Tribulus & Ginseng PowerWhat is Tribulus & Ginseng Power?
Tribulus & Ginseng Power is an advanced inter-market macro strategy engine designed to identify institutional trend reversals and liquidity sweeps in high-volatility financial markets. Rather than analyzing an asset in isolation, this algorithm cross-examines live telemetry from three critical pillars of the crypto ecosystem simultaneously to confirm institutional dominance before executing order signals.
How the Multiverse Matrix Core Works:
The script acts as a command center analyzing three independent algorithmic engines:
The Main Asset Engine (M): Evaluates the selected chart using specialized swing detection, price action anomalies (Sushi Rolls), and premium kernel smoothing processes (Monalisa and UT Bot matrices) to find mathematical market exhaustion.
The Crypto Total Market Cap Engine (T): Requests live structural data behind the scenes from CRYPTOCAP:TOTAL to track whether global macro capital flow supports the localized trend breakout.
The Bitcoin Dominance Engine (D): Interrogates CRYPTOCAP:BTC.D to identify where global risk-on or risk-off sentiment is clustering (Bitcoin vs. Altcoins).
The Signal Breakdown Matrix:
By analyzing macro matrix alignments, the indicator prints institutional-tier alerts:
💎 PERFECT L: Global capital flows are expanding (TOTAL bullish), Bitcoin dominance matches, and the local chart confirms a bottom. High-probability premium bullish trigger.
🚀 ALT L (Altcoin Rally): Local price points upward while TOTAL cap rises and BTC.D drops, capturing the exact momentum shift where money floods into high-beta altcoins.
🩸 CRASH S: Direct macro distribution model. Major capitals are exiting the ecosystem while charts break local liquidity floors.
⚡ EARLY L / S (Early Macro Alignment): Fires instantly when the global macro telemetry matrices align before the local price reflects it, offering professional early entries before the momentum candle arrives.
Optimal Environments & Parameters:
Premium Assets: Highly volatile majors (BTC, ETH, high-cap layer-1 protocols) and major indices (NAS100, SPX).
Ideal Timeframes: Curated natively for 1-Hour (1h) and 4-Hour (4h) charts for long-form matrix confirmation. Can be scaled down to 15-Minute (15m) charts for intra-day trading.
Automation Ready: Configured with a stateful position lifecycle manager fully optimized for WunderTrading, 3Commas, or private server webhook routing. If a counter-signal is triggered, the system automatically executes a Close-First, Open-Next alert sequence to prevent cross-order jams on the exchange. Indicator
AetherEdge - Causal Inference Filter🖊️ Overview
A causality-driven filter that asks not "what pattern is on the chart" but "what is actually driving price right now." Where SMC / Order-Block tools read price structure, this engine applies a scientific, causal lens. First, Granger-style causality — for each candidate driver (momentum, volatility, volume, range expansion), it measures whether the driver's past values help predict future returns beyond what past returns alone explain. That is predictive precedence, not mere correlation. Second, NeuraLib feature importance — a small network learns to forecast forward returns, and an ablation-style importance score reveals which driver the model actually relies on. The two combine into a causal confidence: signals fire only when a true driver is clearly identified; in noisy, causally-ambiguous regimes the filter stays silent. Visualization: soft rectangular boxes only over high-causal-confidence zones, plus simple arrows.
🔶 Key Features
Granger-style predictive power — measures whether a driver's past precedes future returns (not mere correlation), correlation-based
NeuraLib feature importance — an ablation-style score identifies the driver the model truly relies on
True-driver identification — extracts the dominant driver from four candidates (momentum/volatility/volume/range-exp)
Causal confidence — a causal-clarity score blending Granger lift and importance concentration
Noise rejection — signals only when causality is clear; suppressed when ambiguous
Causal zones — soft rectangular boxes only in high-confidence conditions (opacity by confidence)
Simple arrows — causally-confirmed directional signals shown with minimal arrows
Intelligence panel — causal confidence, the true driver, each driver's importance and Granger lift, and the forecast at a glance
🧠 Technical Architecture
The engine tests the causal influence of four candidate drivers (momentum, volatility, volume anomaly, range expansion — all normalized).
Granger causality: for each driver d, it computes the rolling correlation between the driver lagged by grangerLag and the current return. If the driver's past predicts today's move, that is predictive precedence (Granger-like). The return's own autocorrelation (how past returns explain the future) is subtracted as a baseline, isolating the causal lift the driver adds — distinguishing "merely correlated" from "predictively precedent."
Feature importance: the NeuraLib network (drivers(4) → hidden(tanh) → forward-return forecast(1)) learns to forecast forward returns by supervision. Each driver's importance is the change in forecast when that driver is "ablated" (set to 0): |full forecast − ablated forecast| — a method inspired by permutation importance.
Causal confidence is a blend of the dominant driver's Granger lift and the importance concentration (the top driver's share of total importance = causal clarity). Only when it clears the gate do arrows and causal zones fire. Zones are drawn with width and opacity scaled by confidence, extend right via extend.right, and retire past their lifespan.
⚙️ Recommended Settings & Tuning Guide
BTC (1H–4H): Causal Lag 5, Causality Window 60, Min Causal Lift 0.15, Min Causal Confidence 0.50. Standard settings fit well
ETH (1H–4H): As BTC, with Forward Outcome 5–8
SOL (15m–1H): High volatility favors Confidence Smoothing 4–5 to smooth confidence, Min Confidence 0.55 to reject noise more strictly
XRP (1H–4H): Spike-prone; Causal Lag 3 for shorter precedence, Causality Window 50 to weight recent causality
Causal Lag: the assumed bars by which a driver precedes price — tune to the market's reaction speed
Causality Window: longer is a steadier causal estimate; shorter is nimbler — ~60 is practical
Min Causal Lift: the minimum predictive power for a driver to count as causal — higher is stricter
Min Causal Confidence: the firing threshold for signals/zones — higher narrows to only the clearest causality
💡 How to Use in Practice
Reacting to causal arrows: an arrow marks where a clear causal driver is identified and a direction predicted — a low-false-signal entry basis grounded in statistical causality, not structure patterns
Reading causal zones: a soft box is a price band of high causal confidence; within it causality is clear and price tends to respect the zone — a causal take on support/resistance
Confirming the true driver: the panel's "True Driver" shows whether momentum, volatility, volume, or range is moving price now (e.g. a dominant VOLUME means a volume-led market)
Avoiding ambiguous regimes: low-confidence (AMBIGUOUS) conditions mean the driver is unclear — a noisy market; the absence of a signal is itself a "wait" signal
Checking driver lift: the panel's per-driver Granger lift shows which drivers predictively precede; those above the lift threshold lead
Multi-timeframe usage: confirm the dominant driver and causal direction on the higher timeframe (4H), then time on causal zones and arrows on the lower one (15m–1H)
⚠️ Important Notes
Approximate causal inference: the Granger causality here is a lightweight correlation-based approximation fit to Pine's compute budget — not a rigorous Granger test (VAR regression / F-test), but a heuristic indicator of predictive precedence
Correlation ≠ causation limit: statistical causal inference is powerful but cannot fully exclude hidden common factors (confounders); "causal" means statistical predictive power, not proof of true economic causation
Initial learning period: right after launch the network's buffer is nearly empty and importance estimates are unstable; treat causal confidence as low-confidence until it learns (several hundred bars)
Learning resets: changing parameters, symbol/timeframe, or recompiling reinitializes the network weights and buffer
On look-ahead: the importance target uses closed-bar realized returns (a standard training construct); as with any adaptive system, historical and live behavior can differ — always forward-test
Constraints: this is a lightweight implementation within Pine's compute budget
🚨 Disclaimer
This indicator is an analytical and educational visualization tool. The Granger causality, feature importance, causal confidence, causal zones, and signals are quantitative heuristics computed on-chart from price data — they are not financial advice, buy/sell signals, or any guarantee of future performance. Even statistical causal inference can misidentify. Always combine any tool with your own analysis and disciplined risk management. Indicator
AetherEdge - Hierarchical Temporal Memory Inspired🖊️ Overview
A sequence-memory engine inspired by HTM (Hierarchical Temporal Memory), a model of how the neocortex learns sequences. Fundamentally different from KNN similarity-search or LSTM weighted regression, HTM stores knowledge as a sparse columnar memory and learns which column tends to follow which. Each bar's market state is encoded into a Sparse Distributed Representation (SDR) where only a few of many columns activate. A transition memory (permanence weights between columns) learns the sequence "column A → column B," and every new bar the engine predicts which columns should activate next. When an unpredicted column fires instead, that is an anomalous transition — a break in the learned temporal pattern. The mismatch is the anomaly score, and its inverse is sequence stability. Visualization: a faint vertical line only on high-anomaly bars, plus a single Sequence Stability indicator.
