max Zone Age: Maximum bars a zone remains active before forced removal (default: 300)
Mitigation Count: Number of closes through a zone required for mitigation (default: 2)
Max Active Zones Per Side: Maximum simultaneous demand or supply zones displayed (default: 5)
Decay Settings:
Decay Half-Life: Number of bars at which zone opacity reaches 50% of initial value (default: 75)
KM Settings:
KM Training Window: Bar lookback for Kaplan-Meier training data collection (default: 500)
Show Survival Labels: Toggle KM probability labels on active zones (default: true)
Display Settings:
Show Demand Zones: Toggle demand (bull) zone boxes (default: true)
Show Supply Zones: Toggle supply (bear) zone boxes (default: true)
Show Dashboard: Toggle the seven-row information table (default: true)
How to Use This Indicator
Step 1: Understand Zone Creation Conditions
Zones are not created on every bar — they are created only when a statistically significant directional impulse (Z-score above threshold) occurs on above-average volume. This selectivity is intentional. In any given trading session, you will likely see only a few zone creation events, each backed by a genuine momentum surge that suggests institutional participation. When you see a new zone appear, note the Z-score values in the dashboard and the volume RSI reading — higher values on both indicate a stronger impulse and more confident zone placement.
Step 2: Prioritize Fresh, High-Survival Zones
Not all zones on the chart are equally relevant. A fresh zone (low age, full opacity) at a KM survival rate of 80% is a far stronger candidate for price reaction than an old zone (high age, near-transparent) at 30% survival probability. Use both the visual opacity and the KM label together: as a zone ages and fades, reduce your expectation that it will provide meaningful support or resistance. When price approaches a zone that is both visually fresh and shows high KM survival probability, the statistical expectation of reaction is at its highest.
Step 3: Watch for Zone Rejection Alerts
The zone rejection alert fires when price tests a zone (enters the box boundary) and then closes back away from it without mitigating it. This is the core trade setup: price returning to the institutional order block level, briefly penetrating it, and then reversing. The rejection alert provides a timely notification for potential entries in the direction of the original impulse that created the zone, with the zone's near boundary serving as the natural stop-loss reference.
Step 4: Monitor KM Training Size for Statistical Validity
The dashboard displays the KM training sample size — the number of completed zone events (both mitigated and aged-out) available for the survival analysis. With fewer than 10 training events, the KM estimate has high variance and should be treated as rough guidance. With 30 or more training events, the estimate becomes statistically stable. On instruments or timeframes where the indicator has run for extended periods, the KM estimates become increasingly reliable as the training dataset grows.
Indicator Limitations
The Z-score cumulative impulse and volume gate require sufficient chart history for the rolling normalization periods to be seeded. In the first Z-lookback bars of a new chart, zone creation signals may be less reliable as the mean and standard deviation are not yet fully established.
Kaplan-Meier survival estimates are only as reliable as the training dataset. On instruments or timeframes that have not accumulated many completed zone events, the survival probabilities should be treated as rough estimates rather than statistically precise values.
The mitigation definition (two closes through the zone) is a configurable approximation. In real order block theory, mitigation can be defined in several ways; this indicator's specific definition may not match every trader's conceptual framework.
Zones are based on the most recent opposing candle at the time of the impulse event. In fast markets where multiple large candles cluster closely together, the marked candle may not represent the most significant institutional order location.
This indicator requires volume data. On instruments where volume is unavailable or unreliable (some synthetic indices, certain forex pairs), the volume gate will not function as intended and should be disabled or its threshold lowered significantly.
The exponential decay model assumes a constant half-life across all market conditions. In reality, zone relevance can be regime-dependent — a zone formed during a trending market may remain relevant longer than one formed during a range, or vice versa.
Maximum active zones per side is a hard limit. If the limit is reached, new valid zone creation events will be rejected until an existing zone is mitigated or aged out.
Originality Statement
The Liquidity Zone Harvester is a genuinely original indicator that applies statistical and mathematical frameworks from outside the trading domain to a problem common in technical analysis.
