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. 🙏
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