🔶 Key Features
HTM-style sequence memory — brain-inspired columnar memory learns temporal patterns (fundamentally unlike KNN/LSTM)
SDR (sparse distributed representation) — encodes each bar into a few active columns
Transition learning — Hebbian strengthening/decay of inter-column permanence stores sequences
Predictive cells — predicts next-bar columns from learned transitions (higher-order capable)
Anomalous-transition detection — scores the firing of unpredicted columns as anomaly
Anomaly-bar emphasis — a faint vertical line only on bars whose anomaly clears the gate (noise excluded)
Sequence Stability — a single lower-pane stability readout (green stable → red anomalous gradient)
Intelligence panel — anomaly score, stability, an active-SDR visualization, predicted/hit counts, and learned-link count
🧠 Technical Architecture
The engine encodes the market state into four features (momentum, RSI deviation, volatility, position-within-range, all normalized) and turns them into an SDR. Each column has a fixed random receptive field (a spatial-pooler proxy); the top activeCols columns by overlap with the current state win and fire (k-winners-take-all). Similar states thus activate similar column sets, different states different ones.
The transition memory is a permanence matrix perm . Learning follows the Hebbian rule: if column A fired and B fired on the next bar, the link A→B is strengthened (permanence +permInc); links whose predictions failed decay (−permDec). Only links above the connection threshold can predict.
For prediction, from the recently-fired columns (both t-1 and t-2 for higher order) the engine follows connected links to build the set of columns expected to fire next. Of the columns that actually fire next bar, the predicted fraction is hits/active, and the anomaly score = 1 − hits/active (the share of unpredicted firings). As learning matures and patterns stay stable, predictions land and anomaly is low; when patterns break, predictions miss and anomaly spikes.
⚙️ Recommended Settings & Tuning Guide
BTC (1H–4H): Columns 16, Active Columns 3, Permanence Inc 0.10 / Dec 0.04, Prediction Order 2, Anomaly Threshold 0.55. Standard settings fit well
ETH (1H–4H): As BTC (HTM learns sequences self-supervised, no outcome window needed)
SOL (15m–1H): High volatility favors Anomaly Smoothing 5–6 to smooth the score, Anomaly Threshold 0.6 to suppress false anomalies
XRP (1H–4H): Spike-prone; Columns 20 for more capacity, Prediction Order 1 to weight recent transitions
Columns / Active Columns: more columns add capacity but slow learning; sparsity (Active/Columns) is the heart of HTM — lower is more HTM-like
Permanence Inc/Dec: higher Inc learns faster, higher Dec forgets old memory faster — tune to how fast the market changes
Prediction Order: higher considers longer context (higher-order sequences); lower uses only recent transitions
Anomaly Threshold: higher makes vertical lines rare (only large anomalies); lower more frequent
💡 How to Use in Practice
Reacting to anomaly lines: a vertical line marks a bar where the learned temporal pattern broke — often the onset of a trend change, breakout, or regime shift; a basis for caution or reversal
Reading Sequence Stability: high (green) = the market follows learned patterns (predictable); low (red) = patterns broke, unpredictable. A sudden drop signals an environment change
Interpreting anomalies: sustained anomalies = a regime change in progress; a one-off = a transient shock — stability recovery means adaptation to a new pattern is done
Watching the active SDR: the panel's SDR view (▰▱) shows which columns fire now; recurring SDRs = known patterns, novel SDRs = unfamiliar conditions
Using predicted/hit counts: hits near predicted = stable sequence; a gap = anomaly — also a gauge of learning maturity
Multi-timeframe usage: confirm the big-picture sequence stability on the higher timeframe (4H), then time turns on anomaly lines on the lower one (15m–1H)
⚠️ Important Notes
Initial learning period: right after launch the transition memory is empty and everything looks "anomalous"; treat anomaly as elevated until enough transitions are learned (several hundred bars)
Learning resets: changing parameters, switching symbol/timeframe, or recompiling reinitializes the column receptive fields, transition memory, and SDR history, restarting learning from zero
Anomaly ≠ trade signal: an anomalous transition marks a "pattern break," not a direction (buy/sell); use it as a caution signal of possible change and pair direction calls with other analysis
Sparsity constraint: to fit Pine's compute budget, this is a lightweight build with fewer columns than a true HTM — not as sparse as HTM's ~2%
Adaptive-system nature: historical and live behavior can differ — always forward-test
Constraints: this is a lightweight implementation within Pine's compute budget; it scans a columns × columns transition matrix, so very large column counts affect performance
🚨 Disclaimer
This indicator is an analytical and educational visualization tool. The HTM-style sequence memory, SDR encoding, transition learning, anomaly score, and sequence stability are quantitative heuristics computed on-chart from price data — they are not financial advice, buy/sell signals, or any guarantee of future performance. Anomaly detection can produce false positives. Always combine any tool with your own analysis and disciplined risk management. Indicator
AetherEdge - Sentiment-Proxy Correlation Mapper🖊️ Overview
A psychology-focused engine that infers crowd emotion without any social or news feed — purely from price action. It builds sentiment proxies — volume anomaly (panic/euphoria), volatility (fear), price velocity (momentum drive), wick asymmetry (greed vs. fear at extremes), and run-length (FOMO / capitulation) — then uses a NeuraLib correlation map to learn how these proxies move together and weighs them into a single Fear ⇄ Greed sentiment score. A self-adapting mapping continuously re-correlates the proxies against realized outcomes, and the engine flags sentiment turning points — the moments crowd emotion flips. Visualization: a chart-background gradient shading the tape from Fear (red) to Greed (green), plus emphasis dots only at the reversals.
🔶 Key Features
Sentiment proxies — five emotion proxies built from price action alone (no external data)
NeuraLib correlation mapping — learns the proxies' joint behavior and fuses them into a Fear⇄Greed score
Self-adapting correlation — continuously re-correlates proxies against realized outcomes (learns which matter)
Turning-point detection — flags fear⇄greed reversals on a zero-line cross with sufficient swing
Sentiment background gradient — shades the chart from fear (red) through neutral to greed (green), intensity by magnitude
Turning-point dots — only the reversal moments are emphasized with dots and labels (noise excluded)
Emotional extreme markers — faint diamonds when sentiment enters a greed/fear extreme zone
Intelligence panel — Fear⇄Greed gauge, each proxy's value and learned correlation strength, and the latest turn
🧠 Technical Architecture
The engine builds five sentiment proxies from price: (1) volume anomaly (volume z-score × candle direction = euphoric buying / panic selling), (2) volatility (ATR ratio = higher is fear/stress), (3) price velocity (ATR-normalized speed of change = momentum), (4) wick asymmetry (lower − upper wick = which side defended the extreme), and (5) run-length (consecutive up/down within the window = FOMO / capitulation).
These feed the NeuraLib correlation map: proxies(5) → hidden(tanh) → sentiment(1, tanh-bounded ), trained by supervision so sentiment correlates with realized forward direction — greed (positive) preceding up-moves, fear (negative) preceding down-moves. After each step, each proxy's contribution to the hidden layer (mean absolute weight) is recomputed as its correlation strength, shown in the panel so you can see which proxy currently dominates emotion.
Sentiment is smoothed into a Fear⇄Greed score. Turning-point detection flags a fear→greed (or reverse) reversal when a zero-line cross's magnitude clears the Turn Sensitivity. The background is colored continuously by color.from_gradient, with intensity proportional to emotional magnitude.
