AetherEdge - Multi-Armed Bandit🖊️ Overview
AE-MAB is a self-evolving signal allocator that treats a panel of sub-signals as the "arms" of a bandit and learns which to trust now. Each classic signal — trend, momentum, breakout, mean-reversion, MACD — carries a Bayesian Beta(α,β) posterior over its directional hit-rate, updated online with forgetting from realized hits/misses (the reward). Allocation weights come from Thompson Sampling (sampling each posterior) or UCB (mean + confidence bound), both balancing exploration of uncertain arms against exploitation of proven ones. The final signal is the credibility-weighted vote of the arms.
🔶 Key Features
Multi-armed bandit engine — runs 5 sub-signals as arms and dynamically weights toward whichever is working now.
Reward & Bayesian learning — each arm's directional hit/miss is the reward, updating a Beta(α,β) posterior online with forgetting.
Exploration & exploitation — switch between Thompson Sampling (sample the posteriors, occasionally trying uncertain arms) and UCB (mean + confidence bound).
Self-evolving beliefs — a forgetting factor weights recent performance; as the market shifts, the trusted arm quietly changes.
Credibility panel — lists each arm's direction, credibility bar (Beta posterior mean), and allocation weight, with the current lead signal glowing.
Credibility-weighted allocation — a continuous allocation (−1 to +1) of arm directions weighted by credibility is the final signal, reflected in the price background and markers.
Live statistics panel — final signal, conviction, lead arm, allocation value, bandit P&L, and forgetting factor.
Deterministic, non-repainting design — Thompson's RNG is seed-fixed (no intrabar flicker); learning on confirmed bars only, no look-ahead.
🧠 Technical Architecture
Each bar, the 5 arms emit a directional view (+1/0/−1): trend (EMA cross), momentum (RSI), breakout (Donchian), mean-reversion (z-score, contrarian), MACD. On each confirmed bar, posteriors first decay toward the prior Beta(1,1), then the previous bar's view is checked against the realized return — a hit adds α+=1, a miss β+=1 (one-bar lag, no look-ahead). This is the reward-driven self-evolution.
Scores are: in UCB mode mean + c·std (mean = α/(α+β), std = the Beta distribution's width); in Thompson mode a sample from a Gaussian approximation of the Beta posterior clip(mean + σ·z, 0,1) (z from a deterministic LCG + Box-Muller standard normal). Positive scores are normalized into weights w_a, giving the credibility-weighted allocation alloc = Σ w_a·dir_a ∈ . The lead arm is the highest-scoring one. Allocation beyond the deadband calls Long/Short; inside it, Flat.
🎯 Three design choices stand out. First, the Beta posterior's width (uncertainty) directly drives exploration — arms with little evidence are occasionally sampled high under Thompson and get a higher confidence bound under UCB, so they get tried. Second, forgetting adapts to non-stationary markets, naturally lowering the weight of arms that stop working. Third, a deterministic RNG plus confirmed-bar updates keep historical signals non-repainting.
⚙️ Recommended Settings & Tuning Guide
As a crypto starting point — BTC/ETH (1D, 4H): Strategy = Thompson, adaptation λ = 0.97, deadband 0.15; the lead signal rotates smoothly with regime. High-volatility names (SOL, XRP): lower λ toward 0.95 for faster adaptation, and raise the deadband to 0.20 to avoid trading on weak consensus. For more deterministic behavior, switch to UCB (c ≈ 1.0).
Per parameter: Strategy (Thompson/UCB) sets the quality of exploration — Thompson is stochastic and diverse, UCB deterministic and steady. Adaptation λ sets learning memory (0.99 = stable/long, 0.95 = nimble). UCB c sets exploration strength. Deadband sets the consensus strength required to fire. Sub-signal lengths are tuned to your timeframe.
💡 How to Use in Practice
The core read is final signal × conviction. When allocation swings strongly one way (high conviction) and several arms in the credibility panel agree, that's cross-regime consensus — a tailwind for trend-following. The moment the lead arm changes (e.g., from Trend to MeanRev) signals the type of edge that's working has shifted. The credibility bars show at a glance which logic (trend vs contrarian) currently dominates the market. When conviction is low and arms disagree, no logic is working well — an unstable phase where standing aside is wise.
For multi-timeframe work, read the higher-timeframe lead arm for the macro-effective logic and execute on lower-timeframe signals. Bandit P&L offers a sense of the allocator's in-sample traction.
⚠️ Important Notes
Signals are hidden until the warmup period (default 100 bars) completes. Reloading the indicator rebuilds the posteriors from scratch — learning state is not persisted. Thompson sampling uses a Gaussian approximation of the Beta, not exact Beta draws (the approximation is coarse with little early evidence). The panel's "Bandit P&L" is an in-sample metric that excludes trading cost and slippage. The arms are fixed classic rules — the bandit selects among them, it does not invent new signals. Updates occur on confirmed bars only.
🚨 Disclaimer
This indicator is for educational and informational purposes only and does not constitute financial or investment advice. Past performance is not indicative of future results. All trading involves risk. Use it alongside your own thorough testing and sound risk management; all trading decisions remain solely your own responsibility. Indicator
AetherEdge - Bayesian Changepoint Detection🖊️ Overview
AE-BCP is a Bayesian detector of structural breaks (Adams & MacKay, 2007). Rather than flagging regime shifts with a hard threshold, it maintains a posterior distribution over the run length — the number of bars since the last changepoint — and updates it recursively with every new observation. A Gaussian conjugate predictive (on standardized returns or log-volatility) scores how surprising each bar is under each run-length hypothesis. Beliefs self-evolve recursively, and P(run length = 0) is the live probability that a structural break just occurred. The classic triangular run-length posterior is painted as a heatmap.
🔶 Key Features
BOCPD engine — recursively updates the run-length posterior into growth and changepoint probabilities, outputting structural breaks as a probability.
Self-evolving recursive Bayesian update — each run-length hypothesis's predictive model (sufficient statistics) and the posterior update online with every observation.
Changepoint probability (soft detection) — P(run length = 0) as a continuous 0–1 probability; threshold crossings are marked on price.
Run-length posterior heatmap — the time × run-length × probability triangular heatmap (BOCPD's signature visual), aligned to the price timeline.
Expected run length (regime age) — estimates how many bars the current regime has lasted, collapsing to zero at changepoints (shown as a line).
Volatility or drift — monitor log squared returns to detect volatility-regime changes, or returns to detect drift changes.
Live statistics panel — changepoint probability, regime age, MAP run length, last changepoint, and hazard rate.
Non-repainting design — all updates on confirmed bars only, with no look-ahead.
🧠 Technical Architecture
The monitored feature (Volatility mode = log squared return, Returns mode = return) is standardized over a long window, fixing the predictive variance at ~1. On each confirmed bar, the predictive probability of each run-length hypothesis r is pred(x_t|r) = N(x_t | m_r, σ²(1+1/r)) (m_r = the run's mean). From these, growth P(r_t=r+1) ∝ P(r_{t-1}=r)·pred·(1−H) and changepoint P(r_t=0) ∝ Σ_r P(r_{t-1}=r)·pred·H are computed and normalized. H is the hazard rate (= 1 / mean regime length), the prior probability of a changepoint.
Each run-length's sufficient statistics (sum of the run's observations) update as sum = sum + x_t on growth and sum = 0 (prior) on a changepoint. Run length is truncated at Rmax for tractability. Outputs are the changepoint probability P(r_t=0), expected run length E =Σr·P(r), and MAP run length argmax P(r). The posterior is binned into a rolling buffer and, on the last bar, drawn as a heatmap (boxes) aligned to the price timeline.
🎯 Three design choices stand out. First, standardization fixes the predictive variance, allowing a robust Gaussian-conjugate implementation with no gamma functions. Second, monitoring log squared returns turns "variance changes" into "mean changes," so a simple mean-change model captures volatility-regime breaks. Third, confining updates to confirmed bars keeps the historical posterior non-repainting.
⚙️ Recommended Settings & Tuning Guide
As a crypto starting point — BTC/ETH (1D, 4H): Monitor = Volatility, mean regime length 60, standardization length 200, max run length 100, changepoint threshold 0.30; volatility-regime breaks are clearly marked. For more frequent turns: lower mean regime length toward 30 (higher hazard, more sensitive) and lower the threshold to 0.25. To suppress noise: raise mean regime length to 100 and the threshold to 0.40.
Per parameter: Mean regime length is the key knob (hazard = its reciprocal) — shorter detects changepoints more often, longer is conservative. Monitor selects Volatility (vol breaks) or Returns (drift changes) for your purpose. Standardization length sets the baseline scale window (longer is steadier). Max run length sets how far back regimes are retained. Changepoint threshold sets marker sensitivity.
💡 How to Use in Practice
The core read is changepoint probability × regime age. When the changepoint probability is low and stable and the regime age keeps rising, the current regime (trend or range) is persisting — strategies aligned with that premise tend to work. The moment the changepoint probability spikes above the threshold (diamond marker fires) signals the volatility or drift structure has changed — a point to review existing positions or switch strategy to the new regime. Each time the heatmap triangle resets (run length collapses to zero), you can see the market entering a new phase visually.
For multi-timeframe work, read higher-timeframe changepoints for major structural turns and execute on a lower timeframe. It also serves as a meta-filter to run trend or mean-reversion strategies only while the regime is stable.
⚠️ Important Notes
Nothing displays until the warmup period (default 200 bars) completes. Reloading the indicator rebuilds the posterior from scratch — learning state is not persisted. This is a Gaussian-conjugate model with a known (≈1, via standardization) predictive variance that detects mean changes in the monitored feature (Volatility mode captures volatility changes via the log-squared transform). Run length is truncated at Rmax, so for very long regimes the expected run length saturates near the cap. The changepoint probability is a posterior belief, not a certain verdict, and detection requires evidence to accumulate, so it lags by a few bars. Updates occur on confirmed bars only.