The Z-score cumulative impulse detection — using consecutive close-open accumulation normalized against rolling sma/stdev — as the primary trigger for order block marking is an original signal architecture. Most order block indicators use visual pattern matching (e.g., a large candle followed by a gap) rather than statistical significance thresholds.
Applying the Kaplan-Meier survival estimator — a nonparametric method from biostatistics — to estimate the probability that a liquidity zone will survive future price tests is a novel application of medical statistics to market analysis. This provides a mathematically grounded probability estimate that no standard order block indicator offers.
The Bayesian exponential decay applied to zone visual transparency — using a configurable half-life to continuously encode zone freshness as opacity — is an original visual design that treats zone relevance as a continuously diminishing probability rather than a binary active/inactive state.
The overlap prevention function that iterates over all existing zone arrays before creating a new zone — with the index-crash guard for empty arrays — is a specific engineering solution to a concrete problem in box-based indicator design.
The volume RSI quality gate, applied specifically to filter Z-score impulse events rather than as a standalone signal, is an original confluence filter design that specifically addresses the problem of thin-market false signals in order block detection.
Disclaimer
The Liquidity Zone Harvester is provided for educational and informational purposes only. It is a technical analysis tool and does not constitute financial advice. Liquidity zones and order blocks are analytical constructs; they do not guarantee price reactions. Past zone behavior as encoded in Kaplan-Meier estimates does not predict future zone performance. All trading involves risk of loss. Users are solely responsible for their own trading decisions. Please consider your individual risk tolerance and consult a licensed financial professional before engaging in any trading activity.
-Made with passion by officialjackofalltrades
Indicator
Volume-Weighted RSI with Adaptive SmoothingThis indicator is designed to provide traders with insights into the relative strength of a security by incorporating volume-weighted elements, effectively combining the concepts of Relative Strength Index (RSI) and volume-weighted averages to generate meaningful trading signals.
The indicator calculates the traditional RSI, which measures the speed and change of price movements, as well as the volume-weighted RSI, which considers the influence of trading volume on price action. It then applies adaptive smoothing to the volume-weighted RSI, allowing for customization of the smoothing process. The resulting smoothed volume-weighted RSI is plotted alongside the original RSI, providing traders with a comprehensive view of the price strength dynamics.
The line coloration in this indicator is designed to provide visual cues about the relationship between the RSI and the volume-weighted RSI. When the RSI line is above or equal to the volume-weighted RSI line, it suggests a potentially bullish condition with positive market momentum. In such cases, the line is colored lime. Conversely, when the RSI line (fuchsia) is below the volume-weighted RSI line, it indicates a potentially bearish condition with negative market momentum. The line color is set to fuchsia. By observing the line color, traders can quickly assess the relative strength between the RSI and the volume-weighted RSI, aiding their decision-making process.
The bar color and background color further enhance the visual interpretation of the indicator. The bar color reflects the RSI's relationship with the volume-weighted RSI and the predefined thresholds. If the RSI line is above both the volume-weighted RSI line and the overbought threshold (70), the bar color is set to lime, indicating a potentially overbought condition. Conversely, if the RSI line is below both the volume-weighted RSI line and the oversold threshold (30), the bar color is set to fuchsia, suggesting a potentially oversold condition. When the RSI line is between these two thresholds, the bar color is set to yellow, indicating a neutral or intermediate state. The background color, displayed with a semi-transparent shade, provides additional context by reflecting the prevailing market conditions. It turns lime if the volume-weighted RSI is above the overbought threshold, fuchsia if below the oversold threshold, and yellow if it falls between these two thresholds. This coloration scheme aids traders in quickly assessing market conditions and potential trading opportunities.
Calculations:
-- RSI Calculation : The traditional RSI is calculated based on the price movements of the asset. The up and down movements are determined, and exponential moving averages are used to smooth the values. The RSI value ranges from 0 to 100, with levels above 70 indicating overbought conditions and levels below 30 indicating oversold conditions.