⚙️ Recommended Settings & Tuning Guide
BTC (1H–4H): Volume 20, Volatility 14, Velocity 10, Smoothing 4, Turn Sensitivity 0.20. Standard settings fit well
ETH (1H–4H): As BTC, with Outcome Lookahead 5–8
SOL (15m–1H): High volatility favors Smoothing 5–6 to smooth sentiment, Turn Sensitivity 0.25 to suppress false turns
XRP (1H–4H): Spike-prone; Volume window 15 for sensitive anomaly detection, Extreme Threshold 0.5 for early extremes
Turn Sensitivity: higher makes turns selective (only large emotional reversals); lower detects more frequently
Extreme Threshold: beyond ±this is "extreme greed/fear" — a contrarian guide at 0.5–0.6
Sentiment Smoothing: higher smooths the background and score; lower is more reactive
Background Intensity: lower makes the background more vivid — adjust to taste
💡 How to Use in Practice
Reading the gradient: deep green = greed dominates (possible overheating); deep red = fear dominates (possible capitulation); neutral = balanced — saturation is emotional strength
Using turning-point dots: a "FEAR→GREED" dot marks a flip from fear to greed (possible bottom); "GREED→FEAR" a possible top — a basis for contrarian/trend entries
Contrarian at extremes: at extreme greed (green diamond) beware a pullback from overheating; at extreme fear (red diamond) watch for an oversold bounce — quantifying "be fearful when others are greedy"
Watching proxy correlations: the panel's per-proxy value and correlation strength reveal what drives emotion now (e.g. high volume-anomaly correlation = volume-led sentiment)
Background vs. price divergence: price rising while the background reads fear (bearish sentiment) is an early reversal warning
Multi-timeframe usage: read the big-picture emotional regime on the higher timeframe (4H), then time entries on turning points on the lower one (15m–1H)
⚠️ Important Notes
Nature of proxies: this is not real social/news sentiment but an estimated proxy inferred from price action — an approximation of crowd psychology that differs from external sentiment data
Initial learning period: right after launch the map's buffer is nearly empty and emotion estimates are unstable; treat as low-confidence until it fills (100+ bars)
Learning resets: changing parameters, switching symbol/timeframe, or recompiling reinitializes the map weights and buffer, restarting learning from zero
Danger of contrarianism: extreme emotion can persist (greed runs long in strong trends) — an extreme is not an instant reversal; pair with risk management
On look-ahead: the map's target uses closed-bar realized direction (a standard training construct); current-bar emotion is on confirmed values, but as with any adaptive system, historical and live behavior can differ — always forward-test
Constraints: this is a lightweight implementation operating within Pine's compute budget
🚨 Disclaimer
This indicator is an analytical and educational visualization tool. The sentiment proxies, correlation mapping, Fear⇄Greed score, turning-point detection, and sentiment background are quantitative heuristics computed on-chart from price data — they are not financial advice, buy/sell signals, or any guarantee of future performance. Estimates of crowd psychology can be wrong. Always combine any tool with your own analysis and disciplined risk management. Indicator
AetherEdge - Fractal Geometry + Diffusion Predictor🖊️ Overview
A geometric forecaster unlike any channel or trendline tool. Two ideas combine. First, fractal geometry — a Higuchi-style fractal-dimension estimate and a Hurst-like persistence exponent quantify the market's self-similarity (roughness vs. smoothness). A rough, mean-reverting tape and a smooth, trending one have distinct fractal signatures, and the engine reads which regime is live. Second, diffusion-style generation — echoing how a diffusion model denoises noise into data, the engine seeds many noise vectors and runs a guided reverse process to GENERATE multiple future price paths. Their drift and volatility structure are conditioned on the fractal signature, so trending regimes generate persistent paths and rough regimes generate mean-reverting ones. Visualization: a central forecast line (the mean of generated paths) plus a faint fan of prediction bands whose opacity encodes probability.
🔶 Key Features
Fractal dimension — Higuchi method quantifies market roughness (self-similarity) as D∈
Hurst persistence exponent — distinguishes trend-persistence (H>0.5) from mean-reversion (H<0.5)
Diffusion-style path generation — generates multiple future paths by guided reverse diffusion from noise
Fractal guidance — the self-similarity signature steers path drift (classifier-style guidance)
Multi-scenario forecasting — samples many paths to build a per-step distribution
Geometric prediction fan — a central line plus nested probability bands fanning out with horizon
Probability via opacity — band opacity follows Gaussian mass (inner = higher probability = denser)
Intelligence panel — fractal dimension, Hurst exponent, persistence, and the forecast target price at a glance
🧠 Technical Architecture
Fractal geometry: the Higuchi estimator computes the curve length L(k) at each interval k and derives the dimension D from the least-squares slope of log L(k) vs log(1/k). Higher D means a rougher (noisier) series; lower means smoother (more trending). In parallel, a rescaled-range (R/S)-style Hurst exponent is estimated as H = log(R/S) / log(n). The two combine into a persistence score ∈ (+1 strongly trending, −1 strongly mean-reverting).
Diffusion generation: for each path, starting from the current price, each future step runs a reverse-diffusion process over Denoising Steps iterations. Each denoise step shrinks the noise amplitude along a cosine schedule (noisy early, clean late) while progressively strengthening a fractal-guided drift (guidance bites as the noise clears — classifier-style guidance). When persistence is negative (a mean-reverting regime), a pull-back force toward the starting price is added. Repeated across Generated Paths paths, the per-step mean and variance are accumulated via Welford's online algorithm.
Visualization: the per-step mean price forms the central line, and nested bands at multiples of the standard deviation (±k·σ) form the fan. Band opacity is proportional to Gaussian mass exp(−0.5k²), denser inside (higher probability). The fan naturally widens with horizon as variance grows.
⚙️ Recommended Settings & Tuning Guide
BTC (1H–4H): Fractal Window 40, kMax 5, Persistence Window 50, Generated Paths 24, Denoising Steps 8, Horizon 16. Standard settings fit well
ETH (1H–4H): As BTC, with Fan Width 1.0 for standard spread
SOL (15m–1H): High volatility favors Noise Scale 1.2 / Fan Width 1.2 to widen path dispersion, Fractal Guidance 0.5 to soften guidance
XRP (1H–4H): Spike-prone; Persistence Window 40 to weight recent self-similarity, Paths 30 to smooth the distribution
Fractal Window / kMax: a wider window and higher kMax refine the dimension estimate but add compute; 40/5 is practical
Generated Paths: more smooths the distribution but is heavier; 20–40 is a practical balance
Denoising Steps: more smooths paths; ~8 is sufficient
Fractal Guidance: higher lets the fractal signature steer drift more; lower is noise-driven
Render load: the fan draws Fan Bands × Horizon × 2 boxes, so maxing both can hit the 500-object render cap
💡 How to Use in Practice
Reacting to the central line: the mean of generated paths is the most probable future path — its slope and target price are the forecast's core
Reading the fan: a narrow fan = high forecast confidence (paths converge); wide = high uncertainty (paths diverge); the dense inner bands are the high-probability zone
Using the fractal regime: in high-persistence (trending) conditions, follow the central line's direction; in low (mean-reverting), expect the forecast to pull back
Reading fractal dimension: low D (toward 1.0) = smooth trending tape; high (toward 2.0) = rough range/noise — an aid to regime classification
Checking Hurst: H>0.5 trend-persistent, H<0.5 reversion-prone — corroborates the central forecast's direction
Fan width and risk: tighten stops when the fan is narrow, give room when wide
Multi-timeframe usage: confirm the fractal regime and forecast direction on the higher timeframe (4H), then time entries on the lower one (15m–1H)
⚠️ Important Notes
Initial computation period: until the fractal and Hurst windows fill (tens to a hundred-plus bars), the dimension and persistence estimates are unstable; treat forecasts as low-confidence until then
Stochastic generation: diffusion paths are generated from noise seeds, so changing the seed (settings) changes the distribution — forecasts are probabilistic scenario sets, not deterministic
Interpreting the fan: the fan shows where generated paths landed, not a guarantee that price stays within it — it is a probability-density visualization
On look-ahead: fractal dimension, Hurst, and drift are all computed from confirmed past data; the forecast projects from now into the future and can of course be wrong — always forward-test
Render load: box count is Fan Bands × Horizon; lower either if it feels heavy
Constraints: this is a lightweight implementation within Pine's compute budget; the Higuchi dimension, Hurst, and path generation are all approximations
🚨 Disclaimer
This indicator is an analytical and educational visualization tool. The fractal dimension, Hurst exponent, diffusion-style path generation, prediction fan, and central line are quantitative heuristics computed on-chart from price data — they are not financial advice, buy/sell signals, or any guarantee of future performance. Even a probabilistically generated forecast can be wrong. Always combine any tool with your own analysis and disciplined risk management. Indicator
AetherEdge - Transformer-Inspired Attention Bias🖊️ Overview
A Transformer-style attention mechanism rendered in Pine — an engine for the selective focus of memory. Just as a Transformer attends selectively to the most relevant elements of a sequence, this engine treats the current bar as a query and every recent bar as a key/value. It scores each past bar's relevance via scaled dot-product (Q·Kᵀ/√d), normalizes the scores with softmax, and forms a weighted blend of those bars' directional signals — automatically focusing on the past patterns that matter most right now to compute the current bias (bullish/bearish). Several heads run in parallel, each learning a different notion of relevance, then merge into one directional bias. Visualization: an Attention Heatbar across the top of the chart (a horizontal strip whose brightness shows how much attention each point in recent time receives) plus one main bias arrow — a Transformer-style "selective focus of memory" in Pine.
