ADX Filtered Average True RangeOverview
This indicator combines ATR-based volatility measurement with ADX directional filtering to highlight only those moments when volatility expansion is aligned with confirmed trend momentum. Raw ATR measures how much price moves per bar, but a rising ATR alone says nothing about direction or conviction. By layering an ADX filter on top, the indicator separates meaningful expansion — where volatility is growing in the direction of a strengthening trend — from choppy noise where volatility rises without directional follow-through.
The result is a two-signal system displayed in a separate pane: a smoothed ATR line that changes colour only when the trend is directionally confirmed, and a background highlight that activates only when all three conditions align — expanding ATR, a rising directional ADX signal, and a bar that closes in the direction of the trend. This gives traders a compact, at-a-glance read on whether current volatility is actionable or should be ignored.
The indicator is designed for swing and intraday traders on instruments with clear trending behaviour, such as NSE:NIFTY , NSE:BANKNIFTY , MCX:CRUDEOIL1! , and major crypto pairs.
How It Works
ATR is calculated by applying the selected smoothing method to the true range series. A second SWMA pass (smoothed ATR, atrs ) acts as a baseline — when raw ATR rises above atrs and atrs itself is also rising, the indicator treats volatility as actively expanding:
atr = ma_function(ta.tr(true), atrlen)
atrs = ta.swma(atr)
ATRExpanding = (atrs > atrs and atr > atrs)
The ADX filter uses Pine's native ta.dmi() to compute DI+, DI−, and raw ADX. A SWMA of ADX ( sadx ) acts as the directional baseline. Bullish ADX is confirmed when sadx is rising, ADX is above sadx , DI+ shows momentum or dominance over DI−, and DI+ is above 15. Bearish ADX mirrors this using DI−:
sadx = ta.swma(adx)
ADXUp = sadx > sadx and adx > sadx and (diplus > diplus or diplus > diminus) and diplus > 15
ADXDn = sadx > sadx and adx > sadx and (diminus > diminus or diminus > diplus) and diminus > 15
Background highlighting only fires when all three conditions are simultaneously true — expanding volatility, directional ADX confirmation, and a bar closing in the expected direction.
Colour key:
Dark red line — raw ATR; always visible as the volatility baseline
Thick white line — smoothed ATR (SWMA of ATR); the trend-adjusted volatility baseline
Green background — ATR expanding + bullish ADX confirmed + bullish bar (close > open)
Red background — ATR expanding + bearish ADX confirmed + bearish bar (close < open)
No background — ATR contracting, or ADX directional filter not met, or bar indecisive
Inputs
ATR – Length — Number of bars used to smooth the true range for ATR calculation. Constrained to the Fibonacci sequence for harmonic alignment. Low (e.g. 5): fast, reactive ATR that tracks short-term volatility spikes. High (e.g. 55): slow, structural ATR that filters out minor fluctuations. Default: 13.
Smoothing — MA method applied to the true range when computing ATR. RMA (Wilder's moving average) is the industry standard and matches most platform ATR implementations. SWMA is the most responsive with zero lag; VWMA incorporates volume weight. Default: RMA.
ADX – Length — Lookback period for the DMI/ADX calculation via ta.dmi() . Low (e.g. 8): ADX responds faster, triggers more frequently but with more false positives. High (e.g. 55): ADX requires sustained directional pressure before confirming. Default: 34.
ADX – Smoothing — Smoothing period applied inside ta.dmi() to derive the signal line (lensig). Low (e.g. 1): raw, unsmoothed ADX. High (e.g. 13): heavily smoothed ADX that lags but reduces whipsaws. Default: 3.
Usage Notes
A green or red background is a confluence signal, not a standalone entry. Use it to confirm setups identified on your primary chart — price action, structure, or another trend tool.
When the background is absent despite a strong price move, treat this as a caution flag. Either ATR is contracting (move may be running out of energy) or ADX disagrees with direction (possible counter-trend move).
The DI threshold of 15 acts as a minimum directional noise filter. In low-volatility instruments or compressed consolidation phases, ADX rarely satisfies this condition — this is intentional behaviour, not a bug.
The Fibonacci-constrained length inputs encourage harmonic consistency across ATR and ADX settings. Consider pairing lengths that share a ratio — for example ATR 13 with ADX 34, or ATR 21 with ADX 55.
On shorter timeframes (1m–5m), set ATR Length to 5 or 8 and ADX Length to 13 or 21 for responsive signals. On daily or weekly charts, 34/89 or 55/144 combinations work well.
This indicator plots in a separate pane. Keep your price chart uncluttered — the background colour bleeds through to the main chart, providing context without requiring you to watch the pane constantly.
Recommended Pairings
This indicator pairs well with a trend-following overlay such as a Hull MA, VWAP, or a multi-timeframe EMA ribbon to confirm the broader bias before the background activates. Volume indicators — particularly Volume Delta or On-Balance Volume — add a useful layer of confirmation when the background fires, helping distinguish absorption-driven moves from thin-market noise. For Indian derivative traders, pairing with an open-interest change indicator on NSE:NIFTY or NSE:BANKNIFTY options can sharpen entry timing during background signals. Indicator
Bitcoin Logarithmic MACD Risk Metric (W, M)Description
The Bitcoin Logarithmic MACD Risk Metric (W, M) is a long-term macro analysis tool designed to identify generational buying and selling opportunities for Bitcoin. By applying logarithmic and linear regression models to the Logarithmic MACD (LMACD), this indicator normalizes price momentum relative to historical market cycle extremes.
Unlike standard MACD, the logarithmic version used here accounts for Bitcoin's exponential growth over time, allowing for a consistent comparison of momentum across different cycles.
IMPORTANT: Timeframe Compatibility
Note: This indicator is strictly designed for Weekly (W) and Monthly (M) timeframes. The regression lines won't show on other timeframes.
The regression lines (Log and Linear fits) were calculated using data points from these high-level timeframes because they contain less "noise" and are far more reliable for predicting global cyclical tops and bottoms.
It is highly recommended to wait for the candle close on the respective timeframe to confirm any signal, as mid-candle fluctuations can significantly alter the Risk Metric value.
How to Use It
The metric provides a "Risk" score between 0 and 1, visualizing where Bitcoin sits within its historical growth channel.
Sell Zone (Red): When the LMACD line enters the red zone, it indicates that Bitcoin is overbought and potentially nearing a cyclical peak.
Buy Zone (Green): When the LMACD drops into the green zone, it suggests the asset is oversold and approaching a cyclical bottom.
Confluence: While these zones have historically marked major reversals, this indicator should not be used in isolation. Always seek confluence with other technical or fundamental indicators.
Customizable Settings
Line Fits: You can choose between "Log Fit" and "Linear Fit" for both Top and Bottom lines to see which model best aligns with your current analysis.
View Mode: Switch between Fit Lines (seeing the MACD inside the channel) or Normalized (viewing the risk as a 0-1 oscillator).
Risk Levels: Adjust the thresholds for the Buy and Sell zones to fit your risk tolerance.
Price Overlay: Toggle the candle coloring on the main chart to see risk levels directly on the price action.
VIsualization Tip
If the price overlay makes the chart look too cluttered, you can hide the Bitcoin price bars in the main chart settings to focus purely on the colored risk overlay. Alternatively, you can disable the "Plot on Main Pane" option in the indicator settings if you only want to see the metric in its own separate pane below.
Limitations
Asset Focus: This indicator is specifically calibrated for Bitcoin. While you can apply it to other charts, the indicator pane will always display Bitcoin's MACD risk, whereas the price overlay will color the candles of the currently selected instrument.
Fixed Parameters: The LMACD settings (12, 26, Close) are hardcoded. This is intentional, as the regression fitting was performed specifically using these values; changing them would make the historical bands irrelevant.
Hardcoded Coefficients: The regression lines are based on historical tops and bottoms up to 2023. Future market cycles may require new approximations if Bitcoin's volatility profile shifts significantly.
Regression Specifics: The Linear Fit model treats the early 2011 peaks/bottoms as outliers. On the Monthly timeframe, the bottom line is available only as a linear fit due to the limited number of historical macro data points. The upper linear boundary is guaranteed to be broken by future price action, but it can serve as a "conservative" macro target in the meantime.
No Guarantees: Past performance does not guarantee future results. There is no certainty that the metric will reach the boundaries in every cycle or remain within them indefinitely.
Indicator
[GYTS-CE] Pattern Texture Codex (PTC)Pattern Texture Codex (Community Edition)
🌸 Part of GoemonYae Trading System (GYTS) 🌸
🌸 --------- INTRODUCTION --------- 🌸
💮 What is the Pattern Texture Codex?
Standard technical analysis focuses on two dimensions: Amplitude (how far price moves) and Momentum (how fast price moves). The Pattern Texture Codex introduces a third: Structure (how price moves).
This indicator implements Permutation Entropy (PE), a complexity measure from dynamical systems theory that captures whether price evolves in structured patterns or chaotic noise. We call this "Market Texture":
🫧 Smooth Texture (Low Entropy): Price evolves in ordered, predictable patterns. Trends are durable and causality is high. Momentum strategies favoured.
🌵 Rough Texture (High Entropy): Price evolves chaotically. The market is noisy, trends are fragile. Mean reversion or defensive sizing recommended.
💮 Why Use This Indicator?
Most "entropy" indicators on PulseWire fall into common traps:
Shannon entropy of price histograms — measures value distribution but ignores temporal sequence
Approximate Entropy (ApEn) — measures amplitude similarity with O(N²) computational cost
Volatility metrics labelled "entropy" — captures magnitude, not pattern structure
Mathematical errors — normalised values that don't form valid probability distributions
The Pattern Texture Codex provides true ordinal pattern analysis — it examines the sequence of price movements, not just their distribution or magnitude. A smooth uptrend and choppy consolidation may have identical volatility, but their texture is completely different.
↑ Pattern Texture Codex with dynamic threshold bands. Rough Texture (high entropy) often indicates reversals, while Smooth Texture (low entropy) often precedes trend continuation.
🌸 --------- HOW IT WORKS --------- 🌸
💮 Core Concept: Ordinal Patterns
Instead of analysing raw price values, Permutation Entropy converts price into ordinal patterns — the relative ordering of consecutive data points.
For example, with embedding dimension D=3 (three points per pattern):
Pattern "012": first < second < third → ascending
Pattern "210": first > second > third → descending
Pattern "102": middle value smallest → valley shape
The indicator counts how often each pattern appears over a lookback window, then calculates the Shannon entropy of this distribution.
↑ With D=3, three consecutive data points determine each pattern. Left: ascending pattern "012" (first point smallest, second middle, third greatest). Right: descending pattern "210" (first point greatest, second middle, third smallest).
💮 Calculation Overview
The normalised Permutation Entropy is computed as:
H = −∑ p(π) · ln(p(π)) / ln(D!)
Where:
• p(π) is the probability of each ordinal pattern π
• D is the embedding dimension (number of points per pattern)
• D! is the total possible patterns (e.g., 4! = 24 for D=4)
The result ranges from 0 to 1:
• H → 0 : One pattern dominates — highly structured, predictable
• H → 1 : All patterns equally likely — pure randomness
↑ Full calculation example at D=3 showing the lowest entropy point (green vertical line). With 3!=6 possible patterns, the monotonic rising pattern "012" dominates while three other patterns occur only once. The frequency column feeds into the entropy contribution formula, yielding H(3)=1.039. Normalising produces 0.4019 — matching the indicator output exactly.
💮 Theoretical Background
Permutation Entropy has deep theoretical foundations. For piecewise monotone maps, it converges to the Kolmogorov-Sinai entropy — the fundamental measure of chaos in dynamical systems.