🚨 Disclaimer
This indicator is for educational and informational purposes only and does not constitute financial or investment advice. Past performance is not indicative of future results. All trading involves risk. Use it alongside your own thorough testing and sound risk management; all trading decisions remain solely your own responsibility. Indicator
AetherEdge - Principal Component Analysis🖊️ Overview
AE-PCA extracts the market's eigen-state from the correlation structure of a basket of assets. It builds a rolling correlation matrix online and extracts the leading principal components — PC1 (market factor), PC2 (rotation/dispersion) — via power iteration with deflation. From the eigenvalues it derives the Absorption Ratio (λ₁/N) — the share of basket variance explained by one factor — which reveals, at a glance, the "everything moves together" systemic fragility of the market. The eigenbasis continuously re-orients each bar to the current correlation structure.
🔶 Key Features
Online PCA engine — builds a rolling correlation matrix with forgetting and extracts the top two principal components by power iteration (heavy use of the matrix type).
Absorption Ratio (systemic stress) — λ₁/N quantifies market concentration = fragility; sharp rises capture the "correlation convergence" that precedes crashes.
Self-evolving eigenbasis — eigenvectors are warm-started and re-converged each bar, tracking shifts in correlation smoothly (subspace tracking) with sign continuity preserved.
Phase-space comet — projects the live return vector onto PC1/PC2 and traces the market's path through factor space as a comet trail on a floating canvas to the right of the chart.
Factor loadings — identifies the asset that loads most heavily on PC1 (the one leading the market).
Dispersion gauge — 1 − Absorption Ratio shows the degree of diversification.
Live statistics panel — absorption ratio, cumulative (PC1+PC2), eigenvalues λ₁/λ₂, dispersion, lead asset, and PC1/PC2 scores.
Macro-overlay design — the basket is independent of the chart symbol, so you can overlay market-wide stress on any chart you view. Confirmed-bar updates, no look-ahead.
🧠 Technical Architecture
From 6 asset returns, first and second moments (means, covariances) are updated online with forgetting factor λ, then divided by each asset's standard deviation to build the correlation matrix R (N×N). PC1 is found by power iteration v ← Rv/‖Rv‖, with eigenvalue λ₁ from the Rayleigh quotient. Then deflation R₂ = R − λ₁v₁v₁ᵀ is applied, and PC2 obtained by power iteration with Gram-Schmidt orthogonalization. Since the correlation matrix has trace N, the absorption ratio is λ₁/N and the cumulative is (λ₁+λ₂)/N.
Factor scores are computed by projecting the standardized current returns z_i=(r_i−μ_i)/σ_i onto the eigenvectors (PC1 score = Σz_i·v₁_i). Eigenvectors are warm-started from the previous bar for fast convergence and sign stability across bars (PC1 is oriented so Σv₁ > 0). A high absorption ratio means "correlation convergence = single-factor dominance = fragility"; a low one means "dispersion = diversification." All updates occur on confirmed bars.
🎯 Three design choices stand out. First, using the correlation (not covariance) matrix yields a scale-free absorption ratio that isn't dominated by a single high-volatility asset. Second, warm-starting the eigenvectors suppresses sign flips and flicker, giving smooth scores. Third, confining updates to confirmed bars keeps historical values non-repainting.
⚙️ Recommended Settings & Tuning Guide
As a crypto starting point — a basket of the 6 majors (BTC/ETH/SOL/BNB/XRP/ADA), 1D/4H: adaptation λ = 0.97, Calm threshold 0.40, Stress threshold 0.60; crypto-wide risk-on/off separates cleanly. For nimbler anomaly detection: lower λ toward 0.95 to react faster to correlation shifts, and raise the Stress threshold to 0.65 so only genuine convergence triggers, reducing noise.
Per parameter: Adaptation λ is the main knob — near 0.99 correlations are smooth and long-run; near 0.95 they respond quickly to shocks. Calm/Stress thresholds should be tuned to your basket's composition and its typical absorption ratio (closer to uncorrelated → lower λ₁/N; strongly correlated → higher). Comet length / canvas width tune the phase-space display. The basket can be freely swapped for the assets whose correlation you want to track.
💡 How to Use in Practice
The core read is the absorption-ratio regime. When the ratio is low and stable (CALM), the market is diversified and stock-picking tends to work. When it spikes into the Stress zone, correlations have converged into an "everything moves together" fragile state — a cue to reduce concentration as a leading sign of self-reinforcing declines or risk-off. When the phase-space comet stretches strongly in one direction, the market is factor-driven (trending); when it swirls near the center, it's ranging/rotating. The Lead Asset is the one currently driving the whole market.
For multi-timeframe work, read the higher-timeframe absorption ratio for the macro risk backdrop and execute single-name trades on the lower timeframe. Paired with trend and volatility tools, it serves as a top-level risk filter (e.g., avoid fading during stress spikes).
⚠️ Important Notes
This indicator fetches external data (request.security) for 6 symbols. Specifying invalid or illiquid symbols drops their contribution and degrades accuracy — compose the basket from liquid symbols. Nothing displays until warmup completes. Reloading the indicator rebuilds the correlation moments from scratch — learning state is not persisted. The absorption ratio is a proxy for systemic fragility (akin to Kritzman's absorption ratio), not a directional signal by itself. The sign of the PC2 score is arbitrary. The phase-space comet is drawn in the chart's right margin, so display requires right-side space. Power iteration is an approximation of the top two components, and updates occur on confirmed bars only.
🚨 Disclaimer
This indicator is for educational and informational purposes only and does not constitute financial or investment advice. Past performance is not indicative of future results. All trading involves risk. Use it alongside your own thorough testing and sound risk management; all trading decisions remain solely your own responsibility. Indicator
AetherEdge - Spectral Cycle Engine🖊️ Overview
AE-SCE is a market frequency lens that exposes the cycles hiding inside price. It runs a Discrete Fourier Transform over a rolling window of linearly-detrended price, tracks the dominant cycle and its phase (rising/falling, bars-to-turn), reconstructs a denoised waveform from the strongest components and extrapolates it forward (Fourier projection), and paints a live spectrogram — period × time × amplitude — so you can watch cycles strengthen, fade, and migrate. The lens re-focuses every bar onto whichever cycles dominate now.
🔶 Key Features
DFT spectral engine — analyzes the frequency content (amplitude, phase, period) of a rolling window, quantifying the market's cyclical structure.
Dominant-cycle tracking — identifies the strongest period each bar and measures its strength (share of total spectrum).
Phase & turn forecast — from the dominant cycle's current phase, estimates rising/falling and "how many bars to the next peak/trough."
Fourier reconstruction + projection — rebuilds the waveform from the top-N components and extrapolates a forecast curve onto the chart.
Live spectrogram — a period (rows) × time (columns) × amplitude (color) heatmap, revealing the rise, fall, and migration of cycles at a glance.
Adaptive focus — rolling re-estimation keeps the lens trained on the current dominant cycle.
Live statistics panel — dominant period, strength, phase (turn forecast), projected return, and components used.
Efficient design — the trig basis is precomputed once, and the heavy transform runs only on the last bar, staying within Pine's runtime budget.
🧠 Technical Architecture
On each last bar, the newest length-N window is linearly detrended via least squares, and a DFT is applied to the residual. For each bin k=1..N/2 it computes real and imaginary parts, then amplitude A_k = (2/N)√(Re²+Im²), phase φ_k = atan2(Im,Re), and period T_k = N/k. The highest-amplitude bin within the displayed band is the dominant cycle, its strength measured as A* / ΣA. Reconstruction is the sum of the top-N components Σ A_k·cos(2πkm/N − φ_k) plus the linear trend; extending m beyond the window turns it into a forward projection.
The spectrogram applies the same transform to several windows shifted back by a stride, encoding each time-and-period amplitude as color. The phase-based turn forecast derives the dominant cycle's phase angle at the newest bar and converts the phase distance to a peak (cos=1) or trough (cos=−1) into bars. To keep it light, the cos/sin basis matrices are built once and reused, and the DFT itself runs only on the last bar. This is a spectral-analysis tool — not a learning model — that adapts to "the cycle of now" through rolling re-estimation.
🎯 Three design choices stand out. First, linear detrending suppresses trend leakage (spectral leakage), letting genuine cycles surface. Second, retaining phase lets it report not just amplitude but where in the cycle price sits. Third, the precomputed basis and last-bar concentration completely avoid the cost of recomputing across all history.
⚙️ Recommended Settings & Tuning Guide
As a crypto starting point — BTC/ETH (1D, 4H): window N = 64, 5 reconstruction components, spectrogram 16×20, stride 4, horizon 16; medium-to-long cyclical structure separates cleanly. High-volatility / short-term (SOL, XRP): shorten N toward 48 for faster response to shorter cycles, and narrow components to 3–4 to avoid pulling in noise, yielding a cleaner forecast curve.
Per parameter: Window (N) is the key — larger resolves longer cycles but adds lag and load; smaller is nimbler but misses long cycles. Reconstruction components set forecast smoothness — fewer give a smooth dominant-cycle curve, more track finer detail. Stride / columns set how far the spectrogram reaches back. Horizon sets projection length.
💡 How to Use in Practice
The core read is dominant cycle × phase × strength. When cycle strength is high and the phase reads "few bars to trough," it flags a potential dip + cycle reversal — a timing cue. Conversely "few bars to peak" is a candidate for taking profit or fading rallies. By watching the forecast curve's slope and whether price tracks it, you can judge whether cycles are in control (i.e., forecast reliability is high). When the spectrogram shows the dominant cycle migrating or splitting, the cyclical structure is changing — a sign your assumptions may be shifting.