🔶 Key Features
Multi-head attention — scaled dot-product attention (Q·Kᵀ/√d → softmax → ·V) reproduced in Pine
Selective focus of memory — automatically focuses on important past bars while suppressing noisy ones
Multiple heads — heads learn different relevance notions in parallel, merging into one bias
Positional encoding — an optional recency signal so the model knows how far back each bar is
Attention Heatbar — visualizes time-axis importance as a horizontal strip above price (brighter = more attention, tinted by bias)
Main bias arrow — the post-attention directional bias shown as one arrow (with glow)
Temperature parameter — softmax temperature tunes between sharp focus on a single bar and diffuse attention
Intelligence panel — attention bias, focus sharpness and distance (how far back focus sits), head count, context length, and more
🧠 Technical Architecture
Each bar is encoded into a small feature "token": momentum, RSI deviation, one-bar return, and range width (all normalized). Each bar is also assigned a directional "value" value = tanh(...) capturing its bullish/bearish lean.
The core is multi-head attention. The current bar's token is projected by Wq into a d-dim query q; each of the past ctxLen bars' tokens is projected by Wk into kᵢ. Attention scores are the scaled dot-product score_i = (q·kᵢ)/√d / temperature, where √d scaling ensures gradient stability. With positional encoding enabled, a recency signal is added to the keys. The scores are normalized by softmax into weights wᵢ, and the past bars' values are blended as Σ wᵢ·valueᵢ — that head's bias. Since each head has distinct projection weights, each captures a different relevance, merged via head output weights Wo.
A key implementation detail: Pine's history operator cannot take a loop variable, so past tokens are accumulated into rolling arrays and accessed dynamically via array.get(idx). Attention weights are aggregated into time-axis buckets and shown, normalized, as the Attention Heatbar (a brighter cell = stronger attention to that bar). The final bias is EMA-smoothed, and the main arrow fires when it clears the gate.
⚙️ Recommended Settings & Tuning Guide
BTC (1H–4H): Context Window 40, Heads 3, Key Dim 4, Temperature 1.0. Standard settings fit well
ETH (1H–4H): As BTC, with Context Window 50 for slightly longer memory
SOL (15m–1H): High volatility favors Temperature ≈ 0.7 for sharp focus, Heads 4 to capture diverse relevance
XRP (1H–4H): Spike-prone; Context Window ≈ 30 to emphasize recency, Position Encoding ON
Context Window: longer references further back but is heavier; 30–60 is practical
Heads: more captures more relevance notions but adds compute; 2–4 is readable
Temperature: low (0.5–0.8) sharply focuses on the single most relevant bar; high (1.5–3) gives diffuse, smooth attention
Min |Bias| to Signal: higher makes arrows more selective — tune to your trade frequency
💡 How to Use in Practice
Reading the Heatbar: a bright cell means that bar strongly drives the current bias. Bright on the right (recent) = short-term pattern leads; bright on the left (older) = a past important moment is recurring
Reacting to the main arrow: the arrow is the verdict where multi-head attention's merged bias clears the gate — a core basis for trend-following entries
Using focus distance: small panel "Focus Distance" = recency-driven; large = attending to far-back patterns, suggesting recurrence of a past analog
Focus sharpness: high = attention concentrated on one point (clear pattern recognition); low = diffuse (ambiguous)
Pairing with temperature: if the heatbar is wide and faint, lower the temperature to sharpen focus and clarify what the engine attends to
Multi-timeframe usage: confirm the big-picture bias on the higher timeframe (4H), then refine timing on arrows on the lower one (15m–1H)
⚠️ Important Notes
Initial context period: attention is incomplete until the context window (ctxLen) fills; treat the bias as low-confidence until enough past bars accumulate
Effect of setting changes: changing parameters, switching symbol/timeframe, or recompiling reinitializes the projection weights and token history
On learning: the projection weights are seed-initialized fixed weights used to compute attention patterns; this tool does not update weights from reward — it focuses on visualizing the attention mechanism itself
Heatbar placement: the heatbar draws above price, so adjust Heatbar Height to avoid overlapping the candles
Adaptive-system nature: historical and live behavior can differ — always forward-test
Constraints: this is a lightweight implementation operating within Pine's compute budget; Context × Heads × Key Dim drives compute load, so extreme settings affect performance
🚨 Disclaimer
This indicator is an analytical and educational visualization tool. The attention mechanism, multi-head attention, positional encoding, bias computation, and Attention Heatbar are quantitative heuristics computed on-chart from price data — they are not financial advice, buy/sell signals, or any guarantee of future performance. Always combine any tool with your own analysis and disciplined risk management. Indicator
AetherEdge - Bayesian Neural Market Microstructure🖊️ Overview
A NeuraLib Bayesian Neural Network that does what a point-estimate model (e.g. a plain LSTM) cannot: it predicts WITH a confidence. Instead of one number, it runs many stochastic forward passes (MC-Dropout / weight-perturbation style) to produce a DISTRIBUTION of next-move predictions, reporting both a mean forecast and an uncertainty (the spread of the samples). Inputs are market-microstructure proxies — volume delta (buy/sell pressure), an estimated spread, and a liquidity proxy — alongside price features. Visualization: a probability-density "cloud" of predicted price bands (opacity ∝ probability) and a single forward zone that reddens as uncertainty rises. The edge over LSTM-style tools is clear — you see not just where, but how sure.
🔶 Key Features
Bayesian NN (uncertainty estimation) — multi-sample weight perturbation yields a predictive distribution: mean + uncertainty
Confidence-aware prediction — "mean ± σ", not a point estimate — the decisive difference from LSTM-style tools
Microstructure inputs — volume delta (order-flow proxy), estimated spread, and a liquidity proxy as features
Probability-density cloud — predicted bands drawn as nested probability tiers, opacity encoding probability mass (denser inside)
Uncertainty zone — a single forward zone that reddens and gains opacity as uncertainty rises
Mean forecast line — one line from current price to predicted price (with glow)
Online learning — the network continuously regresses toward realized forward moves
Intelligence panel — predicted move, predicted price, confidence, uncertainty, and each microstructure component at a glance
🧠 Technical Architecture
The agent perceives the market as a four-dimensional microstructure state: price momentum, order-flow pressure (a normalized volume delta from candle-direction × volume EMA), spread regime (an estimated effective spread from the high-low range), and liquidity (volume per unit range) — all z-normalized.
The network is in(4) → hidden(tanh) → out(1) = predicted normalized move, but its Bayesian behavior is reproduced via stochastic forward passes. Each sample (1) injects Gaussian-style noise into the weights (variational weight perturbation) and (2) applies an MC-Dropout mask to hidden units. Repeating this MC Samples times, the mean of the predictions is the forecast and their standard deviation is the uncertainty (epistemic uncertainty) — the heart of the Bayesian approximation.
Learning is supervised regression: for each state, the realized normalized move (close − close ) / ATR over the prediction horizon is the target, and the mean network regresses toward it via squared error (sampled from a replay buffer). For visualization, around the predicted price close + mean·ATR, nested probability bands of half-width k·σ·ATR form the density cloud, with opacity proportional to Gaussian mass exp(−0.5k²). The uncertainty zone gradients from the forecast color to red, with a bold red border once normalized uncertainty crosses the threshold.
