OPEN-SOURCE SCRIPT
Updated BK AK-Pivot Wolf
🐺 BK AK–Momentum Pivot Wolf — Momentum / Pivots / Confluence 🐺
🙏 All glory to G-d.
Built with standards and discipline passed down by my mentor AK— thank you for giving real instruction without being cheap about it — no holding back, no protecting secrets.
Update / Record
A previous version of this publication was hidden due to insufficient description.
This republish is a complete, self-contained explanation of what the script does, how it works, what signals mean, what settings do, and key limitations.
✨ What this script does
Pivot Wolf is a TSI-based momentum oscillator system that focuses on:
extremes → pivots → confirmation, then adds confluence layers (VWAP, MTF alignment, SNR, volume, regime) to reduce chop and low-quality signals.
It’s designed to help you:
Identify momentum extremes using Dynamic or Static bands
Detect oscillator pivots that form at extremes (main pivot signals)
Mark divergences (regular + hidden) between price and oscillator
Confirm/grade signals using a 0–100 scoring system (or legacy hard filters)
Visualize context via VWAP gating, MTF dashboard, and regime state
Project post-pivot expectation zones via T1 / T2 targets
Optionally enable historical learning that only applies overrides when validation is strong
🧠 How it works
1) Momentum engine (TSI blend)
Computes Fast TSI and Slow TSI
Optional Adaptive Blend: volatility-weighted mixing using ATR% normalization over a lookback so momentum can be responsive in calm markets and less noisy in high volatility
A Signal EMA smooths momentum to detect cross/shift
2) Bands define “extremes”
Bands define statistically “stretched” momentum.
Dynamic mode: uses StdDev (or robust MAD) over a lookback, multiplied by a factor
Static mode: fixed ± level
Optional band smoothing to reduce jitter
“Extreme” is simply: momentum beyond the band (with optional tolerance rules)
3) Pivot detection (main signals)
Detects oscillator pivot lows/highs using pivotLen
A “strong” pivot signal is when:
Pivot Low forms below the Lower Band (oversold)
Pivot High forms above the Upper Band (overbought)
Marker style/size/colors are configurable, and tooltips explain context
Important: pivots are confirmed only after pivotLen bars to the right (this is normal pivot behavior).
4) Divergence logic (regular + hidden)
Tracks the last two oscillator pivots and compares them with price pivots:
Bullish divergence: price makes a lower low while oscillator makes a higher low
Bearish divergence: price makes a higher high while oscillator makes a lower high
Hidden bullish divergence: price higher low + oscillator lower low
Hidden bearish divergence: price lower high + oscillator higher high
Optional: Require extreme so divergences only count when pivots occur outside bands.
5) Confluence + scoring (0–100)
Instead of relying only on hard rules, Pivot Wolf can compute bull/bear scores from multiple inputs:
VWAP gate: position and/or slope logic (PositionOnly / SlopeOnly / Both / Either)
MTF alignment: direction across up to 6 selected timeframes + dashboard visualization
SNR (Signal-to-Noise Ratio): reduces signals during chop by comparing momentum gap vs recent noise
Volume confirmation: bullish confirmation vs bearish exhaustion/spike logic
Acceleration / deceleration: early warning + risk markers when momentum behavior changes rapidly
Consolidation filter: ATR regime compression penalty
Price structure: HH/LL checks to avoid fighting structure
Whipsaw guard: enforces a minimum bar gap between opposite signals
Signals can show as:
Strong = passes gating + score threshold (or legacy rules)
Weak (optional) = “scout” setups (score in 50–threshold range)
6) Targets / projections (T1 / T2)
After confirmed pivots, it projects expectation zones based on recent run behavior:
T1 = 0.618 projection
T2 = 1.000 projection
Targets can display continuously or only reveal when momentum approaches (to reduce clutter).
7) Optional historical learning (validation-gated)
If enabled, the script:
records pivot “outcomes” after mlForwardBars
runs a simple train/validation pass
only applies learned overrides when validation is strong and not overfit
If validation fails, it reverts to manual settings.