Key scientific properties:
• Scale invariance — analyses rank orderings, invariant to monotonic transformations
• Noise robustness — ordinal encoding naturally filters high-frequency noise
• Computational efficiency — O(1) amortised per bar with lookup acceleration
• Micro-structure sensitivity — captures specific sequence patterns, not just distribution
🌸 --------- KEY FEATURES --------- 🌸
💮 Lookup Table Acceleration
Standard PE requires sorting each embedding vector — O(D log D) operations per bar. This implementation uses precomputed transition tables from Unakafova & Keller (2013), reducing complexity to O(D) per bar.
The key insight: successive ordinal patterns in overlapping windows share (D−1) data points. Rather than recomputing from scratch, the algorithm derives the new pattern from the previous one using a single table lookup.
Enabled by default for Delay=1 and Dimension ∈ {3, 4, 5}
Falls back to standard calculation for D=2, D=6
💮 Flexible Threshold Modes
The indicator supports four independent threshold modes for both Smooth and Rough detection:
Static — Fixed threshold values you define
Dynamic — Adaptive bands based on entropy baseline ± standard deviation
Percentile — Threshold at Nth percentile of recent entropy history
Disabled — No threshold for that direction
You can mix modes: for example, Dynamic for Smooth detection while using Percentile for Rough detection.
🌸 --------- CONFIGURATION --------- 🌸
💮 General Settings
• Source — Price series to analyse (default: close)
• Smoothing Critical Period — Smoothing via 2-pole Ultimate Smoother (default: 0 = disabled). Reveals underlying texture by filtering entropy noise.
💮 Entropy Calculation
Dimension (D) — Points per ordinal pattern:
• D=3: 6 patterns — fast, stable. Good for limited lookback.
• D=4: 24 patterns — balanced, captures V-reversals. Recommended.
• D=5: 120 patterns — sensitive, captures W/M patterns. Noisier.
• D=6: 720 patterns — maximum sensitivity. No lookup acceleration.
Lookback — Estimation window size. Hard minimum: D × 5. Statistical recommendation: 5 × D! (e.g., 120 for D=4). Default: 100.
Delay (τ) — Stride between points in each pattern (default: 1). Change only for oversampled data.
Lookup Acceleration — Enables transition tables for D ∈ {3, 4, 5} with Delay=1.
💮 Threshold Management
Rough/Smooth Mode — Static, Dynamic, Percentile, or Disabled for each threshold
Static Values — Fixed threshold when Static mode selected
Std Dev Multiplier — Band width for Dynamic mode (default: 2.0σ)
Baseline/Deviation Length — Lookback for Dynamic mode calculations
Percentile Settings — Lookback and percentile values for Percentile mode
💮 Visualisation & Alerts
Palette — Colour scheme (default: GYTS Purple)
Colouring Bars — Applies texture colours to chart candles
Dark Mode — Optimises colour intensity for dark backgrounds
Alerts — Triggers when entropy crosses above/below thresholds
🌸 --------- USAGE GUIDE --------- 🌸
💮 Getting Started
Apply the indicator with default settings:
• Dimension: 4 (balanced complexity, captures V-shaped reversals)
• Lookback: 100 (responsive; statistical ideal: ≥120 for 5× coverage)
• Smooth Threshold: Dynamic mode with 2.0σ multiplier
Observe how entropy rises during choppy consolidation and falls during clean trends.
💮 Interpretation
Entropy Value (0 to 1):
Below 0.6: Strong pattern dominance — highly ordered
0.7–0.9: Normal complexity — typical market behaviour
Above 0.95: Near-random — patterns uniformly distributed
Transitions:
Watch for regime changes. When entropy rises sharply after a prolonged smooth period, the trend may be losing coherence. When entropy falls from elevated levels, a new trend may be establishing.
↑ Texture transitions during a trend-to-consolidation regime change. During the uptrend, similar patterns (D=3) recur frequently and entropy decreases. As the market transitions to consolidation, entropy rises — signalling the trend is losing structure.
💮 Trading Applications
Trend Filtering — Only take trend-following signals during Smooth texture.
Mean Reversion Timing — Elevated entropy often precedes mean reversion.
Position Sizing — Reduce exposure during Rough texture.
Exit Management — Tighten stops when entropy rises during a position.
💮 Integration with GYTS Suite
The Pattern Texture Codex exports two signals:
PE Continuous — Raw entropy value (0 to 1)
PE Ternary — State signal (+1 = Rough, 0 = Neutral, −1 = Smooth)
These can be read by Flux Composer or used as filter conditions with Market Regime Detector .
🌸 --------- PARAMETER TUNING --------- 🌸
💮 Lookback Selection
Shorter lookbacks react faster but may produce unstable estimates:
50–100: Responsive. Good for intraday regime detection.
100–200: Balanced. Recommended for swing trading.
200–500: Stable. Better for position trading or noisy instruments.
The indicator enforces a hard minimum of D × 5 to prevent meaningless output.
↑ Three dimensions (D=3, D=4, D=5) with their academically recommended minimum lookback periods.
💮 Threshold Tuning
For Smooth Detection:
Dynamic mode with 2.0σ works well across most instruments. Lower multipliers (1.5σ) detect structure earlier but may false-trigger.
For Rough Detection:
Percentile mode at 90th percentile identifies only extreme chaos. Static threshold of 0.95+ focuses on near-random conditions.
Consider disabling Rough detection entirely if your strategy only cares about identifying structured trends.
↑ The three threshold modes on the same PTC calculation. Both upper and lower thresholds are independently configurable.
🌸 --------- LIMITATIONS --------- 🌸
Amplitude Blindness — PE treats all patterns equally regardless of magnitude. A 0.1% drift and a 10% crash produce identical entropy if their ordinal patterns match.
Equal Values (Ties) — Handled via temporal tie-breaking (recent values rank higher), but heavily discretised data may produce edge-case behaviour.
Sample Size Requirements — Very short lookbacks (below D × 5) produce unreliable estimates. Statistical reliability improves with larger samples.
Not Predictive Alone — Low entropy indicates structure exists, not that the trend will continue. Combine with directional analysis for trading decisions.
Lag During Transitions — The sliding window approach introduces inherent lag when market texture changes.
🌸 --------- CREDITS --------- 🌸
💮 Academic Sources
Bandt, C., & Pompe, B. (2002). Permutation entropy: A natural complexity measure for time series. Physical Review Letters, 88 (17), 174102. DOI
Unakafova, V., & Keller, K. (2013). Efficiently measuring complexity on the basis of real-world data. Entropy, 15 (10), 4392-4415. DOI
Ehlers, J. F. (2024). The Ultimate Smoother. Technical Analysis of Stocks & Commodities , 2024-04. TASC
💮 Libraries Used
FiltersToolkit — Ultimate Smoother and other curated filters
PatternTransitionTables — Precomputed lookup tables for O(1) pattern transitions
ColourUtilities — Gradient palette generation and colour management
Indicator
Indicator
Arc Momentum Oscillator [BOSWaves]Arc Momentum Oscillator - Acceleration-Driven Arc Engine with Adaptive Momentum Tracking and Divergence Detection
Overview
Arc Momentum Oscillator is a momentum-based trend oscillator that tracks directional price commitment through an acceleration-driven arc engine, where arc positioning, trend state, and flip signal generation are determined by the relationship between a smoothed centered RSI momentum line and a dynamically accelerating arc that chases momentum from a volatility-scaled starting distance rather than from fixed thresholds or static level crossovers.
Instead of relying on conventional overbought and oversold threshold crossings or moving average crossovers, trend state is determined by the relative position of momentum and the arc, with the arc launching from a standard deviation-scaled offset on each trend flip and accelerating progressively toward momentum until the relationship reverses. This creates a trend detection mechanism that adapts its sensitivity to the instrument's actual momentum volatility rather than applying identical thresholds regardless of market conditions.
This creates an oscillator that reflects genuine momentum dynamics rather than arbitrary indicator levels. The arc contracts toward momentum during sustained trending conditions as acceleration builds, produces flip signals only when momentum crosses the arc with sufficient separation to clear a volatility-scaled buffer, and maps momentum strength visually through cloud fill intensity, strength bands, and extreme zone highlighting that collectively reveal conviction state at a glance.
Price is therefore evaluated against an arc that adapts to momentum volatility, accelerates with trend persistence, and resets to a calibrated starting distance on each directional change rather than reacting identically to every momentum fluctuation.
Conceptual Framework
Arc Momentum Oscillator is founded on the principle that meaningful trend signals in momentum space emerge from the acceleration dynamics between price momentum and a trailing arc, not from momentum crossing fixed reference levels that carry no adaptive relationship to the instrument's current volatility regime.
Traditional momentum oscillators identify directional changes through threshold crossovers at fixed levels such as 50, 70, or 30, which treat all momentum readings identically regardless of how significant they are relative to recent momentum behavior. This framework replaces static-threshold logic with an acceleration-based arc system where flip significance is measured relative to the current standard deviation of momentum, and arc behavior evolves continuously based on how long the current trend has persisted.
Three core principles guide the design:
Trend flips should require momentum to breach the arc by a volatility-scaled buffer, ensuring signal generation reflects statistically meaningful momentum displacement rather than marginal crossovers.
The arc should accelerate progressively during trend persistence, contracting toward momentum as conviction builds and reflecting weakening trend strength as the gap narrows.
Momentum strength and extreme conditions should be visualized through adaptive fills and strength bands rather than fixed reference lines, providing continuous conviction context across varying volatility regimes.
This shifts oscillator analysis from static threshold monitoring into dynamic arc-momentum relationship tracking where signal significance is continuously calibrated to prevailing momentum volatility.
Theoretical Foundation
The indicator combines RSI-based momentum sourcing, exponential smoothing and centering, standard deviation-based adaptive unit calculation, acceleration-driven arc mechanics, and pivot-based divergence detection between price and the oscillator.
The RSI is calculated over a short configurable period and centered around zero by subtracting fifty, creating a momentum reading that oscillates between negative and positive territory reflecting bearish and bullish momentum respectively. An EMA smoothing pass reduces noise before the arc engine processes the momentum line. The standard deviation of momentum over a rolling window provides the adaptive unit that scales all arc behavior, including launch distance, flip buffer, and step size, ensuring the system responds proportionally to actual momentum variance. The arc accelerates by incrementing its velocity on a configurable smoothing interval, producing progressively faster convergence toward momentum during sustained trends.
Four internal systems operate in tandem:
Momentum Source Engine : Calculates RSI over the configured length, centers it around zero, and applies EMA smoothing to produce a clean directional momentum reading that serves as the primary oscillator line.
Adaptive Unit Calculation : Derives a rolling standard deviation of momentum to produce a volatility-normalized unit that scales arc launch distance, flip buffer threshold, and step size adaptively to current momentum behavior.
Arc Acceleration Engine : Maintains arc position and velocity state, launching the arc from a standard deviation-scaled offset on each flip, incrementing velocity on each smoothing interval, and advancing the arc toward momentum at a rate that grows with trend persistence.
Divergence Detection System : Identifies confirmed pivot highs and lows on the momentum oscillator and compares them against corresponding price pivots, detecting regular bearish divergence where price makes higher highs against lower oscillator highs and regular bullish divergence where price makes lower lows against higher oscillator lows.
This design allows the oscillator to reflect both the direction and the conviction dynamics of momentum while simultaneously monitoring the relationship between price structure and oscillator structure for divergence conditions.
How It Works
Arc Momentum Oscillator evaluates price through a sequence of momentum-aware and acceleration-driven processes:
RSI Calculation and Centering : RSI is calculated over the configured length and shifted by subtracting fifty, producing a centered momentum reading where positive values reflect bullish RSI conditions and negative values reflect bearish conditions.
Momentum Smoothing : EMA smoothing is applied to the centered RSI over the configured smoothing length, reducing bar-to-bar noise and producing the primary momentum line that the arc engine tracks.
Adaptive Unit Derivation : The standard deviation of momentum over a 100-bar rolling window provides the adaptive unit that governs all volatility-scaled arc behavior throughout the session.