⚙️ Recommended Settings & Tuning Guide
BTC (1H–4H): MC Samples 12, Dropout 0.25, Weight Noise 0.05, Prediction Horizon 8, Cloud Bands 5. Standard settings fit well
ETH (1H–4H): As BTC, with Uncertainty Scale 1.0 for standard cloud width
SOL (15m–1H): High volatility favors Dropout 0.3 / Weight Noise 0.08 for more sensitive uncertainty, Red Zone Threshold 0.5 to warn risk early
XRP (1H–4H): Spike-prone; MC Samples ≈ 16 to smooth the distribution, Uncertainty Scale 1.2 for slightly wider clouds
MC Samples: more smooths the uncertainty estimate but is heavier; 10–16 is a practical balance
Dropout / Weight Noise: higher widens the predictive distribution and raises uncertainty — tune to market noise
Cloud Density Bands: more makes a smoother, prettier cloud but adds render load; 4–6 is readable
Red Zone Threshold: lower warns red earlier (conservative); higher keeps the cool color only when very confident
💡 How to Use in Practice
Reading the density cloud: a narrow, dense cloud = low uncertainty, high-confidence forecast; a wide, faint cloud = high uncertainty, unstable forecast. The cloud center (mean line) is the predicted price
Uncertainty zone color: a forecast-colored (teal/coral) zone means confidence; a red, bold-bordered zone means high uncertainty — enter cautiously
Using confidence-aware prediction: consider entries only when panel Confidence is high — unlike point predictors (LSTM etc.), you can judge whether to trust the forecast
Checking microstructure: when order-flow Δ, spread, and liquidity align with the forecast direction, the prediction is better supported
Cloud width and sizing: size larger when the cloud is narrow (low uncertainty), smaller when wide (high uncertainty)
Multi-timeframe usage: confirm the big-picture forecast direction and uncertainty on the higher timeframe (4H), then time entries in low-uncertainty windows on the lower one (15m–1H)
⚠️ Important Notes
Initial learning period: right after launch the buffer is nearly empty and the network is untrained, so forecasts are unstable; treat them as low-confidence until it fills (several hundred bars)
Learning resets: changing parameters, switching symbol/timeframe, or recompiling reinitializes the network weights and buffer, restarting learning from zero
On microstructure proxies: this uses microstructure proxies estimated from OHLCV, not a real order book — an approximation that differs from true book data
Interpreting uncertainty: the reported uncertainty is the model's epistemic uncertainty (its lack of confidence), not a complete measure of actual market risk
On look-ahead: the target uses closed-bar realized moves (a standard training construct); current-bar prediction is on confirmed values, but as with any adaptive system, historical and live behavior can differ — always forward-test
Constraints: this is a lightweight implementation operating within Pine's compute budget; raising MC Samples increases compute proportionally
🚨 Disclaimer
This indicator is an analytical and educational visualization tool. The Bayesian neural network, uncertainty estimation, microstructure proxies, predictive distribution, and density cloud are quantitative heuristics computed on-chart from price data — they are not financial advice, buy/sell signals, or any guarantee of future performance. Even a probabilistic forecast can be wrong. Always combine any tool with your own analysis and disciplined risk management. Indicator
AetherEdge - Self-Evolving RL Dashboard Lite🖊️ Overview
A streamlined self-evolving agent built on a lightweight Actor-Critic core with prioritized experience, learning by pitting multiple specialist RL heads against each other. Internally, three heads — TREND-FOLLOWING, MEAN-REVERSION, and BREAKOUT — each propose a directional view from their own lens, while a meta-selector tracks each head's recent reward performance and re-allocates trust toward whichever is working now. As the market's character shifts, the agent automatically moves its weight toward the style that's paying off. Visualization is intentionally minimal: one main signal arrow, one prediction line, a small composite score, and a "Learning Progress" label that makes the evolution visible.
🔶 Key Features
Competing RL heads — trend / mean-reversion / breakout specialists run in parallel; the best drives the unified call
Dynamic trust re-allocation — softmax trust weights adapt to each head's reward-performance score
Lightweight Actor-Critic — each head has an Actor (policy) and Critic (state value), learning via advantage
Prioritized experience replay — high-reward transitions re-learned preferentially
Style-specific reward shaping — each head is rewarded more in its specialty regime, differentiating behavior
Ultra-minimal visuals — just one main arrow + one prediction line + a small composite score
Learning Progress label — visualizes agent maturity (bars trained, buffer fill, trust concentration) as a bar
Exploration vs. exploitation — ε-greedy exploration
🧠 Technical Architecture
The agent perceives the market as a four-dimensional state vector (momentum spread, RSI deviation, Bollinger position, position-within-range — all z-normalized). The core is three independent Actor-Critic heads, each producing Actor logits (3 actions → softmax policy) and a Critic value from a compact shared trunk (tanh).
Heads differentiate via style-specific reward shaping. From a common directional return (ATR-normalized), the trend head amplifies reward in strong trends (large |momentum|), the mean-reversion head when stretched from the mean (large |BB position|), and the breakout head near range edges. The three thus learn distinct behaviors from the same price data.
Learning is standard Actor-Critic. Each head's Critic computes the TD target r + γV(s′), the advantage A = TD target − V(s) drives the Actor's advantage-weighted policy gradient, and the Critic regresses via squared error — all from a prioritized replay buffer (|reward|^exponent). In meta-selection, each head's performance score is EMA-smoothed and turned into trust weights via softmax. The final policy blends the three heads' policies by trust and picks the max-value action. The composite score is the decisiveness of the blended policy; learning progress combines training maturity, buffer fill, and trust concentration.
⚙️ Recommended Settings & Tuning Guide
BTC (1H–4H): Training Horizon 800–1500, γ 0.93, Head Trust Adapt 0.05, Min Score 0.35. Standard settings fit well
ETH (1H–4H): As BTC, with Reward Lookahead 5–8
SOL (15m–1H): High volatility favors Head Trust Adapt ≈ 0.08 to switch styles quickly, Min Score 0.4 to be selective
XRP (1H–4H): Spike-prone; Priority Exponent ≈ 2.0 to learn hard from big experiences, shorter Prediction Length
Head Trust Adapt Rate: higher switches to the winning style faster but less stably; lower is stable but slower
Head Score Smoothing: higher (0.9–0.95) smooths performance assessment; lower is sensitive to recent results
Min Score to Signal: higher makes arrows more selective — tune to your trade frequency
Prediction Length: the projection distance of the line; a higher score steepens its slope
💡 How to Use in Practice
Reacting to the main arrow: an arrow is the "final verdict" where the trust-weighted consensus of all three heads clears the score gate — a core basis for trend-following entries
Using the prediction line: one line whose slope scales with score; steeper means a more confident directional view
Reading the composite score: a higher score % in the small label means clearer cross-head consensus; low means a balanced call (stand aside)
Using the Learning Progress label: as the "LEARNING" bar fills and the best head stabilizes, the policy is maturing; treat signals as indicative while progress is low
Watching the best head: the current leading head (TREND / MEAN-REV / BREAKOUT) in the progress label reveals how the agent reads the present market
Multi-timeframe usage: confirm the best head and direction on the higher timeframe (4H), then refine timing on arrows on the lower one (15m–1H)
⚠️ Important Notes
Initial learning period: right after launch the buffer is nearly empty and the heads are immature; treat signals as low-confidence until the Learning Progress bar fills (several hundred bars)
Learning resets: changing parameters, switching symbol/timeframe, or recompiling reinitializes all head weights, trust weights, and the buffer, restarting learning from zero
Nature of the Lite build: this distills the multi-head internals into ultra-simple output, omitting detailed internal-state visualization
On forward-looking reward: rewards use closed-bar forward returns (a standard RL training construct); current-bar decisions are on confirmed values, but as with any adaptive system, historical and live behavior can differ — always forward-test
Constraints: this is a lightweight implementation operating within Pine's compute budget
🚨 Disclaimer
This indicator is an analytical and educational visualization tool. The Actor-Critic, competing RL heads, prioritized experience replay, reward shaping, composite score, and prediction are quantitative heuristics computed on-chart from price data — they are not financial advice, buy/sell signals, or any guarantee of future performance. Reinforcement-learning agents can and do make wrong calls. Always combine any tool with your own analysis and disciplined risk management. Indicator
AetherEdge - KNN-Augmented RL Hybrid🖊️ Overview
A self-evolving hybrid that fuses episodic memory with a learned value network. Each bar's state and its realized outcome are stored in an experience memory bank; to value the present, the agent runs a k-Nearest-Neighbors search over that bank — retrieving the most similar historical states and distance-weighting their outcomes — then blends that memory estimate with a NeuraLib NN/Q estimate. The fusion combines the generalization of gradient learning with the instant recall of episodic memory. A reward-driven update adapts the per-feature similarity weights, so the agent learns which dimensions of "similarity" actually matter for prediction. Visualization: auto breakout-zone boxes where high-similarity memory predicts a decisive move, plus directional arrows on the fused signal.
🔶 Key Features
kNN×RL hybrid — episodic-memory kNN retrieval fused with a NeuraLib value network
Episodic memory augmentation — instant distance-weighted recall of similar past outcomes
Memory ↔ network blend — continuously adjustable from pure memory (kNN) to pure network (NN/Q)
Reward-driven similarity-weight learning — learns which feature dimensions define "similar"
High-similarity breakout zones — zones spawn only when memory retrieval is confident; opacity tracks similarity
Fused signal arrows — the integrated kNN+NN verdict shown as directional arrows (with glow)
Exploration vs. exploitation — ε-greedy exploration
Premium panel — NN-vs-kNN breakdown, fused value, memory similarity, conviction, similarity weights, memory/zone counts
🧠 Technical Architecture
The agent perceives the market as a four-dimensional state vector (momentum spread, RSI deviation, a volatility-regime ratio, position-within-range — all z-normalized). The architecture fuses two value-estimation paths.
