AB VolCore

ATR tells you yesterday's range. VolCore models the volatility cycle itself:

–  Estimator stack — Parkinson, Garman-Klass and Yang-Zhang realized volatility (OHLC-based estimators are 5–8× more efficient than close-to-close). Select one or blend.

–  Percentile ranking — current volatility ranked within its own 250-bar distribution, giving a universal 0–100 Vol Rank comparable across all symbols.

–  Squeeze detection — Vol Rank below a threshold and still compressing (negative vol-of-vol slope) marks a coiled spring; squeeze intensity is scored 1–3.

–  Expansion forecasting — after expansion begins, VolCore projects an expected-range cone for the next N bars using a volatility-clustering heuristic.

–  Session decomposition — Vol Rank computed per session (Asia/London/NY) so intraday traders see which session is abnormal.

Marketplace description

Direction is hard to predict. Volatility isn't — it clusters, compresses and expands in cycles, and VolCore maps that cycle with professional-grade estimators (Parkinson, Garman-Klass, Yang-Zhang) instead of a simple ATR. A universal 0–100 Vol Rank makes every symbol comparable. The squeeze engine detects compression while it is still compressing and scores its intensity; the release alert fires with the direction of the first displacement bar. After release, an expected-range cone projects how far the expansion should carry. Session decomposition shows Asia/London/NY volatility separately. Perfect standalone, and a devastating filter in front of any breakout or mean-reversion system. Non-repainting, EA-ready.


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How it works –   Strict validation chain — a candidate becomes an order block only if all hold: it is the last opposing candle before a displacement move; the displacement breaks structure; the move leaves an imbalance (FVG) behind; and displacement volume exceeds its time-of-day baseline. This removes roughly 90% of what other indicators mislabel as order blocks. –   Grading (A/B/C) — from displacement magnitude, liquidity swept by the block before departing, and higher-timeframe alignment. –  
How it works –   Impulse-leg detection — identifies displacement legs (velocity + range expansion + structural break) worth continuing with. –   Zone projection — for each impulse, projects the confluence-cluster pullback zone: the overlap of the 38–62% retracement band, the breakout origin (old resistance/new support), any fair value gap inside the leg, and the auto-anchored VWAP from the leg's origin. Where two or more overlap, a graded continuation zone prints. –   Regime gate — zones only pr
How it works –   Detection with quality filters — true three-candle imbalances filtered by minimum gap size (ATR-scaled), displacement quality of the middle candle and volume, killing the micro-gap spam that makes FVG tools unusable on low timeframes. –   Fill-state tracking — each gap tracked live: Untouched, Partially filled (with fill %), Fully mitigated, Inverted. –   The headline feature — Inversion (failed FVG): when price closes entirely through a gap, the imbalance inverts. Failed bullis
How it works –   Pool detection — equal highs/lows (within an ATR-scaled tolerance), unswept swing highs/lows, session and daily/weekly extremes, and trendline liquidity (3+ touch diagonal clusters where stops accumulate). –   Pool weighting — each pool scored by touch count, age, round-number proximity and origin timeframe, then drawn with thickness/opacity proportional to score. The chart literally shows where the fuel is. –   Sweep engine — a sweep registers when price wicks through a pool an
How it works An inducement trap is a sequence, not a candle pattern. TrapRadar's state machine requires all four legs: –   Bait — an obvious level forms (minor swing, equal highs/lows, range boundary) that retail will trade the breakout of. –   Trigger — price breaks the level and actually attracts participation; relative volume rises on the breakout bar, confirming traders took the bait. –   Trap spring — within K bars a displacement candle (range greater than 1.5× ATR, closing 70%+ through its
How it works No RSI, no stochastic. VectorMomentum measures momentum three independent ways and demands agreement: –   Regression-slope t-statistic — a linear regression on log price over N bars; the t-stat of the slope (slope divided by its standard error) gives statistically-significant momentum that self-normalizes across symbols and volatility levels. –   Volume-pressure-weighted rate of change — ROC weighted by relative tick volume, so momentum on dead volume scores lower than the same move
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