Arc Initialization : On first activation after sufficient bars for standard deviation calculation, the arc is placed below momentum at a distance equal to the adaptive unit multiplied by the Start Distance parameter, with velocity initialized to zero.
Flip Detection : Each bar tests whether momentum has crossed the arc by more than a volatility-scaled buffer in the opposing direction. A cross below the arc minus buffer triggers a bearish flip; a cross above the arc plus buffer triggers a bullish flip.
Arc Reset on Flip : When a flip is detected, the arc resets to a new starting position on the opposite side of momentum at the full Start Distance offset and velocity resets to zero, beginning a fresh acceleration cycle in the new trend direction.
Arc Acceleration : On bars that fall on the configured smoothing interval, arc velocity increments by the configured Arc Speed value and the arc advances toward momentum by the adaptive step size multiplied by current velocity, producing progressively faster arc convergence as the trend persists.
Cloud Fill Rendering : The area between the arc and momentum line is filled with the trend direction color at a configurable transparency, with fill intensity providing a visual representation of the gap between arc and momentum.
Strength Band Rendering : Gradient-filled bands in the extreme oscillator zones scale their opacity based on how far momentum sits from the midline relative to the configured range, providing a continuous strength visualization that intensifies as momentum approaches extreme territory.
Divergence Evaluation : On each confirmed momentum pivot, recent pivot history is scanned for divergence conditions between price and oscillator structure within the configured minimum and maximum pivot distance range, with qualifying divergences drawing connecting lines on both the oscillator and the price chart.
Together, these elements form a continuously updating momentum framework where arc dynamics reveal trend conviction, flip signals reflect statistically significant momentum shifts, and divergence detection identifies structural disagreement between price and momentum before directional changes occur.
Interpretation
Arc Momentum Oscillator should be interpreted as an acceleration-driven momentum conviction system with structural divergence monitoring:
Bullish Trend State (Green) : Active when momentum is above the arc with sufficient buffer separation, indicating upward momentum has breached the arc threshold and the arc is accelerating from below to track the developing upside conviction.
Bearish Trend State (Red) : Active when momentum is below the arc with sufficient buffer separation, indicating downward momentum has breached the arc threshold and the arc is accelerating from above to track the developing downside conviction.
Arc Line : The grey trailing arc represents the dynamic boundary that momentum must cross to trigger a trend flip. Its proximity to momentum reflects accumulated trend age as the arc accelerates and converges toward the momentum line during sustained conditions.
Momentum Line : The colored primary oscillator line reflects smoothed centered RSI momentum, with positive readings above zero indicating bullish momentum dominance and negative readings below zero indicating bearish momentum dominance.
Cloud Fill : The gradient fill between arc and momentum line is colored by current trend direction, with a wider gap producing a more prominent fill that visually represents the distance between momentum and its trailing arc.
Strength Bands : Gradient-filled zones in the upper and lower extreme areas intensify in opacity as momentum approaches and enters extreme territory, providing a continuous strength reading that reflects how far momentum has extended relative to the oscillator's range.
Extreme Zone Background : Subtle background tinting activates when momentum exceeds the configured extreme threshold in either direction, providing an immediate visual cue that momentum is in statistically elevated territory.
Reference Rails : The zero midline, plus and minus 25 reference levels, and the configurable extreme threshold lines provide orientation anchors across the oscillator's momentum range without imposing fixed signal thresholds.
Flip Arrows : Small triangle markers appear on the price chart at arc flip bars, with upward triangles for bullish flips and downward triangles for bearish flips, positioned at a configurable offset from the arc for visibility.
Divergence Lines : Connecting lines drawn on both the oscillator and the price chart at divergence pivot points highlight structural disagreement between price and momentum, with bullish divergence lines in green and bearish divergence lines in red.
Divergence Labels : Bull and Bear labels appear at confirmed divergence pivot points on the oscillator pane, identifying the divergence type for immediate reference.
Trend Candles : Optional candle coloring on the price chart reflects current arc trend state, providing directional context on the main chart without requiring the oscillator pane to be in view.
Arc proximity to momentum, flip signal frequency, strength band opacity, and divergence conditions collectively provide more information than any element in isolation.
Signal Logic & Visual Cues
Arc Momentum Oscillator presents two primary arc interaction signals alongside continuous divergence monitoring:
Bullish Arc Flip : Triggered when momentum crosses above the arc by more than the volatility-scaled buffer, causing the arc to reset below momentum at the full Start Distance offset and begin a fresh bullish acceleration cycle.
Bearish Arc Flip : Triggered when momentum crosses below the arc by more than the volatility-scaled buffer, causing the arc to reset above momentum at the full Start Distance offset and begin a fresh bearish acceleration cycle.
Divergence detection provides secondary reversal context, with bullish divergence identifying price weakness not confirmed by oscillator structure and bearish divergence identifying price strength not confirmed by oscillator structure.
Alert generation covers bullish and bearish arc flips and entry into bull and bear extreme zones for systematic momentum monitoring workflows.
Strategy Integration
Arc Momentum Oscillator fits within momentum-informed and conviction-tracking approaches across trend-following and reversal frameworks:
Flip-Based Directional Entries : Use arc flip signals as momentum regime change confirmations, entering in the flip direction when the arc reset occurs after a statistically significant momentum breach rather than at arbitrary oscillator levels.
Arc Convergence Monitoring : Track the closing distance between the arc and momentum during established trends as a real-time conviction gauge; tight arc-to-momentum gaps indicate mature trend conditions where a flip becomes increasingly possible with minimal additional momentum deterioration.
Extreme Zone Context : Use extreme background tinting and strength band intensity as momentum extension warnings rather than reversal signals, treating extended extreme readings with the caution appropriate to overextended momentum states.
Divergence-Confirmed Reversals : Use divergence signals as anticipatory context for potential arc flips, weighting flip signals more heavily when a divergence condition has formed in the bars preceding the flip rather than treating them as independent signals.
Trend Candle Alignment : Use trend candle coloring on the price chart to maintain arc trend state awareness when analyzing price action directly, ensuring entry and exit decisions remain aligned with the current oscillator regime.
Multi-Timeframe Momentum Confirmation : Apply higher-timeframe arc trend state as a directional momentum filter, favoring trades where lower-timeframe flip signals align with the established higher-timeframe arc direction rather than opposing it.
Technical Implementation Details
Momentum Source : RSI-centered around zero with configurable length and EMA smoothing pass
Adaptive Unit : Rolling 100-bar standard deviation of momentum for volatility-normalized arc scaling
Arc Engine : Stateful acceleration system with velocity accumulation, smoothing-interval advancement, and flip-triggered reset
Flip Buffer : Volatility-scaled separation requirement preventing marginal arc crossovers from triggering flips
Divergence System : Pivot array-based regular divergence detection with configurable pivot size, minimum and maximum distance, and cooloff between successive divergences of the same type
Visualization : Cloud fill, adaptive strength bands, extreme background tinting, reference rails, and divergence lines on both oscillator and price chart
Signal Output : Flip arrows on price chart, divergence labels on oscillator pane, and optional trend candle coloring
Performance Profile : Optimized for real-time execution across all timeframes with adaptive unit calculation ensuring consistent behavior across instruments with different volatility characteristics
Optimal Application Parameters
Timeframe Guidance:
1 - 5 min : Rapid flip detection for scalping with shorter RSI length and higher arc speed for faster momentum tracking
15 - 60 min : Intraday momentum regime identification with balanced smoothing and moderate arc acceleration settings
4H - Daily : Swing-level momentum trend tracking with longer smoothing and slower arc speed for sustained conviction measurement
Suggested Baseline Configuration:
RSI Length : 5
Momentum Smoothing : 17
Arc Speed : 0.11
Start Distance (σx) : 3.5
Arc Smoothing : 5
Extreme Threshold : 30
Show Cloud Fill : Enabled
Show Reference Rails : Enabled
Highlight Extremes : Enabled
Calculate Divergences : Enabled
Show Trend Candles : Enabled
These suggested parameters should be used as a baseline; their effectiveness depends on the instrument's momentum characteristics, volatility profile, and preferred flip frequency, so fine-tuning is expected for optimal performance.
Parameter Calibration Notes
Use the following adjustments to refine behavior without altering the core logic:
Too many arc flips : Increase Start Distance to place the arc further from momentum on each reset, requiring more sustained momentum displacement before a flip registers, or increase Momentum Smoothing to reduce the noise feeding into the arc engine.
Arc flips too infrequent : Decrease Start Distance toward 2.0 for a tighter initial arc offset, or increase Arc Speed so the arc converges faster and becomes more sensitive to momentum reversals as the trend matures.
Arc converges too slowly : Increase Arc Speed to accelerate the rate at which the arc closes the gap to momentum, producing earlier flip sensitivity during sustained trend conditions.
Arc converges too quickly : Decrease Arc Speed toward 0.02 to slow convergence, maintaining a wider arc-to-momentum gap for longer and requiring more significant momentum deterioration before a flip becomes possible.
Momentum line too noisy : Increase Momentum Smoothing toward 20 for a cleaner momentum line with less bar-to-bar reactivity, or increase RSI Length for a slower underlying momentum source.
Extreme highlights activating too often : Increase Extreme Threshold toward 40 or 50 to restrict background tinting to only the most significant momentum extension events.
Too many divergence signals : Increase Pivot Left and Right values to demand more structurally significant pivot confirmations, or increase the minimum pivot distance to prevent closely-spaced pivots from qualifying for divergence comparison.
Adjustments should be incremental and evaluated across multiple session types rather than isolated market conditions.
Performance Characteristics
High Effectiveness:
Trending markets where momentum builds and sustains in one direction, allowing arc acceleration to develop meaningful convergence before the next flip
Instruments with consistent RSI behavior where centered momentum reliably reflects directional conviction and the standard deviation unit stabilizes quickly after initialization
Momentum continuation strategies using arc flip signals as regime change confirmations rather than as precise entry timing tools
Divergence-aware reversal approaches where oscillator structure disagrees with price structure ahead of trend changes, providing anticipatory context for flip signals
Reduced Effectiveness:
Choppy, low-momentum markets where rapid alternation between bullish and bearish RSI conditions produces frequent arc flips without sustained directional follow-through
Extremely low-volatility environments where momentum standard deviation collapses and the adaptive unit loses its discriminative power for scaling arc behavior
News-driven or gap-heavy instruments where sudden momentum spikes trigger arc flips that immediately reverse before the acceleration cycle develops meaningful convergence
Markets with inconsistent RSI dynamics where the centered momentum line fails to produce clean pivot structure, reducing divergence detection reliability
Consolidation and sideways conditions where momentum oscillates near zero without sufficient directional displacement to sustain arc trend states or produce clean flip sequences
Integration Guidelines
Confluence : Combine with BOSWaves structural tools, volume analysis, or adaptive band indicators to validate arc flip signals with price structure and participation context
Arc Convergence Awareness : Monitor arc-to-momentum distance as a real-time trend maturity indicator; tightly converged arc conditions suggest a flip requires minimal further momentum deterioration and warrant increased vigilance
Divergence Respect : Treat divergence signals as context-building evidence rather than standalone triggers; their value increases when they precede an arc flip in the divergence direction rather than occurring in isolation
Extreme Zone Caution : Avoid initiating new positions in the direction of extreme momentum readings; use extreme zone detection as a signal to monitor for arc convergence and potential flip conditions rather than as a continuation entry context
State Discipline : Maintain directional bias aligned with the current arc trend state until a confirmed flip occurs. Short-term momentum oscillations toward zero that do not breach the arc with sufficient buffer do not constitute trend changes.