Note: This “learning” is heuristic optimization inside Pine (not external ML), and overrides are applied only when conditions are met.
🧭 How to use
Check the MTF dashboard for alignment (avoid fighting the stack).
Let momentum reach band extremes (OB/OS).
Treat pivot signals as highest value when Score is strong + VWAP gate agrees.
Use divergence as added weight, not as the sole trigger.
Manage around T1/T2 as structured expectation zones.
📌 Signals & visuals (what you’ll see)
Momentum line with optional gradient (strength/quality feel)
Signal line (EMA)
Upper/Lower bands + optional fills
Extreme dots/edges at band breaks (optional)
Cross stars on momentum/signal crosses (optional)
Divergence markers (◆ regular, ◇ hidden) + optional connector lines
MTF dashboard (direction + strength + confluence)
Info panel meters (Bull, Bear, Net, Osc Position, MTF, Quality, Regime, VWAP Pressure)
Optional stop suggestion markers (ATR/Swing/Pivot/Band methods)
⚙️ Key settings
Core Momentum: TSI lengths, signal EMA, adaptive blend & volatility lookback
Bands/Extremes: Dynamic vs Static bands, basis (StdDev/MAD), smoothing
Pivots & Divergence: pivot sensitivity, max bars between pivots, line/marker toggles
Filters: VWAP gate, MTF bias, SNR, volume, consolidation, structure, whipsaw
Targets/ML: T1/T2 projection logic + optional historical learning validation
Dashboards/Panels: MTF dashboard + Info panel positioning & styling
Performance mode: reduces heavy visual updates if needed
🔔 Alerts included
Bullish/Bearish signal alerts
Divergence detected
Early warning acceleration alerts
Optional regime peak/valley switch alert
(Alerts can be throttled via “Alert Settings”.)
✅ Repainting / confirmation notes (important)
Pivot highs/lows confirm after pivotLen bars by design. Signals appear once the pivot is confirmed.
MTF calculations use request.security(..., lookahead=barmerge.lookahead_off) to avoid forward-looking HTF values.
Anything based on confirmed pivots is inherently delayed by the pivot confirmation window.
⚠️ Known limitations / best practices
VWAP/Volume-based logic depends on reliable volume data. Some symbols/feeds may behave differently.
The script is information-dense; if you hit resource limits, use:
Limit labels
Reduce divergence lines
Turn off heavy visuals (fills, heatmap, dashboards)
Enable Performance mode
This tool is built for structure and confluence, not prediction. It will often stay quiet during chop—by design.
👁️🗨️ King Solomon Lens
“Solomon didn’t predict. He judged. He built tests that made truth show itself. Pivot Wolf is that: pivots as boundary stones, momentum as witness, acceleration as the confession. No hammer in the Temple — rules are cut before entry. When it’s quiet, it’s saving you. When it speaks, it’s a ruling.”
Disclaimer
This script is for educational and informational purposes only. It does not provide financial advice, and it does not guarantee results. You are responsible for your own decisions, testing, and risk management.
🙏 All glory to G-d—the source of all wisdom and every true edge. 🙏
🙏 All glory to G-d.
Built with standards and discipline passed down by my mentor AK— thank you for giving real instruction without being cheap about it — no holding back, no protecting secrets.
Update / Record
A previous version of this publication was hidden due to insufficient description.
This republish is a complete, self-contained explanation of what the script does, how it works, what signals mean, what settings do, and key limitations.
✨ What this script does
Pivot Wolf is a TSI-based momentum oscillator system that focuses on:
extremes → pivots → confirmation, then adds confluence layers (VWAP, MTF alignment, SNR, volume, regime) to reduce chop and low-quality signals.