For multi-timeframe work, read the larger cycle's phase on the higher timeframe and use shorter-cycle turns on the lower timeframe for execution. Pair it with trend tools and de-weight cycle forecasts when trends are strong.
⚠️ Important Notes
Nothing displays until warmup (window + spectrogram reach-back) completes. Fourier extrapolation assumes cycles persist, so forecast reliability drops sharply in strong trends or at structural breaks (regime changes). The reconstruction/forecast curve is redrawn every bar from the current spectrum and is not a frozen historical fit — it updates as new bars arrive. Spectral leakage from the finite window is mitigated by detrending, but cycles are not strictly stationary. Large windows or many columns over long history increase compute. This is a forecasting tool, not a certain future.
🚨 Disclaimer
This indicator is for educational and informational purposes only and does not constitute financial or investment advice. Past performance is not indicative of future results. All trading involves risk. Use it alongside your own thorough testing and sound risk management; all trading decisions remain solely your own responsibility. Indicator
AetherEdge - Kalman State Filter🖊️ Overview
AE-KSF is a self-evolving state-space estimator that treats price as a hidden state. It models the true price as a **local linear trend — a level and a velocity — and recovers it from noisy observations with a Kalman filter. Critically, it does more than smooth: it carries the full uncertainty (covariance) of its estimate and projects it forward as a widening confidence cone. And because it estimates measurement noise online from the innovation stream, the Kalman gain self-tunes to every instrument and regime.
🔶 Key Features
A full Kalman filter engine — predict (x'=Fx, P'=FPF^T+Q) and update (x=x'+K(z−Hx')) run every bar, jointly estimating level and velocity.
Self-evolving adaptive noise — measurement noise R is estimated from the innovation stream, so the Kalman gain self-adjusts to volatility and noise level.
Uncertainty cone — the covariance is propagated forward into a probability cone that widens with confidence, visualizing how far the estimate can be trusted rather than a bare point line.
In-sample confidence band — a translucent ±σ band hugs the centerline, conveying current state uncertainty at a glance.
Velocity & trend strength — velocity (per-bar drift) and its signal-to-noise ratio (a t-statistic) quantify how certain the trend is.
Semantic coloring — centerline, band, and cone are auto-colored by velocity sign and confidence (Rising / Falling / Flat).
Live statistics panel — trend direction, velocity, trend strength, Kalman gain, estimated noise R, state uncertainty, and innovation.
Non-repainting design — state updates on confirmed bars only, with no look-ahead.
🧠 Technical Architecture
The state is two-dimensional — level p and velocity v. Transition F = [ , ] (constant-velocity), observation H = (level only). Each confirmed bar runs a predict step (advancing state and covariance P) and an update step (folding in the innovation z−Hx' through gain K). Observation is in log-price space by default, so the cone becomes multiplicative and asymmetric in price — a financially natural shape.
The heart of the self-evolution is adaptive noise estimation. An EMA tracks the squared innovation, and measurement noise is estimated as R ≈ EMA(innov²) − P'_position (floored). The filter thus dials its gain down in noisy phases (smoother) and up when structure is clear (snappier) — balancing itself. Process noise q is set as a ratio to that estimated R via the "Responsiveness" knob, auto-scaling to the instrument's noise level. The forward cone is built by propagating state and covariance with no measurements; its width starts at the current state uncertainty and widens with horizon.
🎯 Three design choices stand out. First, carrying covariance delivers a confidence-aware estimate beyond a smooth line. Second, adaptive R auto-calibrates the cone to real price noise. Third, confining state updates to confirmed bars keeps the historical estimate non-repainting.
⚙️ Recommended Settings & Tuning Guide
As a crypto starting point — BTC/ETH (1D, 4H): Responsiveness 20, Adaptive Noise on, Confidence σ = 2.0, horizon 16; smooth, low-lag trend tracking. High-volatility names (SOL, XRP): lower Responsiveness to 10–15 to absorb noise, and widen Confidence σ toward 2.5 for a steadier centerline and cone in rough action.
Per parameter: Responsiveness is the main knob — higher tracks price faster (less lag, less smoothing); lower is smoother (more lag, more noise tolerance). Adaptive Noise is best left on — it auto-calibrates per market; for manual control, set Manual Noise (R) directly. Confidence σ sets band and cone width; Horizon sets projection length. Trend Deadband (t-stat) sets how much trend certainty counts as Rising / Falling.
💡 How to Use in Practice
The core read is centerline × velocity × trend strength. When the centerline tilts up (cyan) with a high trend-strength (t-statistic), it reads as a tailwind for buying dips. A tag-and-reject at the in-sample band edge marks a deviation from the state estimate — a mean-reversion cue. A contracting band/cone means a calm, high-confidence state; an expanding one means rising uncertainty — useful for sizing. The forward cone's slope and width convey trend direction and confidence at a glance.
For multi-timeframe work, read the higher-timeframe centerline for the backdrop and use velocity turns (t-stat sign flips) on a lower timeframe for execution. Layered over support/resistance or volume, the Kalman centerline acts as a proxy for the "smooth price path institutions watch" — a reference line for entries and exits.
⚠️ Important Notes
Estimates are hidden until the warmup period (default 30 bars) completes (kept short, as the Kalman converges fast). Reloading the indicator makes the filter reprocess history from scratch — state is not persisted. This is a constant-velocity (local linear trend) model, so the forward point estimate is a straight line — it does not foretell sharp moves or reversals themselves. The uncertainty cone is a probabilistic range under the model's assumptions, not a certain forecast. State updates on confirmed bars; on the forming bar the centerline holds its last confirmed value.
🚨 Disclaimer
This indicator is for educational and informational purposes only and does not constitute financial or investment advice. Past performance is not indicative of future results. All trading involves risk. Use it alongside your own thorough testing and sound risk management; all trading decisions remain solely your own responsibility. Indicator
AetherEdge - Gaussian Mixture Regimes🖊️ Overview
AE-GMM is a self-evolving regime detector that treats the market as a probability distribution rather than carving it up with rigid rules. It models the joint distribution of momentum × volatility as a mixture of K Gaussian components — one per regime — and keeps learning their means, variances, and weights through online Expectation-Maximization with forgetting. Every bar receives a soft probability vector (a posterior) over regimes, rendered as a flowing probability ribbon that lets the market's state blend and shift before your eyes.
🔶 Key Features
Gaussian mixture + online EM engine — the E-step (responsibilities) and M-step (sufficient statistics) run every bar, estimating the regime distribution incrementally.
Self-evolving forgetting mechanism — a forgetting factor λ weights recent data, so the model quietly reshapes itself as regimes emerge and dissolve.
Soft probability ribbon — the K regime probabilities, stacked into a flow in the lower pane; not hard boundaries, but "how much of each regime is present now."
Semantic regime coloring — each component is auto-colored by the character of its learned centroid (Risk-On / Range / Risk-Off / Stress), sidestepping the label-switching problem.
Projection onto price — force_overlay tints the main chart's background by the dominant regime, deepening with confidence.
Live statistics panel — dominant regime, confidence, per-regime probabilities, regime duration, the adaptation factor λ, and model fit (log-likelihood).
Diagonal-covariance robustness — no matrix inversion, numerically stable; learning on confirmed bars only, with no look-ahead.
🧠 Technical Architecture
The feature space is two-dimensional — a momentum axis (z-scored ATR-unit trend deviation) and a volatility axis (z-scored log realized-volatility). Each component is a diagonal-covariance Gaussian with mean μ_k, variance σ²_k, and weight π_k. Every bar, responsibilities (posteriors) are computed as γ_k(x) = π_k·N(x|μ_k,σ²_k) / Σ_j π_j·N(x|μ_j,σ²_j), normalized stably via log-sum-exp in the log domain.
Learning proceeds by incremental EM. On each confirmed bar, the sufficient statistics (responsibility mass N_k, Σγx, Σγx²) are updated with a forgetting factor λ, and π_k, μ_k, σ²_k are re-derived from them. Lower λ weights recent data and adapts quickly; higher λ acts as longer memory and stays steady. Components are initialized spread around a ring in feature space, starting from diverse regimes and migrating toward the data. Each regime's color is decided every bar from its learned centroid (high volatility → Stress; positive momentum → Risk-On; negative → Risk-Off; in between → Range).
🎯 Three design choices stand out. First, soft responsibilities let regime transitions be expressed as a blend of probabilities — the "in-between" is visible. Second, character-based coloring keeps colors meaningful regardless of index shuffling. Third, confining parameter updates to confirmed bars — with only the forming bar's posterior updating live — keeps historical output non-repainting.
⚙️ Recommended Settings & Tuning Guide
As a crypto starting point — BTC/ETH (1D, 4H): K = 3, λ = 0.99, standardization length 200, vol length 20; Risk-On / Range / Stress separate cleanly. High-volatility names (SOL, XRP): lower λ toward 0.97 for faster adaptation, and set K = 4 to split Stress into upside vs downside stress, revealing the internal structure of rough action.
Per parameter: λ (adaptation) is the key knob — near 0.999 regimes are smooth and persistent; near 0.95 they switch nimbly. K (regimes) ranges from 2 (on/off) to 4 (finer states). Standardization length sets the feature baseline window — longer is steadier, shorter more locally adaptive. Stress Vol (z) sets how much of a volatility rise counts as "Stress."