Disclaimer
Arc Momentum Oscillator is a professional-grade momentum analysis and divergence detection tool. It uses acceleration-driven arc mechanics with volatility-normalized momentum measurement but does not predict future price movements. Results depend on market conditions, instrument momentum characteristics, parameter selection, and disciplined execution. BOSWaves recommends deploying this indicator within a broader analytical framework that incorporates price structure, volume context, and comprehensive risk management. Indicator
Indicator
Indicator
Indicator
Dynamic Time Warped Stochastic. Arc-Lenght Stochastic Oscillator𝐓𝐇𝐄 𝐀𝐑𝐂-𝐊𝐈𝐍𝐄𝐌𝐀𝐓𝐈𝐂 𝐎𝐒𝐂𝐈𝐋𝐋𝐀𝐓𝐎𝐑: 𝐁𝐞𝐲𝐨𝐧𝐝 𝐂𝐡𝐫𝐨𝐧𝐨𝐥𝐨𝐠𝐢𝐜𝐚𝐥 𝐓𝐢𝐦𝐞OverviewFor decades, the standard approach to measuring market momentum has relied on a fatal flaw: chronological time. Traditional oscillators force a rigid, fixed lookback window onto an environment that is fundamentally non-linear. When volatility compresses, chronological indicators generate false signals from microscopic chop. When volatility expands, they lag.This tool abandons chronological time. Instead, it processes market structure through a topological lens, measuring the actual path traversed by price action rather than the arbitrary ticking of a clock. It is an exploration of market kinematics designed to filter out spurious noise and identify true regime shifts.Under the Hood: The Mechanics of Topological Data AnalysisThis is not a smoothed RSI or a standard stochastic oscillator. The underlying engine operates on principles derived from digital signal processing and econophysics.Time-Dilated Lookback: Instead of a fixed N-period lookback, the calculation matrix expands and contracts dynamically. It measures the geometric arc-length (ds) of the price vector. During tight, sideways consolidation, the window stretches to absorb the noise. During explosive expansion, the window shrinks, snapping to the current regime with near-zero lag.Kinematic Tokenization: The script analyzes the primary and secondary derivatives of price to understand the velocity and acceleration of the order flow, separating true institutional displacement from retail trap-moves.Advanced Signal Processing: To present this data cleanly, the raw kinematic output is passed through a multi-pole Gaussian filtration system and a non-linear bounding transform. This creates a hyper-responsive oscillator that maps purely between +1 and -1 remaining completely flat during untradeable chop and only oscillating when true structural momentum is present.
How to Use This Tool
This indicator does not predict the future. It categorizes the present.Regime Identification: Observe the boundaries. When the oscillator is pinned at the extremes, the market is in a dominant, unidirectional regime. Divergence against Time: Because the tool operates outside of chronological time, standard divergences (price making a higher high while the oscillator makes a lower high) carry significantly higher structural weight than those found on traditional indicators.Confluence: Use this as a confirming layer. It is designed to validate structural market shifts, identifying when a swept level or a break in market structure has the underlying mathematical velocity to sustain a new trend.DisclaimerThis script is published strictly for educational and analytical purposes. It is a mathematical exploration of market topology and signal processing. It does not constitute financial advice, nor does it guarantee profitable trades. Past performance of mathematical models does not guarantee future market behavior. Always use strict risk management. Indicator
Indicator
WaveTrend XWaveTrend X (WVTX)
A WaveTrend rebuild from first principles. Where the classic WaveTrend ( LazyBear ) normalizes its Channel Index by an arbitrary 0.015 constant inherited from CCI and smooths the result with double exponential moving averages, WVTX replaces both with components that adapt to the data:
The CI normalization uses rolling standard deviation instead of 0.015.
The smoothing uses a 1-D Kalman filter with ATR-adaptive process noise instead of double-EMA.
The fixed ±60 / ±53 OB/OS bands become adaptive rolling-percentile bands.
Signals are gated by a Hurst regime proxy (mean-reversion vs trending) and a Shannon entropy filter (noise suppression).
Liquidity quality scales line opacity so low-volume readings visually fade.
All markers fire only on confirmed bars — explicitly no-repaint.
The default channel and signal periods are tuned for slower, more structural use than classic WaveTrend — see the Notes section for how to revert to classic intraday WT speeds.
How it works
The oscillator is built in five layers:
1. Channel Index (std-normalized). The standard WaveTrend CI is price's deviation from a moving average, normalized by a hard-coded constant. WVTX uses:
CI = (src − anchor) / stdev × 50
The anchor is VWMA (default) or EMA over the channel period. The stdev is computed over the same window. The constant disappears — the oscillator's amplitude now reflects actual recent volatility, not a one-size-fits-all multiplier.
2. Kalman smoothing. A 1-D Kalman filter replaces the double-EMA that produces the WT1 line in classic WaveTrend. The filter balances two tunable noise terms:
Process noise Q — how much you trust the CI to change between bars. Higher = faster, less smooth.
Measurement noise R — how much measurement error you assume. Higher = smoother, more lag.
With ATR-adaptive Q enabled (default), Q scales with normalized ATR — so the filter responds faster in volatile conditions and smooths harder in calm ones. This addresses classic WaveTrend's weakness of being either too slow during impulsive moves or too noisy during chop, never both right at once.
WT2 is then a longer EMA of WT1 (the signal line, classic WaveTrend convention).
3. Hurst regime proxy. A two-window standard-deviation ratio approximates the Hurst exponent:
H ≈ log₂(stdev(src, n) / stdev(src, n/2))
H > 0.5 = trending (volatility grows faster than √n); H < 0.5 = mean-reverting; H ≈ 0.5 = random walk. The configurable dead-band around 0.5 prevents constant regime-flipping near the boundary.
Signals are gated accordingly: in trending regimes, both cross directions are allowed; in mean-reverting regimes, signals only fire from the extremes (WT1 in the bottom or top 30%) where reversion probability is structurally elevated.
4. Shannon entropy noise gate. The recent return distribution is binned into 8 equal-width bins, and Shannon entropy computed:
H = −Σ pᵢ · log(pᵢ) / log(N_bins)
Bounded . Low entropy = concentrated return distribution (structured market); high entropy = uniform distribution (random/noise). When entropy exceeds the configured maximum (default 0.72), signals are suppressed — the oscillator stops emitting markers in conditions where the return statistics suggest no exploitable structure.
5. Adaptive OB/OS. Instead of fixed thresholds, OB and OS bands are the configurable percentiles (default 82% and 18%) of WT1's distribution over a rolling window. The bands move as the oscillator's range shifts — useful for instruments whose volatility regime changes substantially over time.
How to read it
Three lines and an area:
WT1 (main oscillator) — colored by its rolling percentile rank. Cool teal at the high extreme, slate near the middle, coral at the low extreme.
WT2 (signal line) — light slate, follows WT1 with EMA lag.
Diff = WT1 − WT2 (area) — teal area above zero, coral below. Visualizes the momentum between WT1 and the signal line.
Adaptive OB and OS bands are colored by direction (coral above, teal below), with tinted fills extending to the ±50 boundaries.
Background regime tint:
Teal — trending regime (Hurst above the band)
Purple — mean-reverting regime (Hurst below)
No tint — neutral / random walk
Entropy trace (amber, optional) — recentered around zero and scaled for visibility in the same pane. Above zero = recent returns more random than baseline; below zero = more structured.
Signal markers:
Teal triangle below pane — confirmed bullish cross (WT1 crosses up WT2, entropy structured, regime allows, WT1 in lower half)
Coral triangle above pane — confirmed bearish cross (same gates, opposite direction, WT1 in upper half)
Markers fire only on closed bars. There is no intra-bar signal display, by design.
Inputs
Price source — default hlc3.
Channel period — lookback for the VWMA/EMA anchor and stdev normalization. Default 40.
Signal period — EMA period for the WT2 signal line. Default 16.
VWMA anchor — use VWMA instead of EMA as the price anchor. Default on.
Kalman Q / R — process and measurement noise. Defaults 0.02 / 0.8.
ATR-adaptive Q — scale Q with normalized ATR. Default on.
Hurst regime gate / window / band — toggle, window, dead-band around 0.5. Defaults on / 60 / ±0.05.
Entropy noise gate / window / max — toggle, window, entropy ceiling. Defaults on / 40 / 0.72.
Liquidity quality scale / window — fade line opacity by recent volume rank. Defaults on / 50.
OB/OS rank window / overbought / oversold percentile — sets the adaptive bands. Defaults 100 / 82% / 18%.
Regime background / entropy trace — display toggles.
Built-in alerts
Bull cross — confirmed bullish crossover, gates passed
Bear cross — confirmed bearish crossover, gates passed
Bull cross, deep OS — bull cross from below the 20th percentile (high-quality setup)
Bear cross, deep OB — bear cross from above the 80th percentile (high-quality setup)
Trending regime — Hurst transitions into the trending zone
Mean-rev regime — Hurst transitions into the mean-reverting zone
The two "deep" alerts are the most actionable — a confirmed cross from the extremes of the rolling distribution is structurally more interesting than a cross near the middle.
Notes
The classic LazyBear WaveTrend uses channel period 10 and signal period 21 — fast, oscillator-style behavior suited to intraday momentum trading. WVTX's defaults are 40 and 16, four times slower. This is deliberate: with adaptive smoothing (Kalman + ATR), liquidity scaling, and entropy/regime gating layered on top, fast periods produce too many marginal signals for the gates to filter cleanly. Slower periods produce fewer but more structural crosses, which is where the gates earn their keep. If you want classic intraday WaveTrend behavior, set channel period to 10 and signal period to 21 — the math still works, just with more frequent (and noisier) output.
The Kalman smoother is the most consequential of the rebuilds. Classic WaveTrend's double-EMA introduces predictable lag that traders learn to anticipate; the Kalman filter's adaptive smoothing means the lag isn't constant — it tightens in fast moves and relaxes in chop. For mean-reversion traders, this means signal markers are closer to the actual reversal pivot. For trend-followers, it means the oscillator follows directional moves more responsively.
The Hurst regime gate makes the indicator behave differently in different markets. In a trending instrument (most major indices in their secular uptrends), the gate is permissive — crosses fire freely. In a mean-reverting instrument (ranging FX pairs, sideways equities), the gate restricts crosses to the extremes of the rolling distribution, where reversion probability is highest. The dead-band around 0.5 prevents flickering when the regime is ambiguous.
The entropy gate is the most novel filter. It asks a different question from regime detection: not "what kind of market is this" but "is there any structure in the recent return distribution?" When entropy is high (close to 1.0), the distribution looks essentially uniform — no recurring pattern for an oscillator to exploit. The gate stops signals from firing in those conditions, which historically catches a lot of false WaveTrend crosses during news-driven chop.
The adaptive OB/OS bands are tuned for traders who switch between instruments. Fixed ±60/±53 levels work fine on a single instrument with stable volatility; they're systematically wrong on instruments whose volatility regime changes substantially. Percentile bands self-calibrate, so the same indicator settings work across SPY, BTC, and EURUSD without manual retuning.
The non-repaint design is enforced by barstate.isconfirmed — signals fire only when the bar has closed and won't be revised. Intra-bar, you may see WT1 approaching a cross; the marker only appears once the bar closes with the cross confirmed. The conservative choice: signals you can rely on for backtesting and live execution, at the cost of waiting until the bar ends.
This is a signal generator with multiple filtering layers — but a signal is information, not an instruction.
Five years of work on a trading system left me with dozens of indicators that ultimately didn't earn a place in the final build. They're not failures — they're tools that solved problems I no longer needed solved. So instead of shelving them, I'm publishing the majority of them open-source.
If you're a discretionary trader, take what's useful. If you're a systems builder, the source is yours to dissect, modify, and improve. The best return on five years of work is for it to keep working — for someone.
If you use this script — or part of it — in your own work, please credit the original with a link back to my profile.