It’s designed to help you:
Identify momentum extremes using Dynamic or Static bands
Detect oscillator pivots that form at extremes (main pivot signals)
Mark divergences (regular + hidden) between price and oscillator
Confirm/grade signals using a 0–100 scoring system (or legacy hard filters)
Visualize context via VWAP gating, MTF dashboard, and regime state
Project post-pivot expectation zones via T1 / T2 targets
Optionally enable historical learning that only applies overrides when validation is strong
🧠 How it works
1) Momentum engine (TSI blend)
Computes Fast TSI and Slow TSI
Optional Adaptive Blend: volatility-weighted mixing using ATR% normalization over a lookback so momentum can be responsive in calm markets and less noisy in high volatility
A Signal EMA smooths momentum to detect cross/shift
2) Bands define “extremes”
Bands define statistically “stretched” momentum.
Dynamic mode: uses StdDev (or robust MAD) over a lookback, multiplied by a factor
Static mode: fixed ± level
Optional band smoothing to reduce jitter
“Extreme” is simply: momentum beyond the band (with optional tolerance rules)
3) Pivot detection (main signals)
Detects oscillator pivot lows/highs using pivotLen
A “strong” pivot signal is when:
Pivot Low forms below the Lower Band (oversold)
Pivot High forms above the Upper Band (overbought)
Marker style/size/colors are configurable, and tooltips explain context
Important: pivots are confirmed only after pivotLen bars to the right (this is normal pivot behavior).
4) Divergence logic (regular + hidden)
Tracks the last two oscillator pivots and compares them with price pivots:
Bullish divergence: price makes a lower low while oscillator makes a higher low
Bearish divergence: price makes a higher high while oscillator makes a lower high
Hidden bullish divergence: price higher low + oscillator lower low
Hidden bearish divergence: price lower high + oscillator higher high
Optional: Require extreme so divergences only count when pivots occur outside bands.
5) Confluence + scoring (0–100)
Instead of relying only on hard rules, Pivot Wolf can compute bull/bear scores from multiple inputs:
VWAP gate: position and/or slope logic (PositionOnly / SlopeOnly / Both / Either)
MTF alignment: direction across up to 6 selected timeframes + dashboard visualization
SNR (Signal-to-Noise Ratio): reduces signals during chop by comparing momentum gap vs recent noise
Volume confirmation: bullish confirmation vs bearish exhaustion/spike logic
Acceleration / deceleration: early warning + risk markers when momentum behavior changes rapidly
Consolidation filter: ATR regime compression penalty
Price structure: HH/LL checks to avoid fighting structure
Whipsaw guard: enforces a minimum bar gap between opposite signals
Signals can show as:
Strong = passes gating + score threshold (or legacy rules)
Weak (optional) = “scout” setups (score in 50–threshold range)
6) Targets / projections (T1 / T2)
After confirmed pivots, it projects expectation zones based on recent run behavior:
T1 = 0.618 projection
T2 = 1.000 projection
Targets can display continuously or only reveal when momentum approaches (to reduce clutter).
7) Optional historical learning (validation-gated)
If enabled, the script:
records pivot “outcomes” after mlForwardBars
runs a simple train/validation pass
only applies learned overrides when validation is strong and not overfit
If validation fails, it reverts to manual settings.
Note: This “learning” is heuristic optimization inside Pine (not external ML), and overrides are applied only when conditions are met.
🧭 How to use
Check the MTF dashboard for alignment (avoid fighting the stack).
Let momentum reach band extremes (OB/OS).
Treat pivot signals as highest value when Score is strong + VWAP gate agrees.
Use divergence as added weight, not as the sole trigger.
Manage around T1/T2 as structured expectation zones.