💡 How to Use in Practice
The core read is dominant regime × confidence. When the ribbon is thick in a single color (high confidence) and stable, strategies aligned with that regime tend to work (trend-following in Risk-On, fading in Range). When ribbon colors blend, it signals a regime transition — a cue to cut size or stand aside. When the Stress (amber) probability rises, volatility is expanding — useful for staging breakouts or de-risking.
For multi-timeframe work, read the higher-timeframe regime for the backdrop and execute on a lower timeframe. With the price-chart background tint enabled, regime "epochs" sit directly over the candles, making context easy to combine with trend or volume tools.
⚠️ Important Notes
Regimes are hidden until the warmup period (default 200 bars) completes. Reloading the indicator, or changing settings, makes the model relearn across the entire history from scratch — learning state is not persisted. This is a diagonal-covariance approximation and does not explicitly model correlation between features. Regime probabilities are the model's probabilistic beliefs, not certain forecasts. Parameters update on confirmed bars, while the forming bar's probabilities move live.
🚨 Disclaimer
This indicator is for educational and informational purposes only and does not constitute financial or investment advice. Past performance is not indicative of future results. All trading involves risk. Use it alongside your own thorough testing and sound risk management; all trading decisions remain solely your own responsibility. Indicator
AetherEdge - Q-Learning Regime Agent🖊️ Overview
AE-QRA is a reinforcement-learning agent that learns its policy from experience, right on your chart. It discretizes the market into 27 regimes (Trend × Momentum × Volatility) and learns which action to take in each — Short, Flat, or Long — by maximizing reward through tabular Q-learning. No instructions, no labels: the agent relies on reward alone, sharpening its judgment as it shifts from exploration to exploitation.
🔶 Key Features
A real Q-learning engine — the TD(0) update Q(s,a) ← Q(s,a) + α runs every bar, updating a value table over states × actions.
Reward function — reward = position × next-bar return − turnover cost. Profitable decisions are reinforced; needless trading is penalized.
Exploration–exploitation, made real — an ε-greedy policy whose ε decays over time, automatically shifting from random exploration to exploiting the learned policy.
Policy heatmap — the learned policy across 27 regimes, visualized as a 9-cell color grid at the live volatility slice; the cell for the current regime glows.
Regime coloring — bars and background are tinted by the agent's current stance, growing more saturated with conviction.
Action-flip markers — triangle markers mark switches to Long or Short.
Live statistics panel — action, conviction, exploration rate ε, cumulative reward (policy P&L), regime coverage, and the current state.
Fully deterministic, non-repainting design — the exploration RNG is seed-fixed; learning and decisions occur on confirmed bars only, on a one-bar lag.
🧠 Technical Architecture
The state space is built from three discrete features — ATR-unit trend deviation (↓/·/↑), RSI momentum (↓/·/↑), and volatility percentile rank (low/mid/high). Their product gives 27 states, with three actions (Short = −1 / Flat = 0 / Long = +1). The value table Q is persisted as a var matrix (27 × 3).
On each confirmed bar, the agent first observes the reward of its previous action (position × realized return − switching cost), then updates that value via TD(0) using the maximum Q at the next state. It then selects the next action ε-greedily. ε decays as ε = ε_min + (ε_start − ε_min)·exp(−step/decay) — exploratory early on (frequent random actions), exploiting the learned policy as it matures. Conviction is computed as the separation of the three Q-values at the current state and feeds both the background tint and the panel. Because the RNG is a deterministic LCG, the policy is reproducible under identical settings.
🎯 Three design choices stand out. First, embedding a switching cost in the reward suppresses over-trading at the learning level and makes Flat a meaningful choice. Second, confining learning and decisions to confirmed bars on a one-bar lag eliminates future leakage. Third, the deliberately compact 27-state design helps the policy converge even on shorter histories.
⚙️ Recommended Settings & Tuning Guide
As a crypto starting point — BTC/ETH (1D, 4H): α = 0.10, γ = 0.95, ε-Start = 0.90, ε-Decay = 500, switching cost 3 bps; this yields clean trend adaptation. High-volatility names (SOL, XRP): raise switching cost to 5–8 bps to curb trade frequency, and shorten ε-Decay toward 300 so the agent reaches the exploitation phase faster through the noise.
Per parameter: Learning rate α sets adaptation speed — 0.15–0.25 in fast-rotating regimes, 0.05–0.10 in stable ones. Discount γ sets foresight — higher weights long-run reward (0.95–0.99 for swings, 0.85–0.92 for scalps). ε-Decay sets the length of the exploration phase — larger explores longer and learns more cautiously. Switching cost doubles as a direct knob on trade frequency.
💡 How to Use in Practice
The core read is the agent's stance × conviction. A flip to Long (triangle marker) with high conviction and a rising cumulative reward reads as a tailwind for buying dips. The policy heatmap is a powerful context tool: by reading the colors of the cells adjacent to the current regime (the glowing cell), you can anticipate how the agent will act if the market shifts slightly. When ε is still high, the policy is undecided, so treat signals as informational only.
For multi-timeframe work, confirm the macro bias from the higher-timeframe stance (1D), then execute aligned flips on a lower timeframe (1H–4H). Layered over market structure (S/R, order blocks) or volume, it adds which regime, and with what conviction, the agent chooses to go long — useful confirmation context.
⚠️ Important Notes
Until the warmup period (default 300 bars) and the exploration phase pass, actions are largely exploratory (random). Reloading the indicator, or changing settings/seed, makes the agent relearn across the entire history from scratch — learning state is not persisted. The panel's "Policy P&L" is an in-sample, while-learning metric that does not fully account for switching cost or fill slippage; it is not a backtest or forward result. The 27-state tabular design is intentionally coarse — it learns a regime policy, not fine price structure. Decisions update on confirmed bars, and the agent's stance is not a direct buy/sell instruction.
🚨 Disclaimer
This indicator is for educational and informational purposes only and does not constitute financial or investment advice. Past performance is not indicative of future results. All trading involves risk. Use it alongside your own thorough testing and sound risk management; all trading decisions remain solely your own responsibility. Indicator
AetherEdge - Echo State Network🖊️ Overview
AE-ESN doesn't train on price — it pours price into a fixed, random recurrent core (a reservoir) and reads the future out of its rich nonlinear dynamics. No backpropagation, no offline training. Only the readout layer self-evolves online, bar by bar, continuously adapting to the prevailing regime. The result is projected forward as a confidence cone whose width is shaped by the model's own error distribution.
🔶 Key Features
Reservoir-computing engine — a fixed, sparsely-wired recurrent layer (4–48 neurons) maps the price stream into a high-dimensional state space.
Self-evolving online learning — the linear readout is updated every bar via normalized LMS; as the market shifts, the model quietly rewrites itself.
Guaranteed Echo State Property — the spectral radius is measured by power iteration and auto-scaled to the target ρ, ensuring stable memory dynamics.
Forward forecast cone — a free-running projection of multiple bars ahead, rendered as a confidence cone that widens with horizon.
Fully deterministic, non-repainting design — neuron wiring is reproducibly generated from a seed; learning and statistics update on confirmed bars only.
No look-ahead learning — weights are trained on a strict one-bar lag: the previous forecast versus the now-realized outcome.
Live statistics panel — directional accuracy, an R²-like confidence score, reservoir energy, and the projected return in real time.
Neon-grade visuals — gradient confidence cone, a glowing forecast line, and a directional background tint.
🧠 Technical Architecture
At the core is a reservoir driven by three normalized features (a standardized return, an ATR-normalized trend deviation, and momentum). State evolves through leaky integration — x(t) = (1−a)·x(t−1) + a·tanh(W_res·x(t−1) + W_in·u(t)). Both W_res and W_in are generated once via a deterministic LCG and then held fixed; the only thing that learns is the readout vector W_out.
That readout evolves on every confirmed bar through normalized LMS: each bar, the error between the previous forecast and the realized return is projected back along the state vector to correct the weights. An exponential moving variance of that error drives the cone's width (σ), while confidence is computed as explanatory skill over a zero-forecast baseline (an R²-like measure). The forward projection clones the current state and runs the reservoir free, feeding its own predictions back as input across the horizon.
🎯 Three design choices are worth highlighting. First, the spectral radius is measured via power
iteration before being normalized to ρ, so memory quality stays stable even as neuron count changes. Second, learning is confined to confirmed bars on a one-bar lag, eliminating future leakage. Third, because the wiring is seed-deterministic, identical settings reproduce an identical topology on any chart.
⚙️ Recommended Settings & Tuning Guide
As a crypto starting point — BTC/ETH (1D, 4H): 16 neurons, ρ = 0.90, Leak = 0.30, learning rate 0.15, horizon 12; it tracks trending instruments cleanly. High-volatility names (SOL, XRP): lower Leak to 0.20–0.25 to smooth noise, and widen Band σ toward 2.0 so the cone reflects the rougher price action.
Per parameter: Learning rate (μ) sets adaptation speed — use 0.2–0.3 for fast regime turns, 0.08–0.12 for stability in ranges. Spectral radius (ρ) sets memory length — higher retains longer context (0.95–1.05 reactive, 0.80–0.90 calmer). Neuron count sets expressiveness — 12–16 for lower timeframes, 24–32 for higher timeframes or complex structure. Horizon should match your trading style — 6–8 for scalps, 20–30 for swings.
💡 How to Use in Practice
The core read is cone direction × confidence. When the cone tilts up with both confidence and directional accuracy elevated, treat it as a tailwind for buying dips. A tag-and-reject at the cone's outer edge marks the boundary of the model's expected range — a mean-reversion cue. A rapidly expanding cone signals rising volatility; a contracting cone suggests a transition into consolidation. When the one-bar fit trail hugs actual price tightly, the model is reliable in that regime.