Note: these indicators have been updated to Pine Script v6 — some manually, some with AI assistance. Indicator
Volume Weighted RSI PRO | AnonycryptousVolume Weighted RSI Pro | Anonycryptous
Description & user manual
Why this indicator is different
Standard RSI treats every bar equally. A bar where 100 lots traded and a bar where 100,000 lots traded produce the same RSI value if the price change was the same. That is a fundamental problem. Price moves on low volume are noise. Price moves on high volume are institutional participation. RSI cannot tell the difference.
Volume Weighted RSI Pro fixes this at the calculation level.
Instead of averaging raw price changes, every gain and loss is multiplied by its relative volume before entering the RSI calculation. A strong move on elevated volume pushes the oscillator harder than the same move on thin participation. A drift in price on below-average volume barely registers. The result is an RSI that reflects who was actually behind the move — not just that a move happened.
But volume-weighted RSI alone is one perspective. The indicator adds a second independent layer through the Money Flow Index — a separate oscillator that weights typical price by volume rather than price change. When both VW RSI and MFI agree on an extreme reading, the confluence is structurally different from either line alone. One can be driven by a single large candle. Two separate calculation methods arriving at the same conclusion is harder to dismiss.
And then there are the liquidity levels.
Most RSI-based indicators live in isolation from price structure. They show you when the oscillator is extended, but not where on the price chart that extension corresponds to meaningful levels. Volume Weighted RSI Pro draws the nearest swing highs and lows directly on the price chart — the levels where stops cluster, where institutions defend positions, and where reversals tend to form. Each level shows the volume ratio at the moment it was created. Each level disappears automatically when price closes through it, and the indicator immediately identifies the next relevant level below or above.
The divergence engine connects oscillator behavior with price structure by drawing lines, endpoint markers, and a highlighted box on the price chart that spans the exact bars involved. Not just a signal — a spatial map of what happened and where.
This is an RSI indicator that knows where it is on the chart.
Important notice
Volume Weighted RSI Pro generates signals based on oscillator behavior, volume analysis, and price structure detection.
These signals are not financial advice.
They do not predict future price movement.
They do not guarantee profitability.
All trading decisions are made entirely by the user.
Always manage your own risk. Always apply your own judgment.
1. Overview
Volume Weighted RSI Pro is a multi-layer oscillator built around a volume-weighted RSI calculation. It combines momentum analysis, divergence detection, volume flow confirmation, and liquidity level mapping in a single indicator across both the oscillator pane and the price chart.
What it includes:
- Volume weighted RSI using relative volume to scale price change contributions
- Money Flow Index as a second independent momentum layer
- EMA signal line with configurable length and crossover markers
- Divergence detection with RSI pane lines and mainchart box visualization
- Liquidity level lines on the price chart from confirmed swing highs and lows
- Volume ratio label per liquidity level showing institutional activity at formation
- Automatic level mitigation: levels disappear when price closes through them
- OB/OS zone boxes with per-zone volume intensity tracking
- Gradient glow fill between the RSI line and midline
- OB/OS exit signals and signal line crossover markers
- Bar coloring and mainchart background based on RSI position
- Live dashboard with RSI value, volume ratio, zone, divergence, signal, and liquidity levels
- Seven alerts covering divergence, exits, and signal line crossovers
2. Core calculation
2.1 Volume weighted RSI
Standard RSI sums gains and losses over a lookback period using Wilder's smoothing (RMA). Volume Weighted RSI Pro applies the same structure but multiplies each bar's price change by its relative volume — the ratio of bar volume to the smoothed volume average — before the RMA smoothing step.
A bar with twice the average volume contributes twice as much to the gain or loss accumulation. A bar with half the average volume contributes half as much. This means the oscillator level reflects participation quality, not just price displacement. In practical terms: trending moves on rising volume push the oscillator to extremes faster. Pullbacks on thin volume barely move it. This creates a cleaner, more institutionally-aware reading than standard RSI.
The volume weighted mode can be toggled off to revert to standard RSI behavior for direct comparison.
2.2 Standard RSI reference line
A standard RSI line is plotted as a secondary reference in the same pane. Its color and width are configurable. Divergence between the volume weighted and standard lines reveals moments where volume is distorting the picture — a large move on thin participation that standard RSI registers but the VW version largely ignores, or vice versa.
2.3 Signal line
An EMA of the volume weighted RSI value acts as a signal line, similar in concept to the signal line in a MACD. The default length is 9. When the VW RSI crosses above the signal line, momentum is accelerating to the upside. When it crosses below, momentum is decelerating. Crossover markers appear at the exact cross point and can be toggled on or off. The signal line is most useful as a filter — only take a setup if the RSI and signal line agree on direction.
2.4 Money Flow Index
The MFI is calculated using typical price ((high + low + close) / 3) multiplied by volume, producing separate positive and negative money flow sums that are then converted to an index between 0 and 100. It shares the same scale as the VW RSI, making direct visual comparison possible.
The MFI responds differently from VW RSI because it weights price level rather than price change. Elevated MFI without elevated VW RSI suggests buying pressure at current levels without strong directional momentum. Both indicators in overbought territory simultaneously is a stronger condition than either alone.
3. Divergence detection
Divergence is detected by comparing pivots in the volume weighted RSI against pivots in price over a configurable lookback window
Bearish divergence: price makes a higher high while VW RSI makes a lower high. Momentum is weakening as price extends — a structural warning.
Bullish divergence: price makes a lower low while VW RSI makes a higher low. Selling pressure is exhausting even as price continues lower — a structural opportunity.
Sensitivity controls the pivot lookback window:
- High: 3-bar pivots. More signals, more false positives.
- Medium: 5-bar pivots. Balanced default.
- Low: 10-bar pivots. Fewer signals, higher quality.
When a divergence confirms, two things are drawn simultaneously. In the RSI pane: a solid line connecting the two pivot RSI values. On the price chart: a box spanning the full price range of the bars involved in the divergence. This makes the spatial relationship between the oscillator event and the price structure immediately visible.
The divergence box does not confirm a trade. It confirms that a structural disagreement between price and momentum occurred, and where on the chart it happened.
4. Liquidity levels
Liquidity levels are drawn on the price chart at confirmed swing highs and lows using a pivot detection engine. They represent the price levels where stop orders are likely to cluster — below swing lows for buy stops and above swing highs for sell stops. These are the levels that institutional participants use as targets when running liquidity.
Each level is a horizontal line that starts at the pivot bar and extends to the right in real time. The nearest level is fully opaque. Additional levels fade with distance from current price.
Each level displays a volume ratio label at its origin — the bar's volume at the time of pivot formation relative to the recent average. A level formed on 2.1x average volume is more institutionally significant than one formed on 0.7x volume. This context is part of reading the level.
When price closes through a level, it is removed immediately. The indicator repopulates from the remaining valid pivots. There is no manual cleanup and no visual clutter from levels that have already been swept.
The detail level setting controls how aggressively levels are detected:
- Minimal: wide pivot lookback, only the most significant structural highs and lows qualify.
- Standard: balanced detection, practical default across most timeframes.
- Full: tighter pivot lookback, more levels are identified.
The dashboard shows the nearest bull side level (BSL) and bear side level (SSL) by price, updated in real time.
5. OB/OS zone tracking
When the VW RSI enters overbought or oversold territory, the indicator begins accumulating the total volume transacted during that period. When price exits the zone, a filled box is drawn over the duration of the zone on the RSI pane.
The box includes a volume intensity label showing how the average volume inside the zone compared to the baseline average. A zone with 1.8x average volume indicates elevated institutional activity during the extreme reading — the extension was not just price drift but active participation. A zone below 1.0x is thin and less meaningful.
OB/OS zones are off by default.
6. Visual guide
RSI pane elements:
- Bright green/red RSI line — volume weighted RSI, color intensity increases toward OB/OS extremes
- Grey reference line — standard RSI, configurable color and width
- Gold line — signal line (EMA of VW RSI)
- Purple line — MFI
- Gradient glow fill — color intensity increases from midline toward the RSI line, creating a visual depth effect that reflects how extended the oscillator is
- OB/OS background — deepens in red or green when RSI is in extreme territory
- ▲ marker — RSI exiting oversold territory
- ▼ marker — RSI exiting overbought territory
- ✕ marker — RSI/signal line crossover (when enabled)
- Divergence line — solid colored line between the two pivot RSI values
Mainchart elements:
- Horizontal lines — liquidity levels, color and opacity by distance from current price
- Volume label at origin — volume ratio at pivot formation bar
- Divergence box — spans the full price range of the divergence bars
- Background color — subtle green above RSI 50, subtle red below
- Bar coloring — gradient intensity based on RSI position
7. Dashboard reference
The dashboard is positioned bottom right by default and updates on every bar close.
VW RSI — current VW RSI value, colored by position.
Vol ratio — current bar volume relative to the smoothed average. Values above 1.5x are highlighted in gold.
Zone — current RSI zone: overbought, oversold, or neutral.
Divergence — active divergence state if detected on the most recent pivot.
Signal — most recent signal condition.
— Liquidity —
Near BSL — nearest bull side liquidity level below current price.
Near SSL — nearest sell side liquidity level above current price.
Liq levels — count of active levels on each side.
Mode — VW (volume weighted) or STD (standard RSI mode).
Anonycryptous — indicator brand and version.
8. Alerts
Seven alert conditions are available:
- Bullish divergence: price lower low with VW RSI higher low confirmed.
- Bearish divergence: price higher high with VW RSI lower high confirmed.
- OS exit signal: RSI crosses back above the oversold level.
- OB exit signal: RSI crosses back below the overbought level.
- Any divergence: fires on either divergence type.
- Signal cross up: VW RSI crosses above the signal line.
- Signal cross down: VW RSI crosses below the signal line.
9. Settings reference
9.1 RSI settings
- RSI length: lookback period for the VW RSI calculation. Default 14.
- Volume smoothing: lookback for the volume moving average. Default 14.
- Volume weighted mode: toggle between volume weighted and standard RSI.
- Show signal line: toggle the EMA signal line.
- Signal line length: EMA period for the signal line. Default 9.
- Show signal crossovers: toggle ✕ markers at signal line crossovers. Default off.
- Show MFI line: toggle the Money Flow Index line.
- MFI length: lookback for MFI calculation. Default 14.
- MFI color: default brand purple.
- MFI line width: 1 to 4. Default 2.
- Overbought level: threshold for OB signals and zone tracking. Default 70.
- Oversold level: threshold for OS signals and zone tracking. Default 30.
9.2 Divergence
- Sensitivity: pivot lookback window — high (3), medium (5), low (10).
- Show bullish divergence.
- Show bearish divergence.
- Divergence box on mainchart: draws the price range box on the price chart.
- Div line width: stroke weight of divergence lines. 1 to 4. Default 2.
9.3 Liquidity levels
- Show liquidity levels: toggle all liquidity lines on the price chart.
- Detail level: minimal, standard, or full pivot sensitivity.
- Pivot lookback: swing detection window. Default 10.
- Max levels each side: maximum lines shown above and below current price. Default 2.
9.4 OB/OS zones
- Show OB/OS zones: toggle zone boxes in the RSI pane. Default off.
- Volume intensity label: show per-zone volume ratio label.
- Zone transparency: fill opacity for OB/OS zone boxes.
9.5 Visuals
- Bull color: primary bull color across all elements.
- Bear color: primary bear color across all elements.
- Bull div color: color for bullish divergence lines and box.
- Bear div color: color for bearish divergence lines and box.
- Std RSI color: color of the standard RSI reference line.
- Std RSI width: stroke weight of the reference line.
- Bar coloring: gradient bar color based on RSI position.
- Signal size: size of OB/OS exit markers — tiny, small, or normal.
- Show background color: subtle mainchart background based on RSI direction.
- Background transparency: opacity of the mainchart background.
9.6 Dashboard
- Show dashboard.
- Position: top left, top right, bottom left, or bottom right.