📌 Signals & visuals (what you’ll see)
Momentum line with optional gradient (strength/quality feel)
Signal line (EMA)
Upper/Lower bands + optional fills
Extreme dots/edges at band breaks (optional)
Cross stars on momentum/signal crosses (optional)
Divergence markers (◆ regular, ◇ hidden) + optional connector lines
MTF dashboard (direction + strength + confluence)
Info panel meters (Bull, Bear, Net, Osc Position, MTF, Quality, Regime, VWAP Pressure)
Optional stop suggestion markers (ATR/Swing/Pivot/Band methods)
⚙️ Key settings
Core Momentum: TSI lengths, signal EMA, adaptive blend & volatility lookback
Bands/Extremes: Dynamic vs Static bands, basis (StdDev/MAD), smoothing
Pivots & Divergence: pivot sensitivity, max bars between pivots, line/marker toggles
Filters: VWAP gate, MTF bias, SNR, volume, consolidation, structure, whipsaw
Targets/ML: T1/T2 projection logic + optional historical learning validation
Dashboards/Panels: MTF dashboard + Info panel positioning & styling
Performance mode: reduces heavy visual updates if needed
🔔 Alerts included
Bullish/Bearish signal alerts
Divergence detected
Early warning acceleration alerts
Optional regime peak/valley switch alert
(Alerts can be throttled via “Alert Settings”.)
✅ Repainting / confirmation notes (important)
Pivot highs/lows confirm after pivotLen bars by design. Signals appear once the pivot is confirmed.
MTF calculations use request.security(..., lookahead=barmerge.lookahead_off) to avoid forward-looking HTF values.
Anything based on confirmed pivots is inherently delayed by the pivot confirmation window.
⚠️ Known limitations / best practices
VWAP/Volume-based logic depends on reliable volume data. Some symbols/feeds may behave differently.
The script is information-dense; if you hit resource limits, use:
Limit labels
Reduce divergence lines
Turn off heavy visuals (fills, heatmap, dashboards)
Enable Performance mode
This tool is built for structure and confluence, not prediction. It will often stay quiet during chop—by design.
👁️🗨️ King Solomon Lens
“Solomon didn’t predict. He judged. He built tests that made truth show itself. Pivot Wolf is that: pivots as boundary stones, momentum as witness, acceleration as the confession. No hammer in the Temple — rules are cut before entry. When it’s quiet, it’s saving you. When it speaks, it’s a ruling.”
Disclaimer
This script is for educational and informational purposes only. It does not provide financial advice, and it does not guarantee results. You are responsible for your own decisions, testing, and risk management.
🙏 All glory to G-d—the source of all wisdom and every true edge. 🙏
Release Notes
🐺 BK AK–Momentum Pivot Wolf (AK-Pivot Wolf) — Momentum Extremes → Pivots → Confluence ScoringWhat this is
AK-Pivot Wolf is a momentum-pivot oscillator system built around a TSI-style momentum core, designed to identify high-probability turning points and continuation pullbacks by requiring:
(1) Momentum reaches an extreme → (2) A pivot forms → (3) Confluence confirms.
This is not a “mashup.” All modules feed a single decision engine that outputs one thing: a graded long/short signal with contextual guidance and optional projections.
✅ What’s original / why it’s useful
TSI and pivots are known concepts. The originality here is the integrated decision pipeline:
Adaptive momentum blend (fast/slow weighting) using volatility regime (ATR%) so momentum is responsive in calm conditions and less noisy in high volatility.
Dynamic extremes framework (StdDev or robust MAD bands) with optional smoothing + tolerance rules for consistent “stretch” detection.
Unified scoring engine (0–100) that converts confluence inputs into a single quality grade instead of stacking unrelated indicators.
Noise controls designed to prevent “indicator soup” behavior: SNR filter, whipsaw guard, consolidation penalty, optional structure checks.
Actionable UI: tooltips explain why a signal qualified (or failed), not just that it fired.
🧠 How the engine works (step-by-step)
1) Momentum core (TSI-style)
Calculates fast and slow momentum signals.
Optional adaptive volatility-weighted blend selects how much “fast” vs “slow” to use based on ATR% regime.
A Signal EMA smooths momentum to define cross/shift context.
2) Bands define “extremes”
Extremes are defined statistically so “overbought/oversold” adapts to symbol and volatility.
Dynamic mode: StdDev or robust MAD-based bands (lookback + multiplier).
Static mode: fixed ± level.
Optional smoothing reduces band jitter.
3) Pivot detection (primary signal generator)
Oscillator pivots are detected using pivotLen.