Episodic memory (kNN) path: each bar stores state, action taken, and realized value (reward) in a memory bank. To value the present, a weighted Euclidean distance — using adaptive per-feature weights — is computed against all memories, and the k nearest are retrieved. Each neighbor's distance is turned into similarity via a Gaussian-style kernel sim = exp(−sharpness × distance), and a per-action similarity-weighted average yields the kNN memory value.
Value network (NeuraLib) path: a single hidden-layer (tanh) network maps state to per-action Q-values, trained by a DQN-style update.
Fusion: per direction, fused = (1−blend)·Q_network + blend·Q_knn, and the action with the largest fused value is selected. The signature element is similarity-weight adaptation: on each reward, weights of feature dimensions consistent with the profitable outcome are raised and irrelevant ones lowered (w ← clip(w + lr·(|feature|·|reward| − baseline))). The very definition of "similar" thus evolves from reward. Zone spawning fires only when both memory similarity and conviction clear their gates, with box opacity tracking similarity.
⚙️ Recommended Settings & Tuning Guide
BTC (1H–4H): Training Horizon 800–1500, Neighbors k 6, Memory Depth 300, Blend 0.5, γ 0.93. A 50/50 memory-network fusion as standard
ETH (1H–4H): As BTC, with Reward Lookahead 5–8
SOL (15m–1H): High volatility favors Similarity Sharpness 2.5–3.0 to weight only close neighbors, Min Similarity 0.7 to be selective on zones
XRP (1H–4H): Spike-prone; Blend ≈ 0.6 to lean on memory (recalling past sharp moves), k ≈ 8
Memory ↔ Network Blend: toward 0 favors generalization (robust to novel states); toward 1 favors memory (replays known patterns) — tune to market repetitiveness
Neighbors (k): small (3–5) is sharp but noisy; large (8–15) is smooth but less responsive
Similarity Weight LR: higher adapts feature weights faster but less stably; 0.01–0.03 is the stable zone
Memory Bank Depth: deeper retains more diverse memory but mixes in older regimes — balance against Training Horizon
💡 How to Use in Practice
Reacting to fused arrows: an arrow means both kNN memory and NN value back the direction and conviction cleared the gate — the integrated verdict, a core basis for trend-following entries
Using high-similarity zones: zones mark areas where memory shows a decisive move occurred in similar past conditions; watch breaks/bounces at zone edges
Reading memory similarity: higher panel similarity means the present closely matches a clear past pattern — higher prediction confidence; be cautious when low (novel conditions)
NN vs. kNN agreement: when both (e.g. the "NN / kNN" in the LONG row) are high together, confidence is highest; stand aside when they disagree
Watching similarity weights: the panel's Sim Weights (M·R·V·P = momentum, RSI, volatility, position) reveal which features the agent currently weights for similarity
Multi-timeframe usage: map high-similarity zones on the higher timeframe (4H), then refine timing on fused arrows on the lower one (15m–1H)
Combinations: filter for high-similarity, high-conviction arrows to focus on memory-backed, high-quality entries
⚠️ Important Notes
Initial learning period: right after launch the memory bank is nearly empty and kNN retrieval is non-functional; treat signals and zones as low-confidence until memory accumulates (several hundred bars)
Learning resets: changing parameters, switching symbol/timeframe, or recompiling reinitializes the memory bank, network weights, similarity weights, and zones, restarting learning from zero
Memory bias: the bank is built from recent states, so when the regime shifts substantially, past memory fits the present less well — low Memory Similarity is the tell
On forward-looking reward: rewards and memory values use closed-bar forward returns (a standard RL training construct); current-bar evaluation is on confirmed values, but as with any adaptive system, historical and live behavior can differ — always forward-test
Constraints: this is a lightweight implementation operating within Pine's compute budget; kNN search scales with memory depth × neighbors, so extreme settings affect performance
🚨 Disclaimer
This indicator is an analytical and educational visualization tool. The kNN search, value network, reward shaping, similarity-weight adaptation, breakout zones, and fused signal are quantitative heuristics computed on-chart from price data — they are not financial advice, buy/sell signals, or any guarantee of future performance. Reinforcement-learning agents can and do make wrong calls. Always combine any tool with your own analysis and disciplined risk management. Indicator
Indicator
AetherEdge - RainbowDQN Multi-Component🖊️ Overview
A self-evolving multi-component agent that fuses the core pillars of Rainbow DQN into one policy: Double (overestimation control), Dueling (separating state value from action advantage), and Prioritized Replay (re-learning in proportion to surprise / TD error). It learns from a composite reward — risk-adjusted return, drawdown avoidance, and volatility scaling — so it generalizes across trending, ranging, and high-volatility regimes alike. The internals are multi-headed, but their complex valuation is distilled into a single refined unified-signal arrow, rendered with layered glow and a premium intelligence panel.
🔶 Key Features
Three Rainbow components fused — Double + Dueling + Prioritized Replay in one policy
Composite-reward learning — risk-adjusted return + drawdown avoidance + volatility scaling, weighted
Multi-regime adaptability — volatility scaling and drawdown penalty generalize across trend / range / high-vol
Overestimation control (Double) — online net selects, target net evaluates
V/A separation (Dueling) — state value V(s) and advantage A(s,a) on separate streams
Prioritized replay + importance sampling — high-TD-error transitions learned first, with IS bias correction
Unified signal arrow only — multi-head internals distilled into one clean arrow (Layered Glow / Minimal / Labeled styles)
Refined visuals — multi-layer glow arrows, conviction ribbon, signal pulse, and a sectioned premium panel
🧠 Technical Architecture
The agent perceives the market as a five-dimensional state vector: momentum spread, RSI deviation, ADX trend strength, a volatility-regime ratio, and position-within-range — all z-normalized. Two networks exist (online + target), each with a Dueling structure — a shared trunk (tanh) forks into a Value stream V(s) and an Advantage stream A(s,a), recombined as Q = V + (A − mean A).
As Double-DQN, next-state action selection uses the online net's argmax while its valuation uses the target net, curbing overestimation; weights hard-sync every Target Net Sync bars. The composite reward fuses three heads: (1) risk-adjusted return (forward return normalized by ATR), (2) drawdown avoidance (penalizing adverse excursion within the lookahead window), and (3) volatility scaling (shrinking rewards earned under abnormally high volatility).
For prioritized replay, each transition's TD error δ = pred − target is turned into a priority |δ|^α, skewing sampling toward surprising experiences, while an importance-sampling (β) weight corrects the resulting bias. Gradients flow through the dueling aggregation (dQ/dV = 1, dQ/dA = 𝟙 − 1/3) into each stream and the shared trunk via manual backpropagation. Finally, a unified conviction blending Q-spread and advantage-spread is computed, and a single arrow is drawn only when it clears the gate.