- Size: tiny, small, or normal.
10. How to use
10.1 Reading divergence
Divergence is not a signal to enter immediately. It is a warning that the relationship between momentum and price is breaking down. The most effective approach is to wait for the divergence box to appear on the price chart and then look for a second confirmation — a signal line crossover, an OB/OS exit marker, or a price reaction at a nearby liquidity level — before treating the setup as actionable.
Divergence on its own can persist for many bars before price reacts. Use it as directional context, not as a trigger.
10.2 Using liquidity levels
The liquidity lines show where the market has unfinished business — swing levels that formed on meaningful volume and have not yet been revisited. When the VW RSI is approaching overbought or showing bearish divergence and price is simultaneously approaching a sell-side liquidity level above, those two conditions are pointing at the same structural event from different angles.
The volume ratio label at each level is particularly useful. A level formed on 0.6x average volume is a weak level that may not generate a meaningful reaction. A level formed on 2.5x average volume suggests a move was initiated or defended with institutional size. Treat these differently.
10.3 Using VW RSI and MFI together
When both lines are in overbought territory simultaneously, the condition is stronger than either alone. VW RSI is extended on momentum. MFI confirms that money flow at current price levels is also elevated. The two calculations are independent — their agreement is not trivial.
When they diverge — VW RSI overbought while MFI is not — one of the components is not confirming the other. This does not mean the move is wrong, but the confluence is weaker.
10.4 OB/OS zone volume
When the zone volume label shows above 1.5x, the extreme RSI reading occurred during elevated participation. That tells you the extension was not just mechanical drift — there was active buying or selling pressure behind it. An exit from that zone after a high-volume OB/OS period carries more weight than an exit from a thin zone.
10.5 Illustrative bull scenario
Educational example only. Not a trading recommendation.
VW RSI drops into oversold on above-average volume. A bull-side liquidity level sits 0.8% below current price, formed three sessions ago on 2.1x volume. VW RSI begins making a higher low while price makes a lower low — bullish divergence is confirmed. A divergence box appears on the price chart. The RSI crosses back above the oversold level, firing a ▲ marker. The signal line crossover fires shortly after. Three separate conditions align: oversold exit, bullish divergence, and signal line confirmation.
10.6 Illustrative bear scenario
Educational example only. Not a trading recommendation.
Price rallies into a sell-side liquidity level visible on the chart at 2.2x formation volume. VW RSI is in overbought territory while MFI is also elevated. Price makes a higher high but VW RSI makes a lower high — bearish divergence is drawn on the RSI pane and a box appears on the price chart covering the divergence range. RSI crosses back below overbought. A ▼ marker fires. The setup has divergence, OB exit, and a liquidity level all at the same location.
11. Tips
The volume ratio in the dashboard is one of the most underused readings. A vol ratio below 0.7 means current price action is thin — institutions are not participating. Signals that fire on low volume ratio are less reliable than those that fire on 1.5x or above.
Signal line crossovers are most useful as filters. Toggle them on during active sessions to see where momentum flips are occurring relative to the rest of the setup. Too many crossovers on a given session usually means the market is ranging — reduce position size or wait for the RSI to expand toward an extreme before taking the cross seriously.
The standard RSI reference line reveals when volume weighting is changing the picture. If the VW RSI is significantly above the standard line, it means recent price movement was driven by above-average volume. If the VW RSI is below the standard line, price moved on thin participation — the market did not commit to the direction.
Liquidity level count in the dashboard tells you how many valid structural references remain. When the count drops — because levels are being swept — it means the market is clearing stops. That is meaningful context for the direction of the current move.
12. Disclaimer
This indicator is provided for educational and informational purposes only. Nothing in this document or in the indicator output constitutes financial advice or any form of recommendation. Trading financial instruments involves substantial risk of loss. Past performance is not indicative of future results. You may lose all of your invested capital.
Anonycryptous accepts no responsibility or liability for any losses incurred as a result of using this indicator. Indicator
Indicator
[3Commas] Pullback Sniper - Indicator🔷 What it does:
This indicator visualizes a long-only Dollar-Cost-Averaging signal framework driven by a classic RSI oversold-exit trigger on a 3-minute timeframe. It marks base order entries, projects the full safety order ladder, tracks a virtual deal lifecycle on the chart, and exposes webhook-ready alerts for automated execution through an external DCA Bot. No orders are placed by the indicator itself — it is a pure signal and visualization layer.
Base Order signal: RSI(14) crosses below 31 on 3m
Safety Order ladder: 5 levels projected as horizontal lines from base, with filled-state coloring (martingale ×1.30 deviation, ×1.25 volume)
Exit signal: RSI(14) crosses above 69 AND profit ≥ 2.4% from average entry (dual-gate)
On-chart virtual P&L tracker: Net Profit, Max Drawdown, Trades, Win Rate, Profit Factor
🔷 Who is it for:
Bot operators who need a visual confirmation layer for automated intraday DCA execution.
Discretionary traders who use structured averaging logic manually and want clear on-chart triggers.
Free-tier PulseWire users who want access to the same signal logic as the Strategy version without requiring backtest functionality.
Cross-instrument testers who run the same signal framework across crypto, perpetuals, and tokenized equities.
🔷 How does it work:
Long Entry: A base order signal fires when RSI(14, 3m) crosses below 31. The indicator marks this with a green triangle below the bar and stores the base price as the reference for the safety order ladder.
Short Entry: Not used - long-only signal framework by design.
Exit Management: A take-profit signal fires when two conditions align simultaneously - RSI(14, 3m) crosses above 69 AND the unrealized profit from average entry reaches the minimum threshold (default 2.4%). This dual gate prevents premature exits during shallow rebounds and structural exits when profit has not yet recovered. The indicator marks the exit with a cyan diamond and resets the virtual deal state.
🔷 Why it's unique:
Dual-gate exit logic - closing requires both an RSI sell signal AND a minimum profit threshold from average entry. This combination filters out RSI-triggered exits that would close deep-averaged positions at a loss, ensuring every signaled close is structurally sound.
Cumulative martingale architecture - safety order trigger prices are computed from the base entry using a cumulative compounding deviation (each successive step multiplied by ×1.30), not by chaining from the previous SO. This produces a smoother, more controlled deepening of the ladder versus pure linear stepping. The full ladder is projected on chart from the moment of base entry, so traders see the entire risk envelope upfront.
Bot Integration - entry and exit alerts ship with webhook-ready JSON payloads, enabling direct trigger of a connected DCA Bot. Two alert conditions ("Deal Start" and "Deal Close") map cleanly to bot deal-start and close-deal endpoints, with Bot ID, Email Token, and pair label all configurable through inputs.
🔷 What you'll see on the chart:
Green triangle (below bar) - Base Order entry signal fired
Red triangle (above bar) - RSI sell signal fired
Cyan diamond - Dual-gate exit triggered, virtual deal closed
Cyan line - Base Entry price (reference for the SO ladder)
Yellow line - Average Entry price (recalculates as SOs fill)
Lime line - Take Profit target (Average × (1 + minProfit%))
Red lines (5) - Full SO ladder projected from base; fades as each level fills
Stats card (top-left, configurable) - Live virtual backtest results: Net Profit, Max Drawdown, Total Trades, Win Rate, Profit Factor
🔷 Considerations Before Using the Indicator:
Market & Timeframe: Designed for a 3-minute base chart on liquid, volatile instruments with active intraday RSI cycling - perpetual contracts, tokenized equities, and major altcoins. Performance degrades on low-volatility assets where RSI rarely reaches the oversold trigger.
Limitations: The indicator does not place orders. It tracks a "virtual deal" state on the chart for visualization purposes only — actual execution must be performed through a connected bot or manually. The signal framework carries no stop-loss component; in sustained downtrends extending beyond the deepest safety order, the virtual deal holds unrealized loss until either the average is recovered or the alert flow is manually overridden. The dual-gate exit can hold positions for extended periods if RSI never crosses above 69 while the profit threshold is unmet — pair this with a manual review cadence.
Virtual P&L Accuracy: The on-chart stats card uses a simplified internal accounting model - it does not factor exchange commission or slippage. Use the Strategy version for fee-adjusted backtest results.
Backtesting & Demo Testing: Always validate the signal framework on historical data before connecting to a live bot. The companion Strategy version of this script is available on the same profile for full backtest analysis with realistic commissions and slippage. Demo-trade for at least one month to observe behavior in conditions not represented in historical data. Past performance is not indicative of future results.
Parameter Adjustments: RSI thresholds (31 / 69) and the SO step (1.3%) should be tuned per instrument volatility profile. Tighter levels on lower-volatility assets, wider on more volatile pairs. The minimum profit threshold can be lowered to 1.5–2.0% for higher trade frequency or raised to 3.0–4.0% for fewer, larger captures.
🔷 Backtest Validation:
This indicator shares identical signal logic with the Strategy version of the same framework, available on this profile for full historical performance review with realistic commission and slippage:
Strategy version:
Reference results from the Strategy version on BYBIT:ADAUSDT (Spot), 3m chart, tested period Feb 9 — May 11, 2026:
Net Profit: +95.00 USDT (+0.95%) | Max Drawdown: 67.16 USDT (0.67%) | Total Trades: 47 | Win Rate: 72.34% (34/47) | Profit Factor: 7.377
Refer to the Strategy publication for the complete equity curve, trade-by-trade breakdown, and Strategy Tester report.
🔷 How to Use It:
🔸 Adjust Settings: Configure the RSI thresholds, SO step, and minimum profit parameters for the instrument you intend to trade. Defaults are calibrated for liquid altcoin spot pairs on a 3-minute chart. For lower-volatility instruments, tighten the SO step and lower the take-profit threshold; for higher-volatility instruments, do the opposite.
🔸 Visual Confirmation: Use the on-chart projections (base entry, SO ladder, average entry, TP target) to verify that the active virtual deal aligns with your bot's actual position. The indicator's virtual deal state is a 1-to-1 mirror of the Strategy version's signal logic, so any divergence between chart visuals and bot position is a flag for investigation.
🔸 Create alerts to trigger the DCA Bot: Two alert events are exposed by the indicator - "Deal Start" fires on each new base order signal, and "Deal Close" fires when the dual-gate exit triggers. Configure both alerts in PulseWire with the webhook URL pointing to your DCA Bot's signal endpoint. The Bot ID, Email Token, and Pair label can be set in the script's inputs and are automatically embedded into the alert JSON payload. Once configured, the indicator publishes the signal and the bot handles execution on the exchange autonomously.
🔷 INDICATOR SETTINGS
RSI Length - Period for the RSI calculation (default 14).
RSI Buy Trigger (cross below) - Oversold threshold for the base order signal (default 31).
RSI Sell Trigger (cross above) - Overbought threshold for the exit signal (default 69).
Base Order Volume (USDT, ref) - Reference notional for the initial entry; used for Average Entry projection only.
Safety Order Volume (USDT, ref) - Reference notional for the first averaging-down order; subsequent SOs scale by Volume Coefficient.
Max Safety Orders - Total number of averaging steps tracked in the virtual deal.
Price Deviation % (SO step from base) - Percentage deviation for the first safety order; subsequent steps scale by Step Coefficient.
Martingale Volume Coefficient - Size multiplier applied to each successive safety order.
Martingale Step Coefficient - Multiplier applied to each successive deviation step.
Min Profit % (from avg entry) - Minimum unrealized profit threshold required for the exit gate.
Require RSI Sell Signal to Close - When enabled, both the RSI sell crossover AND the min profit threshold must align (dual-gate exit).
Initial Capital (ref for % calc) - Reference capital base for computing return and drawdown percentages in the stats card.
Visual Layer toggles - Show/hide base line, average line, TP line, full SO ladder, signal triangles.
Stats card / Watermark - Display layer controls for on-chart virtual backtest summary and branding.