-- Volume-Weighted RSI Calculation : The volume-weighted RSI incorporates the trading volume of the asset into the calculations. The closing price is multiplied by the corresponding volume, and the average is taken over a specific length. The up and down movements are smoothed using exponential moving averages to generate the volume-weighted RSI value.
-- Adaptive Smoothing : The indicator offers an adaptive smoothing option, allowing traders to customize the smoothing process of the volume-weighted RSI. By adjusting the smoothing length, traders can fine-tune the responsiveness of the indicator to changes in market conditions. Smoothing helps reduce noise and enhances the clarity of the signals.
Interpretation:
The indicator provides two main components for interpretation:
-- RSI : The traditional RSI reflects the price momentum and potential overbought or oversold conditions. Traders can look for RSI values above 70 as potential overbought signals, suggesting a possible price reversal or correction. Conversely, RSI values below 30 indicate potential oversold signals, indicating a potential price rebound or rally.
-- Volume-Weighted RSI : The volume-weighted RSI incorporates trading volume, which provides insights into the strength of price movements. When the volume-weighted RSI is above the traditional RSI, it suggests that the buying pressure supported by higher volume is stronger, potentially indicating a more reliable trend. Conversely, when the volume-weighted RSI is below the traditional RSI, it suggests that the selling pressure supported by higher volume is stronger, potentially indicating a more significant price reversal.
Potential Strategies:
-- Overbought and Oversold Signals : Traders can utilize the RSI component of the indicator to identify overbought and oversold conditions. A potential strategy is to consider taking short positions when the RSI is above 70 and long positions when the RSI is below 30. These levels can act as dynamic support and resistance areas, indicating possible price reversals.
-- Confirmation with Volume : Traders can use the volume-weighted RSI as a confirmation tool to validate price movements. When the volume-weighted RSI is above the traditional RSI, it may provide additional confirmation for long positions, suggesting stronger buying pressure. Conversely, when the volume-weighted RSI is below the traditional RSI, it may provide confirmation for short positions, indicating stronger selling pressure.
-- Trend Reversal Strategy : Watch for the volume-weighted RSI to reach extreme levels above 70 (overbought) or below 30 (oversold). Look for a reversal signal where the RSI line (green or fuchsia) crosses below or above the volume-weighted RSI line. Enter a trade when the reversal signal occurs, and the RSI line changes color. Exit the trade when the RSI line crosses back in the opposite direction or reaches the opposite extreme level.
-- Divergence Strategy : Compare the direction of the RSI line (green or fuchsia) with the volume-weighted RSI line. A bullish divergence occurs when the RSI line makes higher lows while the volume-weighted RSI line makes lower lows. A bearish divergence occurs when the RSI line makes lower highs while the volume-weighted RSI line makes higher highs. Once a divergence is identified, wait for the RSI line to cross above or below the volume-weighted RSI line as confirmation of a potential trend reversal. Consider using additional indicators or price action analysis to time the entry more accurately. Use stop-loss orders and profit targets to manage risk and secure profits.
-- Trend Continuation Strategy : Assess the overall trend direction by observing the RSI line's position relative to the volume-weighted RSI line. When the RSI line consistently stays above the volume-weighted RSI line, it indicates a bullish trend, while the opposite suggests a bearish trend. Look for temporary pullbacks within the ongoing trend where the RSI line (green or fuchsia) touches or crosses the volume-weighted RSI line. Enter trades in the direction of the dominant trend when the RSI line crosses back in the trend direction. Exit the trade when the RSI line starts to deviate significantly from the volume-weighted RSI line or when the trend shows signs of weakening through other technical or fundamental factors.
Limitations:
-- False Signals : Like any indicator, the "Volume-Weighted RSI with Adaptive Smoothing" may produce false signals, especially during periods of low liquidity or choppy market conditions. Traders should exercise caution and consider using additional confirmation indicators or tools to validate the signals generated by this indicator.