For multi-timeframe work, confirm the macro bias from the cone on the higher timeframe (1D), then execute aligned setups on a lower timeframe (1H–4H). Layering it over volume profile or market structure (S/R, order blocks) lets the cone add where and with what conviction, giving mutual confirmation.
⚠️ Important Notes
Signals are hidden until the warmup period (default 200 bars) completes. Reloading the indicator, or changing settings/seed, makes the network relearn across the entire history from scratch — learning state is not persisted between sessions. Reproducibility on a given chart is preserved, but expect confidence and accuracy to take time to settle after any change. The forecast cone is a probabilistic expected range, not a guarantee. Learning and statistics update on confirmed bars only, and the cone refreshes when a bar closes. Pushing neuron count high (40+) over very long history can approach Pine's ~40-second runtime limit.
🚨 Disclaimer
This indicator is for educational and informational purposes only and does not constitute financial or investment advice. Past performance is not indicative of future results. All trading involves risk. Use it alongside your own thorough testing and sound risk management; all trading decisions remain solely your own responsibility. Indicator
Bitcoin ETF Dominance and Accumulation Ratio | Astral Vision Bitcoin ETF Market Dominance and Accumulation Ratio | Astral Vision 🌠💠
Since the launch of US spot Bitcoin ETFs, institutional custody of Bitcoin has grown to represent a significant and expanding share of the total 21 million supply. This indicator tracks that relationship through two complementary analytical lenses: how much of Bitcoin's fixed supply is now held in institutional custody across all ten ETFs, and whether the pace at which ETFs are accumulating Bitcoin is increasing or decreasing relative to price, providing a forward-looking measure of institutional conviction that goes beyond raw holdings data.
The indicator operates in two modes that address fundamentally different questions about the same underlying data.
ETF Bitcoin Market Dominance mode answers the question of how much of Bitcoin's total supply institutional investors have removed from circulation through ETF wrappers. The oscillator shows the total ETF holdings as a percentage of the 21 million hard cap, updated daily from on-chain Glassnode data. A rising line means ETFs are capturing an increasing share of the fixed supply, reducing the available float for other market participants and historically creating upward price pressure. The table shows each issuer's contribution to that dominance percentage alongside the change in their supply share across three configurable periods, making it possible to see both the current structural picture and the direction of change at the issuer level.
Accumulation Ratio mode answers a more nuanced question: are ETFs accumulating Bitcoin faster or slower than price is rising? The ratio is constructed as the cumulative net flow of all ETFs divided by the Bitcoin price. A rising ratio means ETFs are adding holdings faster than price appreciation alone would explain, indicating genuine net accumulation rather than passive appreciation of existing holdings. A falling ratio means price is rising faster than ETF accumulation, which can indicate that ETF demand is not the primary driver of the current price move. This ratio is then Z-Score normalized against its own rolling history and inverted so that negative Z-Scores, representing periods where price has outrun ETF accumulation, appear above zero as a warning signal, while positive Z-Scores, representing periods of active ETF accumulation, appear below zero as a supportive condition.
Analytical Framework
Type of analysis: institutional supply absorption, accumulation pace relative to price
Recommended timeframe: daily chart, suited for position trading and long-term cycle analysis
Signal type: continuous oscillator with per-issuer multi-period breakdown table, two analytical modes
How to interpret the signals
In ETF Bitcoin Market Dominance mode, the oscillator shows a steadily rising line as long as ETFs continue to absorb supply. The rate of that rise is what matters most. A steeply rising oscillator means ETFs are pulling Bitcoin off the market faster than recent history, historically a condition associated with price strength. A flattening oscillator means absorption has stalled, which has historically preceded consolidation phases. Watch the supply delta columns in the table for each issuer: issuers showing positive supply deltas across all three periods are in sustained accumulation, while those showing consistent negative deltas across all periods are in structural outflow that is reducing their share of the fixed supply.
In Accumulation Ratio mode, the Z-Score oscillator inverted interpretation is the key. When the oscillator is below the lower threshold, it means ETFs are accumulating strongly relative to price, a structurally bullish signal indicating that institutional demand is driving or supporting the price move. When the oscillator rises above the upper threshold, price is running ahead of ETF accumulation, which has historically been a caution signal indicating reduced institutional sponsorship of the current price level. The table ROC columns show which specific issuers are driving changes in the ratio, allowing the identification of the products most actively accumulating relative to their own history.
Across both modes, the total row in the table is the most important single reading. It gives the aggregate institutional picture without needing to process each issuer individually.
How it differs from existing tools
No standard PulseWire indicator tracks ETF holdings as a percentage of Bitcoin's fixed 21 million supply across all ten issuers simultaneously with multi-period supply delta analysis. The Accumulation Ratio mode is entirely original: dividing cumulative net flows by price and Z-Score normalizing the result produces a signal that is fundamentally different from both raw flow data and simple holdings levels, capturing the relationship between institutional demand intensity and price that neither measure alone can express.
Plots 📊
Oscillator showing ETF supply percentage of the 21M hard cap with fill (ETF Bitcoin Market Dominance mode)
Inverted Z-Score oscillator of the cumulative flow-to-price ratio with threshold lines and fill (Accumulation Ratio mode)
Zero baseline
Table with per-issuer supply dominance, supply percentage, and three-period supply delta (ETF Bitcoin Market Dominance mode)
Table with per-issuer holdings ROC across three periods and accumulation ratio delta across three periods (Accumulation Ratio mode)
Inputs 🎛️
Mode: ETF Bitcoin Market Dominance or Accumulation Ratio
Period A, B, C: configurable lookback windows in days for the three table columns
Z-Score Length: rolling normalization window for the Accumulation Ratio mode
Threshold High and Low: configurable extreme zone boundaries for the Accumulation Ratio oscillator
Colors 🎨
5 Astral Vision presets + custom override. Default: Hermes.
Disclaimer ⭕️
This indicator is for informational and educational purposes only. It does not constitute financial advice. Past performance is not indicative of future results. Always do your own research before making investment decisions. Indicator
Bitcoin ETF Holdings Analysis | Astral Vision Bitcoin ETF Holdings Analysis | Astral Vision 🌠💠
Knowing how much Bitcoin the ETFs hold today is only half the picture. What institutional analysts and serious traders actually watch is how those holdings are changing: whether the aggregate balance is growing or shrinking, at what rate, and which specific products are driving that change. This indicator provides that complete picture in a single view, combining a stacked area chart of absolute on-chain holdings across all ten US spot Bitcoin ETFs with a detailed table that shows the rate of change and raw Bitcoin delta for each issuer across two configurable time periods.
The stacked area chart plots the total Bitcoin held across all ten ETFs as a cumulative area, with each issuer's contribution as its own color-coded band. This makes it immediately visible not only how the total is evolving but also how the internal composition is shifting. A rising total with one band expanding rapidly indicates that a specific product is driving the accumulation. A flat or falling total with bands shifting relative to each other indicates rotation between products rather than net new demand.
The table provides the quantitative detail behind what the chart shows visually. For each ETF, it displays the current holdings in BTC and USD, the percentage rate of change over two configurable lookback periods, and the absolute Bitcoin delta over the same periods. The combination of rate of change and raw delta is deliberately designed to give two different analytical perspectives simultaneously: the rate of change shows whether the pace of accumulation is historically significant for that product, while the raw delta shows the absolute quantity of Bitcoin moving in absolute terms.
Analytical Framework
Type of analysis: institutional custody tracking, absolute ETF holdings monitoring
Recommended timeframe: daily chart, best used as a structural demand context layer for position trading and long-term analysis.
Signal type: absolute holdings tracking with multi-period rate of change and raw delta per issuer
How to interpret the signals
The stacked chart is the macro view. A steadily rising top line means the aggregate institutional Bitcoin custody is growing, which is structurally bullish regardless of short-term price action. A flattening or declining top line means institutional accumulation has stalled or reversed, which historically has preceded or accompanied price weakness. The shape of the individual bands tells you where demand is concentrated: when a single band is expanding disproportionately, that product is capturing the lion's share of new institutional allocation.
The table is where you make practical decisions. The ROC columns tell you whether the pace of holdings change is accelerating or decelerating. A product with a strongly positive short-period ROC and a weaker long-period ROC is accelerating recently after a period of slower growth, indicating fresh institutional interest in that specific vehicle. A product with the reverse, strong long-period ROC but weakening short-period, is showing momentum deceleration and may be approaching a plateau.
The raw delta columns are particularly important for GBTC. Because GBTC started with an enormous legacy balance, even a small negative percentage ROC translates to a very large absolute Bitcoin outflow. Watching the absolute delta column for GBTC tells you the actual Bitcoin quantity leaving that product each week or month, which directly represents selling pressure that the market must absorb from new-product inflows.
The total row at the bottom is the single most important summary: it tells you in plain numbers how many Bitcoin have entered or left the entire ETF ecosystem over each time window. When both periods show a positive delta, institutional custody is growing on both short and medium horizons, a structurally supportive condition. When the short-period delta turns negative while the long-period remains positive, a temporary pause or reversal is occurring within an ongoing accumulation trend.
How it differs from existing tools
Standard on-chain ETF trackers display either a single product's balance or a simple total without per-issuer breakdown or rate-of-change context. This indicator provides both BTC and USD holdings simultaneously for each of the ten issuers, computes percentage ROC and absolute delta independently across two configurable periods, and renders the full composition as a stacked area chart. No standard PulseWire indicator combines all-issuer absolute custody tracking, dual-metric multi-period analysis, and stacked composition visualization in a single tool.