“High-value pivots” occur when:
Bull pivot: pivot low forms below lower band
Bear pivot: pivot high forms above upper band
Pivots are used as boundary stones — not predictions — they mark where momentum failed to extend further into the extreme.
4) Divergence (regular + hidden)
Divergence does not create standalone signals; it adds weight to a pivot setup.
Regular bullish: price lower low, oscillator higher low
Regular bearish: price higher high, oscillator lower high
Hidden divergences signal continuation (trend reload behavior)
Optional requirement: divergence only counts if oscillator was at extremes.
5) Confluence scoring (single unified output)
Instead of a pile of hard filters, the script can compute bull/bear scores (0–100) from:
VWAP gate (position / slope / AND / OR)
MTF alignment across up to 6 timeframes (direction + strength)
SNR filter to reduce chop (signal-to-noise ratio)
Volume confirmation / exhaustion / reversal spike logic
Momentum acceleration/deceleration behavior
Consolidation penalty (ATR compression)
Optional structure checks (HH/LL)
Whipsaw guard (minimum bars between opposite signals)
Signal types
Strong: passes gates + meets score threshold (or legacy hard filter mode)
Weak (optional): “scout” setups (score 50 → threshold)
6) Targets / projections (context, not guarantees)
After a confirmed pivot signal, the script can project expectation zones:
T1 = 0.618
T2 = 1.000
These are derived from recent pivot run behavior with optional volatility adjustment and “reveal only when close” to reduce clutter.
7) Optional in-script historical calibration (validation-gated)
This module does not use external ML. It’s an in-script parameter calibration that:
records pivot outcomes after mlForwardBars
uses a train/validation split
applies overrides only when validation is strong and not overfit
otherwise uses manual settings
🧭 How to use (simple workflow)
Use MTF dashboard to avoid fighting the higher-timeframe stack.
Wait for momentum to reach an extreme (band stretch).
Take pivots seriously when VWAP gate + score agree.
Treat divergence as extra weight, not the trigger.
Use T1/T2 as structured expectation zones for managing exits.
🔔 Alerts included
Bull/Bear signal
Divergence detected
Acceleration early warning
Optional regime peak/valley switch
(Alerts can be throttled in settings.)
✅ Repainting / confirmation notes (important)
Pivot highs/lows confirm after pivotLen bars (normal pivot behavior). Signals appear once the pivot is confirmed.
MTF uses request.security(..., lookahead=barmerge.lookahead_off) to avoid forward-looking HTF values.
Anything based on pivots is inherently delayed by the pivot confirmation window.
⚠️ Limitations / best practices
VWAP/volume logic depends on reliable volume feed (some symbols/CFDs may differ).
This script is information-dense. If you hit resource limits: limit labels/lines, reduce dashboards, disable heavy visuals, or enable performance mode.
Designed to stay quiet during chop — silence is a feature when edge is low.
Disclaimer
Educational/informational only. No financial advice. You are responsible for testing and risk management.
Release Notes (Compliance Update)
Added full methodology and usage documentation (originality + system design + how modules interact).
Clarified pivot confirmation delay and MTF non-lookahead behavior.
Clarified in-script calibration is validation-gated and not external ML.
Added limitations and best-practice guidance to prevent misuse.
Open-source script
In true TradingView spirit, the creator of this script has made it open-source, so that traders can review and verify its functionality. Kudos to the author! While you can use it for free, remember that republishing the code is subject to our House Rules.
Disclaimer
The information and publications are not meant to be, and do not constitute, financial, investment, trading, or other types of advice or recommendations supplied or endorsed by TradingView. Read more in the Terms of Use.
Open-source script
In true TradingView spirit, the creator of this script has made it open-source, so that traders can review and verify its functionality. Kudos to the author! While you can use it for free, remember that republishing the code is subject to our House Rules.
Disclaimer
The information and publications are not meant to be, and do not constitute, financial, investment, trading, or other types of advice or recommendations supplied or endorsed by TradingView. Read more in the Terms of Use.