Webhook - Bot ID, Email Token, and Pair label for DCA Bot signal routing.
👨🏻💻💭 We hope this tool helps enhance your trading. Your feedback is invaluable, so feel free to share any suggestions for improvements or new features you'd like to see implemented.
__
The information and publications within the 3Commas PulseWire account are not meant to be and do not constitute financial, investment, trading, or other types of advice or recommendations supplied or endorsed by 3Commas and any of the parties acting on behalf of 3Commas, including its employees, contractors, ambassadors, etc. Indicator
[3Commas] Pullback Sniper Strategy🔷 What it does:
This strategy executes a long-only Dollar-Cost-Averaging approach driven by a single classic RSI signal on a 3-minute timeframe. It opens a base order when RSI(14) crosses below an oversold threshold, layers a martingale-scaled safety order ladder at predefined cumulative price deviations from the base, and exits the full position when RSI crosses back above an overbought threshold combined with a minimum profit gate from the average entry. The design is built for high-frequency intraday DCA on volatile liquid instruments — clean signal logic, structured averaging, no stop-loss dependency.
Base Order entry: RSI(14) crosses below 31 on 3m
Safety Order ladder: 5 levels, martingale volume scale ×1.25, deviation scale ×1.30
Exit: RSI(14) crosses above 69 on 3m AND profit ≥ 2.4% from average entry
Cumulative deviation mode (deviations measured from BASE price)
🔷 Who is it for:
Intraday DCA traders who prefer aggressive averaging with tight take-profit cycles.
Bot operators who run automated execution through webhook integration with a DCA Bot.
Discretionary traders who want a single-signal framework that is easy to backtest and tune.
Cross-instrument testers who want one signal layer portable across crypto, perpetuals, and tokenized equities.
🔷 How does it work:
Long Entry: A base order opens when RSI(14, 3m) crosses below 31, signaling entry into an oversold regime. The crossover is a single-shot confirmation per cycle to prevent re-entries during the same RSI sweep.
Short Entry: Not used — long-only by design.
Exit Management: The full position closes when two conditions align — RSI(14, 3m) crosses above 69 (overbought exit signal) AND the unrealized profit from average entry reaches the configured minimum threshold (default 2.4%). This dual gate prevents premature exits during shallow rebounds and structural exits when profit has not yet recovered.
🔷 Why it's unique:
Dual-gate exit logic — closing requires both an RSI sell signal AND a minimum profit threshold from average entry. This combination filters out RSI-triggered exits that would close deep-averaged positions at a loss, ensuring every deal closes only when both the signal and the math agree.
Cumulative martingale architecture — safety order trigger prices are computed from the base entry using a cumulative compounding deviation (each successive step multiplied by ×1.30), not by chaining from the previous SO. This produces a smoother, more controlled deepening of the ladder versus pure linear stepping.
Bot Integration — entry and exit alerts ship with webhook-ready JSON payloads, enabling direct trigger of a connected DCA Bot. The strategy publishes the signal, the bot handles order routing on the exchange.
🔷 Considerations Before Using the Indicator:
Market & Timeframe: Designed for a 3-minute base chart on liquid, volatile instruments with active intraday RSI cycling — perpetual contracts, tokenized equities, and major altcoins. Performance degrades on low-volatility assets where RSI rarely reaches the oversold trigger or where 3-minute candles are illiquid.
Limitations: The strategy carries no stop loss. In sustained downtrends extending beyond the deepest safety order, the position holds unrealized loss until either the average is recovered or the deal is closed manually. Consider adding a regime filter (e.g., long-term moving average direction) for deployment in non-trending or sustained bearish markets. The dual-gate exit can hold positions for extended periods if RSI never crosses above 69 while profit threshold is unmet — pair this with a manual review cadence.
Backtesting & Demo Testing: Always run extended backtests across multiple market regimes — uptrend, downtrend, and ranging — before deploying real capital. Demo-trade for at least one month to observe behavior in conditions not represented in historical data. Past performance is not indicative of future results.
Parameter Adjustments: Default commission and slippage values are calibrated for typical perpetual venues. Adjust commission_value to match your exchange (~0.05 for OKX Perpetual, ~0.055 for Bybit Perpetual, ~0.04 for Binance USD-M Futures, ~0.10 for Binance Spot, ~0.40 for Kraken Spot). Slippage of 5 ticks accounts for taker execution on liquid pairs — reduce for very tight markets, increase for thin order books. RSI thresholds (31 / 69) and the SO step (1.3%) should be tuned per instrument volatility profile.
🔷 STRATEGY PROPERTIES
Symbol: Cross-instrument — tested on Cardano/Tether (BYBIT:ADAUSDT Spot) on 3m. Logic is identical for any liquid pair.
Timeframe: 3m chart.
Test Period: Feb 16, 2026 — May 11, 2026 (≈3 months of recent active trading).
Initial Capital: 10,000 USDT.
Order Size per Trade: Base Order 45 USDT, Safety Orders 55 / 68.75 / 85.94 / 107.42 / 134.28 USDT (martingale ×1.25). Maximum cumulative position notional ≈ 496.39 USDT (≈ 5% of capital). Observed Max Drawdown 0.67% — far inside the ≤10% threshold.
Commission: 0.10% taker — neutral default; adjust per venue (see notes above).
Slippage: 5 ticks — typical taker execution on liquid pairs.
Margin for Long and Short Positions: 100% (1× leverage assumed; no margin amplification applied).
Indicator Settings: Default Configuration.
Base Order Volume: 45 USDT
Safety Order Volume (1st): 55 USDT
Max Safety Orders: 5
Price Step (1st SO): 1.3%
Step Coefficient: 1.30
Volume Coefficient: 1.25
Min Profit (from AVG): 2.4%
Entry: RSI(14, 3m) crossunder 31
Exit Signal: RSI(14, 3m) crossover 69 AND profit ≥ 2.4%
Strategy: Long Only.
🔷 STRATEGY RESULTS
⚠️ Remember, past results do not guarantee future performance.
Net Profit: +95.00 USDT (+0.95%)
Max Drawdown: 67.16 USDT (0.67%)
Total Closed Trades: 47
Percent Profitable: 72.34% (34 / 47)
Profit Factor: 7.377
Average Trade:
Average # Bars in Trades:
Backtest run on BYBIT:ADAUSDT (Spot) on 3m base chart. Re-run on your own venue with venue-specific commission and slippage before drawing conclusions for live deployment.
🔷 How to Use It:
🔸 Adjust Settings: Set Base Order and Safety Order volumes proportional to your account size. The default 45 / 55-USDT structure is calibrated for a 10,000-USDT test account, so total maximum exposure stays below 5% of capital. Scale linearly to your equity. RSI thresholds (31 / 69) and the SO step (1.3%) can be tightened on lower-volatility instruments or widened on more volatile pairs.
🔸 Results Review: Verify Maximum Drawdown stays within your personal risk budget. The strategy is configured for a very conservative position-size envelope, but extended history may shift the profile. Re-test on your own venue using venue-specific commission and slippage values. Demo-trade for at least one month before any live deployment.
🔸 Create alerts to trigger the DCA Bot: Two alert messages are exposed by the script — "Deal Start" fires on each new base order, and "Deal Close" fires when the dual-gate exit condition triggers. Configure both alerts in PulseWire with the webhook URL pointing to your DCA Bot's signal endpoint. Once configured, the strategy publishes the signal and the bot handles execution on the exchange autonomously.
🔷 INDICATOR SETTINGS
RSI Length — Period for the RSI calculation (default 14).
RSI Buy Trigger — Oversold threshold for the base order signal (default 31, cross-below).
RSI Sell Trigger — Overbought threshold for the exit signal (default 69, cross-above).
Base Order Volume (USDT) — Notional value of the initial entry per cycle.
Safety Order Volume (USDT) — Notional value of the first averaging-down order; subsequent SOs scale by Volume Coefficient.
Max Safety Orders — Total number of averaging steps available per deal.
Price Deviation % (SO step from base) — Percentage deviation from base price for the first safety order; subsequent steps scale by Step Coefficient.
Martingale Volume Coefficient — Size multiplier applied to each successive safety order.
Martingale Step Coefficient — Multiplier applied to each successive deviation step.
Min Profit % (from avg entry) — Minimum unrealized profit threshold required for the exit gate.
Require RSI Sell Signal to Close — When enabled, both the RSI sell crossover AND the min profit threshold must align for the position to close (dual-gate exit).
Limit by Date Range — Toggle to constrain backtest to a specific date window.
Stats card / Watermark — Display layer controls for on-chart backtest summary and branding.
Webhook — Bot ID, Email Token, and Pair label for DCA Bot signal routing.
👨🏻💻💭 We hope this tool helps enhance your trading. Your feedback is invaluable, so feel free to share any suggestions for improvements or new features you'd like to see implemented.
__
The information and publications within the 3Commas PulseWire account are not meant to be and do not constitute financial, investment, trading, or other types of advice or recommendations supplied or endorsed by 3Commas and any of the parties acting on behalf of 3Commas, including its employees, contractors, ambassadors, etc. Strategy
Indicator
Chosen Instrument Real Price Line - S.Grey v.1The Chosen Instrument Real Price Line indicator is designed to display the live price of any selected stock, index, futures contract, CFD, or custom PulseWire symbol directly on your chart as a dynamic horizontal reference level. Unlike traditional comparison tools that only display percentage relationships or secondary panels, this indicator projects the real-time price level of the chosen instrument directly onto the active chart, allowing traders to visually compare markets in real time from a single chart window.
The indicator continuously tracks the selected instrument and plots its current live price as a customizable horizontal line. The line can be styled with different colors, thicknesses, and extension modes, giving the trader full visual control over how the reference level appears on the chart. The line can extend to the right, left, both directions, or remain as a fixed-width segment. A separate toggle also allows the line itself to be hidden completely while still keeping the label visible at the correct price level.
The label system is designed specifically for real-time intermarket comparison and spread monitoring. The label can display the selected instrument name, its current live price, the live price difference between the chosen instrument and the current chart instrument, or both values simultaneously. This makes the indicator especially useful for traders who monitor the relationship between CFDs and their underlying base instruments.
One of the most practical applications of this tool is tracking the difference between CFD products and their corresponding futures or index markets in real time. For example, a trader using a broker CFD such as SPX500USD can compare it directly against the underlying SPX index or ES futures contract. The indicator instantly shows whether the CFD is trading above or below the base instrument and by how many points. This allows traders to monitor broker spreads, temporary pricing inefficiencies, overnight premium or discount conditions, and market synchronization across different instruments without switching charts.
Because the difference is updated live, the indicator can also be used as a spread-monitoring tool during volatile market conditions. Traders can quickly identify moments when a CFD deviates unusually far from the underlying futures contract, which may help in execution timing, spread analysis, or broker comparison. This is particularly useful during high-impact news events, session transitions, overnight trading, or periods of reduced liquidity.
The indicator is also highly flexible for broader intermarket analysis. Traders can compare futures against cash indexes, ETFs against futures, sector ETFs against indexes, or even different brokers’ CFD feeds against each other. Since the selected instrument is fully customizable using PulseWire symbols, virtually any supported market can be tracked as a live reference level.
The label itself is fully customizable and can be configured to display:
* only the real-time price,
* only the price difference versus the active chart,
* or both values together.
Importantly, the instrument name always remains visible regardless of the selected label mode, ensuring clear identification at all times.
This tool is particularly valuable for futures traders, CFD traders, index traders, arbitrage-oriented traders, and anyone monitoring the live relationship between correlated markets from a single chart environment.
Indicator
Indicator
Indicator
Strategy_470 - NY Open 3H Sweep ContinuationStrategy_470 is an intraday opening-session strategy designed to identify liquidity sweeps around the New York open and trade the potential continuation move that follows.