Plots 📊
Stacked area chart showing total Bitcoin holdings across all ten ETFs with per-issuer color-coded bands
Table with current BTC holdings, current USD holdings, short and long-period ROC, and short and long-period absolute BTC delta for all ten ETFs plus aggregate total row
Inputs 🎛️
Period A: short lookback window in days for the first ROC and delta columns
Period B: medium lookback window in days for the second ROC and delta columns
Colors 🎨
5 Astral Vision presets + custom override. Default: Futura. Each ETF retains its fixed color across all indicators in the ETF suite for visual consistency.
Disclaimer ⭕️
This indicator is for informational and educational purposes only. It does not constitute financial advice. Past performance is not indicative of future results. Always do your own research before making investment decisions. Indicator
Bitcoin ETF Flows Z-Score | Astral Vision Bitcoin ETF Flows Z-Score | Astral Vision 🌠💠
Daily ETF flow data is noisy by nature. A single large institutional redemption or creation event can dominate the raw number on any given day, making it difficult to distinguish between a statistically meaningful shift in institutional demand and normal day-to-day variation. This indicator solves that problem by normalizing each ETF's daily flow against its own rolling distribution, producing a Z-Score that measures how unusual today's flow is relative to that specific product's own history, and then combining those normalized readings into a single aggregate signal that is far more statistically robust than any raw flow total.
The key insight behind this approach is that absolute flow size is not what matters most. What matters is whether today's flow is unusual relative to what that ETF normally sees. A 5,000 BTC inflow into IBIT is unremarkable if IBIT regularly sees flows of that magnitude, but a 5,000 BTC inflow into a smaller product like EZBC would be an extreme outlier. By normalizing each product independently before aggregating, this indicator treats all ten ETFs as equal contributors to the signal regardless of their size, detecting coordinated multi-issuer flow anomalies that would be invisible in a simple raw total.
The oscillator in the sub-panel shows the 7-day smoothed aggregate Z-Score, filtered to reduce single-day noise. The table provides the real-time picture: the current Z-Score for each individual ETF plus its rolling average across three configurable periods, making it possible to see which issuers are driving or dragging the aggregate and whether their anomalous behavior is a single-day spike or a sustained trend.
Analytical Framework
Type of analysis: institutional flow anomaly detection, ETF demand quality assessment
Recommended timeframe: daily chart, best used as a macro demand quality overlay for position trading.
Signal type: normalized Z-Score oscillator with per-issuer multi-period breakdown table
How to interpret the signals
The oscillator and background color on the price chart are the primary real-time signals. When the smoothed Z-Score is above the upper threshold, ETF flows across the aggregate are statistically elevated relative to their own recent history, meaning institutional demand is running hotter than normal. This has historically preceded or accompanied upward price momentum. When it is below the lower threshold, flows are statistically depressed, meaning institutional demand is unusually weak even if some flows remain technically positive in raw terms.
The zero line is the most important structural reference. A sustained period of the Z-Score above zero means that more often than not, each day's flows are above each ETF's own historical average, indicating a regime of structurally elevated institutional demand. A sustained period below zero indicates the opposite: a regime where demand is consistently running below each product's own baseline.
The table is where the signal becomes actionable at the issuer level. Focus on the relationship between the current Z-Score and the rolling period averages for each ETF. An issuer with a high current Z-Score but a near-zero or negative long-period average is experiencing a sudden spike that is not part of a sustained trend, which may indicate a one-off event rather than a genuine shift in institutional appetite for that product. An issuer with a high Z-Score across all three periods, current, short, and long, is in a confirmed sustained flow expansion that is genuinely anomalous relative to its own history.
The most powerful signals come when multiple issuers simultaneously show elevated Z-Scores across all time periods. This indicates a broad coordinated increase in institutional demand across the entire ETF ecosystem, not just a rotation from one product to another, and has historically been associated with the strongest price performance windows.
GBTC's Z-Score readings deserve particular attention interpreted in reverse: a very negative GBTC Z-Score means outflows from that product are running unusually high even by GBTC's own elevated-outflow standards, adding unusual selling pressure on top of the structural outflow trend. When GBTC's Z-Score rises toward zero or positive, its outflow rate is normalizing, which historically has been a net positive for the aggregate balance.
How it differs from existing tools
Standard ETF flow tools compare raw Bitcoin amounts across issuers of vastly different sizes, which systematically overstates the significance of large-cap product flows and understates signals from smaller products. This indicator normalizes each issuer independently against its own rolling mean and standard deviation, then aggregates. A +2σ reading from EZBC carries the same weight as a +2σ reading from IBIT, making the combined signal sensitive to broad-based demand shifts rather than dominated by the largest product.
Plots 📊
7-day smoothed aggregate Z-Score oscillator with upper and lower threshold lines
Zero baseline
Background color on the price chart when aggregate Z-Score is in extreme zones
Table with current Z-Score and three-period rolling averages for all ten ETFs plus the aggregate total, color-coded by sign
Inputs 🎛️
Z-Score Length: rolling window for per-ETF mean and standard deviation normalization
Threshold High and Low: configurable extreme zone boundaries for the oscillator
Period A, B, C: rolling average windows in days for the three period columns in the table
Colors 🎨
5 Astral Vision presets + custom override. Default: Hermes.
Disclaimer ⭕️
This indicator is for informational and educational purposes only. It does not constitute financial advice. Past performance is not indicative of future results. Always do your own research before making investment decisions. Indicator
Bitcoin ETF Flows Analysis | Astral Vision Bitcoin ETF Flows Analysis | Astral Vision 🌠💠
ETF flows are the single most watched institutional signal in the Bitcoin market since the launch of US spot products in January 2024. A positive flow day means authorized participants are creating new ETF shares by depositing Bitcoin into the trust, indicating net buying pressure from institutional and retail investors accessing Bitcoin through regulated wrappers. A negative flow day means shares are being redeemed and Bitcoin is leaving the trust, indicating net selling pressure. This indicator tracks those daily flows across all ten US spot Bitcoin ETFs simultaneously, breaks them down per issuer, and aggregates them across three configurable time windows so that both the current momentum and the medium-term trend of institutional accumulation are immediately visible.
The histogram plots the combined net daily flow across all selected ETFs in Bitcoin. Each bar represents the aggregate Bitcoin that moved into or out of the entire ETF ecosystem on that day. The optional mean line smooths this into a trend, filtering out the day-to-day noise of individual large transactions or weekend reporting distortions and revealing the underlying directional bias of institutional flows. The table provides the per-issuer breakdown across three rolling periods, making it possible to see not just the total but which products are driving or dragging the aggregate.
Each issuer can be toggled independently, allowing the aggregate to be computed for any subset of ETFs. This is particularly useful for isolating the behavior of a specific issuer cohort, such as comparing the new-launch products against GBTC, or analyzing the two largest products in isolation.
Analytical Framework
Type of analysis: institutional flow tracking, ETF demand monitoring
Recommended timeframe: daily chart. Best used as a macro demand context overlay for position trading.
Signal type: daily net flow histogram with multi-period cumulative table
How to interpret the signals
The histogram is the primary real-time signal. Consecutive positive days, especially with increasing bar height, indicate sustained institutional accumulation and historically correlate with upward price momentum. Consecutive negative days indicate persistent redemption pressure, which has historically been a headwind for price regardless of on-chain fundamentals. The transition from negative to positive histogram bars after a period of outflows has historically been one of the cleaner short-to-medium-term entry signals in the post-ETF Bitcoin market.
The mean line mode is more useful for identifying trend direction than individual days. When the smoothed line is rising and positive, institutional demand is structurally present. When it is flattening from positive territory, momentum is slowing even if individual days remain positive, which has historically been an early warning of near-term consolidation or correction. When the mean line turns negative, institutional selling is becoming the dominant force.
The table columns are where the most nuanced signals emerge. Look at each issuer's flow sum across all three periods simultaneously. An issuer showing large positive flows in the short period but flat or negative flows in the long period is accelerating recently after a previous pause, indicating fresh demand entering that product. An issuer showing the reverse is losing momentum. When GBTC shows persistent negative flows across all periods while other issuers show positive, the structural outflow from the legacy product is continuing, and the question is whether new-product inflows are more than offsetting it.
The total row at the bottom of the table is the most important single number: it shows whether the entire ETF complex is in net accumulation or net distribution across each time horizon. When the 7-day, 30-day, and 90-day totals are all positive and growing, the institutional bid is structurally strong. When they diverge, with short-term negative but long-term positive, the market is in a temporary pullback within an ongoing accumulation trend.
How it differs from existing tools
Standard ETF flow tools on PulseWire either display a single ETF's balance or a simple total without per-issuer breakdown. This indicator computes daily deltas from on-chain balance data for all ten issuers simultaneously, aggregates them with per-ETF toggle control, and provides rolling cumulative sums across three independent time windows per issuer. No standard PulseWire indicator combines per-issuer flow isolation, multi-period rolling aggregation, and a toggleable issuer selection into a single tool.
Plots 📊
Daily net flow histogram across all selected ETFs in Bitcoin, colored positive or negative
Optional smoothed mean line replacing the histogram for trend visualization
Zero baseline
Table with per-issuer cumulative flows across three configurable periods, plus aggregate total row, color-coded by direction
Inputs 🎛️
ETF toggles: individual on/off control for each of the ten US spot Bitcoin ETFs
Period A, B, C: rolling windows in days for the three cumulative sum columns in the table
Plot Mode: Daily histogram or smoothed Mean line
Mean Length: smoothing period when Mean mode is selected
Colors 🎨
5 Astral Vision presets + custom override. Default: Futura. Each ETF retains its fixed color in the table for consistency with the Dominance indicator.