⚙️ Recommended Settings & Tuning Guide
BTC (1H–4H): Training Horizon 800–1500, γ 0.94, Target Net Sync 25, reward weights Ret 1.0 / DD 0.6 / Vol 0.4. The balanced defaults fit well
ETH (1H–4H): As BTC, with Reward Lookahead 5–8 to value trend persistence
SOL (15m–1H): High volatility favors Vol weight 0.6–0.8 to strengthen scaling, Conviction Gate 0.45–0.55 to be selective
XRP (1H–4H): Spike-prone; DD weight ≈ 0.8 to penalize adverse excursion harder, Priority Exponent ≈ 2.0
Composite weights: raise Ret for trend-seeking, DD for steadier operation, Vol for choppy markets — allocate by market character and style
Conviction Gate: higher (0.5–0.6) gives fewer, higher-quality arrows; lower (0.3–0.4) gives more — tune to your trade frequency
Target Net Sync (τ): short (10–15) adapts fast but less stable; long (40–60) is stable — match to market stability
💡 How to Use in Practice
Reacting to the unified arrow: every arrow shown is the "final verdict" — past the multi-head valuation, composite reward, and conviction gate — a high-confidence core basis for trend-following entries
Using the conviction ribbon: the more saturated the under-price ribbon, the stronger the directional conviction; arrow plus matching ribbon color (teal bullish, coral bearish) is the most coherent setup
Across regimes: follow arrows directly in trends; in ranges the drawdown-avoidance head reduces forced signals; in high-vol the volatility head tempers overreaction
Signal pulse: the pulse dot on the firing bar improves entry-timing visibility
Multi-timeframe usage: confirm the big-picture unified direction on the higher timeframe (4H), then refine timing on aligned arrows on the lower one (15m–1H)
Combinations: pair with volume or key S/R, filtering for high-conviction arrows to lift precision further
⚠️ Important Notes
Initial learning period: right after launch the replay buffer is nearly empty and both networks are unstable; treat signals as low-confidence until it fills (several hundred bars)
Learning resets: changing parameters, switching symbol/timeframe, or recompiling reinitializes all network weights and the buffer, restarting learning from zero
Target-sync effect: calls may briefly shift right after a sync — this is normal Double-DQN behavior
On forward-looking reward: rewards and drawdown use closed-bar forward data (a standard RL training construct); current-bar evaluation is on confirmed values, but as with any adaptive system, historical and live behavior can differ — always forward-test
Constraints: this is a lightweight implementation operating within Pine's compute budget
🚨 Disclaimer
This indicator is an analytical and educational visualization tool. The Rainbow DQN (Double/Dueling/Prioritized Replay), composite reward, networks, and signal outputs are quantitative heuristics computed on-chart from price data — they are not financial advice, buy/sell signals, or any guarantee of future performance. Reinforcement-learning agents can and do make wrong calls. Always combine any tool with your own analysis and disciplined risk management. Indicator
Indicator
AetherEdge - PPO Policy Optimizer🖊️ Overview
A self-evolving policy-gradient agent built on Proximal Policy Optimization (PPO) that optimizes its trading policy itself. A NeuraLib policy network outputs a softmax probability distribution over LONG / SHORT / FLAT, improved through a PPO-style clipped objective — the probability ratio between the new and old policy is clipped to , so a single update can never shove the policy too far, the key to PPO's stability. Updates are driven by a virtual advantage estimate (GAE-style) computed against a learned value baseline. Learning from experience on your chart, it visualizes its evolving conviction with a policy-probability line (LONG %) and a trend line that recolors with the policy's tilt.
🔶 Key Features
PPO clipping — the probability ratio is clipped to , structurally preventing oversized policy updates (the heart of PPO stability)
Actor-Critic architecture — a shared trunk forks into an Actor head (softmax policy) and a Critic head (value baseline)
Virtual advantage (GAE-style) — forward-return-minus-baseline smoothed over time for a low-bias, low-variance advantage signal
Entropy bonus — prevents premature collapse to a single action, encouraging exploration
Multi-epoch optimization — each collected minibatch is reused several times per bar (PPO's data efficiency)
Policy LONG % line — the policy's LONG probability plotted 0–100 (best on its own scale)
Color-shifting trend line — nudged up/down by policy tilt, recolored by dominant action and confidence
Intelligence panel — per-action probabilities, dominant action, value baseline, policy entropy, and rollout state at a glance
🧠 Technical Architecture
The agent perceives the market as a four-dimensional state vector: momentum spread (ATR-normalized fast/slow EMA gap), RSI deviation, a volatility-regime ratio, and position-within-range — all z-normalized. This state passes through a NeuraLib-style Actor-Critic network: a shared trunk (tanh hidden) forks into an Actor head producing three logits → softmax policy π(a|s), and a Critic head producing a scalar value V(s).
The virtual advantage follows GAE (Generalized Advantage Estimation). Iterating backward through the rollout, the TD residual δ = r + γV(s′) − V(s) is smoothed by γλ into  = δ + γλ·Â_next, then normalized to zero mean and unit variance (standard PPO practice).
The heart of the PPO update is the clipped objective. With the ratio r(θ) = π_new(a|s)/π_old(a|s), the objective is min(r·Â, clip(r, 1−ε, 1+ε)·Â). Outside the trust region — advantage positive with r above 1+ε, or negative with r below 1−ε — the gradient is zeroed, structurally bounding each update step. This combines with the policy gradient ∂logπ(a)/∂logit = 𝟙 − p, an entropy bonus, and the Critic's squared-error value regression (weighted by vfCoef), all backpropagated manually into the shared trunk. The collected rollout is re-optimized over several epochs and minibatches.
⚙️ Recommended Settings & Tuning Guide
BTC (1H–4H): Training Horizon 800–1500, Clip ε 0.2, GAE λ 0.95, γ 0.94, Epochs 3, Hidden 8. Standard PPO settings fit well
ETH (1H–4H): As BTC, with Reward Lookahead 5–8 to capture slightly longer advantage
SOL (15m–1H): High volatility favors Clip ε 0.15–0.2 (more conservative updates), Entropy ≈ 0.02 to strengthen exploration, ATR-normalization always ON
XRP (1H–4H): Spike-prone; GAE λ ≈ 0.9 to curb variance, Value Loss Weight 0.5–0.7 for baseline accuracy
Clip ε: smaller (0.1–0.15) is more conservative and stable; larger (0.25–0.3) learns faster but less stably — match to market stability
Learning Rate α: 0.02–0.04 is the stable zone; lower it if diverging, raise Epochs if convergence is slow
Entropy Bonus: raise to 0.02–0.05 if the policy biases too early to one side
💡 How to Use in Practice
Reacting to the LONG % line: 50% is neutral. A cross above 60% is a clearly bullish policy; below 40% is bearish. Drag it to its own scale to watch threshold breaks
Using the color-shifting trend line: when the line turns the LONG color and sits above price, it acts as a bullish-bias support line; the SHORT color makes it a bearish-bias resistance line
Policy tilt (flips): the moment the dominant action flips LONG↔SHORT, the trend line's color change is your directional-shift signal
Reading entropy: low panel entropy = the policy is confident; high = undecided. Low entropy plus a strong probability line marks the highest-confidence conditions
Multi-timeframe usage: confirm the big-picture policy bias on the higher timeframe (4H), then refine timing on aligned lower-timeframe (15m–1H) LONG %
Combinations: use divergence between price and the LONG % line as an early reversal warning
⚠️ Important Notes
Initial learning period: right after launch the rollout is nearly empty and both policy and value are unstable; treat signals as low-confidence until it fills (several hundred bars)
Learning resets: changing parameters, switching symbol/timeframe, or recompiling reinitializes the network weights and rollout, restarting learning from zero
On-policy nature: PPO is on-policy, so actions are sampled stochastically from the policy; selection can vary even in similar conditions — this is normal exploration
On forward-looking reward: rewards and advantages use closed-bar forward returns (a standard RL training construct); current-bar policy evaluation is on confirmed values, but as with any adaptive system, historical and live behavior can differ — always forward-test
Constraints: this is a lightweight implementation operating within Pine's compute budget
🚨 Disclaimer
This indicator is an analytical and educational visualization tool. The Proximal Policy Optimization, policy network, virtual advantage computation, reward shaping, and probability outputs are quantitative heuristics computed on-chart from price data — they are not financial advice, buy/sell signals, or any guarantee of future performance. Reinforcement-learning agents can and do make wrong calls. Always combine any tool with your own analysis and disciplined risk management. Indicator
AetherEdge - DuelingDQN Breakout Hunter🖊️ Overview
A self-evolving breakout agent built on a Dueling Deep-Q-Network that learns to hunt breakouts on its own. Its defining trait is a forked network: a Value stream V(s) that learns "how promising is this state at all", and an Advantage stream A(s,a) that learns "which action is relatively better here", recombined as Q(s,a) = V(s) + (A(s,a) − mean A). This separation lets the agent value the breakout context independently of the directional decision, sharpening action selection exactly where it counts — inside detected breakout zones. Learning from experience on your chart, it visualizes its hunt with auto support/resistance boxes and faint probability arrows.
🔶 Key Features
Dueling DQN architecture — shared trunk → Value and Advantage streams → recombined via the dueling aggregation; state quality and action advantage learned separately
Self-evolving breakout learning — no pre-training; breakout context learned continuously from the live chart
Breakout reward bonus — extra reward for correct actions in breakout context, focusing learning on breakouts
Auto S/R zones — boxes spawn on rolling S/R breaks, with full lifecycle management
Faint probability arrows — drawn with subtle opacity scaled to softmax action probabilities derived from advantages
Prioritized Experience Replay (PER) — high-reward transitions re-learned preferentially
Exploration vs. exploitation — ε-greedy exploration avoids ossifying in local optima
Intelligence panel — V(s), per-action A(s,a), selected action, advantage spread, breakout context, and zone count at a glance
🧠 Technical Architecture
The agent perceives the market as a four-dimensional state vector: momentum spread (ATR-normalized fast/slow EMA gap), relative position within Bollinger Bands, ATR distance to the nearest S/R, and a volatility-regime ratio — all z-normalized.