-- Lagging Nature : The indicator relies on historical price data and volume to calculate the RSI and volume-weighted RSI. As a result, the signals provided may have a certain degree of lag compared to real-time price action. Traders should be aware of this inherent lag and consider combining the indicator with other timely indicators to enhance the accuracy of their trading decisions.
-- Parameter Sensitivity : The indicator's effectiveness can be influenced by the choice of parameters, such as the length of the RSI, smoothing length, and adaptive smoothing option. Different market conditions may require adjustments to these parameters to optimize performance. Traders are encouraged to conduct thorough testing and analysis to determine the most suitable parameter values for their specific trading strategies and preferences.
-- Market Conditions : The indicator's performance may vary depending on the prevailing market conditions. It is essential to understand that no indicator can guarantee accurate predictions or consistently profitable trades. Traders should consider the broader market context, fundamental factors, and other technical indicators to complement the insights provided by the "Volume-Weighted RSI with Adaptive Smoothing" indicator.
-- Subjectivity : Interpretation of the indicator's signals involves subjective judgment. Traders may have varying interpretations of overbought and oversold levels, as well as the significance of the volume-weighted RSI in relation to the traditional RSI. It is crucial to combine the indicator with personal analysis and trading experience to make informed trading decisions.
Remember, no single indicator can provide foolproof trading signals. The "Volume-Weighted RSI with Adaptive Smoothing" indicator serves as a valuable tool for analyzing price strength and volume dynamics. It can assist traders in identifying potential entry and exit points, validating trends, and managing risk. However, it should be used as part of a comprehensive trading strategy that considers multiple factors and indicators to increase the likelihood of successful trades. Indicator
KINSKI RSI/RSX DivergenceThe Relative Strength Index (RSI) is a momentum indicator that measures the magnitude of recent price changes to analyse overbought or oversold conditions. RSI values range from 0 to 100.
The Relative Strength Index (RSI) is calculated using the following formula: RSI = 100 - 100 / (1 + RS) Where RS = average gain of upward phases during the specified time frame / average loss of downward phases during the specified time frame.
An asset price is considered overbought (due for a correction) if the RSI is above 70 and oversold (due for a recovery) if it is below 30. More extreme values (80/20) are also used to avoid false readings.
In a strong uptrend, the RSI often reaches 70 and above for long periods, and downtrends can remain at 30 or below for long periods.
Divergence detection in RSI is one of the important functions of this indicator. The reason is that an RSI divergence is a more reliable signal than the overbought and oversold indicators themselves. You will get overbought and oversold signals all the time. However, the divergence is a rare event.
In general, RSI divergence means that the RSI indicator is moving in the opposite direction compared to the price. So while the price is moving, the RSI is telling us in advance to expect a change in direction.
Positive RSI divergence
A positive RSI divergence is when the price trend has lower lows and lower highs, while the RSI indicator does the opposite - higher highs and higher lows. The price continues to fall while the RSI indicator begins to rise.
Negative RSI divergence
Negative RSI divergence is the opposite of positive divergence. It applies to uptrends where the price reaches higher highs and higher lows. However, the RSI shows lower highs and lower lows - the price goes up but the RSI goes down. The price closes with higher highs and higher lows, while the RSI indicator does the opposite - lower lows and lower highs, confirming a negative divergence. As a result, there is a sharp decline in the price.
RSX Indicator - Base script: SharkCIA by Jaggedsoft (Linked in the source code)
The RSX is the noise-free variant of the more popular RSI oscillator. Typically, any indicator can be smoothed by applying a moving average. However, a major disadvantage of such a method is that there is a time lag between the indicator and the price. RSX Indicator attempts to do this without signal delay.
What distinguishes this indicator from others of this type?
Display of RSI indicator together/alone with RSX and RSI smoothed
display of the RSI indicator (option: "RSI: On/Off")
display of the RSX indicator (option: "RSX: On/Off")
display of the RSI indicator as smoothed version (option: "RSI Smoothed: On/Off")
offers the possibility to choose between different view variants
many settings for additional information, layout and divergence identification
enables completely new comparison possibilities and insights with the additional RSI variants
Indicator