Disclaimer ⭕️
This indicator is for informational and educational purposes only. It does not constitute financial advice. Past performance is not indicative of future results. Always do your own research before making investment decisions. Indicator
Bitcoin ETF Dominance | Astral Vision Bitcoin ETF Dominance | Astral Vision 🌠💠
Since the approval of spot Bitcoin ETFs in January 2024, institutional capital flows have become one of the most important signals in the Bitcoin market. But knowing the total ETF balance alone is not enough: what matters is which issuers are capturing market share, whether that share is shifting, and how fast those shifts are happening. This indicator tracks the internal competitive landscape of the US spot Bitcoin ETF ecosystem, showing in real time which products are gaining or losing dominance within the total ETF allocation and how that distribution is evolving across three configurable time windows.
The indicator fetches the on-chain Bitcoin balances of ten US spot ETFs from Glassnode and computes each issuer's percentage share of the total, updated daily. These shares are displayed as a stacked area chart where each band represents one ETF, making it immediately visible how the total pie is divided and how the boundaries between issuers are shifting over time. The table alongside provides the precise current dominance percentage for each ETF together with the change in that share over three configurable periods.
The dominance delta columns are the most actionable output. A positive delta for an issuer means it is capturing an increasing share of total ETF assets, indicating that new flows are preferentially entering that product or that other issuers are seeing net outflows. A negative delta means the issuer is losing share, which can happen either because it is experiencing outflows or because competitors are growing faster. These shifts in relative dominance often precede or accompany significant movements in total ETF flows, which in turn are a leading indicator for Bitcoin price action.
Analytical Framework
Type of analysis: institutional flow structure, ETF market share dynamics
Recommended timeframe: daily chart. Best used as a macro context overlay for position trading and long-term analysis.
Signal type: continuous dominance tracking with configurable multi-period delta comparison
How to interpret the signals
The stacked area chart provides a visual map of the ETF ecosystem at a glance. When a single issuer's band is expanding visually, it is capturing a disproportionate share of new inflows. When multiple bands are shifting simultaneously, it indicates a rotation between products rather than a change in total ETF appetite.
The table is where the actionable signals live. Focus on the dominance delta columns rather than the absolute dominance percentage. An issuer showing a large positive delta over the short period but a smaller positive delta over the long period is accelerating its market share capture, which is historically associated with that product being the primary destination for institutional inflows in the current cycle. An issuer showing consistent negative deltas across all three periods is losing share persistently, often because it carries higher fees or because a competing product has become the preferred vehicle for new institutional allocation.
BlackRock's IBIT has been the dominant flow vehicle since launch and its dominance delta is the most watched: when IBIT is gaining share, it typically means new institutional money is entering the market. When it is losing share to smaller issuers, it can indicate rotation by more sophisticated allocators toward lower-cost products, which is structurally bullish for the broader ETF ecosystem.
GBTC deserves special attention as the legacy product: persistent negative deltas for GBTC confirm that its structural outflow trend is continuing, which has historically been a source of selling pressure that absorbed bullish ETF inflows. When GBTC's negative delta begins to flatten, it removes a persistent headwind.
Use the three time windows together: short-period deltas for momentum, medium-period for trend, long-period for structural shift. When all three agree for a given issuer, the dominance trend is confirmed.
How it differs from existing tools
Standard ETF flow indicators on PulseWire display either the total Bitcoin balance across all ETFs or the flow for a single issuer, neither of which shows how the internal competitive structure of the ETF market is evolving. This indicator normalizes each issuer's balance to a percentage of the total, making the relative competitive dynamics visible in a way that absolute balance charts cannot. The three-period delta table provides temporal depth that a single current-day reading cannot, allowing the distinction between a momentary shift and a sustained structural trend.
Plots 📊
Stacked area chart with one band per ETF, color-coded by issuer, covering the full 0-100% range
Table with current dominance percentage and configurable-period delta for all ten ETFs, color-coded by direction
Inputs 🎛️
Period A, B, C: the three lookback windows in days for the dominance delta columns in the table
Colors 🎨
5 Astral Vision presets + custom override. Default: Infinito. Each ETF has a fixed distinct color in the stacked area chart for visual identification.
Disclaimer ⭕️
This indicator is for informational and educational purposes only. It does not constitute financial advice. Past performance is not indicative of future results. Always do your own research before making investment decisions. Indicator
Volatility Bands Using t-Distribution & Prediction IntervalsThis indicator is a statistical tool designed to project a dynamic range where the next asset price is expected to fall within a specific level of confidence.
Unlike standard volatility bands, a prediction interval is a statistical range that estimates where a single future observation will fall, given a specific probability. Because predicting a single future outcome introduces more inherent uncertainty than predicting an average, these bands are wider and mathematically tighter for forecasting next-bar anomalies.
How it Works
This indicator uses a Student's t-distribution (or an optional z-distribution for performance) to calculate historical volatility thresholds.
Finding the Estimated t-distribution
When you select a 95% Confidence Level, the script sets an error alpha of 5% (1 - 0.95). Because asset price movement can deviate to either the upside or downside, it splits this error equally into both tails (a two-tailed test). The script normalizes this, targeting an exact area of 0.025 (2.5%) in the extreme tails of the curve.
To find where that target area lies, the script reconstructs the Probability Density Function (PDF) of the Student's t-distribution. The height of the curve depends heavily on the Degrees of Freedom (df = length - 1).
Because Pine Script doesn't have a native Gamma function, the script uses double factorials to calculate the complex math coefficients. If your sample size (length) is exceptionally high (>300), the t-distribution naturally mirrors a regular normal distribution, so it switches to a standard Gaussian curve equation to save some time.
// Calculate double factorial
double_fac(int n) =>
float res = 1.0
int curr = n
while curr > 1
res := res * curr
curr := curr - 2
res
// t-distribution formula
cur_dist(float x, int cur_df) =>
float res = 0.0
if cur_df > 300
res := 0.3989422804014326779 * math.exp(-x * x * 0.5)
else
float coeff = double_fac(cur_df - 1) / (math.sqrt(cur_df) * double_fac(cur_df - 2) * (cur_df % 2 == 0 ? 2.0 : math.pi))
res := coeff * math.pow(1.0 + x * x / cur_df, -0.5 * (cur_df + 1))
res
To find the approximate t-value that corresponds to our target tail area, the script performs numerical integration using the Trapezoid Rule (trapezoid_reverse). This is the most time consuming part.
// Reverse trapezoidal to calculate t-value from area
trapezoid_reverse(float p, int cur_df) =>
float max_p = 0.5
float max_stat = 300.0
float add_prec = 1.0
float result_i = 0.0
if p == max_p
result_i := 100000000.0 // Infinity
else
float trap_sum = 0.0
float gap_width = p / (add_prec * 1250.0)
float i = 0.0
float last_func = cur_dist(0.0, cur_df)
while trap_sum < (2500.0 * add_prec) and i < max_stat
i := i + gap_width
trap_sum := trap_sum + last_func
last_func := cur_dist(i, cur_df)
trap_sum := trap_sum + last_func
result_i := i
result_i
Calculating the Prediction Interval
Once the t-critical value is found, it is then put into the standard prediction interval formula. The script uses EMA instead of SMA to improve responsiveness to current trends.
float prediction_interval_top = mean + tCrit * stdev * math.sqrt(1 + 1/length)
float prediction_interval_bot = mean - tCrit * stdev * math.sqrt(1 + 1/length)
Because the calculations for the t-distribution estimates are computationally expensive, you can select the lookback window for using the t-distribution. You can also choose to use the faster z-score for historical bars.
Note 1: The larger the sample size, the smaller you may have to set your lookback window in order to fit within the execution time limits.
You can use this for:
Overbought/Oversold, Mean reversion signals
Dynamic Stop-loss/Take-profit (SL/TP)
1. Overbought/Oversold/Mean Reversion
The indicator creates a red background to indicate that the candlestick has broken above the top 95% prediction interval band. It can be interpreted as a potential reversal.
2. Dynamic Stop-loss/Take-profit (SL/TP)
You can utilize the plotted mean and the confidence interval as entry/exit points. For example, you could put a stop-loss at the bottom band, and a take profit at the top band.
Alerts:
Price Above Top PI (Close crossed out)
Price Below Bottom PI (Close crossed out)
High Above Top PI (Wick touched/pierced top)
Low Below Bottom PI (Wick touched/pierced bottom)
Settings:
Length: The size of the sample for the standard deviation & mean calculations. 30 is recommended.
Source: Used for standard deviation & mean calculations. For example: Changing the source to 'high' would make the indicator predict the range of the next 'high'.
Confidence Level: Dictates how wide the bands should be. A 95% confidence level is default. Must be entered as a decimal between 0 and 1.
Lookback Length: Used to limit the amount of bars to back calculate the prediction interval using the t-distribution. Default is 300.
Use z-score: Use the z-score to calculated past values. Note that this will introduce error. For example, for a sample size of 30 and a 95% confidence level, using a z-score instead of a t-score will introduce a ~4.2% error in your interval width, causing your actual prediction interval to drop from 95% down to ~94.0%.
Background Colors:
Green: The low/close of the candle breached the lower band. Potential bullish reversal.
Red: The high/close of the candle breached the upper band. Potential bearish reversal.
Limitations:
The indicator assumes that the data follows a bell curve, which the markets do not.
The indicator may lag behind actual price action.
The indicator may produce false signals.
The indicator does not predict future prices.
Disclaimer: All trading decisions and responsibilities rest solely on the user of the indicator. Indicator
Android Smart Trend (AST)# 🤖 Android Smart Trend (AST)
Android Smart Trend (AST) is a multi-engine trend intelligence system designed to transform complex market information into a single adaptive trend structure.