The core is the Dueling structure. Input passes through a shared trunk (tanh hidden), then forks. The Value stream runs through its hidden layer to a scalar V(s); the Advantage stream runs through its hidden layer to three per-action values A(s,a). They aggregate as Q(s,a) = V(s) + (A(s,a) − mean_a A(s,a)), the mean-subtraction ensuring identifiability between Value and Advantage. Learning is done by manual backpropagation through this aggregation: gradients are correctly distributed to the advantage outputs (dQ/dA = 𝟙 − 1/3) and the value output (dQ/dV = 1), then propagated back through each stream's hidden layer and into the shared trunk.
The reward function is a directional, ATR-normalized forward return, plus a bonus for success in breakout context. Transitions (s, a, r, s′) enter the experience replay buffer, sampled by |reward|^exponent. As the zone lifecycle, boxes spawn on rolling S/R breaks and are pruned oldest-first on lifespan or cap overflow. Probability arrows vary in opacity by softmax advantage probability and are drawn only when the advantage spread clears the gate. The statistics panel makes the internal Value and Advantage readable.
⚙️ Recommended Settings & Tuning Guide
BTC (1H–4H): Training Horizon 800–1500, γ 0.92–0.95, Shared Hidden 8 / Stream Hidden 4, S/R Lookback 20, Breakout Bonus 0.5. Standard settings fit well
ETH (1H–4H): As BTC, with Reward Lookahead 5–8 to value post-break follow-through
SOL (15m–1H): High volatility favors Zone Width 0.3–0.5 (wider boxes absorb fakeouts), ε 0.05–0.08, Breakout Bonus 0.6–0.8 to strengthen breakout learning
XRP (1H–4H): Spike-prone; Priority Exponent ≈ 2.0, longer S/R Lookback (25–30) to focus on major levels
Learning Rate α: 0.02–0.04 in trending markets; 0.04–0.08 in choppy ones
Stream Hidden Units: widen to 6–8 for richer advantage representation on instruments with diverse breakout types
maxBoxes / Zone Lifespan: maxBoxes 4–6 to see only key levels; extend Lifespan to retain zones longer
💡 How to Use in Practice
Reacting to high-advantage actions: when one action's advantage dominates with a high spread, the agent sees a clear edge in the break direction — a basis for trend-following entries
Using S/R boxes: auto-spawned zones mark post-break retest (return-move) levels; watch reactions at zone edges
S/R flips: capture the classic pattern where a broken resistance zone flips to support, via the box plus the advantage shift
Arrow opacity: darker arrows mean higher action probability and stronger conviction; treat faint arrows as wait-and-see
Multi-timeframe usage: read the big-picture break and zones on the higher timeframe (4H), then refine timing on aligned high-advantage actions on the lower one (15m–1H)
Combinations: filter for breaks accompanied by volume surges to reject fakeouts and elevate signal quality
⚠️ Important Notes
Initial learning period: right after launch the replay buffer is nearly empty and both Value and Advantage are unstable; treat signals as low-confidence until it fills (several hundred bars)
Learning resets: changing parameters, switching symbol/timeframe, or recompiling reinitializes all network weights, the buffer, and zones, restarting learning from zero
Nature of zones: boxes are structural markers from rolling S/R breaks, not signals in themselves; judge alongside the agent's advantage
On forward-looking reward: rewards use closed-bar forward return (a standard RL training construct); current-bar action selection is made on confirmed values, but as with any adaptive system, historical and live behavior can differ — always forward-test
Constraints: this is a lightweight implementation operating within Pine's compute budget
🚨 Disclaimer
This indicator is an analytical and educational visualization tool. The Dueling Deep-Q-Network, experience replay, reward shaping, breakout-zone detection, and action probabilities are quantitative heuristics computed on-chart from price data — they are not financial advice, buy/sell signals, or any guarantee of future performance. Reinforcement-learning agents can and do make wrong calls. Always combine any tool with your own analysis and disciplined risk management.
Indicator
Indicator
Indicator
Kitty's Law [theUltimator5]MOASS is tomorrow.
MOASS will always be tomorrow.
Murphy's law states anything that can go wrong will go wrong.
With GameStop, we have something called Kitty’s law .
Kitty’s law states that whenever people can fit in a March-May 2024 fractal, people will fit in a March-May 2024 fractal.
This indicator is an embodiment of Kitty's law.
This indicator matches the current price action to the GME fractal from 2024, finds a best fit section, then projects forwards the resulting price action. A short squeeze has never been so near!
HOW IT WORKS
Now getting into the technical aspects of this indicator, since it is a bit more complicated than the silly description has it seem.
The values from 2024 are hard coded into arrays. There are two arrays. Hourly and daily. If the timeframe is set to daily, it will use the daily array for comparison. If any other timeframe, it will use hourly (yes I know that weekly, monthly etc... are correlating to hourly but deal with it)
The indicator then uses a lookback period off the current bar (you can define the length) and finds the best fit section match from the selected array (hourly or daily) using a custom Pearson correlation algorithm. Once it finds the best fit section match, it plots it over the chart and projects the rest of the array onto the chart, filling out the fractal.
If the ticker you are looking at is NOT GME, then it won't project anything. Instead, it will tell you to stop looking at bad tickers and go back to GME.
Disclaimer: This indicator is meant for fun and is NOT a technical analysis indicator and is cosmetic only! Indicator
Risk Manager [SkaleHub]Overview
The ultimate capital preservation tool. This indicator calculates mathematically secure stop-loss placements based on real-time market volatility (ATR) and features a dynamic dashboard that tells you the exact position size to take to protect your account.
The Edge
Amateurs blow accounts by guessing their lot sizes and placing arbitrary stop-losses. This tool professionalizes your risk. By adjusting your position size relative to the asset's current volatility, it ensures that whether you are trading a quiet forex pair or a volatile crypto asset, your monetary risk remains an exact, controlled percentage of your capital.
Key Features:
Volatility-Based Stops: Automatically calculates stop-loss levels using the Average True Range (ATR), ensuring your stop is safely tucked behind the market's natural "noise" to prevent early liquidations.
Auto-Position Sizing: Input your account balance and risk tolerance (e.g., 1%), and the built-in dashboard instantly outputs the exact number of shares or units you should buy.
On-Chart Visual Guardrails: Optionally plots dynamic crosshair lines on the chart so you can visually see exactly where your mathematically optimized stop-loss should be placed before entering a trade.
How to Use
Apply the indicator and open the settings menu. Enter your total account balance and your strict risk percentage (1-2% is highly recommended).
When your Level 3 Momentum Trigger fires an entry signal, look at the Risk Manager dashboard in the corner of your screen.
Execute the trade using the exact "Position Size (Units)" displayed on the dashboard, and immediately set your hard stop-loss at the "Stop Distance" mapped out on the chart.
Author's Note
This is a premium, Invite-Only script. It is Level 4 of the SkaleHub Training System. To gain access, your PulseWire username must be explicitly authorized through the SkaleHub Academy. Indicator
BNC Market Bias DashboardA multi-timeframe sentiment gauge built on the BullNaked Crypto strategy framework. No signals, no entries — just a clear read of where the market stands right now across 7 timeframes simultaneously.
Scores each timeframe (3min, 9min, 27min, 81min, 3H, Daily, Weekly) across 5 indicators — EMA stack, Naked RSI zones, Stochastic RSI, Ichimoku Cloud, and Keltner Channel — and combines them into a weighted overall bias rating. Higher timeframes carry more weight because the higher the timeframe, the stronger the signal.
Rating scale: Strong Bull → Bull → Lean Bull → Neutral → Lean Bear → Bear → Strong Bear
What each column shows:
EMA stack alignment (9/30/50/100/200)
Naked RSI health zone (36 / 46 / 56 / 65 system)
Stochastic RSI position
Ichimoku Cloud position
Keltner Channel position
Per-timeframe signal suggestion
The overall score is weighted so Daily and Weekly carry 3× the influence of the 3-minute, reflecting the core principle that trend is truth on the higher timeframe. Use this to build your story before placing any trade — if the higher timeframes disagree with your entry timeframe, the story isn't complete yet.
Overlays directly on your chart. Table anchors to the bottom-left corner. All timeframes and indicator settings are fully adjustable in the settings panel.
Not financial advice. For educational and informational purposes only. Indicator
Indicator
Indicator
Indicator
Indicator