The model uses the previous 3-hour candle as the reference range. During the New York trading window, the strategy watches for price to sweep either the previous 3H high or low. After that sweep, it waits for a confirmation move through a recent swing level before entering.
For long setups, price must sweep below the previous 3H low and then reclaim strength by breaking above a recent swing high.
For short setups, price must sweep above the previous 3H high and then confirm weakness by breaking below a recent swing low.
The strategy includes a fixed risk structure:
Stop loss is placed beyond the sweep extreme.
Take profit is placed at the opposite side of the previous 3H range.
Optional break-even management is triggered once price reaches +1R.
The model can be limited to one trade per day.
Longs and shorts can be enabled or disabled independently.
This strategy is intended to study the behavior of index markets around the New York open, especially instruments such as QQQ, NASDAQ futures, NQ, MNQ, NAS100 or similar products.
It is not based on indicators. The logic focuses on time, liquidity, range structure and price reaction after a sweep.
Core concept:
Liquidity sweep → confirmation break → continuation toward the opposite side of the 3H range.
Recommended use:
5-minute chart, New York session, with session settings adjusted depending on the instrument. For QQQ, regular session testing may work better with a 09:30–10:30 New York window. For futures, 09:00–10:30 New York can be tested.
This script is for research, education and backtesting purposes. It should not be considered financial advice. Strategy
DSS Bressert - Trend, Valuation & Equity | Astral Vision DSS Bressert - Trend, Valuation & Equity | Astral Vision 🌠💠
The Double Smoothed Stochastic, developed by William Bressert, applies a double layer of EMA smoothing to a standard stochastic oscillator, producing a signal that is significantly less noisy than raw stochastics while retaining sensitivity to momentum shifts.
The result oscillates between 0 and 100, with crossovers between the DSS line and its trigger EMA identifying trend changes at the selected resolution.
This indicator extends the standard DSS with three distinct analytical layers:
Trend mode colors by bull/bear regime established at each DSS/trigger crossover, persisting the signal until the next cross.
Extremes mode activates only at overbought and oversold zones, filtering the middle ground.
The Equity view simulates a long-only strategy that holds when the DSS is in a bullish regime and exits to cash otherwise, displaying a cumulative equity curve alongside the price chart, translating the signal's historical performance into a directly readable return series.
Calculation ⚙️
`xPreCalc = EMA(Stochastic(close, high, low, period), EMAlen)`
`DSS = EMA(Stochastic(xPreCalc, xPreCalc, xPreCalc, period), EMAlen)`
`Trigger = EMA(DSS, TriggerLen)`
A bull regime is established when DSS crosses above the trigger and persists until the next bearish cross. In No Repaint mode, crossover signals are shifted by one bar to prevent intrabar repainting. The equity curve compounds close-to-close returns only during confirmed bullish regime bars, starting from a user-defined year at a base value of 100.
Plots 📊
DSS line and trigger line in the indicator panel, colored by active mode and regime (Indicator view)
Overbought and oversold threshold lines (Indicator view)
Equity curve starting from the configured year (Equity view)
Candle coloring on the price chart by DSS regime in both views
Inputs 🎛️
`View`: Indicator (DSS oscillator) or Equity (cumulative strategy curve)
`Mode`: Trend (regime-persistent coloring) or Extremes (overbought/oversold zones only)
`No Repaint`: shifts crossover signals one bar forward to eliminate intrabar repainting (Trend mode only)
`Equity Start Year`: anchor year for equity curve initialization (default 2019)
`Period`: stochastic lookback for both smoothing stages (default 10)
`EMA Length`: smoothing applied to each stochastic stage (default 9)
`Trigger EMA`: EMA of the DSS line used as crossover signal (default 5)
`Overbought / Oversold`: threshold levels for Extremes mode (defaults 90 / 10)
`Resolution`: timeframe at which DSS is computed (default 60 minutes)
Colors 🎨
5 Astral Vision presets + custom override. Default: Paradiso.
In Trend mode, positive color applies during bullish regimes and negative during bearish. In Extremes mode, positive color activates at overbought and negative at oversold, with neutral between thresholds.
Purpose 🎯
Standard stochastic indicators generate excessive crossover noise, particularly on lower timeframes, making regime identification unreliable without significant manual filtering.
The double smoothing of the DSS reduces that noise substantially, but most implementations still present only a raw oscillator with no mechanism for translating the signal into a persistent regime read or a measurable performance track record.
This indicator addresses both gaps.
The Trend mode converts the DSS crossover into a persistent regime signal that remains active until explicitly invalidated, removing ambiguity about the current directional bias.
The Equity view goes further: rather than asking the trader to visually backtest the signal, it computes and displays the cumulative return of following the DSS regime directly on the chart, making the signal's historical value immediately quantifiable without any external tools.
The configurable resolution allows the same indicator to serve as both an intraday momentum tool and a macro trend filter depending on the selected timeframe.
Disclaimer ⭕️
It is not financial advice, not an investment recommendation, and not affiliated with any financial institution, research firm, or organization of any kind. All content is provided for educational and informational purposes only. Always conduct your own research before making any financial decision. Indicator
Neural Weight Oscillator (Zeiierman)█ Overview
The Neural Weight Oscillator (Zeiierman) is an adaptive multi-factor oscillator that combines structured decision-making with dynamic market learning.
The script analyzes three core market behaviors: Trend, Mean Reversion, and Momentum. Instead of treating these components equally, the oscillator uses the Best-Worst Method (BWM) to determine which market behavior should have the greatest influence under current market conditions.
An adaptive training layer then studies historical market reactions and gradually amplifies the features that have recently produced the strongest directional behavior.
The result is a hybrid oscillator that blends:
Human-defined market logic
Adaptive feature weighting
Multi-factor momentum analysis
Dynamic market learning
Unlike traditional oscillators that rely on static formulas, the Neural Weight Oscillator continuously adjusts its internal structure based on both trader-defined weighting preferences and changing market behavior.
█ How It Works
⚪ Market Structure Engine
The oscillator builds its analysis from three independent behavioral models: Trend, Mean Reversion, and Momentum.
The Trend component measures structural direction by comparing the fast EMA against the slow EMA, then adds the EMA slope to capture acceleration.
trendSpread = (emaFast - emaSlow) / atr
trendSlope = (emaFast - emaFast ) / atr
trendScore = normalize(trendSpread + trendSlope, -2.5, 2.5)
The Mean Reversion component measures stretched conditions using RSI exhaustion and statistical deviation from the market mean.
zScore = dev == 0 ? 0 : (close - basis) / dev
meanScore = (100 - rsi) * 0.5 + normalize(-zScore, -2.5, 2.5) * 0.5
The Momentum component measures directional acceleration using ROC, RSI momentum, and EMA velocity.
rocNorm = normalize(close / close - 1.0, -0.05, 0.05)
momentumScore = rocNorm * 0.45 + rsi * 0.35 + emaMomentum * 0.20
Each component produces its own normalized score before being blended into the final oscillator.
⚪ Best-Worst Method (BWM)
The core weighting system in the oscillator is based on the Best-Worst Method (BWM), a structured decision-making framework that creates balanced weighting relationships among multiple factors.
bestIdx = criterionIndex(bestCriterion)
worstIdx = criterionIndex(worstCriterion)
array.set(bo, bestIdx, 1.0)
array.set(ow, worstIdx, 1.0)
Instead of assigning arbitrary percentages manually, BWM allows the trader to define which market behavior matters most and which matters least. The script then automatically calculates balanced internal weights.
The process begins by selecting:
The “Best” factor → the market behavior trusted most
The “Worst” factor → the market behavior trusted least
relWeight = math.sqrt((aBW / boVal) * owVal)
The oscillator then compares all remaining factors relative to those two extremes and converts those relationships into normalized internal weights.
⚪ How To Think About The BWM Weights
The easiest way to think about BWM is:
“What type of market behavior do I trust most in the current environment?”
Different market conditions naturally favor different behaviors.
In strong directional trends , traders often prioritize Trend because structural continuation becomes the dominant force.
In choppy or range-bound markets , Mean Reversion may become more important because the market repeatedly returns back toward equilibrium.
During aggressive breakout environments , Momentum may deserve the highest weighting because acceleration becomes the primary driver.
The goal is not to find a “perfect” weight configuration, but rather to align the oscillator with the type of behavior currently dominating the market.
⚪ Adaptive Neural Training Layer
The oscillator includes an adaptive learning layer that learns how the market has recently reacted to the model’s internal features.
The script looks back at prior Trend, Mean Reversion, and Momentum feature values, then compares them to the future price reaction.
target = close / close - 1.0
targetDirection = target > 0 ? 1.0 : target < 0 ? -1.0 : 0.0
High-quality samples are ranked by how strong the move was relative to volatility.
sampleScore = math.abs(target) / qualityVol
The model then compares its internal prediction against the actual market direction and adjusts the learned feature weights over time.
pred = twTrend * s.trend + twMean * s.mean + twMomentum * s.momentum + tbias
err = pred - s.target
This allows the oscillator to gradually learn which features are producing the strongest directional behavior.
⚪ Adaptive Feature Amplification
The learned weights are converted into feature amplifiers.
trendAmplifier = 1.0 + learnTrend * blend
meanAmplifier = 1.0 + learnMean * blend
momentumAmplifier = 1.0 + learnMomentum * blend
This allows stronger features to gain more influence, while weaker features receive less influence.
█ How to Use
⚪ Reading the Oscillator
The oscillator operates between 0 and 100.
Values above 50 suggest bullish pressure dominates the market, while values below 50 suggest bearish pressure dominates.
As the oscillator moves farther away from the neutral 50 level, directional imbalance becomes stronger.
Readings above 70 typically indicate strong bullish expansion, while readings below 30 indicate strong bearish pressure. Extreme zones above 80 or below 20 may signal exhaustion conditions where reversals become more likely.
⚪ Using the BWM Weighting System
The BWM system allows traders to align the oscillator with current market behavior by controlling how much influence Trend, Mean Reversion, and Momentum should have inside the model.
Imagine the market is trending strongly upward.
You may believe:
Trend is the dominant market behavior.
Mean Reversion still matters during pullbacks.
Momentum should have the least influence.
In this case, you could choose:
Best = Trend
Worst = Momentum
You then control how strongly Trend dominates the other factors through the comparison inputs.
For example:
Best-to-Others:
Trend = 1
Mean = 3
Mom = 6
Relative-to-Worst:
Trend = 4
Mean = 2
Mom = 1
This tells the oscillator:
Trend is selected as the strongest market behavior.
Momentum is selected as the weakest market behavior.
Trend is 3x more important than Mean Reversion.
Trend is 6x more important than Momentum.
Mean Reversion is 2x more important than Momentum.
The script automatically converts these relationships into balanced internal weights.
As a result, the oscillator becomes more trend-sensitive while reducing the influence of short-term momentum fluctuations and weak counter-trend behavior.
If the market becomes highly rotational or range-bound, traders may instead increase the importance of Mean Reversion so the oscillator becomes more responsive to exhaustion and reversal conditions.
During aggressive breakout environments, increasing Momentum weighting can help the oscillator react faster to acceleration phases.
The weighting system is designed to adapt the oscillator’s personality to different market environments rather than forcing one static interpretation onto every condition.
█ Settings
Fast EMA: controls the responsiveness of the Trend and Momentum calculations.
Slow EMA: controls the structural trend baseline used throughout the oscillator.
Smoothing: controls the smoothness of the final oscillator line.
The Best and Worst: determine how the BWM weighting model prioritizes market behaviors.
Best-to-Others: define how strongly the selected Best factor dominates the remaining components.
Relative-to-Worst: define how much stronger each component is compared to the selected Worst factor.
Use Training: enables the adaptive learning layer.
Influence: controls how strongly the learned model amplifies features.
Line Impact: controls how much the adaptive model can directly influence the oscillator line itself.
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Disclaimer
The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
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