Instead of relying on one indicator or a single market condition, AST combines multiple layers of analysis including trend strength, momentum, volatility, market structure, volume behavior, moving average relationships, and multi-timeframe confirmation. The result is a clean and easy-to-read trend environment that helps traders focus on direction rather than noise.
The core philosophy behind AST is simple:
**Hide complexity. Show clarity.**
While many indicators overwhelm the chart with dozens of signals, panels, and calculations, AST processes those components internally and presents only the information that matters most:
• Smart Trend Line
• Adaptive Power Strength
• Multi-Timeframe Confirmation
• Dynamic Trend Aura
• Visual Market Direction
The trend line automatically adapts to changing market conditions. As trend strength increases, the visual structure becomes more dominant, helping traders quickly identify high-confidence environments. During weaker or uncertain conditions, the system naturally reduces visual intensity, allowing traders to recognize potential consolidation phases.
### Key Features
✅ Adaptive Smart Trend Line
A dynamic trend structure that adjusts according to market strength and directional conviction.
✅ Power-Based Visualization
Trend intensity is visually represented through line strength and aura expansion, making market conditions instantly recognizable.
✅ Multi-Timeframe Intelligence
Combines information from multiple timeframes to create a more balanced and reliable directional assessment.
✅ Clean Chart Experience
Designed to reduce chart clutter while maintaining high informational value.
✅ Non-Repainting Logic
Signals and trend state changes are generated using confirmed market data.
### How To Read AST
🟢 Green Environment
Bullish trend conditions with positive directional strength.
🔴 Red Environment
Bearish trend conditions with negative directional strength.
🟡 Neutral Environment
Market is transitioning, consolidating, or lacking directional conviction.
Higher power values indicate stronger trend alignment and broader market agreement across the underlying engines.
### Philosophy
AST is not designed to predict the future.
Its purpose is to organize market information, measure trend quality, and help traders make more informed decisions within existing market conditions.
The goal is simple:
**Less noise. More clarity. Smarter trends.**
Android Smart Trend (AST)
Adaptive Market Intelligence.
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Strongest Price NodesStrongest Price Nodes highlights the price levels where the market has built the strongest activity across recent sessions.
Instead of displaying a full profile or filling the chart with too many levels, this indicator extracts only the most important price nodes and plots them directly on the chart as clean decision levels.
These nodes represent areas where price has spent time, traded volume, or built both time and volume together.
In simple terms, the indicator helps answer one question: Where has the market shown the strongest acceptance?
What It Shows? The indicator plots two types of nodes:
1. Historical Price Nodes
Historical nodes are calculated from completed sessions using the selected lookback period.
They show where the strongest market participation occurred in recent history.
These levels can act as important areas for reaction, support, resistance, retest, breakout confirmation, or mean reversion.
2. Developing Session Node
The developing session node shows the strongest value area forming during the current session.
It updates live as the session develops.To keep the chart clean, the developing session node is always limited to one level.
Calculation Modes The indicator includes three profile modes:
Volume
Finds price levels where the most volume traded.
Time
Finds price levels where price spent the most time.
Volume x Time
Finds price levels where both volume and time align.
This mode is useful for identifying stronger acceptance zones because it combines participation with time spent at price.
Why These Nodes Matter
Strong price nodes often become important market reference points.
Price may pause around them, reject from them, retest them after a breakout, rotate back toward them, or use them as support or resistance.
A strong node is not a buy or sell signal by itself. It is a decision zone. What matters is how price behaves when it reaches the node.
Display Controls
Nodes can be displayed as lines or ATR-based zones.
ATR-based zones adjust automatically with market volatility.
The extension mode can also be controlled.
You can choose to stop levels at session end, extend them for a fixed number of bars, extend them to the right, extend them to the left, or extend them in both directions.
This helps keep the chart clean and avoids unnecessary infinite lines when they are not needed.
How Traders Can Use It
Use historical nodes to mark important prior value areas.
Use the developing node to understand where the current session is building value.
If price holds above a node, it may suggest acceptance.
If price rejects from a node, it may suggest failed acceptance.
If price breaks away and later retests the node, it can become a useful continuation or reversal area.
The indicator works best when combined with price action, VWAP, session highs and lows, trend context, and volume behavior.
Summary
Strongest Price Nodes is built for traders who want a cleaner way to identify important price levels.
It focuses only on the strongest areas of market activity and removes unnecessary profile clutter.
The goal is not to predict direction.
The goal is to show where the market has accepted price and where the next important reaction may happen.
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Sessions Flow [Cartel Console] Sessions Flow
# Overview
Sessions Flow is a session-based market activity visualization tool designed to provide a detailed view of how trading volume is distributed throughout the major global forex and index trading sessions.
Rather than displaying volume as a single aggregated value, the indicator breaks each session into multiple price levels and visualizes where trading activity was concentrated during that session. This allows traders to study session structure, identify high-participation and low-participation areas, and compare how different sessions interact with price.
The indicator automatically tracks and analyzes the four major trading sessions:
• Sydney Session
• Tokyo Session
• London Session
• New York Session
Each session is processed independently and displayed directly on the chart using volume distribution heatmaps, volume profiles, Point of Control calculations, and Value Area measurements.
---
# Core Features
## Session Detection
The indicator automatically identifies the start and end of each selected trading session and creates a dedicated session structure on the chart.
Users can enable or disable individual sessions and customize session times according to their preferences.
Supported sessions include:
• Sydney
• Tokyo
• London
• New York
---
### Session Heatmap
Each session contains a heatmap that displays the relative distribution of trading activity throughout the session range.
The heatmap highlights:
• Areas with greater participation
• Areas with moderate participation
• Areas with lower participation
This provides a quick visual overview of where the market spent the most and least volume during a session.
Heatmap density and transparency settings can be fully customized.
---
### Session Volume Profile
For every session, the indicator constructs a volume profile based on price distribution inside the session range.
The profile is displayed as a side histogram showing how activity was distributed vertically across different price levels.
This can help traders observe:
• High-volume areas
• Low-volume areas
• Session acceptance regions
• Session rejection regions
The profile width and display settings are adjustable.
---
## Point of Control (POC)
The Point of Control represents the price level that accumulated the highest amount of volume during a session.
The indicator automatically calculates and plots the POC for each completed session.
POC levels often serve as useful reference points when reviewing historical session activity and market structure.
---
### Value Area Analysis
The indicator calculates a configurable Value Area based on the percentage of session volume selected by the user.
Displayed levels include:
• Value Area High (VAH)
• Value Area Low (VAL)
These levels help visualize the region where the majority of session activity occurred.
The default setting uses a 70% Value Area, but users may customize this value.
---
### Historical Session Management
To maintain chart performance, the indicator includes controls for:
• Maximum detailed sessions displayed
• Historical session lookback period
• Simplified rendering of older sessions
Recent sessions can retain full heatmap and profile information while older sessions transition into lightweight background structures.
This allows extensive historical analysis without excessive chart clutter.
---
### Active Session Dashboard
A built-in dashboard displays the currently active trading sessions in real time.
The dashboard provides:
• Active session names
• Live session status indicators
This makes it easy to determine which global markets are currently open without leaving the chart.
---
## Customization Options
The indicator includes a wide range of configurable settings:
### Session Settings
• Individual session visibility
• Custom session times
### Heatmap Settings
• Heatmap resolution
• Number of price bins
• Density visualization controls
### Volume Profile Settings
• Histogram width
• Detailed session limits
### Value Area Settings
• Value Area percentage
• VA visibility controls
### Styling Settings
• Session colors
• Border transparency
• Historical session appearance
• Dashboard position
---
## Intended Usage
Sessions Flow is designed for traders who want to study how market activity develops during different trading sessions.
It can be used for:
• Session analysis
• Market structure observation
• Historical session review
• Volume distribution study
• Contextual chart analysis
The indicator focuses on visualization and analysis rather than signal generation.
---
## Disclaimer
This indicator is intended for educational and analytical purposes only. It does not provide financial advice, trading recommendations, or guaranteed outcomes. Trading involves risk, and users should perform their own analysis before making trading decisions.
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The Strat Sequence Continuation / Reclaim Engine v1.0The Strat Sequence Continuation / Reclaim Engine
The Strat Sequence Continuation / Reclaim Engine is a body-close-based study designed to help traders review how specific Strat candle sequences have historically resolved on the selected chart timeframe.
This indicator combines two separate views:
1. **Markov Regime Permission Panel**
A regime-style table that evaluates recent price behavior and displays market state, return, transition probabilities, permission, and trade behavior context. This table can be turned on/off by the user.
2. **Strat Sequence Continuation / Reclaim Table**
A focused table that measures whether selected Strat sequences continued by body close or failed/reclaimed the relevant reference level.
The sequence table evaluates setups such as:
* Failed 2U → x
* Failed 2D → x
* F2U → 2D → x
* F2D → 2U → x
* F2U → 2D → 2D → x
* F2D → 2U → 2U → x
* 2-2U → x
* 2-2D → x
* 2-1-2U → x
* 2-1-2D → x
* 3-1-2U → x
* 3-1-2D → x
* 3H → x
* 3L → x
For bullish sequences, continuation is counted only when the next candle closes above the applicable reference candle high.
For bearish sequences, continuation is counted only when the next candle closes below the applicable reference candle low.
If price does not close beyond the reference level, the event is classified as reclaim/fail.
The table also includes:
* User-selected lookback
* Live setup candle read
* Live sequence watch
* Body-close decision rule
* Row visibility controls
* Minimum sample threshold filtering
* Optional bull/bear color coding
This tool is intended for historical sequence review, market context, and discretionary analysis. It does not predict future price movement, generate buy/sell signals, or provide financial advice. All outputs depend on the selected symbol, timeframe, lookback settings, and available chart history.
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